[ { "path": ".devcontainer/README.md", "content": "# `.devcontainer` Config\n\nThis directory houses a set of Dev Container config files, which streamline contributions from team and community members.\n\n## Developing in the Browser using Codespaces\n\nGitHub Codespaces allows maintainers and contributors to launch directly into a web browser window that hosts the VS Code IDE.\n\n## Container Prebuild Optimizations\n\nPrebuilds of these dev containers can significantly speed up launch times.\n\n## Sharing Codespace Links\n\nPer the [GitHub Docs](https://docs.github.com/en/codespaces/setting-up-your-project-for-codespaces/setting-up-your-repository/facilitating-quick-creation-and-resumption-of-codespaces#creating-a-link-to-the-codespace-creation-page-for-your-repository), you can:\n\n- Create a codespace for the default branch:\n - [`https://codespaces.new/airbytehq/quickstarts`](https://codespaces.new/airbytehq/airbyte)\n- Create a codespace for a specific branch of the repository:\n - `https://codespaces.new/airbytehq/quickstarts/tree/BRANCH-NAME`\n - `https://codespaces.new/FORK-NAME/quickstarts/tree/BRANCH-NAME`\n - E.g. https://codespaces.new/aaronsteers/quickstarts/tree/aj%2Ffeat%2Fdevcontainers\n- Create a codespace for a pull request:\n - https://codespaces.new/airbytehq/quickstarts/pull/PR-SHA\n" }, { "path": ".devcontainer/devcontainer.json", "content": "// This is a generic devcontainer definition for working with Quickstarts.\n//\n// Included in this devcontainer:\n// - Python (3.10)\n// - Terraform CLI\n// - dbt (BigQuery variant)\n// - Docker-In-Docker support (DinD)\n// - Various VS Code extensions supporting the above 👆\n\n{\n \"name\": \"Airbyte Quickstarts Dev Container (Generic)\",\n\n // For general devcontainer config, see: https://aka.ms/devcontainer.json\n // For Python-specific options, see: https://github.com/devcontainers/templates/tree/main/src/python\n \"image\": \"mcr.microsoft.com/devcontainers/python:0-3.10\",\n\n \"features\": {\n // Features to add to the dev container.\n // More info: https://containers.dev/features.\n \"ghcr.io/devcontainers/features/docker-in-docker:2\": {},\n \"ghcr.io/devcontainers-contrib/features/poetry:2\": {},\n \"ghcr.io/devcontainers/features/terraform\": {},\n \"ghcr.io/devcontainers-contrib/features/pipx-package:1\": {\n \"package\": \"dbt-bigquery\",\n \"version\": \"1.7.2\",\n \"interpreter\": \"python3\",\n \"includeDeps\": true // ...because dbt-query doesn't directly surface the dbt CLI\n }\n },\n \"overrideFeatureInstallOrder\": [\n // Strict ordering gives best chance of cache reuse.\n // Put things that aren't changing at top of list:\n \"ghcr.io/devcontainers/features/docker-in-docker:2\",\n \"ghcr.io/devcontainers/features/terraform\",\n \"ghcr.io/devcontainers-contrib/features/poetry:2\",\n \"ghcr.io/devcontainers-contrib/features/pipx-package:1\"\n ],\n\n // Configure tool-specific properties.\n \"customizations\": {\n \"vscode\": {\n \"extensions\": [\n // Python extensions:\n \"charliermarsh.ruff\",\n \"ms-python.black-formatter\",\n \"ms-python.mypy-type-checker\",\n \"ms-python.python\",\n \"ms-python.vscode-pylance\",\n \"ms-toolsai.jupyter\",\n // Toml support:\n \"tamasfe.even-better-toml\",\n // Yaml and JSON Schema support:\n \"redhat.vscode-yaml\",\n // Contributing:\n \"GitHub.vscode-pull-request-github\"\n ],\n \"settings\": {\n \"extensions.ignoreRecommendations\": true,\n \"git.autofetch\": true,\n \"git.openRepositoryInParentFolders\": \"always\",\n \"python.defaultInterpreterPath\": \".venv/bin/python\",\n \"python.interpreter.infoVisibility\": \"always\",\n \"python.terminal.activateEnvironment\": true,\n \"python.testing.pytestEnabled\": true\n }\n }\n },\n \"containerEnv\": {\n \"POETRY_VIRTUALENVS_IN_PROJECT\": \"true\"\n }\n\n // Use 'forwardPorts' to make a list of ports inside the container available locally.\n // \"forwardPorts\": [],\n // Use 'postCreateCommand' to run commands after the container is created.\n // \"postCreateCommand\": \"pip3 install --user -r requirements.txt\",\n // Uncomment to connect as root instead. More info: https://aka.ms/dev-containers-non-root.\n // \"remoteUser\": \"root\"\n}\n" }, { "path": ".gitignore", "content": "# Environments\n.env\n.venv\nenv/\nvenv/\nENV/\nenv.bak/\nvenv.bak/\n\n#Desktop Services Store\n.DS_Store\n\n# PyAirbyte caches and virtual environments\n.cache\n.venv*\n" }, { "path": "CONTRIBUTING.md", "content": "# Contributing to Airbyte Quickstarts\n\nThank you for considering contributing to Airbyte Quickstarts! 🌟 It’s people like you that make this project valuable for the community. Whether it’s fixing bugs or adding new Quickstarts, we welcome your contributions.\n\n## How Can I Contribute?\n\n### 1. Reporting Bugs\n\n- First, check the [Issues](https://github.com/airbytehq/quickstarts/issues) to see if the bug has already been reported.\n- If it hasn’t, [open a new issue](https://github.com/airbytehq/quickstarts/issues/new), providing a descriptive title and a clear description.\n\n### 2. Suggesting Enhancements\n\n- Before suggesting enhancements, please read the [documentation](https://github.com/airbytehq/quickstarts/blob/main/README.md) and check the [Issues](https://github.com/airbytehq/quickstarts/issues) to see if it has been discussed before.\n- If it hasn’t, [open a new issue](https://github.com/airbytehq/quickstarts/issues/new), providing a descriptive title, detailed description, and use case.\n\n### 3. Submitting Changes and New Quickstarts\n\n1. **Fork the Repository**: Create your own fork of the [quickstarts repository](https://github.com/airbytehq/quickstarts).\n2. **Clone the Repository**: Clone your forked repository to your local machine.\n ```sh\n git clone https://github.com/your-username/quickstarts.git\n ```\n3. **Create a Branch**: Create a new branch from `main` for your changes.\n ```sh\n git checkout -b feature/my-new-feature\n ```\n4. **Make Changes**: Make your changes or additions to the new branch.\n5. **Commit Changes**: Commit your changes with a clear and descriptive commit message.\n ```sh\n git commit -m \"Add a new quickstart\"\n ```\n6. **Push Changes**: Push your changes to your fork on GitHub.\n ```sh\n git push origin feature/my-new-feature\n ```\n7. **Submit a Pull Request**: Go to the [Pull Requests](https://github.com/airbytehq/quickstarts/pulls) of the original repository and create a new pull request. Provide a clear description of the changes and reference any related issues.\n\n### 4. Notes for New Quickstarts\n\n1. **Create it in a New Directory**: Each Quickstart should live in its own directory and be a standalone project. \n2. **Add a README.md**: All Quickstarts should have clear and detailed instructions about how to set them up.\n\n## Style Guides\n\n- Write clean and simple code, following the existing code structure and naming conventions.\n- For Markdown files, adhere to [Markdown Guide](https://www.markdownguide.org/extended-syntax/).\n- Include comments with clear explanations of your code.\n- Update the documentation (README.md) if needed, to reflect the changes made.\n\n## Review Process\n\nOnce your pull request is submitted, maintainers will review it. They may ask for additional changes or clarifications. Once the pull request is approved, it will be merged into the main branch.\n\n## Contact\n\nFor questions or help with the contributing process, please reach out in the #hackathons channel in the [Airbyte Slack](https://airbytehq.slack.com/).\n\nThank you for contributing to Airbyte Quickstarts! 🚀" }, { "path": "README.md", "content": "# Airbyte Quickstarts\n\nWelcome to Airbyte Quickstarts! This repository provides various templates to help you quickly build your data stack tailored to different domains like Marketing, Product, Finance, Operations, and more.\n\n## Objective\n\nTo empower data teams by providing ready-to-use code templates, enabling the swift and efficient deployment of data stacks with minimal configuration.\n\n## How To Start?\n\n1. **Choose a Template**: Navigate to the Quickstart that suits you needs. Each folder in this repository is a Quickstart and can be used as a standalone project.\n2. **Follow Setup Instructions**: Each Quickstart contains a `README.md` file with step-by-step instructions to set up the stack.\n3. **Customize**: Modify the Quickstart as needed to suit your specific requirements.\n\n## List Of Available Quickstarts\n\n- [Airbyte, dbt, Airflow and BigQuery E-commerce Stack](./airbyte_dbt_airflow_bigquery)\n- [Airbyte, dbt, Airflow and Snowflake Basic Stack](./airbyte_dbt_airflow_snowflake)\n- [Airbyte, dbt, Dagster and BigQuery Basic Stack](./airbyte_dbt_dagster)\n- [Airbyte, dbt, Dagster and Snowflake Basic Stack](./airbyte_dbt_dagster_snowflake)\n- [Airbyte, dbt, Prefect and BigQuery (PAD) Stack](./airbyte_dbt_prefect_bigquery)\n- [Airbyte, dbt, Prefect and Snowflake Basic Stack](./airbyte_dbt_prefect_snowflake)\n- [Airbyte, dbt, Snowflake and Looker Basic Stack](./airbyte_dbt_snowflake_looker)\n- [API to Data Warehouse Integration Stack](./api_to_warehouse)\n- [Customer Satisfaction Analytics Stack With Zendesk Support, Airbyte, dbt, Dagster and BigQuery](./satisfaction_analytics_zendesk_support)\n- [Customer Ticket Volume Analytics Stack With Zendesk Support, Airbyte, dbt, Dagster and BigQuery](./ticket_volume_analytics_zendesk_support)\n- [Database Snapshot Stack](./database_snapshot)\n- [E-commerce Analytics with Airbyte, dbt, Dagster and BigQuery](./ecommerce_analytics_bigquery)\n- [ELT simplified stack with Airbyte, dbt, Prefect, Github and Bigquery](./elt_simplified_stack)\n- [Github Insight Stack with Airbyte, dbt, Dagster and BigQuery](./github_insight_stack)\n- [Low-Latency Data Availability Stack](./low_latency_data_availability)\n- [MongoDB MySQL Integration Stack](./mongodb_mysql_integration)\n- [Multisource Database Aggregtion Stack](./multisource_aggregation)\n- [Postgres Data Replication Stack](./postgres_data_replication)\n- [Postgres to MySQL Database Migration Stack](./postgres_to_mysql_migration)\n- [Postgres to Snowflake Data Integraton](./postgres_snowflake_integration)\n\n## Contribution Guidelines\n\nWe highly encourage community contributions to help improve, expand and add new Quickstarts! Please read our [Contribution Guidelines](CONTRIBUTING.md) before making a submission.\n\nIf you're looking to contribute with a new Quickstart, you can look for inspiration in the [Issues](https://github.com/airbytehq/quickstarts/issues) tab. There, we keep a list of our most wanted Quickstarts and often offer rewards for contributions, for example, during our different Hackathons.\n\n## Contact\n\nFor questions or help with the contributing process, please reach out in the #hackathons channel in the [Airbyte Slack](https://airbytehq.slack.com/).\n" }, { "path": "airbyte_dbt_airflow_bigquery/.gitignore", "content": "# Byte-compiled / optimized / DLL files\n__pycache__/\n*.py[cod]\n*$py.class\n\n# C extensions\n*.so\n\n# Distribution / packaging\n.Python\nbuild/\ndevelop-eggs/\ndist/\ndownloads/\neggs/\n.eggs/\nlib/\nlib64/\nparts/\nsdist/\nvar/\nwheels/\nshare/python-wheels/\n*.egg-info/\n.installed.cfg\n*.egg\nMANIFEST\n\n# PyInstaller\n# Usually these files are written by a python script from a template\n# before PyInstaller builds the exe, so as to inject date/other infos into it.\n*.manifest\n*.spec\n\n# Installer logs\npip-log.txt\npip-delete-this-directory.txt\n\n# Unit test / coverage reports\nhtmlcov/\n.tox/\n.nox/\n.coverage\n.coverage.*\n.cache\nnosetests.xml\ncoverage.xml\n*.cover\n*.py,cover\n.hypothesis/\n.pytest_cache/\ncover/\n\n# Translations\n*.mo\n*.pot\n\n# Django stuff:\n*.log\nlocal_settings.py\ndb.sqlite3\ndb.sqlite3-journal\n\n# Flask stuff:\ninstance/\n.webassets-cache\n\n# Scrapy stuff:\n.scrapy\n\n# Sphinx documentation\ndocs/_build/\n\n# PyBuilder\n.pybuilder/\ntarget/\n\n# Jupyter Notebook\n.ipynb_checkpoints\n\n# IPython\nprofile_default/\nipython_config.py\n\n# pyenv\n# For a library or package, you might want to ignore these files since the code is\n# intended to run in multiple environments; otherwise, check them in:\n# .python-version\n\n# pipenv\n# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.\n# However, in case of collaboration, if having platform-specific dependencies or dependencies\n# having no cross-platform support, pipenv may install dependencies that don't work, or not\n# install all needed dependencies.\n#Pipfile.lock\n\n# poetry\n# Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.\n# This is especially recommended for binary packages to ensure reproducibility, and is more\n# commonly ignored for libraries.\n# https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control\n#poetry.lock\n\n# pdm\n# Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control.\n#pdm.lock\n# pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it\n# in version control.\n# https://pdm.fming.dev/#use-with-ide\n.pdm.toml\n\n# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm\n__pypackages__/\n\n# Celery stuff\ncelerybeat-schedule\ncelerybeat.pid\n\n# SageMath parsed files\n*.sage.py\n\n# Environments\n.env\n.venv\nenv/\nvenv/\nENV/\nenv.bak/\nvenv.bak/\n\n# Spyder project settings\n.spyderproject\n.spyproject\n\n# Rope project settings\n.ropeproject\n\n# mkdocs documentation\n/site\n\n# mypy\n.mypy_cache/\n.dmypy.json\ndmypy.json\n\n# Pyre type checker\n.pyre/\n\n# pytype static type analyzer\n.pytype/\n\n# Cython debug symbols\ncython_debug/\n\n# PyCharm\n# JetBrains specific template is maintained in a separate JetBrains.gitignore that can\n# be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore\n# and can be added to the global gitignore or merged into this file. For a more nuclear\n# option (not recommended) you can uncomment the following to ignore the entire idea folder.\n#.idea/\n\n#Desktop Services Store\n.DS_Store" }, { "path": "airbyte_dbt_airflow_bigquery/README.md", "content": "# E-commerce Analytics Stack with Airbyte, dbt, Airflow (ADA) and BigQuery\n\nWelcome to the Airbyte, dbt and Airflow (ADA) Stack with BigQuery quickstart! This repo contains the code to show how to utilize Airbyte and dbt for data extraction and transformation, and implement Apache Airflow to orchestrate the data workflows, providing a end-to-end ELT pipeline. With this setup, you can pull fake e-commerce data, put it into BigQuery, and play around with it using dbt and Airflow.\n\nHere's the diagram of the end to end data pipeline you will build, from the Airflow DAG Graph view:\n\n![elt_dag](assets/elt_dag.png)\n\nAnd here are the transformations happening when the dbt DAG is executed:\n\n![ecommerce_dag](assets/ecommerce_dag.png)\n\n## Table of Contents\n\n- [Prerequisites](#prerequisites)\n- [Setting an environment for your project](#1-setting-an-environment-for-your-project)\n- [Setting Up BigQuery](#2-setting-up-bigquery)\n- [Setting Up Airbyte Connectors](#3-setting-up-airbyte-connectors)\n- [Setting Up the dbt Project](#4-setting-up-the-dbt-project)\n- [Setting Up Airflow](#5-setting-up-airflow)\n- [Orchestrating with Airflow](#6-orchestrating-with-airflow)\n- [Next Steps](#7-next-steps)\n\n## Prerequisites\n\nBefore you embark on this integration, ensure you have the following set up and ready:\n\n1. **Python 3.10 or later**: If not installed, download and install it from [Python's official website](https://www.python.org/downloads/).\n\n2. **Docker and Docker Compose (Docker Desktop)**: Install [Docker](https://docs.docker.com/get-docker/) following the official documentation for your specific OS.\n\n3. **Airbyte OSS version**: Deploy the open-source version of Airbyte locally. Follow the installation instructions from the [Airbyte Documentation](https://docs.airbyte.com/quickstart/deploy-airbyte/).\n\n4. **Terraform (Optional)**: Terraform will help you provision and manage the Airbyte resources. If you haven't installed it, follow the [official Terraform installation guide](https://developer.hashicorp.com/terraform/tutorials/aws-get-started/install-cli). This is an optional step because you can also create and manage Airbyte resources via the UI. Both ways will be described below.\n\n5. **Google Cloud account with BigQuery**: You will also need to add the necessary permissions to allow Airbyte and dbt to access the data in BigQuery. A step-by-step guide is provided [below](#2-setting-up-bigquery).\n\n## 1. Setting an environment for your project\n\nGet the project up and running on your local machine by following these steps:\n\n1. **Clone the repository (Clone only this quickstart)**: \n ```bash\n git clone --filter=blob:none --sparse https://github.com/airbytehq/quickstarts.git\n ```\n\n ```bash\n cd quickstarts\n ```\n\n ```bash\n git sparse-checkout add airbyte_dbt_airflow_bigquery\n ```\n\n2. **Navigate to the directory**: \n ```bash\n cd airbyte_dbt_airflow_bigquery\n ```\n\n At this point you can view the code in your preferred IDE. \n \n The next steps are only necessary if want to develop or test the dbt models locally, since Airbyte and Airflow are running on Docker.\n\n3. **Set up a virtual environment**: \n \n You can use the following commands, just make sure to adapt to your specific python installation.\n\n - For Linux and Mac:\n ```bash\n python3 -m venv venv\n source venv/bin/activate\n ```\n\n - For Windows:\n ```bash\n python -m venv venv\n .\\venv\\Scripts\\activate\n ```\n\n4. **Install dependencies**: \n\n ```bash\n pip install -e \".[dev]\"\n ```\n\n## 2. Setting up BigQuery\n\n1. **Create a Google Cloud project**:\n - If you have a Google Cloud project, you can skip this step.\n - Go to the [Google Cloud Console](https://console.cloud.google.com/).\n - Click on the \"Select a project\" dropdown at the top right and select \"New Project\".\n - Give your project a name and follow the steps to create it.\n\n2. **Create BigQuery datasets**:\n - In the Google Cloud Console, go to BigQuery.\n - Make two new datasets: `raw_data` for Airbyte and `transformed_data` for dbt.\n - If you pick different names, remember to change the names in the code too.\n \n **How to create a dataset:**\n - In the left sidebar, click on your project name.\n - Click “Create Dataset”.\n - Enter the dataset ID (either `raw_data` or `transformed_data`).\n - Click \"Create Dataset\".\n\n3. **Create a Service Account and Assign Roles**:\n - Go to “IAM & Admin” > “Service accounts” in the Google Cloud Console.\n - Click “Create Service Account”.\n - Name your service account.\n - Assign the “BigQuery Data Editor” and “BigQuery Job User” roles to the service account.\n\n **How to create a service account and assign roles:**\n - While creating the service account, under the “Grant this service account access to project” section, click the “Role” dropdown.\n - Choose the “BigQuery Data Editor” and “BigQuery Job User” roles.\n - Finish the creation process.\n \n4. **Generate a JSON key for the Service Account**:\n - Make a JSON key to let the service account sign in.\n \n **How to generate a JSON key:**\n - Find the service account in the “Service accounts” list.\n - Click on the service account name.\n - In the “Keys” section, click “Add Key” and pick JSON.\n - The key will download automatically. Keep it safe and don’t share it.\n\n## 3. Setting Up Airbyte Connectors\n\nTo set up your Airbyte connectors, you can choose to do it via Terraform, or the UI. Choose one of the two following options.\n\n### 3.1. Setting Up Airbyte Connectors with Terraform\n\nAirbyte allows you to create connectors for sources and destinations via Terraform, facilitating data synchronization between various platforms. Here's how you can set this up:\n\n1. **Navigate to the Airbyte Configuration Directory**:\n\n ```bash\n cd infra/airbyte\n ```\n\n2. **Modify Configuration Files**:\n\n Within the `infra/airbyte` directory, you'll find three crucial Terraform files:\n - `provider.tf`: Defines the Airbyte provider.\n - `main.tf`: Contains the main configuration for creating Airbyte resources.\n - `variables.tf`: Holds various variables, including credentials.\n\n Adjust the configurations in these files to suit your project's needs: \n\n - Provide credentials for your BigQuery connection in the `main.tf` file.\n - `dataset_id`: The name of the BigQuery dataset where Airbyte will load data. In this case, enter “raw_data”.\n - `project_id`: Your BigQuery project ID.\n - `credentials_json`: The contents of the service account JSON file. You should input a string, so you need to convert the JSON content to string beforehand.\n - `workspace_id`: Your Airbyte workspace ID, which can be found in the webapp url. For example, in this url: http://localhost:8000/workspaces/910ab70f-0a67-4d25-a983-999e99e1e395/ the workspace id would be `910ab70f-0a67-4d25-a983-999e99e1e395`.\n\n - Alternatively, you can utilize the `variables.tf` file to manage these credentials:\n - You’ll be prompted to enter the credentials when you execute `terraform plan` and `terraform apply`. If going for this option, just move to the next step. If you don’t want to use variables, remove them from the file.\n\n3. **Initialize Terraform**:\n \n This step prepares Terraform to create the resources defined in your configuration files.\n ```bash\n terraform init\n ```\n\n4. **Review the Plan**:\n\n Before applying any changes, review the plan to understand what Terraform will do.\n ```bash\n terraform plan\n ```\n\n5. **Apply Configuration**:\n\n After reviewing and confirming the plan, apply the Terraform configurations to create the necessary Airbyte resources.\n ```bash\n terraform apply\n ```\n\n6. **Verify in Airbyte UI**:\n\n Once Terraform completes its tasks, navigate to the [Airbyte UI](http://localhost:8000/). Here, you should see your source and destination connectors, as well as the connection between them, set up and ready to go 🎉.\n\n### 3.2. Setting Up Airbyte Connectors Using the UI\n\nStart by launching the Airbyte UI by going to http://localhost:8000/ in your browser. Then:\n\n1. **Create a source**:\n\n - Go to the Sources tab and click on `+ New source`.\n - Search for “faker” using the search bar and select `Sample Data (Faker)`.\n - Adjust the Count and optional fields as needed for your use case. You can also leave as is. \n - Click on `Set up source`.\n\n2. **Create a destination**:\n\n - Go to the Destinations tab and click on `+ New destination`.\n - Search for “bigquery” using the search bar and select `BigQuery`.\n - Enter the connection details as needed.\n - For simplicity, you can use `Standard Inserts` as the loading method.\n - In the `Service Account Key JSON` field, enter the contents of the JSON file. Yes, the full JSON.\n - Click on `Set up destination`.\n\n3. **Create a connection**:\n\n - Go to the Connections tab and click on `+ New connection`.\n - Select the source and destination you just created.\n - Enter the connection details as needed.\n - Click on `Set up connection`.\n\nThat’s it! Your connection is set up and ready to go! 🎉 \n\n## 4. Setting Up the dbt Project\n\n[dbt (data build tool)](https://www.getdbt.com/) allows you to transform your data by writing, documenting, and executing SQL workflows. Setting up the dbt project requires specifying connection details for your data platform, in this case, BigQuery. Here’s a step-by-step guide to help you set this up:\n\n1. **Navigate to the dbt Project Directory**:\n\n Move to the directory containing the dbt configuration:\n ```bash\n cd ../../dbt_project\n ```\n\n2. **Update Connection Details**:\n\n - You'll find a `profiles.yml` file within the directory. This file contains configurations for dbt to connect with your data platform. Update this file with your BigQuery connection details. Specifically, you need to update the Service Account JSON file path, the dataset location and your BigQuery project ID.\n - Provide your BigQuery project ID in the `database` field of the `/models/ecommerce/sources/faker_sources.yml` file.\n\n If you want to avoid hardcoding credentials in the `profiles.yml` file, you can leverage environment variables. Here's an example: `keyfile: \"{{ env_var('DBT_BIGQUERY_KEYFILE_PATH', '') }}\"`\n\n3. **Test the Connection (Optional)**:\n You can test the connection to your BigQuery instance using the following command. Just take into account that you would need to provide the local path to your service account key file instead.\n \n ```bash\n dbt debug\n ```\n \n If everything is set up correctly, this command should report a successful connection to BigQuery 🎉.\n\n## 5. Setting Up Airflow\n\nLet's set up Airflow for our project, following the steps below. We are basing our setup on the Running Airflow in Docker guide, with some customizations:\n\n1. **Navigate to the Orchestration Directory**:\n\n ```bash\n cd ../orchestration\n ```\n\n2. **Set Environment Variables**:\n\n - Open the `.env.example` file located in the `orchestration` directory.\n - Update the necessary fields, paying special attention to the `GCP_SERVICE_ACCOUNT_PATH`, which should point to your local service account JSON key directory path.\n - Rename the file from `.env.example` to `.env` after filling in the details.\n\n3. **Build the custom Airflow image**:\n\n ```bash\n docker compose build\n ```\n\n4. **Launch the Airflow container**:\n\n ```bash\n docker compose up\n ```\n\n This might take a few minutes initially as it sets up necessary databases and metadata.\n\n5. **Setting up Airflow Connections**:\n\n Both for using Airbyte and dbt, we need to set up connections in Airflow:\n\n - Access the Airflow UI by navigating to `http://localhost:8080` in your browser. The default username and password are both `airflow`, unless you changed it on the `.env` file.\n - Go to the \"Admin\" > \"Connections\" tab.\n\n **5.1. Create Airbyte Connection**:\n\n Click on the `+` button to create a new connection and fill in the following details to create an Airbyte connection:\n\n - **Connection Id**: The name of the connection, this will be used in the DAGs responsible for triggering Airbyte syncs. Name it `airbyte_connection`.\n - **Connection Type**: The type of the connection. In this case, select `Airbyte`.\n - **Host**: The host of the Airbyte instance. Since we're running it locally, use `airbyte-proxy`, which is the name of the container running Airbyte. In case you have a remote instance, you can use the URL of the instance.\n - **Port**: The port of the Airbyte instance. By default the API is exposed on port `8001`.\n - **Login**: If you're using the proxy (it's used by default in the official Airbyte Docker Compose file), this is required. By default it's `airbyte`.\n - **Password**: If you're using the proxy (it's used by default in the official Airbyte Docker Compose file), this is required. By default it's `password`.\n\n Click on the `Test` button, and make sure you get a `Connection successfully tested` message at the top. Then, you can `Save` the connection.\n\n **5.2. Create Google Cloud (BigQuery) connection**:\n\n Click on the `+` button to create a new connection and fill in the following details to create an Google Cloud connection:\n\n - **Connection Id**: The name of the connection, this one will be used in the DAGs responsible for triggering dbt runs. Name it `dbt_file_connection`.\n - **Connection Type**: The type of the connection. Select `Google Cloud` from the drop down menu.\n - **Project ID**: The Google Cloud project ID. \n - **Keyfile path**: The path to the service account key file. In this case, it's mounted to `/opt/airflow/service_accounts/[your-service-account-key-file].json`. \n - Alternatively, you can use the **Keyfile JSON** field and paste the contents of the key file.\n\n Click on the `Test` button, and make sure you get a `Connection successfully tested` message at the top. Then, you can `Save` the connection.\n\n6. **Integrate dbt with Airflow**:\n\n We use [Astronomer Cosmos](https://astronomer.github.io/astronomer-cosmos/) to integrate dbt with Airflow. This library parses DAGs and Task Groups from dbt models, and allows us to use Airflow connections instead of dbt profiles. Additionally, it runs tests automatically after each model is completed. To set it up, we've created the file `orchestration/airflow/config/dbt_config.py` with the necessary configurations.\n\n Update the following in the `dbt_config.py` file, if necessary:\n\n - The `location` key inside `google_config` with the location of your BigQuery `transformed_data` dataset, if it's not `US`.\n - The method used to create the `google_condig`. The code uses the `GoogleCloudServiceAccountFileProfileMapping` method, assuming that the Google Cloud connection in Airflow was created using the *Keyfile Path*. If you used the *Keyfile JSON*, you should use the `GoogleCloudServiceAccountDictProfileMapping` method instead.\n\n7. **Link Airbyte connection to the Airflow DAG**:\n\n The last step being being able to execute the DAG in Airflow, is to include the `connection_id` from Airbyte:\n\n - Visit the Airbyte UI at http://localhost:8000/.\n - In the \"Connections\" tab, select the \"Faker to BigQuery\" connection and copy its connection id from the URL.\n - Update the `connection_id` in the `extract_data` task within `orchestration/airflow/dags/elt_dag.py` with this id.\n\n That's it! Airflow has been configured to work with dbt and Airbyte. 🎉 \n\n## 6. Orchestrating with Airflow\nNow that everything is set up, it's time to run your data pipeline!\n\n- In the Airflow UI, go to the \"DAGs\" section.\n- Locate `elt_dag` and click on \"Trigger DAG\" under the \"Actions\" column.\n\nThis will initiate the complete data pipeline, starting with the Airbyte sync from Faker to BigQuery, followed by dbt transforming the raw data into `staging` and `marts` models. As the last step, it generates dbt docs.\n\n- Confirm the sync status in the Airbyte UI.\n- After dbt jobs completion, check the BigQuery console to see the newly created views in the `transformed_data` dataset.\n- Once the dbt pipeline completes, you can check the dbt docs from the Airflow UI by going to the \"Custom Docs\" > \"dbt\" tab.\n\nCongratulations! You've successfully run an end-to-end workflow with Airflow, dbt and Airbyte. 🎉\n\n## 7. Next Steps\n\nOnce you've gone through the steps above, you should have a working Airbyte, dbt and Airflow (ADA) Stack with BigQuery. You can use this as a starting point for your project, and adapt it to your needs. There are lots of things you can do beyond this point, and these tools are evolving fast and adding new features almost every week. Here are some ideas to continue your project:\n\n1. **Expand your data sources**:\n\n This quickstart uses a very simple data source. Airbyte provides hundreds of sources that might be integrated into your pipeline. And besides configuring and orchestrating them, don't forget to add them as sources in your dbt project. This will make sure you have a lineage graph like the one we showed in the beginning of this document.\n\n2. **Dive into dbt and improve your transformations**:\n\n dbt is a very powerful tool, and it has lots of features that can help you improve your transformations. You can find more details in the [dbt Documentation](https://docs.getdbt.com/). It's very important that you understand the types of materializations and incremental models, as well as understanding the models, sources, metrics and everything else that dbt provides.\n\n3. **Apply Data Quality into your pipeline**\n\n dbt provides a simple test framework that is a good starting point, but there is a lot more you can do to ensure your data is correct. You can use Airflow to run manual data quality checks, by using [Sensors](https://airflow.apache.org/docs/apache-airflow/stable/core-concepts/sensors.html) or operators that run custom queries. You can also use specialized tools such as [Great Expectations](https://greatexpectations.io/) to create more complex data quality checks.\n\n4. **Monitoring and alerts**\n\n Airflow's UI is a good start for simple monitoring, but as your pipelines scale it might be useful to have a more robust monitoring solution. You can use tools such as [Prometheus](https://prometheus.io/) and [Grafana](https://grafana.com/) to create dashboards and alerts for your pipelines, but you can create [notifications using Airflow](https://airflow.apache.org/docs/apache-airflow/2.6.0/howto/notifications.html) or other tools such as [re_data](https://docs.getre.io/latest/docs/re_data/introduction/whatis_data/).\n\n5. **Contribute with the community**\n\n All tools mentioned here are open-source and have very active communities. You can contribute with them by creating issues, suggesting features, or even creating pull requests. You can also contribute with the Airbyte community by creating [connectors](https://docs.airbyte.io/connector-development) for new sources and destinations.\n" }, { "path": "airbyte_dbt_airflow_bigquery/dbt_project/.gitignore", "content": "\ntarget/\ndbt_packages/\nlogs/\n\n#Desktop Services Store\n.DS_Store\n\n#User cookie\n.user.yml" }, { "path": "airbyte_dbt_airflow_bigquery/dbt_project/README.md", "content": "Welcome to your new dbt project!\n\n### Using the starter project\n\nTry running the following commands:\n- dbt run\n- dbt test\n\n\n### Resources:\n- Learn more about dbt [in the docs](https://docs.getdbt.com/docs/introduction)\n- Check out [Discourse](https://discourse.getdbt.com/) for commonly asked questions and answers\n- Join the [chat](https://community.getdbt.com/) on Slack for live discussions and support\n- Find [dbt events](https://events.getdbt.com) near you\n- Check out [the blog](https://blog.getdbt.com/) for the latest news on dbt's development and best practices\n\n## This project\n\nWe've created two dbt models: example (which contains the default dbt example from jaffle-shop) and ecommerce, which uses data from the dataset extracted via airbyte using the Faker source.\n\nThis project is being orchestrated via Apache Airflow using the [Astronomer Cosmos](https://astronomer.github.io/astronomer-cosmos/) project. For more details in orchestrating dbt models with Airflow, you can check the `orchestration` folder in this quickstart.\n\nThe ecommerce dbt model was forked and updated from the [Ecommerce Analytics Bigquery Quickstart](https://github.com/airbytehq/quickstarts/tree/main/ecommerce_analytics_bigquery)." }, { "path": "airbyte_dbt_airflow_bigquery/dbt_project/analyses/.gitkeep", "content": "" }, { "path": "airbyte_dbt_airflow_bigquery/dbt_project/dbt_project.yml", "content": "# Name your project! Project names should contain only lowercase characters\n# and underscores. A good package name should reflect your organization's\n# name or the intended use of these models\nname: 'dbt_project'\nversion: '1.0.0'\nconfig-version: 2\n\n# This setting configures which \"profile\" dbt uses for this project.\nprofile: 'dbt_project'\n\n# These configurations specify where dbt should look for different types of files.\n# The `model-paths` config, for example, states that models in this project can be\n# found in the \"models/\" directory. You probably won't need to change these!\nmodel-paths: ['models']\nanalysis-paths: ['analyses']\ntest-paths: ['tests']\nseed-paths: ['seeds']\nmacro-paths: ['macros']\nsnapshot-paths: ['snapshots']\n\nclean-targets: # directories to be removed by `dbt clean`\n - 'target'\n - 'dbt_packages'\n\n# Configuring models\n# Full documentation: https://docs.getdbt.com/docs/configuring-models\n\n# In this example config, we tell dbt to build all models in the example/\n# directory as views. These settings can be overridden in the individual model\n# files using the `{{ config(...) }}` macro.\nmodels:\n dbt_project:\n # Config indicated by + and applies to all files under models/example/\n ecommerce:\n +materialized: view\n staging:\n +materialized: view\n marts:\n +materialized: view\n" }, { "path": "airbyte_dbt_airflow_bigquery/dbt_project/macros/.gitkeep", "content": "" }, { "path": "airbyte_dbt_airflow_bigquery/dbt_project/models/ecommerce/marts/product_popularity.sql", "content": "WITH base AS (\n SELECT \n product_id,\n COUNT(id) AS purchase_count\n FROM {{ ref('stg_purchases') }}\n GROUP BY 1\n)\n\nSELECT \n p.id,\n p.make,\n p.model,\n b.purchase_count\nFROM {{ ref('stg_products') }} p\nLEFT JOIN base b ON p.id = b.product_id\nORDER BY b.purchase_count DESC\n" }, { "path": "airbyte_dbt_airflow_bigquery/dbt_project/models/ecommerce/marts/purchase_patterns.sql", "content": "SELECT \n user_id,\n product_id,\n purchased_at,\n added_to_cart_at,\n TIMESTAMP_DIFF(purchased_at, added_to_cart_at, SECOND) AS time_to_purchase_seconds,\n returned_at\nFROM {{ ref('stg_purchases') }}\n" }, { "path": "airbyte_dbt_airflow_bigquery/dbt_project/models/ecommerce/marts/schema.yml", "content": "version: 2\n\nmodels:\n - name: product_popularity\n columns:\n - name: id\n tests:\n - unique\n - name: make\n tests:\n - not_null\n - name: model\n tests:\n - not_null\n - name: purchase_count\n tests:\n - not_null\n\n - name: purchase_patterns\n columns:\n - name: user_id\n tests:\n - not_null\n - name: product_id\n tests:\n - not_null\n - name: purchased_at\n - name: added_to_cart_at\n tests:\n - not_null\n - name: time_to_purchase_seconds\n - name: returned_at\n\n - name: user_demographics\n columns:\n - name: gender\n tests:\n - not_null\n - name: academic_degree\n tests:\n - not_null\n - name: nationality\n tests:\n - not_null\n - name: average_age\n tests:\n - not_null\n - name: user_count\n tests:\n - not_null\n" }, { "path": "airbyte_dbt_airflow_bigquery/dbt_project/models/ecommerce/marts/user_demographics.sql", "content": "WITH base AS (\n SELECT \n id AS user_id,\n gender,\n academic_degree,\n nationality,\n age\n FROM {{ ref('stg_users') }}\n)\n\nSELECT \n gender,\n academic_degree,\n nationality,\n AVG(age) AS average_age,\n COUNT(user_id) AS user_count\nFROM base\nGROUP BY 1, 2, 3\n" }, { "path": "airbyte_dbt_airflow_bigquery/dbt_project/models/ecommerce/sources/faker_sources.yml", "content": "version: 2\n\nsources:\n - name: faker\n project: your_project_id # Update this field with your BigQuery project ID\n dataset: raw_data\n tables:\n - name: users\n description: 'Simulated user data from the Faker connector.'\n columns:\n - name: id\n description: 'Unique identifier for the user.'\n - name: address\n - name: occupation\n - name: gender\n - name: academic_degree\n - name: weight\n - name: created_at\n - name: language\n - name: telephone\n - name: title\n - name: updated_at\n - name: nationality\n - name: blood_type\n - name: name\n - name: age\n - name: email\n - name: height\n - name: _airbyte_raw_id\n - name: _airbyte_extracted_at\n - name: _airbyte_meta\n\n - name: products\n description: 'Simulated product data from the Faker connector.'\n columns:\n - name: id\n description: 'Unique identifier for the product.'\n - name: updated_at\n - name: year\n - name: price\n - name: created_at\n - name: model\n - name: make\n - name: _airbyte_raw_id\n - name: _airbyte_extracted_at\n - name: _airbyte_meta\n\n - name: purchases\n description: 'Simulated purchase data from the Faker connector.'\n columns:\n - name: id\n description: 'Unique identifier for the purchase.'\n - name: updated_at\n - name: purchased_at\n - name: user_id\n - name: returned_at\n - name: product_id\n - name: created_at\n - name: added_to_cart_at\n - name: _airbyte_raw_id\n - name: _airbyte_extracted_at\n - name: _airbyte_meta\n" }, { "path": "airbyte_dbt_airflow_bigquery/dbt_project/models/ecommerce/staging/schema.yml", "content": "version: 2\n\nmodels:\n - name: stg_users\n columns:\n - name: id\n tests:\n - unique\n - name: gender\n tests:\n - not_null\n - name: academic_degree\n tests:\n - not_null\n - name: title\n tests:\n - not_null\n - name: nationality\n tests:\n - not_null\n - name: age\n tests:\n - not_null\n - name: name\n tests:\n - not_null\n - name: email\n tests:\n - not_null\n - name: created_at\n tests:\n - not_null\n - name: updated_at\n tests:\n - not_null\n - name: _airbyte_extracted_at\n tests:\n - not_null\n\n - name: stg_purchases\n columns:\n - name: id\n tests:\n - unique\n - name: user_id\n tests:\n - not_null\n - name: product_id\n tests:\n - not_null\n - name: updated_at\n tests:\n - not_null\n - name: purchased_at\n - name: returned_at\n - name: created_at\n tests:\n - not_null\n - name: added_to_cart_at\n - name: _airbyte_extracted_at\n tests:\n - not_null\n\n - name: stg_products\n columns:\n - name: id\n tests:\n - unique\n - name: year\n tests:\n - not_null\n - name: price\n tests:\n - not_null\n - name: model\n tests:\n - not_null\n - name: make\n tests:\n - not_null\n - name: created_at\n tests:\n - not_null\n - name: updated_at\n tests:\n - not_null\n - name: _airbyte_extracted_at\n tests:\n - not_null\n" }, { "path": "airbyte_dbt_airflow_bigquery/dbt_project/models/ecommerce/staging/stg_products.sql", "content": "select\n id,\n year,\n price,\n model,\n make,\n created_at,\n updated_at,\n _airbyte_extracted_at\nfrom {{ source('faker', 'products') }}\n" }, { "path": "airbyte_dbt_airflow_bigquery/dbt_project/models/ecommerce/staging/stg_purchases.sql", "content": "select\n id,\n user_id,\n product_id,\n updated_at,\n purchased_at,\n returned_at,\n created_at,\n added_to_cart_at,\n _airbyte_extracted_at\nfrom {{ source('faker', 'purchases') }}\n" }, { "path": "airbyte_dbt_airflow_bigquery/dbt_project/models/ecommerce/staging/stg_users.sql", "content": "select\n id,\n gender,\n academic_degree,\n title,\n nationality,\n age,\n name,\n email,\n created_at,\n updated_at,\n _airbyte_extracted_at,\nfrom {{ source('faker', 'users') }}\n" }, { "path": "airbyte_dbt_airflow_bigquery/dbt_project/profiles.yml", "content": "dbt_project:\n outputs:\n dev:\n dataset: transformed_data\n job_execution_timeout_seconds: 300\n job_retries: 1\n keyfile: /opt/airflow/service_accounts/your_keyfile_path.json # Update this field with your file name, example: /opt/airflow/service_accounts/airflow-***116-83db69931a10.json\n location: your_dataset_location # Update this field with your dataset location, example: US\n method: service-account\n priority: interactive\n project: your_project_id # Update this field with your BigQuery project ID\n threads: 1\n type: bigquery\n target: dev\n\n " }, { "path": "airbyte_dbt_airflow_bigquery/dbt_project/seeds/.gitkeep", "content": "" }, { "path": "airbyte_dbt_airflow_bigquery/dbt_project/seeds/raw_customers.csv", "content": "id,first_name,last_name\n1,Michael,P.\n2,Shawn,M.\n3,Kathleen,P.\n4,Jimmy,C.\n5,Katherine,R.\n6,Sarah,R.\n7,Martin,M.\n8,Frank,R.\n9,Jennifer,F.\n10,Henry,W.\n11,Fred,S.\n12,Amy,D.\n13,Kathleen,M.\n14,Steve,F.\n15,Teresa,H.\n16,Amanda,H.\n17,Kimberly,R.\n18,Johnny,K.\n19,Virginia,F.\n20,Anna,A.\n21,Willie,H.\n22,Sean,H.\n23,Mildred,A.\n24,David,G.\n25,Victor,H.\n26,Aaron,R.\n27,Benjamin,B.\n28,Lisa,W.\n29,Benjamin,K.\n30,Christina,W.\n31,Jane,G.\n32,Thomas,O.\n33,Katherine,M.\n34,Jennifer,S.\n35,Sara,T.\n36,Harold,O.\n37,Shirley,J.\n38,Dennis,J.\n39,Louise,W.\n40,Maria,A.\n41,Gloria,C.\n42,Diana,S.\n43,Kelly,N.\n44,Jane,R.\n45,Scott,B.\n46,Norma,C.\n47,Marie,P.\n48,Lillian,C.\n49,Judy,N.\n50,Billy,L.\n51,Howard,R.\n52,Laura,F.\n53,Anne,B.\n54,Rose,M.\n55,Nicholas,R.\n56,Joshua,K.\n57,Paul,W.\n58,Kathryn,K.\n59,Adam,A.\n60,Norma,W.\n61,Timothy,R.\n62,Elizabeth,P.\n63,Edward,G.\n64,David,C.\n65,Brenda,W.\n66,Adam,W.\n67,Michael,H.\n68,Jesse,E.\n69,Janet,P.\n70,Helen,F.\n71,Gerald,C.\n72,Kathryn,O.\n73,Alan,B.\n74,Harry,A.\n75,Andrea,H.\n76,Barbara,W.\n77,Anne,W.\n78,Harry,H.\n79,Jack,R.\n80,Phillip,H.\n81,Shirley,H.\n82,Arthur,D.\n83,Virginia,R.\n84,Christina,R.\n85,Theresa,M.\n86,Jason,C.\n87,Phillip,B.\n88,Adam,T.\n89,Margaret,J.\n90,Paul,P.\n91,Todd,W.\n92,Willie,O.\n93,Frances,R.\n94,Gregory,H.\n95,Lisa,P.\n96,Jacqueline,A.\n97,Shirley,D.\n98,Nicole,M.\n99,Mary,G.\n100,Jean,M.\n" }, { "path": "airbyte_dbt_airflow_bigquery/dbt_project/seeds/raw_orders.csv", "content": "id,user_id,order_date,status\r\n1,1,2018-01-01,returned\r\n2,3,2018-01-02,completed\r\n3,94,2018-01-04,completed\r\n4,50,2018-01-05,completed\r\n5,64,2018-01-05,completed\r\n6,54,2018-01-07,completed\r\n7,88,2018-01-09,completed\r\n8,2,2018-01-11,returned\r\n9,53,2018-01-12,completed\r\n10,7,2018-01-14,completed\r\n11,99,2018-01-14,completed\r\n12,59,2018-01-15,completed\r\n13,84,2018-01-17,completed\r\n14,40,2018-01-17,returned\r\n15,25,2018-01-17,completed\r\n16,39,2018-01-18,completed\r\n17,71,2018-01-18,completed\r\n18,64,2018-01-20,returned\r\n19,54,2018-01-22,completed\r\n20,20,2018-01-23,completed\r\n21,71,2018-01-23,completed\r\n22,86,2018-01-24,completed\r\n23,22,2018-01-26,return_pending\r\n24,3,2018-01-27,completed\r\n25,51,2018-01-28,completed\r\n26,32,2018-01-28,completed\r\n27,94,2018-01-29,completed\r\n28,8,2018-01-29,completed\r\n29,57,2018-01-31,completed\r\n30,69,2018-02-02,completed\r\n31,16,2018-02-02,completed\r\n32,28,2018-02-04,completed\r\n33,42,2018-02-04,completed\r\n34,38,2018-02-06,completed\r\n35,80,2018-02-08,completed\r\n36,85,2018-02-10,completed\r\n37,1,2018-02-10,completed\r\n38,51,2018-02-10,completed\r\n39,26,2018-02-11,completed\r\n40,33,2018-02-13,completed\r\n41,99,2018-02-14,completed\r\n42,92,2018-02-16,completed\r\n43,31,2018-02-17,completed\r\n44,66,2018-02-17,completed\r\n45,22,2018-02-17,completed\r\n46,6,2018-02-19,completed\r\n47,50,2018-02-20,completed\r\n48,27,2018-02-21,completed\r\n49,35,2018-02-21,completed\r\n50,51,2018-02-23,completed\r\n51,71,2018-02-24,completed\r\n52,54,2018-02-25,return_pending\r\n53,34,2018-02-26,completed\r\n54,54,2018-02-26,completed\r\n55,18,2018-02-27,completed\r\n56,79,2018-02-28,completed\r\n57,93,2018-03-01,completed\r\n58,22,2018-03-01,completed\r\n59,30,2018-03-02,completed\r\n60,12,2018-03-03,completed\r\n61,63,2018-03-03,completed\r\n62,57,2018-03-05,completed\r\n63,70,2018-03-06,completed\r\n64,13,2018-03-07,completed\r\n65,26,2018-03-08,completed\r\n66,36,2018-03-10,completed\r\n67,79,2018-03-11,completed\r\n68,53,2018-03-11,completed\r\n69,3,2018-03-11,completed\r\n70,8,2018-03-12,completed\r\n71,42,2018-03-12,shipped\r\n72,30,2018-03-14,shipped\r\n73,19,2018-03-16,completed\r\n74,9,2018-03-17,shipped\r\n75,69,2018-03-18,completed\r\n76,25,2018-03-20,completed\r\n77,35,2018-03-21,shipped\r\n78,90,2018-03-23,shipped\r\n79,52,2018-03-23,shipped\r\n80,11,2018-03-23,shipped\r\n81,76,2018-03-23,shipped\r\n82,46,2018-03-24,shipped\r\n83,54,2018-03-24,shipped\r\n84,70,2018-03-26,placed\r\n85,47,2018-03-26,shipped\r\n86,68,2018-03-26,placed\r\n87,46,2018-03-27,placed\r\n88,91,2018-03-27,shipped\r\n89,21,2018-03-28,placed\r\n90,66,2018-03-30,shipped\r\n91,47,2018-03-31,placed\r\n92,84,2018-04-02,placed\r\n93,66,2018-04-03,placed\r\n94,63,2018-04-03,placed\r\n95,27,2018-04-04,placed\r\n96,90,2018-04-06,placed\r\n97,89,2018-04-07,placed\r\n98,41,2018-04-07,placed\r\n99,85,2018-04-09,placed\r\n" }, { "path": "airbyte_dbt_airflow_bigquery/dbt_project/seeds/raw_payments.csv", "content": "id,order_id,payment_method,amount\n1,1,credit_card,1000\n2,2,credit_card,2000\n3,3,coupon,100\n4,4,coupon,2500\n5,5,bank_transfer,1700\n6,6,credit_card,600\n7,7,credit_card,1600\n8,8,credit_card,2300\n9,9,gift_card,2300\n10,9,bank_transfer,0\n11,10,bank_transfer,2600\n12,11,credit_card,2700\n13,12,credit_card,100\n14,13,credit_card,500\n15,13,bank_transfer,1400\n16,14,bank_transfer,300\n17,15,coupon,2200\n18,16,credit_card,1000\n19,17,bank_transfer,200\n20,18,credit_card,500\n21,18,credit_card,800\n22,19,gift_card,600\n23,20,bank_transfer,1500\n24,21,credit_card,1200\n25,22,bank_transfer,800\n26,23,gift_card,2300\n27,24,coupon,2600\n28,25,bank_transfer,2000\n29,25,credit_card,2200\n30,25,coupon,1600\n31,26,credit_card,3000\n32,27,credit_card,2300\n33,28,bank_transfer,1900\n34,29,bank_transfer,1200\n35,30,credit_card,1300\n36,31,credit_card,1200\n37,32,credit_card,300\n38,33,credit_card,2200\n39,34,bank_transfer,1500\n40,35,credit_card,2900\n41,36,bank_transfer,900\n42,37,credit_card,2300\n43,38,credit_card,1500\n44,39,bank_transfer,800\n45,40,credit_card,1400\n46,41,credit_card,1700\n47,42,coupon,1700\n48,43,gift_card,1800\n49,44,gift_card,1100\n50,45,bank_transfer,500\n51,46,bank_transfer,800\n52,47,credit_card,2200\n53,48,bank_transfer,300\n54,49,credit_card,600\n55,49,credit_card,900\n56,50,credit_card,2600\n57,51,credit_card,2900\n58,51,credit_card,100\n59,52,bank_transfer,1500\n60,53,credit_card,300\n61,54,credit_card,1800\n62,54,bank_transfer,1100\n63,55,credit_card,2900\n64,56,credit_card,400\n65,57,bank_transfer,200\n66,58,coupon,1800\n67,58,gift_card,600\n68,59,gift_card,2800\n69,60,credit_card,400\n70,61,bank_transfer,1600\n71,62,gift_card,1400\n72,63,credit_card,2900\n73,64,bank_transfer,2600\n74,65,credit_card,0\n75,66,credit_card,2800\n76,67,bank_transfer,400\n77,67,credit_card,1900\n78,68,credit_card,1600\n79,69,credit_card,1900\n80,70,credit_card,2600\n81,71,credit_card,500\n82,72,credit_card,2900\n83,73,bank_transfer,300\n84,74,credit_card,3000\n85,75,credit_card,1900\n86,76,coupon,200\n87,77,credit_card,0\n88,77,bank_transfer,1900\n89,78,bank_transfer,2600\n90,79,credit_card,1800\n91,79,credit_card,900\n92,80,gift_card,300\n93,81,coupon,200\n94,82,credit_card,800\n95,83,credit_card,100\n96,84,bank_transfer,2500\n97,85,bank_transfer,1700\n98,86,coupon,2300\n99,87,gift_card,3000\n100,87,credit_card,2600\n101,88,credit_card,2900\n102,89,bank_transfer,2200\n103,90,bank_transfer,200\n104,91,credit_card,1900\n105,92,bank_transfer,1500\n106,92,coupon,200\n107,93,gift_card,2600\n108,94,coupon,700\n109,95,coupon,2400\n110,96,gift_card,1700\n111,97,bank_transfer,1400\n112,98,bank_transfer,1000\n113,99,credit_card,2400\n" }, { "path": "airbyte_dbt_airflow_bigquery/dbt_project/snapshots/.gitkeep", "content": "" }, { "path": "airbyte_dbt_airflow_bigquery/dbt_project/tests/.gitkeep", "content": "" }, { "path": "airbyte_dbt_airflow_bigquery/infra/.gitignore", "content": "# Local .terraform directories\n**/.terraform/*\n\n# .tfstate files\n*.tfstate\n*.tfstate.*\n\n# Crash log files\ncrash.log\ncrash.*.log\n\n# Exclude all .tfvars files, which are likely to contain sensitive data, such as\n# password, private keys, and other secrets. These should not be part of version \n# control as they are data points which are potentially sensitive and subject \n# to change depending on the environment.\n*.tfvars.json\n\n# Ignore override files as they are usually used to override resources locally and so\n# are not checked in\noverride.tf\noverride.tf.json\n*_override.tf\n*_override.tf.json\n\n# Include override files you do wish to add to version control using negated pattern\n# !example_override.tf\n\n# Include tfplan files to ignore the plan output of command: terraform plan -out=tfplan\n# example: *tfplan*\n\n# Ignore CLI configuration files\n.terraformrc\nterraform.rc" }, { "path": "airbyte_dbt_airflow_bigquery/infra/README.md", "content": "# Airbyte setup with terraform\n\nThis folder contains the terraform code to setup a source, destination and connection in Airbyte using terraform.\n\nWe're using the [airbyte official provider](https://registry.terraform.io/providers/airbytehq/airbyte/latest/docs), and any details can be found in the documentation.\n\nFor this example we're using:\n- [Airbyte Source Faker](https://registry.terraform.io/providers/airbytehq/airbyte/latest/docs/resources/source_faker)\n- [Airbyte Destination BigQuery](https://registry.terraform.io/providers/airbytehq/airbyte/latest/docs/resources/destination_bigquery)\n- [Airbyte Connection](https://registry.terraform.io/providers/airbytehq/airbyte/latest/docs/resources/connection)\n\nThis is all optional, since part of the advantage of using Airbyte is setting up the sources and destinations via the UI. However, if you want to automate this process, you can use this terraform code as a starting point." }, { "path": "airbyte_dbt_airflow_bigquery/infra/airbyte/main.tf", "content": "// Airbyte Terraform provider documentation: https://registry.terraform.io/providers/airbytehq/airbyte/latest/docs\n\n// Sources\nresource \"airbyte_source_faker\" \"faker\" {\n configuration = {\n always_updated = false\n count = 1000\n parallelism = 9\n records_per_slice = 10\n seed = 6\n source_type = \"faker\"\n }\n name = \"Faker\"\n workspace_id = var.workspace_id\n}\n\n// Destinations\nresource \"airbyte_destination_bigquery\" \"bigquery\" {\n configuration = {\n dataset_id = var.dataset_id\n dataset_location = \"US\"\n destination_type = \"bigquery\"\n project_id = var.project_id\n credentials_json = file(var.credentials_json_path)\n loading_method = {\n destination_bigquery_loading_method_standard_inserts = {\n method = \"Standard\"\n }\n }\n }\n name = \"BigQuery\"\n workspace_id = var.workspace_id\n}\n\n// Connections\nresource \"airbyte_connection\" \"faker_to_bigquery\" {\n name = \"Faker to BigQuery\"\n source_id = airbyte_source_faker.faker.source_id\n destination_id = airbyte_destination_bigquery.bigquery.destination_id\n configurations = {\n streams = [\n {\n name = \"users\"\n },\n {\n name = \"products\"\n },\n {\n name = \"purchases\"\n },\n ]\n }\n}\n" }, { "path": "airbyte_dbt_airflow_bigquery/infra/airbyte/provider.tf", "content": "// Airbyte Terraform provider documentation: https://registry.terraform.io/providers/airbytehq/airbyte/latest/docs\n\nterraform {\n required_providers {\n airbyte = {\n source = \"airbytehq/airbyte\"\n version = \"0.3.3\"\n }\n }\n}\n\nprovider \"airbyte\" {\n // If running locally (Airbyte OSS) with docker-compose using the airbyte-proxy, \n // include the actual password/username you've set up (or use the defaults below)\n username = \"airbyte\"\n password = \"password\"\n \n // if running locally (Airbyte OSS), include the server url to the airbyte-api-server\n server_url = \"http://localhost:8006/v1\"\n}" }, { "path": "airbyte_dbt_airflow_bigquery/infra/airbyte/terraform.tfvars", "content": "workspace_id=\"\"\ndataset_id=\"sample_ecommerce\"\nproject_id=\"\"\ncredentials_json_path = \"\"" }, { "path": "airbyte_dbt_airflow_bigquery/infra/airbyte/variables.tf", "content": "variable \"workspace_id\" {\n type = string\n}\n\nvariable \"dataset_id\" {\n type = string\n}\n\nvariable \"project_id\" {\n type = string\n}\n\nvariable \"credentials_json_path\" {\n type = string\n}\n\n\n" }, { "path": "airbyte_dbt_airflow_bigquery/orchestration/.gitignore", "content": "logs\n__pycache__" }, { "path": "airbyte_dbt_airflow_bigquery/orchestration/Dockerfile", "content": "FROM apache/airflow:2.7.2-python3.11\nCOPY requirements.txt /\nRUN pip install --no-cache-dir -r /requirements.txt" }, { "path": "airbyte_dbt_airflow_bigquery/orchestration/README.md", "content": "# Airflow setup with Airbyte and DBT\n\nThis folder contains the code to setup Airflow with Airbyte and DBT.\n\n## Setup\n\nWe're using the [Running Airflow in Docker](https://airflow.apache.org/docs/apache-airflow/stable/howto/docker-compose/index.html) as a starting point.\n\nWe've downloaded the official `docker-compose.yaml` file provided by Airflow and adapted it to:\n- Use some configurations from an .env file\n- Add the Airbyte operator, dbt and astronomer-cosmos packages\n- Mount our dbt project folder into the container image\n- For running locally, we've set up the network to use the one deployed by the Airbyte container setup (from [Airbyte Local Deployment](https://docs.airbyte.com/deploying-airbyte/local-deployment))\n- Admitting you're\n\n## Features\n\n- Providing dbt docs as a plugin from airflow, and making it available in the UI (and behing authentication)\n- Example dag with the airbyte operator\n- Example dag rendering dbt docs\n- Example dag orchestrating specific dbt-models inside a dag with multiple tasks\n- Example dag orchestrating specific dbt models as a dag\n\nWe're also using dataset aware schedules, and the airflow decorator to write the dag code." }, { "path": "airbyte_dbt_airflow_bigquery/orchestration/airflow/config/dbt_config.py", "content": "from cosmos.config import ProjectConfig, ProfileConfig\nfrom cosmos.profiles import GoogleCloudServiceAccountDictProfileMapping, GoogleCloudServiceAccountFileProfileMapping\n\n\nproject_config = ProjectConfig(\n dbt_project_path=\"/opt/airflow/dbt_project\",\n)\n\ngoogle_config = GoogleCloudServiceAccountFileProfileMapping(\n conn_id=\"dbt_file_connection\",\n profile_args={\n \"dataset\": \"transformed_data\",\n \"location\": \"US\", # Update if you're using a different location for your dataset\n \"threads\": 1,\n \"retries\": 1,\n \"priority\": \"interactive\",\n }\n)\n\nprofile_config = ProfileConfig(\n profile_name=\"dbt_project\",\n target_name=\"dev\",\n profile_mapping=google_config\n)" }, { "path": "airbyte_dbt_airflow_bigquery/orchestration/airflow/dags/elt_dag.py", "content": "from pendulum import datetime\nfrom airflow.decorators import dag\nfrom airflow.providers.airbyte.operators.airbyte import AirbyteTriggerSyncOperator\nfrom airflow.operators.trigger_dagrun import TriggerDagRunOperator\n\nfrom cosmos import DbtDag\nfrom cosmos.operators import DbtDocsOperator\nfrom cosmos.config import RenderConfig\n\nfrom dbt_config import project_config, profile_config # type:ignore\nfrom dbt_upload_docs import upload_docs # type:ignore\n\n# Define the ELT DAG\n@dag(\n dag_id=\"elt_dag\",\n start_date=datetime(2023, 10, 1),\n schedule=\"@daily\",\n tags=[\"airbyte\", \"dbt\", \"bigquery\", \"ecommerce\"],\n catchup=False,\n)\ndef extract_and_transform():\n \"\"\"\n Runs the connection \"Faker to BigQuery\" on Airbyte and then triggers the dbt DAG.\n \"\"\"\n # Airbyte sync task\n extract_data = AirbyteTriggerSyncOperator(\n task_id=\"trigger_airbyte_faker_to_bigquery\",\n airbyte_conn_id=\"airbyte_connection\",\n connection_id=\"your_connection_id\", # Update with your Airbyte connection ID\n asynchronous=False,\n timeout=3600,\n wait_seconds=3\n )\n\n # Trigger for dbt DAG\n trigger_dbt_dag = TriggerDagRunOperator(\n task_id=\"trigger_dbt_dag\",\n trigger_dag_id=\"dbt_ecommerce\",\n wait_for_completion=True,\n poke_interval=30,\n )\n\n render_dbt_docs = DbtDocsOperator(\n task_id=\"render_dbt_docs\",\n profile_config=profile_config,\n project_dir=\"/opt/airflow/dbt_project\",\n callback=upload_docs,\n )\n\n # Set the order of tasks\n extract_data >> trigger_dbt_dag >> render_dbt_docs\n\n# Instantiate the ELT DAG\nextract_and_transform_dag = extract_and_transform()\n\n# Define the dbt DAG using DbtDag from the cosmos library\ndbt_cosmos_dag = DbtDag(\n dag_id=\"dbt_ecommerce\",\n start_date=datetime(2023, 10, 1),\n tags=[\"dbt\", \"ecommerce\"],\n catchup=False,\n project_config=project_config,\n profile_config=profile_config,\n render_config=RenderConfig(select=[\"path:models/ecommerce\"]),\n)\n\n# Instantiate the dbt DAG\ndbt_cosmos_dag\n" }, { "path": "airbyte_dbt_airflow_bigquery/orchestration/airflow/plugins/custom_docs_plugin.py", "content": "\"\"\"Plugins example\"\"\"\nfrom __future__ import annotations\n\nfrom flask import Blueprint\nfrom flask_appbuilder import BaseView, expose\n\nfrom airflow.plugins_manager import AirflowPlugin\nfrom airflow.security import permissions\nfrom airflow.www.auth import has_access\n\nbp = Blueprint(\n \"Docs Plugin\",\n __name__,\n template_folder=\"templates\",\n static_folder=\"static\",\n static_url_path=\"/dbtdocspluginview\",\n)\n\nclass DbtDocsPluginView(BaseView):\n \"\"\"Creating a Flask-AppBuilder View\"\"\"\n default_view = \"index\"\n @expose(\"/\")\n @has_access(\n [\n (permissions.ACTION_CAN_READ, permissions.RESOURCE_WEBSITE),\n ]\n )\n def index(self):\n \"\"\"Create default view\"\"\"\n return self.render_template(\"dbt/index.html\", name=\"DBT\")\n\n# Creating a flask blueprint\n\nclass CustomDocsPlugin(AirflowPlugin):\n \"\"\"Defining the plugin class\"\"\"\n\n name = \"Docs Plugin\"\n flask_blueprints = [bp]\n appbuilder_views = [{\n \"name\": \"dbt\",\n \"category\": \"Custom Docs\",\n \"view\": DbtDocsPluginView()\n }]" }, { "path": "airbyte_dbt_airflow_bigquery/orchestration/airflow/plugins/dbt_upload_docs.py", "content": "import shutil, os, re\ndef fix_file():\n \n with open('/opt/airflow/plugins/templates/dbt/dbt_index.html') as f:\n html_contents = f.read()\n \n # Define a regular expression to match the script tag\n script_regex = r''\n\n # Find the script tag that you want to extract\n script_match = re.search(script_regex, html_contents, re.DOTALL)\n\n # Get the contents of the script tag\n script_contents = script_match.group(1)\n\n # Write the script contents to a separate JavaScript file\n if not os.path.exists('/opt/airflow/plugins/static'):\n os.makedirs('/opt/airflow/plugins/static')\n with open('/opt/airflow/plugins/static/script.js', 'w') as f:\n f.write(script_contents)\n\n # Remove the script tag from the HTML contents\n html_contents = html_contents.replace(script_contents,\"\")\n\n # Add a new script tag to the head section of the HTML contents\n new_script_tag = f''\n head_regex = r'(.*?)'\n head_match = re.search(head_regex, html_contents, re.DOTALL)\n head_contents = head_match.group(1)\n head_contents += new_script_tag\n html_contents = re.sub(head_regex, '' + head_contents + '', html_contents, flags=re.DOTALL)\n\n # Write the modified HTML contents to a new file\n with open('/opt/airflow/plugins/templates/dbt/index.html', 'w') as f:\n f.write(html_contents)\n\ndef upload_docs(project_dir):\n # upload docs to a storage of your choice\n # you only need to upload the following files:\n # - f\"{project_dir}/target/index.html\"\n # - f\"{project_dir}/target/manifest.json\"\n # - f\"{project_dir}/target/graph.gpickle\"\n # - f\"{project_dir}/target/catalog.json\"\n\n shutil.move(f\"{project_dir}/target/index.html\", \"/opt/airflow/plugins/templates/dbt/dbt_index.html\")\n shutil.move(f\"{project_dir}/target/manifest.json\", \"/opt/airflow/plugins/static/manifest.json\")\n shutil.move(f\"{project_dir}/target/graph.gpickle\", \"/opt/airflow/plugins/static/graph.gpickle\")\n shutil.move(f\"{project_dir}/target/catalog.json\", \"/opt/airflow/plugins/static/catalog.json\")\n fix_file()\n pass" }, { "path": "airbyte_dbt_airflow_bigquery/orchestration/airflow/plugins/static/.gitkeep", "content": "" }, { "path": "airbyte_dbt_airflow_bigquery/orchestration/airflow/plugins/templates/dbt/.gitkeep", "content": "" }, { "path": "airbyte_dbt_airflow_bigquery/orchestration/docker-compose.yaml", "content": "# Licensed to the Apache Software Foundation (ASF) under one\n# or more contributor license agreements. See the NOTICE file\n# distributed with this work for additional information\n# regarding copyright ownership. The ASF licenses this file\n# to you under the Apache License, Version 2.0 (the\n# \"License\"); you may not use this file except in compliance\n# with the License. You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing,\n# software distributed under the License is distributed on an\n# \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\n# KIND, either express or implied. See the License for the\n# specific language governing permissions and limitations\n# under the License.\n#\n\n# Basic Airflow cluster configuration for CeleryExecutor with Redis and PostgreSQL.\n#\n# WARNING: This configuration is for local development. Do not use it in a production deployment.\n#\n# This configuration supports basic configuration using environment variables or an .env file\n# The following variables are supported:\n#\n# AIRFLOW_IMAGE_NAME - Docker image name used to run Airflow.\n# Default: apache/airflow:2.7.2\n# AIRFLOW_UID - User ID in Airflow containers\n# Default: 50000\n# AIRFLOW_PROJ_DIR - Base path to which all the files will be volumed.\n# Default: .\n# Those configurations are useful mostly in case of standalone testing/running Airflow in test/try-out mode\n#\n# _AIRFLOW_WWW_USER_USERNAME - Username for the administrator account (if requested).\n# Default: airflow\n# _AIRFLOW_WWW_USER_PASSWORD - Password for the administrator account (if requested).\n# Default: airflow\n# _PIP_ADDITIONAL_REQUIREMENTS - Additional PIP requirements to add when starting all containers.\n# Use this option ONLY for quick checks. Installing requirements at container\n# startup is done EVERY TIME the service is started.\n# A better way is to build a custom image or extend the official image\n# as described in https://airflow.apache.org/docs/docker-stack/build.html.\n# Default: ''\n#\n# Feel free to modify this file to suit your needs.\n---\nversion: '3.8'\nx-airflow-common: &airflow-common\n # In order to add custom dependencies or upgrade provider packages you can use your extended image.\n # Comment the image line, place your Dockerfile in the directory where you placed the docker-compose.yaml\n # and uncomment the \"build\" line below, Then run `docker-compose build` to build the images.\n # image: ${AIRFLOW_IMAGE_NAME:-apache/airflow:2.7.2}\n build: .\n env_file:\n - ./.env\n environment: &airflow-common-env\n AIRFLOW__CORE__EXECUTOR: CeleryExecutor\n AIRFLOW__DATABASE__SQL_ALCHEMY_CONN: postgresql+psycopg2://${POSTGRES_USER:-airflow}:${POSTGRES_PASSWORD:-airflow}@postgres/airflow\n # For backward compatibility, with Airflow <2.3\n AIRFLOW__CORE__SQL_ALCHEMY_CONN: postgresql+psycopg2://${POSTGRES_USER:-airflow}:${POSTGRES_PASSWORD:-airflow}@postgres/airflow\n AIRFLOW__CELERY__RESULT_BACKEND: db+postgresql://${POSTGRES_USER:-airflow}:${POSTGRES_PASSWORD:-airflow}@postgres/airflow\n AIRFLOW__CELERY__BROKER_URL: redis://:@redis:6379/0\n AIRFLOW__CORE__FERNET_KEY: ''\n AIRFLOW__CORE__DAGS_ARE_PAUSED_AT_CREATION: 'true'\n AIRFLOW__CORE__LOAD_EXAMPLES: ${LOAD_EXAMPLES:-true}\n AIRFLOW__API__AUTH_BACKENDS: 'airflow.api.auth.backend.basic_auth,airflow.api.auth.backend.session'\n AIRFLOW__SCHEDULER__ENABLE_HEALTH_CHECK: 'true'\n AIRFLOW__CORE__LAZY_LOAD_PLUGINS: 'false'\n # WARNING: Use _PIP_ADDITIONAL_REQUIREMENTS option ONLY for a quick checks\n # for other purpose (development, test and especially production usage) build/extend Airflow image.\n _PIP_ADDITIONAL_REQUIREMENTS: ${_PIP_ADDITIONAL_REQUIREMENTS:-}\n volumes:\n - ${AIRFLOW_PROJ_DIR:-.}/dags:/opt/airflow/dags\n - ${AIRFLOW_PROJ_DIR:-.}/logs:/opt/airflow/logs\n - ${AIRFLOW_PROJ_DIR:-.}/config:/opt/airflow/config\n - ${AIRFLOW_PROJ_DIR:-.}/plugins:/opt/airflow/plugins\n - ${DBT_PROJ_DIR:-.}:/opt/airflow/dbt_project\n - ${GCP_SERVICE_ACCOUNT_PATH:-.}:/opt/airflow/service_accounts\n user: '${AIRFLOW_UID:-50000}:0'\n depends_on: &airflow-common-depends-on\n redis:\n condition: service_healthy\n postgres:\n condition: service_healthy\n networks:\n - airbyte_airbyte_public\nservices:\n postgres:\n image: postgres:15\n environment:\n POSTGRES_USER: ${POSTGRES_USER:-airflow}\n POSTGRES_PASSWORD: ${POSTGRES_PASSWORD:-airflow}\n POSTGRES_DB: airflow\n volumes:\n - postgres-db-volume:/var/lib/postgresql/data\n healthcheck:\n test: ['CMD', 'pg_isready', '-U', 'airflow']\n interval: 10s\n retries: 5\n start_period: 5s\n restart: always\n networks:\n - airbyte_airbyte_public\n\n redis:\n image: redis:latest\n expose:\n - 6379\n healthcheck:\n test: ['CMD', 'redis-cli', 'ping']\n interval: 10s\n timeout: 30s\n retries: 50\n start_period: 30s\n restart: always\n networks:\n - airbyte_airbyte_public\n\n airflow-webserver:\n <<: *airflow-common\n command: webserver\n ports:\n - '8080:8080'\n healthcheck:\n test: ['CMD', 'curl', '--fail', 'http://localhost:8080/health']\n interval: 30s\n timeout: 10s\n retries: 5\n start_period: 30s\n restart: always\n depends_on:\n <<: *airflow-common-depends-on\n airflow-init:\n condition: service_completed_successfully\n\n airflow-scheduler:\n <<: *airflow-common\n command: scheduler\n healthcheck:\n test: ['CMD', 'curl', '--fail', 'http://localhost:8974/health']\n interval: 30s\n timeout: 10s\n retries: 5\n start_period: 30s\n restart: always\n depends_on:\n <<: *airflow-common-depends-on\n airflow-init:\n condition: service_completed_successfully\n\n airflow-worker:\n <<: *airflow-common\n command: celery worker\n healthcheck:\n # yamllint disable rule:line-length\n test:\n - 'CMD-SHELL'\n - 'celery --app airflow.providers.celery.executors.celery_executor.app inspect ping -d \"celery@$${HOSTNAME}\" || celery --app airflow.executors.celery_executor.app inspect ping -d \"celery@$${HOSTNAME}\"'\n interval: 30s\n timeout: 10s\n retries: 5\n start_period: 30s\n environment:\n <<: *airflow-common-env\n # Required to handle warm shutdown of the celery workers properly\n # See https://airflow.apache.org/docs/docker-stack/entrypoint.html#signal-propagation\n DUMB_INIT_SETSID: '0'\n restart: always\n depends_on:\n <<: *airflow-common-depends-on\n airflow-init:\n condition: service_completed_successfully\n\n airflow-triggerer:\n <<: *airflow-common\n command: triggerer\n healthcheck:\n test:\n [\n 'CMD-SHELL',\n 'airflow jobs check --job-type TriggererJob --hostname \"$${HOSTNAME}\"',\n ]\n interval: 30s\n timeout: 10s\n retries: 5\n start_period: 30s\n restart: always\n depends_on:\n <<: *airflow-common-depends-on\n airflow-init:\n condition: service_completed_successfully\n\n airflow-init:\n <<: *airflow-common\n entrypoint: /bin/bash\n # yamllint disable rule:line-length\n command:\n - -c\n - |\n function ver() {\n printf \"%04d%04d%04d%04d\" $${1//./ }\n }\n airflow_version=$$(AIRFLOW__LOGGING__LOGGING_LEVEL=INFO && gosu airflow airflow version)\n airflow_version_comparable=$$(ver $${airflow_version})\n min_airflow_version=2.2.0\n min_airflow_version_comparable=$$(ver $${min_airflow_version})\n if (( airflow_version_comparable < min_airflow_version_comparable )); then\n echo\n echo -e \"\\033[1;31mERROR!!!: Too old Airflow version $${airflow_version}!\\e[0m\"\n echo \"The minimum Airflow version supported: $${min_airflow_version}. Only use this or higher!\"\n echo\n exit 1\n fi\n if [[ -z \"${AIRFLOW_UID}\" ]]; then\n echo\n echo -e \"\\033[1;33mWARNING!!!: AIRFLOW_UID not set!\\e[0m\"\n echo \"If you are on Linux, you SHOULD follow the instructions below to set \"\n echo \"AIRFLOW_UID environment variable, otherwise files will be owned by root.\"\n echo \"For other operating systems you can get rid of the warning with manually created .env file:\"\n echo \" See: https://airflow.apache.org/docs/apache-airflow/stable/howto/docker-compose/index.html#setting-the-right-airflow-user\"\n echo\n fi\n one_meg=1048576\n mem_available=$$(($$(getconf _PHYS_PAGES) * $$(getconf PAGE_SIZE) / one_meg))\n cpus_available=$$(grep -cE 'cpu[0-9]+' /proc/stat)\n disk_available=$$(df / | tail -1 | awk '{print $$4}')\n warning_resources=\"false\"\n if (( mem_available < 4000 )) ; then\n echo\n echo -e \"\\033[1;33mWARNING!!!: Not enough memory available for Docker.\\e[0m\"\n echo \"At least 4GB of memory required. You have $$(numfmt --to iec $$((mem_available * one_meg)))\"\n echo\n warning_resources=\"true\"\n fi\n if (( cpus_available < 2 )); then\n echo\n echo -e \"\\033[1;33mWARNING!!!: Not enough CPUS available for Docker.\\e[0m\"\n echo \"At least 2 CPUs recommended. You have $${cpus_available}\"\n echo\n warning_resources=\"true\"\n fi\n if (( disk_available < one_meg * 10 )); then\n echo\n echo -e \"\\033[1;33mWARNING!!!: Not enough Disk space available for Docker.\\e[0m\"\n echo \"At least 10 GBs recommended. You have $$(numfmt --to iec $$((disk_available * 1024 )))\"\n echo\n warning_resources=\"true\"\n fi\n if [[ $${warning_resources} == \"true\" ]]; then\n echo\n echo -e \"\\033[1;33mWARNING!!!: You have not enough resources to run Airflow (see above)!\\e[0m\"\n echo \"Please follow the instructions to increase amount of resources available:\"\n echo \" https://airflow.apache.org/docs/apache-airflow/stable/howto/docker-compose/index.html#before-you-begin\"\n echo\n fi\n mkdir -p /sources/logs /sources/dags /sources/plugins\n chown -R \"${AIRFLOW_UID}:0\" /sources/{logs,dags,plugins}\n exec /entrypoint airflow version\n # yamllint enable rule:line-length\n environment:\n <<: *airflow-common-env\n _AIRFLOW_DB_MIGRATE: 'true'\n _AIRFLOW_WWW_USER_CREATE: 'true'\n _AIRFLOW_WWW_USER_USERNAME: ${_AIRFLOW_WWW_USER_USERNAME:-airflow}\n _AIRFLOW_WWW_USER_PASSWORD: ${_AIRFLOW_WWW_USER_PASSWORD:-airflow}\n _PIP_ADDITIONAL_REQUIREMENTS: ''\n user: '0:0'\n volumes:\n - ${AIRFLOW_PROJ_DIR:-.}:/sources\n\n airflow-cli:\n <<: *airflow-common\n profiles:\n - debug\n environment:\n <<: *airflow-common-env\n CONNECTION_CHECK_MAX_COUNT: '0'\n # Workaround for entrypoint issue. See: https://github.com/apache/airflow/issues/16252\n command:\n - bash\n - -c\n - airflow\n\n # You can enable flower by adding \"--profile flower\" option e.g. docker-compose --profile flower up\n # or by explicitly targeted on the command line e.g. docker-compose up flower.\n # See: https://docs.docker.com/compose/profiles/\n flower:\n <<: *airflow-common\n command: celery flower\n profiles:\n - flower\n ports:\n - '5555:5555'\n healthcheck:\n test: ['CMD', 'curl', '--fail', 'http://localhost:5555/']\n interval: 30s\n timeout: 10s\n retries: 5\n start_period: 30s\n restart: always\n depends_on:\n <<: *airflow-common-depends-on\n airflow-init:\n condition: service_completed_successfully\n networks:\n - airbyte_airbyte_public\n\nvolumes:\n postgres-db-volume:\nnetworks:\n airbyte_airbyte_public:\n external: true\n" }, { "path": "airbyte_dbt_airflow_bigquery/orchestration/requirements.txt", "content": "dbt-core~=1.6.0\nastronomer-cosmos~=1.1.0\nastronomer-cosmos[dbt-bigquery]~=1.1.0\napache-airflow-providers-google~=10.9.0\napache-airflow-providers-airbyte~=3.3.2" }, { "path": "airbyte_dbt_airflow_bigquery/setup.py", "content": "from setuptools import find_packages, setup\n\nsetup(\n name=\"airbyte-dbt-airflow-bigquery\",\n packages=find_packages(),\n install_requires=[\n \"dbt-bigquery\",\n \"astronomer-cosmos[dbt-bigquery]\",\n \"apache-airflow-providers-google\",\n \"apache-airflow-providers-airbyte\",\n \"apache-airflow\",\n ],\n extras_require={\"dev\": [\"pytest\"]},\n)\n\n\n" }, { "path": "airbyte_dbt_airflow_snowflake/.gitignore", "content": "# Byte-compiled / optimized / DLL files\n__pycache__/\n*.py[cod]\n*$py.class\n\n# C extensions\n*.so\n\n# Distribution / packaging\n.Python\nbuild/\ndevelop-eggs/\ndist/\ndownloads/\neggs/\n.eggs/\nlib/\nlib64/\nparts/\nsdist/\nvar/\nwheels/\nshare/python-wheels/\n*.egg-info/\n.installed.cfg\n*.egg\nMANIFEST\n\n# PyInstaller\n# Usually these files are written by a python script from a template\n# before PyInstaller builds the exe, so as to inject date/other infos into it.\n*.manifest\n*.spec\n\n# Installer logs\npip-log.txt\npip-delete-this-directory.txt\n\n# Unit test / coverage reports\nhtmlcov/\n.tox/\n.nox/\n.coverage\n.coverage.*\n.cache\nnosetests.xml\ncoverage.xml\n*.cover\n*.py,cover\n.hypothesis/\n.pytest_cache/\ncover/\n\n# Translations\n*.mo\n*.pot\n\n# Django stuff:\n*.log\nlocal_settings.py\ndb.sqlite3\ndb.sqlite3-journal\n\n# Flask stuff:\ninstance/\n.webassets-cache\n\n# Scrapy stuff:\n.scrapy\n\n# Sphinx documentation\ndocs/_build/\n\n# PyBuilder\n.pybuilder/\ntarget/\n\n# Jupyter Notebook\n.ipynb_checkpoints\n\n# IPython\nprofile_default/\nipython_config.py\n\n# pyenv\n# For a library or package, you might want to ignore these files since the code is\n# intended to run in multiple environments; otherwise, check them in:\n# .python-version\n\n# pipenv\n# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.\n# However, in case of collaboration, if having platform-specific dependencies or dependencies\n# having no cross-platform support, pipenv may install dependencies that don't work, or not\n# install all needed dependencies.\n#Pipfile.lock\n\n# poetry\n# Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.\n# This is especially recommended for binary packages to ensure reproducibility, and is more\n# commonly ignored for libraries.\n# https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control\n#poetry.lock\n\n# pdm\n# Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control.\n#pdm.lock\n# pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it\n# in version control.\n# https://pdm.fming.dev/#use-with-ide\n.pdm.toml\n\n# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm\n__pypackages__/\n\n# Celery stuff\ncelerybeat-schedule\ncelerybeat.pid\n\n# SageMath parsed files\n*.sage.py\n\n# Environments\n.env\n.venv\nenv/\nvenv/\nENV/\nenv.bak/\nvenv.bak/\n\n# Spyder project settings\n.spyderproject\n.spyproject\n\n# Rope project settings\n.ropeproject\n\n# mkdocs documentation\n/site\n\n# mypy\n.mypy_cache/\n.dmypy.json\ndmypy.json\n\n# Pyre type checker\n.pyre/\n\n# pytype static type analyzer\n.pytype/\n\n# Cython debug symbols\ncython_debug/\n\n# PyCharm\n# JetBrains specific template is maintained in a separate JetBrains.gitignore that can\n# be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore\n# and can be added to the global gitignore or merged into this file. For a more nuclear\n# option (not recommended) you can uncomment the following to ignore the entire idea folder.\n#.idea/\n\n#Desktop Services Store\n.DS_Store" }, { "path": "airbyte_dbt_airflow_snowflake/README.md", "content": "# Airbyte-dbt-Airflow-Snowflake Integration\n\nWelcome to the \"Airbyte-dbt-Airflow-Snowflake Integration\" repository! This repo provides a quickstart template for building a full data stack using Airbyte, Airflow, dbt, and Snowflake. Easily extract data from Postgres and load it into Snowflake using Airbyte, and apply necessary transformations using dbt, all orchestrated seamlessly with Airflow. While this template doesn't delve into specific data or transformations, its goal is to showcase the synergy of these tools.\n\nThis quickstart is designed to minimize setup hassles and propel you forward.\n\n## Table of Contents\n\n- [Airbyte-dbt-Airflow-Snowflake Integration](#airbyte-dbt-airflow-snowflake-integration)\n - [Table of Contents](#table-of-contents)\n - [Infrastructure Layout](#infrastructure-layout)\n - [Airflow Pipeline DAG](#airflow-pipeline-dag)\n - [Prerequisites](#prerequisites)\n - [1. Setting an environment for your project](#1-setting-an-environment-for-your-project)\n - [2. Setting Up Airbyte Connectors with Terraform](#2-setting-up-airbyte-connectors-with-terraform)\n - [3. Setting Up the dbt Project](#3-setting-up-the-dbt-project)\n - [4. Orchestrating with Airflow](#4-orchestrating-with-airflow)\n - [Next Steps](#next-steps)\n\n## Infrastructure Layout\n![insfrastructure layout](images/ada_snowflake_stack.png)\n\n## Airflow Pipeline DAG\n![pipeline dag](images/dag.png)\n\n## Prerequisites\n\nBefore you embark on this integration, ensure you have the following set up and ready:\n\n1. **Python 3.10 or later**: If not installed, download and install it from [Python's official website](https://www.python.org/downloads/).\n\n2. **Docker and Docker Compose (Docker Desktop)**: Install [Docker](https://docs.docker.com/get-docker/) following the official documentation for your specific OS.\n\n3. **Airbyte OSS version**: Deploy the open-source version of Airbyte. Follow the installation instructions from the [Airbyte Documentation](https://docs.airbyte.com/quickstart/deploy-airbyte/).\n\n4. **Terraform**: Terraform will help you provision and manage the Airbyte resources. If you haven't installed it, follow the [official Terraform installation guide](https://developer.hashicorp.com/terraform/tutorials/aws-get-started/install-cli).\n\n\n## 1. Setting an environment for your project\n\nGet the project up and running on your local machine by following these steps:\n\n1. **Clone the repository (Clone only this quickstart)**: \n ```bash\n git clone --filter=blob:none --sparse https://github.com/airbytehq/quickstarts.git\n ```\n\n ```bash\n cd quickstarts\n ```\n\n ```bash\n git sparse-checkout add airbyte_dbt_airflow_snowflake\n ```\n\n \n2. **Navigate to the directory**: \n ```bash\n cd airbyte_dbt_airflow_snowflake\n ```\n\n3. **Set Up a Virtual Environment**: \n - For Mac:\n ```bash\n python3 -m venv venv\n source venv/bin/activate\n ```\n - For Windows:\n ```bash\n python -m venv venv\n .\\venv\\Scripts\\activate\n ```\n\n4. **Install Dependencies**: \n ```bash\n pip install -e \".[dev]\"\n ```\n\n## 2. Setting Up Airbyte Connectors with Terraform\n\nAirbyte allows you to create connectors for sources and destinations, facilitating data synchronization between various platforms. In this project, we're harnessing the power of Terraform to automate the creation of these connectors and the connections between them. Here's how you can set this up:\n\n1. **Navigate to the Airbyte Configuration Directory**:\n \n Change to the relevant directory containing the Terraform configuration for Airbyte:\n ```bash\n cd infra/airbyte\n ```\n\n2. **Modify Configuration Files**:\n\n Within the `infra/airbyte` directory, you'll find three crucial Terraform files:\n - `provider.tf`: Defines the Airbyte provider.\n - `main.tf`: Contains the main configuration for creating Airbyte resources.\n - `variables.tf`: Holds various variables, including credentials.\n\n Adjust the configurations in these files to suit your project's needs. Specifically, provide credentials for your Postgres and Snowflake connections. You can utilize the `variables.tf` file to manage these credentials.\n\n3. **Initialize Terraform**:\n \n This step prepares Terraform to create the resources defined in your configuration files.\n ```bash\n terraform init\n ```\n\n4. **Review the Plan**:\n\n Before applying any changes, review the plan to understand what Terraform will do.\n ```bash\n terraform plan\n ```\n\n5. **Apply Configuration**:\n\n After reviewing and confirming the plan, apply the Terraform configurations to create the necessary Airbyte resources.\n ```bash\n terraform apply\n ```\n\n6. **Verify in Airbyte UI**:\n\n Once Terraform completes its tasks, navigate to the Airbyte UI. Here, you should see your source and destination connectors, as well as the connection between them, set up and ready to go.\n\n## 3. Setting Up the dbt Project\n\n[dbt (data build tool)](https://www.getdbt.com/) allows you to transform your data by writing, documenting, and executing SQL workflows. Setting up the dbt project requires specifying connection details for your data platform, in this case, Snowflake. Here’s a step-by-step guide to help you set this up:\n\n1. **Navigate to the dbt Project Directory**:\n\n Change to the directory containing the dbt configuration:\n ```bash\n cd ../../dbt_project\n ```\n\n2. **Update Connection Details**:\n\n You'll find a `profiles.yml` file within the directory. This file contains configurations for dbt to connect with your data platform. Update this file with your Snowflake connection details.\n\n3. **Utilize Environment Variables (Optional but Recommended)**:\n\n To keep your credentials secure, you can leverage environment variables. An example is provided within the `profiles.yml` file.\n\n4. **Test the Connection**:\n\n Once you’ve updated the connection details, you can test the connection to your Snowflake instance using:\n ```bash\n dbt debug\n ```\n\n If everything is set up correctly, this command should report a successful connection to Snowflake.\n\n## 4. Orchestrating with Airflow\n\n[Airflow](https://airflow.apache.org/) is a modern data orchestrator designed to help you build, test, and monitor your data workflows. In this section, we'll walk you through setting up Airflow to oversee both the Airbyte and dbt workflows:\n\n1. **Navigate to the Orchestration Directory**:\n\n Switch to the directory containing the Airflow orchestration configurations:\n ```bash\n cd ../../orchestration\n ```\n\n2. **Set Environment Variables**:\n\n The Airflow pipeline requires certain environment variables to run successfully. The variables will be set using the `.env` file. Populate the `.env` file with the contents of the `.env.example` file and modify to suit your use case. \n \n Particularly, modify the `AIRFLOW_AIRBYTE_CONN` value which is the connection URI that Airflow uses to connect to the Airbyte API. See [here](https://airflow.apache.org/docs/apache-airflow/2.0.2/howto/connection.html#connection-uri-format) for more details. \n \n Also modify the `AIRBYTE_CONN_ID` value which is the id of the connection you have set up in Airbyte.\n\n3. **Build and Run Airflow Locally**:\n\n Build our Airflow image with the necessary packages and services\n ```bash\n docker compose build\n ```\n\n And then run it\n ```bash\n docker compose up\n ```\n\n4. **Access Airflow in Your Browser**:\n\n When it's done, you can access the Airflow UI at `http://127.0.0.1:8080`. The default username and password are both `airflow`, unless you changed it on the `.env` file.\n\n Here, you should see the DAG for the Extract, Load and Transformation pipeline. To get an overview of DAG, click on the DAG's name and select the Graph view. This will give you a clear picture of the process lineage and visualize how the operation flows from extraction to transformation.\n\n## Next Steps\n\nOnce you've set up and launched this initial integration, the real power lies in its adaptability and extensibility. Here’s a roadmap to help you customize and harness this project tailored to your specific data needs:\n\n1. **Create dbt Sources for Airbyte Data**:\n\n Your raw data extracted via Airbyte can be represented as sources in dbt. Start by [creating new dbt sources](https://docs.getdbt.com/docs/build/sources) to represent this data, allowing for structured transformations down the line.\n\n2. **Add Your dbt Transformations**:\n\n With your dbt sources in place, you can now build upon them. Add your custom SQL transformations in dbt, ensuring that you treat the sources as an upstream dependency. This ensures that your transformations work on the most up-to-date raw data.\n\n3. **Execute the Pipeline in Airflow**:\n\n Navigate to the Airflow UI and Trigger the DAG. This triggers the entire pipeline, encompassing the extraction via Airbyte, transformations via dbt, and any other subsequent steps. Modify the schedule as well to suit your use case.\n\n4. **Extend the Project**:\n\n The real beauty of this integration is its extensibility. Whether you want to add more data sources, integrate additional tools, or enhance your transformation logic – the floor is yours. With the foundation set, sky's the limit for how you want to extend and refine your data processes." }, { "path": "airbyte_dbt_airflow_snowflake/dbt_project/.gitignore", "content": "\ntarget/\ndbt_packages/\nlogs/\n\n#Desktop Services Store\n.DS_Store\n\n#User cookie\n.user.yml" }, { "path": "airbyte_dbt_airflow_snowflake/dbt_project/README.md", "content": "Welcome to your new dbt project!\n\n### Using the starter project\n\nTry running the following commands:\n- dbt run\n- dbt test\n\n\n### Resources:\n- Learn more about dbt [in the docs](https://docs.getdbt.com/docs/introduction)\n- Check out [Discourse](https://discourse.getdbt.com/) for commonly asked questions and answers\n- Join the [chat](https://community.getdbt.com/) on Slack for live discussions and support\n- Find [dbt events](https://events.getdbt.com) near you\n- Check out [the blog](https://blog.getdbt.com/) for the latest news on dbt's development and best practices\n" }, { "path": "airbyte_dbt_airflow_snowflake/dbt_project/analyses/.gitkeep", "content": "" }, { "path": "airbyte_dbt_airflow_snowflake/dbt_project/dbt_project.yml", "content": "\n# Name your project! Project names should contain only lowercase characters\n# and underscores. A good package name should reflect your organization's\n# name or the intended use of these models\nname: 'dbt_project'\nversion: '1.0.0'\nconfig-version: 2\n\n# This setting configures which \"profile\" dbt uses for this project.\nprofile: 'dbt_project'\n\n# These configurations specify where dbt should look for different types of files.\n# The `model-paths` config, for example, states that models in this project can be\n# found in the \"models/\" directory. You probably won't need to change these!\nmodel-paths: [\"models\"]\nanalysis-paths: [\"analyses\"]\ntest-paths: [\"tests\"]\nseed-paths: [\"seeds\"]\nmacro-paths: [\"macros\"]\nsnapshot-paths: [\"snapshots\"]\n\nclean-targets: # directories to be removed by `dbt clean`\n - \"target\"\n - \"dbt_packages\"\n\n\n# Configuring models\n# Full documentation: https://docs.getdbt.com/docs/configuring-models\n\n# In this example config, we tell dbt to build all models in the example/\n# directory as views. These settings can be overridden in the individual model\n# files using the `{{ config(...) }}` macro.\nmodels:\n dbt_project:\n # Config indicated by + and applies to all files under models/example/\n example:\n +materialized: view\n" }, { "path": "airbyte_dbt_airflow_snowflake/dbt_project/macros/.gitkeep", "content": "" }, { "path": "airbyte_dbt_airflow_snowflake/dbt_project/models/example/my_first_dbt_model.sql", "content": "\n/*\n Welcome to your first dbt model!\n Did you know that you can also configure models directly within SQL files?\n This will override configurations stated in dbt_project.yml\n\n Try changing \"table\" to \"view\" below\n*/\n\n{{ config(materialized='table') }}\n\nwith source_data as (\n\n select * from {{ source('snowflake', 'sample_table') }}\n\n)\n\nselect *\nfrom source_data\n\n/*\n Uncomment the line below to remove records with null `id` values\n*/\n\n-- where id is not null\n" }, { "path": "airbyte_dbt_airflow_snowflake/dbt_project/models/example/my_second_dbt_model.sql", "content": "\n-- Use the `ref` function to select from other models\n\nselect *\nfrom {{ ref('my_first_dbt_model') }}\nwhere id = 1\n" }, { "path": "airbyte_dbt_airflow_snowflake/dbt_project/models/example/schema.yml", "content": "\nversion: 2\n\nmodels:\n - name: my_first_dbt_model\n description: \"A starter dbt model\"\n columns:\n - name: id\n description: \"The primary key for this table\"\n tests:\n - unique\n - not_null\n\n - name: my_second_dbt_model\n description: \"A starter dbt model\"\n columns:\n - name: id\n description: \"The primary key for this table\"\n tests:\n - unique\n - not_null\n" }, { "path": "airbyte_dbt_airflow_snowflake/dbt_project/models/sources.yml", "content": "version: 2\n\nsources:\n - name: snowflake\n tables:\n - name: sample_table\n" }, { "path": "airbyte_dbt_airflow_snowflake/dbt_project/profiles.yml", "content": "dbt_project:\n outputs:\n dev:\n\n type: snowflake\n account: \"{{ env_var('DBT_SNOWFLAKE_ACCOUNT_ID', '') }}\"\n\n # User/password auth\n user: username\n password: \"{{ env_var('DBT_SNOWFLAKE_PASSWORD', '') }}\"\n\n role: user_role\n database: database_name\n warehouse: warehouse_name\n schema: dbt_schema\n threads: 1\n client_session_keep_alive: False\n query_tag: anything\n\n # optional\n connect_retries: 0 # default 0\n connect_timeout: 10 # default: 10\n retry_on_database_errors: False # default: false\n retry_all: False # default: false\n reuse_connections: False # default: false (available v1.4+)\n\n target: dev" }, { "path": "airbyte_dbt_airflow_snowflake/dbt_project/seeds/.gitkeep", "content": "" }, { "path": "airbyte_dbt_airflow_snowflake/dbt_project/snapshots/.gitkeep", "content": "" }, { "path": "airbyte_dbt_airflow_snowflake/dbt_project/tests/.gitkeep", "content": "" }, { "path": "airbyte_dbt_airflow_snowflake/infra/.gitignore", "content": "# Local .terraform directories\n**/.terraform/*\n\n# .tfstate files\n*.tfstate\n*.tfstate.*\n\n# Crash log files\ncrash.log\ncrash.*.log\n\n# Exclude all .tfvars files, which are likely to contain sensitive data, such as\n# password, private keys, and other secrets. These should not be part of version \n# control as they are data points which are potentially sensitive and subject \n# to change depending on the environment.\n*.tfvars\n*.tfvars.json\n\n# Ignore override files as they are usually used to override resources locally and so\n# are not checked in\noverride.tf\noverride.tf.json\n*_override.tf\n*_override.tf.json\n\n# Include override files you do wish to add to version control using negated pattern\n# !example_override.tf\n\n# Include tfplan files to ignore the plan output of command: terraform plan -out=tfplan\n# example: *tfplan*\n\n# Ignore CLI configuration files\n.terraformrc\nterraform.rc" }, { "path": "airbyte_dbt_airflow_snowflake/infra/airbyte/main.tf", "content": "// Airbyte Terraform provider documentation: https://registry.terraform.io/providers/airbytehq/airbyte/latest/docs\n\n// Sources\nresource \"airbyte_source_postgres\" \"postgres\" {\n configuration = {\n database = \"...my_database...\"\n host = \"...my_host...\"\n username = \"...my_username...\"\n password = \"...my_password...\"\n port = 5432\n source_type = \"postgres\"\n schemas = [\n \"...my_schema...\"\n ]\n ssl_mode = {\n allow = {}\n }\n tunnel_method = {\n no_tunnel = {}\n }\n replication_method = {\n scan_changes_with_user_defined_cursor = {}\n }\n }\n name = \"Postgres\"\n workspace_id = var.workspace_id\n}\n\n// Destinations\nresource \"airbyte_destination_snowflake\" \"snowflake\" {\n configuration = {\n credentials = {\n destination_snowflake_authorization_method_key_pair_authentication = {\n auth_type = \"Key Pair Authentication\"\n private_key = \"...my_private_key...\"\n private_key_password = \"...my_private_key_password...\"\n }\n }\n database = \"AIRBYTE_DATABASE\"\n destination_type = \"snowflake\"\n host = \"accountname.us-east-2.aws.snowflakecomputing.com\"\n jdbc_url_params = \"...my_jdbc_url_params...\"\n raw_data_schema = \"...my_raw_data_schema...\"\n role = \"AIRBYTE_ROLE\"\n schema = \"AIRBYTE_SCHEMA\"\n username = \"AIRBYTE_USER\"\n warehouse = \"AIRBYTE_WAREHOUSE\"\n }\n name = \"Snowflake\"\n workspace_id = var.workspace_id\n} \n\n// Connections\nresource \"airbyte_connection\" \"postgres_to_snowflake\" {\n name = \"Postgres to Snowflake\"\n source_id = airbyte_source_postgres.postgres.source_id\n destination_id = airbyte_destination_snowflake.snowflake.destination_id\n configurations = {\n streams = [\n {\n name = \"...my_table_name_1...\"\n },\n {\n name = \"...my_table_name_2...\"\n },\n ]\n }\n}" }, { "path": "airbyte_dbt_airflow_snowflake/infra/airbyte/provider.tf", "content": "// Airbyte Terraform provider documentation: https://registry.terraform.io/providers/airbytehq/airbyte/latest/docs\n\nterraform {\n required_providers {\n airbyte = {\n source = \"airbytehq/airbyte\"\n version = \"0.3.4\"\n }\n }\n}\n\nprovider \"airbyte\" {\n // If running locally (Airbyte OSS) with docker-compose using the airbyte-proxy, \n // include the actual password/username you've set up (or use the defaults below)\n username = \"airbyte\"\n password = \"password\"\n \n // if running locally (Airbyte OSS), include the server url to the airbyte-api-server\n server_url = \"http://localhost:8006/v1\"\n}" }, { "path": "airbyte_dbt_airflow_snowflake/infra/airbyte/variables.tf", "content": "variable \"workspace_id\" {\n type = string\n}\n\n\n\n\n\n\n" }, { "path": "airbyte_dbt_airflow_snowflake/orchestration/.gitignore", "content": "logs\n__pycache__" }, { "path": "airbyte_dbt_airflow_snowflake/orchestration/Dockerfile", "content": "FROM apache/airflow:2.7.2-python3.11\nCOPY requirements.txt /\nRUN pip install --no-cache-dir -r /requirements.txt" }, { "path": "airbyte_dbt_airflow_snowflake/orchestration/README.md", "content": "# Airflow setup with Airbyte and DBT\n\nThis folder contains the code to setup Airflow with Airbyte and DBT.\n\n## Setup\n\nWe're using the [Running Airflow in Docker](https://airflow.apache.org/docs/apache-airflow/stable/howto/docker-compose/index.html) as a starting point.\n\nWe've downloaded the official `docker-compose.yaml` file provided by Airflow and adapted it to:\n- Use some configurations from an .env file\n- Add the Airbyte operator\n- Mount our dbt project folder into the container image\n- For running locally, we've set up the network to use the one deployed by the Airbyte container setup (from [Airbyte Local Deployment](https://docs.airbyte.com/deploying-airbyte/local-deployment))\n" }, { "path": "airbyte_dbt_airflow_snowflake/orchestration/airflow/dags/my_elt_dag.py", "content": "import pendulum, os\n\nfrom datetime import timedelta\n\nfrom airflow import DAG\nfrom airflow.operators.bash import BashOperator\nfrom airflow.operators.empty import EmptyOperator\nfrom airflow.sensors.python import PythonSensor\n\nfrom airflow.providers.airbyte.operators.airbyte import AirbyteTriggerSyncOperator\nfrom airflow.providers.airbyte.sensors.airbyte import AirbyteJobSensor\nfrom airflow.providers.airbyte.hooks.airbyte import AirbyteHook\n\n\n\nAIRFLOW_AIRBYTE_CONN_ID = os.getenv(\"AIRFLOW_AIRBYTE_CONN\") # The name of the Airflow connection to get connection information for Airbyte.\nAIRBYTE_CONNECTION_ID = os.getenv(\"AIRBYTE_CONN_ID\") # the Airbyte ConnectionId UUID between a source and destination.\nDBT_DIR = \"/opt/airflow/dbt_project\"\n\ndefault_args = {\n 'owner': 'airflow',\n 'depends_on_past': False,\n 'start_date': pendulum.today('UTC').add(days=-1),\n 'retries': 1,\n 'retry_delay': timedelta(minutes=5),\n }\n\ndef check_airbyte_health():\n airbyte_hook = AirbyteHook(airbyte_conn_id=AIRFLOW_AIRBYTE_CONN_ID)\n is_healthy, message = airbyte_hook.test_connection()\n print(message)\n return is_healthy\n\nwith DAG(\n dag_id='ELT_DAG',\n default_args=default_args,\n schedule='@daily',\n ) as dag:\n\n start_pipeline_task = EmptyOperator(task_id=\"start_pipeline\")\n end_pipeline_task = EmptyOperator(task_id=\"end_pipeline\")\n\n airbyte_precheck_task = PythonSensor(\n task_id=\"check_airbyte_health\",\n poke_interval=10,\n timeout=3600,\n mode=\"poke\",\n python_callable=check_airbyte_health,\n )\n \n trigger_airbyte_sync_task = AirbyteTriggerSyncOperator(\n task_id='airbyte_trigger_sync',\n airbyte_conn_id=AIRFLOW_AIRBYTE_CONN_ID,\n connection_id=AIRBYTE_CONNECTION_ID,\n asynchronous=True\n )\n\n wait_for_sync_completion_task = AirbyteJobSensor(\n task_id='airbyte_check_sync',\n airbyte_conn_id=AIRFLOW_AIRBYTE_CONN_ID,\n airbyte_job_id=trigger_airbyte_sync_task.output\n )\n\n run_dbt_check_task = BashOperator(\n task_id='run_dbt_precheck',\n bash_command='pwd && dbt debug && dbt list',\n cwd=DBT_DIR\n )\n\n run_dbt_model_task = BashOperator(\n task_id='run_dbt_model',\n bash_command='dbt run',\n cwd=DBT_DIR\n )\n\n start_pipeline_task >> airbyte_precheck_task >> trigger_airbyte_sync_task \\\n >> [wait_for_sync_completion_task, run_dbt_check_task] \\\n >> run_dbt_model_task >> end_pipeline_task\n" }, { "path": "airbyte_dbt_airflow_snowflake/orchestration/airflow/plugins/static/.gitkeep", "content": "" }, { "path": "airbyte_dbt_airflow_snowflake/orchestration/airflow/plugins/templates/dbt/.gitkeep", "content": "" }, { "path": "airbyte_dbt_airflow_snowflake/orchestration/docker-compose.yaml", "content": "# Licensed to the Apache Software Foundation (ASF) under one\n# or more contributor license agreements. See the NOTICE file\n# distributed with this work for additional information\n# regarding copyright ownership. The ASF licenses this file\n# to you under the Apache License, Version 2.0 (the\n# \"License\"); you may not use this file except in compliance\n# with the License. You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing,\n# software distributed under the License is distributed on an\n# \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\n# KIND, either express or implied. See the License for the\n# specific language governing permissions and limitations\n# under the License.\n#\n\n# Basic Airflow cluster configuration for CeleryExecutor with Redis and PostgreSQL.\n#\n# WARNING: This configuration is for local development. Do not use it in a production deployment.\n#\n# This configuration supports basic configuration using environment variables or an .env file\n# The following variables are supported:\n#\n# AIRFLOW_IMAGE_NAME - Docker image name used to run Airflow.\n# Default: apache/airflow:2.7.2\n# AIRFLOW_UID - User ID in Airflow containers\n# Default: 50000\n# AIRFLOW_PROJ_DIR - Base path to which all the files will be volumed.\n# Default: .\n# Those configurations are useful mostly in case of standalone testing/running Airflow in test/try-out mode\n#\n# _AIRFLOW_WWW_USER_USERNAME - Username for the administrator account (if requested).\n# Default: airflow\n# _AIRFLOW_WWW_USER_PASSWORD - Password for the administrator account (if requested).\n# Default: airflow\n# _PIP_ADDITIONAL_REQUIREMENTS - Additional PIP requirements to add when starting all containers.\n# Use this option ONLY for quick checks. Installing requirements at container\n# startup is done EVERY TIME the service is started.\n# A better way is to build a custom image or extend the official image\n# as described in https://airflow.apache.org/docs/docker-stack/build.html.\n# Default: ''\n#\n# Feel free to modify this file to suit your needs.\n---\nversion: '3.8'\nx-airflow-common: &airflow-common\n # In order to add custom dependencies or upgrade provider packages you can use your extended image.\n # Comment the image line, place your Dockerfile in the directory where you placed the docker-compose.yaml\n # and uncomment the \"build\" line below, Then run `docker-compose build` to build the images.\n # image: ${AIRFLOW_IMAGE_NAME:-apache/airflow:2.7.2}\n build: .\n env_file:\n - ./.env\n environment: &airflow-common-env\n AIRFLOW__CORE__EXECUTOR: CeleryExecutor\n AIRFLOW__DATABASE__SQL_ALCHEMY_CONN: postgresql+psycopg2://${POSTGRES_USER:-airflow}:${POSTGRES_PASSWORD:-airflow}@postgres/airflow\n # For backward compatibility, with Airflow <2.3\n AIRFLOW__CORE__SQL_ALCHEMY_CONN: postgresql+psycopg2://${POSTGRES_USER:-airflow}:${POSTGRES_PASSWORD:-airflow}@postgres/airflow\n AIRFLOW__CELERY__RESULT_BACKEND: db+postgresql://${POSTGRES_USER:-airflow}:${POSTGRES_PASSWORD:-airflow}@postgres/airflow\n AIRFLOW__CELERY__BROKER_URL: redis://:@redis:6379/0\n AIRFLOW__CORE__FERNET_KEY: ''\n AIRFLOW__CORE__DAGS_ARE_PAUSED_AT_CREATION: 'true'\n AIRFLOW__CORE__LOAD_EXAMPLES: ${LOAD_EXAMPLES:-true}\n AIRFLOW__API__AUTH_BACKENDS: 'airflow.api.auth.backend.basic_auth,airflow.api.auth.backend.session'\n AIRFLOW__SCHEDULER__ENABLE_HEALTH_CHECK: 'true'\n AIRFLOW__CORE__LAZY_LOAD_PLUGINS: 'false'\n # WARNING: Use _PIP_ADDITIONAL_REQUIREMENTS option ONLY for a quick checks\n # for other purpose (development, test and especially production usage) build/extend Airflow image.\n _PIP_ADDITIONAL_REQUIREMENTS: ${_PIP_ADDITIONAL_REQUIREMENTS:-}\n volumes:\n - ${AIRFLOW_PROJ_DIR:-.}/dags:/opt/airflow/dags\n - ${AIRFLOW_PROJ_DIR:-.}/logs:/opt/airflow/logs\n - ${AIRFLOW_PROJ_DIR:-.}/config:/opt/airflow/config\n - ${AIRFLOW_PROJ_DIR:-.}/plugins:/opt/airflow/plugins\n - ${DBT_PROJ_DIR:-.}:/opt/airflow/dbt_project\n user: '${AIRFLOW_UID:-50000}:0'\n depends_on: &airflow-common-depends-on\n redis:\n condition: service_healthy\n postgres:\n condition: service_healthy\n networks:\n - airbyte_airbyte_public\nservices:\n postgres:\n image: postgres:15\n environment:\n POSTGRES_USER: ${POSTGRES_USER:-airflow}\n POSTGRES_PASSWORD: ${POSTGRES_PASSWORD:-airflow}\n POSTGRES_DB: airflow\n volumes:\n - postgres-db-volume:/var/lib/postgresql/data\n healthcheck:\n test: ['CMD', 'pg_isready', '-U', 'airflow']\n interval: 10s\n retries: 5\n start_period: 5s\n restart: always\n networks:\n - airbyte_airbyte_public\n\n redis:\n image: redis:latest\n expose:\n - 6379\n healthcheck:\n test: ['CMD', 'redis-cli', 'ping']\n interval: 10s\n timeout: 30s\n retries: 50\n start_period: 30s\n restart: always\n networks:\n - airbyte_airbyte_public\n\n airflow-webserver:\n <<: *airflow-common\n command: webserver\n ports:\n - '8080:8080'\n healthcheck:\n test: ['CMD', 'curl', '--fail', 'http://localhost:8080/health']\n interval: 30s\n timeout: 10s\n retries: 5\n start_period: 30s\n restart: always\n depends_on:\n <<: *airflow-common-depends-on\n airflow-init:\n condition: service_completed_successfully\n\n airflow-scheduler:\n <<: *airflow-common\n command: scheduler\n healthcheck:\n test: ['CMD', 'curl', '--fail', 'http://localhost:8974/health']\n interval: 30s\n timeout: 10s\n retries: 5\n start_period: 30s\n restart: always\n depends_on:\n <<: *airflow-common-depends-on\n airflow-init:\n condition: service_completed_successfully\n\n airflow-worker:\n <<: *airflow-common\n command: celery worker\n healthcheck:\n # yamllint disable rule:line-length\n test:\n - 'CMD-SHELL'\n - 'celery --app airflow.providers.celery.executors.celery_executor.app inspect ping -d \"celery@$${HOSTNAME}\" || celery --app airflow.executors.celery_executor.app inspect ping -d \"celery@$${HOSTNAME}\"'\n interval: 30s\n timeout: 10s\n retries: 5\n start_period: 30s\n environment:\n <<: *airflow-common-env\n # Required to handle warm shutdown of the celery workers properly\n # See https://airflow.apache.org/docs/docker-stack/entrypoint.html#signal-propagation\n DUMB_INIT_SETSID: '0'\n restart: always\n depends_on:\n <<: *airflow-common-depends-on\n airflow-init:\n condition: service_completed_successfully\n\n airflow-triggerer:\n <<: *airflow-common\n command: triggerer\n healthcheck:\n test:\n [\n 'CMD-SHELL',\n 'airflow jobs check --job-type TriggererJob --hostname \"$${HOSTNAME}\"',\n ]\n interval: 30s\n timeout: 10s\n retries: 5\n start_period: 30s\n restart: always\n depends_on:\n <<: *airflow-common-depends-on\n airflow-init:\n condition: service_completed_successfully\n\n airflow-init:\n <<: *airflow-common\n entrypoint: /bin/bash\n # yamllint disable rule:line-length\n command:\n - -c\n - |\n function ver() {\n printf \"%04d%04d%04d%04d\" $${1//./ }\n }\n airflow_version=$$(AIRFLOW__LOGGING__LOGGING_LEVEL=INFO && gosu airflow airflow version)\n airflow_version_comparable=$$(ver $${airflow_version})\n min_airflow_version=2.2.0\n min_airflow_version_comparable=$$(ver $${min_airflow_version})\n if (( airflow_version_comparable < min_airflow_version_comparable )); then\n echo\n echo -e \"\\033[1;31mERROR!!!: Too old Airflow version $${airflow_version}!\\e[0m\"\n echo \"The minimum Airflow version supported: $${min_airflow_version}. Only use this or higher!\"\n echo\n exit 1\n fi\n if [[ -z \"${AIRFLOW_UID}\" ]]; then\n echo\n echo -e \"\\033[1;33mWARNING!!!: AIRFLOW_UID not set!\\e[0m\"\n echo \"If you are on Linux, you SHOULD follow the instructions below to set \"\n echo \"AIRFLOW_UID environment variable, otherwise files will be owned by root.\"\n echo \"For other operating systems you can get rid of the warning with manually created .env file:\"\n echo \" See: https://airflow.apache.org/docs/apache-airflow/stable/howto/docker-compose/index.html#setting-the-right-airflow-user\"\n echo\n fi\n one_meg=1048576\n mem_available=$$(($$(getconf _PHYS_PAGES) * $$(getconf PAGE_SIZE) / one_meg))\n cpus_available=$$(grep -cE 'cpu[0-9]+' /proc/stat)\n disk_available=$$(df / | tail -1 | awk '{print $$4}')\n warning_resources=\"false\"\n if (( mem_available < 4000 )) ; then\n echo\n echo -e \"\\033[1;33mWARNING!!!: Not enough memory available for Docker.\\e[0m\"\n echo \"At least 4GB of memory required. You have $$(numfmt --to iec $$((mem_available * one_meg)))\"\n echo\n warning_resources=\"true\"\n fi\n if (( cpus_available < 2 )); then\n echo\n echo -e \"\\033[1;33mWARNING!!!: Not enough CPUS available for Docker.\\e[0m\"\n echo \"At least 2 CPUs recommended. You have $${cpus_available}\"\n echo\n warning_resources=\"true\"\n fi\n if (( disk_available < one_meg * 10 )); then\n echo\n echo -e \"\\033[1;33mWARNING!!!: Not enough Disk space available for Docker.\\e[0m\"\n echo \"At least 10 GBs recommended. You have $$(numfmt --to iec $$((disk_available * 1024 )))\"\n echo\n warning_resources=\"true\"\n fi\n if [[ $${warning_resources} == \"true\" ]]; then\n echo\n echo -e \"\\033[1;33mWARNING!!!: You have not enough resources to run Airflow (see above)!\\e[0m\"\n echo \"Please follow the instructions to increase amount of resources available:\"\n echo \" https://airflow.apache.org/docs/apache-airflow/stable/howto/docker-compose/index.html#before-you-begin\"\n echo\n fi\n mkdir -p /sources/logs /sources/dags /sources/plugins\n chown -R \"${AIRFLOW_UID}:0\" /sources/{logs,dags,plugins}\n exec /entrypoint airflow version\n # yamllint enable rule:line-length\n environment:\n <<: *airflow-common-env\n _AIRFLOW_DB_MIGRATE: 'true'\n _AIRFLOW_WWW_USER_CREATE: 'true'\n _AIRFLOW_WWW_USER_USERNAME: ${_AIRFLOW_WWW_USER_USERNAME:-airflow}\n _AIRFLOW_WWW_USER_PASSWORD: ${_AIRFLOW_WWW_USER_PASSWORD:-airflow}\n _PIP_ADDITIONAL_REQUIREMENTS: ''\n user: '0:0'\n volumes:\n - ${AIRFLOW_PROJ_DIR:-.}:/sources\n\n airflow-cli:\n <<: *airflow-common\n profiles:\n - debug\n environment:\n <<: *airflow-common-env\n CONNECTION_CHECK_MAX_COUNT: '0'\n # Workaround for entrypoint issue. See: https://github.com/apache/airflow/issues/16252\n command:\n - bash\n - -c\n - airflow\n\n # You can enable flower by adding \"--profile flower\" option e.g. docker-compose --profile flower up\n # or by explicitly targeted on the command line e.g. docker-compose up flower.\n # See: https://docs.docker.com/compose/profiles/\n flower:\n <<: *airflow-common\n command: celery flower\n profiles:\n - flower\n ports:\n - '5555:5555'\n healthcheck:\n test: ['CMD', 'curl', '--fail', 'http://localhost:5555/']\n interval: 30s\n timeout: 10s\n retries: 5\n start_period: 30s\n restart: always\n depends_on:\n <<: *airflow-common-depends-on\n airflow-init:\n condition: service_completed_successfully\n networks:\n - airbyte_airbyte_public\n\nvolumes:\n postgres-db-volume:\nnetworks:\n airbyte_airbyte_public:\n external: true\n" }, { "path": "airbyte_dbt_airflow_snowflake/orchestration/requirements.txt", "content": "dbt-core~=1.6.0\ndbt-snowflake\napache-airflow-providers-airbyte~=3.3.2\n" }, { "path": "airbyte_dbt_airflow_snowflake/setup.py", "content": "from setuptools import find_packages, setup\n\nsetup(\n name=\"airbyte-dbt-airflow-snowflake\",\n packages=find_packages(),\n install_requires=[\n \"dbt-snowflake\",\n \"apache-airflow[airbyte]\",\n \"apache-airflow\",\n ],\n extras_require={\"dev\": [\"pytest\"]},\n)" }, { "path": "airbyte_dbt_dagster/.gitignore", "content": "# Byte-compiled / optimized / DLL files\n__pycache__/\n*.py[cod]\n*$py.class\n\n# C extensions\n*.so\n\n# Distribution / packaging\n.Python\nbuild/\ndevelop-eggs/\ndist/\ndownloads/\neggs/\n.eggs/\nlib/\nlib64/\nparts/\nsdist/\nvar/\nwheels/\nshare/python-wheels/\n*.egg-info/\n.installed.cfg\n*.egg\nMANIFEST\n\n# PyInstaller\n# Usually these files are written by a python script from a template\n# before PyInstaller builds the exe, so as to inject date/other infos into it.\n*.manifest\n*.spec\n\n# Installer logs\npip-log.txt\npip-delete-this-directory.txt\n\n# Unit test / coverage reports\nhtmlcov/\n.tox/\n.nox/\n.coverage\n.coverage.*\n.cache\nnosetests.xml\ncoverage.xml\n*.cover\n*.py,cover\n.hypothesis/\n.pytest_cache/\ncover/\n\n# Translations\n*.mo\n*.pot\n\n# Django stuff:\n*.log\nlocal_settings.py\ndb.sqlite3\ndb.sqlite3-journal\n\n# Flask stuff:\ninstance/\n.webassets-cache\n\n# Scrapy stuff:\n.scrapy\n\n# Sphinx documentation\ndocs/_build/\n\n# PyBuilder\n.pybuilder/\ntarget/\n\n# Jupyter Notebook\n.ipynb_checkpoints\n\n# IPython\nprofile_default/\nipython_config.py\n\n# pyenv\n# For a library or package, you might want to ignore these files since the code is\n# intended to run in multiple environments; otherwise, check them in:\n# .python-version\n\n# pipenv\n# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.\n# However, in case of collaboration, if having platform-specific dependencies or dependencies\n# having no cross-platform support, pipenv may install dependencies that don't work, or not\n# install all needed dependencies.\n#Pipfile.lock\n\n# poetry\n# Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.\n# This is especially recommended for binary packages to ensure reproducibility, and is more\n# commonly ignored for libraries.\n# https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control\n#poetry.lock\n\n# pdm\n# Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control.\n#pdm.lock\n# pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it\n# in version control.\n# https://pdm.fming.dev/#use-with-ide\n.pdm.toml\n\n# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm\n__pypackages__/\n\n# Celery stuff\ncelerybeat-schedule\ncelerybeat.pid\n\n# SageMath parsed files\n*.sage.py\n\n# Environments\n.env\n.venv\nenv/\nvenv/\nENV/\nenv.bak/\nvenv.bak/\n\n# Spyder project settings\n.spyderproject\n.spyproject\n\n# Rope project settings\n.ropeproject\n\n# mkdocs documentation\n/site\n\n# mypy\n.mypy_cache/\n.dmypy.json\ndmypy.json\n\n# Pyre type checker\n.pyre/\n\n# pytype static type analyzer\n.pytype/\n\n# Cython debug symbols\ncython_debug/\n\n# PyCharm\n# JetBrains specific template is maintained in a separate JetBrains.gitignore that can\n# be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore\n# and can be added to the global gitignore or merged into this file. For a more nuclear\n# option (not recommended) you can uncomment the following to ignore the entire idea folder.\n#.idea/\n\n#Desktop Services Store\n.DS_Store" }, { "path": "airbyte_dbt_dagster/README.md", "content": "# Airbyte-dbt-Dagster Integration\n\nWelcome to the \"Airbyte-dbt-Dagster Integration\" repository! This repo provides a quickstart template for building a full data stack using Airbyte, Dagster, dbt, and BigQuery. Easily extract data from Postgres, load it into BigQuery, and apply necessary transformations using dbt, all orchestrated seamlessly with Dagster. While this template doesn't delve into specific data or transformations, its goal is to showcase the synergy of these tools.\n\nThis quickstart is designed to minimize setup hassles and propel you forward.\n\n## Table of Contents\n\n- [Prerequisites](#prerequisites)\n- [Setting an environment for your project](#1-setting-an-environment-for-your-project)\n- [Setting Up Airbyte Connectors with Terraform](#2-setting-up-airbyte-connectors-with-terraform)\n- [Setting Up the dbt Project](#3-setting-up-the-dbt-project)\n- [Orchestrating with Dagster](#4-orchestrating-with-dagster)\n- [Next Steps](#next-steps)\n\n## Prerequisites\n\nBefore you embark on this integration, ensure you have the following set up and ready:\n\n1. **Python 3.10 or later**: If not installed, download and install it from [Python's official website](https://www.python.org/downloads/).\n\n2. **Docker and Docker Compose (Docker Desktop)**: Install [Docker](https://docs.docker.com/get-docker/) following the official documentation for your specific OS.\n\n3. **Airbyte OSS version**: Deploy the open-source version of Airbyte. Follow the installation instructions from the [Airbyte Documentation](https://docs.airbyte.com/quickstart/deploy-airbyte/).\n\n4. **Terraform**: Terraform will help you provision and manage the Airbyte resources. If you haven't installed it, follow the [official Terraform installation guide](https://developer.hashicorp.com/terraform/tutorials/aws-get-started/install-cli).\n\n\n## 1. Setting an environment for your project\n\nGet the project up and running on your local machine by following these steps:\n\n1. **Clone the repository (Clone only this quickstart)**: \n ```bash\n git clone --filter=blob:none --sparse https://github.com/airbytehq/quickstarts.git\n ```\n\n ```bash\n cd quickstarts\n ```\n\n ```bash\n git sparse-checkout add airbyte_dbt_dagster\n ```\n\n \n2. **Navigate to the directory**: \n ```bash\n cd airbyte_dbt_dagster\n ```\n\n3. **Set Up a Virtual Environment**: \n - For Mac:\n ```bash\n python3 -m venv venv\n source venv/bin/activate\n ```\n - For Windows:\n ```bash\n python -m venv venv\n .\\venv\\Scripts\\activate\n ```\n\n4. **Install Dependencies**: \n ```bash\n pip install -e \".[dev]\"\n ```\n\n## 2. Setting Up Airbyte Connectors with Terraform\n\nAirbyte allows you to create connectors for sources and destinations, facilitating data synchronization between various platforms. In this project, we're harnessing the power of Terraform to automate the creation of these connectors and the connections between them. Here's how you can set this up:\n\n1. **Navigate to the Airbyte Configuration Directory**:\n \n Change to the relevant directory containing the Terraform configuration for Airbyte:\n ```bash\n cd infra/airbyte\n ```\n\n2. **Modify Configuration Files**:\n\n Within the `infra/airbyte` directory, you'll find three crucial Terraform files:\n - `provider.tf`: Defines the Airbyte provider.\n - `main.tf`: Contains the main configuration for creating Airbyte resources.\n - `variables.tf`: Holds various variables, including credentials.\n\n Adjust the configurations in these files to suit your project's needs. Specifically, provide credentials for your Postgres and BigQuery connections. You can utilize the `variables.tf` file to manage these credentials.\n\n3. **Initialize Terraform**:\n \n This step prepares Terraform to create the resources defined in your configuration files.\n ```bash\n terraform init\n ```\n\n4. **Review the Plan**:\n\n Before applying any changes, review the plan to understand what Terraform will do.\n ```bash\n terraform plan\n ```\n\n5. **Apply Configuration**:\n\n After reviewing and confirming the plan, apply the Terraform configurations to create the necessary Airbyte resources.\n ```bash\n terraform apply\n ```\n\n6. **Verify in Airbyte UI**:\n\n Once Terraform completes its tasks, navigate to the Airbyte UI. Here, you should see your source and destination connectors, as well as the connection between them, set up and ready to go.\n\n## 3. Setting Up the dbt Project\n\n[dbt (data build tool)](https://www.getdbt.com/) allows you to transform your data by writing, documenting, and executing SQL workflows. Setting up the dbt project requires specifying connection details for your data platform, in this case, BigQuery. Here’s a step-by-step guide to help you set this up:\n\n1. **Navigate to the dbt Project Directory**:\n\n Change to the directory containing the dbt configuration:\n ```bash\n cd dbt_project\n ```\n\n2. **Update Connection Details**:\n\n You'll find a `profiles.yml` file within the directory. This file contains configurations for dbt to connect with your data platform. Update this file with your BigQuery connection details.\n\n3. **Utilize Environment Variables (Optional but Recommended)**:\n\n To keep your credentials secure, you can leverage environment variables. An example is provided within the `profiles.yml` file.\n\n4. **Test the Connection**:\n\n Once you’ve updated the connection details, you can test the connection to your BigQuery instance using:\n ```bash\n dbt debug\n ```\n\n If everything is set up correctly, this command should report a successful connection to BigQuery.\n\n## 4. Orchestrating with Dagster\n\n[Dagster](https://dagster.io/) is a modern data orchestrator designed to help you build, test, and monitor your data workflows. In this section, we'll walk you through setting up Dagster to oversee both the Airbyte and dbt workflows:\n\n1. **Navigate to the Orchestration Directory**:\n\n Switch to the directory containing the Dagster orchestration configurations:\n ```bash\n cd orchestration\n ```\n\n2. **Set Environment Variables**:\n\n Dagster requires certain environment variables to be set to interact with other tools like dbt and Airbyte. Set the following variables:\n\n ```bash\n export DAGSTER_DBT_PARSE_PROJECT_ON_LOAD=1\n export AIRBYTE_PASSWORD=password\n ```\n \n Note: The `AIRBYTE_PASSWORD` is set to `password` as a default for local Airbyte instances. If you've changed this during your Airbyte setup, ensure you use the appropriate password here.\n\n3. **Launch the Dagster UI**:\n\n With the environment variables in place, kick-start the Dagster UI:\n ```bash\n dagster dev\n ```\n\n4. **Access Dagster in Your Browser**:\n\n Open your browser and navigate to:\n ```\n http://127.0.0.1:3000\n ```\n\n Here, you should see assets for both Airbyte and dbt. To get an overview of how these assets interrelate, click on \"view global asset lineage\". This will give you a clear picture of the data lineage, visualizing how data flows between the tools.\n\n## Next Steps\n\nOnce you've set up and launched this initial integration, the real power lies in its adaptability and extensibility. Here’s a roadmap to help you customize and harness this project tailored to your specific data needs:\n\n1. **Create dbt Sources for Airbyte Data**:\n\n Your raw data extracted via Airbyte can be represented as sources in dbt. Start by [creating new dbt sources](https://docs.getdbt.com/docs/build/sources) to represent this data, allowing for structured transformations down the line.\n\n2. **Add Your dbt Transformations**:\n\n With your dbt sources in place, you can now build upon them. Add your custom SQL transformations in dbt, ensuring that you treat the sources as an upstream dependency. This ensures that your transformations work on the most up-to-date raw data.\n\n3. **Execute the Pipeline in Dagster**:\n\n Navigate to the Dagster UI and click on \"Materialize all\". This triggers the entire pipeline, encompassing the extraction via Airbyte, transformations via dbt, and any other subsequent steps.\n\n4. **Extend the Project**:\n\n The real beauty of this integration is its extensibility. Whether you want to add more data sources, integrate additional tools, or enhance your transformation logic – the floor is yours. With the foundation set, sky's the limit for how you want to extend and refine your data processes." }, { "path": "airbyte_dbt_dagster/dbt_project/.gitignore", "content": "\ntarget/\ndbt_packages/\nlogs/\n\n#Desktop Services Store\n.DS_Store\n\n#User cookie\n.user.yml" }, { "path": "airbyte_dbt_dagster/dbt_project/README.md", "content": "Welcome to your new dbt project!\n\n### Using the starter project\n\nTry running the following commands:\n- dbt run\n- dbt test\n\n\n### Resources:\n- Learn more about dbt [in the docs](https://docs.getdbt.com/docs/introduction)\n- Check out [Discourse](https://discourse.getdbt.com/) for commonly asked questions and answers\n- Join the [chat](https://community.getdbt.com/) on Slack for live discussions and support\n- Find [dbt events](https://events.getdbt.com) near you\n- Check out [the blog](https://blog.getdbt.com/) for the latest news on dbt's development and best practices\n" }, { "path": "airbyte_dbt_dagster/dbt_project/analyses/.gitkeep", "content": "" }, { "path": "airbyte_dbt_dagster/dbt_project/dbt_project.yml", "content": "\n# Name your project! Project names should contain only lowercase characters\n# and underscores. A good package name should reflect your organization's\n# name or the intended use of these models\nname: 'dbt_project'\nversion: '1.0.0'\nconfig-version: 2\n\n# This setting configures which \"profile\" dbt uses for this project.\nprofile: 'dbt_project'\n\n# These configurations specify where dbt should look for different types of files.\n# The `model-paths` config, for example, states that models in this project can be\n# found in the \"models/\" directory. You probably won't need to change these!\nmodel-paths: [\"models\"]\nanalysis-paths: [\"analyses\"]\ntest-paths: [\"tests\"]\nseed-paths: [\"seeds\"]\nmacro-paths: [\"macros\"]\nsnapshot-paths: [\"snapshots\"]\n\nclean-targets: # directories to be removed by `dbt clean`\n - \"target\"\n - \"dbt_packages\"\n\n\n# Configuring models\n# Full documentation: https://docs.getdbt.com/docs/configuring-models\n\n# In this example config, we tell dbt to build all models in the example/\n# directory as views. These settings can be overridden in the individual model\n# files using the `{{ config(...) }}` macro.\nmodels:\n dbt_project:\n # Config indicated by + and applies to all files under models/example/\n example:\n +materialized: view\n" }, { "path": "airbyte_dbt_dagster/dbt_project/macros/.gitkeep", "content": "" }, { "path": "airbyte_dbt_dagster/dbt_project/models/example/my_first_dbt_model.sql", "content": "\n/*\n Welcome to your first dbt model!\n Did you know that you can also configure models directly within SQL files?\n This will override configurations stated in dbt_project.yml\n\n Try changing \"table\" to \"view\" below\n*/\n\n{{ config(materialized='table') }}\n\nwith source_data as (\n\n select 1 as id\n union all\n select null as id\n\n)\n\nselect *\nfrom source_data\n\n/*\n Uncomment the line below to remove records with null `id` values\n*/\n\n-- where id is not null\n" }, { "path": "airbyte_dbt_dagster/dbt_project/models/example/my_second_dbt_model.sql", "content": "\n-- Use the `ref` function to select from other models\n\nselect *\nfrom {{ ref('my_first_dbt_model') }}\nwhere id = 1\n" }, { "path": "airbyte_dbt_dagster/dbt_project/models/example/schema.yml", "content": "\nversion: 2\n\nmodels:\n - name: my_first_dbt_model\n description: \"A starter dbt model\"\n columns:\n - name: id\n description: \"The primary key for this table\"\n tests:\n - unique\n - not_null\n\n - name: my_second_dbt_model\n description: \"A starter dbt model\"\n columns:\n - name: id\n description: \"The primary key for this table\"\n tests:\n - unique\n - not_null\n" }, { "path": "airbyte_dbt_dagster/dbt_project/profiles.yml", "content": "dbt_project:\n outputs:\n dev:\n dataset: my_dataset\n job_execution_timeout_seconds: 300\n job_retries: 1\n keyfile: \"{{ env_var('DBT_BIGQUERY_KEYFILE_PATH', '') }}\"\n location: my_dataset_location\n method: service-account\n priority: interactive\n project: my_project_id\n threads: 1\n type: bigquery\n target: dev" }, { "path": "airbyte_dbt_dagster/dbt_project/seeds/.gitkeep", "content": "" }, { "path": "airbyte_dbt_dagster/dbt_project/snapshots/.gitkeep", "content": "" }, { "path": "airbyte_dbt_dagster/dbt_project/tests/.gitkeep", "content": "" }, { "path": "airbyte_dbt_dagster/infra/.gitignore", "content": "# Local .terraform directories\n**/.terraform/*\n\n# .tfstate files\n*.tfstate\n*.tfstate.*\n\n# Crash log files\ncrash.log\ncrash.*.log\n\n# Exclude all .tfvars files, which are likely to contain sensitive data, such as\n# password, private keys, and other secrets. These should not be part of version \n# control as they are data points which are potentially sensitive and subject \n# to change depending on the environment.\n*.tfvars\n*.tfvars.json\n\n# Ignore override files as they are usually used to override resources locally and so\n# are not checked in\noverride.tf\noverride.tf.json\n*_override.tf\n*_override.tf.json\n\n# Include override files you do wish to add to version control using negated pattern\n# !example_override.tf\n\n# Include tfplan files to ignore the plan output of command: terraform plan -out=tfplan\n# example: *tfplan*\n\n# Ignore CLI configuration files\n.terraformrc\nterraform.rc" }, { "path": "airbyte_dbt_dagster/infra/airbyte/.terraform.lock.hcl", "content": "# This file is maintained automatically by \"terraform init\".\n# Manual edits may be lost in future updates.\n\nprovider \"registry.terraform.io/airbytehq/airbyte\" {\n version = \"0.3.3\"\n constraints = \"0.3.3\"\n hashes = [\n \"h1:a6g5uWP/pt1/popVNlKwnTssWNfdYY4KVFPMisN/yvU=\",\n \"zh:0efa470b34d9b912b47efe4469c51713bfc3c2413e52c17e1e903f2a3cddb2f6\",\n \"zh:1bddd69fa2c2d4f3e239d60555446df9bc4ce0c0cabbe7e092fe1d44989ab004\",\n \"zh:2e20540403a0010007b53456663fb037b24e30f6c8943f65da1bcf7fa4dfc8a6\",\n \"zh:2f415369ad884e8b7115a5c5ff229d052f7af1fca27abbfc8ebef379ed11aec4\",\n \"zh:46fd9a906f4b6461112dcc5a5aa01a3fcd7a19a72d4ad0b2e37790da37701fe1\",\n \"zh:83503ebb77bb6d6941c42ba323cf22380d08a1506554a2dcc8ac54e74c0886a1\",\n \"zh:890df766e9b839623b1f0437355032a3c006226a6c200cd911e15ee1a9014e9f\",\n \"zh:8fd770eff726826d3a63b9e3733c5455b5cde004027b04ee3f75888eb8538c90\",\n \"zh:b0fc890ed4f9b077bf70ed121cc3550e7a07d16e7798ad517623274aa62ad7b0\",\n \"zh:c2a01612362da9b73cd5958f281e1aa7ff09af42182e463097d11ed78e778e72\",\n \"zh:c64b2bb1887a0367d64ba3393d4b3a16c418cf5b1792e2e7aae7c0b5413eb334\",\n \"zh:ce14ebbf0ed91913ec62655a511763dec62b5779de9a209bd6f1c336640cddc0\",\n \"zh:e0662ca837eee10f7733ea9a501d995281f56bd9b410ae13ad03eb106011db14\",\n \"zh:e103d480fc6066004bc98e9e04a141a1f55b918cc2912716beebcc6fc4c872fb\",\n \"zh:e2507049098f0f1b21cb56870f4a5ef624bcf6d3959e5612eada1f8117341648\",\n ]\n}\n" }, { "path": "airbyte_dbt_dagster/infra/airbyte/main.tf", "content": "// Airbyte Terraform provider documentation: https://registry.terraform.io/providers/airbytehq/airbyte/latest/docs\n\n// Sources\nresource \"airbyte_source_postgres\" \"postgres\" {\n configuration = {\n database = \"...my_database...\"\n host = \"...my_host...\"\n username = \"...my_username...\"\n password = \"...my_password...\"\n port = 5432\n source_type = \"postgres\"\n schemas = [\n \"...my_schema...\"\n ]\n ssl_mode = {\n allow = {}\n }\n tunnel_method = {\n no_tunnel = {}\n }\n replication_method = {}\n }\n name = \"Postgres\"\n workspace_id = var.workspace_id\n}\n\n// Destinations\nresource \"airbyte_destination_bigquery\" \"bigquery\" {\n configuration = {\n dataset_id = \"...my_dataset_id...\"\n dataset_location = \"...my_dataset_location...\"\n destination_type = \"bigquery\"\n project_id = \"...my_project_id...\"\n credentials_json = \"...my_credentials_json_file_path...\"\n loading_method = {\n destination_bigquery_loading_method_standard_inserts = {\n method = \"Standard\"\n }\n }\n }\n name = \"BigQuery\"\n workspace_id = var.workspace_id\n}\n\n// Connections\nresource \"airbyte_connection\" \"postgres_to_bigquery\" {\n name = \"Postgres to BigQuery\"\n source_id = airbyte_source_postgres.postgres.source_id\n destination_id = airbyte_destination_bigquery.bigquery.destination_id\n configurations = {\n streams = [\n {\n name = \"...my_table_name_1...\"\n },\n {\n name = \"...my_table_name_2...\"\n },\n ]\n }\n}" }, { "path": "airbyte_dbt_dagster/infra/airbyte/provider.tf", "content": "// Airbyte Terraform provider documentation: https://registry.terraform.io/providers/airbytehq/airbyte/latest/docs\n\nterraform {\n required_providers {\n airbyte = {\n source = \"airbytehq/airbyte\"\n version = \"0.3.3\"\n }\n }\n}\n\nprovider \"airbyte\" {\n // If running locally (Airbyte OSS) with docker-compose using the airbyte-proxy, \n // include the actual password/username you've set up (or use the defaults below)\n username = \"airbyte\"\n password = \"password\"\n \n // if running locally (Airbyte OSS), include the server url to the airbyte-api-server\n server_url = \"http://localhost:8006/v1\"\n}" }, { "path": "airbyte_dbt_dagster/infra/airbyte/variables.tf", "content": "variable \"workspace_id\" {\n type = string\n}\n\n\n\n\n\n\n" }, { "path": "airbyte_dbt_dagster/orchestration/orchestration/__init__.py", "content": "" }, { "path": "airbyte_dbt_dagster/orchestration/orchestration/assets.py", "content": "import os\nfrom dagster import OpExecutionContext\nfrom dagster_dbt import DbtCliResource, dbt_assets\nfrom dagster_airbyte import AirbyteResource, load_assets_from_airbyte_instance\n\nfrom .constants import dbt_manifest_path\n\n@dbt_assets(manifest=dbt_manifest_path)\ndef dbt_project_dbt_assets(context: OpExecutionContext, dbt: DbtCliResource):\n yield from dbt.cli([\"build\"], context=context).stream()\n\nairbyte_instance = AirbyteResource(\n host=\"localhost\",\n port=\"8000\",\n # If using basic auth, include username and password:\n username=\"airbyte\",\n password=os.getenv(\"AIRBYTE_PASSWORD\")\n)\n\nairbyte_assets = load_assets_from_airbyte_instance(airbyte_instance)" }, { "path": "airbyte_dbt_dagster/orchestration/orchestration/constants.py", "content": "import os\nfrom pathlib import Path\n\nfrom dagster_dbt import DbtCliResource\n\ndbt_project_dir = Path(__file__).joinpath(\"..\", \"..\", \"..\", \"dbt_project\").resolve()\ndbt = DbtCliResource(project_dir=os.fspath(dbt_project_dir))\n\n# If DAGSTER_DBT_PARSE_PROJECT_ON_LOAD is set, a manifest will be created at runtime.\n# Otherwise, we expect a manifest to be present in the project's target directory.\nif os.getenv(\"DAGSTER_DBT_PARSE_PROJECT_ON_LOAD\"):\n dbt_parse_invocation = dbt.cli([\"parse\"], manifest={}).wait()\n dbt_manifest_path = dbt_parse_invocation.target_path.joinpath(\"manifest.json\")\nelse:\n dbt_manifest_path = dbt_project_dir.joinpath(\"target\", \"manifest.json\")" }, { "path": "airbyte_dbt_dagster/orchestration/orchestration/definitions.py", "content": "import os\n\nfrom dagster import Definitions\nfrom dagster_dbt import DbtCliResource\n\nfrom .assets import dbt_project_dbt_assets, airbyte_assets\nfrom .constants import dbt_project_dir\nfrom .schedules import schedules\n\ndefs = Definitions(\n assets=[dbt_project_dbt_assets, airbyte_assets],\n schedules=schedules,\n resources={\n \"dbt\": DbtCliResource(project_dir=os.fspath(dbt_project_dir)),\n },\n)" }, { "path": "airbyte_dbt_dagster/orchestration/orchestration/schedules.py", "content": "\"\"\"\nTo add a daily schedule that materializes your dbt assets, uncomment the following lines.\n\"\"\"\nfrom dagster_dbt import build_schedule_from_dbt_selection\n\nfrom .assets import dbt_project_dbt_assets\n\nschedules = [\n# build_schedule_from_dbt_selection(\n# [dbt_project_dbt_assets],\n# job_name=\"materialize_dbt_models\",\n# cron_schedule=\"0 0 * * *\",\n# dbt_select=\"fqn:*\",\n# ),\n]" }, { "path": "airbyte_dbt_dagster/orchestration/pyproject.toml", "content": "[build-system]\nrequires = [\"setuptools\"]\nbuild-backend = \"setuptools.build_meta\"\n\n[tool.dagster]\nmodule_name = \"orchestration.definitions\"\ncode_location_name = \"orchestration\"" }, { "path": "airbyte_dbt_dagster/orchestration/setup.py", "content": "from setuptools import find_packages, setup\n\nsetup(\n name=\"orchestration\",\n version=\"0.0.1\",\n packages=find_packages(),\n install_requires=[\n \"dagster\",\n \"dagster-cloud\",\n \"dagster-dbt\",\n \"dbt-core>=1.4.0\",\n \"dbt-bigquery\",\n ],\n extras_require={\n \"dev\": [\n \"dagster-webserver\",\n ]\n },\n)" }, { "path": "airbyte_dbt_dagster/setup.py", "content": "from setuptools import find_packages, setup\n\nsetup(\n name=\"airbyte-dbt-dagster\",\n packages=find_packages(),\n install_requires=[\n \"dbt-bigquery\",\n \"dagster\",\n \"dagster-cloud\",\n \"dagster-dbt\",\n \"dagster-airbyte\",\n ],\n extras_require={\"dev\": [\"dagit\", \"pytest\"]},\n)" }, { "path": "airbyte_dbt_dagster_snowflake/.gitignore", "content": "# Byte-compiled / optimized / DLL files\n__pycache__/\n*.py[cod]\n*$py.class\n\n# C extensions\n*.so\n\n# Distribution / packaging\n.Python\nbuild/\ndevelop-eggs/\ndist/\ndownloads/\neggs/\n.eggs/\nlib/\nlib64/\nparts/\nsdist/\nvar/\nwheels/\nshare/python-wheels/\n*.egg-info/\n.installed.cfg\n*.egg\nMANIFEST\n\n# PyInstaller\n# Usually these files are written by a python script from a template\n# before PyInstaller builds the exe, so as to inject date/other infos into it.\n*.manifest\n*.spec\n\n# Installer logs\npip-log.txt\npip-delete-this-directory.txt\n\n# Unit test / coverage reports\nhtmlcov/\n.tox/\n.nox/\n.coverage\n.coverage.*\n.cache\nnosetests.xml\ncoverage.xml\n*.cover\n*.py,cover\n.hypothesis/\n.pytest_cache/\ncover/\n\n# Translations\n*.mo\n*.pot\n\n# Django stuff:\n*.log\nlocal_settings.py\ndb.sqlite3\ndb.sqlite3-journal\n\n# Flask stuff:\ninstance/\n.webassets-cache\n\n# Scrapy stuff:\n.scrapy\n\n# Sphinx documentation\ndocs/_build/\n\n# PyBuilder\n.pybuilder/\ntarget/\n\n# Jupyter Notebook\n.ipynb_checkpoints\n\n# IPython\nprofile_default/\nipython_config.py\n\n# pyenv\n# For a library or package, you might want to ignore these files since the code is\n# intended to run in multiple environments; otherwise, check them in:\n# .python-version\n\n# pipenv\n# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.\n# However, in case of collaboration, if having platform-specific dependencies or dependencies\n# having no cross-platform support, pipenv may install dependencies that don't work, or not\n# install all needed dependencies.\n#Pipfile.lock\n\n# poetry\n# Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.\n# This is especially recommended for binary packages to ensure reproducibility, and is more\n# commonly ignored for libraries.\n# https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control\n#poetry.lock\n\n# pdm\n# Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control.\n#pdm.lock\n# pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it\n# in version control.\n# https://pdm.fming.dev/#use-with-ide\n.pdm.toml\n\n# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm\n__pypackages__/\n\n# Celery stuff\ncelerybeat-schedule\ncelerybeat.pid\n\n# SageMath parsed files\n*.sage.py\n\n# Environments\n.env\n.venv\nenv/\nvenv/\nENV/\nenv.bak/\nvenv.bak/\n\n# Spyder project settings\n.spyderproject\n.spyproject\n\n# Rope project settings\n.ropeproject\n\n# mkdocs documentation\n/site\n\n# mypy\n.mypy_cache/\n.dmypy.json\ndmypy.json\n\n# Pyre type checker\n.pyre/\n\n# pytype static type analyzer\n.pytype/\n\n# Cython debug symbols\ncython_debug/\n\n# PyCharm\n# JetBrains specific template is maintained in a separate JetBrains.gitignore that can\n# be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore\n# and can be added to the global gitignore or merged into this file. For a more nuclear\n# option (not recommended) you can uncomment the following to ignore the entire idea folder.\n#.idea/\n\n#Desktop Services Store\n.DS_Store" }, { "path": "airbyte_dbt_dagster_snowflake/README.md", "content": "# Airbyte-dbt-Dagster-Snowflake Integration\n\nWelcome to the \"Airbyte-dbt-Dagster-Snowflake Integration\" repository! This repo provides a quickstart template for building a full data stack using Airbyte, Dagster, dbt, and Snowflake. Easily extract data from Postgres and load it into Snowflake using Airbyte, and apply necessary transformations using dbt, all orchestrated seamlessly with Dagster. While this template doesn't delve into specific data or transformations, its goal is to showcase the synergy of these tools.\n\nThis quickstart is designed to minimize setup hassles and propel you forward.\n\n## Table of Contents\n\n- [Airbyte-dbt-Dagster-Snowflake Integration](#airbyte-dbt-dagster-snowflake-integration)\n - [Table of Contents](#table-of-contents)\n - [Infrastructure Layout](#infrastructure-layout)\n - [Pipeline DAG](#pipeline-dag)\n - [Prerequisites](#prerequisites)\n - [1. Setting an environment for your project](#1-setting-an-environment-for-your-project)\n - [2. Setting Up Airbyte Connectors with Terraform](#2-setting-up-airbyte-connectors-with-terraform)\n - [3. Setting Up the dbt Project](#3-setting-up-the-dbt-project)\n - [4. Orchestrating with Dagster](#4-orchestrating-with-dagster)\n - [Next Steps](#next-steps)\n\n## Infrastructure Layout\n![insfrastructure layout](images/dad_snowflake_stack.png)\n\n## Pipeline DAG\n![pipeline dag](images/dag.svg)\n\n## Prerequisites\n\nBefore you embark on this integration, ensure you have the following set up and ready:\n\n1. **Python 3.10 or later**: If not installed, download and install it from [Python's official website](https://www.python.org/downloads/).\n\n2. **Docker and Docker Compose (Docker Desktop)**: Install [Docker](https://docs.docker.com/get-docker/) following the official documentation for your specific OS.\n\n3. **Airbyte OSS version**: Deploy the open-source version of Airbyte. Follow the installation instructions from the [Airbyte Documentation](https://docs.airbyte.com/quickstart/deploy-airbyte/).\n\n4. **Terraform**: Terraform will help you provision and manage the Airbyte resources. If you haven't installed it, follow the [official Terraform installation guide](https://developer.hashicorp.com/terraform/tutorials/aws-get-started/install-cli).\n\n\n## 1. Setting an environment for your project\n\nGet the project up and running on your local machine by following these steps:\n\n1. **Clone the repository (Clone only this quickstart)**: \n ```bash\n git clone --filter=blob:none --sparse https://github.com/airbytehq/quickstarts.git\n ```\n\n ```bash\n cd quickstarts\n ```\n\n ```bash\n git sparse-checkout add airbyte_dbt_dagster_snowflake\n ```\n\n \n2. **Navigate to the directory**: \n ```bash\n cd airbyte_dbt_dagster_snowflake\n ```\n\n3. **Set Up a Virtual Environment**: \n - For Mac:\n ```bash\n python3 -m venv venv\n source venv/bin/activate\n ```\n - For Windows:\n ```bash\n python -m venv venv\n .\\venv\\Scripts\\activate\n ```\n\n4. **Install Dependencies**: \n ```bash\n pip install -e \".[dev]\"\n ```\n\n## 2. Setting Up Airbyte Connectors with Terraform\n\nAirbyte allows you to create connectors for sources and destinations, facilitating data synchronization between various platforms. In this project, we're harnessing the power of Terraform to automate the creation of these connectors and the connections between them. Here's how you can set this up:\n\n1. **Navigate to the Airbyte Configuration Directory**:\n \n Change to the relevant directory containing the Terraform configuration for Airbyte:\n ```bash\n cd infra/airbyte\n ```\n\n2. **Modify Configuration Files**:\n\n Within the `infra/airbyte` directory, you'll find three crucial Terraform files:\n - `provider.tf`: Defines the Airbyte provider.\n - `main.tf`: Contains the main configuration for creating Airbyte resources.\n - `variables.tf`: Holds various variables, including credentials.\n\n Adjust the configurations in these files to suit your project's needs. Specifically, provide credentials for your Postgres and Snowflake connections. You can utilize the `variables.tf` file to manage these credentials.\n\n3. **Initialize Terraform**:\n \n This step prepares Terraform to create the resources defined in your configuration files.\n ```bash\n terraform init\n ```\n\n4. **Review the Plan**:\n\n Before applying any changes, review the plan to understand what Terraform will do.\n ```bash\n terraform plan\n ```\n\n5. **Apply Configuration**:\n\n After reviewing and confirming the plan, apply the Terraform configurations to create the necessary Airbyte resources.\n ```bash\n terraform apply\n ```\n\n6. **Verify in Airbyte UI**:\n\n Once Terraform completes its tasks, navigate to the Airbyte UI. Here, you should see your source and destination connectors, as well as the connection between them, set up and ready to go.\n\n## 3. Setting Up the dbt Project\n\n[dbt (data build tool)](https://www.getdbt.com/) allows you to transform your data by writing, documenting, and executing SQL workflows. Setting up the dbt project requires specifying connection details for your data platform, in this case, Snowflake. Here’s a step-by-step guide to help you set this up:\n\n1. **Navigate to the dbt Project Directory**:\n\n Change to the directory containing the dbt configuration:\n ```bash\n cd ../../dbt_project\n ```\n\n2. **Update Connection Details**:\n\n You'll find a `profiles.yml` file within the directory. This file contains configurations for dbt to connect with your data platform. Update this file with your Snowflake connection details.\n\n3. **Utilize Environment Variables (Optional but Recommended)**:\n\n To keep your credentials secure, you can leverage environment variables. An example is provided within the `profiles.yml` file.\n\n4. **Test the Connection**:\n\n Once you’ve updated the connection details, you can test the connection to your Snowflake instance using:\n ```bash\n dbt debug\n ```\n\n If everything is set up correctly, this command should report a successful connection to Snowflake.\n\n## 4. Orchestrating with Dagster\n\n[Dagster](https://dagster.io/) is a modern data orchestrator designed to help you build, test, and monitor your data workflows. In this section, we'll walk you through setting up Dagster to oversee both the Airbyte and dbt workflows:\n\n1. **Navigate to the Orchestration Directory**:\n\n Switch to the directory containing the Dagster orchestration configurations:\n ```bash\n cd ../../orchestration\n ```\n\n2. **Set Environment Variables**:\n\n Dagster requires certain environment variables to be set to interact with other tools like dbt and Airbyte. Set the following variables:\n\n ```bash\n export DAGSTER_DBT_PARSE_PROJECT_ON_LOAD=1\n export AIRBYTE_PASSWORD=password\n ```\n \n Note: The `AIRBYTE_PASSWORD` is set to `password` as a default for local Airbyte instances. If you've changed this during your Airbyte setup, ensure you use the appropriate password here.\n\n3. **Launch the Dagster UI**:\n\n With the environment variables in place, kick-start the Dagster UI:\n ```bash\n dagster dev\n ```\n\n4. **Access Dagster in Your Browser**:\n\n Open your browser and navigate to:\n ```\n http://127.0.0.1:3000\n ```\n\n Here, you should see assets for both Airbyte and dbt. To get an overview of how these assets interrelate, click on \"view global asset lineage\". This will give you a clear picture of the data lineage, visualizing how data flows between the tools.\n\n## Next Steps\n\nOnce you've set up and launched this initial integration, the real power lies in its adaptability and extensibility. Here’s a roadmap to help you customize and harness this project tailored to your specific data needs:\n\n1. **Create dbt Sources for Airbyte Data**:\n\n Your raw data extracted via Airbyte can be represented as sources in dbt. Start by [creating new dbt sources](https://docs.getdbt.com/docs/build/sources) to represent this data, allowing for structured transformations down the line.\n\n2. **Add Your dbt Transformations**:\n\n With your dbt sources in place, you can now build upon them. Add your custom SQL transformations in dbt, ensuring that you treat the sources as an upstream dependency. This ensures that your transformations work on the most up-to-date raw data.\n\n3. **Execute the Pipeline in Dagster**:\n\n Navigate to the Dagster UI and click on \"Materialize all\". This triggers the entire pipeline, encompassing the extraction via Airbyte, transformations via dbt, and any other subsequent steps.\n\n4. **Extend the Project**:\n\n The real beauty of this integration is its extensibility. Whether you want to add more data sources, integrate additional tools, or enhance your transformation logic – the floor is yours. With the foundation set, sky's the limit for how you want to extend and refine your data processes." }, { "path": "airbyte_dbt_dagster_snowflake/dbt_project/.gitignore", "content": "\ntarget/\ndbt_packages/\nlogs/\n\n#Desktop Services Store\n.DS_Store\n\n#User cookie\n.user.yml" }, { "path": "airbyte_dbt_dagster_snowflake/dbt_project/README.md", "content": "Welcome to your new dbt project!\n\n### Using the starter project\n\nTry running the following commands:\n- dbt run\n- dbt test\n\n\n### Resources:\n- Learn more about dbt [in the docs](https://docs.getdbt.com/docs/introduction)\n- Check out [Discourse](https://discourse.getdbt.com/) for commonly asked questions and answers\n- Join the [chat](https://community.getdbt.com/) on Slack for live discussions and support\n- Find [dbt events](https://events.getdbt.com) near you\n- Check out [the blog](https://blog.getdbt.com/) for the latest news on dbt's development and best practices\n" }, { "path": "airbyte_dbt_dagster_snowflake/dbt_project/analyses/.gitkeep", "content": "" }, { "path": "airbyte_dbt_dagster_snowflake/dbt_project/dbt_project.yml", "content": "\n# Name your project! Project names should contain only lowercase characters\n# and underscores. A good package name should reflect your organization's\n# name or the intended use of these models\nname: 'dbt_project'\nversion: '1.0.0'\nconfig-version: 2\n\n# This setting configures which \"profile\" dbt uses for this project.\nprofile: 'dbt_project'\n\n# These configurations specify where dbt should look for different types of files.\n# The `model-paths` config, for example, states that models in this project can be\n# found in the \"models/\" directory. You probably won't need to change these!\nmodel-paths: [\"models\"]\nanalysis-paths: [\"analyses\"]\ntest-paths: [\"tests\"]\nseed-paths: [\"seeds\"]\nmacro-paths: [\"macros\"]\nsnapshot-paths: [\"snapshots\"]\n\nclean-targets: # directories to be removed by `dbt clean`\n - \"target\"\n - \"dbt_packages\"\n\n\n# Configuring models\n# Full documentation: https://docs.getdbt.com/docs/configuring-models\n\n# In this example config, we tell dbt to build all models in the example/\n# directory as views. These settings can be overridden in the individual model\n# files using the `{{ config(...) }}` macro.\nmodels:\n dbt_project:\n # Config indicated by + and applies to all files under models/example/\n example:\n +materialized: view\n" }, { "path": "airbyte_dbt_dagster_snowflake/dbt_project/macros/.gitkeep", "content": "" }, { "path": "airbyte_dbt_dagster_snowflake/dbt_project/models/example/my_first_dbt_model.sql", "content": "\n/*\n Welcome to your first dbt model!\n Did you know that you can also configure models directly within SQL files?\n This will override configurations stated in dbt_project.yml\n\n Try changing \"table\" to \"view\" below\n*/\n\n{{ config(materialized='table') }}\n\nwith source_data as (\n\n select * from {{ source('snowflake', 'sample_table') }}\n\n)\n\nselect *\nfrom source_data\n\n/*\n Uncomment the line below to remove records with null `id` values\n*/\n\n-- where id is not null\n" }, { "path": "airbyte_dbt_dagster_snowflake/dbt_project/models/example/my_second_dbt_model.sql", "content": "\n-- Use the `ref` function to select from other models\n\nselect *\nfrom {{ ref('my_first_dbt_model') }}\nwhere id = 1\n" }, { "path": "airbyte_dbt_dagster_snowflake/dbt_project/models/example/schema.yml", "content": "\nversion: 2\n\nmodels:\n - name: my_first_dbt_model\n description: \"A starter dbt model\"\n columns:\n - name: id\n description: \"The primary key for this table\"\n tests:\n - unique\n - not_null\n\n - name: my_second_dbt_model\n description: \"A starter dbt model\"\n columns:\n - name: id\n description: \"The primary key for this table\"\n tests:\n - unique\n - not_null\n" }, { "path": "airbyte_dbt_dagster_snowflake/dbt_project/models/sources.yml", "content": "version: 2\n\nsources:\n - name: snowflake\n tables:\n - name: sample_table\n meta:\n dagster:\n asset_key: [\"sample_table\"] # This metadata specifies the corresponding Dagster asset for this dbt source.\n" }, { "path": "airbyte_dbt_dagster_snowflake/dbt_project/profiles.yml", "content": "dbt_project:\n outputs:\n dev:\n\n type: snowflake\n account: \"{{ env_var('DBT_SNOWFLAKE_ACCOUNT_ID', '') }}\"\n\n # User/password auth\n user: username\n password: \"{{ env_var('DBT_SNOWFLAKE_PASSWORD', '') }}\"\n\n role: user_role\n database: database_name\n warehouse: warehouse_name\n schema: dbt_schema\n threads: 1\n client_session_keep_alive: False\n query_tag: anything\n\n # optional\n connect_retries: 0 # default 0\n connect_timeout: 10 # default: 10\n retry_on_database_errors: False # default: false\n retry_all: False # default: false\n reuse_connections: False # default: false (available v1.4+)\n\n target: dev" }, { "path": "airbyte_dbt_dagster_snowflake/dbt_project/seeds/.gitkeep", "content": "" }, { "path": "airbyte_dbt_dagster_snowflake/dbt_project/snapshots/.gitkeep", "content": "" }, { "path": "airbyte_dbt_dagster_snowflake/dbt_project/tests/.gitkeep", "content": "" }, { "path": "airbyte_dbt_dagster_snowflake/infra/.gitignore", "content": "# Local .terraform directories\n**/.terraform/*\n\n# .tfstate files\n*.tfstate\n*.tfstate.*\n\n# Crash log files\ncrash.log\ncrash.*.log\n\n# Exclude all .tfvars files, which are likely to contain sensitive data, such as\n# password, private keys, and other secrets. These should not be part of version \n# control as they are data points which are potentially sensitive and subject \n# to change depending on the environment.\n*.tfvars\n*.tfvars.json\n\n# Ignore override files as they are usually used to override resources locally and so\n# are not checked in\noverride.tf\noverride.tf.json\n*_override.tf\n*_override.tf.json\n\n# Include override files you do wish to add to version control using negated pattern\n# !example_override.tf\n\n# Include tfplan files to ignore the plan output of command: terraform plan -out=tfplan\n# example: *tfplan*\n\n# Ignore CLI configuration files\n.terraformrc\nterraform.rc" }, { "path": "airbyte_dbt_dagster_snowflake/infra/airbyte/main.tf", "content": "// Airbyte Terraform provider documentation: https://registry.terraform.io/providers/airbytehq/airbyte/latest/docs\n\n// Sources\nresource \"airbyte_source_postgres\" \"postgres\" {\n configuration = {\n database = \"...my_database...\"\n host = \"...my_host...\"\n username = \"...my_username...\"\n password = \"...my_password...\"\n port = 5432\n source_type = \"postgres\"\n schemas = [\n \"...my_schema...\"\n ]\n ssl_mode = {\n source_postgres_ssl_modes_allow = {\n mode = \"allow\"\n }\n }\n tunnel_method = {\n source_postgres_ssh_tunnel_method_no_tunnel = {\n tunnel_method = \"NO_TUNNEL\"\n }\n }\n replication_method = {\n source_postgres_update_method_scan_changes_with_user_defined_cursor = {\n method = \"Standard\"\n }\n }\n }\n name = \"Postgres\"\n workspace_id = var.workspace_id\n}\n\n// Destinations\nresource \"airbyte_destination_snowflake\" \"snowflake\" {\n configuration = {\n credentials = {\n destination_snowflake_authorization_method_key_pair_authentication = {\n auth_type = \"Key Pair Authentication\"\n private_key = \"...my_private_key...\"\n private_key_password = \"...my_private_key_password...\"\n }\n }\n database = \"AIRBYTE_DATABASE\"\n destination_type = \"snowflake\"\n host = \"accountname.us-east-2.aws.snowflakecomputing.com\"\n jdbc_url_params = \"...my_jdbc_url_params...\"\n raw_data_schema = \"...my_raw_data_schema...\"\n role = \"AIRBYTE_ROLE\"\n schema = \"AIRBYTE_SCHEMA\"\n username = \"AIRBYTE_USER\"\n warehouse = \"AIRBYTE_WAREHOUSE\"\n }\n name = \"Snowflake\"\n workspace_id = var.workspace_id\n} \n\n// Connections\nresource \"airbyte_connection\" \"postgres_to_snowflake\" {\n name = \"Postgres to Snowflake\"\n source_id = airbyte_source_postgres.postgres.source_id\n destination_id = airbyte_destination_snowflake.snowflake.destination_id\n configurations = {\n streams = [\n {\n name = \"...my_table_name_1...\"\n },\n {\n name = \"...my_table_name_2...\"\n },\n ]\n }\n}" }, { "path": "airbyte_dbt_dagster_snowflake/infra/airbyte/provider.tf", "content": "// Airbyte Terraform provider documentation: https://registry.terraform.io/providers/airbytehq/airbyte/latest/docs\n\nterraform {\n required_providers {\n airbyte = {\n source = \"airbytehq/airbyte\"\n version = \"0.3.4\"\n }\n }\n}\n\nprovider \"airbyte\" {\n // If running locally (Airbyte OSS) with docker-compose using the airbyte-proxy, \n // include the actual password/username you've set up (or use the defaults below)\n username = \"airbyte\"\n password = \"password\"\n \n // if running locally (Airbyte OSS), include the server url to the airbyte-api-server\n server_url = \"http://localhost:8006/v1\"\n}" }, { "path": "airbyte_dbt_dagster_snowflake/infra/airbyte/variables.tf", "content": "variable \"workspace_id\" {\n type = string\n}\n\n\n\n\n\n\n" }, { "path": "airbyte_dbt_dagster_snowflake/orchestration/orchestration/__init__.py", "content": "" }, { "path": "airbyte_dbt_dagster_snowflake/orchestration/orchestration/assets.py", "content": "import os\nfrom dagster import OpExecutionContext\nfrom dagster_dbt import DbtCliResource, dbt_assets\nfrom dagster_airbyte import AirbyteResource, load_assets_from_airbyte_instance\n\nfrom .constants import dbt_manifest_path\n\n@dbt_assets(manifest=dbt_manifest_path)\ndef dbt_project_dbt_assets(context: OpExecutionContext, dbt: DbtCliResource):\n yield from dbt.cli([\"build\"], context=context).stream()\n\nairbyte_instance = AirbyteResource(\n host=\"localhost\",\n port=\"8000\",\n # If using basic auth, include username and password:\n username=\"airbyte\",\n password=os.getenv(\"AIRBYTE_PASSWORD\")\n)\n\nairbyte_assets = load_assets_from_airbyte_instance(airbyte_instance)" }, { "path": "airbyte_dbt_dagster_snowflake/orchestration/orchestration/constants.py", "content": "import os\nfrom pathlib import Path\n\nfrom dagster_dbt import DbtCliResource\n\ndbt_project_dir = Path(__file__).joinpath(\"..\", \"..\", \"..\", \"dbt_project\").resolve()\ndbt = DbtCliResource(project_dir=os.fspath(dbt_project_dir))\n\n# If DAGSTER_DBT_PARSE_PROJECT_ON_LOAD is set, a manifest will be created at runtime.\n# Otherwise, we expect a manifest to be present in the project's target directory.\nif os.getenv(\"DAGSTER_DBT_PARSE_PROJECT_ON_LOAD\"):\n dbt_parse_invocation = dbt.cli([\"parse\"], manifest={}).wait()\n dbt_manifest_path = dbt_parse_invocation.target_path.joinpath(\"manifest.json\")\nelse:\n dbt_manifest_path = dbt_project_dir.joinpath(\"target\", \"manifest.json\")" }, { "path": "airbyte_dbt_dagster_snowflake/orchestration/orchestration/definitions.py", "content": "import os\n\nfrom dagster import Definitions\nfrom dagster_dbt import DbtCliResource\n\nfrom .assets import dbt_project_dbt_assets, airbyte_assets\nfrom .constants import dbt_project_dir\nfrom .schedules import schedules\n\ndefs = Definitions(\n assets=[dbt_project_dbt_assets, airbyte_assets],\n schedules=schedules,\n resources={\n \"dbt\": DbtCliResource(project_dir=os.fspath(dbt_project_dir)),\n },\n)" }, { "path": "airbyte_dbt_dagster_snowflake/orchestration/orchestration/schedules.py", "content": "\"\"\"\nTo add a daily schedule that materializes your dbt assets, uncomment the following lines.\n\"\"\"\nfrom dagster_dbt import build_schedule_from_dbt_selection\n\nfrom .assets import dbt_project_dbt_assets\n\nschedules = [\n# build_schedule_from_dbt_selection(\n# [dbt_project_dbt_assets],\n# job_name=\"materialize_dbt_models\",\n# cron_schedule=\"0 0 * * *\",\n# dbt_select=\"fqn:*\",\n# ),\n]" }, { "path": "airbyte_dbt_dagster_snowflake/orchestration/pyproject.toml", "content": "[build-system]\nrequires = [\"setuptools\"]\nbuild-backend = \"setuptools.build_meta\"\n\n[tool.dagster]\nmodule_name = \"orchestration.definitions\"\ncode_location_name = \"orchestration\"" }, { "path": "airbyte_dbt_dagster_snowflake/orchestration/setup.py", "content": "from setuptools import find_packages, setup\n\nsetup(\n name=\"orchestration\",\n version=\"0.0.1\",\n packages=find_packages(),\n install_requires=[\n \"dagster\",\n \"dagster-cloud\",\n \"dagster-dbt\",\n \"dbt-core>=1.4.0\",\n \"dbt-snowflake\",\n ],\n extras_require={\n \"dev\": [\n \"dagster-webserver\",\n ]\n },\n)" }, { "path": "airbyte_dbt_dagster_snowflake/setup.py", "content": "from setuptools import find_packages, setup\n\nsetup(\n name=\"airbyte-dbt-dagster-snowflake\",\n packages=find_packages(),\n install_requires=[\n \"dbt-snowflake\",\n \"dagster\",\n \"dagster-cloud\",\n \"dagster-dbt\",\n \"dagster-airbyte\",\n ],\n extras_require={\"dev\": [\"dagit\", \"pytest\"]},\n)" }, { "path": "airbyte_dbt_prefect_bigquery/.gitignore", "content": "# Byte-compiled / optimized / DLL files\n__pycache__/\n*.py[cod]\n*$py.class\n\n# C extensions\n*.so\n\n# Distribution / packaging\n.Python\nbuild/\ndevelop-eggs/\ndist/\ndownloads/\neggs/\n.eggs/\nlib/\nlib64/\nparts/\nsdist/\nvar/\nwheels/\nshare/python-wheels/\n*.egg-info/\n.installed.cfg\n*.egg\nMANIFEST\n\n# PyInstaller\n# Usually these files are written by a python script from a template\n# before PyInstaller builds the exe, so as to inject date/other infos into it.\n*.manifest\n*.spec\n\n# Installer logs\npip-log.txt\npip-delete-this-directory.txt\n\n# Unit test / coverage reports\nhtmlcov/\n.tox/\n.nox/\n.coverage\n.coverage.*\n.cache\nnosetests.xml\ncoverage.xml\n*.cover\n*.py,cover\n.hypothesis/\n.pytest_cache/\ncover/\n\n# Translations\n*.mo\n*.pot\n\n# Django stuff:\n*.log\nlocal_settings.py\ndb.sqlite3\ndb.sqlite3-journal\n\n# Flask stuff:\ninstance/\n.webassets-cache\n\n# Scrapy stuff:\n.scrapy\n\n# Sphinx documentation\ndocs/_build/\n\n# PyBuilder\n.pybuilder/\ntarget/\n\n# Jupyter Notebook\n.ipynb_checkpoints\n\n# IPython\nprofile_default/\nipython_config.py\n\n# pyenv\n# For a library or package, you might want to ignore these files since the code is\n# intended to run in multiple environments; otherwise, check them in:\n# .python-version\n\n# pipenv\n# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.\n# However, in case of collaboration, if having platform-specific dependencies or dependencies\n# having no cross-platform support, pipenv may install dependencies that don't work, or not\n# install all needed dependencies.\n#Pipfile.lock\n\n# poetry\n# Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.\n# This is especially recommended for binary packages to ensure reproducibility, and is more\n# commonly ignored for libraries.\n# https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control\n#poetry.lock\n\n# pdm\n# Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control.\n#pdm.lock\n# pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it\n# in version control.\n# https://pdm.fming.dev/#use-with-ide\n.pdm.toml\n\n# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm\n__pypackages__/\n\n# Celery stuff\ncelerybeat-schedule\ncelerybeat.pid\n\n# SageMath parsed files\n*.sage.py\n\n# Environments\n.env\n.venv\nenv/\nvenv/\nENV/\nenv.bak/\nvenv.bak/\n\n# Spyder project settings\n.spyderproject\n.spyproject\n\n# Rope project settings\n.ropeproject\n\n# mkdocs documentation\n/site\n\n# mypy\n.mypy_cache/\n.dmypy.json\ndmypy.json\n\n# Pyre type checker\n.pyre/\n\n# pytype static type analyzer\n.pytype/\n\n# Cython debug symbols\ncython_debug/\n\n# PyCharm\n# JetBrains specific template is maintained in a separate JetBrains.gitignore that can\n# be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore\n# and can be added to the global gitignore or merged into this file. For a more nuclear\n# option (not recommended) you can uncomment the following to ignore the entire idea folder.\n#.idea/\n\n#Desktop Services Store\n.DS_Store" }, { "path": "airbyte_dbt_prefect_bigquery/README.md", "content": "# Airbyte-dbt-Prefect-BigQuery Integration\n\nWelcome to the Prefect, Airbyte, dbt (PAD) Stack with BigQuery quickstart! This repo contains the code to show how to utilize Airbyte and dbt for data extraction and transformation, and utilize Prefect to orchestrate the data workflows, providing a end-to-end ELT pipeline. With this setup, you can pull fake e-commerce data, put it into BigQuery, and play around with it using dbt and Prefect.\n\n## Infrastructure Layout\n![insfrastructure layout](images/pad_bigquery_stack.png)\n\n## Pipeline DAG\n![pipeline dag](images/dag.png)\n\n## Table of Contents\n - [Prerequisites](#prerequisites)\n - [Setting an environment for your project](#1-setting-an-environment-for-your-project)\n - [Setting Up BigQuery to work with Airbyte and dbt](#2-setting-up-bigquery)\n - [Setting Up Airbyte Connectors](#3-setting-up-airbyte-connectors)\n - [Setting Up the dbt Project](#4-setting-up-the-dbt-project)\n - [Orchestrating with Prefect](#5)\n - [Next Steps](#next-steps)\n\n## Prerequisites\n\nBefore you embark on this integration, ensure you have the following set up and ready:\n\n1. **Python 3.10 or later**: If not installed, download and install it from [Python's official website](https://www.python.org/downloads/).\n\n2. **Docker and Docker Compose (Docker Desktop)**: Install [Docker](https://docs.docker.com/get-docker/) following the official documentation for your specific OS.\n\n3. **Airbyte OSS version**: Deploy the open-source version of Airbyte locally. Follow the installation instructions from the [Airbyte Documentation](https://docs.airbyte.com/quickstart/deploy-airbyte/).\n\n4. **Terraform (Optional)**: Terraform will help you provision and manage the Airbyte resources. If you haven't installed it, follow the [official Terraform installation guide](https://developer.hashicorp.com/terraform/tutorials/aws-get-started/install-cli). This is an optional step because you can also create and manage Airbyte resources via the UI. Both ways will be described below.\n\n5. **Google Cloud account with BigQuery**: You will also need to add the necessary permissions to allow Airbyte and dbt to access the data in BigQuery. A step-by-step guide is provided [below](#2-setting-up-bigquery).\n\n\n## 1. Setting an environment for your project\n\nGet the project up and running on your local machine by following these steps:\n\n1. **Clone the repository (Clone only this quickstart)**: \n ```bash\n git clone --filter=blob:none --sparse https://github.com/airbytehq/quickstarts.git\n ```\n\n ```bash\n cd quickstarts\n ```\n\n ```bash\n git sparse-checkout add airbyte_dbt_prefect_bigquery\n ```\n\n \n2. **Navigate to the directory**: \n ```bash\n cd airbyte_dbt_prefect_bigquery\n ```\n\n At this point you can view the code in your preferred IDE.\n\n3. **Set Up a Virtual Environment**: \n - For Mac:\n ```bash\n python3 -m venv venv\n source venv/bin/activate\n ```\n\n - For Windows:\n ```bash\n python -m venv venv\n .\\venv\\Scripts\\activate\n ```\n\n4. **Install Dependencies**: \n ```bash\n pip install -e \".[dev]\"\n ```\n\n## 2. Setting up BigQuery\nIf you don't have a Google Cloud account, you can sign up and get free credits, which are more than enough to implement this project.\n\n1. **Create a Google Cloud project**:\n - Go to the [Google Cloud Console](https://console.cloud.google.com/).\n - Click on the \"Select a project\" dropdown at the top right and select \"New Project\".\n - Give your project a name and follow the steps to create it.\n\n2. **Create BigQuery datasets**:\n - In the Google Cloud Console, go to BigQuery.\n - Make two new datasets: `raw_data` for Airbyte and `transformed_data` for dbt.\n - If you pick different names, remember to change the names in the code too.\n \n **How to create a dataset:**\n - In the left sidebar, click on your project name.\n - Click “Create Dataset”.\n - Enter the dataset ID (either `raw_data` or `transformed_data`).\n - Click \"Create Dataset\".\n\n3. **Create a Service Account and Assign Roles**:\n - Go to “IAM & Admin” > “Service accounts” in the Google Cloud Console.\n - Click “Create Service Account”.\n - Name your service account.\n - Assign the “BigQuery Data Editor” and “BigQuery Job User” roles to the service account.\n\n **How to create a service account and assign roles:**\n - While creating the service account, under the “Grant this service account access to project” section, click the “Role” dropdown.\n - Choose the “BigQuery Data Editor” and “BigQuery Job User” roles.\n - Finish the creation process.\n \n4. **Generate a JSON key for the Service Account**:\n - Make a JSON key to let the service account sign in.\n \n **How to generate a JSON key:**\n - Find the service account in the “Service accounts” list.\n - Click on the service account name.\n - In the “Keys” section, click “Add Key” and pick JSON.\n - The key will download automatically. Keep it safe and don’t share it.\n\n## 3. Setting Up Airbyte Connectors\n\nTo set up your Airbyte connectors, you can choose to do it via Terraform, or the UI. Choose one of the two following options.\n\n### 3.1. Setting Up Airbyte Connectors with Terraform\n\nAirbyte allows you to create connectors for sources and destinations via Terraform, facilitating data synchronization between various platforms. Here's how you can set this up:\n\n1. **Navigate to the Airbyte Configuration Directory**:\n\n ```bash\n cd infra/airbyte\n ```\n\n2. **Modify Configuration Files**:\n\n Within the `infra/airbyte` directory, you'll find three crucial Terraform files:\n - `provider.tf`: Defines the Airbyte provider.\n - `main.tf`: Contains the main configuration for creating Airbyte resources.\n - `variables.tf`: Holds various variables, including credentials.\n\n Adjust the configurations in these files to suit your project's needs: \n\n - Provide credentials for your BigQuery connection in the `main.tf` file.\n - `dataset_id`: The name of the BigQuery dataset where Airbyte will load data. In this case, enter “raw_data”.\n - `project_id`: Your BigQuery project ID.\n - `credentials_json`: The contents of the service account JSON file. You should input a string, so you need to convert the JSON content to string beforehand.\n - `workspace_id`: Your Airbyte workspace ID, which can be found in the webapp url. For example, in this url: http://localhost:8000/workspaces/910ab70f-0a67-4d25-a983-999e99e1e395/ the workspace id would be `910ab70f-0a67-4d25-a983-999e99e1e395`.\n\n - Alternatively, you can utilize the `variables.tf` file to manage these credentials:\n - You’ll be prompted to enter the credentials when you execute `terraform plan` and `terraform apply`. If going for this option, just move to the next step. If you don’t want to use variables, remove them from the file.\n\n3. **Initialize Terraform**:\n \n This step prepares Terraform to create the resources defined in your configuration files.\n ```bash\n terraform init\n ```\n\n4. **Review the Plan**:\n\n Before applying any changes, review the plan to understand what Terraform will do.\n ```bash\n terraform plan\n ```\n\n5. **Apply Configuration**:\n\n After reviewing and confirming the plan, apply the Terraform configurations to create the necessary Airbyte resources.\n ```bash\n terraform apply\n ```\n\n6. **Verify in Airbyte UI**:\n\n Once Terraform completes its tasks, navigate to the [Airbyte UI](http://localhost:8000/). Here, you should see your source and destination connectors, as well as the connection between them, set up and ready to go 🎉.\n\n### 3.2. Setting Up Airbyte Connectors Using the UI\n\nStart by launching the Airbyte UI by going to http://localhost:8000/ in your browser. Then:\n\n1. **Create a source**:\n\n - Go to the Sources tab and click on `+ New source`.\n - Search for “faker” using the search bar and select `Sample Data (Faker)`.\n - Adjust the Count and optional fields as needed for your use case. You can also leave as is. \n - Click on `Set up source`.\n\n2. **Create a destination**:\n\n - Go to the Destinations tab and click on `+ New destination`.\n - Search for “bigquery” using the search bar and select `BigQuery`.\n - Enter the connection details as needed.\n - For simplicity, you can use `Standard Inserts` as the loading method.\n - In the `Service Account Key JSON` field, enter the contents of the JSON file. Yes, the full JSON.\n - Click on `Set up destination`.\n\n3. **Create a connection**:\n\n - Go to the Connections tab and click on `+ New connection`.\n - Select the source and destination you just created.\n - Enter the connection details as needed.\n - Click on `Set up connection`.\n\nThat’s it! Your connection is set up and ready to go! 🎉 \n\n## 4. Setting Up the dbt Project\n\n[dbt (data build tool)](https://www.getdbt.com/) allows you to transform your data by writing, documenting, and executing SQL workflows. Setting up the dbt project requires specifying connection details for your data platform, in this case, BigQuery. Here’s a step-by-step guide to help you set this up:\n\n1. **Navigate to the dbt Project Directory**:\n\n Move to the directory containing the dbt configuration:\n ```bash\n cd ../../dbt_project\n ```\n\n2. **Update Connection Details**:\n\n - You'll find a `profiles.yml` file within the directory. This file contains configurations for dbt to connect with your data platform. Update this file with your BigQuery connection details. Specifically, you need to update the Service Account JSON file path, the dataset location and your BigQuery project ID.\n - Provide your BigQuery project ID in the `database` field of the `/models/ecommerce/sources/faker_sources.yml` file.\n\n If you want to avoid hardcoding credentials in the `profiles.yml` file, you can leverage environment variables. Here's an example: `keyfile: \"{{ env_var('DBT_BIGQUERY_KEYFILE_PATH', '') }}\"`\n\n3. **Test the Connection (Optional)**:\n You can test the connection to your BigQuery instance using the following command. Just take into account that you would need to provide the local path to your service account key file instead.\n \n ```bash\n dbt debug\n ```\n \n If everything is set up correctly, this command should report a successful connection to BigQuery 🎉.\n\n## 5. Orchestrating with Prefect\n\n[Prefect](https://prefect.io/) is an orchestration workflow tool that makes it easy to build, run, and monitor data workflows by writing Python code. In this section, we'll walk you through creating a Prefect flow to orchestrate both Airbyte extract and load operations, and dbt transformations with Python:\n\n1. **Navigate to the Orchestration Directory**:\n\n Switch to the directory containing the Prefect orchestration configurations:\n ```bash\n cd ../orchestration\n ```\n\n2. **Update the Airbyte Connection ID**:\n\n Open the `my_elt_flow.py` Python script and update the `connection_id` key in the `airbyte_connection` object.\n\n To find your connection id go to the [Airbyte UI](http://localhost:8000/), then select the connection you want to use from the \"Connections\" tab and copy the ID from the URL (you'll find it after `/connections/`, i.e., `e3646db8-6612-4142-8edf-1e51932b6836`).\n\n3. **Set Environment Variables**:\n\n Prefect requires certain environment variables to be set to interact with other tools, like Airbyte. Set the following variables:\n\n ```bash\n export AIRBYTE_PASSWORD=password\n ```\n\n Additionally, set the following environment variable to avoid unnecessary notifications from Prefect:\n\n ```bash\n export PREFECT_API_SERVICES_FLOW_RUN_NOTIFICATIONS_ENABLED=false\n ```\n \n4. **Connect to Prefect's API**:\n\n Open a new terminal window. Start a local Prefect server instance in your virtual environment:\n\n ```bash\n prefect server start\n ```\n\n5. **Deploy the Flow**:\n\n Go back to your previous terminal and execute the following python script:\n\n ```bash\n python my_elt_flow.py\n ```\n\n When you run the flow script, Prefect will automatically create a flow deployment that you can interact with via the UI and API. The script will stay running so that it can listen for scheduled or triggered runs of this flow; once a run is found, it will be executed within a subprocess.\n\n6. **Access Prefect UI in Your Browser**:\n\n Open your browser and navigate to:\n ```\n http://127.0.0.1:4200\n ```\n You can now begin interacting with your newly created deployment! 🎉\n\n## Next Steps\n\nCongratulations on deploying and running this quickstart! 🎉 Here are some suggestions on what you can explore next to dive deeper and get more out of your project:\n\n### 1. **Explore the Data and Insights**\n - Dive into the datasets in BigQuery, run some queries, and explore the data you've collected and transformed. This is your chance to uncover insights and understand the data better!\n\n### 2. **Optimize Your dbt Models**\n - Review the transformations you’ve applied using dbt. Try optimizing the models or create new ones based on your evolving needs and insights you want to extract.\n\n### 3. **Expand Your Data Sources**\n - Add more data sources to Airbyte. Explore different types of sources available, and see how they can enrich your existing datasets and broaden your analytical capabilities.\n\n### 4. **Enhance Data Quality and Testing**\n - Implement data quality tests in dbt to ensure the reliability and accuracy of your transformations. Use dbt's testing features to validate your data and catch issues early on.\n\n### 5. **Scale Your Setup**\n - Consider scaling your setup to handle more data, more sources, and more transformations. Optimize your configurations and resources to ensure smooth and efficient processing of larger datasets.\n\n### 7. **Contribute to the Community**\n - Share your learnings, optimizations, and new configurations with the community. Contribute to the respective tool’s communities and help others learn and grow." }, { "path": "airbyte_dbt_prefect_bigquery/dbt_project/.gitignore", "content": "\ntarget/\ndbt_packages/\nlogs/\n\n#Desktop Services Store\n.DS_Store\n\n#User cookie\n.user.yml" }, { "path": "airbyte_dbt_prefect_bigquery/dbt_project/README.md", "content": "Welcome to your new dbt project!\n\n### Using the starter project\n\nTry running the following commands:\n- dbt run\n- dbt test\n\n\n### Resources:\n- Learn more about dbt [in the docs](https://docs.getdbt.com/docs/introduction)\n- Check out [Discourse](https://discourse.getdbt.com/) for commonly asked questions and answers\n- Join the [chat](https://community.getdbt.com/) on Slack for live discussions and support\n- Find [dbt events](https://events.getdbt.com) near you\n- Check out [the blog](https://blog.getdbt.com/) for the latest news on dbt's development and best practices\n" }, { "path": "airbyte_dbt_prefect_bigquery/dbt_project/analyses/.gitkeep", "content": "" }, { "path": "airbyte_dbt_prefect_bigquery/dbt_project/dbt_project.yml", "content": "\n# Name your project! Project names should contain only lowercase characters\n# and underscores. A good package name should reflect your organization's\n# name or the intended use of these models\nname: 'dbt_project'\nversion: '1.0.0'\nconfig-version: 2\n\n# This setting configures which \"profile\" dbt uses for this project.\nprofile: 'dbt_project'\n\n# These configurations specify where dbt should look for different types of files.\n# The `model-paths` config, for example, states that models in this project can be\n# found in the \"models/\" directory. You probably won't need to change these!\nmodel-paths: [\"models\"]\nanalysis-paths: [\"analyses\"]\ntest-paths: [\"tests\"]\nseed-paths: [\"seeds\"]\nmacro-paths: [\"macros\"]\nsnapshot-paths: [\"snapshots\"]\n\nclean-targets: # directories to be removed by `dbt clean`\n - \"target\"\n - \"dbt_packages\"\n\n\n# Configuring models\n# Full documentation: https://docs.getdbt.com/docs/configuring-models\n\n# In this example config, we tell dbt to build all models in the example/\n# directory as views. These settings can be overridden in the individual model\n# files using the `{{ config(...) }}` macro.\nmodels:\n dbt_project:\n # Config indicated by + and applies to all files under models/example/\n example:\n +materialized: view\n" }, { "path": "airbyte_dbt_prefect_bigquery/dbt_project/macros/.gitkeep", "content": "" }, { "path": "airbyte_dbt_prefect_bigquery/dbt_project/models/marts/product_popularity.sql", "content": "WITH base AS (\n SELECT \n product_id,\n COUNT(id) AS purchase_count\n FROM {{ ref('stg_purchases') }}\n GROUP BY 1\n)\n\nSELECT \n p.id,\n p.make,\n p.model,\n b.purchase_count\nFROM {{ ref('stg_products') }} p\nLEFT JOIN base b ON p.id = b.product_id\nORDER BY b.purchase_count DESC\n" }, { "path": "airbyte_dbt_prefect_bigquery/dbt_project/models/marts/purchase_patterns.sql", "content": "SELECT \n user_id,\n product_id,\n purchased_at,\n added_to_cart_at,\n TIMESTAMP_DIFF(purchased_at, added_to_cart_at, SECOND) AS time_to_purchase_seconds,\n returned_at\nFROM {{ ref('stg_purchases') }}\n" }, { "path": "airbyte_dbt_prefect_bigquery/dbt_project/models/marts/user_demographics.sql", "content": "WITH base AS (\n SELECT \n id AS user_id,\n gender,\n academic_degree,\n nationality,\n age\n FROM {{ ref('stg_users') }}\n)\n\nSELECT \n gender,\n academic_degree,\n nationality,\n AVG(age) AS average_age,\n COUNT(user_id) AS user_count\nFROM base\nGROUP BY 1, 2, 3\n" }, { "path": "airbyte_dbt_prefect_bigquery/dbt_project/models/sources/faker_sources.yml", "content": "version: 2\n\nsources:\n - name: faker\n database: your_bigquery_project_id # Update with your BigQuery project ID\n schema: raw_data\n\n tables:\n - name: users\n description: \"Simulated user data from the Faker connector.\"\n columns:\n - name: id\n description: \"Unique identifier for the user.\"\n - name: address\n - name: occupation\n - name: gender\n - name: academic_degree\n - name: weight\n - name: created_at\n - name: language\n - name: telephone\n - name: title\n - name: updated_at\n - name: nationality\n - name: blood_type\n - name: name\n - name: age\n - name: email\n - name: height\n - name: _airbyte_extracted_at\n - name: _airbyte_raw_id\n - name: _airbyte_meta\n\n - name: products\n description: \"Simulated product data from the Faker connector.\"\n columns:\n - name: id\n description: \"Unique identifier for the product.\"\n - name: updated_at\n - name: year\n - name: price\n - name: created_at\n - name: model\n - name: make\n - name: _airbyte_extracted_at\n - name: _airbyte_raw_id\n - name: _airbyte_meta\n\n - name: purchases\n description: \"Simulated purchase data from the Faker connector.\"\n columns:\n - name: id\n description: \"Unique identifier for the purchase.\"\n - name: updated_at\n - name: purchased_at\n - name: user_id\n - name: returned_at\n - name: product_id\n - name: created_at\n - name: added_to_cart_at\n - name: _airbyte_extracted_at\n - name: _airbyte_raw_id\n - name: _airbyte_meta\n\n" }, { "path": "airbyte_dbt_prefect_bigquery/dbt_project/models/staging/stg_products.sql", "content": "select\n id,\n year,\n price,\n model,\n make,\n created_at,\n updated_at,\n _airbyte_extracted_at,\nfrom {{ source('faker', 'products') }}\n" }, { "path": "airbyte_dbt_prefect_bigquery/dbt_project/models/staging/stg_purchases.sql", "content": "select\n id,\n user_id,\n product_id,\n updated_at,\n purchased_at,\n returned_at,\n created_at,\n added_to_cart_at,\n _airbyte_extracted_at,\nfrom {{ source('faker', 'purchases') }}\n" }, { "path": "airbyte_dbt_prefect_bigquery/dbt_project/models/staging/stg_users.sql", "content": "select\n id,\n gender,\n academic_degree,\n title,\n nationality,\n age,\n name,\n email,\n created_at,\n updated_at,\n _airbyte_extracted_at,\nfrom {{ source('faker', 'users') }}\n" }, { "path": "airbyte_dbt_prefect_bigquery/dbt_project/profiles.yml", "content": "dbt_project:\n outputs:\n dev:\n dataset: transformed_data\n job_execution_timeout_seconds: 300\n job_retries: 1\n keyfile: \"{{ env_var('DBT_BIGQUERY_KEYFILE_PATH', '') }}\"\n location: my_dataset_location\n method: service-account\n priority: interactive\n project: my_project_id\n threads: 1\n type: bigquery\n target: dev" }, { "path": "airbyte_dbt_prefect_bigquery/dbt_project/seeds/.gitkeep", "content": "" }, { "path": "airbyte_dbt_prefect_bigquery/dbt_project/snapshots/.gitkeep", "content": "" }, { "path": "airbyte_dbt_prefect_bigquery/dbt_project/tests/.gitkeep", "content": "" }, { "path": "airbyte_dbt_prefect_bigquery/infra/.gitignore", "content": "# Local .terraform directories\n**/.terraform/*\n\n# .tfstate files\n*.tfstate\n*.tfstate.*\n\n# Crash log files\ncrash.log\ncrash.*.log\n\n# Exclude all .tfvars files, which are likely to contain sensitive data, such as\n# password, private keys, and other secrets. These should not be part of version \n# control as they are data points which are potentially sensitive and subject \n# to change depending on the environment.\n*.tfvars\n*.tfvars.json\n\n# Ignore override files as they are usually used to override resources locally and so\n# are not checked in\noverride.tf\noverride.tf.json\n*_override.tf\n*_override.tf.json\n\n# Include override files you do wish to add to version control using negated pattern\n# !example_override.tf\n\n# Include tfplan files to ignore the plan output of command: terraform plan -out=tfplan\n# example: *tfplan*\n\n# Ignore CLI configuration files\n.terraformrc\nterraform.rc" }, { "path": "airbyte_dbt_prefect_bigquery/infra/airbyte/main.tf", "content": "// Airbyte Terraform provider documentation: https://registry.terraform.io/providers/airbytehq/airbyte/latest/docs\n\n// Sources\nresource \"airbyte_source_faker\" \"faker\" {\n configuration = {\n always_updated = false\n count = 1000\n parallelism = 9\n records_per_slice = 10\n seed = 6\n source_type = \"faker\"\n }\n name = \"Faker\"\n workspace_id = var.workspace_id\n}\n\n// Destinations\nresource \"airbyte_destination_bigquery\" \"bigquery\" {\n configuration = {\n dataset_id = \"raw_data\"\n dataset_location = \"US\"\n destination_type = \"bigquery\"\n project_id = var.project_id\n credentials_json = file(var.credentials_json_path)\n loading_method = {\n destination_bigquery_loading_method_standard_inserts = {\n method = \"Standard\"\n }\n }\n }\n name = \"BigQuery\"\n workspace_id = var.workspace_id\n}\n\n// Connections\nresource \"airbyte_connection\" \"faker_to_bigquery\" {\n name = \"Faker to BigQuery\"\n source_id = airbyte_source_faker.faker.source_id\n destination_id = airbyte_destination_bigquery.bigquery.destination_id\n configurations = {\n streams = [\n {\n name = \"users\"\n },\n {\n name = \"products\"\n },\n {\n name = \"purchases\"\n },\n ]\n }\n}\n" }, { "path": "airbyte_dbt_prefect_bigquery/infra/airbyte/provider.tf", "content": "// Airbyte Terraform provider documentation: https://registry.terraform.io/providers/airbytehq/airbyte/latest/docs\n\nterraform {\n required_providers {\n airbyte = {\n source = \"airbytehq/airbyte\"\n version = \"0.3.4\"\n }\n }\n}\n\nprovider \"airbyte\" {\n // If running locally (Airbyte OSS) with docker-compose using the airbyte-proxy, \n // include the actual password/username you've set up (or use the defaults below)\n username = \"airbyte\"\n password = \"password\"\n \n // if running locally (Airbyte OSS), include the server url to the airbyte-api-server\n server_url = \"http://localhost:8006/v1\"\n}" }, { "path": "airbyte_dbt_prefect_bigquery/infra/airbyte/variables.tf", "content": "variable \"workspace_id\" {\n type = string\n}\n\nvariable \"project_id\" {\n type = string\n}\n\nvariable \"credentials_json_path\" {\n type = string\n}" }, { "path": "airbyte_dbt_prefect_bigquery/orchestration/my_elt_flow.py", "content": "import os\n\nfrom prefect import flow, task\n\nfrom prefect_airbyte.server import AirbyteServer\nfrom prefect_airbyte.connections import AirbyteConnection, AirbyteSyncResult\nfrom prefect_airbyte.flows import run_connection_sync\n\nfrom prefect_dbt.cli.commands import DbtCoreOperation\n\n\n\nremote_airbyte_server = AirbyteServer(\n username=\"airbyte\",\n password=os.getenv(\"AIRBYTE_PASSWORD\"),\n server_host=\"localhost\",\n server_port=\"8000\"\n)\n\nremote_airbyte_server.save(\"my-remote-airbyte-server\", overwrite=True)\n\nairbyte_connection = AirbyteConnection(\n airbyte_server=remote_airbyte_server,\n connection_id=\"...my_airbyte_connection_id...\", # Replace the value with your Airbyte connection ID\n status_updates=True,\n)\n\n@task(name=\"Extract, Load with Airbyte\")\ndef run_airbyte_sync(connection: AirbyteConnection) -> AirbyteSyncResult:\n job_run = connection.trigger()\n job_run.wait_for_completion()\n return job_run.fetch_result()\n\ndef run_dbt_commands(commands, prev_task_result):\n dbt_task = DbtCoreOperation(\n commands=commands,\n project_dir=\"../dbt_project\",\n profiles_dir=\"../dbt_project\",\n wait_for=prev_task_result\n )\n return dbt_task\n\n@flow(log_prints=True)\ndef my_elt_flow():\n \n # run Airbyte sync\n airbyte_sync_result = run_airbyte_sync(airbyte_connection)\n\n # run dbt precheck \n dbt_init_task = task(name=\"dbt Precheck\")(run_dbt_commands)(\n commands=[\"pwd\", \"dbt debug\", \"dbt list\"], \n prev_task_result=airbyte_sync_result\n )\n dbt_init_task.run()\n\n # run dbt models\n dbt_run_task = task(name=\"Transform with dbt\")(run_dbt_commands)(\n commands=[\"dbt run\"], \n prev_task_result=dbt_init_task\n )\n dbt_run_task.run()\n \n\n\nif __name__ == \"__main__\":\n my_elt_flow()\n" }, { "path": "airbyte_dbt_prefect_bigquery/setup.py", "content": "from setuptools import find_packages, setup\n\nsetup(\n name=\"airbyte-dbt-prefect-bigquery\",\n packages=find_packages(),\n install_requires=[\n \"prefect\",\n \"prefect-airbyte\",\n \"prefect-dbt\",\n \"dbt-bigquery\",\n ],\n extras_require={\"dev\": [\"pytest\"]},\n)" }, { "path": "airbyte_dbt_prefect_snowflake/.gitignore", "content": "# Byte-compiled / optimized / DLL files\n__pycache__/\n*.py[cod]\n*$py.class\n\n# C extensions\n*.so\n\n# Distribution / packaging\n.Python\nbuild/\ndevelop-eggs/\ndist/\ndownloads/\neggs/\n.eggs/\nlib/\nlib64/\nparts/\nsdist/\nvar/\nwheels/\nshare/python-wheels/\n*.egg-info/\n.installed.cfg\n*.egg\nMANIFEST\n\n# PyInstaller\n# Usually these files are written by a python script from a template\n# before PyInstaller builds the exe, so as to inject date/other infos into it.\n*.manifest\n*.spec\n\n# Installer logs\npip-log.txt\npip-delete-this-directory.txt\n\n# Unit test / coverage reports\nhtmlcov/\n.tox/\n.nox/\n.coverage\n.coverage.*\n.cache\nnosetests.xml\ncoverage.xml\n*.cover\n*.py,cover\n.hypothesis/\n.pytest_cache/\ncover/\n\n# Translations\n*.mo\n*.pot\n\n# Django stuff:\n*.log\nlocal_settings.py\ndb.sqlite3\ndb.sqlite3-journal\n\n# Flask stuff:\ninstance/\n.webassets-cache\n\n# Scrapy stuff:\n.scrapy\n\n# Sphinx documentation\ndocs/_build/\n\n# PyBuilder\n.pybuilder/\ntarget/\n\n# Jupyter Notebook\n.ipynb_checkpoints\n\n# IPython\nprofile_default/\nipython_config.py\n\n# pyenv\n# For a library or package, you might want to ignore these files since the code is\n# intended to run in multiple environments; otherwise, check them in:\n# .python-version\n\n# pipenv\n# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.\n# However, in case of collaboration, if having platform-specific dependencies or dependencies\n# having no cross-platform support, pipenv may install dependencies that don't work, or not\n# install all needed dependencies.\n#Pipfile.lock\n\n# poetry\n# Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.\n# This is especially recommended for binary packages to ensure reproducibility, and is more\n# commonly ignored for libraries.\n# https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control\n#poetry.lock\n\n# pdm\n# Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control.\n#pdm.lock\n# pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it\n# in version control.\n# https://pdm.fming.dev/#use-with-ide\n.pdm.toml\n\n# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm\n__pypackages__/\n\n# Celery stuff\ncelerybeat-schedule\ncelerybeat.pid\n\n# SageMath parsed files\n*.sage.py\n\n# Environments\n.env\n.venv\nenv/\nvenv/\nENV/\nenv.bak/\nvenv.bak/\n\n# Spyder project settings\n.spyderproject\n.spyproject\n\n# Rope project settings\n.ropeproject\n\n# mkdocs documentation\n/site\n\n# mypy\n.mypy_cache/\n.dmypy.json\ndmypy.json\n\n# Pyre type checker\n.pyre/\n\n# pytype static type analyzer\n.pytype/\n\n# Cython debug symbols\ncython_debug/\n\n# PyCharm\n# JetBrains specific template is maintained in a separate JetBrains.gitignore that can\n# be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore\n# and can be added to the global gitignore or merged into this file. For a more nuclear\n# option (not recommended) you can uncomment the following to ignore the entire idea folder.\n#.idea/\n\n#Desktop Services Store\n.DS_Store\n" }, { "path": "airbyte_dbt_prefect_snowflake/README.md", "content": "# Airbyte-dbt-Prefect-Snowflake Integration\n\nWelcome to the \"Airbyte-dbt-Prefect-Snowflake Integration\" repository! This repo provides a quickstart template for building a full data stack using Airbyte, Prefect, dbt, and Snowflake. Easily extract data from Postgres and load it into Snowflake using Airbyte, and apply necessary transformations using dbt, all orchestrated seamlessly with Prefect. While this template doesn't delve into specific data or transformations, its goal is to showcase the synergy of these tools.\n\nThis quickstart is designed to minimize setup hassles and propel you forward.\n\n## Table of Contents\n\n- [Airbyte-dbt-Prefect-Snowflake Integration](#airbyte-dbt-prefect-snowflake-integration)\n - [Table of Contents](#table-of-contents)\n - [Infrastructure Layout](#infrastructure-layout)\n - [Prerequisites](#prerequisites)\n - [1. Setting an environment for your project](#1-setting-an-environment-for-your-project)\n - [2. Setting Up Airbyte Connectors with Terraform](#2-setting-up-airbyte-connectors-with-terraform)\n - [3. Setting Up the dbt Project](#3-setting-up-the-dbt-project)\n - [4. Orchestrating with Prefect](#4-orchestrating-with-Prefect)\n - [Next Steps](#next-steps)\n\n## Infrastructure Layout\n![insfrastructure layout](images/infrastructure.png)\n\n## Prerequisites\n\nBefore you embark on this integration, ensure you have the following set up and ready:\n\n1. **Python 3.10 or later**: If not installed, download and install it from [Python's official website](https://www.python.org/downloads/).\n\n2. **Docker and Docker Compose (Docker Desktop)**: Install [Docker](https://docs.docker.com/get-docker/) following the official documentation for your specific OS.\n\n3. **Airbyte OSS version**: Deploy the open-source version of Airbyte. Follow the installation instructions from the [Airbyte Documentation](https://docs.airbyte.com/quickstart/deploy-airbyte/).\n\n4. **Terraform**: Terraform will help you provision and manage the Airbyte resources. If you haven't installed it, follow the [official Terraform installation guide](https://developer.hashicorp.com/terraform/tutorials/aws-get-started/install-cli).\n\n\n## 1. Setting an environment for your project\n\nGet the project up and running on your local machine by following these steps:\n\n1. **Clone the repository (Clone only this quickstart)**: \n ```bash\n git clone --filter=blob:none --sparse https://github.com/airbytehq/quickstarts.git\n ```\n\n ```bash\n cd quickstarts\n ```\n\n ```bash\n git sparse-checkout add airbyte_dbt_Prefect_snowflake\n ```\n\n \n2. **Navigate to the directory**: \n ```bash\n cd airbyte_dbt_prefect_snowflake\n ```\n\n3. **Set Up a Virtual Environment**: \n - For Mac:\n ```bash\n python3 -m venv venv\n source venv/bin/activate\n ```\n - For Windows:\n ```bash\n python -m venv venv\n .\\venv\\Scripts\\activate\n ```\n\n4. **Install Dependencies**: \n ```bash\n pip install -e \".[dev]\"\n ```\n\n## 2. Setting Up Airbyte Connectors with Terraform\n\nAirbyte allows you to create connectors for sources and destinations, facilitating data synchronization between various platforms. In this project, we're harnessing the power of Terraform to automate the creation of these connectors and the connections between them. Here's how you can set this up:\n\n1. **Navigate to the Airbyte Configuration Directory**:\n \n Change to the relevant directory containing the Terraform configuration for Airbyte:\n ```bash\n cd infra/airbyte\n ```\n\n2. **Modify Configuration Files**:\n\n Within the `infra/airbyte` directory, you'll find three crucial Terraform files:\n - `provider.tf`: Defines the Airbyte provider.\n - `main.tf`: Contains the main configuration for creating Airbyte resources.\n - `variables.tf`: Holds various variables, including credentials.\n\n Adjust the configurations in these files to suit your project's needs. Specifically, provide credentials for your Postgres and Snowflake connections. You can utilize the `variables.tf` file to manage these credentials.\n\n3. **Initialize Terraform**:\n \n This step prepares Terraform to create the resources defined in your configuration files.\n ```bash\n terraform init\n ```\n\n4. **Review the Plan**:\n\n Before applying any changes, review the plan to understand what Terraform will do.\n ```bash\n terraform plan\n ```\n\n5. **Apply Configuration**:\n\n After reviewing and confirming the plan, apply the Terraform configurations to create the necessary Airbyte resources.\n ```bash\n terraform apply\n ```\n\n6. **Verify in Airbyte UI**:\n\n Once Terraform completes its tasks, navigate to the Airbyte UI. Here, you should see your source and destination connectors, as well as the connection between them, set up and ready to go.\n\n## 3. Setting Up the dbt Project\n\n[dbt (data build tool)](https://www.getdbt.com/) allows you to transform your data by writing, documenting, and executing SQL workflows. Setting up the dbt project requires specifying connection details for your data platform, in this case, Snowflake. Here’s a step-by-step guide to help you set this up:\n\n1. **Navigate to the dbt Project Directory**:\n\n Change to the directory containing the dbt configuration:\n ```bash\n cd ../../dbt_project\n ```\n\n2. **Update Connection Details**:\n\n You'll find a `profiles.yml` file within the directory. This file contains configurations for dbt to connect with your data platform. Update this file with your Snowflake connection details.\n\n3. **Utilize Environment Variables (Optional but Recommended)**:\n\n To keep your credentials secure, you can leverage environment variables. An example is provided within the `profiles.yml` file.\n\n4. **Test the Connection**:\n\n Once you’ve updated the connection details, you can test the connection to your Snowflake instance using:\n ```bash\n dbt debug\n ```\n\n If everything is set up correctly, this command should report a successful connection to Snowflake.\n\n## 4. Orchestrating with Prefect\n\n[Prefect](https://prefect.io/) is an orchestration workflow tool that makes it easy to build, run, and monitor data workflows by writing Python code. In this section, we'll walk you through creating a Prefect flow to orchestrate both Airbyte extract and load operations, and dbt transformations with Python:\n\n1. **Navigate to the Orchestration Directory**:\n\n Switch to the directory containing the Prefect orchestration configurations:\n ```bash\n cd ../orchestration\n ```\n\n2. **Set Environment Variables**:\n\n Prefect requires certain environment variables to be set to interact with other tools like dbt and Airbyte. Set the following variables:\n\n ```bash\n export AIRBYTE_PASSWORD=password\n ```\n\n3. **Connect to Prefect's API**:\n\n Open a new terminal window. Start a local Prefect server instance in your virtual environment:\n\n ```bash\n prefect server start\n ```\n\n4. **Deploy the Flow**:\n\n When we run the flow script, Prefect will automatically create a flow deployment that you can interact with via the UI and API. The script will stay running so that it can listen for scheduled or triggered runs of this flow; once a run is found, it will be executed within a subprocess.\n\n ```bash\n python my_elt_flow.py\n ```\n\n5. **Access Prefect UI in Your Browser**:\n\n Open your browser and navigate to:\n ```\n http://127.0.0.1:4200\n ```\n You can now begin interacting with your newly created deployment!\n\n## Next Steps\n\nOnce you've set up and launched this initial integration, the real power lies in its adaptability and extensibility. Here’s a roadmap to help you customize and harness this project tailored to your specific data needs:\n\n1. **Create dbt Sources for Airbyte Data**:\n\n - Your raw data extracted via Airbyte can be represented as sources in dbt. Start by [creating new dbt sources](https://docs.getdbt.com/docs/build/sources) to represent this data, allowing for structured transformations down the line.\n\n2. **Add Your dbt Transformations**:\n\n - With your dbt sources in place, you can now build upon them. Add your custom SQL transformations in dbt, ensuring that you treat the sources as an upstream dependency. This ensures that your transformations work on the most up-to-date raw data.\n\n### 3. **Extend the Prefect Pipeline**:\n\n - You can create flow runs from this deployment via API calls to be triggered by new data sync in Airbyte rather than on a schedule. You can customize your dbt runs based on the results got from AirbyteSyncResult. You can also migrate the deployment to the Prefect cloud.\n\n4. **Extend the Project**:\n\n - The real beauty of this integration is its extensibility. Whether you want to add more data sources, integrate additional tools, or enhance your transformation logic – the floor is yours. With the foundation set, sky's the limit for how you want to extend and refine your data processes.\n\n" }, { "path": "airbyte_dbt_prefect_snowflake/dbt_project/.gitignore", "content": "\ntarget/\ndbt_packages/\nlogs/\n\n#Desktop Services Store\n.DS_Store\n\n#User cookie\n.user.yml\n" }, { "path": "airbyte_dbt_prefect_snowflake/dbt_project/README.md", "content": "Welcome to your new dbt project!\n\n### Using the starter project\n\nTry running the following commands:\n- dbt run\n- dbt test\n\n\n### Resources:\n- Learn more about dbt [in the docs](https://docs.getdbt.com/docs/introduction)\n- Check out [Discourse](https://discourse.getdbt.com/) for commonly asked questions and answers\n- Join the [chat](https://community.getdbt.com/) on Slack for live discussions and support\n- Find [dbt events](https://events.getdbt.com) near you\n- Check out [the blog](https://blog.getdbt.com/) for the latest news on dbt's development and best practices\n" }, { "path": "airbyte_dbt_prefect_snowflake/dbt_project/analyses/.gitkeep", "content": "" }, { "path": "airbyte_dbt_prefect_snowflake/dbt_project/dbt_project.yml", "content": "\n# Name your project! Project names should contain only lowercase characters\n# and underscores. A good package name should reflect your organization's\n# name or the intended use of these models\nname: 'dbt_project'\nversion: '1.0.0'\nconfig-version: 2\n\n# This setting configures which \"profile\" dbt uses for this project.\nprofile: 'dbt_project'\n\n# These configurations specify where dbt should look for different types of files.\n# The `model-paths` config, for example, states that models in this project can be\n# found in the \"models/\" directory. You probably won't need to change these!\nmodel-paths: [\"models\"]\nanalysis-paths: [\"analyses\"]\ntest-paths: [\"tests\"]\nseed-paths: [\"seeds\"]\nmacro-paths: [\"macros\"]\nsnapshot-paths: [\"snapshots\"]\n\nclean-targets: # directories to be removed by `dbt clean`\n - \"target\"\n - \"dbt_packages\"\n\n\n# Configuring models\n# Full documentation: https://docs.getdbt.com/docs/configuring-models\n\n# In this example config, we tell dbt to build all models in the example/\n# directory as views. These settings can be overridden in the individual model\n# files using the `{{ config(...) }}` macro.\nmodels:\n dbt_project:\n # Config indicated by + and applies to all files under models/example/\n example:\n +materialized: view\n" }, { "path": "airbyte_dbt_prefect_snowflake/dbt_project/macros/.gitkeep", "content": "" }, { "path": "airbyte_dbt_prefect_snowflake/dbt_project/models/example/my_first_dbt_model.sql", "content": "\n/*\n Welcome to your first dbt model!\n Did you know that you can also configure models directly within SQL files?\n This will override configurations stated in dbt_project.yml\n\n Try changing \"table\" to \"view\" below\n*/\n\n{{ config(materialized='table') }}\n\nwith source_data as (\n\n select * from {{ source('snowflake', 'sample_table') }}\n\n)\n\nselect *\nfrom source_data\n\n/*\n Uncomment the line below to remove records with null `id` values\n*/\n\n-- where id is not null\n" }, { "path": "airbyte_dbt_prefect_snowflake/dbt_project/models/example/my_second_dbt_model.sql", "content": "\n-- Use the `ref` function to select from other models\n\nselect *\nfrom {{ ref('my_first_dbt_model') }}\nwhere id = 1\n" }, { "path": "airbyte_dbt_prefect_snowflake/dbt_project/models/example/schema.yml", "content": "\nversion: 2\n\nmodels:\n - name: my_first_dbt_model\n description: \"A starter dbt model\"\n columns:\n - name: id\n description: \"The primary key for this table\"\n tests:\n - unique\n - not_null\n\n - name: my_second_dbt_model\n description: \"A starter dbt model\"\n columns:\n - name: id\n description: \"The primary key for this table\"\n tests:\n - unique\n - not_null\n" }, { "path": "airbyte_dbt_prefect_snowflake/dbt_project/models/sources.yml", "content": "version: 2\n\nsources:\n - name: snowflake\n tables:\n - name: sample_table\n meta:\n dagster:\n asset_key: [\"sample_table\"] # This metadata specifies the corresponding Dagster asset for this dbt source.\n" }, { "path": "airbyte_dbt_prefect_snowflake/dbt_project/profiles.yml", "content": "dbt_project:\n outputs:\n dev:\n\n type: snowflake\n account: \"{{ env_var('DBT_SNOWFLAKE_ACCOUNT_ID', '') }}\"\n\n # User/password auth\n user: username\n password: \"{{ env_var('DBT_SNOWFLAKE_PASSWORD', '') }}\"\n\n role: user_role\n database: database_name\n warehouse: warehouse_name\n schema: dbt_schema\n threads: 1\n client_session_keep_alive: False\n query_tag: anything\n\n # optional\n connect_retries: 0 # default 0\n connect_timeout: 10 # default: 10\n retry_on_database_errors: False # default: false\n retry_all: False # default: false\n reuse_connections: False # default: false (available v1.4+)\n\n target: dev\n" }, { "path": "airbyte_dbt_prefect_snowflake/dbt_project/seeds/.gitkeep", "content": "" }, { "path": "airbyte_dbt_prefect_snowflake/dbt_project/snapshots/.gitkeep", "content": "" }, { "path": "airbyte_dbt_prefect_snowflake/dbt_project/tests/.gitkeep", "content": "" }, { "path": "airbyte_dbt_prefect_snowflake/infra/.gitignore", "content": "# Local .terraform directories\n**/.terraform/*\n\n# .tfstate files\n*.tfstate\n*.tfstate.*\n\n# Crash log files\ncrash.log\ncrash.*.log\n\n# Exclude all .tfvars files, which are likely to contain sensitive data, such as\n# password, private keys, and other secrets. These should not be part of version \n# control as they are data points which are potentially sensitive and subject \n# to change depending on the environment.\n*.tfvars\n*.tfvars.json\n\n# Ignore override files as they are usually used to override resources locally and so\n# are not checked in\noverride.tf\noverride.tf.json\n*_override.tf\n*_override.tf.json\n\n# Include override files you do wish to add to version control using negated pattern\n# !example_override.tf\n\n# Include tfplan files to ignore the plan output of command: terraform plan -out=tfplan\n# example: *tfplan*\n\n# Ignore CLI configuration files\n.terraformrc\nterraform.rc\n" }, { "path": "airbyte_dbt_prefect_snowflake/infra/airbyte/main.tf", "content": "// Airbyte Terraform provider documentation: https://registry.terraform.io/providers/airbytehq/airbyte/latest/docs\n\n// Sources\nresource \"airbyte_source_postgres\" \"postgres\" {\n configuration = {\n database = \"...my_database...\"\n host = \"...my_host...\"\n username = \"...my_username...\"\n password = \"...my_password...\"\n port = 5432\n source_type = \"postgres\"\n schemas = [\n \"...my_schema...\"\n ]\n ssl_mode = {\n allow = {}\n }\n tunnel_method = {\n no_tunnel = {}\n }\n replication_method = {\n scan_changes_with_user_defined_cursor = {}\n }\n }\n name = \"Postgres\"\n workspace_id = var.workspace_id\n}\n\n// Destinations\nresource \"airbyte_destination_snowflake\" \"snowflake\" {\n configuration = {\n credentials = {\n destination_snowflake_authorization_method_key_pair_authentication = {\n auth_type = \"Key Pair Authentication\"\n private_key = \"...my_private_key...\"\n private_key_password = \"...my_private_key_password...\"\n }\n }\n database = \"AIRBYTE_DATABASE\"\n destination_type = \"snowflake\"\n host = \"accountname.us-east-2.aws.snowflakecomputing.com\"\n jdbc_url_params = \"...my_jdbc_url_params...\"\n raw_data_schema = \"...my_raw_data_schema...\"\n role = \"AIRBYTE_ROLE\"\n schema = \"AIRBYTE_SCHEMA\"\n username = \"AIRBYTE_USER\"\n warehouse = \"AIRBYTE_WAREHOUSE\"\n }\n name = \"Snowflake\"\n workspace_id = var.workspace_id\n} \n\n// Connections\nresource \"airbyte_connection\" \"postgres_to_snowflake\" {\n name = \"Postgres to Snowflake\"\n source_id = airbyte_source_postgres.postgres.source_id\n destination_id = airbyte_destination_snowflake.snowflake.destination_id\n configurations = {\n streams = [\n {\n name = \"...my_table_name_1...\"\n },\n {\n name = \"...my_table_name_2...\"\n },\n ]\n }\n}\n" }, { "path": "airbyte_dbt_prefect_snowflake/infra/airbyte/provider.tf", "content": "// Airbyte Terraform provider documentation: https://registry.terraform.io/providers/airbytehq/airbyte/latest/docs\n\nterraform {\n required_providers {\n airbyte = {\n source = \"airbytehq/airbyte\"\n version = \"0.3.4\"\n }\n }\n}\n\nprovider \"airbyte\" {\n // If running locally (Airbyte OSS) with docker-compose using the airbyte-proxy, \n // include the actual password/username you've set up (or use the defaults below)\n username = \"airbyte\"\n password = \"password\"\n \n // if running locally (Airbyte OSS), include the server url to the airbyte-api-server\n server_url = \"http://localhost:8006/v1\"\n}\n" }, { "path": "airbyte_dbt_prefect_snowflake/infra/airbyte/variables.tf", "content": "variable \"workspace_id\" {\n type = string\n}\n" }, { "path": "airbyte_dbt_prefect_snowflake/orchestration/my_elt_flow.py", "content": "import os\n\nfrom prefect import flow, task\n\nfrom prefect_airbyte.server import AirbyteServer\nfrom prefect_airbyte.connections import AirbyteConnection, AirbyteSyncResult\nfrom prefect_airbyte.flows import run_connection_sync\n\nfrom prefect_dbt.cli.commands import DbtCoreOperation\n\n\n\nremote_airbyte_server = AirbyteServer(\n username=\"airbyte\",\n password=os.getenv(\"AIRBYTE_PASSWORD\"),\n server_host=\"localhost\",\n server_port=\"8000\"\n)\n\nremote_airbyte_server.save(\"my-remote-airbyte-server\", overwrite=True)\n\nairbyte_connection = AirbyteConnection(\n airbyte_server=remote_airbyte_server,\n connection_id=\"...my_airbyte_connection_id...\",\n status_updates=True,\n)\n\n@task(name=\"Extract, Load with Airbyte\")\ndef run_airbyte_sync(connection: AirbyteConnection) -> AirbyteSyncResult:\n job_run = connection.trigger()\n job_run.wait_for_completion()\n return job_run.fetch_result()\n\ndef run_dbt_commands(commands, prev_task_result):\n dbt_task = DbtCoreOperation(\n commands=commands,\n project_dir=\"../dbt_project\",\n profiles_dir=\"../dbt_project\",\n wait_for=prev_task_result\n )\n return dbt_task\n\n@flow(log_prints=True)\ndef my_elt_flow():\n\n # run Airbyte sync\n # airbyte_sync_result: AirbyteSyncResult = run_connection_sync(\n # airbyte_connection=airbyte_connection,\n # )\n airbyte_sync_result = run_airbyte_sync(airbyte_connection)\n\n # run dbt precheck \n dbt_init_task = task(name=\"dbt Precheck\")(run_dbt_commands)(\n commands=[\"pwd\", \"dbt debug\", \"dbt list\"], \n prev_task_result=airbyte_sync_result\n )\n dbt_init_task.run()\n\n # run dbt models\n dbt_run_task = task(name=\"Transform with dbt\")(run_dbt_commands)(\n commands=[\"dbt run\"], \n prev_task_result=dbt_init_task\n )\n dbt_run_task.run()\n\n\n\nif __name__ == \"__main__\":\n # my_elt_flow.visualize()\n my_elt_flow()\n" }, { "path": "airbyte_dbt_prefect_snowflake/setup.py", "content": "from setuptools import find_packages, setup\n\nsetup(\n name=\"airbyte-dbt-prefect-snowflake\",\n packages=find_packages(),\n install_requires=[\n \"dbt-snowflake\",\n \"prefect\",\n \"prefect-airbyte\",\n \"prefect-dbt\",\n \"dbt-core>=1.4.0\",\n ],\n extras_require={\"dev\": [\"pytest\"]},\n)\n" }, { "path": "airbyte_dbt_snowflake_looker/.gitignore", "content": "# Byte-compiled / optimized / DLL files\n__pycache__/\n*.py[cod]\n*$py.class\n\n# C extensions\n*.so\n\n# Distribution / packaging\n.Python\nbuild/\ndevelop-eggs/\ndist/\ndownloads/\neggs/\n.eggs/\nlib/\nlib64/\nparts/\nsdist/\nvar/\nwheels/\nshare/python-wheels/\n*.egg-info/\n.installed.cfg\n*.egg\nMANIFEST\n\n# PyInstaller\n# Usually these files are written by a python script from a template\n# before PyInstaller builds the exe, so as to inject date/other infos into it.\n*.manifest\n*.spec\n\n# Installer logs\npip-log.txt\npip-delete-this-directory.txt\n\n# Unit test / coverage reports\nhtmlcov/\n.tox/\n.nox/\n.coverage\n.coverage.*\n.cache\nnosetests.xml\ncoverage.xml\n*.cover\n*.py,cover\n.hypothesis/\n.pytest_cache/\ncover/\n\n# Translations\n*.mo\n*.pot\n\n# Django stuff:\n*.log\nlocal_settings.py\ndb.sqlite3\ndb.sqlite3-journal\n\n# Flask stuff:\ninstance/\n.webassets-cache\n\n# Scrapy stuff:\n.scrapy\n\n# Sphinx documentation\ndocs/_build/\n\n# PyBuilder\n.pybuilder/\ntarget/\n\n# Jupyter Notebook\n.ipynb_checkpoints\n\n# IPython\nprofile_default/\nipython_config.py\n\n# pyenv\n# For a library or package, you might want to ignore these files since the code is\n# intended to run in multiple environments; otherwise, check them in:\n# .python-version\n\n# pipenv\n# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.\n# However, in case of collaboration, if having platform-specific dependencies or dependencies\n# having no cross-platform support, pipenv may install dependencies that don't work, or not\n# install all needed dependencies.\n#Pipfile.lock\n\n# poetry\n# Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.\n# This is especially recommended for binary packages to ensure reproducibility, and is more\n# commonly ignored for libraries.\n# https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control\n#poetry.lock\n\n# pdm\n# Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control.\n#pdm.lock\n# pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it\n# in version control.\n# https://pdm.fming.dev/#use-with-ide\n.pdm.toml\n\n# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm\n__pypackages__/\n\n# Celery stuff\ncelerybeat-schedule\ncelerybeat.pid\n\n# SageMath parsed files\n*.sage.py\n\n# Environments\n.env\n.venv\nenv/\nvenv/\nENV/\nenv.bak/\nvenv.bak/\n\n# Spyder project settings\n.spyderproject\n.spyproject\n\n# Rope project settings\n.ropeproject\n\n# mkdocs documentation\n/site\n\n# mypy\n.mypy_cache/\n.dmypy.json\ndmypy.json\n\n# Pyre type checker\n.pyre/\n\n# pytype static type analyzer\n.pytype/\n\n# Cython debug symbols\ncython_debug/\n\n# PyCharm\n# JetBrains specific template is maintained in a separate JetBrains.gitignore that can\n# be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore\n# and can be added to the global gitignore or merged into this file. For a more nuclear\n# option (not recommended) you can uncomment the following to ignore the entire idea folder.\n#.idea/\n\n#Desktop Services Store\n.DS_Store" }, { "path": "airbyte_dbt_snowflake_looker/README.md", "content": "# Airbyte-dbt-Snowflake-Looker Integration\n\nWelcome to the \"Airbyte-dbt-Snowflake-Looker Integration\" repository! This repo provides a quickstart template for building a full data stack using Airbyte, Dagster, dbt, Snowflake and Looker. Easily extract data from Postgres and load it into Snowflake using Airbyte, and apply necessary transformations using dbt, all orchestrated seamlessly with Dagster. Then connect your Snowflake instance to Looker for business intelligence, analytics, data modeling, etc. While this template doesn't delve into specific data or transformations, its goal is to showcase the synergy of these tools.\n\nThis quickstart is designed to minimize setup hassles and propel you forward.\n\n## Table of Contents\n\n- [Airbyte-dbt-Snowflake-Looker Integration](#airbyte-dbt-snowflake-looker-integration)\n - [Table of Contents](#table-of-contents)\n - [Infrastructure Layout](#infrastructure-layout)\n - [Pipeline DAG](#pipeline-dag)\n - [Prerequisites](#prerequisites)\n - [1. Setting an environment for your project](#1-setting-an-environment-for-your-project)\n - [2. Setting Up Airbyte Connectors with Terraform](#2-setting-up-airbyte-connectors-with-terraform)\n - [3. Setting Up the dbt Project](#3-setting-up-the-dbt-project)\n - [4. Orchestrating with Dagster](#4-orchestrating-with-dagster)\n - [5. Integrating with Looker](#5-integrating-with-looker)\n - [Next Steps](#next-steps)\n\n## Infrastructure Layout\n![insfrastructure layout](images/adsl_stack.png)\n\n## Pipeline DAG\n![pipeline dag](images/dag.svg)\n\n## Prerequisites\n\nBefore you embark on this integration, ensure you have the following set up and ready:\n\n1. **Python 3.10 or later**: If not installed, download and install it from [Python's official website](https://www.python.org/downloads/).\n\n2. **Docker and Docker Compose (Docker Desktop)**: Install [Docker](https://docs.docker.com/get-docker/) following the official documentation for your specific OS.\n\n3. **Airbyte OSS version**: Deploy the open-source version of Airbyte. Follow the installation instructions from the [Airbyte Documentation](https://docs.airbyte.com/quickstart/deploy-airbyte/).\n\n4. **Terraform**: Terraform will help you provision and manage the Airbyte resources. If you haven't installed it, follow the [official Terraform installation guide](https://developer.hashicorp.com/terraform/tutorials/aws-get-started/install-cli).\n\n\n## 1. Setting an environment for your project\n\nGet the project up and running on your local machine by following these steps:\n\n1. **Clone the repository (Clone only this quickstart)**: \n ```bash\n git clone --filter=blob:none --sparse https://github.com/airbytehq/quickstarts.git\n ```\n\n ```bash\n cd quickstarts\n ```\n\n ```bash\n git sparse-checkout add airbyte_dbt_dagster_snowflake\n ```\n\n \n2. **Navigate to the directory**: \n ```bash\n cd airbyte_dbt_dagster_snowflake\n ```\n\n3. **Set Up a Virtual Environment**: \n - For Mac:\n ```bash\n python3 -m venv venv\n source venv/bin/activate\n ```\n - For Windows:\n ```bash\n python -m venv venv\n .\\venv\\Scripts\\activate\n ```\n\n4. **Install Dependencies**: \n ```bash\n pip install -e \".[dev]\"\n ```\n\n## 2. Setting Up Airbyte Connectors with Terraform\n\nAirbyte allows you to create connectors for sources and destinations, facilitating data synchronization between various platforms. In this project, we're harnessing the power of Terraform to automate the creation of these connectors and the connections between them. Here's how you can set this up:\n\n1. **Navigate to the Airbyte Configuration Directory**:\n \n Change to the relevant directory containing the Terraform configuration for Airbyte:\n ```bash\n cd infra/airbyte\n ```\n\n2. **Modify Configuration Files**:\n\n Within the `infra/airbyte` directory, you'll find three crucial Terraform files:\n - `provider.tf`: Defines the Airbyte provider.\n - `main.tf`: Contains the main configuration for creating Airbyte resources.\n - `variables.tf`: Holds various variables, including credentials.\n\n Adjust the configurations in these files to suit your project's needs. Specifically, provide credentials for your Postgres and Snowflake connections. You can utilize the `variables.tf` file to manage these credentials.\n\n3. **Initialize Terraform**:\n \n This step prepares Terraform to create the resources defined in your configuration files.\n ```bash\n terraform init\n ```\n\n4. **Review the Plan**:\n\n Before applying any changes, review the plan to understand what Terraform will do.\n ```bash\n terraform plan\n ```\n\n5. **Apply Configuration**:\n\n After reviewing and confirming the plan, apply the Terraform configurations to create the necessary Airbyte resources.\n ```bash\n terraform apply\n ```\n\n6. **Verify in Airbyte UI**:\n\n Once Terraform completes its tasks, navigate to the Airbyte UI. Here, you should see your source and destination connectors, as well as the connection between them, set up and ready to go.\n\n## 3. Setting Up the dbt Project\n\n[dbt (data build tool)](https://www.getdbt.com/) allows you to transform your data by writing, documenting, and executing SQL workflows. Setting up the dbt project requires specifying connection details for your data platform, in this case, Snowflake. Here’s a step-by-step guide to help you set this up:\n\n1. **Navigate to the dbt Project Directory**:\n\n Change to the directory containing the dbt configuration:\n ```bash\n cd ../../dbt_project\n ```\n\n2. **Update Connection Details**:\n\n You'll find a `profiles.yml` file within the directory. This file contains configurations for dbt to connect with your data platform. Update this file with your Snowflake connection details.\n\n3. **Utilize Environment Variables (Optional but Recommended)**:\n\n To keep your credentials secure, you can leverage environment variables. An example is provided within the `profiles.yml` file.\n\n4. **Test the Connection**:\n\n Once you’ve updated the connection details, you can test the connection to your Snowflake instance using:\n ```bash\n dbt debug\n ```\n\n If everything is set up correctly, this command should report a successful connection to Snowflake.\n\n## 4. Orchestrating with Dagster\n\n[Dagster](https://dagster.io/) is a modern data orchestrator designed to help you build, test, and monitor your data workflows. In this section, we'll walk you through setting up Dagster to oversee both the Airbyte and dbt workflows:\n\n1. **Navigate to the Orchestration Directory**:\n\n Switch to the directory containing the Dagster orchestration configurations:\n ```bash\n cd ../../orchestration\n ```\n\n2. **Set Environment Variables**:\n\n Dagster requires certain environment variables to be set to interact with other tools like dbt and Airbyte. Set the following variables:\n\n ```bash\n export DAGSTER_DBT_PARSE_PROJECT_ON_LOAD=1\n export AIRBYTE_PASSWORD=password\n ```\n \n Note: The `AIRBYTE_PASSWORD` is set to `password` as a default for local Airbyte instances. If you've changed this during your Airbyte setup, ensure you use the appropriate password here.\n\n3. **Launch the Dagster UI**:\n\n With the environment variables in place, kick-start the Dagster UI:\n ```bash\n dagster dev\n ```\n\n4. **Access Dagster in Your Browser**:\n\n Open your browser and navigate to:\n ```\n http://127.0.0.1:3000\n ```\n\n Here, you should see assets for both Airbyte and dbt. To get an overview of how these assets interrelate, click on \"view global asset lineage\". This will give you a clear picture of the data lineage, visualizing how data flows between the tools.\n\n## 5. Integrating with Looker\n\nLooker is a product that helps you explore, share, and visualize your company's data so that you can make better business decisions. It is an enterprise platform for BI, data applications, and embedded analytics that helps you explore and share insights in real time. It also has the ability to convert user input via a Graphical User Interface (GUI) into SQL queries and subsequently transmit them directly to the database in real-time. To get started with Looker and learn more about it, check [here](https://cloud.google.com/looker/docs).\n\nFollow the steps below to integrate your Snowflake instance with your Looker studio.\n\n1. Create a Looker User in Snowflake\n\n To allow Looker run queries in Snowflake, you need to create a dedicated user for the looker instance in Snowflake and provision access for the user. Run the queries below in Snowflake to do this.\n\n You can add in the `ON FUTURE` keyword to persist `GRANT` statements for future tables and objects in the database to prevent re-running the GRANT statements as new tables are created.\n\n ```sql\n -- change role to ACCOUNTADMIN\n use role ACCOUNTADMIN;\n\n -- create role for looker\n create role if not exists looker_role;\n grant role looker_role to role SYSADMIN;\n -- Note that we are not making the looker_role a SYSADMIN, but rather granting users with the SYSADMIN role to modify the looker_role\n \n -- create a user for looker\n create user if not exists looker_user\n password = '';\n\n grant role looker_role to user looker_user;\n\n alter user looker_user\n set default_role = looker_role\n default_warehouse = 'looker_wh';\n \n -- this part is to executed only if the user roles are to be changed \n -- change role\n use role SYSADMIN;\n \n -- create a warehouse for looker (optional)\n create warehouse if not exists looker_wh\n \n -- set the size based on your dataset\n warehouse_size = medium\n warehouse_type = standard\n auto_suspend = 1800\n auto_resume = true\n initially_suspended = true;\n \n grant all privileges\n on warehouse looker_wh\n to role looker_role;\n\n -- grant read only database access (repeat for all database/schemas)\n grant usage on database to role looker_role;\n grant usage on schema . to role looker_role;\n\n -- rerun the following any time a table is added to the schema\n grant select on all tables in schema . to role looker_role;\n -- or\n grant select on future tables in schema . to role looker_role;\n\n -- create schema for looker to write back to\n use database ;\n create schema if not exists looker_scratch;\n use role ACCOUNTADMIN;\n grant ownership on schema looker_scratch to role SYSADMIN revoke current grants;\n grant all on schema looker_scratch to role looker_role;\n ```\n\n2. Adding the Database Connection in Looker\n\n After creating the looker user in snowflake, we need to create a connection from Looker to Snowflake using our Snowflake credentials and the looker user we just created in Snowflake. To do this, follow the steps below.\n \n - Navigate to the **Admin** panel of the Looker interface.\n - Select **Connections**.\n - Click **Add Connection**. A Configuration Section will open up where you will be required to fill the connection details below. For more details see [here](https://cloud.google.com/looker/docs/connecting-to-your-db).\n\n - **Name**: Give the connection an Arbitrary name. \n - **Dialect**: Select **Snowflake**.\n - **Host**: It is of the format .snowflakecomputing.com. See [here](https://docs.snowflake.com/developer-guide/jdbc/jdbc-configure#connection-parameters) to validate your host value.\n - **Port**: The default is 443. \n - **Database**: Provide the name of the default database that is required for use. Note that this field is case-sensitive.\n - **Schema**: This the default Database Schema that is used in your Snowflake Deployment. \n - **Authentication**: Select **Database Account** or **OAuth**:\n - Use **Database Account** to specify the Username and Password of the Snowflake user account that will be used to connect to Looker.\n - Follow the steps below to get credentials to use **OAuth** for the connection.\n\n To set up an OAuth based connection, you will require a user account with `ACCOUNTADMIN` permission on Snowflake. Firstly you are required to run the following command in Snowflake, where `` is the hostname of the Looker Instance: \n\n ```sql\n CREATE SECURITY INTEGRATION LOOKER\n TYPE = OAUTH\n ENABLED = TRUE\n OAUTH_CLIENT = LOOKER\n OAUTH_REDIRECT_URI = 'https:///external_oauth/redirect';\n ```\n\n To obtain the OAuth Client ID and Client Secret, you need to run the following command: \n\n ```sql\n SELECT SYSTEM$SHOW_OAUTH_CLIENT_SECRETS('LOOKER');\n ```\n\n Paste in the OAUTH_CLIENT_ID and OAUTH_CLIENT_SECRET values in the Looker OAuth fields.\n\n - **Enable PDTs**: Use this to enable persistent derived tables (PDTs). See [here](https://cloud.google.com/looker/docs/derived-tables#persistent-derived-tables) for more information.\n - **Temp Database**: If PDTs [Persistent Derived Tables] are enabled, this section needs to be set to a Database Schema where the user has full privileges to create, drop, rename, and alter tables.\n - **(Optional) Max Connections per node**: See more about this [here](https://cloud.google.com/looker/docs/connecting-to-your-db#max_connections).\n - **Database Time Zone**: Default is UTC.\n - **Query Time Zone**: Default is UTC.\n - Additional JDBC parameters: Add additional JDBC parameters from the Snowflake JDBC driver.\n - Add `warehouse=`.\n - Additionally, by default, Looker will set the following Snowflake parameters on each session:\n\n - `TIMESTAMP_TYPE_MAPPING=TIMESTAMP_LTZ`\n - `JDBC_TREAT_DECIMAL_AS_INT=FALSE`\n - `TIMESTAMP_INPUT_FORMAT=AUTO`\n - `AUTOCOMMIT=TRUE`\n \n You can override each of these parameters by setting an alternative value in the Additional JDBC parameters field, for example: `&AUTOCOMMIT=FALSE`\n\n - Click on **Test** to check if the connection is Successful. For troubleshooting, see [here](https://cloud.google.com/looker/docs/testing-db-connectivity).\n - Click **Connect** to save these settings.\n\n\nNow that the connection to Snowflake has been created, you are good to go to explore your Snowflake data in Looker.\n \n\n\n## Next Steps\n\nOnce you've set up and launched this initial integration, the real power lies in its adaptability and extensibility. Here’s a roadmap to help you customize and harness this project tailored to your specific data needs:\n\n1. **Create dbt Sources for Airbyte Data**:\n\n Your raw data extracted via Airbyte can be represented as sources in dbt. Start by [creating new dbt sources](https://docs.getdbt.com/docs/build/sources) to represent this data, allowing for structured transformations down the line.\n\n2. **Add Your dbt Transformations**:\n\n With your dbt sources in place, you can now build upon them. Add your custom SQL transformations in dbt, ensuring that you treat the sources as an upstream dependency. This ensures that your transformations work on the most up-to-date raw data.\n\n3. **Execute the Pipeline in Dagster**:\n\n Navigate to the Dagster UI and click on \"Materialize all\". This triggers the entire pipeline, encompassing the extraction via Airbyte, transformations via dbt, and any other subsequent steps.\n\n4. **Explore in Looker**:\n\n You can use the SQL Runner to create queries and Explores, create and share Looks (reports and dashboards), or use LookML to create a data model that Looker will use to query your data. However way you wish to go with your Snowflake data, the choice is yours.\n\n5. **Extend the Project**:\n\n The real beauty of this integration is its extensibility. Whether you want to add more data sources, integrate additional tools, or enhance your transformation logic – the floor is yours. With the foundation set, sky's the limit for how you want to extend and refine your data processes." }, { "path": "airbyte_dbt_snowflake_looker/dbt_project/.gitignore", "content": "\ntarget/\ndbt_packages/\nlogs/\n\n#Desktop Services Store\n.DS_Store\n\n#User cookie\n.user.yml" }, { "path": "airbyte_dbt_snowflake_looker/dbt_project/README.md", "content": "Welcome to your new dbt project!\n\n### Using the starter project\n\nTry running the following commands:\n- dbt run\n- dbt test\n\n\n### Resources:\n- Learn more about dbt [in the docs](https://docs.getdbt.com/docs/introduction)\n- Check out [Discourse](https://discourse.getdbt.com/) for commonly asked questions and answers\n- Join the [chat](https://community.getdbt.com/) on Slack for live discussions and support\n- Find [dbt events](https://events.getdbt.com) near you\n- Check out [the blog](https://blog.getdbt.com/) for the latest news on dbt's development and best practices\n" }, { "path": "airbyte_dbt_snowflake_looker/dbt_project/analyses/.gitkeep", "content": "" }, { "path": "airbyte_dbt_snowflake_looker/dbt_project/dbt_project.yml", "content": "\n# Name your project! Project names should contain only lowercase characters\n# and underscores. A good package name should reflect your organization's\n# name or the intended use of these models\nname: 'dbt_project'\nversion: '1.0.0'\nconfig-version: 2\n\n# This setting configures which \"profile\" dbt uses for this project.\nprofile: 'dbt_project'\n\n# These configurations specify where dbt should look for different types of files.\n# The `model-paths` config, for example, states that models in this project can be\n# found in the \"models/\" directory. You probably won't need to change these!\nmodel-paths: [\"models\"]\nanalysis-paths: [\"analyses\"]\ntest-paths: [\"tests\"]\nseed-paths: [\"seeds\"]\nmacro-paths: [\"macros\"]\nsnapshot-paths: [\"snapshots\"]\n\nclean-targets: # directories to be removed by `dbt clean`\n - \"target\"\n - \"dbt_packages\"\n\n\n# Configuring models\n# Full documentation: https://docs.getdbt.com/docs/configuring-models\n\n# In this example config, we tell dbt to build all models in the example/\n# directory as views. These settings can be overridden in the individual model\n# files using the `{{ config(...) }}` macro.\nmodels:\n dbt_project:\n # Config indicated by + and applies to all files under models/example/\n example:\n +materialized: view\n" }, { "path": "airbyte_dbt_snowflake_looker/dbt_project/macros/.gitkeep", "content": "" }, { "path": "airbyte_dbt_snowflake_looker/dbt_project/models/example/my_first_dbt_model.sql", "content": "\n/*\n Welcome to your first dbt model!\n Did you know that you can also configure models directly within SQL files?\n This will override configurations stated in dbt_project.yml\n\n Try changing \"table\" to \"view\" below\n*/\n\n{{ config(materialized='table') }}\n\nwith source_data as (\n\n select * from {{ source('snowflake', 'sample_table') }}\n\n)\n\nselect *\nfrom source_data\n\n/*\n Uncomment the line below to remove records with null `id` values\n*/\n\n-- where id is not null\n" }, { "path": "airbyte_dbt_snowflake_looker/dbt_project/models/example/my_second_dbt_model.sql", "content": "\n-- Use the `ref` function to select from other models\n\nselect *\nfrom {{ ref('my_first_dbt_model') }}\nwhere id = 1\n" }, { "path": "airbyte_dbt_snowflake_looker/dbt_project/models/example/schema.yml", "content": "\nversion: 2\n\nmodels:\n - name: my_first_dbt_model\n description: \"A starter dbt model\"\n columns:\n - name: id\n description: \"The primary key for this table\"\n tests:\n - unique\n - not_null\n\n - name: my_second_dbt_model\n description: \"A starter dbt model\"\n columns:\n - name: id\n description: \"The primary key for this table\"\n tests:\n - unique\n - not_null\n" }, { "path": "airbyte_dbt_snowflake_looker/dbt_project/models/sources.yml", "content": "version: 2\n\nsources:\n - name: snowflake\n tables:\n - name: sample_table\n meta:\n dagster:\n asset_key: [\"sample_table\"] # This metadata specifies the corresponding Dagster asset for this dbt source.\n" }, { "path": "airbyte_dbt_snowflake_looker/dbt_project/profiles.yml", "content": "dbt_project:\n outputs:\n dev:\n\n type: snowflake\n account: \"{{ env_var('DBT_SNOWFLAKE_ACCOUNT_ID', '') }}\"\n\n # User/password auth\n user: username\n password: \"{{ env_var('DBT_SNOWFLAKE_PASSWORD', '') }}\"\n\n role: user_role\n database: database_name\n warehouse: warehouse_name\n schema: dbt_schema\n threads: 1\n client_session_keep_alive: False\n query_tag: anything\n\n # optional\n connect_retries: 0 # default 0\n connect_timeout: 10 # default: 10\n retry_on_database_errors: False # default: false\n retry_all: False # default: false\n reuse_connections: False # default: false (available v1.4+)\n\n target: dev" }, { "path": "airbyte_dbt_snowflake_looker/dbt_project/seeds/.gitkeep", "content": "" }, { "path": "airbyte_dbt_snowflake_looker/dbt_project/snapshots/.gitkeep", "content": "" }, { "path": "airbyte_dbt_snowflake_looker/dbt_project/tests/.gitkeep", "content": "" }, { "path": "airbyte_dbt_snowflake_looker/infra/.gitignore", "content": "# Local .terraform directories\n**/.terraform/*\n\n# .tfstate files\n*.tfstate\n*.tfstate.*\n\n# Crash log files\ncrash.log\ncrash.*.log\n\n# Exclude all .tfvars files, which are likely to contain sensitive data, such as\n# password, private keys, and other secrets. These should not be part of version \n# control as they are data points which are potentially sensitive and subject \n# to change depending on the environment.\n*.tfvars\n*.tfvars.json\n\n# Ignore override files as they are usually used to override resources locally and so\n# are not checked in\noverride.tf\noverride.tf.json\n*_override.tf\n*_override.tf.json\n\n# Include override files you do wish to add to version control using negated pattern\n# !example_override.tf\n\n# Include tfplan files to ignore the plan output of command: terraform plan -out=tfplan\n# example: *tfplan*\n\n# Ignore CLI configuration files\n.terraformrc\nterraform.rc" }, { "path": "airbyte_dbt_snowflake_looker/infra/airbyte/main.tf", "content": "// Airbyte Terraform provider documentation: https://registry.terraform.io/providers/airbytehq/airbyte/latest/docs\n\n// Sources\nresource \"airbyte_source_postgres\" \"postgres\" {\n configuration = {\n database = \"...my_database...\"\n host = \"...my_host...\"\n username = \"...my_username...\"\n password = \"...my_password...\"\n port = 5432\n source_type = \"postgres\"\n schemas = [\n \"...my_schema...\"\n ]\n ssl_mode = {\n allow = {}\n }\n tunnel_method = {\n no_tunnel = {}\n }\n replication_method = {\n scan_changes_with_user_defined_cursor = {}\n }\n }\n name = \"Postgres\"\n workspace_id = var.workspace_id\n}\n\n// Destinations\nresource \"airbyte_destination_snowflake\" \"snowflake\" {\n configuration = {\n credentials = {\n destination_snowflake_authorization_method_key_pair_authentication = {\n auth_type = \"Key Pair Authentication\"\n private_key = \"...my_private_key...\"\n private_key_password = \"...my_private_key_password...\"\n }\n }\n database = \"AIRBYTE_DATABASE\"\n destination_type = \"snowflake\"\n host = \"accountname.us-east-2.aws.snowflakecomputing.com\"\n jdbc_url_params = \"...my_jdbc_url_params...\"\n raw_data_schema = \"...my_raw_data_schema...\"\n role = \"AIRBYTE_ROLE\"\n schema = \"AIRBYTE_SCHEMA\"\n username = \"AIRBYTE_USER\"\n warehouse = \"AIRBYTE_WAREHOUSE\"\n }\n name = \"Snowflake\"\n workspace_id = var.workspace_id\n} \n\n// Connections\nresource \"airbyte_connection\" \"postgres_to_snowflake\" {\n name = \"Postgres to Snowflake\"\n source_id = airbyte_source_postgres.postgres.source_id\n destination_id = airbyte_destination_snowflake.snowflake.destination_id\n configurations = {\n streams = [\n {\n name = \"...my_table_name_1...\"\n },\n {\n name = \"...my_table_name_2...\"\n },\n ]\n }\n}" }, { "path": "airbyte_dbt_snowflake_looker/infra/airbyte/provider.tf", "content": "// Airbyte Terraform provider documentation: https://registry.terraform.io/providers/airbytehq/airbyte/latest/docs\n\nterraform {\n required_providers {\n airbyte = {\n source = \"airbytehq/airbyte\"\n version = \"0.3.4\"\n }\n }\n}\n\nprovider \"airbyte\" {\n // If running locally (Airbyte OSS) with docker-compose using the airbyte-proxy, \n // include the actual password/username you've set up (or use the defaults below)\n username = \"airbyte\"\n password = \"password\"\n \n // if running locally (Airbyte OSS), include the server url to the airbyte-api-server\n server_url = \"http://localhost:8006/v1\"\n}" }, { "path": "airbyte_dbt_snowflake_looker/infra/airbyte/variables.tf", "content": "variable \"workspace_id\" {\n type = string\n}\n\n\n\n\n\n\n" }, { "path": "airbyte_dbt_snowflake_looker/orchestration/orchestration/__init__.py", "content": "" }, { "path": "airbyte_dbt_snowflake_looker/orchestration/orchestration/assets.py", "content": "import os\nfrom dagster import OpExecutionContext\nfrom dagster_dbt import DbtCliResource, dbt_assets\nfrom dagster_airbyte import AirbyteResource, load_assets_from_airbyte_instance\n\nfrom .constants import dbt_manifest_path\n\n@dbt_assets(manifest=dbt_manifest_path)\ndef dbt_project_dbt_assets(context: OpExecutionContext, dbt: DbtCliResource):\n yield from dbt.cli([\"build\"], context=context).stream()\n\nairbyte_instance = AirbyteResource(\n host=\"localhost\",\n port=\"8000\",\n # If using basic auth, include username and password:\n username=\"airbyte\",\n password=os.getenv(\"AIRBYTE_PASSWORD\")\n)\n\nairbyte_assets = load_assets_from_airbyte_instance(airbyte_instance)" }, { "path": "airbyte_dbt_snowflake_looker/orchestration/orchestration/constants.py", "content": "import os\nfrom pathlib import Path\n\nfrom dagster_dbt import DbtCliResource\n\ndbt_project_dir = Path(__file__).joinpath(\"..\", \"..\", \"..\", \"dbt_project\").resolve()\ndbt = DbtCliResource(project_dir=os.fspath(dbt_project_dir))\n\n# If DAGSTER_DBT_PARSE_PROJECT_ON_LOAD is set, a manifest will be created at runtime.\n# Otherwise, we expect a manifest to be present in the project's target directory.\nif os.getenv(\"DAGSTER_DBT_PARSE_PROJECT_ON_LOAD\"):\n dbt_parse_invocation = dbt.cli([\"parse\"], manifest={}).wait()\n dbt_manifest_path = dbt_parse_invocation.target_path.joinpath(\"manifest.json\")\nelse:\n dbt_manifest_path = dbt_project_dir.joinpath(\"target\", \"manifest.json\")" }, { "path": "airbyte_dbt_snowflake_looker/orchestration/orchestration/definitions.py", "content": "import os\n\nfrom dagster import Definitions\nfrom dagster_dbt import DbtCliResource\n\nfrom .assets import dbt_project_dbt_assets, airbyte_assets\nfrom .constants import dbt_project_dir\nfrom .schedules import schedules\n\ndefs = Definitions(\n assets=[dbt_project_dbt_assets, airbyte_assets],\n schedules=schedules,\n resources={\n \"dbt\": DbtCliResource(project_dir=os.fspath(dbt_project_dir)),\n },\n)" }, { "path": "airbyte_dbt_snowflake_looker/orchestration/orchestration/schedules.py", "content": "\"\"\"\nTo add a daily schedule that materializes your dbt assets, uncomment the following lines.\n\"\"\"\nfrom dagster_dbt import build_schedule_from_dbt_selection\n\nfrom .assets import dbt_project_dbt_assets\n\nschedules = [\n# build_schedule_from_dbt_selection(\n# [dbt_project_dbt_assets],\n# job_name=\"materialize_dbt_models\",\n# cron_schedule=\"0 0 * * *\",\n# dbt_select=\"fqn:*\",\n# ),\n]" }, { "path": "airbyte_dbt_snowflake_looker/orchestration/pyproject.toml", "content": "[build-system]\nrequires = [\"setuptools\"]\nbuild-backend = \"setuptools.build_meta\"\n\n[tool.dagster]\nmodule_name = \"orchestration.definitions\"\ncode_location_name = \"orchestration\"" }, { "path": "airbyte_dbt_snowflake_looker/orchestration/setup.py", "content": "from setuptools import find_packages, setup\n\nsetup(\n name=\"orchestration\",\n version=\"0.0.1\",\n packages=find_packages(),\n install_requires=[\n \"dagster\",\n \"dagster-cloud\",\n \"dagster-dbt\",\n \"dbt-core>=1.4.0\",\n \"dbt-snowflake\",\n ],\n extras_require={\n \"dev\": [\n \"dagster-webserver\",\n ]\n },\n)" }, { "path": "airbyte_dbt_snowflake_looker/setup.py", "content": "from setuptools import find_packages, setup\n\nsetup(\n name=\"airbyte-dbt-snowflake-looker\",\n packages=find_packages(),\n install_requires=[\n \"dbt-snowflake\",\n \"dagster\",\n \"dagster-cloud\",\n \"dagster-dbt\",\n \"dagster-airbyte\",\n ],\n extras_require={\"dev\": [\"dagit\", \"pytest\"]},\n)" }, { "path": "airbyte_lib_notebooks/AirbyteLib_Basic_Features_Demo.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"This demo has been be moved. Click [here](https://github.com/airbytehq/quickstarts/blob/main/pyairbyte_notebooks/PyAirbyte_Basic_Features_Demo.ipynb) to go to the demo. \"\n ]\n }\n ],\n \"metadata\": {\n \"colab\": {\n \"include_colab_link\": true,\n \"provenance\": []\n },\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.9.6\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 0\n}\n" }, { "path": "airbyte_lib_notebooks/AirbyteLib_CoinAPI_Demo.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"This demo has been be moved. Click [here](https://github.com/airbytehq/quickstarts/blob/main/pyairbyte_notebooks/PyAirbyte_CoinAPI_Demo.ipynb) to go to the demo. \"\n ]\n }\n ],\n \"metadata\": {\n \"colab\": {\n \"include_colab_link\": true,\n \"provenance\": []\n },\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"name\": \"python\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 0\n}\n" }, { "path": "airbyte_lib_notebooks/AirbyteLib_GA4_Demo.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"This demo has been be moved. Click [here](https://github.com/airbytehq/quickstarts/blob/main/pyairbyte_notebooks/PyAirbyte_GA4_Demo.ipynb) to go to the demo. \"\n ]\n }\n ],\n \"metadata\": {\n \"colab\": {\n \"include_colab_link\": true,\n \"provenance\": []\n },\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"name\": \"python\"\n },\n \"widgets\": {\n \"application/vnd.jupyter.widget-state+json\": {\n \"24a7bb9210cc4c00b19f12715f36ef8a\": {\n \"model_module\": \"@jupyter-widgets/base\",\n \"model_module_version\": \"1.2.0\",\n \"model_name\": \"LayoutModel\",\n \"state\": {\n \"_model_module\": \"@jupyter-widgets/base\",\n \"_model_module_version\": \"1.2.0\",\n \"_model_name\": \"LayoutModel\",\n \"_view_count\": null,\n \"_view_module\": \"@jupyter-widgets/base\",\n \"_view_module_version\": \"1.2.0\",\n \"_view_name\": \"LayoutView\",\n \"align_content\": null,\n \"align_items\": null,\n \"align_self\": null,\n \"border\": null,\n \"bottom\": 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\"IPY_MODEL_42b10528846241e0aac16458a1b8655f\",\n \"value\": 100\n }\n },\n \"d7c54b0aaa4442a19254501027caf793\": {\n \"model_module\": \"@jupyter-widgets/controls\",\n \"model_module_version\": \"1.5.0\",\n \"model_name\": \"FloatProgressModel\",\n \"state\": {\n \"_dom_classes\": [],\n \"_model_module\": \"@jupyter-widgets/controls\",\n \"_model_module_version\": \"1.5.0\",\n \"_model_name\": \"FloatProgressModel\",\n \"_view_count\": null,\n \"_view_module\": \"@jupyter-widgets/controls\",\n \"_view_module_version\": \"1.5.0\",\n \"_view_name\": \"ProgressView\",\n \"bar_style\": \"\",\n \"description\": \"\",\n \"description_tooltip\": null,\n \"layout\": \"IPY_MODEL_24a7bb9210cc4c00b19f12715f36ef8a\",\n \"max\": 100,\n \"min\": 0,\n \"orientation\": \"horizontal\",\n \"style\": \"IPY_MODEL_3031c476bc464ed08e90b5e91ee6393b\",\n \"value\": 100\n }\n }\n }\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 0\n}\n" }, { "path": "airbyte_lib_notebooks/AirbyteLib_Github_Incremental_Demo.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"This demo has been be moved. Click [here](https://github.com/airbytehq/quickstarts/blob/main/pyairbyte_notebooks/PyAirbyte_Github_Incremental_Demo.ipynb) to go to the demo. \"\n ]\n }\n ],\n \"metadata\": {\n \"colab\": {\n \"include_colab_link\": true,\n \"provenance\": []\n },\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"name\": \"python\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 0\n}\n" }, { "path": "airbyte_lib_notebooks/PyAirbyte_Postgres_Custom_Cache_Demo.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"This demo has been be moved. Click [here](https://github.com/airbytehq/quickstarts/blob/main/pyairbyte_notebooks/PyAirbyte_Postgres_Custom_Cache_Demo.ipynb) to go to the demo. \"\n ]\n }\n ],\n \"metadata\": {\n \"colab\": {\n \"provenance\": []\n },\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.9.6\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 0\n}\n" }, { "path": "airbyte_lib_notebooks/PyAirbyte_Shopify_Demo.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"This demo has been be moved. Click [here](https://github.com/airbytehq/quickstarts/blob/main/pyairbyte_notebooks/PyAirbyte_Shopify_Demo.ipynb) to go to the demo. \"\n ]\n }\n ],\n \"metadata\": {\n \"colab\": {\n \"provenance\": []\n },\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"name\": \"python\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 0\n}\n" }, { "path": "airbyte_lib_notebooks/README.md", "content": "# PyAirbyte Notebooks Quickstart\n\nThis quickstart will help you get started quickly with PyAirbyte.\n\n> **Note:** **PyAirbyte is currently in private beta and is not intended for production use.**\n\n## Quickstart Quicklinks\n\nTo jump right in, click on any of the below links to open a new Colab notebook from the provided quickstart template.\n\n1. [Basic Features Demo](https://colab.research.google.com/github/airbytehq/quickstarts/blob/master/airbyte_lib_notebooks/AirbyteLib_Basic_Features_Demo.ipynb) - Walks through the basic functionality of PyAirbyte and how to use it in a Notebook environment.\n2. [CoinAPI Demo](https://colab.research.google.com/github/airbytehq/quickstarts/blob/master/airbyte_lib_notebooks/AirbyteLib_CoinAPI_Demo.ipynb) - Shows how to provide credentials securely and perform basic graphing.\n3. [GitHub Demo](https://colab.research.google.com/github/airbytehq/quickstarts/blob/master/airbyte_lib_notebooks/AirbyteLib_Github_Incremental_Demo.ipynb) - Demonstrates how to get data from GitHub, how to analyze GitHub metrics and how to refresh your cache data incrementally.\n4. [GA4 Demo](https://colab.research.google.com/github/airbytehq/quickstarts/blob/master/airbyte_lib_notebooks/AirbyteLib_GA4_Demo.ipynb) - A Google Analytics demo showing how to analyze page views and other GA metrics.\n\n## How to use these Quickstarts\n\nThere are three ways to use the quickstart resources here.\n\n### Google Colab\n\nGoogle Colab (\"Colab\" for short) is a hosted version of Jupyter. Because it is hosted by google, most people can access Colab using their existing Google account. To use these notebooks in Colab, click on the \"Open in Colab\" badge at the top of the file.\n\nNote:\n\n- Colab doesn't come with virtual environment (\"venv\") support by default. For this reason, our demo workbooks start by installing venv support as a prerequisite, using `apt-get`.\n\n### Self-Hosted Jupyter\n\nIf you have a self-hosted Jupyter instance, you can load any of the notebooks in this directory.\n\n### VS Code Notebooks\n\nYou can run these notebooks natively in VS Code if you have the Python extension installed. You can also use GitHub Codespaces to open a new VS Code devcontainer in your web browser.\n\n## Securely Managed Secrets\n\nYou can pass secrets to PyAirbyte by using the `get_secret()` function. This call will retrieve a named secret from any of the following locations:\n\n1. Google Colab Secrets\n2. Environment Variables\n3. Masked User Input (via [getpass](https://docs.python.org/3/library/getpass.html))\n\nIf you are using Google Colab, we suggest using the Colab secrets feature. For other environments, you can set your secret values in environment variables.\n\nNote: The `get_secret()` implementation in PyAirbyte is provided for your convenience as a secure runtime-agnostic default secrets interface. You are always free to use any secrets management platform you are most familiar with.\n\n**Warning:** Please do not enter your secrets directly into notebook cells. Doing so can cause the secret to be leaked into logs and/or in the \"previous versions\" look-back of the notebook. Instead, simply call `get_secret()` without pre-initializing the value. If the value is not already initialized, you will be prompted for secret values interactively and all values will be masked during input to avoid accidental leakage. This is performed using the Python standard library [`getpass`](https://docs.python.org/3/library/getpass.html).\n" }, { "path": "airbyte_s3_pinecone_rag/.gitignore", "content": "# Byte-compiled / optimized / DLL files\n__pycache__/\n*.py[cod]\n*$py.class\n\n# C extensions\n*.so\n\n# Distribution / packaging\n.Python\nbuild/\ndevelop-eggs/\ndist/\ndownloads/\neggs/\n.eggs/\nlib/\nlib64/\nparts/\nsdist/\nvar/\nwheels/\nshare/python-wheels/\n*.egg-info/\n.installed.cfg\n*.egg\nMANIFEST\n\n# PyInstaller\n# Usually these files are written by a python script from a template\n# before PyInstaller builds the exe, so as to inject date/other infos into it.\n*.manifest\n*.spec\n\n# Installer logs\npip-log.txt\npip-delete-this-directory.txt\n\n# Unit test / coverage reports\nhtmlcov/\n.tox/\n.nox/\n.coverage\n.coverage.*\n.cache\nnosetests.xml\ncoverage.xml\n*.cover\n*.py,cover\n.hypothesis/\n.pytest_cache/\ncover/\n\n# Translations\n*.mo\n*.pot\n\n# Django stuff:\n*.log\nlocal_settings.py\ndb.sqlite3\ndb.sqlite3-journal\n\n# Flask stuff:\ninstance/\n.webassets-cache\n\n# Scrapy stuff:\n.scrapy\n\n# Sphinx documentation\ndocs/_build/\n\n# PyBuilder\n.pybuilder/\ntarget/\n\n# Jupyter Notebook\n.ipynb_checkpoints\n\n# IPython\nprofile_default/\nipython_config.py\n\n# pyenv\n# For a library or package, you might want to ignore these files since the code is\n# intended to run in multiple environments; otherwise, check them in:\n# .python-version\n\n# pipenv\n# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.\n# However, in case of collaboration, if having platform-specific dependencies or dependencies\n# having no cross-platform support, pipenv may install dependencies that don't work, or not\n# install all needed dependencies.\n#Pipfile.lock\n\n# poetry\n# Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.\n# This is especially recommended for binary packages to ensure reproducibility, and is more\n# commonly ignored for libraries.\n# https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control\n#poetry.lock\n\n# pdm\n# Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control.\n#pdm.lock\n# pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it\n# in version control.\n# https://pdm.fming.dev/#use-with-ide\n.pdm.toml\n\n# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm\n__pypackages__/\n\n# Celery stuff\ncelerybeat-schedule\ncelerybeat.pid\n\n# SageMath parsed files\n*.sage.py\n\n# Environments\n.env\n.venv\nenv/\nvenv/\nENV/\nenv.bak/\nvenv.bak/\n\n# Spyder project settings\n.spyderproject\n.spyproject\n\n# Rope project settings\n.ropeproject\n\n# mkdocs documentation\n/site\n\n# mypy\n.mypy_cache/\n.dmypy.json\ndmypy.json\n\n# Pyre type checker\n.pyre/\n\n# pytype static type analyzer\n.pytype/\n\n# Cython debug symbols\ncython_debug/\n\n# PyCharm\n# JetBrains specific template is maintained in a separate JetBrains.gitignore that can\n# be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore\n# and can be added to the global gitignore or merged into this file. For a more nuclear\n# option (not recommended) you can uncomment the following to ignore the entire idea folder.\n#.idea/\n\n#Desktop Services Store\n.DS_Store\nsecrets/\n" }, { "path": "airbyte_s3_pinecone_rag/README.md", "content": "# Airbyte S3 Pinecone RAG\n\nAirbyte S3 Pinecone RAG repo provides a quickstart template for building a full data stack using Airbyte cloud, Terraform, and dbt to move data from S3 -> BigQuery -> Pinecone for interacting with fetched data through an LLM and form a full fledged Retrieval-Augmented Generation.\n\nThis quickstart is designed to minimize setup hassles and propel you forward.\n\n![Quickstart overview](assets/dataflow.png)\n\n## Table of Contents\n\n- [Prerequisites](#prerequisites)\n- [Creating an Environment For Your Project](#1-creating-an-environment-for-your-project)\n- [Setting Up BigQuery](#2-setting-up-bigquery)\n- [Adding Configuration Values](#3-adding-configuration-values)\n- [Setting Up Airbyte Connectors](#4-setting-up-airbyte-connectors)\n- [Sync S3 Data into BigQuery](#5-sync-s3-data-into-bigquery)\n- [Setting Up the dbt Project](#6-setting-up-the-dbt-project)\n- [Publishing Into Pinecone](#7-publishing-into-pinecone)\n- [Asking Questions About Your Data](#8-asking-questions-about-your-data)\n- [Next Steps](#9-next-steps)\n\n## Prerequisites\n\n### Amazon S3\n\nThe source to fetch data is from s3 to make the content searchable in Pinecone. Follow the [S3 source docs](https://docs.airbyte.com/integrations/sources/s3) for information on configuring a S3 source.\n\n### BigQuery\n\nBigQuery will store the raw API data (csv in example) from our sources and also the transformed data from dbt. You'll need a BigQuery project and a dataset with a service account that can control the dataset. Airbyte's [BigQuery destination docs](https://docs.airbyte.com/integrations/destinations/bigquery) lists the requirements and links describing how to configure.\n\n### Pinecone\n\nPinecone is the vector database we will use to index documents and their metadata, and also for finding documents that provide context for a query. You'll need a Pinecone account, an API key, and an index created with 1536 dimensions, as OpenAI returns vectors of 1536 dimensions. See the [Pinecone docs](https://docs.pinecone.io/docs/quickstart) for more information.\n\n### OpenAI\n\nOpenAI is used both in processing the query and also provides the LLM for generating a response. The query is vectorized so it can be used to identify relevant items in the Pinecone index, and these items are provided to the LLM as context to better respond to the query. You'll need an OpenAI account with credits and an API key. If you already have an account with OpenAI, you can generate a new API key by visiting this link: [platform.openai.com/api-keys](https://platform.openai.com/api-keys)\n\n### Software\n\n1. **Python 3.10 or later**: If not installed, download and install it from [Python's official website](https://www.python.org/downloads/).\n\n2. **Docker and Docker Compose (Docker Desktop)**: Install [Docker](https://docs.docker.com/get-docker/) following the official documentation for your specific OS.\n\n3. **Airbyte OSS version**: If you want to deploy the open-source version instead of using Airbyte Cloud, follow the installation instructions from the [Deploy Airbyte documentation](https://docs.airbyte.com/quickstart/deploy-airbyte/).\n\n4. **Terraform**: Terraform will help you provision and manage the Airbyte resources. If you haven't installed it, follow the [official Terraform installation guide](https://developer.hashicorp.com/terraform/tutorials/aws-get-started/install-cli).\n\n## 1. Creating an Environment For Your Project\n\nGet the project up and running on your local machine by following these steps:\n\n1. **Clone the repository (Clone only this quickstart)**:\n\n ```bash\n git clone --filter=blob:none --sparse https://github.com/airbytehq/quickstarts.git\n ```\n\n ```bash\n cd quickstarts\n ```\n\n ```bash\n git sparse-checkout add pyairbyte_s3_pinecone_rag\n ```\n\n2. **Navigate to the directory**:\n\n ```bash\n cd pyairbyte_s3_pinecone_rag\n ```\n\n3. **Set Up a Virtual Environment**:\n\n - For Mac:\n ```bash\n python3 -m venv .venv\n source .venv/bin/activate\n ```\n - For Windows:\n ```bash\n python -m venv .venv\n .\\.venv\\Scripts\\activate\n ```\n\n4. **Install Dependencies**:\n ```bash\n pip install -e \".[dev]\"\n ```\n\n\n## 2. Setting Up BigQuery\n\n#### 1. **Create a Google Cloud Project**\n\n- If you have a Google Cloud project, you can skip this step.\n- Go to the [Google Cloud Console](https://console.cloud.google.com/).\n- Click on the \"Select a project\" dropdown at the top right and select \"New Project\".\n- Give your project a name and follow the steps to create it.\n\n#### 2. **Create BigQuery Datasets**\n\n- In the Google Cloud Console, go to BigQuery.\n- Make two new datasets: `raw_data` for Airbyte and `transformed_data` for dbt.\n - If you pick different names, remember to change the names in the code too.\n\n**How to create a dataset:**\n\n- In the left sidebar, click on your project name.\n- Click “Create Dataset”.\n- Enter the dataset ID (either `raw_data` or `transformed_data`).\n- Click \"Create Dataset\".\n\n#### 3. **Create Service Accounts and Assign Roles**\n\n- Go to “IAM & Admin” > “Service accounts” in the Google Cloud Console.\n- Click “Create Service Account”.\n- Name your service account (like `airbyte-service-account`).\n- Assign the “BigQuery Data Editor” and “BigQuery Job User” roles to the service account.\n- Follow the same steps to make another service account for dbt (like `dbt-service-account`) and assign the roles.\n\n**How to create a service account and assign roles:**\n\n- While creating the service account, under the “Grant this service account access to project” section, click the “Role” dropdown.\n- Choose the “BigQuery Data Editor” and “BigQuery Job User” roles.\n- Finish the creation process.\n\n#### 4. **Generate JSON Keys for Service Accounts**\n\n- For both service accounts, make a JSON key to let the service accounts sign in.\n\n**How to generate JSON key:**\n\n- Find the service account in the “Service accounts” list.\n- Click on the service account name.\n- In the “Keys” section, click “Add Key” and pick JSON.\n- The key will download automatically. Keep it safe and don’t share it.\n- Do this for the other service account too.\n\n\n## 3. Adding Configuration Values\n\nThe following steps will execute Terraform and dbt workflows to create the necessary resources for the integration. To do this, you'll need to provide some configuration values. Copy the provided `.env.template` file to `.env` and set its values. Then run the following command to source the environment variables into your shell so they are available when running Terraform and dbt:\n\n```bash\nset -o allexport && source .env && set +o allexport\n```\n\nDon't forget to re-run the above command after making any changes to the `.env` file.\n\n## 4. Setting Up Airbyte Connectors\n\n### Manually via the Airbyte UI\n\nCreate the [sources](https://docs.airbyte.com/quickstart/add-a-source) and [destinations](https://docs.airbyte.com/quickstart/add-a-destination) within your Airbyte environment, you can follow the [create connections](https://docs.airbyte.com/quickstart/create-a-connection) to define connections between them to control how and when the data will sync.\n\nYou can find the Airbyte workspace ID from the URL, e.g. `https://cloud.airbyte.com/workspaces/{workspace-id}/connections`.\n\n### With Terraform\n\nAirbyte allows you to create connectors for sources and destinations, facilitating data synchronization between various platforms. In this project, we're harnessing the power of Terraform to automate the creation of these connectors and the connections between them. Here's how you can set it up:\n\n1. **Navigate to the Airbyte Configuration Directory**:\n\n Change to the relevant directory containing the Terraform configuration for Airbyte:\n\n ```bash\n cd infra/airbyte\n ```\n\n2. **Modify Configuration Files**:\n\n Within the `infra/airbyte` directory, you'll find three crucial Terraform files:\n\n - `provider.tf`: Defines the Airbyte provider.\n - `main.tf`: Contains the main configuration for creating Airbyte resources.\n - `variables.tf`: Defines variables whose values are populated from the `.env` file.\n\n If you're using Airbyte Cloud instead of a local deployment you will need to update the Airbyte provider configuration in _infra/airbyte/provider.tf_, setting the `bearer_auth` to an API key generated at https://portal.airbyte.com/.\n\n3. **Initialize Terraform**:\n\n This step prepares Terraform to create the resources defined in your configuration files.\n\n ```bash\n terraform init\n ```\n\n4. **Review the Plan**:\n\n Before applying any changes, review the plan to understand what Terraform will do.\n\n ```bash\n terraform plan\n ```\n\n5. **Apply Configuration**:\n\n After reviewing and confirming the plan, apply the Terraform configurations to create the necessary Airbyte resources.\n\n ```bash\n terraform apply\n ```\n\n6. **Verify in Airbyte UI**:\n\n Once Terraform completes its tasks, navigate to the Airbyte UI. Here, you should see your source and destination connectors, as well as the connection between them, set up and ready to go.\n\n If you're using Airbyte Cloud, we configured Terraform to assemble the URL for you:\n\n ```bash\n terraform output\n ```\n\n will print something like:\n\n ```\n airbyte_cloud_url = \"https://cloud.airbyte.com/workspaces/{workspace-id}/connections\"\n ```\n\n## 5. Sync S3 Data into BigQuery\n\nBefore building the dbt project, transforming the raw s3 data, the source tables must exist in the BigQuery dataset. Open the Airbyte UI and navigate to the Connections page. Click the _Sync Now_ button for `S3 to BigQuery` to start the sync.\n\nOnce the sync is complete you can inspect the tables in BigQuery.\n\n## 6. Setting Up the dbt Project\n\n[dbt (data build tool)](https://www.getdbt.com/) allows you to transform your data by writing, documenting, and executing SQL workflows. Setting up the dbt project requires specifying connection details for your data platform, in this case, BigQuery. Here’s a step-by-step guide to help you set this up:\n\n1. **Navigate to the dbt Project Directory**:\n\n Change to the directory containing the dbt configuration:\n\n ```bash\n cd dbt_project\n ```\n\n You'll find a `profiles.yml` file within the directory. This file contains configurations for dbt to connect with your data platform, and is preconfigured to pull the connection details from environment variables.\n\n You can use the `dbt_project/example/schema.yml` to make your own dbt_model and create your own sql files corresponding to the dbt_model names\n\n2. **Utilize Environment Variables (Optional but Recommended)**:\n\n To keep your credentials secure, you can leverage environment variables. An example is provided as the `dev` config in `profiles.yml`.\n\n3. **Test the Connection**:\n\n Once you’ve updated the connection details, you can test the connection to your BigQuery instance using:\n\n ```bash\n dbt debug\n ```\n\n If everything is set up correctly, this command should report a successful connection to BigQuery.\n\n4. **Run the Model**\n\n With the connection in place, you can now build the model to create the `S3` view in BigQuery, which the configured Airbyte connection will use to sync S3 data into Pinecone.\n\n You can inspect the provided _dbt_project/models/marts/purchase_data.sql_ to see removing null values after querying for purchase patterns.\n\n ```bash\n dbt build\n ```\n\n You should now see the `s3_data` view in BigQuery.\n\n## 7. Publishing Into Pinecone\n\nWith the source data transformed it is now ready to publish into the Pinecone index. Head back to the Connections and start a sync for `Publish BigQuery Data to Pinecone`.\n\n\n## 8. Asking Questions About Your Data\n\nAfter Airbyte has published the S3 data text into your Pinecone index, it is ready to be interacted with via an LLM model. After providing a couple more environment variables, the provided _query.py_ will provide an interactive session to ask questions about your data.\n\n```bash\nexport OPENAI_API_KEY=openai_api_key\nexport PINECONE_API_KEY=pinecone_api_key\nexport PINECONE_ENVIRONMENT=pinecone_environment\nexport PINECONE_INDEX=pinecone_index\n\npython query.py\n```\n\n## 9. Next Steps\n\nOnce you've set up and launched this initial integration, the real power lies in its adaptability and extensibility. Here’s a roadmap to help you customize and harness this project tailored to your specific data needs:\n\n1. **Use Additional Data Sources**:\n\n Pinecone allows you to store vectors from multiple sources in a single index. This allows multiple document types to be queried together, and even referenced between themselves by the LLM. Add additional sources to the Terraform configuration or manually through the Airbyte UI.\n\n2. **Try Different LLM Chains**:\n\n The provided _query.py_ uses many default values when creating the LLM chain, which is quick to create but limits flexibility. One constraint is that the `stuff` chain type requires the query and discovered documents all fit within a single tokenized input to the LLM. This is fine for short queries and documents, but if you use longer sources you will need to use another chain type such as `map_reduce`.\n\n3. **Extend Access to the LLM**:\n\n The provided _query.py_ is a simple example of how to interact with the LLM locally. You could build a web-based UI that triggers a query and displays the results, or even integrate the LLM into a Slack bot to provide answers to questions in real-time.\n\n4. **Make Use of Metadata**\n\n When configuring the Pinecone destination you can choose to include metadata fields from the data. This allows you to filter results based on the metadata fields. For example, you could filter the results of a query to only include documents from a specific author, or provide a time range that a matching item must have been created or edited within. Metadata values are also provided back to the query executor so you can use them to provide additional context to the LLM.\n\n See [Filtering with metadata](https://docs.pinecone.io/docs/metadata-filtering) for more details.\n" }, { "path": "airbyte_s3_pinecone_rag/dbt_project/.gitignore", "content": "\ntarget/\ndbt_packages/\nlogs/\n\n#Desktop Services Store\n.DS_Store\n\n#User cookie\n.user.yml\n.vscode\n" }, { "path": "airbyte_s3_pinecone_rag/dbt_project/README.md", "content": "Welcome to your new dbt project!\n\n### Using the starter project\n\nTry running the following commands:\n- dbt run\n- dbt test\n\n\n### Resources:\n- Learn more about dbt [in the docs](https://docs.getdbt.com/docs/introduction)\n- Check out [Discourse](https://discourse.getdbt.com/) for commonly asked questions and answers\n- Join the [chat](https://community.getdbt.com/) on Slack for live discussions and support\n- Find [dbt events](https://events.getdbt.com) near you\n- Check out [the blog](https://blog.getdbt.com/) for the latest news on dbt's development and best practices\n" }, { "path": "airbyte_s3_pinecone_rag/dbt_project/dbt_project.yml", "content": "# Name your project! Project names should contain only lowercase characters\n# and underscores. A good package name should reflect your organization's\n# name or the intended use of these models\nname: \"dbt_project\"\nversion: \"1.0.0\"\nconfig-version: 2\n\n# This setting configures which \"profile\" dbt uses for this project.\nprofile: \"dbt_project\"\n\n# These configurations specify where dbt should look for different types of files.\n# The `model-paths` config, for example, states that models in this project can be\n# found in the \"models/\" directory. You probably won't need to change these!\nmodel-paths: [\"models\"]\nanalysis-paths: [\"analyses\"]\ntest-paths: [\"tests\"]\nseed-paths: [\"seeds\"]\nmacro-paths: [\"macros\"]\nsnapshot-paths: [\"snapshots\"]\n\nclean-targets: # directories to be removed by `dbt clean`\n - \"target\"\n - \"dbt_packages\"\n\n# Configuring models\n# Full documentation: https://docs.getdbt.com/docs/configuring-models\n\n# In this example config, we tell dbt to build all models in the example/\n# directory as views. These settings can be overridden in the individual model\n# files using the `{{ config(...) }}` macro.\nmodels:\n dbt_project:\n example:\n +materialized: view\n" }, { "path": "airbyte_s3_pinecone_rag/dbt_project/models/marts/purchase_data.sql", "content": "/* As example csv data is typiclly flat,\n getting purchase patterns with a basic sql query and cleansing null */\n\nWITH purchase_data AS (\n SELECT\n user_id,\n CASE WHEN category IS NULL THEN 'unknown' ELSE category END AS product_category,\n CASE WHEN brand IS NULL THEN 'unknown' ELSE brand END AS brand,\n purchased_at,\n added_to_cart_at,\n TIMESTAMP_DIFF(purchased_at, added_to_cart_at, SECOND) AS time_to_purchase_seconds,\n returned_at\n FROM {{ ref('stg_purchases') }}\n)\n\nSELECT\n user_id,\n product_category,\n brand,\n time_to_purchase_seconds,\n CASE WHEN description IS NULL THEN '' ELSE description END AS product_description\nFROM purchase_data\n" }, { "path": "airbyte_s3_pinecone_rag/dbt_project/models/sources/s3.source.yml", "content": "version: 2\n\nsources:\n - name: s3_raw\n database: \"{{ env_var('BIGQUERY_PROJECT_ID') }}\"\n schema: \"{{ env_var('BIGQUERY_DATASET_ID') }}\"\n\n tables:\n - name: s3_csv\n description: \"Purchase data gathered from csv\"\n columns:\n - name: id\n description: \"Unique identifier for the purchase.\"\n - name: updated_at\n - name: purchased_at\n - name: user_id\n - name: returned_at\n - name: product_id\n - name: created_at\n - name: added_to_cart_at\n - name: _airbyte_extracted_at\n - name: _airbyte_raw_id\n - name: _airbyte_meta\n" }, { "path": "airbyte_s3_pinecone_rag/dbt_project/models/staging/stg_purchases.sql", "content": "-- models/stg_purchases.sql\nselect\n id,\n user_id,\n product_id,\n updated_at,\n purchased_at,\n returned_at,\n created_at,\n added_to_cart_at,\n _airbyte_extracted_at,\nfrom {{ source('s3', 'purchases') }}\n" }, { "path": "airbyte_s3_pinecone_rag/dbt_project/profiles.yml", "content": "dbt_project:\n outputs:\n dev:\n type: bigquery\n project: \"{{ env_var('BIGQUERY_PROJECT_ID') }}\"\n dataset: \"{{ env_var('BIGQUERY_DATASET_ID') }}\"\n location: \"{{ env_var('BIGQUERY_DATASET_LOCATION') }}\"\n keyfile: \"{{ env_var('DBT_BIGQUERY_KEYFILE_PATH') }}\"\n method: service-account\n priority: interactive\n job_execution_timeout_seconds: 300\n job_retries: 1\n threads: 1\n target: dev\n" }, { "path": "airbyte_s3_pinecone_rag/infra/.gitignore", "content": "# Local .terraform directories\n**/.terraform/*\n\n# .tfstate files\n*.tfstate\n*.tfstate.*\n\n# Crash log files\ncrash.log\ncrash.*.log\n\n# Exclude all .tfvars files, which are likely to contain sensitive data, such as\n# password, private keys, and other secrets. These should not be part of version \n# control as they are data points which are potentially sensitive and subject \n# to change depending on the environment.\n*.tfvars\n*.tfvars.json\n\n# Ignore override files as they are usually used to override resources locally and so\n# are not checked in\noverride.tf\noverride.tf.json\n*_override.tf\n*_override.tf.json\n\n# Include override files you do wish to add to version control using negated pattern\n# !example_override.tf\n\n# Include tfplan files to ignore the plan output of command: terraform plan -out=tfplan\n# example: *tfplan*\n\n# Ignore CLI configuration files\n.terraformrc\nterraform.rc" }, { "path": "airbyte_s3_pinecone_rag/infra/airbyte/.terraform.lock.hcl", "content": "# This file is maintained automatically by \"terraform init\".\n# Manual edits may be lost in future updates.\n\nprovider \"registry.terraform.io/airbytehq/airbyte\" {\n version = \"0.3.4\"\n constraints = \"0.3.4\"\n hashes = [\n \"h1:E97NK92naRr/9iAtDxA1PJ1aQYWR/vqWN10ThuQjUn8=\",\n \"zh:02167e00f7e89b6f09ae8796b9ee0ac2d8702b5cb295cb27d7a79266ffafe196\",\n \"zh:1ddad39354af090e830caf1e5cce845f24ff0bcef61b73e77ebc7703c2ecf90d\",\n \"zh:223a0a46d354ad0709d5f28d60accb3448ba5f256b84438238fb05235d1e5b34\",\n \"zh:29efd8848b9560456ec3d90f54984670e9d5b7e36f1edd2adb15c5fec3f57166\",\n \"zh:33d31310ba7ec699b5bd64edbb63b0a89bd55d87fae0f55409bbfa5fd7dd4d90\",\n \"zh:35ed0e2894e28ec7762406a18510b789b76b0649ace309eec22acaf10c982f08\",\n \"zh:4ba860918b65c00cc596d0b5b40068b89a72a300604a62bca7d286073779e684\",\n \"zh:59a0d1128477e587d9dac71f93598bae6050d176d29c840b6ad1bf95529d61e8\",\n \"zh:890df766e9b839623b1f0437355032a3c006226a6c200cd911e15ee1a9014e9f\",\n \"zh:8d9bb37e9094eba02acf8d08cf9f3331cd7c26478441d70e74e8d1ec9cb33aaa\",\n \"zh:9e5243eac43950889781a88d4e6186aea240898045e0e3c8fffd3291c5e74b6f\",\n \"zh:a0c31a5bc0cbc4a7341a0d185806a1c6797508580bede71a5009ad7b078d68c2\",\n \"zh:af341259999c6639a1c27e8f116a40b088dd192a3057096dc23a42affc97113f\",\n \"zh:b9779f8f695b4fab56e062abab61eaa58853f20c6411d53b2bd82a66d79a8b49\",\n \"zh:e284d898e5a30e507f1292635542dafe0e95ea8a5a215103a9d96d699aed9e75\",\n ]\n}\n" }, { "path": "airbyte_s3_pinecone_rag/infra/airbyte/main.tf", "content": "// Airbyte Terraform provider documentation: https://registry.terraform.io/providers/airbytehq/airbyte/latest/docs\n\nlocals {\n // The BigQuery destination expects a JSON credentials file, but the source expects a JSON string.\n // Read JSON file into a string variable, so we can use the same contents also for the source connector.\n bigquery_credentials_json = replace(\n file(var.bigquery_credentials_json_file_path), \"\\n\", \"\"\n ) // <- If this fails, please double check that the file exists.\n\n airbyte_cloud_url = var.airbyte_workspace_id != null ? \"https://cloud.airbyte.com/workspaces/${var.airbyte_workspace_id}/connections\" : \"(n/a)\"\n}\n\n// Sources\nresource \"airbyte_source_bigquery\" \"bigquery\" {\n configuration = {\n credentials_json = local.bigquery_credentials_json\n project_id = var.bigquery_project_id\n dataset_id = var.bigquery_dataset_id\n source_type = \"bigquery\"\n }\n name = \"BigQuery Publishing Source\"\n workspace_id = var.airbyte_workspace_id\n}\n\nresource \"airbyte_source_s3\" \"s3\" {\n configuration = {\n source_type = \"s3\"\n bucket = var.bucket\n aws_access_key_id = var.aws_access_key_id\n aws_secret_access_key = var.aws_secret_access_key\n endpoint = \"https://s3.amazonaws.com\"\n path_pattern = \"**\"\n format = {\n csv_format = {\n ignore_errors_on_fields_mismatch = true\n }\n }\n provider = {\n aws_access_key_id = var.aws_access_key_id\n aws_secret_access_key = var.aws_secret_access_key\n bucket = var.bucket #Change to your need\n endpoint = \"https://s3.amazonaws.com\" #Change to your need\n path_prefix = \"/Wallpapers/\" #Change to your need\n region_name = \"us-west-2\" #Change to your need\n start_date = \"2021-01-01T00:00:00Z\" #Change to your need\n }\n region_name = \"us-west-2\"\n streams = [\n {\n name = \"Wallpapers\"\n file_type = \"csv\"\n format = {\n csv_format = {\n filetype = \"csv\"\n }\n }\n },\n ]\n }\n name = \"usgs-landsat\" #Change to your need\n workspace_id = var.airbyte_workspace_id\n}\n\n\n// Destinations\nresource \"airbyte_destination_bigquery\" \"bigquery\" {\n configuration = {\n dataset_id = var.bigquery_dataset_id\n dataset_location = var.bigquery_dataset_location\n destination_type = \"bigquery\"\n project_id = var.bigquery_project_id\n credentials_json = local.bigquery_credentials_json\n loading_method = {\n destination_bigquery_loading_method_standard_inserts = {\n method = \"Standard\"\n }\n }\n }\n name = \"BigQuery Raw Data Destination\"\n workspace_id = var.airbyte_workspace_id\n}\nresource \"airbyte_destination_pinecone\" \"pinecone\" {\n configuration = {\n destination_type = \"pinecone\"\n embedding = {\n destination_pinecone_embedding_open_ai = {\n openai_key = var.openai_key\n mode = \"openai\"\n }\n }\n indexing = {\n index = var.pinecone_index\n pinecone_environment = var.pinecone_environment\n pinecone_key = var.pinecone_key\n }\n processing = {\n chunk_overlap = 16\n chunk_size = 1024\n metadata_fields = [\"url\", \"last_edited_time\"]\n text_fields = [\"s3_text\"]\n }\n }\n name = \"Pinecone Publish Destination\"\n workspace_id = var.airbyte_workspace_id\n}\n\n// Connections\nresource \"airbyte_connection\" \"s3_connection\" {\n name = \"S3 to BigQuery\"\n source_id = airbyte_source_s3.s3.source_id\n destination_id = airbyte_destination_bigquery.bigquery.destination_id\n namespace_definition = \"custom_format\"\n namespace_format = var.bigquery_dataset_id\n format = {\n csv = {}\n }\n prefix = \"s3_\"\n configurations = {\n streams = [\n {\n name = \"Wallpapers\"\n file_type = \"csv\"\n format = {\n csv_format = {\n filetype = \"csv\"\n }\n }\n },\n ]\n }\n}\nresource \"airbyte_connection\" \"bigquery_to_pinecone\" {\n name = \"Publish BigQuery Data to Pinecone\"\n source_id = airbyte_source_bigquery.bigquery.source_id\n destination_id = airbyte_destination_pinecone.pinecone.destination_id\n configurations = {\n streams = [\n {\n name = \"s3_data\",\n cursor_field = [\"last_edited_time\"],\n primary_key = [[\"url\"]]\n sync_mode = \"incremental_deduped_history\"\n }\n ]\n }\n}\n" }, { "path": "airbyte_s3_pinecone_rag/infra/airbyte/output.tf", "content": "// These variables will be printed at the end of a successful `terraform apply`,\n// or by running `terraform output` at any time.\n\noutput \"airbyte_cloud_url\" {\n value = local.airbyte_cloud_url \n}\n" }, { "path": "airbyte_s3_pinecone_rag/infra/airbyte/provider.tf", "content": "// Airbyte Terraform provider documentation: https://registry.terraform.io/providers/airbytehq/airbyte/latest/docs\n\nterraform {\n required_providers {\n airbyte = {\n source = \"airbytehq/airbyte\"\n version = \"0.3.4\"\n }\n }\n}\n\n/////////////////////////////////\n// Airbyte Provider Definition //\n/////////////////////////////////\n\n// Uncomment the OSS or Cloud block, depending on your desired deployment location:\n\n// Airbyte Cloud:\nprovider \"airbyte\" {\n bearer_auth = var.airbyte_cloud_auth_key\n}\n\n# // Airbyte OSS:\n# provider \"airbyte\" {\n# // Optionally override the airbyte-api-server URL\n# server_url = \"http://localhost:8006/v1\"\n\n# // Optionally override the default password/username below\n# username = \"airbyte\"\n# password = \"password\" \n# }\n" }, { "path": "airbyte_s3_pinecone_rag/infra/airbyte/variables.tf", "content": "// Airbyte\nvariable \"airbyte_workspace_id\" {\n description = < What do you want to know?\"))\nconsole.print(\"\")\nwhile True:\n try:\n query = input(\"\")\n except KeyboardInterrupt:\n console.print(\"\\n\")\n console.print(Markdown(\"_Goodbye!_ 👋\"))\n exit(0)\n\n answer = qa.run(query)\n console.print(Markdown(answer))\n\n console.print(Markdown(\"\\n------\\n> What else do you want to know?\\n\"))\n console.print(\"\\n\")\n" }, { "path": "airbyte_s3_pinecone_rag/quickstart.md", "content": "# Quick Start\n\n```bash\n# Change to the quickstart directory\ncd pyairbyte_s3_pinecone_rag\n\n# Create a new `.env` file from the template\ncp .env.template .env\n\n# Edit the `.env` file and provide all needed variables\ncode .env\n\n# Export the .env file variables\nset -o allexport && source .env && set +o allexport\n\n# Deploy terraform\nterraform -chdir=infra/airbyte init\nterraform -chdir=infra/airbyte apply\n\n# Print Airbyte Cloud workspace URL\nterraform -chdir=infra/airbyte output\n\n# In Airbyte Cloud:\n# 1. Peform any needed fine-tuning or debugging.\n# 2. Run the source data connection at least once.\n\n# Build dbt models\ndbt run --project-dir=dbt_project --profiles-dir=dbt_project\n\n# In Airbyte Cloud:\n# 1. Create the publish connection if needed.\n# 2. Run the publish connection at least once.\n\n# Create and activate the virtual environment for the AI chatbot\npython -m venv .venv\nsource .venv/bin/activate\n\n# Run the chatbot\n./query.py\n```\n\nPlease see the [Readme](README.md) for more detailed instructions.\n" }, { "path": "airbyte_s3_pinecone_rag/setup.py", "content": "from setuptools import find_packages, setup\n\nsetup(\n name=\"pyairbyte_s3_pinecone_rag\",\n packages=find_packages(),\n install_requires=[\n \"dbt-bigquery\",\n \"pinecone-client\",\n \"langchain\",\n \"openai==0.28.1\",\n \"tiktoken\",\n \"rich\"\n ],\n extras_require={\"dev\": [\"pytest\"]},\n)\n" }, { "path": "api_to_warehouse/.gitignore", "content": "# Byte-compiled / optimized / DLL files\n__pycache__/\n*.py[cod]\n*$py.class\n\n# C extensions\n*.so\n\n# Distribution / packaging\n.Python\nbuild/\ndevelop-eggs/\ndist/\ndownloads/\neggs/\n.eggs/\nlib/\nlib64/\nparts/\nsdist/\nvar/\nwheels/\nshare/python-wheels/\n*.egg-info/\n.installed.cfg\n*.egg\nMANIFEST\n\n# PyInstaller\n# Usually these files are written by a python script from a template\n# before PyInstaller builds the exe, so as to inject date/other infos into it.\n*.manifest\n*.spec\n\n# Installer logs\npip-log.txt\npip-delete-this-directory.txt\n\n# Unit test / coverage reports\nhtmlcov/\n.tox/\n.nox/\n.coverage\n.coverage.*\n.cache\nnosetests.xml\ncoverage.xml\n*.cover\n*.py,cover\n.hypothesis/\n.pytest_cache/\ncover/\n\n# Translations\n*.mo\n*.pot\n\n# Django stuff:\n*.log\nlocal_settings.py\ndb.sqlite3\ndb.sqlite3-journal\n\n# Flask stuff:\ninstance/\n.webassets-cache\n\n# Scrapy stuff:\n.scrapy\n\n# Sphinx documentation\ndocs/_build/\n\n# PyBuilder\n.pybuilder/\ntarget/\n\n# Jupyter Notebook\n.ipynb_checkpoints\n\n# IPython\nprofile_default/\nipython_config.py\n\n# pyenv\n# For a library or package, you might want to ignore these files since the code is\n# intended to run in multiple environments; otherwise, check them in:\n# .python-version\n\n# pipenv\n# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.\n# However, in case of collaboration, if having platform-specific dependencies or dependencies\n# having no cross-platform support, pipenv may install dependencies that don't work, or not\n# install all needed dependencies.\n#Pipfile.lock\n\n# poetry\n# Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.\n# This is especially recommended for binary packages to ensure reproducibility, and is more\n# commonly ignored for libraries.\n# https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control\n#poetry.lock\n\n# pdm\n# Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control.\n#pdm.lock\n# pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it\n# in version control.\n# https://pdm.fming.dev/#use-with-ide\n.pdm.toml\n\n# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm\n__pypackages__/\n\n# Celery stuff\ncelerybeat-schedule\ncelerybeat.pid\n\n# SageMath parsed files\n*.sage.py\n\n# Environments\n.env\n.venv\nenv/\nvenv/\nENV/\nenv.bak/\nvenv.bak/\n\n# Spyder project settings\n.spyderproject\n.spyproject\n\n# Rope project settings\n.ropeproject\n\n# mkdocs documentation\n/site\n\n# mypy\n.mypy_cache/\n.dmypy.json\ndmypy.json\n\n# Pyre type checker\n.pyre/\n\n# pytype static type analyzer\n.pytype/\n\n# Cython debug symbols\ncython_debug/\n\n# PyCharm\n# JetBrains specific template is maintained in a separate JetBrains.gitignore that can\n# be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore\n# and can be added to the global gitignore or merged into this file. For a more nuclear\n# option (not recommended) you can uncomment the following to ignore the entire idea folder.\n#.idea/\n\n#Desktop Services Store\n.DS_Store" }, { "path": "api_to_warehouse/Readme.md", "content": "# API to Warehouse Stack\n\n\nWelcome to the \"API to Warehouse Stack\" repository! This repository offers a simple template to help you get data from various APIs and put it into your data warehouse for further analysis using Airbyte. You can use several supported APIs as your data sources for this process. Here are a few examples of the APIs you can set up to extract data using Airbyte.\n\n- [Twitter API](https://docs.airbyte.com/integrations/sources/twitter)\n- [US census API](https://docs.airbyte.com/integrations/sources/us-census)\n- [Rocket.chat API](https://docs.airbyte.com/integrations/sources/rocket-chat)\n- [Recreation.gov API](https://docs.airbyte.com/integrations/sources/recreation)\n- [Polygon Stock API](https://docs.airbyte.com/integrations/sources/polygon-stock-api)\n- [PokeAPI](https://docs.airbyte.com/integrations/sources/pokeapi)\n- Many more\n\nHere are some data warehouses that users can choose as a destination to load the data extracted from APIs.\n\n- [Snowflake](https://docs.airbyte.com/integrations/destinations/snowflake)\n- [BigQuery](https://docs.airbyte.com/integrations/destinations/bigquery)\n- [Amazon Redshift](https://docs.airbyte.com/integrations/destinations/redshift)\n- Many more\n\nIn this process, we'll use the Github API to get data and Snowflake as the data warehouse to store the data.\n\n## Table of Contents\n\n- [API to Warehouse Stack](#api-to-warehouse-stack)\n - [Table of Contents](#table-of-contents)\n - [Infrastructure Layout](#infrastructure-layout)\n - [Setting an environment for your project](#setting-an-environment-for-your-project)\n - There are two ways to setup the connectors of airbyte.\n - [1. Using Airbyte UI](#1-using-airbyte-ui)\n - [2. Using Terraform to Setup the Connector](#2-using-terraform-to-setup-the-connector)\n - [Next Steps](#next-steps)\n\n\n## Infrastructure Layout\n\n![infrastructure layout](images/infrastructure.jpg)\n\n## Prerequisites\n\nBefore you embark on this integration, ensure you have the following set up and ready:\n\n1. **Python 3.10 or later**: If not installed, download and install it from [Python's official website](https://www.python.org/downloads/).\n\n2. **Docker and Docker Compose (Docker Desktop)**: Install [Docker](https://docs.docker.com/get-docker/) following the official documentation for your specific OS.\n\n3. **Airbyte OSS version**: Deploy the open-source version of Airbyte. Follow the installation instructions from the [Airbyte Documentation](https://docs.airbyte.com/quickstart/deploy-airbyte/).\n\n4. **Terraform**: Terraform will help you provision and manage the Airbyte resources. If you haven't installed it, follow the [official Terraform installation guide](https://developer.hashicorp.com/terraform/tutorials/aws-get-started/install-cli).\n\n## Setting an environment for your project\n\nGet the project up and running on your local machine by following these steps:\n\n1. **Clone the repository (Clone only this quickstart)**: \n ```bash\n git clone --filter=blob:none --sparse https://github.com/airbytehq/quickstarts.git\n ```\n\n ```bash\n cd quickstarts\n ```\n\n ```bash\n git sparse-checkout add api_to_warehouse\n ```\n\n \n2. **Navigate to the directory**: \n ```bash\n cd api_to_warehouse\n ```\n\n3. **Set Up a Virtual Environment**: \n - For Mac:\n ```bash\n python3 -m venv venv\n source venv/bin/activate\n ```\n - For Windows:\n ```bash\n python -m venv venv\n .\\venv\\Scripts\\activate\n ```\n\n4. **Install Dependencies**: \n ```bash\n pip install -e \".[dev]\"\n ```\n\n\n\n## 1. Using Airbyte UI\n\nTo establish the connection and import data from the Github API into the Snowflake warehouse, kindly proceed by utilizing the Airbyte user interface. The following steps should be adhered to:\n\n1. Run the Airbyte OSS version by following the [documentation](https://docs.airbyte.com/quickstart/deploy-airbyte).\n\n2. Setup the Github API as source by following [these steps](https://docs.airbyte.com/integrations/sources/github).\n\n3. Setup the Snowflake as destination by following [these steps](https://docs.airbyte.com/integrations/destinations/snowflake)\n\n4. Please proceed to configure the synchronization time and select the specific tables you wish to load into Snowflake from GitHub. You can make your selection from the list of available streams.\n\n5. Enjoy :smile:, your data loaded into Snowflake data warehouse from Github API.\n\n\n## 2. Using Terraform to Setup the Connector\n\nAirbyte enables you to make connections between different platforms by creating connectors for sources and destinations. In this project, we're using Terraform to automate the setup of these connectors and their connections. Here's how you can do it:\n\n1. **Navigate to the Airbyte Configuration Directory**:\n \n Change to the relevant directory containing the Terraform configuration for Airbyte:\n ```bash\n cd infra/airbyte\n ```\n\n2. **Modify Configuration Files**:\n\n Within the `infra/airbyte` directory, you'll find three crucial Terraform files:\n - `provider.tf`: Defines the Airbyte provider.\n - `main.tf`: Contains the main configuration for creating Airbyte resources.\n - `variables.tf`: Holds various variables, including credentials.\n\n Adjust the configurations in these files to suit your project's needs. Specifically, provide credentials for your Postgres connections. You can utilize the `variables.tf` file to manage these credentials.\n\n3. **Initialize Terraform**:\n \n This step prepares Terraform to create the resources defined in your configuration files.\n ```bash\n terraform init\n ```\n\n4. **Review the Plan**:\n\n Before applying any changes, review the plan to understand what Terraform will do.\n ```bash\n terraform plan\n ```\n\n5. **Apply Configuration**:\n\n After reviewing and confirming the plan, apply the Terraform configurations to create the necessary Airbyte resources.\n ```bash\n terraform apply\n ```\n\n6. **Verify in Airbyte UI**:\n\n After Terraform finishes its tasks, go to the Airbyte user interface. You will find your source and destination connectors already set up, along with the connection between them, all ready to use.\n\n## Next Steps\n\nAfter you extract and load data from an API into a data warehouse, you can analyze the data. For example, we used Snowflake data warehouse, which supports analytical tools like [Tableau](https://www.snowflake.com/resource/best-practices-for-using-tableau-with-snowflake/?utm_cta=website-be-trending-snowflake-tableau-ek), [Talend](https://www.snowflake.com/technology-partners/talend/) and [Sigma](https://www.snowflake.com/technology-partners/sigma/). " }, { "path": "api_to_warehouse/infra/.gitignore", "content": "# Local .terraform directories\n**/.terraform/*\n\n# .tfstate files\n*.tfstate\n*.tfstate.*\n\n# Crash log files\ncrash.log\ncrash.*.log\n\n# Exclude all .tfvars files, which are likely to contain sensitive data, such as\n# password, private keys, and other secrets. These should not be part of version \n# control as they are data points which are potentially sensitive and subject \n# to change depending on the environment.\n*.tfvars\n*.tfvars.json\n\n# Ignore override files as they are usually used to override resources locally and so\n# are not checked in\noverride.tf\noverride.tf.json\n*_override.tf\n*_override.tf.json\n\n# Include override files you do wish to add to version control using negated pattern\n# !example_override.tf\n\n# Include tfplan files to ignore the plan output of command: terraform plan -out=tfplan\n# example: *tfplan*\n\n# Ignore CLI configuration files\n.terraformrc\nterraform.rc" }, { "path": "api_to_warehouse/infra/airbyte/.terraform.lock.hcl", "content": "# This file is maintained automatically by \"terraform init\".\n# Manual edits may be lost in future updates.\n\nprovider \"registry.terraform.io/airbytehq/airbyte\" {\n version = \"0.3.4\"\n constraints = \"0.3.4\"\n hashes = [\n \"h1:0AHJKsRTlX6BCJZCJw5/oHsN97zi1AP33JeuPMwoX6U=\",\n \"zh:02167e00f7e89b6f09ae8796b9ee0ac2d8702b5cb295cb27d7a79266ffafe196\",\n \"zh:1ddad39354af090e830caf1e5cce845f24ff0bcef61b73e77ebc7703c2ecf90d\",\n \"zh:223a0a46d354ad0709d5f28d60accb3448ba5f256b84438238fb05235d1e5b34\",\n \"zh:29efd8848b9560456ec3d90f54984670e9d5b7e36f1edd2adb15c5fec3f57166\",\n \"zh:33d31310ba7ec699b5bd64edbb63b0a89bd55d87fae0f55409bbfa5fd7dd4d90\",\n \"zh:35ed0e2894e28ec7762406a18510b789b76b0649ace309eec22acaf10c982f08\",\n \"zh:4ba860918b65c00cc596d0b5b40068b89a72a300604a62bca7d286073779e684\",\n \"zh:59a0d1128477e587d9dac71f93598bae6050d176d29c840b6ad1bf95529d61e8\",\n \"zh:890df766e9b839623b1f0437355032a3c006226a6c200cd911e15ee1a9014e9f\",\n \"zh:8d9bb37e9094eba02acf8d08cf9f3331cd7c26478441d70e74e8d1ec9cb33aaa\",\n \"zh:9e5243eac43950889781a88d4e6186aea240898045e0e3c8fffd3291c5e74b6f\",\n \"zh:a0c31a5bc0cbc4a7341a0d185806a1c6797508580bede71a5009ad7b078d68c2\",\n \"zh:af341259999c6639a1c27e8f116a40b088dd192a3057096dc23a42affc97113f\",\n \"zh:b9779f8f695b4fab56e062abab61eaa58853f20c6411d53b2bd82a66d79a8b49\",\n \"zh:e284d898e5a30e507f1292635542dafe0e95ea8a5a215103a9d96d699aed9e75\",\n ]\n}\n" }, { "path": "api_to_warehouse/infra/airbyte/main.tf", "content": "// Airbyte Terraform provider documentation: https://registry.terraform.io/providers/airbytehq/airbyte/latest/docs\n\n// Sources - Documentation of Source : https://registry.terraform.io/providers/airbytehq/airbyte/latest/docs/resources/source_github\nresource \"airbyte_source_github\" \"my_source_github\" {\n configuration = {\n # branch = \"\"\n credentials = {\n source_github_authentication_personal_access_token = {\n // Log into GitHub and then generate a personal access token. To load balance your API quota consumption across multiple API tokens, input multiple tokens separated with \",\"\n\n personal_access_token = \"\"\n\n }\n }\n repository = \"\"\n # requests_per_hour = 10\n source_type = \"github\"\n // Enter the date\n start_date = \"\"\n }\n name = \"Github API\"\n# secret_id = \"...my_secret_id...\"\n workspace_id = var.workspace_id\n}\n\n// Destinations - Documentation of Destination: https://registry.terraform.io/providers/airbytehq/airbyte/latest/docs/resources/destination_snowflake\nresource \"airbyte_destination_snowflake\" \"my_destination_snowflake\" {\n configuration = {\n credentials = {\n destination_snowflake_authorization_method_username_and_password = {\n password = \"\"\n }\n }\n database = \"\"\n destination_type = \"snowflake\"\n host = \"\"\n # jdbc_url_params = \"...my_jdbc_url_params...\"\n # raw_data_schema = \"...my_raw_data_schema...\"\n role = \"\"\n schema = \"\"\n username = \"\"\n warehouse = \"\"\n }\n name = \"Snowflake Warehouse\"\n workspace_id = var.workspace_id\n}\n\n// Connections\nresource \"airbyte_connection\" \"github_to_snowflake\" {\n name = \"Github API to Snowflake Data Warehouse\"\n source_id = airbyte_source_github.my_source_github.source_id\n destination_id = airbyte_destination_snowflake.my_destination_snowflake.destination_id\n status = \"active\"\n configurations = {\n\n // Available Streams = Comments, Commit comment reactions, Commit comments,\n # Commits, Deployments, Events, Issue comment reactions, Issue events, Issue milestones,\n # Issue reactions, Issues, Project cards, Project columns, Projects, Pull request comment reactions,\n # Pull requests, Pull request stats, Releases, Review comments, Reviews, Stargazers, Workflow runs, \n # Workflows\n streams = [\n {\n name = \"projects\"\n },\n {\n name = \"commits\"\n },\n ]\n sync_mode = \"full_refresh_overwrite\"\n }\n schedule = {\n cron_expression = \"\"\n schedule_type = \"cron\"\n }\n}" }, { "path": "api_to_warehouse/infra/airbyte/provider.tf", "content": "// Airbyte Terraform provider documentation: https://registry.terraform.io/providers/airbytehq/airbyte/latest/docs\n\nterraform {\n required_providers {\n airbyte = {\n source = \"airbytehq/airbyte\"\n version = \"0.3.4\"\n }\n }\n}\n\nprovider \"airbyte\" {\n // If running locally (Airbyte OSS) with docker-compose using the airbyte-proxy, \n // include the actual password/username you've set up (or use the defaults below)\n username = \"airbyte\"\n password = \"password\"\n \n // if running locally (Airbyte OSS), include the server url to the airbyte-api-server\n server_url = \"http://localhost:8006/v1\"\n}" }, { "path": "api_to_warehouse/infra/airbyte/variables.tf", "content": "\nvariable \"workspace_id\" {\n type = string\n}\n\n\n\n\n\n" }, { "path": "api_to_warehouse/setup.py", "content": "from setuptools import find_packages, setup\n\nsetup(\n name=\"api-to-warehouse\",\n packages=find_packages(),\n install_requires=[\n ],\n extras_require={\"dev\": [\"pytest\"]},\n)" }, { "path": "customer_segmentation_analytics_shopify/.gitignore", "content": "# Byte-compiled / optimized / DLL files\n__pycache__/\n*.py[cod]\n*$py.class\n\n# C extensions\n*.so\n\n# Distribution / packaging\n.Python\nbuild/\ndevelop-eggs/\ndist/\ndownloads/\neggs/\n.eggs/\nlib/\nlib64/\nparts/\nsdist/\nvar/\nwheels/\nshare/python-wheels/\n*.egg-info/\n.installed.cfg\n*.egg\nMANIFEST\n\n# PyInstaller\n# Usually these files are written by a python script from a template\n# before PyInstaller builds the exe, so as to inject date/other infos into it.\n*.manifest\n*.spec\n\n# Installer logs\npip-log.txt\npip-delete-this-directory.txt\n\n# Unit test / coverage reports\nhtmlcov/\n.tox/\n.nox/\n.coverage\n.coverage.*\n.cache\nnosetests.xml\ncoverage.xml\n*.cover\n*.py,cover\n.hypothesis/\n.pytest_cache/\ncover/\n\n# Translations\n*.mo\n*.pot\n\n# Django stuff:\n*.log\nlocal_settings.py\ndb.sqlite3\ndb.sqlite3-journal\n\n# Flask stuff:\ninstance/\n.webassets-cache\n\n# Scrapy stuff:\n.scrapy\n\n# Sphinx documentation\ndocs/_build/\n\n# PyBuilder\n.pybuilder/\ntarget/\n\n# Jupyter Notebook\n.ipynb_checkpoints\n\n# IPython\nprofile_default/\nipython_config.py\n\n# pyenv\n# For a library or package, you might want to ignore these files since the code is\n# intended to run in multiple environments; otherwise, check them in:\n# .python-version\n\n# pipenv\n# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.\n# However, in case of collaboration, if having platform-specific dependencies or dependencies\n# having no cross-platform support, pipenv may install dependencies that don't work, or not\n# install all needed dependencies.\n#Pipfile.lock\n\n# poetry\n# Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.\n# This is especially recommended for binary packages to ensure reproducibility, and is more\n# commonly ignored for libraries.\n# https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control\n#poetry.lock\n\n# pdm\n# Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control.\n#pdm.lock\n# pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it\n# in version control.\n# https://pdm.fming.dev/#use-with-ide\n.pdm.toml\n\n# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm\n__pypackages__/\n\n# Celery stuff\ncelerybeat-schedule\ncelerybeat.pid\n\n# SageMath parsed files\n*.sage.py\n\n# Environments\n.env\n.venv\nenv/\nvenv/\nENV/\nenv.bak/\nvenv.bak/\n\n# Spyder project settings\n.spyderproject\n.spyproject\n\n# Rope project settings\n.ropeproject\n\n# mkdocs documentation\n/site\n\n# mypy\n.mypy_cache/\n.dmypy.json\ndmypy.json\n\n# Pyre type checker\n.pyre/\n\n# pytype static type analyzer\n.pytype/\n\n# Cython debug symbols\ncython_debug/\n\n# PyCharm\n# JetBrains specific template is maintained in a separate JetBrains.gitignore that can\n# be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore\n# and can be added to the global gitignore or merged into this file. For a more nuclear\n# option (not recommended) you can uncomment the following to ignore the entire idea folder.\n#.idea/\n\n#Desktop Services Store\n.DS_Store" }, { "path": "customer_segmentation_analytics_shopify/README.md", "content": "# Customer Segmentation Analytics Stack With Shopify, Airbyte, Dbt, Dagster and BigQuery\n\nWelcome to the \"Customer Segmentation Analytics Stack\" repository! ✨ This is your go-to place to easily set up a data stack using Shopify, Airbyte, Dbt, BigQuery, and Dagster. With this setup, you can pull Shopify data, extract it using Airbyte, put it into BigQuery, and play around with it using dbt and Dagster.\n\nThis Quickstart is all about making things easy, getting you started quickly and showing you how smoothly all these tools can work together!\n\nBelow is a visual representation of how data flows through our integrated tools in this Quickstart. This comes from Dagster's global asset lineage view:\n\n![Global Asset Lineage](<./assets/Global_Asset_Lineage (7).svg>)\n\n## Table of Contents\n\n- [Prerequisites](#prerequisites)\n- [Setting an environment for your project](#1-setting-an-environment-for-your-project)\n- [Setting Up BigQuery to work with Airbyte and dbt](#2-setting-up-bigquery)\n- [Setting Up Airbyte Connectors with Terraform](#3-setting-up-airbyte-connectors-with-terraform)\n- [Setting Up the dbt Project](#4-setting-up-the-dbt-project)\n- [Orchestrating with Dagster](#5-orchestrating-with-dagster)\n- [Next Steps](#next-steps)\n\n## Prerequisites\n\nBefore you embark on this integration, ensure you have the following set up and ready:\n\n1. **Python 3.10 or later**: If not installed, download and install it from [Python's official website](https://www.python.org/downloads/).\n\n2. **Docker and Docker Compose (Docker Desktop)**: Install [Docker](https://docs.docker.com/get-docker/) following the official documentation for your specific OS.\n\n3. **Airbyte OSS version**: Deploy the open-source version of Airbyte. Follow the installation instructions from the [Airbyte Documentation](https://docs.airbyte.com/quickstart/deploy-airbyte/).\n\n4. **Terraform**: Terraform will help you provision and manage the Airbyte resources. If you haven't installed it, follow the [official Terraform installation guide](https://developer.hashicorp.com/terraform/tutorials/aws-get-started/install-cli).\n\n5. **Google Cloud account with BigQuery**: You will also need to add the necessary permissions to allow Airbyte and dbt to access the data in BigQuery. A step-by-step guide is provided [below](#2-setting-up-bigquery).\n\n## 1. Setting an environment for your project\n\nGet the project up and running on your local machine by following these steps:\n\n1. **Clone the repository (Clone only this quickstart)**:\n\n ```bash\n git clone --filter=blob:none --sparse https://github.com/airbytehq/quickstarts.git\n ```\n\n ```bash\n cd quickstarts\n ```\n\n ```bash\n git sparse-checkout add customer_segmentation_analytics_shopify\n ```\n\n2. **Navigate to the directory**:\n\n ```bash\n cd customer_segmentation_analytics_shopify\n ```\n\n3. **Set Up a Virtual Environment**:\n\n - For Mac:\n ```bash\n python3 -m venv venv\n source venv/bin/activate\n ```\n - For Windows:\n ```bash\n python -m venv venv\n .\\venv\\Scripts\\activate\n ```\n4. **Install Dependencies**:\n ```bash\n pip install -e \".[dev]\"\n ```\n\n## 2. Setting Up BigQuery\n\n#### 1. **Create a Google Cloud Project**\n\n- If you have a Google Cloud project, you can skip this step.\n- Go to the [Google Cloud Console](https://console.cloud.google.com/).\n- Click on the \"Select a project\" dropdown at the top right and select \"New Project\".\n- Give your project a name and follow the steps to create it.\n\n#### 2. **Create BigQuery Datasets**\n\n- In the Google Cloud Console, go to BigQuery.\n- Make two new datasets: `raw_data` for Airbyte and `transformed_data` for dbt.\n - If you pick different names, remember to change the names in the code too.\n\n**How to create a dataset:**\n\n- In the left sidebar, click on your project name.\n- Click “Create Dataset”.\n- Enter the dataset ID (either `raw_data` or `transformed_data`).\n- Click \"Create Dataset\".\n\n#### 3. **Create Service Accounts and Assign Roles**\n\n- Go to “IAM & Admin” > “Service accounts” in the Google Cloud Console.\n- Click “Create Service Account”.\n- Name your service account (like `airbyte-service-account`).\n- Assign the “BigQuery Data Editor” and “BigQuery Job User” roles to the service account.\n- Follow the same steps to make another service account for dbt (like `dbt-service-account`) and assign the roles.\n\n**How to create a service account and assign roles:**\n\n- While creating the service account, under the “Grant this service account access to project” section, click the “Role” dropdown.\n- Choose the “BigQuery Data Editor” and “BigQuery Job User” roles.\n- Finish the creation process.\n\n#### 4. **Generate JSON Keys for Service Accounts**\n\n- For both service accounts, make a JSON key to let the service accounts sign in.\n\n**How to generate JSON key:**\n\n- Find the service account in the “Service accounts” list.\n- Click on the service account name.\n- In the “Keys” section, click “Add Key” and pick JSON.\n- The key will download automatically. Keep it safe and don’t share it.\n- Do this for the other service account too.\n\n## 3. Setting Up Airbyte Connectors with Terraform\n\nAirbyte allows you to create connectors for sources and destinations, facilitating data synchronization between various platforms. In this project, we're harnessing the power of Terraform to automate the creation of these connectors and the connections between them. Here's how you can set this up:\n\n1. **Navigate to the Airbyte Configuration Directory**:\n\n Change to the relevant directory containing the Terraform configuration for Airbyte:\n\n ```bash\n cd infra/airbyte\n ```\n\n2. **Modify Configuration Files**:\n\n Within the `infra/airbyte` directory, you'll find three crucial Terraform files:\n\n - `provider.tf`: Defines the Airbyte provider.\n - `main.tf`: Contains the main configuration for creating Airbyte resources.\n - `variables.tf`: Holds various variables, including credentials.\n\n Adjust the configurations in these files to suit your project's needs. Specifically, provide credentials for your BigQuery connection. You can utilize the `variables.tf` file to manage these credentials.\n\n3. **Initialize Terraform**:\n\n This step prepares Terraform to create the resources defined in your configuration files.\n\n ```bash\n terraform init\n ```\n\n4. **Review the Plan**:\n\n Before applying any changes, review the plan to understand what Terraform will do.\n\n ```bash\n terraform plan\n ```\n\n5. **Apply Configuration**:\n\n After reviewing and confirming the plan, apply the Terraform configurations to create the necessary Airbyte resources.\n\n ```bash\n terraform apply\n ```\n\n6. **Verify in Airbyte UI**:\n\n Once Terraform completes its tasks, navigate to the [Airbyte UI](http://localhost:8000/). Here, you should see your source and destination connectors, as well as the connection between them, set up and ready to go.\n\n## 4. Setting Up the dbt Project\n\n[dbt (data build tool)](https://www.getdbt.com/) allows you to transform your data by writing, documenting, and executing SQL workflows. Setting up the dbt project requires specifying connection details for your data platform, in this case, BigQuery. Here’s a step-by-step guide to help you set this up:\n\n1. **Navigate to the dbt Project Directory**:\n\n Change to the directory containing the dbt configuration:\n\n ```bash\n cd dbt_project\n ```\n\n2. **Update Connection Details**:\n\n You'll find a `profiles.yml` file within the directory. This file contains configurations for dbt to connect with your data platform. Update this file with your BigQuery connection details.\n\n3. **Utilize Environment Variables (Optional but Recommended)**:\n\n To keep your credentials secure, you can leverage environment variables. An example is provided within the `profiles.yml` file.\n\n4. **Test the Connection**:\n\n Once you’ve updated the connection details, you can test the connection to your BigQuery instance using:\n\n ```bash\n dbt debug\n ```\n\n If everything is set up correctly, this command should report a successful connection to BigQuery.\n\n5. **Run the Models**:\n\n If you would like to run the dbt models manually at this point, you can do so by executing:\n\n ```bash\n dbt run\n ```\n\n You can verify the data has been transformed by going to BigQuery and checking the `transformed_data` dataset.\n\n6. **Visualise the Data(optional)**:\n\n This is totally an optional step to visualise the data. We will be using python and matplotlib you can use any of your choice. First we need to install the necessary dependencies and we can do this by the following command.\n\n ```bash\n pip install google-cloud-bigquery matplotlib\n ``` \n\n Now create a folder named \"analyses\" under the dbt_project directory. Make sure to name the folder exactly the same as you've mentioned in the `dbt_project.yml` file otherwise it will throw error. Next, create python file under the \"analyses\" folder with appropriate name like `customer_activity_analysis.py`. Now write down your python script for the analysis. Make sure to set your BigQuery service account json file path as environment variables and use it to authenticate with BigQuery. \n\n Now after you are done writing your python script go to \"analyses\" folder.\n\n ```bash\n cd analyses\n ```\n\n Now run the following command to run the python file. Make sure to replace `customer_activity_analysis.py` with your actual file name. \n\n ```bash\n python customer_activity_analysis.py\n ```\n\n You should then see a window displaying a beautiful chart.\n\n## 5. Orchestrating with Dagster\n\n[Dagster](https://dagster.io/) is a modern data orchestrator designed to help you build, test, and monitor your data workflows. In this section, we'll walk you through setting up Dagster to oversee both the Airbyte and dbt workflows:\n\n1. **Navigate to the Orchestration Directory**:\n\n Switch to the directory containing the Dagster orchestration configurations:\n\n ```bash\n cd orchestration\n ```\n\n2. **Set Environment Variables**:\n\n Dagster requires certain environment variables to be set to interact with other tools like dbt and Airbyte. Set the following variables:\n\n ```bash\n export DAGSTER_DBT_PARSE_PROJECT_ON_LOAD=1\n export AIRBYTE_PASSWORD=password\n ```\n\n Note: The `AIRBYTE_PASSWORD` is set to `password` as a default for local Airbyte instances. If you've changed this during your Airbyte setup, ensure you use the appropriate password here.\n\n3. **Launch the Dagster UI**:\n\n With the environment variables in place, kick-start the Dagster UI:\n\n ```bash\n dagster dev\n ```\n\n4. **Access Dagster in Your Browser**:\n\n Open your browser and navigate to:\n\n ```\n http://127.0.0.1:3000\n ```\n\n Here, you should see assets for both Airbyte and dbt. To get an overview of how these assets interrelate, click on \"view global asset lineage\". This will give you a clear picture of the data lineage, visualizing how data flows between the tools.\n\n5. **Materialize Dagster Assets**:\n In the Dagster UI, click on \"Materialize all\". This should trigger the full pipeline. First the Airbyte sync to extract data from Faker and load it into BigQuery, and then dbt to transform the raw data, materializing the `staging` and `marts` models.\n\n## Next Steps\n\nCongratulations on deploying and running the Customer Satisfaction Analytics Quistart! 🎉 Here are some suggestions on what you can explore next to dive deeper and get more out of your project:\n\n### 1. **Explore the Data and Insights**\n - Dive into the datasets in BigQuery, run some queries, and explore the data you've collected and transformed. This is your chance to uncover insights and understand the data better!\n\n### 2. **Optimize Your dbt Models**\n - Review the transformations you’ve applied using dbt. Try optimizing the models or create new ones based on your evolving needs and insights you want to extract.\n\n### 3. **Automate and Monitor Your Pipelines**\n - Explore more advanced Dagster configurations and setups to automate your pipelines further and set up monitoring and alerting to be informed of any issues immediately." }, { "path": "customer_segmentation_analytics_shopify/dbt_project/.gitignore", "content": "\ntarget/\ndbt_packages/\nlogs/\n\n.user.yml\n" }, { "path": "customer_segmentation_analytics_shopify/dbt_project/README.md", "content": "Welcome to your new dbt project!\n\n### Using the starter project\n\nTry running the following commands:\n\n- dbt run\n- dbt test\n\n### Resources:\n\n- Learn more about dbt [in the docs](https://docs.getdbt.com/docs/introduction)\n- Check out [Discourse](https://discourse.getdbt.com/) for commonly asked questions and answers\n- Join the [chat](https://community.getdbt.com/) on Slack for live discussions and support\n- Find [dbt events](https://events.getdbt.com) near you\n- Check out [the blog](https://blog.getdbt.com/) for the latest news on dbt's development and best practices\n" }, { "path": "customer_segmentation_analytics_shopify/dbt_project/analyses/customer_activity_analysis.py", "content": "from google.cloud import bigquery\nimport os\nfrom matplotlib import pyplot as plt\n\n# Path to your BigQuery service account key\nservice_account_key_path = os.environ.get('DBT_BIGQUERY_KEYFILE_PATH')\n\n# Initialize the BigQuery client\nclient = bigquery.Client.from_service_account_json(service_account_key_path)\n\n# SQL query to categorize customers based on their activity level\nquery = \"\"\"\nWITH customer_activity AS (\n SELECT\n id,\n orders_count,\n CASE\n WHEN orders_count >= 10 THEN 'Highly Active'\n WHEN orders_count >= 5 THEN 'Moderately Active'\n ELSE 'Low Activity'\n END AS activity_level\n FROM transformed_data.stg_customers\n)\nSELECT\n id,\n orders_count,\n activity_level\nFROM customer_activity\n\"\"\"\n\n# Run the query\nquery_job = client.query(query)\n\n# Get the results\nresults = query_job.result()\n\n# Extract the data\ncustomer_ids = []\norders_counts = []\nactivity_levels = []\n\nfor row in results:\n customer_ids.append(row.id)\n orders_counts.append(row.orders_count)\n activity_levels.append(row.activity_level)\n\n# Create a bar chart to visualize the data\nplt.figure(figsize=(10, 6))\nplt.bar(activity_levels, orders_counts, width=0.6, align='center', alpha=0.7)\nplt.xlabel('Activity Level')\nplt.ylabel('Number of Customers')\nplt.title('Customer Activity Level Segmentation')\nplt.grid(axis='y', linestyle='--', alpha=0.6)\nplt.show()\n" }, { "path": "customer_segmentation_analytics_shopify/dbt_project/analyses/purchase_pattern_segmentation_analysis.py", "content": "from google.cloud import bigquery\nimport os\nfrom matplotlib import pyplot as plt\n\n# Path to your BigQuery service account key\nservice_account_key_path = os.environ.get('DBT_BIGQUERY_KEYFILE_PATH')\n\n# Initialize the BigQuery client\nclient = bigquery.Client.from_service_account_json(service_account_key_path)\n\n# SQL query to categorize customers based on their purchase behavior\nquery = \"\"\"\nWITH purchase_data AS (\n SELECT\n id,\n COUNT(*) AS total_purchases,\n SUM(total_spent) AS total_spent\n FROM transformed_data.stg_customers\n GROUP BY id\n),\nbehavioral_segments AS (\n SELECT\n id,\n CASE\n WHEN total_purchases >= 5 AND total_spent >= 500 THEN 'High-Value Shoppers'\n WHEN total_purchases < 5 AND total_spent < 100 THEN 'Low-Value Shoppers'\n ELSE 'Regular Shoppers'\n END AS purchase_segment\n FROM purchase_data\n)\nSELECT * FROM behavioral_segments\n\"\"\"\n\n# Run the query\nquery_job = client.query(query)\n\n# Get the results\nresults = query_job.result()\n\n# Extract the data\ncustomer_ids = []\npurchase_segments = []\n\nfor row in results:\n customer_ids.append(row.id)\n purchase_segments.append(row.purchase_segment)\n\n# Count the number of customers in each segment\nsegment_counts = {segment: purchase_segments.count(segment) for segment in set(purchase_segments)}\n\n# Create a bar chart to visualize the data\nsegments, counts = zip(*segment_counts.items())\n\nplt.figure(figsize=(10, 6))\nplt.bar(segments, counts, width=0.6, align='center', alpha=0.7)\nplt.xlabel('Purchase Behavior Segment')\nplt.ylabel('Number of Customers')\nplt.title('Customer Purchase Behavior Segmentation')\nplt.grid(axis='y', linestyle='--', alpha=0.6)\nplt.show()\n" }, { "path": "customer_segmentation_analytics_shopify/dbt_project/analyses/rfm_segmentation_analysis.py", "content": "from google.cloud import bigquery\nimport os\nfrom matplotlib import pyplot as plt\n\n# Path to your BigQuery service account key\nservice_account_key_path = os.environ.get('DBT_BIGQUERY_KEYFILE_PATH')\n\n# Initialize the BigQuery client\nclient = bigquery.Client.from_service_account_json(service_account_key_path)\n\n# SQL query to categorize customers based on RFM segments\nquery = \"\"\"\nWITH rfm_data AS (\n SELECT\n user_id,\n MAX(created_at) AS last_purchase_date,\n COUNT(DISTINCT order_id) AS total_orders,\n SUM(amount) AS total_spent\n FROM transformed_data.stg_transactions\n GROUP BY user_id\n),\nrfm_segments AS (\n SELECT\n user_id,\n CASE\n WHEN TIMESTAMP_DIFF(CAST(CURRENT_DATE AS TIMESTAMP), last_purchase_date, DAY) <= 30 THEN 'Active'\n WHEN total_orders >= 5 THEN 'Loyal'\n WHEN total_spent >= 500 THEN 'High-Spending'\n ELSE 'Churned'\n END AS rfm_segment\n FROM rfm_data\n)\nSELECT * FROM rfm_segments\n\"\"\"\n\n# Run the query\nquery_job = client.query(query)\n\n# Get the results\nresults = query_job.result()\n\n# Extract the data\nuser_ids = []\nrfm_segments = []\n\nfor row in results:\n user_ids.append(row.user_id)\n rfm_segments.append(row.rfm_segment)\n\n# Count the number of customers in each segment\nsegment_counts = {segment: rfm_segments.count(segment) for segment in set(rfm_segments)}\n\n# Create a bar chart to visualize the data\nsegments, counts = zip(*segment_counts.items())\n\nplt.figure(figsize=(10, 6))\nplt.bar(segments, counts, width=0.6, align='center', alpha=0.7)\nplt.xlabel('RFM Segment')\nplt.ylabel('Number of Customers')\nplt.title('Customer RFM Segmentation')\nplt.grid(axis='y', linestyle='--', alpha=0.6)\nplt.show()\n" }, { "path": "customer_segmentation_analytics_shopify/dbt_project/dbt_project.yml", "content": "\n# Name your project! Project names should contain only lowercase characters\n# and underscores. A good package name should reflect your organization's\n# name or the intended use of these models\nname: 'dbt_project'\nversion: '1.0.0'\nconfig-version: 2\n\n# This setting configures which \"profile\" dbt uses for this project.\nprofile: 'dbt_project'\n\n# These configurations specify where dbt should look for different types of files.\n# The `model-paths` config, for example, states that models in this project can be\n# found in the \"models/\" directory. You probably won't need to change these!\nmodel-paths: [\"models\"]\nanalysis-paths: [\"analyses\"]\ntest-paths: [\"tests\"]\nseed-paths: [\"seeds\"]\nmacro-paths: [\"macros\"]\nsnapshot-paths: [\"snapshots\"]\n\nclean-targets: # directories to be removed by `dbt clean`\n - \"target\"\n - \"dbt_packages\"\n\n\n# Configuring models\n# Full documentation: https://docs.getdbt.com/docs/configuring-models\n\n# In this example config, we tell dbt to build all models in the example/\n# directory as views. These settings can be overridden in the individual model\n# files using the `{{ config(...) }}` macro.\nmodels:\n dbt_project:\n # Config indicated by + and applies to all files under models/example/\n staging:\n +materialized: view\n marts:\n +materialized: view\n" }, { "path": "customer_segmentation_analytics_shopify/dbt_project/models/marts/customer_activity.sql", "content": "\nWITH customer_activity AS (\n SELECT\n id,\n orders_count,\n CASE\n WHEN orders_count >= 10 THEN 'Highly Active'\n WHEN orders_count >= 5 THEN 'Moderately Active'\n ELSE 'Low Activity'\n END AS activity_level\n FROM transformed_data.stg_customers\n)\nSELECT\n id,\n orders_count,\n activity_level\nFROM customer_activity\n" }, { "path": "customer_segmentation_analytics_shopify/dbt_project/models/marts/purchase_pattern_segmentation.sql", "content": "\nwith purchase_data as (\n select\n id,\n count(*) as total_purchases,\n sum(total_spent) as total_spent\n from transformed_data.stg_customers\n group by id\n),\nbehavioral_segments as (\n select\n id,\n case\n when total_purchases >= 5 and total_spent >= 500 then 'High-Value Shoppers'\n when total_purchases < 5 and total_spent < 100 then 'Low-Value Shoppers'\n else 'Regular Shoppers'\n end as purchase_segment\n from purchase_data\n)\nselect * from behavioral_segments\n" }, { "path": "customer_segmentation_analytics_shopify/dbt_project/models/marts/rfm_segmentation.sql", "content": "with rfm_data as (\n select\n user_id,\n max(created_at) as last_purchase_date,\n count(distinct order_id) as total_orders,\n sum(amount) as total_spent\n from transformed_data.stg_transactions\n group by user_id\n),\nrfm_segments as (\n select\n user_id,\n case\n when TIMESTAMP_DIFF(CAST(current_date AS TIMESTAMP), last_purchase_date, DAY) <= 30 then 'Active'\n when total_orders >= 5 then 'Loyal'\n when total_spent >= 500 then 'High-Spending'\n else 'Churned'\n end as rfm_segment\n from rfm_data\n)\nselect * from rfm_segments\n" }, { "path": "customer_segmentation_analytics_shopify/dbt_project/models/sources/shopify_sources.yml", "content": "version: 2\n\nsources:\n - name: shopify\n # Use your BigQuery project ID\n database: \"{{ env_var('BIGQUERY_PROJECT_ID', '') }}\"\n # Use your BigQuery dataset name\n schema: shopify_airbyte\n\n tables:\n - name: customers\n description: \"Simulated customers data from the Shopify connector.\"\n columns:\n - name: id\n description: \"Unique identifier for the customers.\"\n - name: accepts_marketing\n - name: accepts_marketing_updated_at\n - name: addresses\n - name: admin_graphql_api_id\n - name: created_at\n - name: currency\n - name: default_address\n - name: email\n - name: email_marketing_consent\n - name: first_name\n - name: last_name\n - name: last_order_id\n - name: last_order_name\n - name: marketing_opt_in_level\n - name: multipass_identifier\n - name: note\n - name: orders_count\n - name: phone\n - name: shop_url\n - name: sms_marketing_consent\n - name: state\n - name: tags\n - name: tax_exempt\n - name: tax_exemptions\n - name: total_spent\n - name: updated_at\n - name: verified_email\n\n - name: transactions\n description: \"Simulated transactions data from the Shopify connector.\"\n columns:\n - name: id\n description: \"Unique identifier for the transactions.\"\n - name: admin_graphql_api_id\n - name: amount\n - name: authorization\n - name: created_at\n - name: currency\n - name: device_id\n - name: error_code\n - name: gateway\n - name: kind\n - name: location_id\n - name: message\n - name: order_id\n - name: parent_id\n - name: payment_details\n - name: payment_id\n - name: processed_at\n - name: receipt\n - name: shop_url\n - name: source_name\n - name: status\n - name: test\n - name: total_unsettled_set\n - name: user_id\n\n" }, { "path": "customer_segmentation_analytics_shopify/dbt_project/models/staging/stg_customers.sql", "content": "select\n *\nfrom {{ source('shopify', 'customers') }}\n" }, { "path": "customer_segmentation_analytics_shopify/dbt_project/models/staging/stg_transactions.sql", "content": "select\n *\nfrom {{ source('shopify', 'transactions') }}\n" }, { "path": "customer_segmentation_analytics_shopify/dbt_project/profiles.yml", "content": "dbt_project:\n outputs:\n dev:\n dataset: transformed_data\n job_execution_timeout_seconds: 300\n job_retries: 1\n # Use an env variable to indicate your JSON key file path\n keyfile: \"{{ env_var('DBT_BIGQUERY_KEYFILE_PATH', '') }}\"\n location: US\n method: service-account\n priority: interactive\n # Indicate your BigQuery project ID\n project: \"{{ env_var('BIGQUERY_PROJECT_ID', '') }}\"\n threads: 1\n type: bigquery\n target: dev" }, { "path": "customer_segmentation_analytics_shopify/infra/.gitignore", "content": "# Local .terraform directories\n**/.terraform/*\n\n# .tfstate files\n*.tfstate\n*.tfstate.*\n\n# Crash log files\ncrash.log\ncrash.*.log\n\n# Exclude all .tfvars files, which are likely to contain sensitive data, such as\n# password, private keys, and other secrets. These should not be part of version \n# control as they are data points which are potentially sensitive and subject \n# to change depending on the environment.\n*.tfvars\n*.tfvars.json\n\n# Ignore override files as they are usually used to override resources locally and so\n# are not checked in\noverride.tf\noverride.tf.json\n*_override.tf\n*_override.tf.json\n\n# Include override files you do wish to add to version control using negated pattern\n# !example_override.tf\n\n# Include tfplan files to ignore the plan output of command: terraform plan -out=tfplan\n# example: *tfplan*\n\n# Ignore CLI configuration files\n.terraformrc\nterraform.rc" }, { "path": "customer_segmentation_analytics_shopify/infra/airbyte/.terraform.lock.hcl", "content": "# This file is maintained automatically by \"terraform init\".\n# Manual edits may be lost in future updates.\n\nprovider \"registry.terraform.io/airbytehq/airbyte\" {\n version = \"0.3.3\"\n constraints = \"0.3.3\"\n hashes = [\n \"h1:0LmuAc5LvlMuOUPtNEaCAh9FHrV/C877bDJhm9Lz8MU=\",\n \"zh:0efa470b34d9b912b47efe4469c51713bfc3c2413e52c17e1e903f2a3cddb2f6\",\n \"zh:1bddd69fa2c2d4f3e239d60555446df9bc4ce0c0cabbe7e092fe1d44989ab004\",\n \"zh:2e20540403a0010007b53456663fb037b24e30f6c8943f65da1bcf7fa4dfc8a6\",\n \"zh:2f415369ad884e8b7115a5c5ff229d052f7af1fca27abbfc8ebef379ed11aec4\",\n \"zh:46fd9a906f4b6461112dcc5a5aa01a3fcd7a19a72d4ad0b2e37790da37701fe1\",\n \"zh:83503ebb77bb6d6941c42ba323cf22380d08a1506554a2dcc8ac54e74c0886a1\",\n \"zh:890df766e9b839623b1f0437355032a3c006226a6c200cd911e15ee1a9014e9f\",\n \"zh:8fd770eff726826d3a63b9e3733c5455b5cde004027b04ee3f75888eb8538c90\",\n \"zh:b0fc890ed4f9b077bf70ed121cc3550e7a07d16e7798ad517623274aa62ad7b0\",\n \"zh:c2a01612362da9b73cd5958f281e1aa7ff09af42182e463097d11ed78e778e72\",\n \"zh:c64b2bb1887a0367d64ba3393d4b3a16c418cf5b1792e2e7aae7c0b5413eb334\",\n \"zh:ce14ebbf0ed91913ec62655a511763dec62b5779de9a209bd6f1c336640cddc0\",\n \"zh:e0662ca837eee10f7733ea9a501d995281f56bd9b410ae13ad03eb106011db14\",\n \"zh:e103d480fc6066004bc98e9e04a141a1f55b918cc2912716beebcc6fc4c872fb\",\n \"zh:e2507049098f0f1b21cb56870f4a5ef624bcf6d3959e5612eada1f8117341648\",\n ]\n}\n" }, { "path": "customer_segmentation_analytics_shopify/infra/airbyte/main.tf", "content": "// Source\nresource \"airbyte_source_shopify\" \"my_source_shopify\" {\n configuration = {\n credentials = {\n source_shopify_shopify_authorization_method_api_password = {\n api_password = var.api_password\n auth_method = \"api_password\"\n }\n }\n shop = var.shop\n source_type = \"shopify\"\n }\n name = \"Shopify\"\n workspace_id = var.workspace_id\n}\n\n// Destinations\nresource \"airbyte_destination_bigquery\" \"bigquery\" {\n configuration = {\n dataset_id = var.dataset_id\n dataset_location = \"US\"\n destination_type = \"bigquery\"\n project_id = var.project_id\n credentials_json = var.credentials_json\n loading_method = {\n destination_bigquery_loading_method_standard_inserts = {\n method = \"Standard\"\n }\n }\n }\n name = \"BigQuery\"\n workspace_id = var.workspace_id\n}\n\n// Connections\nresource \"airbyte_connection\" \"shopify_bigquery\" {\n name = \"Shopify to BigQuery\"\n source_id = airbyte_source_shopify.my_source_shopify.source_id\n destination_id = airbyte_destination_bigquery.bigquery.destination_id\n configurations = {\n streams = [\n {\n name = \"customers\"\n },\n {\n name = \"transactions\"\n }\n ]\n }\n}\n" }, { "path": "customer_segmentation_analytics_shopify/infra/airbyte/provider.tf", "content": "// Airbyte Terraform provider documentation: https://registry.terraform.io/providers/airbytehq/airbyte/latest/docs\n\nterraform {\n required_providers {\n airbyte = {\n source = \"airbytehq/airbyte\"\n version = \"0.3.3\"\n }\n }\n}\n\nprovider \"airbyte\" {\n // If running locally (Airbyte OSS) with docker-compose using the airbyte-proxy, \n // include the actual password/username you've set up (or use the defaults below)\n username = \"airbyte\"\n password = \"password\"\n\n // if running locally (Airbyte OSS), include the server url to the airbyte-api-server\n server_url = \"http://localhost:8006/v1/\"\n}\n" }, { "path": "customer_segmentation_analytics_shopify/infra/airbyte/variables.tf", "content": "variable \"api_password\" {\n type = string\n}\n\nvariable \"workspace_id\" {\n type = string\n}\n\nvariable \"dataset_id\" {\n type = string\n}\n\nvariable \"project_id\" {\n type = string\n}\n\nvariable \"credentials_json\" {\n type = string\n}\n\nvariable \"shop\" {\n type = string\n}\n\n\n\n\n" }, { "path": "customer_segmentation_analytics_shopify/orchestration/orchestration/__init__.py", "content": "" }, { "path": "customer_segmentation_analytics_shopify/orchestration/orchestration/assets.py", "content": "import os\nfrom dagster import OpExecutionContext\nfrom dagster_dbt import DbtCliResource, dbt_assets\nfrom dagster_airbyte import AirbyteResource, load_assets_from_airbyte_instance\n\nfrom .constants import dbt_manifest_path\n\n\n@dbt_assets(manifest=dbt_manifest_path)\ndef dbt_project_dbt_assets(context: OpExecutionContext, dbt: DbtCliResource):\n yield from dbt.cli([\"build\"], context=context).stream()\n\n\nairbyte_instance = AirbyteResource(\n host=\"localhost\",\n port=\"8000\",\n # If using basic auth, include username and password:\n username=\"airbyte\",\n password=\"password\"\n)\n\nairbyte_assets = load_assets_from_airbyte_instance(airbyte_instance,)\n" }, { "path": "customer_segmentation_analytics_shopify/orchestration/orchestration/constants.py", "content": "import os\nfrom pathlib import Path\n\nfrom dagster_dbt import DbtCliResource\n\ndbt_project_dir = Path(__file__).joinpath(\"..\", \"..\", \"..\", \"dbt_project\").resolve()\ndbt = DbtCliResource(project_dir=os.fspath(dbt_project_dir))\n\n# If DAGSTER_DBT_PARSE_PROJECT_ON_LOAD is set, a manifest will be created at runtime.\n# Otherwise, we expect a manifest to be present in the project's target directory.\nif os.getenv(\"DAGSTER_DBT_PARSE_PROJECT_ON_LOAD\"):\n dbt_parse_invocation = dbt.cli([\"parse\"], manifest={}).wait()\n dbt_manifest_path = dbt_parse_invocation.target_path.joinpath(\"manifest.json\")\nelse:\n dbt_manifest_path = dbt_project_dir.joinpath(\"target\", \"manifest.json\")" }, { "path": "customer_segmentation_analytics_shopify/orchestration/orchestration/definitions.py", "content": "import os\n\nfrom dagster import Definitions\nfrom dagster_dbt import DbtCliResource\n\nfrom .assets import dbt_project_dbt_assets, airbyte_assets\nfrom .constants import dbt_project_dir\nfrom .schedules import schedules\n\ndefs = Definitions(\n assets=[dbt_project_dbt_assets, airbyte_assets],\n schedules=schedules,\n resources={\n \"dbt\": DbtCliResource(project_dir=os.fspath(dbt_project_dir)),\n },\n)\n" }, { "path": "customer_segmentation_analytics_shopify/orchestration/orchestration/schedules.py", "content": "\"\"\"\nTo add a daily schedule that materializes your dbt assets, uncomment the following lines.\n\"\"\"\nfrom dagster_dbt import build_schedule_from_dbt_selection\n\nfrom .assets import dbt_project_dbt_assets\n\nschedules = [\n # build_schedule_from_dbt_selection(\n # [dbt_project_dbt_assets],\n # job_name=\"materialize_dbt_models\",\n # cron_schedule=\"0 0 * * *\",\n # dbt_select=\"fqn:*\",\n # ),\n]\n" }, { "path": "customer_segmentation_analytics_shopify/orchestration/pyproject.toml", "content": "[build-system]\nrequires = [\"setuptools\"]\nbuild-backend = \"setuptools.build_meta\"\n\n[tool.dagster]\nmodule_name = \"orchestration.definitions\"\ncode_location_name = \"orchestration\"" }, { "path": "customer_segmentation_analytics_shopify/orchestration/setup.py", "content": "from setuptools import find_packages, setup\n\nsetup(\n name=\"orchestration\",\n version=\"0.0.1\",\n packages=find_packages(),\n install_requires=[\n \"dagster\",\n \"dagster-cloud\",\n \"dagster-dbt\",\n \"dbt-core>=1.4.0\",\n \"dbt-bigquery\",\n ],\n extras_require={\n \"dev\": [\n \"dagster-webserver\",\n ]\n },\n)" }, { "path": "customer_segmentation_analytics_shopify/setup.py", "content": "from setuptools import find_packages, setup\n\nsetup(\n name=\"airbyte-dbt-dagster\",\n packages=find_packages(),\n install_requires=[\n \"dbt-bigquery\",\n \"dagster\",\n \"dagster-cloud\",\n \"dagster-dbt\",\n \"dagster-airbyte\",\n ],\n extras_require={\"dev\": [\"dagit\", \"pytest\"]},\n)" }, { "path": "data_to_pinecone_llm/.gitignore", "content": "# Byte-compiled / optimized / DLL files\n__pycache__/\n*.py[cod]\n*$py.class\n\n# C extensions\n*.so\n\n# Distribution / packaging\n.Python\nbuild/\ndevelop-eggs/\ndist/\ndownloads/\neggs/\n.eggs/\nlib/\nlib64/\nparts/\nsdist/\nvar/\nwheels/\nshare/python-wheels/\n*.egg-info/\n.installed.cfg\n*.egg\nMANIFEST\n\n# PyInstaller\n# Usually these files are written by a python script from a template\n# before PyInstaller builds the exe, so as to inject date/other infos into it.\n*.manifest\n*.spec\n\n# Installer logs\npip-log.txt\npip-delete-this-directory.txt\n\n# Unit test / coverage reports\nhtmlcov/\n.tox/\n.nox/\n.coverage\n.coverage.*\n.cache\nnosetests.xml\ncoverage.xml\n*.cover\n*.py,cover\n.hypothesis/\n.pytest_cache/\ncover/\n\n# Translations\n*.mo\n*.pot\n\n# Django stuff:\n*.log\nlocal_settings.py\ndb.sqlite3\ndb.sqlite3-journal\n\n# Flask stuff:\ninstance/\n.webassets-cache\n\n# Scrapy stuff:\n.scrapy\n\n# Sphinx documentation\ndocs/_build/\n\n# PyBuilder\n.pybuilder/\ntarget/\n\n# Jupyter Notebook\n.ipynb_checkpoints\n\n# IPython\nprofile_default/\nipython_config.py\n\n# pyenv\n# For a library or package, you might want to ignore these files since the code is\n# intended to run in multiple environments; otherwise, check them in:\n# .python-version\n\n# pipenv\n# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.\n# However, in case of collaboration, if having platform-specific dependencies or dependencies\n# having no cross-platform support, pipenv may install dependencies that don't work, or not\n# install all needed dependencies.\n#Pipfile.lock\n\n# poetry\n# Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.\n# This is especially recommended for binary packages to ensure reproducibility, and is more\n# commonly ignored for libraries.\n# https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control\n#poetry.lock\n\n# pdm\n# Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control.\n#pdm.lock\n# pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it\n# in version control.\n# https://pdm.fming.dev/#use-with-ide\n.pdm.toml\n\n# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm\n__pypackages__/\n\n# Celery stuff\ncelerybeat-schedule\ncelerybeat.pid\n\n# SageMath parsed files\n*.sage.py\n\n# Environments\n.env\n.venv\nenv/\nvenv/\nENV/\nenv.bak/\nvenv.bak/\n\n# Spyder project settings\n.spyderproject\n.spyproject\n\n# Rope project settings\n.ropeproject\n\n# mkdocs documentation\n/site\n\n# mypy\n.mypy_cache/\n.dmypy.json\ndmypy.json\n\n# Pyre type checker\n.pyre/\n\n# pytype static type analyzer\n.pytype/\n\n# Cython debug symbols\ncython_debug/\n\n# PyCharm\n# JetBrains specific template is maintained in a separate JetBrains.gitignore that can\n# be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore\n# and can be added to the global gitignore or merged into this file. For a more nuclear\n# option (not recommended) you can uncomment the following to ignore the entire idea folder.\n#.idea/\n\n#Desktop Services Store\n.DS_Store" }, { "path": "data_to_pinecone_llm/.vscode/quickstart.code-workspace", "content": "{\n\t\"folders\": [\n\t\t{\n\t\t\t\"path\": \"..\"\n\t\t},\n\t\t{\n\t\t\t\"path\": \"../..\"\n\t\t}\n\t],\n\t\"settings\": {}\n}" }, { "path": "data_to_pinecone_llm/README.md", "content": "# Data-to-Pinecone Integration\n\nWelcome to the \"Data-to-Pinecone Integration\" repository! This repo provides a quickstart template for building a full data stack using Airbyte, Terraform, and dbt to move data from Notion -> BigQuery -> Pinecone for interacting with Notion data through an LLM.\n\nThis quickstart is designed to minimize setup hassles and propel you forward.\n\n![Quickstart overview](assets/1-dataflow.png)\n\n## Table of Contents\n\n- [Prerequisites](#prerequisites)\n- [Creating an Environment For Your Project](#1-creating-an-environment-for-your-project)\n- [Adding Configuration Values](#2-adding-configuration-values)\n- [Setting Up Airbyte Connectors](#3-setting-up-airbyte-connectors)\n- [Sync Notion Data into BigQuery](#4-sync-notion-data-into-bigquery)\n- [Setting Up the dbt Project](#5-setting-up-the-dbt-project)\n- [Publishing Into Pinecone](#6-publishing-into-pinecone)\n- [Asking Questions About Your Data](#7-asking-questions-about-your-data)\n- [Next Steps](#8-next-steps)\n\n## Prerequisites\n\n### Notion\n\nWe'll source Notion pages to make the content searchable in Pinecone. Follow the [Notion source docs](https://docs.airbyte.com/integrations/sources/notion) for information on configuring a Notion source.\n\n### BigQuery\n\nBigQuery will store the raw API data from our sources and also the transformed data from dbt. You'll need a BigQuery project and a dataset with a service account that can control the dataset. Airbyte's [BigQuery destination docs](https://docs.airbyte.com/integrations/destinations/bigquery) lists the requirements and links describing how to configure.\n\n### Pinecone\n\nPinecone is the vector database we will use to index documents and their metadata, and also for finding documents that provide context for a query. You'll need a Pinecone account, an API key, and an index created with 1536 dimensions, as OpenAI returns vectors of 1536 dimensions. See the [Pinecone docs](https://docs.pinecone.io/docs/quickstart) for more information.\n\n### OpenAI\n\nOpenAI is used both in processing the query and also provides the LLM for generating a response. The query is vectorized so it can be used to identify relevant items in the Pinecone index, and these items are provided to the LLM as context to better respond to the query. You'll need an OpenAI account with credits and an API key. If you already have an account with OpenAI, you can generate a new API key by visiting this link: [platform.openai.com/api-keys](https://platform.openai.com/api-keys)\n\n### Software\n\n1. **Python 3.10 or later**: If not installed, download and install it from [Python's official website](https://www.python.org/downloads/).\n\n2. **Docker and Docker Compose (Docker Desktop)**: Install [Docker](https://docs.docker.com/get-docker/) following the official documentation for your specific OS.\n\n3. **Airbyte OSS version**: If you want to deploy the open-source version instead of using Airbyte Cloud, follow the installation instructions from the [Deploy Airbyte documentation](https://docs.airbyte.com/quickstart/deploy-airbyte/).\n\n4. **Terraform**: Terraform will help you provision and manage the Airbyte resources. If you haven't installed it, follow the [official Terraform installation guide](https://developer.hashicorp.com/terraform/tutorials/aws-get-started/install-cli).\n\n## 1. Creating an Environment For Your Project\n\nGet the project up and running on your local machine by following these steps:\n\n1. **Clone the repository (Clone only this quickstart)**:\n\n ```bash\n git clone --filter=blob:none --sparse https://github.com/airbytehq/quickstarts.git\n ```\n\n ```bash\n cd quickstarts\n ```\n\n ```bash\n git sparse-checkout add data_to_pinecone_llm\n ```\n\n2. **Navigate to the directory**:\n\n ```bash\n cd data_to_pinecone_llm\n ```\n\n3. **Set Up a Virtual Environment**:\n\n - For Mac:\n ```bash\n python3 -m venv .venv\n source .venv/bin/activate\n ```\n - For Windows:\n ```bash\n python -m venv .venv\n .\\.venv\\Scripts\\activate\n ```\n\n4. **Install Dependencies**:\n ```bash\n pip install -e \".[dev]\"\n ```\n\n## 2. Adding Configuration Values\n\nThe following steps will execute Terraform and dbt workflows to create the necessary resources for the integration. To do this, you'll need to provide some configuration values. Copy the provided `.env.template` file to `.env` and set its values. Then run the following command to source the environment variables into your shell so they are available when running Terraform and dbt:\n\n```bash\nset -o allexport && source .env && set +o allexport\n```\n\nDon't forget to re-run the above command after making any changes to the `.env` file.\n\n## 3. Setting Up Airbyte Connectors\n\n### Manually via the Airbyte UI\n\nCreate the [sources](https://docs.airbyte.com/quickstart/add-a-source) and [destinations](https://docs.airbyte.com/quickstart/add-a-destination) within your Airbyte environment, you can follow the [create connections](https://docs.airbyte.com/quickstart/create-a-connection) to define connections between them to control how and when the data will sync.\n\nYou can find the Airbyte workspace ID from the URL, e.g. `https://cloud.airbyte.com/workspaces/{workspace-id}/connections`.\n\n### With Terraform\n\nAirbyte allows you to create connectors for sources and destinations, facilitating data synchronization between various platforms. In this project, we're harnessing the power of Terraform to automate the creation of these connectors and the connections between them. Here's how you can set it up:\n\n1. **Navigate to the Airbyte Configuration Directory**:\n\n Change to the relevant directory containing the Terraform configuration for Airbyte:\n\n ```bash\n cd infra/airbyte\n ```\n\n2. **Modify Configuration Files**:\n\n Within the `infra/airbyte` directory, you'll find three crucial Terraform files:\n\n - `provider.tf`: Defines the Airbyte provider.\n - `main.tf`: Contains the main configuration for creating Airbyte resources.\n - `variables.tf`: Defines variables whose values are populated from the `.env` file.\n\n If you're using Airbyte Cloud instead of a local deployment you will need to update the Airbyte provider configuration in _infra/airbyte/provider.tf_, setting the `bearer_auth` to an API key generated at https://portal.airbyte.com/.\n\n3. **Initialize Terraform**:\n\n This step prepares Terraform to create the resources defined in your configuration files.\n\n ```bash\n terraform init\n ```\n\n4. **Review the Plan**:\n\n Before applying any changes, review the plan to understand what Terraform will do.\n\n ```bash\n terraform plan\n ```\n\n5. **Apply Configuration**:\n\n After reviewing and confirming the plan, apply the Terraform configurations to create the necessary Airbyte resources.\n\n ```bash\n terraform apply\n ```\n\n6. **Verify in Airbyte UI**:\n\n Once Terraform completes its tasks, navigate to the Airbyte UI. Here, you should see your source and destination connectors, as well as the connection between them, set up and ready to go.\n\n If you're using Airbyte Cloud, we configured Terraform to assemble the URL for you:\n\n ```bash\n terraform output\n ```\n\n will print something like:\n\n ```\n airbyte_cloud_url = \"https://cloud.airbyte.com/workspaces/{workspace-id}/connections\"\n ```\n\n![Airbyte Workspace Sources](assets/2-sources.png)\n\n![Airbyte Workspace Destinations](assets/3-destinations.png)\n\n![Airbyte Workspace Connections](assets/4-connections.png)\n\n## 4. Sync Notion Data into BigQuery\n\nBefore building the dbt project, transforming the raw notion data, the source tables must exist in the BigQuery dataset. Open the Airbyte UI and navigate to the Connections page. Click the _Sync Now_ button for `Notion to BigQuery` to start the sync.\n\n![UI to sync Notion](assets/5-sync-notion.png)\n\nOnce the sync is complete you can inspect the tables in BigQuery.\n\n![Notion source tables in BigQuery](assets/6-bigquery-dataset.png)\n\n## 5. Setting Up the dbt Project\n\n[dbt (data build tool)](https://www.getdbt.com/) allows you to transform your data by writing, documenting, and executing SQL workflows. Setting up the dbt project requires specifying connection details for your data platform, in this case, BigQuery. Here’s a step-by-step guide to help you set this up:\n\n1. **Navigate to the dbt Project Directory**:\n\n Change to the directory containing the dbt configuration:\n\n ```bash\n cd dbt_project\n ```\n\n You'll find a `profiles.yml` file within the directory. This file contains configurations for dbt to connect with your data platform, and is preconfigured to pull the connection details from environment variables.\n\n2. **Utilize Environment Variables (Optional but Recommended)**:\n\n To keep your credentials secure, you can leverage environment variables. An example is provided as the `keyfile` config in `profiles.yml`.\n\n3. **Test the Connection**:\n\n Once you’ve updated the connection details, you can test the connection to your BigQuery instance using:\n\n ```bash\n dbt debug\n ```\n\n If everything is set up correctly, this command should report a successful connection to BigQuery.\n\n4. **Run the Model**\n\n With the connection in place, you can now build the model to create the `notion` view in BigQuery, which the configured Airbyte connection will use to sync Notion pages into Pinecone.\n\n You can inspect the provided _dbt_project/models/notion.sql_ to see how it collates Notion blocks into a single text field which can be vectorized and indexed into Pinecone.\n\n ```bash\n dbt build\n ```\n\n You should now see the `notion_data` view in BigQuery.\n\n ![Notion source tables and view in BigQuery](assets/7-bigquery-dataset-with-view.png)\n\n## 6. Publishing Into Pinecone\n\nWith the source data transformed it is now ready to publish into the Pinecone index. Head back to the Connections and start a sync for `Publish BigQuery Data to Pinecone`.\n\n![Notion source tables and view in BigQuery](assets/8-sync-pinecone.png)\n\n## 7. Asking Questions About Your Data\n\nAfter Airbyte has published the Notion page text into your Pinecone index, it is ready to be interacted with via an LLM model. After providing a couple more environment variables, the provided _query.py_ will provide an interactive session to ask questions about your data.\n\n```bash\nexport OPENAI_API_KEY=openai_api_key\nexport PINECONE_API_KEY=pinecone_api_key\nexport PINECONE_ENVIRONMENT=pinecone_environment\nexport PINECONE_INDEX=pinecone_index\n\npython query.py\n```\n\n## 8. Next Steps\n\nOnce you've set up and launched this initial integration, the real power lies in its adaptability and extensibility. Here’s a roadmap to help you customize and harness this project tailored to your specific data needs:\n\n1. **Use Additional Data Sources**:\n\n Pinecone allows you to store vectors from multiple sources in a single index. This allows multiple document types to be queried together, and even referenced between themselves by the LLM. Add additional sources to the Terraform configuration or manually through the Airbyte UI.\n\n2. **Try Different LLM Chains**:\n\n The provided _query.py_ uses many default values when creating the LLM chain, which is quick to create but limits flexibility. One constraint is that the `stuff` chain type requires the query and discovered documents all fit within a single tokenized input to the LLM. This is fine for short queries and documents, but if you use longer sources you will need to use another chain type such as `map_reduce`.\n\n3. **Extend Access to the LLM**:\n\n The provided _query.py_ is a simple example of how to interact with the LLM locally. You could build a web-based UI that triggers a query and displays the results, or even integrate the LLM into a Slack bot to provide answers to questions in real-time.\n\n4. **Make Use of Metadata**\n\n When configuring the Pinecone destination you can choose to include metadata fields from the data. This allows you to filter results based on the metadata fields. For example, you could filter the results of a query to only include documents from a specific author, or provide a time range that a matching item must have been created or edited within. Metadata values are also provided back to the query executor so you can use them to provide additional context to the LLM.\n\n See [Filtering with metadata](https://docs.pinecone.io/docs/metadata-filtering) for more details.\n" }, { "path": "data_to_pinecone_llm/dbt_project/.gitignore", "content": "\ntarget/\ndbt_packages/\nlogs/\n\n#Desktop Services Store\n.DS_Store\n\n#User cookie\n.user.yml" }, { "path": "data_to_pinecone_llm/dbt_project/README.md", "content": "Welcome to your new dbt project!\n\n### Using the starter project\n\nTry running the following commands:\n- dbt run\n- dbt test\n\n\n### Resources:\n- Learn more about dbt [in the docs](https://docs.getdbt.com/docs/introduction)\n- Check out [Discourse](https://discourse.getdbt.com/) for commonly asked questions and answers\n- Join the [chat](https://community.getdbt.com/) on Slack for live discussions and support\n- Find [dbt events](https://events.getdbt.com) near you\n- Check out [the blog](https://blog.getdbt.com/) for the latest news on dbt's development and best practices\n" }, { "path": "data_to_pinecone_llm/dbt_project/analyses/.gitkeep", "content": "" }, { "path": "data_to_pinecone_llm/dbt_project/dbt_project.yml", "content": "# Name your project! Project names should contain only lowercase characters\n# and underscores. A good package name should reflect your organization's\n# name or the intended use of these models\nname: \"dbt_project\"\nversion: \"1.0.0\"\nconfig-version: 2\n\n# This setting configures which \"profile\" dbt uses for this project.\nprofile: \"dbt_project\"\n\n# These configurations specify where dbt should look for different types of files.\n# The `model-paths` config, for example, states that models in this project can be\n# found in the \"models/\" directory. You probably won't need to change these!\nmodel-paths: [\"models\"]\nanalysis-paths: [\"analyses\"]\ntest-paths: [\"tests\"]\nseed-paths: [\"seeds\"]\nmacro-paths: [\"macros\"]\nsnapshot-paths: [\"snapshots\"]\n\nclean-targets: # directories to be removed by `dbt clean`\n - \"target\"\n - \"dbt_packages\"\n\n# Configuring models\n# Full documentation: https://docs.getdbt.com/docs/configuring-models\n\n# In this example config, we tell dbt to build all models in the example/\n# directory as views. These settings can be overridden in the individual model\n# files using the `{{ config(...) }}` macro.\nmodels:\n dbt_project:\n" }, { "path": "data_to_pinecone_llm/dbt_project/macros/.gitkeep", "content": "" }, { "path": "data_to_pinecone_llm/dbt_project/models/notion.source.yml", "content": "version: 2\n\nsources:\n - name: notion_raw\n database: \"{{ env_var('BIGQUERY_PROJECT_ID') }}\"\n schema: \"{{ env_var('BIGQUERY_DATASET_ID') }}\"\n tables:\n - name: notion_blocks\n - name: notion_iterator\n - name: notion_pages\n" }, { "path": "data_to_pinecone_llm/dbt_project/models/notion_data.sql", "content": "/* Notion data transformation and cleansing.\n\nNotion data will be conformed to plain text:\n1. Parent page nodes are recursively parsed downwards to locate\n all child nodes.\n2. All applicable node types are converted to plain text.\n\nTODO:\n- Consider adding markdownified document metatada cues in the\n future for headers, list items, tables, etc.\n*/\n\nwith recursive iterator as (\n select JSON_VALUE(parent.page_id) as parent_page_id, *\n from {{ source('notion_raw', 'notion_blocks') }} AS blocks\n union all\n select iterator.parent_page_id, next_block.* \n from iterator\n join {{ source('notion_raw', 'notion_blocks') }} AS next_block\n on JSON_VALUE(next_block.parent.block_id) = iterator.id\n),\nextracted_text as (\n select\n ARRAY_TO_STRING(\n ARRAY(\n SELECT JSON_VALUE(json_values, '$.plain_text')\n FROM UNNEST(JSON_QUERY_ARRAY(paragraph, '$.rich_text')) as json_values\n ),\n ' '\n ) as paragraph_text,\n ARRAY_TO_STRING(\n ARRAY(\n SELECT JSON_VALUE(json_values, '$[0].plain_text')\n FROM\n UNNEST(JSON_QUERY_ARRAY(table_row, '$.cells'))\n as json_values\n ),\n ' '\n ) as table_row_text,\n ARRAY_TO_STRING(\n ARRAY(\n SELECT JSON_VALUE(json_values, '$.plain_text')\n FROM\n UNNEST(JSON_QUERY_ARRAY(heading_1, '$.rich_text'))\n as json_values\n ),\n ' '\n ) as heading_1_text,\n ARRAY_TO_STRING(\n ARRAY(\n SELECT JSON_VALUE(json_values, '$.plain_text')\n FROM\n UNNEST(JSON_QUERY_ARRAY(heading_2, '$.rich_text'))\n as json_values\n ),\n ' '\n ) as heading_2_text,\n ARRAY_TO_STRING(\n ARRAY(\n SELECT JSON_VALUE(json_values, '$.plain_text')\n FROM\n UNNEST(JSON_QUERY_ARRAY(heading_3, '$.rich_text'))\n as json_values\n ),\n ' '\n ) as heading_3_text,\n ARRAY_TO_STRING(\n ARRAY(\n SELECT JSON_VALUE(json_values, '$.plain_text')\n FROM UNNEST(JSON_QUERY_ARRAY(numbered_list_item, '$.rich_text')) as json_values\n ),\n ' '\n ) as numbered_list_item_text,\n ARRAY_TO_STRING(\n ARRAY(\n SELECT JSON_VALUE(json_values, '$.plain_text')\n FROM UNNEST(JSON_QUERY_ARRAY(bulleted_list_item, '$.rich_text')) as json_values\n ),\n ' '\n ) as bulleted_list_item_text,\n *\n from iterator\n),\ncombined_text as (\n select\n parent_page_id,\n id,\n type,\n coalesce(\n IF(CHAR_LENGTH(paragraph_text) > 0, paragraph_text, null),\n IF(CHAR_LENGTH(table_row_text) > 0, table_row_text, null),\n IF(CHAR_LENGTH(heading_1_text) > 0, heading_1_text, null),\n IF(CHAR_LENGTH(heading_2_text) > 0, heading_2_text, null),\n IF(CHAR_LENGTH(heading_3_text) > 0, heading_3_text, null),\n IF(CHAR_LENGTH(numbered_list_item_text) > 0, numbered_list_item_text, null),\n IF(CHAR_LENGTH(bulleted_list_item_text) > 0, bulleted_list_item_text, null)\n ) as text\n from extracted_text\n),\naggregated_text as (\n /* Aggregated text string per Notion page */\n select\n parent_page_id, string_agg(text) as text\n from combined_text\n group by parent_page_id\n)\n\nselect\n last_edited_time, url, text as notion_text\nfrom aggregated_text\njoin {{ source('notion_raw', 'notion_pages') }} on id = parent_page_id\nwhere text is not null /* Pinecone connector will fail if we attempt to\n insert with a null text value. */\n" }, { "path": "data_to_pinecone_llm/dbt_project/profiles.yml", "content": "dbt_project:\n outputs:\n dev:\n type: bigquery\n project: \"{{ env_var('BIGQUERY_PROJECT_ID') }}\"\n dataset: \"{{ env_var('BIGQUERY_DATASET_ID') }}\"\n location: \"{{ env_var('BIGQUERY_DATASET_LOCATION') }}\"\n keyfile: \"{{ env_var('DBT_BIGQUERY_KEYFILE_PATH') }}\"\n method: service-account\n priority: interactive\n job_execution_timeout_seconds: 300\n job_retries: 1\n threads: 1\n target: dev\n" }, { "path": "data_to_pinecone_llm/dbt_project/seeds/.gitkeep", "content": "" }, { "path": "data_to_pinecone_llm/dbt_project/snapshots/.gitkeep", "content": "" }, { "path": "data_to_pinecone_llm/dbt_project/tests/.gitkeep", "content": "" }, { "path": "data_to_pinecone_llm/infra/.gitignore", "content": "# Local .terraform directories\n**/.terraform/*\n\n# .tfstate files\n*.tfstate\n*.tfstate.*\n\n# Crash log files\ncrash.log\ncrash.*.log\n\n# Exclude all .tfvars files, which are likely to contain sensitive data, such as\n# password, private keys, and other secrets. These should not be part of version \n# control as they are data points which are potentially sensitive and subject \n# to change depending on the environment.\n*.tfvars\n*.tfvars.json\n\n# Ignore override files as they are usually used to override resources locally and so\n# are not checked in\noverride.tf\noverride.tf.json\n*_override.tf\n*_override.tf.json\n\n# Include override files you do wish to add to version control using negated pattern\n# !example_override.tf\n\n# Include tfplan files to ignore the plan output of command: terraform plan -out=tfplan\n# example: *tfplan*\n\n# Ignore CLI configuration files\n.terraformrc\nterraform.rc" }, { "path": "data_to_pinecone_llm/infra/airbyte/.terraform.lock.hcl", "content": "# This file is maintained automatically by \"terraform init\".\n# Manual edits may be lost in future updates.\n\nprovider \"registry.terraform.io/airbytehq/airbyte\" {\n version = \"0.3.4\"\n constraints = \"0.3.4\"\n hashes = [\n \"h1:0AHJKsRTlX6BCJZCJw5/oHsN97zi1AP33JeuPMwoX6U=\",\n \"h1:E97NK92naRr/9iAtDxA1PJ1aQYWR/vqWN10ThuQjUn8=\",\n \"zh:02167e00f7e89b6f09ae8796b9ee0ac2d8702b5cb295cb27d7a79266ffafe196\",\n \"zh:1ddad39354af090e830caf1e5cce845f24ff0bcef61b73e77ebc7703c2ecf90d\",\n \"zh:223a0a46d354ad0709d5f28d60accb3448ba5f256b84438238fb05235d1e5b34\",\n \"zh:29efd8848b9560456ec3d90f54984670e9d5b7e36f1edd2adb15c5fec3f57166\",\n \"zh:33d31310ba7ec699b5bd64edbb63b0a89bd55d87fae0f55409bbfa5fd7dd4d90\",\n \"zh:35ed0e2894e28ec7762406a18510b789b76b0649ace309eec22acaf10c982f08\",\n \"zh:4ba860918b65c00cc596d0b5b40068b89a72a300604a62bca7d286073779e684\",\n \"zh:59a0d1128477e587d9dac71f93598bae6050d176d29c840b6ad1bf95529d61e8\",\n \"zh:890df766e9b839623b1f0437355032a3c006226a6c200cd911e15ee1a9014e9f\",\n \"zh:8d9bb37e9094eba02acf8d08cf9f3331cd7c26478441d70e74e8d1ec9cb33aaa\",\n \"zh:9e5243eac43950889781a88d4e6186aea240898045e0e3c8fffd3291c5e74b6f\",\n \"zh:a0c31a5bc0cbc4a7341a0d185806a1c6797508580bede71a5009ad7b078d68c2\",\n \"zh:af341259999c6639a1c27e8f116a40b088dd192a3057096dc23a42affc97113f\",\n \"zh:b9779f8f695b4fab56e062abab61eaa58853f20c6411d53b2bd82a66d79a8b49\",\n \"zh:e284d898e5a30e507f1292635542dafe0e95ea8a5a215103a9d96d699aed9e75\",\n ]\n}\n" }, { "path": "data_to_pinecone_llm/infra/airbyte/main.tf", "content": "// Airbyte Terraform provider documentation: https://registry.terraform.io/providers/airbytehq/airbyte/latest/docs\n\nlocals {\n // The BigQuery destination expects a JSON credentials file, but the source expects a JSON string.\n // Read JSON file into a string variable, so we can use the same contents also for the source connector.\n bigquery_credentials_json = replace(\n file(var.bigquery_credentials_json_file_path), \"\\n\", \"\"\n ) // <- If this fails, please double check that the file exists.\n\n airbyte_cloud_url = var.airbyte_workspace_id != null ? \"https://cloud.airbyte.com/workspaces/${var.airbyte_workspace_id}/connections\" : \"(n/a)\"\n}\n\n// Sources\nresource \"airbyte_source_bigquery\" \"bigquery\" {\n configuration = {\n credentials_json = local.bigquery_credentials_json\n project_id = var.bigquery_project_id\n dataset_id = var.bigquery_dataset_id\n source_type = \"bigquery\"\n }\n name = \"BigQuery Publishing Source\"\n workspace_id = var.airbyte_workspace_id\n}\nresource \"airbyte_source_notion\" \"notion_source\" {\n configuration = {\n credentials = {\n source_notion_authenticate_using_access_token = {\n auth_type = \"token\"\n token = var.notion_token\n }\n }\n source_type = \"notion\"\n start_date = \"2023-01-01T00:00:00.001Z\" # Note: Fractional seconds with .000 may not work.\n }\n name = \"Notion Data Source\"\n workspace_id = var.airbyte_workspace_id\n}\n\n// Destinations\nresource \"airbyte_destination_bigquery\" \"bigquery\" {\n configuration = {\n dataset_id = var.bigquery_dataset_id\n dataset_location = var.bigquery_dataset_location\n destination_type = \"bigquery\"\n project_id = var.bigquery_project_id\n credentials_json = local.bigquery_credentials_json\n loading_method = {\n destination_bigquery_loading_method_standard_inserts = {\n method = \"Standard\"\n }\n }\n }\n name = \"BigQuery Raw Data Destination\"\n workspace_id = var.airbyte_workspace_id\n}\nresource \"airbyte_destination_pinecone\" \"pinecone\" {\n configuration = {\n destination_type = \"pinecone\"\n embedding = {\n destination_pinecone_embedding_open_ai = {\n openai_key = var.openai_key\n mode = \"openai\"\n }\n }\n indexing = {\n index = var.pinecone_index\n pinecone_environment = var.pinecone_environment\n pinecone_key = var.pinecone_key\n }\n processing = {\n chunk_overlap = 16\n chunk_size = 1024\n metadata_fields = [\"url\", \"last_edited_time\"]\n text_fields = [\"notion_text\"]\n }\n }\n name = \"Pinecone Publish Destination\"\n workspace_id = var.airbyte_workspace_id\n}\n\n// Connections\nresource \"airbyte_connection\" \"notion_connection\" {\n name = \"Notion to BigQuery\"\n source_id = airbyte_source_notion.notion_source.source_id\n destination_id = airbyte_destination_bigquery.bigquery.destination_id\n namespace_definition = \"custom_format\"\n namespace_format = var.bigquery_dataset_id\n prefix = \"notion_\"\n configurations = {\n streams = [\n { name = \"blocks\" },\n { name = \"pages\" },\n { name = \"users\" }\n ]\n }\n}\nresource \"airbyte_connection\" \"bigquery_to_pinecone\" {\n name = \"Publish BigQuery Data to Pinecone\"\n source_id = airbyte_source_bigquery.bigquery.source_id\n destination_id = airbyte_destination_pinecone.pinecone.destination_id\n configurations = {\n streams = [\n {\n name = \"notion_data\",\n cursor_field = [\"last_edited_time\"],\n primary_key = [[\"url\"]]\n sync_mode = \"incremental_deduped_history\"\n }\n ]\n }\n}" }, { "path": "data_to_pinecone_llm/infra/airbyte/output.tf", "content": "// These variables will be printed at the end of a successful `terraform apply`,\n// or by running `terraform output` at any time.\n\noutput \"airbyte_cloud_url\" {\n value = local.airbyte_cloud_url \n}\n" }, { "path": "data_to_pinecone_llm/infra/airbyte/provider.tf", "content": "// Airbyte Terraform provider documentation: https://registry.terraform.io/providers/airbytehq/airbyte/latest/docs\n\nterraform {\n required_providers {\n airbyte = {\n source = \"airbytehq/airbyte\"\n version = \"0.3.4\"\n }\n }\n}\n\n/////////////////////////////////\n// Airbyte Provider Definition //\n/////////////////////////////////\n\n// Uncomment the OSS or Cloud block, depending on your desired deployment location:\n\n// Airbyte Cloud:\nprovider \"airbyte\" {\n bearer_auth = var.airbyte_cloud_auth_key\n}\n\n# // Airbyte OSS:\n# provider \"airbyte\" {\n# // Optionally override the airbyte-api-server URL\n# server_url = \"http://localhost:8006/v1\"\n\n# // Optionally override the default password/username below\n# username = \"airbyte\"\n# password = \"password\" \n# }\n" }, { "path": "data_to_pinecone_llm/infra/airbyte/variables.tf", "content": "// Airbyte\nvariable \"airbyte_workspace_id\" {\n description = < What do you want to know?\"))\nconsole.print(\"\")\nwhile True:\n try:\n query = input(\"\")\n except KeyboardInterrupt:\n console.print(\"\\n\")\n console.print(Markdown(\"_Goodbye!_ 👋\"))\n exit(0)\n\n answer = qa.run(query)\n console.print(Markdown(answer))\n\n console.print(Markdown(\"\\n------\\n> What else do you want to know?\\n\"))\n console.print(\"\\n\")\n" }, { "path": "data_to_pinecone_llm/quickstart.md", "content": "# Quick Start\n\n```bash\n# Change to the quickstart directory\ncd data_to_pinecone_llm\n\n# Create a new `.env` file from the template\ncp .env.template .env\n\n# Edit the `.env` file and provide all needed variables\ncode .env\n\n# Export the .env file variables\nset -o allexport && source .env && set +o allexport\n\n# Deploy terraform\nterraform -chdir=infra/airbyte init\nterraform -chdir=infra/airbyte apply\n\n# Print Airbyte Cloud workspace URL\nterraform -chdir=infra/airbyte output\n\n# In Airbyte Cloud:\n# 1. Peform any needed fine-tuning or debugging.\n# 2. Run the source data connection at least once.\n\n# Build dbt models\ndbt run --project-dir=dbt_project --profiles-dir=dbt_project\n\n# In Airbyte Cloud:\n# 1. Create the publish connection if needed.\n# 2. Run the publish connection at least once.\n\n# Create and activate the virtual environment for the AI chatbot\npython -m venv .venv\nsource .venv/bin/activate\n\n# Run the chatbot\n./query.py\n```\n\nPlease see the [Readme](README.md) for more detailed instructions.\n" }, { "path": "data_to_pinecone_llm/secrets/.gitignore", "content": "# Git should ignore everything in this directory except this gitignore file and the readme.\n*\n!README.md\n!.gitignore\n" }, { "path": "data_to_pinecone_llm/secrets/README.md", "content": "# Secrets directory\n\nYou can store secrets here and they will be ignored by git.\n\nFor instance, you can save your Google Credentials JSON file here in this directory.\n" }, { "path": "data_to_pinecone_llm/setup.py", "content": "from setuptools import find_packages, setup\n\nsetup(\n name=\"data_to_pinecone_llm\",\n packages=find_packages(),\n install_requires=[\n \"dbt-bigquery\",\n \"pinecone-client\",\n \"langchain\",\n \"openai==0.28.1\",\n \"tiktoken\",\n \"rich\"\n ],\n extras_require={\"dev\": [\"pytest\"]},\n)\n" }, { "path": "database_snapshot/.gitignore", "content": "# Byte-compiled / optimized / DLL files\n__pycache__/\n*.py[cod]\n*$py.class\n\n# C extensions\n*.so\n\n# Distribution / packaging\n.Python\nbuild/\ndevelop-eggs/\ndist/\ndownloads/\neggs/\n.eggs/\nlib/\nlib64/\nparts/\nsdist/\nvar/\nwheels/\nshare/python-wheels/\n*.egg-info/\n.installed.cfg\n*.egg\nMANIFEST\n\n# PyInstaller\n# Usually these files are written by a python script from a template\n# before PyInstaller builds the exe, so as to inject date/other infos into it.\n*.manifest\n*.spec\n\n# Installer logs\npip-log.txt\npip-delete-this-directory.txt\n\n# Unit test / coverage reports\nhtmlcov/\n.tox/\n.nox/\n.coverage\n.coverage.*\n.cache\nnosetests.xml\ncoverage.xml\n*.cover\n*.py,cover\n.hypothesis/\n.pytest_cache/\ncover/\n\n# Translations\n*.mo\n*.pot\n\n# Django stuff:\n*.log\nlocal_settings.py\ndb.sqlite3\ndb.sqlite3-journal\n\n# Flask stuff:\ninstance/\n.webassets-cache\n\n# Scrapy stuff:\n.scrapy\n\n# Sphinx documentation\ndocs/_build/\n\n# PyBuilder\n.pybuilder/\ntarget/\n\n# Jupyter Notebook\n.ipynb_checkpoints\n\n# IPython\nprofile_default/\nipython_config.py\n\n# pyenv\n# For a library or package, you might want to ignore these files since the code is\n# intended to run in multiple environments; otherwise, check them in:\n# .python-version\n\n# pipenv\n# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.\n# However, in case of collaboration, if having platform-specific dependencies or dependencies\n# having no cross-platform support, pipenv may install dependencies that don't work, or not\n# install all needed dependencies.\n#Pipfile.lock\n\n# poetry\n# Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.\n# This is especially recommended for binary packages to ensure reproducibility, and is more\n# commonly ignored for libraries.\n# https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control\n#poetry.lock\n\n# pdm\n# Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control.\n#pdm.lock\n# pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it\n# in version control.\n# https://pdm.fming.dev/#use-with-ide\n.pdm.toml\n\n# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm\n__pypackages__/\n\n# Celery stuff\ncelerybeat-schedule\ncelerybeat.pid\n\n# SageMath parsed files\n*.sage.py\n\n# Environments\n.env\n.venv\nenv/\nvenv/\nENV/\nenv.bak/\nvenv.bak/\n\n# Spyder project settings\n.spyderproject\n.spyproject\n\n# Rope project settings\n.ropeproject\n\n# mkdocs documentation\n/site\n\n# mypy\n.mypy_cache/\n.dmypy.json\ndmypy.json\n\n# Pyre type checker\n.pyre/\n\n# pytype static type analyzer\n.pytype/\n\n# Cython debug symbols\ncython_debug/\n\n# PyCharm\n# JetBrains specific template is maintained in a separate JetBrains.gitignore that can\n# be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore\n# and can be added to the global gitignore or merged into this file. For a more nuclear\n# option (not recommended) you can uncomment the following to ignore the entire idea folder.\n#.idea/\n\n#Desktop Services Store\n.DS_Store" }, { "path": "database_snapshot/README.md", "content": "# Database Snapshot\n\nWelcome to the \"Database Snapshot\" repository! This repo provides a quickstart template for building a full data stack that creates a table snapshot from a database and stores it in an Amazon S3 bucket as a JSONL file using Airbyte and then loads the snapshot file to a preferred data warehouse, also using Airbyte. \n\nIn this quickstart, we will easily snapshot a sample table from Postgres and then load the table snapshot into BigQuery. The snapshot creation from the database and snapshot loading into the data warehouse are scheduled as one time operations. While this template doesn't delve into specific data, its goal is to showcase the synergy of these tools.\n\nLike other quickstarts, this is designed to minimize setup hassles and propel you forward.\n\n## Table of Contents\n\n- [Database Snapshot](#database-snapshot)\n - [Table of Contents](#table-of-contents)\n - [Infrastructure Layout](#infrastructure-layout)\n - [Prerequisites](#prerequisites)\n - [1. Setting an environment for your project](#1-setting-an-environment-for-your-project)\n - [2. Setting Up Airbyte Connectors with Terraform](#2-setting-up-airbyte-connectors-with-terraform)\n - [3. Running the Stack](#3-running-the-stack)\n - [Next Steps](#next-steps)\n\n## Infrastructure Layout\n![insfrastructure layout](images/layout.png)\n\n## Prerequisites\n\nBefore you embark on this integration, ensure you have the following set up and ready:\n\n1. **Python 3.10 or later**: If not installed, download and install it from [Python's official website](https://www.python.org/downloads/).\n\n2. **Docker and Docker Compose (Docker Desktop)**: Install [Docker](https://docs.docker.com/get-docker/) following the official documentation for your specific OS.\n\n3. **Airbyte OSS version**: Deploy the open-source version of Airbyte. Follow the installation instructions from the [Airbyte Documentation](https://docs.airbyte.com/quickstart/deploy-airbyte/).\n\n4. **Terraform**: Terraform will help you provision and manage the Airbyte resources. If you haven't installed it, follow the [official Terraform installation guide](https://developer.hashicorp.com/terraform/tutorials/aws-get-started/install-cli).\n\n\n## 1. Setting an environment for your project\n\nGet the project up and running on your local machine by following these steps:\n\n1. **Clone the repository (Clone only this quickstart)**: \n ```bash\n git clone --filter=blob:none --sparse https://github.com/airbytehq/quickstarts.git\n ```\n\n ```bash\n cd quickstarts\n ```\n\n ```bash\n git sparse-checkout add database_snapshot\n ```\n\n \n2. **Navigate to the directory**: \n ```bash\n cd database_snapshot\n ```\n\n3. **Set Up a Virtual Environment**: \n - For Mac:\n ```bash\n python3 -m venv venv\n source venv/bin/activate\n ```\n - For Windows:\n ```bash\n python -m venv venv\n .\\venv\\Scripts\\activate\n ```\n\n4. **Install Dependencies**: \n ```bash\n pip install -e \".[dev]\"\n ```\n\n## 2. Setting Up Airbyte Connectors with Terraform\n\nAirbyte allows you to create connectors for sources and destinations, facilitating data synchronization between various platforms. In this project, we're harnessing the power of Terraform to automate the creation of these connectors and the connections between them. Here's how you can set this up:\n\n1. **Navigate to the Airbyte Configuration Directory**:\n \n Change to the relevant directory containing the Terraform configuration for Airbyte:\n ```bash\n cd infra/airbyte\n ```\n\n2. **Modify Configuration Files**:\n\n Within the `infra/airbyte` directory, you'll find three crucial Terraform files:\n - `provider.tf`: Defines the Airbyte provider.\n - `main.tf`: Contains the main configuration for creating Airbyte resources.\n - `variables.tf`: Holds various variables, including credentials.\n\n You'll also find three crucial Terraform modules:\n - `connections`: Contains the configuration files for the Airbyte connections.\n - `destinations`: Contains the configuration files for the Airbyte destination connector(s).\n - `sources`: Contains the configuration files for the Airbyte source connector(s).\n\n In each terraform module, you will find the following Terraform configuration files:\n - `provider.tf`: Defines the Airbyte provider.\n - `main.tf`: Contains the main configuration for creating Airbyte resources.\n - `variables.tf`: Holds various variables, including credentials.\n - `outputs.tf`: Defines exported data or metadata about your resources.\n\n Adjust the configurations in these files to suit your project's needs. Specifically, provide credentials for your Postgres, S3 (source and destination) and BigQuery connections. You can utilize the `variables.tf` files to manage these credentials.\n\n3. **Initialize Terraform**:\n \n This step prepares Terraform to create the resources defined in your configuration files.\n ```bash\n terraform init\n ```\n\n4. **Review the Plan**:\n\n Before applying any changes, review the plan to understand what Terraform will do.\n ```bash\n terraform plan\n ```\n\n5. **Apply Configuration**:\n\n After reviewing and confirming the plan, apply the Terraform configurations to create the necessary Airbyte resources. A few things to note:\n\n 1. The S3 source connector will not be created if a bucket with the same name specified is not present is S3.\n 2. JSONL files have to be present in the specified bucket path. Otherwise, the snapshot loading connection (S3 to BigQuery) will not be created because the absence of the JSONL files will prevent Airbyte from detecting a stream.\n\n Fixing the first issue (if you encounter it) is straightforward - just create a new bucket with a unique name. However, since you will be snapshotting the database table for the first time, you will definitely have to fix the second issue before you proceed. There are 2 options for this:\n\n 1. **(Recommended)** Run the snapshot operation first. This populates the S3 bucket with actual JSONL files for the loading connection to pick up for stream detection. Then, create the loading connection and run it.\n 2. Upload an empty JSONL file to your S3 bucket with the bucket path you have specified in the destination connector config.\n\n Now proceed to create the Airbyte resources.\n\n ```bash\n terraform apply\n ```\n\n You will get an error that the `S3 to BigQuery` connection cannot be created. Ignore this and proceed. You should now have 5 resources (4 connectors and 1 connection) created in you Airbyte environment;\n\n 1. The Postgres Database Source\n 2. The S3 Destination\n 3. The S3 Source\n 4. The BigQuery Destination\n 5. The Postgres to S3 Conneection\n\n Confirm this by running the command below\n\n ```bash\n terraform state list\n ```\n\n6. **Verify in Airbyte UI**:\n\n Once Terraform completes its tasks, navigate to the Airbyte UI. Here, you should see your source and destination connectors, as well as the connection between them, set up and ready to go.\n\n## 3. Running the Stack\n\nAfter verifying that the 4 connectors and 1 connection have been created, you can proceed to run the snapshot operation.\n\n1. **Run the Database Snapshot**:\n \n Navigate to the `Postgres to S3` connection and click `Sync now` to snapshot the database table and save it to your S3.\n\n2. **Create the Loading Connection**:\n\n Once the snapshot has successfully completed, navigate back to your terraform home directory and create the second connection.\n\n ```bash\n terraform apply\n ```\n\n Now you should not see an error since the JSONL files are now present in the S3 bucket. Verify that the connection was created in the Airbyte UI and proceed to `Sync now`.\n\n You should now have your table snapshot loaded to your data warehouse.\n\n\n## Next Steps\n\nOnce you've set up and launched this initial integration, the real power lies in its adaptability and extensibility. Here’s a roadmap to help you customize and harness this project tailored to your specific data needs:\n\n1. **Add more Tables from Database**:\n\n You can add more tables from your database to snapshot.\n\n2. **Create dbt transformations**:\n\n You can create transformations using dbt, depending on your use case for the loaded data in your data warehouse. Add SQL transformations in dbt.\n\n3. **Scheduling**:\n\n You can run both the snapshot and the snapshot loading syncs on schedule depending on your use case. This can be done within Airbyte. For extensibility, you can use workflow orchestration tools like Dagster, Airflow or Prefect.\n\n4. **Extend the Project**:\n\n The real beauty of this integration is its extensibility. Whether you want to add more data sources, integrate additional tools, or enhance your transformation logic – the floor is yours. With the foundation set, sky's the limit for how you want to extend and refine your data processes." }, { "path": "database_snapshot/infra/.gitignore", "content": "# Local .terraform directories\n**/.terraform/*\n\n# .tfstate files\n*.tfstate\n*.tfstate.*\n\n# Crash log files\ncrash.log\ncrash.*.log\n\n# Exclude all .tfvars files, which are likely to contain sensitive data, such as\n# password, private keys, and other secrets. These should not be part of version \n# control as they are data points which are potentially sensitive and subject \n# to change depending on the environment.\n*.tfvars\n*.tfvars.json\n\n# Ignore override files as they are usually used to override resources locally and so\n# are not checked in\noverride.tf\noverride.tf.json\n*_override.tf\n*_override.tf.json\n\n# Include override files you do wish to add to version control using negated pattern\n# !example_override.tf\n\n# Include tfplan files to ignore the plan output of command: terraform plan -out=tfplan\n# example: *tfplan*\n\n# Ignore CLI configuration files\n.terraformrc\nterraform.rc" }, { "path": "database_snapshot/infra/airbyte/connections/main.tf", "content": "// Airbyte Terraform provider documentation: https://registry.terraform.io/providers/airbytehq/airbyte/latest/docs\n\n// Connections\nresource \"airbyte_connection\" \"postgres_to_s3\" {\n name = \"Postgres to S3\"\n source_id = var.postgres_id\n destination_id = var.s3_dest_id\n schedule = {\n schedule_type = \"manual\"\n }\n configurations = {\n streams = [\n {\n cursor_field = [\"...\",]\n name = \"...my_table_name_1...\"\n primary_key = [[\"...\",],]\n sync_mode = \"full_refresh_append\"\n }\n ]\n }\n}\n\nresource \"airbyte_connection\" \"s3_to_bigquery\" {\n name = \"S3 to BigQuery\"\n source_id = var.s3_source_id\n destination_id = var.bigquery_id\n schedule = {\n schedule_type = \"manual\"\n }\n configurations = {\n streams = [\n {\n cursor_field = [\"...\",]\n name = \"...my_table_name_1...\"\n primary_key = [[\"...\",],]\n sync_mode = \"full_refresh_append\"\n }\n ]\n }\n}" }, { "path": "database_snapshot/infra/airbyte/connections/provider.tf", "content": "// Airbyte Terraform provider documentation: https://registry.terraform.io/providers/airbytehq/airbyte/latest/docs\n\nterraform {\n required_providers {\n airbyte = {\n source = \"airbytehq/airbyte\"\n version = \"0.3.4\"\n }\n }\n}\n\nprovider \"airbyte\" {\n // If running locally (Airbyte OSS) with docker-compose using the airbyte-proxy, \n // include the actual password/username you've set up (or use the defaults below)\n username = \"airbyte\"\n password = var.airbyte_password\n \n // if running locally (Airbyte OSS), include the server url to the airbyte-api-server\n server_url = \"http://localhost:8006/v1\"\n}" }, { "path": "database_snapshot/infra/airbyte/connections/variables.tf", "content": "variable \"s3_source_id\" {\n type = string\n}\n\nvariable \"postgres_id\" {\n type = string\n}\n\nvariable \"s3_dest_id\" {\n type = string\n}\n\nvariable \"bigquery_id\" {\n type = string\n}\n\nvariable \"airbyte_password\" {\n type = string\n default = \"password\"\n}\n" }, { "path": "database_snapshot/infra/airbyte/destinations/main.tf", "content": "// Airbyte Terraform provider documentation: https://registry.terraform.io/providers/airbytehq/airbyte/latest/docs\n\n// Destinations\nresource \"airbyte_destination_bigquery\" \"bigquery\" {\n configuration = {\n dataset_id = \"...my_dataset_id...\"\n dataset_location = \"...my_dataset_location...\"\n destination_type = \"bigquery\"\n project_id = \"...my_project_id...\"\n credentials_json = \"...my_credentials_json_string...\"\n loading_method = {\n destination_bigquery_loading_method_standard_inserts = {\n method = \"Standard\"\n }\n }\n }\n name = \"BigQuery\"\n workspace_id = var.workspace_id\n}\n\nresource \"airbyte_destination_s3\" \"s3\" {\n configuration = {\n destination_type = \"s3\"\n format = {\n destination_s3_output_format_json_lines_newline_delimited_json = {\n format_type = \"JSONL\"\n }\n }\n s3_bucket_name = \"...my_bucket...\"\n s3_bucket_path = \"postgres_snapshot/table\"\n s3_bucket_region = \"...my_bucket_region...\"\n \n access_key_id = \"...my_aws_access_key_id...\"\n secret_access_key = \"...my_aws_secret_access_key...\"\n }\n name = \"S3\"\n workspace_id = var.workspace_id\n}" }, { "path": "database_snapshot/infra/airbyte/destinations/outputs.tf", "content": "output \"bigquery_id\" {\n value = airbyte_destination_bigquery.bigquery.destination_id\n}\n\noutput \"s3_id\" {\n value = airbyte_destination_s3.s3.destination_id\n}\n" }, { "path": "database_snapshot/infra/airbyte/destinations/provider.tf", "content": "// Airbyte Terraform provider documentation: https://registry.terraform.io/providers/airbytehq/airbyte/latest/docs\n\nterraform {\n required_providers {\n airbyte = {\n source = \"airbytehq/airbyte\"\n version = \"0.3.4\"\n }\n }\n}\n\nprovider \"airbyte\" {\n // If running locally (Airbyte OSS) with docker-compose using the airbyte-proxy, \n // include the actual password/username you've set up (or use the defaults below)\n username = \"airbyte\"\n password = var.airbyte_password\n \n // if running locally (Airbyte OSS), include the server url to the airbyte-api-server\n server_url = \"http://localhost:8006/v1\"\n}" }, { "path": "database_snapshot/infra/airbyte/destinations/variables.tf", "content": "variable \"workspace_id\" {\n type = string\n}\n\nvariable \"airbyte_password\" {\n type = string\n default = \"password\"\n}\n" }, { "path": "database_snapshot/infra/airbyte/main.tf", "content": "// Airbyte Terraform provider documentation: https://registry.terraform.io/providers/airbytehq/airbyte/latest/docs\n\nmodule \"sources\" {\n source = \"./sources\"\n workspace_id = \"...my_workspace_id...\"\n}\n\nmodule \"destination\" {\n source = \"./destinations\"\n workspace_id = \"...my_workspace_id...\"\n}\n\nmodule \"connections\" {\n source = \"./connections\"\n s3_dest_id = module.destination.s3_id\n s3_source_id = module.sources.s3_id\n postgres_id = module.sources.postgres_id\n bigquery_id = module.destination.bigquery_id\n}\n" }, { "path": "database_snapshot/infra/airbyte/provider.tf", "content": "// Airbyte Terraform provider documentation: https://registry.terraform.io/providers/airbytehq/airbyte/latest/docs\n\nterraform {\n required_providers {\n airbyte = {\n source = \"airbytehq/airbyte\"\n version = \"0.3.4\"\n }\n }\n}\n\nprovider \"airbyte\" {\n // If running locally (Airbyte OSS) with docker-compose using the airbyte-proxy, \n // include the actual password/username you've set up (or use the defaults below)\n username = \"airbyte\"\n password = var.airbyte_password\n \n // if running locally (Airbyte OSS), include the server url to the airbyte-api-server\n server_url = \"http://localhost:8006/v1\"\n}" }, { "path": "database_snapshot/infra/airbyte/sources/main.tf", "content": "// Airbyte Terraform provider documentation: https://registry.terraform.io/providers/airbytehq/airbyte/latest/docs\n\n// Sources\nresource \"airbyte_source_postgres\" \"postgres\" {\n configuration = {\n database = \"...my_database...\"\n host = \"...my_host...\"\n username = \"...my_username...\"\n password = \"...my_password...\"\n port = 5432\n source_type = \"postgres\"\n schemas = [\n \"...my_schema...\"\n ]\n ssl_mode = {\n allow = {}\n }\n tunnel_method = {\n no_tunnel = {}\n }\n replication_method = {\n scan_changes_with_user_defined_cursor = {}\n }\n }\n name = \"Postgres\"\n workspace_id = var.workspace_id\n}\n\nresource \"airbyte_source_s3\" \"s3\" {\n configuration = {\n bucket = \"...my_bucket...\"\n source_type = \"s3\"\n streams = [\n {\n file_type = \"...my_file_type...\"\n name = \"...my_stream_name...\"\n\n format = {\n source_s3_file_based_stream_config_format_jsonl_format = {\n filetype = \"jsonl\"\n }\n }\n globs = [\n \"postgres_snapshot/table/...my_stream_name.../*.jsonl*\",\n ]\n primary_key = \"...my_primary_key...\"\n },\n ]\n\n aws_access_key_id = \"...my_aws_access_key_id...\"\n aws_secret_access_key = \"...my_aws_secret_access_key...\"\n }\n name = \"S3\"\n workspace_id = var.workspace_id\n}" }, { "path": "database_snapshot/infra/airbyte/sources/outputs.tf", "content": "\noutput \"postgres_id\" {\n value = airbyte_source_postgres.postgres.source_id\n}\n\noutput \"s3_id\" {\n value = airbyte_source_s3.s3.source_id\n}" }, { "path": "database_snapshot/infra/airbyte/sources/provider.tf", "content": "// Airbyte Terraform provider documentation: https://registry.terraform.io/providers/airbytehq/airbyte/latest/docs\n\nterraform {\n required_providers {\n airbyte = {\n source = \"airbytehq/airbyte\"\n version = \"0.3.4\"\n }\n }\n}\n\nprovider \"airbyte\" {\n // If running locally (Airbyte OSS) with docker-compose using the airbyte-proxy, \n // include the actual password/username you've set up (or use the defaults below)\n username = \"airbyte\"\n password = var.airbyte_password\n \n // if running locally (Airbyte OSS), include the server url to the airbyte-api-server\n server_url = \"http://localhost:8006/v1\"\n}" }, { "path": "database_snapshot/infra/airbyte/sources/variables.tf", "content": "variable \"workspace_id\" {\n type = string\n}\n\nvariable \"airbyte_password\" {\n type = string\n default = \"password\"\n}\n" }, { "path": "database_snapshot/infra/airbyte/variables.tf", "content": "variable \"workspace_id\" {\n type = string\n}\n\nvariable \"airbyte_password\" {\n type = string\n default = \"password\"\n}\n" }, { "path": "database_snapshot/setup.py", "content": "from setuptools import find_packages, setup\n\nsetup(\n name=\"database-snapshot\",\n packages=find_packages(),\n install_requires=[\n ],\n extras_require={\"dev\": [\"pytest\"]},\n)" }, { "path": "developer_productivity_analytics_github/.gitignore", "content": "# Byte-compiled / optimized / DLL files\n__pycache__/\n*.py[cod]\n*$py.class\n\n# C extensions\n*.so\n\n# Distribution / packaging\n.Python\nbuild/\ndevelop-eggs/\ndist/\ndownloads/\neggs/\n.eggs/\nlib/\nlib64/\nparts/\nsdist/\nvar/\nwheels/\nshare/python-wheels/\n*.egg-info/\n.installed.cfg\n*.egg\nMANIFEST\n\n# PyInstaller\n# Usually these files are written by a python script from a template\n# before PyInstaller builds the exe, so as to inject date/other infos into it.\n*.manifest\n*.spec\n\n# Installer logs\npip-log.txt\npip-delete-this-directory.txt\n\n# Unit test / coverage reports\nhtmlcov/\n.tox/\n.nox/\n.coverage\n.coverage.*\n.cache\nnosetests.xml\ncoverage.xml\n*.cover\n*.py,cover\n.hypothesis/\n.pytest_cache/\ncover/\n\n# Translations\n*.mo\n*.pot\n\n# Django stuff:\n*.log\nlocal_settings.py\ndb.sqlite3\ndb.sqlite3-journal\n\n# Flask stuff:\ninstance/\n.webassets-cache\n\n# Scrapy stuff:\n.scrapy\n\n# Sphinx documentation\ndocs/_build/\n\n# PyBuilder\n.pybuilder/\ntarget/\n\n# Jupyter Notebook\n.ipynb_checkpoints\n\n# IPython\nprofile_default/\nipython_config.py\n\n# pyenv\n# For a library or package, you might want to ignore these files since the code is\n# intended to run in multiple environments; otherwise, check them in:\n# .python-version\n\n# pipenv\n# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.\n# However, in case of collaboration, if having platform-specific dependencies or dependencies\n# having no cross-platform support, pipenv may install dependencies that don't work, or not\n# install all needed dependencies.\n#Pipfile.lock\n\n# poetry\n# Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.\n# This is especially recommended for binary packages to ensure reproducibility, and is more\n# commonly ignored for libraries.\n# https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control\n#poetry.lock\n\n# pdm\n# Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control.\n#pdm.lock\n# pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it\n# in version control.\n# https://pdm.fming.dev/#use-with-ide\n.pdm.toml\n\n# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm\n__pypackages__/\n\n# Celery stuff\ncelerybeat-schedule\ncelerybeat.pid\n\n# SageMath parsed files\n*.sage.py\n\n# Environments\n.env\n.venv\nenv/\nvenv/\nENV/\nenv.bak/\nvenv.bak/\n\n# Spyder project settings\n.spyderproject\n.spyproject\n\n# Rope project settings\n.ropeproject\n\n# mkdocs documentation\n/site\n\n# mypy\n.mypy_cache/\n.dmypy.json\ndmypy.json\n\n# Pyre type checker\n.pyre/\n\n# pytype static type analyzer\n.pytype/\n\n# Cython debug symbols\ncython_debug/\n\n# PyCharm\n# JetBrains specific template is maintained in a separate JetBrains.gitignore that can\n# be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore\n# and can be added to the global gitignore or merged into this file. For a more nuclear\n# option (not recommended) you can uncomment the following to ignore the entire idea folder.\n#.idea/\n\n#Desktop Services Store\n.DS_Store" }, { "path": "developer_productivity_analytics_github/README.md", "content": "# Developer Productivity Analytics Stack With Github, Airbyte, Dbt, Dagster and BigQuery\n\nWelcome to the \"Developer Productivity Analytics Stack\" repository! ✨ This is your go-to place to easily set up a data stack using , Airbyte Github, Dbt, BigQuery, and Dagster. With this setup, you can pull Github data, extract it using Airbyte, put it into BigQuery, and play around with it using dbt and Dagster.\n\nThis Quickstart is all about making things easy, getting you started quickly and showing you how smoothly all these tools can work together!\n\nBelow is a visual representation of how data flows through our integrated tools in this Quickstart. This comes from Dagster's global asset lineage view:\n\n![Global Asset Lineage](<./assets/Global_Asset_Lineage (5).svg>)\n\n## Table of Contents\n\n- [Prerequisites](#prerequisites)\n- [Setting an environment for your project](#1-setting-an-environment-for-your-project)\n- [Setting Up BigQuery to work with Airbyte and dbt](#2-setting-up-bigquery)\n- [Setting Up Airbyte Connectors with Terraform](#3-setting-up-airbyte-connectors-with-terraform)\n- [Setting Up the dbt Project](#4-setting-up-the-dbt-project)\n- [Orchestrating with Dagster](#5-orchestrating-with-dagster)\n- [Next Steps](#next-steps)\n\n## Prerequisites\n\nBefore you embark on this integration, ensure you have the following set up and ready:\n\n1. **Python 3.10 or later**: If not installed, download and install it from [Python's official website](https://www.python.org/downloads/).\n\n2. **Docker and Docker Compose (Docker Desktop)**: Install [Docker](https://docs.docker.com/get-docker/) following the official documentation for your specific OS.\n\n3. **Airbyte OSS version**: Deploy the open-source version of Airbyte. Follow the installation instructions from the [Airbyte Documentation](https://docs.airbyte.com/quickstart/deploy-airbyte/).\n\n4. **Terraform**: Terraform will help you provision and manage the Airbyte resources. If you haven't installed it, follow the [official Terraform installation guide](https://developer.hashicorp.com/terraform/tutorials/aws-get-started/install-cli).\n\n5. **Google Cloud account with BigQuery**: You will also need to add the necessary permissions to allow Airbyte and dbt to access the data in BigQuery. A step-by-step guide is provided [below](#2-setting-up-bigquery).\n\n## 1. Setting an environment for your project\n\nGet the project up and running on your local machine by following these steps:\n\n1. **Clone the repository (Clone only this quickstart)**:\n\n ```bash\n git clone --filter=blob:none --sparse https://github.com/airbytehq/quickstarts.git\n ```\n\n ```bash\n cd quickstarts\n ```\n\n ```bash\n git sparse-checkout add developer_productivity_analytics_github\n ```\n\n2. **Navigate to the directory**:\n\n ```bash\n cd developer_productivity_analytics_github\n ```\n\n3. **Set Up a Virtual Environment**:\n\n - For Mac:\n ```bash\n python3 -m venv venv\n source venv/bin/activate\n ```\n - For Windows:\n ```bash\n python -m venv venv\n .\\venv\\Scripts\\activate\n ```\n4. **Install Dependencies**:\n ```bash\n pip install -e \".[dev]\"\n ```\n\n## 2. Setting Up BigQuery\n\n#### 1. **Create a Google Cloud Project**\n\n- If you have a Google Cloud project, you can skip this step.\n- Go to the [Google Cloud Console](https://console.cloud.google.com/).\n- Click on the \"Select a project\" dropdown at the top right and select \"New Project\".\n- Give your project a name and follow the steps to create it.\n\n#### 2. **Create BigQuery Datasets**\n\n- In the Google Cloud Console, go to BigQuery.\n- Make two new datasets: `raw_data` for Airbyte and `transformed_data` for dbt.\n - If you pick different names, remember to change the names in the code too.\n\n**How to create a dataset:**\n\n- In the left sidebar, click on your project name.\n- Click “Create Dataset”.\n- Enter the dataset ID (either `raw_data` or `transformed_data`).\n- Click \"Create Dataset\".\n\n#### 3. **Create Service Accounts and Assign Roles**\n\n- Go to “IAM & Admin” > “Service accounts” in the Google Cloud Console.\n- Click “Create Service Account”.\n- Name your service account (like `airbyte-service-account`).\n- Assign the “BigQuery Data Editor” and “BigQuery Job User” roles to the service account.\n- Follow the same steps to make another service account for dbt (like `dbt-service-account`) and assign the roles.\n\n**How to create a service account and assign roles:**\n\n- While creating the service account, under the “Grant this service account access to project” section, click the “Role” dropdown.\n- Choose the “BigQuery Data Editor” and “BigQuery Job User” roles.\n- Finish the creation process.\n\n#### 4. **Generate JSON Keys for Service Accounts**\n\n- For both service accounts, make a JSON key to let the service accounts sign in.\n\n**How to generate JSON key:**\n\n- Find the service account in the “Service accounts” list.\n- Click on the service account name.\n- In the “Keys” section, click “Add Key” and pick JSON.\n- The key will download automatically. Keep it safe and don’t share it.\n- Do this for the other service account too.\n\n## 3. Setting Up Airbyte Connectors with Terraform\n\nAirbyte allows you to create connectors for sources and destinations, facilitating data synchronization between various platforms. In this project, we're harnessing the power of Terraform to automate the creation of these connectors and the connections between them. Here's how you can set this up:\n\n1. **Navigate to the Airbyte Configuration Directory**:\n\n Change to the relevant directory containing the Terraform configuration for Airbyte:\n\n ```bash\n cd infra/airbyte\n ```\n\n2. **Modify Configuration Files**:\n\n Within the `infra/airbyte` directory, you'll find three crucial Terraform files:\n\n - `provider.tf`: Defines the Airbyte provider.\n - `main.tf`: Contains the main configuration for creating Airbyte resources.\n - `variables.tf`: Holds various variables, including credentials.\n\n Adjust the configurations in these files to suit your project's needs. Specifically, provide credentials for your BigQuery connection. You can utilize the `variables.tf` file to manage these credentials.\n\n3. **Initialize Terraform**:\n\n This step prepares Terraform to create the resources defined in your configuration files.\n\n ```bash\n terraform init\n ```\n\n4. **Review the Plan**:\n\n Before applying any changes, review the plan to understand what Terraform will do.\n\n ```bash\n terraform plan\n ```\n\n5. **Apply Configuration**:\n\n After reviewing and confirming the plan, apply the Terraform configurations to create the necessary Airbyte resources.\n\n ```bash\n terraform apply\n ```\n\n6. **Verify in Airbyte UI**:\n\n Once Terraform completes its tasks, navigate to the [Airbyte UI](http://localhost:8000/). Here, you should see your source and destination connectors, as well as the connection between them, set up and ready to go.\n\n## 4. Setting Up the dbt Project\n\n[dbt (data build tool)](https://www.getdbt.com/) allows you to transform your data by writing, documenting, and executing SQL workflows. Setting up the dbt project requires specifying connection details for your data platform, in this case, BigQuery. Here’s a step-by-step guide to help you set this up:\n\n1. **Navigate to the dbt Project Directory**:\n\n Change to the directory containing the dbt configuration:\n\n ```bash\n cd dbt_project\n ```\n\n2. **Update Connection Details**:\n\n You'll find a `profiles.yml` file within the directory. This file contains configurations for dbt to connect with your data platform. Update this file with your BigQuery connection details.\n\n3. **Utilize Environment Variables (Optional but Recommended)**:\n\n To keep your credentials secure, you can leverage environment variables. An example is provided within the `profiles.yml` file.\n\n4. **Test the Connection**:\n\n Once you’ve updated the connection details, you can test the connection to your BigQuery instance using:\n\n ```bash\n dbt debug\n ```\n\n If everything is set up correctly, this command should report a successful connection to BigQuery.\n\n5. **Run the Models**:\n\n If you would like to run the dbt models manually at this point, you can do so by executing:\n\n ```bash\n dbt run\n ```\n\n You can verify the data has been transformed by going to BigQuery and checking the `transformed_data` dataset.\n\n## 5. Orchestrating with Dagster\n\n[Dagster](https://dagster.io/) is a modern data orchestrator designed to help you build, test, and monitor your data workflows. In this section, we'll walk you through setting up Dagster to oversee both the Airbyte and dbt workflows:\n\n1. **Navigate to the Orchestration Directory**:\n\n Switch to the directory containing the Dagster orchestration configurations:\n\n ```bash\n cd orchestration\n ```\n\n2. **Set Environment Variables**:\n\n Dagster requires certain environment variables to be set to interact with other tools like dbt and Airbyte. Set the following variables:\n\n ```bash\n export DAGSTER_DBT_PARSE_PROJECT_ON_LOAD=1\n export AIRBYTE_PASSWORD=password\n ```\n\n Note: The `AIRBYTE_PASSWORD` is set to `password` as a default for local Airbyte instances. If you've changed this during your Airbyte setup, ensure you use the appropriate password here.\n\n3. **Launch the Dagster UI**:\n\n With the environment variables in place, kick-start the Dagster UI:\n\n ```bash\n dagster dev\n ```\n\n4. **Access Dagster in Your Browser**:\n\n Open your browser and navigate to:\n\n ```\n http://127.0.0.1:3000\n ```\n\n Here, you should see assets for both Airbyte and dbt. To get an overview of how these assets interrelate, click on \"view global asset lineage\". This will give you a clear picture of the data lineage, visualizing how data flows between the tools.\n\n5. **Materialize Dagster Assets**:\n In the Dagster UI, click on \"Materialize all\". This should trigger the full pipeline. First the Airbyte sync to extract data from Faker and load it into BigQuery, and then dbt to transform the raw data, materializing the `staging` and `marts` models.\n\n## Next Steps\n\nCongratulations on deploying and running the Customer Satisfaction Analytics Quistart! 🎉 Here are some suggestions on what you can explore next to dive deeper and get more out of your project:\n\n### 1. **Explore the Data and Insights**\n - Dive into the datasets in BigQuery, run some queries, and explore the data you've collected and transformed. This is your chance to uncover insights and understand the data better!\n\n### 2. **Optimize Your dbt Models**\n - Review the transformations you’ve applied using dbt. Try optimizing the models or create new ones based on your evolving needs and insights you want to extract.\n\n### 3. **Automate and Monitor Your Pipelines**\n - Explore more advanced Dagster configurations and setups to automate your pipelines further and set up monitoring and alerting to be informed of any issues immediately." }, { "path": "developer_productivity_analytics_github/dbt_project/.gitignore", "content": "\ntarget/\ndbt_packages/\nlogs/\n\n#Desktop Services Store\n.DS_Store\n\n#User cookie\n.user.yml" }, { "path": "developer_productivity_analytics_github/dbt_project/dbt_project.yml", "content": "\n# Name your project! Project names should contain only lowercase characters\n# and underscores. A good package name should reflect your organization's\n# name or the intended use of these models\nname: 'dbt_project'\nversion: '1.0.0'\nconfig-version: 2\n\n# This setting configures which \"profile\" dbt uses for this project.\nprofile: 'dbt_project'\n\n# These configurations specify where dbt should look for different types of files.\n# The `model-paths` config, for example, states that models in this project can be\n# found in the \"models/\" directory. You probably won't need to change these!\nmodel-paths: [\"models\"]\nanalysis-paths: [\"analyses\"]\ntest-paths: [\"tests\"]\nseed-paths: [\"seeds\"]\nmacro-paths: [\"macros\"]\nsnapshot-paths: [\"snapshots\"]\n\nclean-targets: # directories to be removed by `dbt clean`\n - \"target\"\n - \"dbt_packages\"\n\n\n# Configuring models\n# Full documentation: https://docs.getdbt.com/docs/configuring-models\n\n# In this example config, we tell dbt to build all models in the example/\n# directory as views. These settings can be overridden in the individual model\n# files using the `{{ config(...) }}` macro.\nmodels:\n dbt_project:\n # Config indicated by + and applies to all files under models/example/\n staging:\n +materialized: view\n marts:\n +materialized: view\n" }, { "path": "developer_productivity_analytics_github/dbt_project/models/marts/avarage_time_to_merge_pr_analysis.sql", "content": "SELECT\n AVG(TIMESTAMP_DIFF(merged_at, created_at, SECOND)) AS avg_merge_time_seconds\nFROM\n transformed_data.stg_pull_requests\nWHERE\n merged_at IS NOT NULL\n" }, { "path": "developer_productivity_analytics_github/dbt_project/models/marts/commits_over_time_per_dev_analysis.sql", "content": "SELECT\n DATE(created_at) AS commit_date,\n JSON_EXTRACT_SCALAR(author, '$.login') AS developer_username,\n COUNT(*) AS num_commits\nFROM\n transformed_data.stg_commits\nWHERE\n JSON_EXTRACT_SCALAR(author, '$.login') = 'developer_username'\nGROUP BY\n commit_date, developer_username\nORDER BY\n commit_date\n" }, { "path": "developer_productivity_analytics_github/dbt_project/models/marts/dev_activity_by_day_of_week_analysis.sql", "content": "SELECT\n EXTRACT(DAYOFWEEK FROM created_at) AS day_of_week,\n EXTRACT(HOUR FROM created_at) AS hour_of_day,\n JSON_EXTRACT_SCALAR(author, '$.login') AS developer_username,\n COUNT(*) AS num_commits\nFROM\n transformed_data.stg_commits\nGROUP BY\n day_of_week, hour_of_day, developer_username\n" }, { "path": "developer_productivity_analytics_github/dbt_project/models/marts/dev_collaboration_network_analysis.sql", "content": "WITH Collaboration AS (\n SELECT\n JSON_EXTRACT_SCALAR(a.author, '$.login') AS developer1,\n JSON_EXTRACT_SCALAR(b.author, '$.login') AS developer2,\n COUNT(*) AS num_collaborations\n FROM\n transformed_data.stg_commits AS a\n JOIN\n transformed_data.stg_commits AS b\n ON\n a.repository = b.repository\n AND a.sha <> b.sha\n GROUP BY\n developer1, developer2\n)\nSELECT\n developer1,\n developer2,\n num_collaborations\nFROM\n Collaboration\nWHERE\n num_collaborations > 5 -- Adjust threshold as needed\n" }, { "path": "developer_productivity_analytics_github/dbt_project/models/marts/freq_of_code_contribution_analysis.sql", "content": "SELECT\n JSON_EXTRACT_SCALAR(author, '$.login') AS developer_username,\n COUNT(*) AS num_contributions\nFROM\n transformed_data.stg_commits\nGROUP BY\n developer_username\n" }, { "path": "developer_productivity_analytics_github/dbt_project/models/marts/no_of_code_reviews_per_dev_analysis.sql", "content": "SELECT\n JSON_EXTRACT_SCALAR(requested_reviewers, '$.users[0].login') AS developer_username,\n COUNT(*) AS num_reviews\nFROM\n transformed_data.stg_pull_requests\nWHERE\n JSON_EXTRACT_SCALAR(requested_reviewers, '$.users[0].login') IS NOT NULL\nGROUP BY\n developer_username\n" }, { "path": "developer_productivity_analytics_github/dbt_project/models/marts/no_of_commits_per_dev_per_repo_analysis.sql", "content": "SELECT\n JSON_EXTRACT_SCALAR(author, '$.login') AS developer_username,\n repository,\n COUNT(*) AS num_commits\nFROM\n transformed_data.stg_commits\nGROUP BY\n developer_username, repository\n" }, { "path": "developer_productivity_analytics_github/dbt_project/models/marts/no_of_pr_per_dev_analysis.sql", "content": "SELECT\n JSON_EXTRACT_SCALAR(user, '$.login') AS developer_username,\n COUNT(*) AS num_pull_requests_opened\nFROM\n transformed_data.stg_pull_requests\nGROUP BY\n developer_username" }, { "path": "developer_productivity_analytics_github/dbt_project/models/marts/number_of_pr_open_or_closed.sql", "content": "SELECT\n JSON_EXTRACT_SCALAR(user, '$.login') AS username,\n SUM(CASE WHEN state = 'opened' THEN 1 ELSE 0 END) AS opened_prs,\n SUM(CASE WHEN state = 'closed' THEN 1 ELSE 0 END) AS closed_prs\nFROM transformed_data.stg_pull_requests\nGROUP BY username\n" }, { "path": "developer_productivity_analytics_github/dbt_project/models/marts/top_collaborators_by_repo_analysis.sql", "content": "WITH TopCollaborators AS (\n SELECT\n repository,\n JSON_EXTRACT_SCALAR(author, '$.login') AS developer_username,\n COUNT(*) AS num_commits\n FROM\n transformed_data.stg_commits\n GROUP BY\n repository, developer_username\n)\nSELECT\n repository,\n developer_username,\n num_commits\nFROM (\n SELECT\n repository,\n developer_username,\n num_commits,\n ROW_NUMBER() OVER (PARTITION BY repository ORDER BY num_commits DESC) AS rn\n FROM\n TopCollaborators\n)\nWHERE\n rn <= 5 -- Adjust the number of top collaborators as needed\n" }, { "path": "developer_productivity_analytics_github/dbt_project/models/marts/track_issues_assigned_by_dev_analysis.sql", "content": "SELECT\n JSON_EXTRACT_SCALAR(assignee, '$.login') AS developer_username,\n COUNT(*) AS num_issues_assigned\nFROM\n transformed_data.stg_issues\nGROUP BY\n developer_username\n" }, { "path": "developer_productivity_analytics_github/dbt_project/models/sources/github_source.yml", "content": "version: 2\n\nsources:\n - name: github\n # Use your BigQuery project ID\n database: \"{{ env_var('BIGQUERY_PROJECT_ID', '') }}\"\n # Use your BigQuery dataset name\n schema: github_airbyte\n \n tables: \n\n - name: users\n description: \"Simulated user data from the Github connector.\"\n columns:\n - name: id \n description: \"Unique identifier for the users.\"\n - name: avatar_url \n - name: events_url\n - name: followers_url\n - name: following_url\n - name: gists_url\n - name: gravatar_url\n - name: html_url\n - name: login\n - name: node_id\n - name: organization\n - name: organizations_url\n - name: received_events_url\n - name: repos_url\n - name: site_admin\n - name: starred_url\n - name: subscriptions_url\n - name: type\n - name: url\n\n - name: teams\n description: \"Simulated team data from the Github connector.\"\n columns:\n - name: id \n description: \"Unique identifier for the teams.\"\n - name: description \n - name: members_url\n - name: name\n - name: notifaction_setting\n - name: parent\n - name: permission\n - name: html_url\n - name: privacy\n - name: node_id\n - name: organization\n - name: repositories_url\n - name: slug\n - name: url\n\n - name: tags\n description: \"Simulated tag data from the Github connector.\"\n columns:\n - name: name \n - name: commit \n - name: tarball_url\n - name: zipball_url\n - name: repository\n - name: node_id\n\n - name: stargazers\n description: \"Simulated stargazer data from the Github connector.\"\n columns:\n - name: user \n - name: user_id \n - name: starred_at\n - name: repository\n\n - name: reviews\n description: \"Simulated review data from the Github connector.\"\n columns:\n - name: id \n description: \"Unique identifier for the reviews.\"\n - name: user \n - name: author_association \n - name: body\n - name: repository\n - name: commit_id\n - name: created_at\n - name: html_url\n - name: node_id\n - name: pull_request_url\n - name: state\n - name: submitted_at\n - name: updated_at\n - name: _links\n\n - name: review_comments\n description: \"Simulated review_comment data from the Github connector.\"\n columns:\n - name: id \n description: \"Unique identifier for the review_comments.\"\n - name: user \n - name: author_association \n - name: body\n - name: repository\n - name: commit_id\n - name: created_at\n - name: html_url\n - name: node_id\n - name: pull_request_url\n - name: diff_hunk\n - name: in_reply_to_id\n - name: updated_at\n - name: _links\n - name: line\n - name: original_commit_id\n - name: original_line\n - name: original_position\n - name: original_start_line\n - name: path\n - name: position\n - name: pull_request_review_id\n - name: reactions\n - name: side\n - name: start_line\n - name: start_side\n - name: subject_type\n - name: url\n\n - name: repositories\n description: \"Simulated repository data from the Github connector.\"\n columns:\n - name: id \n description: \"Unique identifier for the repositories.\"\n - name: allow_forking \n - name: archive_url \n - name: archived\n - name: assignees_url\n - name: blobs_url\n - name: branches_url\n - name: clone_url\n - name: collaborators_url\n - name: comments_url\n - name: commits_url\n - name: compare_url\n - name: contents_url\n - name: contributors_url\n - name: created_at\n - name: default_branch\n - name: deployments_url\n - name: description\n - name: disabled\n - name: downloads_url\n - name: events_url\n - name: fork\n - name: forks\n - name: forks_count\n - name: forks_url\n - name: full_name\n - name: git_commits_url\n - name: git_refs_url \n - name: git_tags_url\n - name: git_url\n - name: has_discussions\n - name: has_downloads\n - name: has_issues\n - name: has_pages\n - name: has_projects\n - name: has_wiki\n - name: homepage\n - name: hooks_url\n - name: html_url\n - name: is_template\n - name: issue_comment_url\n - name: issue_events_url\n - name: issues_url\n - name: keys_url\n - name: labels_url\n - name: language\n - name: languages_url\n - name: license\n - name: merges_url\n - name: milestones_url\n - name: mirror_url\n - name: name\n - name: node_id\n - name: notifications_url\n - name: open_issues\n - name: open_issues_count\n - name: organization\n - name: owner\n - name: permissions\n - name: private\n - name: pulls_url\n - name: pushed_at\n - name: releases_url\n - name: security_and_analysis\n - name: size\n - name: ssh_url\n - name: stargazers_count\n - name: stargazers_url\n - name: statuses_url\n - name: subscribers_url\n - name: subscription_url\n - name: svn_url\n - name: tags_url\n - name: teams_url\n - name: topics\n - name: trees_url\n - name: updated_at\n - name: url\n - name: visibility\n - name: watchers\n - name: watchers_count\n - name: web_commit_signoff_required\n\n - name: pull_requests\n description: \"Simulated pull_request data from the Github connector.\"\n columns:\n - name: id \n description: \"Unique identifier for the pull_requests.\"\n - name: active_lock_reason \n - name: assignee \n - name: assignees\n - name: author_association\n - name: auto_merge\n - name: base\n - name: body\n - name: closed_at\n - name: comments_url\n - name: commits_url\n - name: created_at\n - name: diff_url\n - name: draft\n - name: head\n - name: html_url\n - name: issue_url\n - name: labels\n - name: locked\n - name: merge_commit_sha\n - name: merged_at\n - name: milestone\n - name: node_id\n - name: number\n - name: patch_url\n - name: repository\n - name: requested_reviewers\n - name: requested_teams\n - name: review_comment_url\n - name: review_comments_url\n - name: state\n - name: statuses_url\n - name: title\n - name: updated_at\n - name: url\n - name: user\n\n - name: organizations\n description: \"Simulated organization data from the Github connector.\"\n columns:\n - name: id \n description: \"Unique identifier for the organizations.\"\n - name: advanced_security_enabled_for_new_repositories \n - name: archived_at \n - name: archived_at\n - name: billing_email\n - name: blog\n - name: collaborators\n - name: company\n - name: created_at\n - name: default_repository_permission\n - name: dependabot_alerts_enabled_for_new_repositories\n - name: dependabot_security_updates_enabled_for_new_repositories\n - name: dependency_graph_enabled_for_new_repositories\n - name: description\n - name: disk_usage\n - name: email\n - name: events_url\n - name: followers\n - name: following\n - name: has_organization_projects\n - name: has_repository_projects\n - name: hooks_url\n - name: html_url\n - name: is_verified\n - name: issues_url\n - name: location\n - name: login\n - name: members_allowed_repository_creation_type\n - name: members_can_create_internal_repositories\n - name: members_can_create_pages\n - name: members_can_create_private_pages\n - name: members_can_create_private_repositories\n - name: members_can_create_public_pages\n - name: members_can_create_public_repositories\n - name: members_can_create_repositories\n - name: members_can_fork_private_repositories\n - name: members_url\n - name: name\n - name: node_id\n - name: owned_private_repos\n - name: plan\n - name: private_gists\n - name: public_gists\n - name: public_members_url\n - name: public_repos\n - name: repos_url\n - name: secret_scanning_enabled_for_new_repositories\n - name: secret_scanning_push_protection_custom_link\n - name: secret_scanning_push_protection_custom_link_enabled\n - name: secret_scanning_push_protection_enabled_for_new_repositories\n - name: total_private_repos\n - name: twitter_username\n - name: two_factor_requirement_enabled\n - name: type\n - name: updated_at\n - name: url\n - name: web_commit_signoff_required\n\n - name: issues\n description: \"Simulated issue data from the Github connector.\"\n columns:\n - name: id \n description: \"Unique identifier for the issues.\"\n - name: active_lock_reason \n - name: assignee\n - name: assignees\n - name: author_association\n - name: body\n - name: closed_at\n - name: comments\n - name: comments_url\n - name: created_at\n - name: draft\n - name: events_url\n - name: html_url\n - name: labels\n - name: labels_url\n - name: locked\n - name: milestone\n - name: node_id\n - name: node_id\n - name: performed_via_github_app\n - name: pull_request\n - name: reactions\n - name: repository\n - name: repository_url\n - name: state\n - name: state_reason\n - name: timeline_url\n - name: title\n - name: updated_at\n - name: url\n - name: user\n - name: user_id\n\n - name: commits\n description: \"Simulated commit data from the Github connector.\"\n columns:\n - name: author \n - name: branch\n - name: comments_url\n - name: commit\n - name: committer\n - name: created_at\n - name: html_url\n - name: node_id\n - name: parents\n - name: repository\n - name: sha\n - name: url\n\n - name: comments\n description: \"Simulated comment data from the Github connector.\"\n columns:\n - name: id \n description: \"Unique identifier for the comments.\"\n - name: author_association \n - name: body\n - name: created_at\n - name: html_url\n - name: issue_url\n - name: node_id\n - name: performed_via_github_app\n - name: reactions\n - name: repository\n - name: updated_at\n - name: url\n - name: user\n - name: user_id\n\n - name: branches\n description: \"Simulated branch data from the Github connector.\"\n columns:\n - name: commit \n - name: name\n - name: protected\n - name: protection\n - name: protection_url\n - name: repository\n\n - name: collaborators\n description: \"Simulated collaborators data from the Github connector.\"\n columns:\n - name: id \n description: \"Unique identifier for the collaborators.\"\n - name: avatar_url \n - name: events_url\n - name: followers_url\n - name: following_url\n - name: gists_url\n - name: gravatar_id\n - name: html_url\n - name: login\n - name: node_id\n - name: organizations_url\n - name: permissions\n - name: received_events_url\n - name: repos_url\n - name: repository\n - name: role_name\n - name: site_adminl\n - name: starred_url\n - name: subscriptions_url\n - name: type\n - name: url\n\n " }, { "path": "developer_productivity_analytics_github/dbt_project/models/staging/stg_branches.sql", "content": "select\n *\nfrom {{ source('github', 'branches') }}\n" }, { "path": "developer_productivity_analytics_github/dbt_project/models/staging/stg_collaborators.sql", "content": "select\n *\nfrom {{ source('github', 'collaborators') }}" }, { "path": "developer_productivity_analytics_github/dbt_project/models/staging/stg_comments.sql", "content": "select\n *\nfrom {{ source('github', 'comments') }}" }, { "path": "developer_productivity_analytics_github/dbt_project/models/staging/stg_commits.sql", "content": "select\n *\nfrom {{ source('github', 'commits') }}" }, { "path": "developer_productivity_analytics_github/dbt_project/models/staging/stg_issues.sql", "content": "select\n *\nfrom {{ source('github', 'issues') }}" }, { "path": "developer_productivity_analytics_github/dbt_project/models/staging/stg_organizations.sql", "content": "select\n *\nfrom {{ source('github', 'organizations') }}" }, { "path": "developer_productivity_analytics_github/dbt_project/models/staging/stg_pull_requests.sql", "content": "select\n *\nfrom {{ source('github', 'pull_requests') }}" }, { "path": "developer_productivity_analytics_github/dbt_project/models/staging/stg_repositories.sql", "content": "select\n *\nfrom {{ source('github', 'repositories') }}" }, { "path": "developer_productivity_analytics_github/dbt_project/models/staging/stg_review_comments.sql", "content": "select\n *\nfrom {{ source('github', 'review_comments') }}" }, { "path": "developer_productivity_analytics_github/dbt_project/models/staging/stg_reviews.sql", "content": "select\n *\nfrom {{ source('github', 'reviews') }}" }, { "path": "developer_productivity_analytics_github/dbt_project/models/staging/stg_stargazers.sql", "content": "select\n *\nfrom {{ source('github', 'stargazers') }}" }, { "path": "developer_productivity_analytics_github/dbt_project/models/staging/stg_tags.sql", "content": "select\n *\nfrom {{ source('github', 'tags') }}" }, { "path": "developer_productivity_analytics_github/dbt_project/models/staging/stg_teams.sql", "content": "select\n *\nfrom {{ source('github', 'teams') }}" }, { "path": "developer_productivity_analytics_github/dbt_project/models/staging/stg_users.sql", "content": "select\n *\nfrom {{ source('github', 'users') }}\n" }, { "path": "developer_productivity_analytics_github/dbt_project/profiles.yml", "content": "dbt_project:\n outputs:\n dev:\n dataset: transformed_data\n job_execution_timeout_seconds: 300\n job_retries: 1\n # Use an env variable to indicate your JSON key file path\n keyfile: \"{{ env_var('DBT_BIGQUERY_KEYFILE_PATH', '') }}\"\n location: US\n method: service-account\n priority: interactive\n # Indicate your BigQuery project ID\n project: \"{{ env_var('BIGQUERY_PROJECT_ID', '') }}\"\n threads: 1\n type: bigquery\n target: dev" }, { "path": "developer_productivity_analytics_github/dbt_project/snapshots/.gitkeep", "content": "" }, { "path": "developer_productivity_analytics_github/dbt_project/tests/.gitkeep", "content": "" }, { "path": "developer_productivity_analytics_github/infra/.gitignore", "content": "# Local .terraform directories\n**/.terraform/*\n\n# .tfstate files\n*.tfstate\n*.tfstate.*\n\n# Crash log files\ncrash.log\ncrash.*.log\n\n# Exclude all .tfvars files, which are likely to contain sensitive data, such as\n# password, private keys, and other secrets. These should not be part of version \n# control as they are data points which are potentially sensitive and subject \n# to change depending on the environment.\n*.tfvars\n*.tfvars.json\n\n# Ignore override files as they are usually used to override resources locally and so\n# are not checked in\noverride.tf\noverride.tf.json\n*_override.tf\n*_override.tf.json\n\n# Include override files you do wish to add to version control using negated pattern\n# !example_override.tf\n\n# Include tfplan files to ignore the plan output of command: terraform plan -out=tfplan\n# example: *tfplan*\n\n# Ignore CLI configuration files\n.terraformrc\nterraform.rc" }, { "path": "developer_productivity_analytics_github/infra/airbyte/.terraform.lock.hcl", "content": "# This file is maintained automatically by \"terraform init\".\n# Manual edits may be lost in future updates.\n\nprovider \"registry.terraform.io/airbytehq/airbyte\" {\n version = \"0.3.3\"\n constraints = \"0.3.3\"\n hashes = [\n \"h1:0LmuAc5LvlMuOUPtNEaCAh9FHrV/C877bDJhm9Lz8MU=\",\n \"zh:0efa470b34d9b912b47efe4469c51713bfc3c2413e52c17e1e903f2a3cddb2f6\",\n \"zh:1bddd69fa2c2d4f3e239d60555446df9bc4ce0c0cabbe7e092fe1d44989ab004\",\n \"zh:2e20540403a0010007b53456663fb037b24e30f6c8943f65da1bcf7fa4dfc8a6\",\n \"zh:2f415369ad884e8b7115a5c5ff229d052f7af1fca27abbfc8ebef379ed11aec4\",\n \"zh:46fd9a906f4b6461112dcc5a5aa01a3fcd7a19a72d4ad0b2e37790da37701fe1\",\n \"zh:83503ebb77bb6d6941c42ba323cf22380d08a1506554a2dcc8ac54e74c0886a1\",\n \"zh:890df766e9b839623b1f0437355032a3c006226a6c200cd911e15ee1a9014e9f\",\n \"zh:8fd770eff726826d3a63b9e3733c5455b5cde004027b04ee3f75888eb8538c90\",\n \"zh:b0fc890ed4f9b077bf70ed121cc3550e7a07d16e7798ad517623274aa62ad7b0\",\n \"zh:c2a01612362da9b73cd5958f281e1aa7ff09af42182e463097d11ed78e778e72\",\n \"zh:c64b2bb1887a0367d64ba3393d4b3a16c418cf5b1792e2e7aae7c0b5413eb334\",\n \"zh:ce14ebbf0ed91913ec62655a511763dec62b5779de9a209bd6f1c336640cddc0\",\n \"zh:e0662ca837eee10f7733ea9a501d995281f56bd9b410ae13ad03eb106011db14\",\n \"zh:e103d480fc6066004bc98e9e04a141a1f55b918cc2912716beebcc6fc4c872fb\",\n \"zh:e2507049098f0f1b21cb56870f4a5ef624bcf6d3959e5612eada1f8117341648\",\n ]\n}\n" }, { "path": "developer_productivity_analytics_github/infra/airbyte/main.tf", "content": "\n// source\nresource \"airbyte_source_github\" \"my_source_github\" {\n configuration = {\n credentials = {\n source_github_authentication_personal_access_token = {\n personal_access_token = var.personal_access_token\n }\n }\n repository = var.repository\n source_type = \"github\"\n start_date = \"2023-09-01T00:00:00Z\"\n }\n name = \"your_name\"\n workspace_id = var.workspace_id\n}\n\n\n// destination\nresource \"airbyte_destination_bigquery\" \"bigquery\" {\n configuration = {\n dataset_id = var.dataset_id\n dataset_location = \"US\"\n destination_type = \"bigquery\"\n project_id = var.project_id\n credentials_json = var.credentials_json\n loading_method = {\n destination_bigquery_loading_method_standard_inserts = {\n method = \"Standard\"\n }\n }\n }\n name = \"BigQuery\"\n workspace_id = var.workspace_id\n}\n\n\n// connection\nresource \"airbyte_connection\" \"github_bigquery\" {\n name = \"Github to bigquery\"\n source_id = airbyte_source_github.my_source_github.source_id\n destination_id = airbyte_destination_bigquery.bigquery.destination_id\n configurations = {\n streams = [\n {\n name = \"users\"\n },\n {\n name = \"teams\"\n },\n {\n name = \"tags\"\n },\n {\n name = \"stargazers\"\n },\n {\n name = \"repositories\"\n },\n {\n name = \"pull_requests\"\n },\n {\n name = \"organizations\"\n },\n {\n name = \"issues\"\n },\n {\n name = \"commits\"\n },\n {\n name = \"comments\"\n }, \n {\n name = \"branches\"\n },\n {\n name = \"reviews\"\n },\n {\n name = \"review_comments\"\n },\n {\n name = \"collaborators\"\n }\n ]\n }\n}\n" }, { "path": "developer_productivity_analytics_github/infra/airbyte/provider.tf", "content": "// Airbyte Terraform provider documentation: https://registry.terraform.io/providers/airbytehq/airbyte/latest/docs\n\nterraform {\n required_providers {\n airbyte = {\n source = \"airbytehq/airbyte\"\n version = \"0.3.3\"\n }\n }\n}\n\nprovider \"airbyte\" {\n // If running locally (Airbyte OSS) with docker-compose using the airbyte-proxy, \n // include the actual password/username you've set up (or use the defaults below)\n username = \"airbyte\"\n password = \"password\"\n\n // if running locally (Airbyte OSS), include the server url to the airbyte-api-server\n server_url = \"http://localhost:8006/v1/\"\n}\n" }, { "path": "developer_productivity_analytics_github/infra/airbyte/variables.tf", "content": "variable \"personal_access_token\" {\n type = string\n}\n\nvariable \"project_id\" {\n type = string\n}\n\nvariable \"workspace_id\" {\n type = string\n}\n\nvariable \"dataset_id\" {\n type = string\n}\n\nvariable \"credentials_json\" {\n type = string\n}\n\nvariable \"repository\" {\n type = string\n}\n\n\n\n\n\n\n\n" }, { "path": "developer_productivity_analytics_github/orchestration/orchestration/__init__.py", "content": "" }, { "path": "developer_productivity_analytics_github/orchestration/orchestration/assets.py", "content": "import os\nfrom dagster import OpExecutionContext\nfrom dagster_dbt import DbtCliResource, dbt_assets\nfrom dagster_airbyte import AirbyteResource, load_assets_from_airbyte_instance\n\nfrom .constants import dbt_manifest_path\n\n\n@dbt_assets(manifest=dbt_manifest_path)\ndef dbt_project_dbt_assets(context: OpExecutionContext, dbt: DbtCliResource):\n yield from dbt.cli([\"build\"], context=context).stream()\n\n\nairbyte_instance = AirbyteResource(\n host=\"localhost\",\n port=\"8000\",\n # If using basic auth, include username and password:\n username=\"airbyte\",\n password=\"password\"\n)\n\nairbyte_assets = load_assets_from_airbyte_instance(airbyte_instance,)\n" }, { "path": "developer_productivity_analytics_github/orchestration/orchestration/constants.py", "content": "import os\nfrom pathlib import Path\n\nfrom dagster_dbt import DbtCliResource\n\ndbt_project_dir = Path(__file__).joinpath(\"..\", \"..\", \"..\", \"dbt_project\").resolve()\ndbt = DbtCliResource(project_dir=os.fspath(dbt_project_dir))\n\n# If DAGSTER_DBT_PARSE_PROJECT_ON_LOAD is set, a manifest will be created at runtime.\n# Otherwise, we expect a manifest to be present in the project's target directory.\nif os.getenv(\"DAGSTER_DBT_PARSE_PROJECT_ON_LOAD\"):\n dbt_parse_invocation = dbt.cli([\"parse\"], manifest={}).wait()\n dbt_manifest_path = dbt_parse_invocation.target_path.joinpath(\"manifest.json\")\nelse:\n dbt_manifest_path = dbt_project_dir.joinpath(\"target\", \"manifest.json\")" }, { "path": "developer_productivity_analytics_github/orchestration/orchestration/definitions.py", "content": "import os\n\nfrom dagster import Definitions\nfrom dagster_dbt import DbtCliResource\n\nfrom .assets import dbt_project_dbt_assets, airbyte_assets\nfrom .constants import dbt_project_dir\nfrom .schedules import schedules\n\ndefs = Definitions(\n assets=[dbt_project_dbt_assets, airbyte_assets],\n schedules=schedules,\n resources={\n \"dbt\": DbtCliResource(project_dir=os.fspath(dbt_project_dir)),\n },\n)\n" }, { "path": "developer_productivity_analytics_github/orchestration/orchestration/schedules.py", "content": "\"\"\"\nTo add a daily schedule that materializes your dbt assets, uncomment the following lines.\n\"\"\"\nfrom dagster_dbt import build_schedule_from_dbt_selection\n\nfrom .assets import dbt_project_dbt_assets\n\nschedules = [\n # build_schedule_from_dbt_selection(\n # [dbt_project_dbt_assets],\n # job_name=\"materialize_dbt_models\",\n # cron_schedule=\"0 0 * * *\",\n # dbt_select=\"fqn:*\",\n # ),\n]\n" }, { "path": "developer_productivity_analytics_github/orchestration/pyproject.toml", "content": "[build-system]\nrequires = [\"setuptools\"]\nbuild-backend = \"setuptools.build_meta\"\n\n[tool.dagster]\nmodule_name = \"orchestration.definitions\"\ncode_location_name = \"orchestration\"" }, { "path": "developer_productivity_analytics_github/orchestration/setup.py", "content": "from setuptools import find_packages, setup\n\nsetup(\n name=\"orchestration\",\n version=\"0.0.1\",\n packages=find_packages(),\n install_requires=[\n \"dagster\",\n \"dagster-cloud\",\n \"dagster-dbt\",\n \"dbt-core>=1.4.0\",\n \"dbt-bigquery\",\n ],\n extras_require={\n \"dev\": [\n \"dagster-webserver\",\n ]\n },\n)" }, { "path": "developer_productivity_analytics_github/setup.py", "content": "from setuptools import find_packages, setup\n\nsetup(\n name=\"airbyte-dbt-dagster\",\n packages=find_packages(),\n install_requires=[\n \"dbt-bigquery\",\n \"dagster\",\n \"dagster-cloud\",\n \"dagster-dbt\",\n \"dagster-airbyte\",\n ],\n extras_require={\"dev\": [\"dagit\", \"pytest\"]},\n)" }, { "path": "ecommerce_analytics_bigquery/.gitignore", "content": "# Byte-compiled / optimized / DLL files\n__pycache__/\n*.py[cod]\n*$py.class\n\n# C extensions\n*.so\n\n# Distribution / packaging\n.Python\nbuild/\ndevelop-eggs/\ndist/\ndownloads/\neggs/\n.eggs/\nlib/\nlib64/\nparts/\nsdist/\nvar/\nwheels/\nshare/python-wheels/\n*.egg-info/\n.installed.cfg\n*.egg\nMANIFEST\n\n# PyInstaller\n# Usually these files are written by a python script from a template\n# before PyInstaller builds the exe, so as to inject date/other infos into it.\n*.manifest\n*.spec\n\n# Installer logs\npip-log.txt\npip-delete-this-directory.txt\n\n# Unit test / coverage reports\nhtmlcov/\n.tox/\n.nox/\n.coverage\n.coverage.*\n.cache\nnosetests.xml\ncoverage.xml\n*.cover\n*.py,cover\n.hypothesis/\n.pytest_cache/\ncover/\n\n# Translations\n*.mo\n*.pot\n\n# Django stuff:\n*.log\nlocal_settings.py\ndb.sqlite3\ndb.sqlite3-journal\n\n# Flask stuff:\ninstance/\n.webassets-cache\n\n# Scrapy stuff:\n.scrapy\n\n# Sphinx documentation\ndocs/_build/\n\n# PyBuilder\n.pybuilder/\ntarget/\n\n# Jupyter Notebook\n.ipynb_checkpoints\n\n# IPython\nprofile_default/\nipython_config.py\n\n# pyenv\n# For a library or package, you might want to ignore these files since the code is\n# intended to run in multiple environments; otherwise, check them in:\n# .python-version\n\n# pipenv\n# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.\n# However, in case of collaboration, if having platform-specific dependencies or dependencies\n# having no cross-platform support, pipenv may install dependencies that don't work, or not\n# install all needed dependencies.\n#Pipfile.lock\n\n# poetry\n# Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.\n# This is especially recommended for binary packages to ensure reproducibility, and is more\n# commonly ignored for libraries.\n# https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control\n#poetry.lock\n\n# pdm\n# Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control.\n#pdm.lock\n# pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it\n# in version control.\n# https://pdm.fming.dev/#use-with-ide\n.pdm.toml\n\n# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm\n__pypackages__/\n\n# Celery stuff\ncelerybeat-schedule\ncelerybeat.pid\n\n# SageMath parsed files\n*.sage.py\n\n# Environments\n.env\n.venv\nenv/\nvenv/\nENV/\nenv.bak/\nvenv.bak/\n\n# Spyder project settings\n.spyderproject\n.spyproject\n\n# Rope project settings\n.ropeproject\n\n# mkdocs documentation\n/site\n\n# mypy\n.mypy_cache/\n.dmypy.json\ndmypy.json\n\n# Pyre type checker\n.pyre/\n\n# pytype static type analyzer\n.pytype/\n\n# Cython debug symbols\ncython_debug/\n\n# PyCharm\n# JetBrains specific template is maintained in a separate JetBrains.gitignore that can\n# be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore\n# and can be added to the global gitignore or merged into this file. For a more nuclear\n# option (not recommended) you can uncomment the following to ignore the entire idea folder.\n#.idea/\n\n#Desktop Services Store\n.DS_Store" }, { "path": "ecommerce_analytics_bigquery/README.md", "content": "# E-commerce Analytics Stack with Airbyte, dbt, Dagster and BigQuery\n\nBelow is a visual representation of how data flows through our integrated tools in this Quickstart. This comes from Dagster's global asset lineage view:\n\n![Global Asset Lineage](./assets/Global_Asset_Lineage.svg)\n\n## Table of Contents\n\n- [Prerequisites](#prerequisites)\n- [Setting an environment for your project](#1-setting-an-environment-for-your-project)\n- [Setting Up BigQuery to work with Airbyte and dbt](#2-setting-up-bigquery)\n- [Setting Up Airbyte Connectors](#3-setting-up-airbyte-connectors)\n- [Setting Up the dbt Project](#4-setting-up-the-dbt-project)\n- [Orchestrating with Dagster](#5-orchestrating-with-dagster)\n- [Next Steps](#next-steps)\n\n## Prerequisites\n\nBefore you embark on this integration, ensure you have the following set up and ready:\n\n1. **Python 3.10 or later**: If not installed, download and install it from [Python's official website](https://www.python.org/downloads/).\n\n2. **Docker and Docker Compose (Docker Desktop)**: Install [Docker](https://docs.docker.com/get-docker/) following the official documentation for your specific OS.\n\n3. **Airbyte OSS version**: Deploy the open-source version of Airbyte locally. Follow the installation instructions from the [Airbyte Documentation](https://docs.airbyte.com/quickstart/deploy-airbyte/).\n\n4. **Terraform (Optional)**: Terraform will help you provision and manage the Airbyte resources. If you haven't installed it, follow the [official Terraform installation guide](https://developer.hashicorp.com/terraform/tutorials/aws-get-started/install-cli). This is an optional step because you can also create and manage Airbyte resources via the UI.\n\n5. **Google Cloud account with BigQuery**: You will also need to add the necessary permissions to allow Airbyte and dbt to access the data in BigQuery. A step-by-step guide is provided [below](#2-setting-up-bigquery).\n\n## 1. Setting an environment for your project\n\nGet the project up and running on your local machine by following these steps:\n\n1. **Clone the repository (Clone only this quickstart)**: \n ```bash\n git clone --filter=blob:none --sparse https://github.com/airbytehq/quickstarts.git\n ```\n\n ```bash\n cd quickstarts\n ```\n\n ```bash\n git sparse-checkout add ecommerce_analytics_bigquery\n ```\n\n2. **Navigate to the directory**: \n ```bash\n cd ecommerce_analytics_bigquery\n ```\nAt this point you can view the code in your preferred IDE. For example, if you’re using Visual Studio Code, you can execute `code .` to open the code. \n\n3. **Set Up a Virtual Environment**: \nYou can use the following commands, just make sure to adapt to your specific python installation.\n\n - For Linux and Mac:\n ```bash\n python3 -m venv venv\n source venv/bin/activate\n ```\n\n - For Windows:\n ```bash\n python -m venv venv\n .\\venv\\Scripts\\activate\n ```\n\n4. **Install Dependencies**: \n ```bash\n pip install -e \".[dev]\"\n ```\n\n## 2. Setting Up BigQuery\n\n#### 1. **Create a Google Cloud Project**\n - If you have a Google Cloud project, you can skip this step.\n - Go to the [Google Cloud Console](https://console.cloud.google.com/).\n - Click on the \"Select a project\" dropdown at the top right and select \"New Project\".\n - Give your project a name and follow the steps to create it.\n\n#### 2. **Create BigQuery Datasets**\n - In the Google Cloud Console, go to BigQuery.\n - Make two new datasets: `raw_data` for Airbyte and `transformed_data` for dbt.\n - If you pick different names, remember to change the names in the code too.\n \n **How to create a dataset:**\n - In the left sidebar, click on your project name.\n - Click “Create Dataset”.\n - Enter the dataset ID (either `raw_data` or `transformed_data`).\n - Click \"Create Dataset\".\n\n#### 3. **Create Service Accounts and Assign Roles**\n - Go to “IAM & Admin” > “Service accounts” in the Google Cloud Console.\n - Click “Create Service Account”.\n - Name your service account (like `airbyte-service-account`).\n - Assign the “BigQuery Data Editor” and “BigQuery Job User” roles to the service account.\n - Follow the same steps to make another service account for dbt (like `dbt-service-account`) and assign the roles.\n\n **How to create a service account and assign roles:**\n - While creating the service account, under the “Grant this service account access to project” section, click the “Role” dropdown.\n - Choose the “BigQuery Data Editor” and “BigQuery Job User” roles.\n - Finish the creation process.\n \n#### 4. **Generate JSON Keys for Service Accounts**\n - For both service accounts, make a JSON key to let the service accounts sign in.\n \n **How to generate JSON key:**\n - Find the service account in the “Service accounts” list.\n - Click on the service account name.\n - In the “Keys” section, click “Add Key” and pick JSON.\n - The key will download automatically. Keep it safe and don’t share it.\n - Do this for the other service account too.\n\n## 3. Setting Up Airbyte Connectors\n\nTo set up your Airbyte connectors, you can choose to do it via Terraform, or the UI. Choose one of the two following options.\n\n### 3.1. Setting Up Airbyte Connectors with Terraform\n\nAirbyte allows you to create connectors for sources and destinations via Terraform, facilitating data synchronization between various platforms. Here's how you can set this up:\n\n1. **Navigate to the Airbyte Configuration Directory**:\n\n ```bash\n cd infra/airbyte\n ```\n\n2. **Modify Configuration Files**:\n\n Within the `infra/airbyte` directory, you'll find three crucial Terraform files:\n - `provider.tf`: Defines the Airbyte provider.\n - `main.tf`: Contains the main configuration for creating Airbyte resources.\n - `variables.tf`: Holds various variables, including credentials.\n\n Adjust the configurations in these files to suit your project's needs: \n\n - Provide credentials for your BigQuery connection in the `main.tf` file.\n - `dataset_id`: The name of the BigQuery dataset where Airbyte will load data. In this case, enter “raw_data”.\n - `project_id`: Your BigQuery project ID.\n - `credentials_json`: The contents of the service account JSON file. You should input a string, so you need to convert the JSON content to string beforehand.\n - `workspace_id`: Your Airbyte workspace ID, which can be found in the webapp url. For example, in this url: http://localhost:8000/workspaces/910ab70f-0a67-4d25-a983-999e99e1e395/ the workspace id would be `910ab70f-0a67-4d25-a983-999e99e1e395`.\n\n - Alternatively, you can utilize the `variables.tf` file to manage these credentials:\n - You’ll be prompted to enter the credentials when you execute `terraform plan` and `terraform apply`. If going for this option, just move to the next step. If you don’t want to use variables, remove them from the file.\n\n3. **Initialize Terraform**:\n \n This step prepares Terraform to create the resources defined in your configuration files.\n ```bash\n terraform init\n ```\n\n4. **Review the Plan**:\n\n Before applying any changes, review the plan to understand what Terraform will do.\n ```bash\n terraform plan\n ```\n\n5. **Apply Configuration**:\n\n After reviewing and confirming the plan, apply the Terraform configurations to create the necessary Airbyte resources.\n ```bash\n terraform apply\n ```\n\n6. **Verify in Airbyte UI**:\n\n Once Terraform completes its tasks, navigate to the [Airbyte UI](http://localhost:8000/). Here, you should see your source and destination connectors, as well as the connection between them, set up and ready to go 🎉.\n\n### 3.2. Setting Up Airbyte Connectors Using the UI\n\nStart by launching the Airbyte UI by going to http://localhost:8000/ in your browser. Then:\n\n1. **Create a source**:\n\n - Go to the Sources tab and click on `+ New source`.\n - Search for “faker” using the search bar and select `Sample Data (Faker)`.\n - Adjust the Count and optional fields as needed for your use case. You can also leave as is. \n - Click on `Set up source`.\n\n2. **Create a destination**:\n\n - Go to the Destinations tab and click on `+ New destination`.\n - Search for “bigquery” using the search bar and select `BigQuery`.\n - Enter the connection details as needed.\n - For simplicity, you can use `Standard Inserts` as the loading method.\n - In the `Service Account Key JSON` field, enter the contents of the JSON file. Yes, the full JSON.\n - Click on `Set up destination`.\n\n3. **Create a connection**:\n\n - Go to the Connections tab and click on `+ New connection`.\n - Select the source and destination you just created.\n - Enter the connection details as needed.\n - Click on `Set up connection`.\n\nThat’s it! Your connection is set up and ready to go! 🎉 \n\n## 4. Setting Up the dbt Project\n\n[dbt (data build tool)](https://www.getdbt.com/) allows you to transform your data by writing, documenting, and executing SQL workflows. Setting up the dbt project requires specifying connection details for your data platform, in this case, BigQuery. Here’s a step-by-step guide to help you set this up:\n\n1. **Navigate to the dbt Project Directory**:\n\n Move to the directory containing the dbt configuration:\n ```bash\n cd ../../dbt_project\n ```\n\n2. **Update Connection Details**:\n\n - You'll find a `profiles.yml` file within the directory. This file contains configurations for dbt to connect with your data platform. Update this file with your BigQuery connection details. Specifically, you need to update the Service Account JSON file path and your BigQuery project ID.\n - Provide your BigQuery project ID in the `database` field of the `dbt_project/models/sources/faker_sources.yml` file.\n\nIf you want to avoid hardcoding credentials in the `profiles.yml` file, you can leverage environment variables. An example of how to use them in this file is provided for the `keyfile` key.\n\n3. **Test the Connection**:\n\n Once you’ve updated the connection details, you can test the connection to your BigQuery instance using:\n ```bash\n dbt debug\n ```\nIf everything is set up correctly, this command should report a successful connection to BigQuery 🎉.\n\n## 5. Orchestrating with Dagster\n\n[Dagster](https://dagster.io/) is a modern data orchestrator designed to help you build, test, and monitor your data workflows. In this section, we'll walk you through setting up Dagster to oversee both the Airbyte and dbt workflows:\n\n1. **Navigate to the Orchestration Directory**:\n\n Switch to the directory containing the Dagster orchestration configurations:\n ```bash\n cd ../orchestration\n ```\n\n2. **Set Environment Variables**:\n\n Dagster requires certain environment variables to be set to interact with other tools like dbt and Airbyte. Set the following variables:\n\n ```bash\n export DAGSTER_DBT_PARSE_PROJECT_ON_LOAD=1\n export AIRBYTE_PASSWORD=password\n ```\n \n Note: The `AIRBYTE_PASSWORD` is set to `password` as a default for local Airbyte instances. If you've changed this during your Airbyte setup, ensure you use the appropriate password here.\n\n3. **Launch the Dagster UI**:\n\n With the environment variables in place, kick-start the Dagster UI:\n ```bash\n dagster dev\n ```\n\n4. **Access Dagster in Your Browser**:\n\n Open your browser and navigate to:\n ```\n http://127.0.0.1:3000\n ```\nHere, you should see assets for both Airbyte and dbt. To get an overview of how these assets interrelate, click on `view global asset lineage` at the top right corner of the Dagster UI. This will give you a clear picture of the data lineage, visualizing how data flows between the tools.\n\n5. **Materialize Dagster Assets**:\n\n In the Dagster UI, click on `Materialize all`. This should trigger the full pipeline. First the Airbyte sync to extract data from Faker and load it into BigQuery, and then dbt to transform the raw data, materializing the `staging` and `marts` models.\n\nYou can go to the Airbyte UI and confirm a sync is running, and then, once the dbt jobs have run, go to your BigQuery console and check the views have been created in the `transformed data` dataset.\n\n## Next Steps\n\nCongratulations on deploying and running the E-commerce Analytics Quistart! 🎉 Here are some suggestions on what you can explore next to dive deeper and get more out of your project:\n\n### 1. **Explore the Data and Insights**\n - Dive into the datasets in BigQuery, run some queries, and explore the data you've collected and transformed. This is your chance to uncover insights and understand the data better!\n\n### 2. **Optimize Your dbt Models**\n - Review the transformations you’ve applied using dbt. Try optimizing the models or create new ones based on your evolving needs and insights you want to extract.\n\n### 3. **Expand Your Data Sources**\n - Add more data sources to Airbyte. Explore different types of sources available, and see how they can enrich your existing datasets and broaden your analytical capabilities.\n\n### 4. **Enhance Data Quality and Testing**\n - Implement data quality tests in dbt to ensure the reliability and accuracy of your transformations. Use dbt's testing features to validate your data and catch issues early on.\n\n### 5. **Automate and Monitor Your Pipelines**\n - Explore more advanced Dagster configurations and setups to automate your pipelines further and set up monitoring and alerting to be informed of any issues immediately.\n\n### 6. **Scale Your Setup**\n - Consider scaling your setup to handle more data, more sources, and more transformations. Optimize your configurations and resources to ensure smooth and efficient processing of larger datasets.\n\n### 7. **Contribute to the Community**\n - Share your learnings, optimizations, and new configurations with the community. Contribute to the respective tool’s communities and help others learn and grow.\n\n" }, { "path": "ecommerce_analytics_bigquery/dbt_project/.gitignore", "content": "\ntarget/\ndbt_packages/\nlogs/\n\n.user.yml\n" }, { "path": "ecommerce_analytics_bigquery/dbt_project/README.md", "content": "Welcome to your new dbt project!\n\n### Using the starter project\n\nTry running the following commands:\n- dbt run\n- dbt test\n\n\n### Resources:\n- Learn more about dbt [in the docs](https://docs.getdbt.com/docs/introduction)\n- Check out [Discourse](https://discourse.getdbt.com/) for commonly asked questions and answers\n- Join the [chat](https://community.getdbt.com/) on Slack for live discussions and support\n- Find [dbt events](https://events.getdbt.com) near you\n- Check out [the blog](https://blog.getdbt.com/) for the latest news on dbt's development and best practices\n" }, { "path": "ecommerce_analytics_bigquery/dbt_project/analyses/.gitkeep", "content": "" }, { "path": "ecommerce_analytics_bigquery/dbt_project/dbt_project.yml", "content": "\n# Name your project! Project names should contain only lowercase characters\n# and underscores. A good package name should reflect your organization's\n# name or the intended use of these models\nname: 'dbt_project'\nversion: '1.0.0'\nconfig-version: 2\n\n# This setting configures which \"profile\" dbt uses for this project.\nprofile: 'dbt_project'\n\n# These configurations specify where dbt should look for different types of files.\n# The `model-paths` config, for example, states that models in this project can be\n# found in the \"models/\" directory. You probably won't need to change these!\nmodel-paths: [\"models\"]\nanalysis-paths: [\"analyses\"]\ntest-paths: [\"tests\"]\nseed-paths: [\"seeds\"]\nmacro-paths: [\"macros\"]\nsnapshot-paths: [\"snapshots\"]\n\nclean-targets: # directories to be removed by `dbt clean`\n - \"target\"\n - \"dbt_packages\"\n\n\n# Configuring models\n# Full documentation: https://docs.getdbt.com/docs/configuring-models\n\n# In this example config, we tell dbt to build all models in the example/\n# directory as views. These settings can be overridden in the individual model\n# files using the `{{ config(...) }}` macro.\nmodels:\n dbt_project:\n # Config indicated by + and applies to all files under models/example/\n staging:\n +materialized: view\n marts:\n +materialized: view\n" }, { "path": "ecommerce_analytics_bigquery/dbt_project/macros/.gitkeep", "content": "" }, { "path": "ecommerce_analytics_bigquery/dbt_project/models/marts/product_popularity.sql", "content": "WITH base AS (\n SELECT \n product_id,\n COUNT(id) AS purchase_count\n FROM {{ ref('stg_purchases') }}\n GROUP BY 1\n)\n\nSELECT \n p.id,\n p.make,\n p.model,\n b.purchase_count\nFROM {{ ref('stg_products') }} p\nLEFT JOIN base b ON p.id = b.product_id\nORDER BY b.purchase_count DESC\n" }, { "path": "ecommerce_analytics_bigquery/dbt_project/models/marts/purchase_patterns.sql", "content": "SELECT \n user_id,\n product_id,\n purchased_at,\n added_to_cart_at,\n TIMESTAMP_DIFF(purchased_at, added_to_cart_at, SECOND) AS time_to_purchase_seconds,\n returned_at\nFROM {{ ref('stg_purchases') }}\n" }, { "path": "ecommerce_analytics_bigquery/dbt_project/models/marts/user_demographics.sql", "content": "WITH base AS (\n SELECT \n id AS user_id,\n gender,\n academic_degree,\n nationality,\n age\n FROM {{ ref('stg_users') }}\n)\n\nSELECT \n gender,\n academic_degree,\n nationality,\n AVG(age) AS average_age,\n COUNT(user_id) AS user_count\nFROM base\nGROUP BY 1, 2, 3\n" }, { "path": "ecommerce_analytics_bigquery/dbt_project/models/sources/faker_sources.yml", "content": "version: 2\n\nsources:\n - name: faker\n # Use your BigQuery project ID\n database: your_project_id\n # Use your BigQuery dataset name\n schema: raw_data\n\n tables:\n - name: users\n description: \"Simulated user data from the Faker connector.\"\n columns:\n - name: id\n description: \"Unique identifier for the user.\"\n - name: address\n - name: occupation\n - name: gender\n - name: academic_degree\n - name: weight\n - name: created_at\n - name: language\n - name: telephone\n - name: title\n - name: updated_at\n - name: nationality\n - name: blood_type\n - name: name\n - name: age\n - name: email\n - name: height\n - name: _airbyte_ab_id\n - name: _airbyte_emitted_at\n - name: _airbyte_normalized_at\n - name: _airbyte_users_hashid\n\n - name: products\n description: \"Simulated product data from the Faker connector.\"\n columns:\n - name: id\n description: \"Unique identifier for the product.\"\n - name: updated_at\n - name: year\n - name: price\n - name: created_at\n - name: model\n - name: make\n - name: _airbyte_ab_id\n - name: _airbyte_emitted_at\n - name: _airbyte_normalized_at\n - name: _airbyte_users_hashid\n\n - name: purchases\n description: \"Simulated purchase data from the Faker connector.\"\n columns:\n - name: id\n description: \"Unique identifier for the purchase.\"\n - name: updated_at\n - name: purchased_at\n - name: user_id\n - name: returned_at\n - name: product_id\n - name: created_at\n - name: added_to_cart_at\n - name: _airbyte_ab_id\n - name: _airbyte_emitted_at\n - name: _airbyte_normalized_at\n - name: _airbyte_users_hashid\n\n" }, { "path": "ecommerce_analytics_bigquery/dbt_project/models/staging/stg_products.sql", "content": "select\n id,\n year,\n price,\n model,\n make,\n created_at,\n updated_at,\n _airbyte_extracted_at,\nfrom {{ source('faker', 'products') }}\n" }, { "path": "ecommerce_analytics_bigquery/dbt_project/models/staging/stg_purchases.sql", "content": "select\n id,\n user_id,\n product_id,\n updated_at,\n purchased_at,\n returned_at,\n created_at,\n added_to_cart_at,\n _airbyte_extracted_at,\nfrom {{ source('faker', 'purchases') }}\n" }, { "path": "ecommerce_analytics_bigquery/dbt_project/models/staging/stg_users.sql", "content": "select\n id,\n gender,\n academic_degree,\n title,\n nationality,\n age,\n name,\n email,\n created_at,\n updated_at,\n _airbyte_extracted_at,\nfrom {{ source('faker', 'users') }}\n" }, { "path": "ecommerce_analytics_bigquery/dbt_project/profiles.yml", "content": "dbt_project:\n outputs:\n dev:\n dataset: transformed_data\n job_execution_timeout_seconds: 300\n job_retries: 1\n # Use an env variable to indicate your JSON key file path\n keyfile: \"{{ env_var('DBT_BIGQUERY_KEYFILE_PATH', '') }}\"\n location: US\n method: service-account\n priority: interactive\n # Indicate your BigQuery project ID\n project: your_project_id\n threads: 1\n type: bigquery\n target: dev" }, { "path": "ecommerce_analytics_bigquery/dbt_project/seeds/.gitkeep", "content": "" }, { "path": "ecommerce_analytics_bigquery/dbt_project/snapshots/.gitkeep", "content": "" }, { "path": "ecommerce_analytics_bigquery/dbt_project/tests/.gitkeep", "content": "" }, { "path": "ecommerce_analytics_bigquery/infra/.gitignore", "content": "# Local .terraform directories\n**/.terraform/*\n\n# .tfstate files\n*.tfstate\n*.tfstate.*\n\n# Crash log files\ncrash.log\ncrash.*.log\n\n# Exclude all .tfvars files, which are likely to contain sensitive data, such as\n# password, private keys, and other secrets. These should not be part of version \n# control as they are data points which are potentially sensitive and subject \n# to change depending on the environment.\n*.tfvars\n*.tfvars.json\n\n# Ignore override files as they are usually used to override resources locally and so\n# are not checked in\noverride.tf\noverride.tf.json\n*_override.tf\n*_override.tf.json\n\n# Include override files you do wish to add to version control using negated pattern\n# !example_override.tf\n\n# Include tfplan files to ignore the plan output of command: terraform plan -out=tfplan\n# example: *tfplan*\n\n# Ignore CLI configuration files\n.terraformrc\nterraform.rc" }, { "path": "ecommerce_analytics_bigquery/infra/airbyte/.terraform.lock.hcl", "content": "# This file is maintained automatically by \"terraform init\".\n# Manual edits may be lost in future updates.\n\nprovider \"registry.terraform.io/airbytehq/airbyte\" {\n version = \"0.3.3\"\n constraints = \"0.3.3\"\n hashes = [\n \"h1:a6g5uWP/pt1/popVNlKwnTssWNfdYY4KVFPMisN/yvU=\",\n \"zh:0efa470b34d9b912b47efe4469c51713bfc3c2413e52c17e1e903f2a3cddb2f6\",\n \"zh:1bddd69fa2c2d4f3e239d60555446df9bc4ce0c0cabbe7e092fe1d44989ab004\",\n \"zh:2e20540403a0010007b53456663fb037b24e30f6c8943f65da1bcf7fa4dfc8a6\",\n \"zh:2f415369ad884e8b7115a5c5ff229d052f7af1fca27abbfc8ebef379ed11aec4\",\n \"zh:46fd9a906f4b6461112dcc5a5aa01a3fcd7a19a72d4ad0b2e37790da37701fe1\",\n \"zh:83503ebb77bb6d6941c42ba323cf22380d08a1506554a2dcc8ac54e74c0886a1\",\n \"zh:890df766e9b839623b1f0437355032a3c006226a6c200cd911e15ee1a9014e9f\",\n \"zh:8fd770eff726826d3a63b9e3733c5455b5cde004027b04ee3f75888eb8538c90\",\n \"zh:b0fc890ed4f9b077bf70ed121cc3550e7a07d16e7798ad517623274aa62ad7b0\",\n \"zh:c2a01612362da9b73cd5958f281e1aa7ff09af42182e463097d11ed78e778e72\",\n \"zh:c64b2bb1887a0367d64ba3393d4b3a16c418cf5b1792e2e7aae7c0b5413eb334\",\n \"zh:ce14ebbf0ed91913ec62655a511763dec62b5779de9a209bd6f1c336640cddc0\",\n \"zh:e0662ca837eee10f7733ea9a501d995281f56bd9b410ae13ad03eb106011db14\",\n \"zh:e103d480fc6066004bc98e9e04a141a1f55b918cc2912716beebcc6fc4c872fb\",\n \"zh:e2507049098f0f1b21cb56870f4a5ef624bcf6d3959e5612eada1f8117341648\",\n ]\n}\n" }, { "path": "ecommerce_analytics_bigquery/infra/airbyte/main.tf", "content": "// Airbyte Terraform provider documentation: https://registry.terraform.io/providers/airbytehq/airbyte/latest/docs\n\n// Sources\nresource \"airbyte_source_faker\" \"faker\" {\n configuration = {\n always_updated = false\n count = 1000\n parallelism = 9\n records_per_slice = 10\n seed = 6\n source_type = \"faker\"\n }\n name = \"Faker\"\n workspace_id = var.workspace_id\n}\n\n// Destinations\nresource \"airbyte_destination_bigquery\" \"bigquery\" {\n configuration = {\n dataset_id = var.dataset_id\n dataset_location = \"US\"\n destination_type = \"bigquery\"\n project_id = var.project_id\n credentials_json = var.credentials_json\n loading_method = {\n destination_bigquery_loading_method_standard_inserts = {\n method = \"Standard\"\n }\n }\n }\n name = \"BigQuery\"\n workspace_id = var.workspace_id\n}\n\n// Connections\nresource \"airbyte_connection\" \"faker_to_bigquery\" {\n name = \"Faker to BigQuery\"\n source_id = airbyte_source_faker.faker.source_id\n destination_id = airbyte_destination_bigquery.bigquery.destination_id\n configurations = {\n streams = [\n {\n name = \"users\"\n },\n {\n name = \"products\"\n },\n {\n name = \"purchases\"\n },\n ]\n }\n}" }, { "path": "ecommerce_analytics_bigquery/infra/airbyte/provider.tf", "content": "// Airbyte Terraform provider documentation: https://registry.terraform.io/providers/airbytehq/airbyte/latest/docs\n\nterraform {\n required_providers {\n airbyte = {\n source = \"airbytehq/airbyte\"\n version = \"0.3.3\"\n }\n }\n}\n\nprovider \"airbyte\" {\n // If running locally (Airbyte OSS) with docker-compose using the airbyte-proxy, \n // include the actual password/username you've set up (or use the defaults below)\n username = \"airbyte\"\n password = \"password\"\n \n // if running locally (Airbyte OSS), include the server url to the airbyte-api-server\n server_url = \"http://localhost:8006/v1\"\n}" }, { "path": "ecommerce_analytics_bigquery/infra/airbyte/variables.tf", "content": "variable \"workspace_id\" {\n type = string\n}\n\nvariable \"dataset_id\" {\n type = string\n}\n\nvariable \"project_id\" {\n type = string\n}\n\nvariable \"credentials_json\" {\n type = string\n}\n\n\n\n\n" }, { "path": "ecommerce_analytics_bigquery/orchestration/orchestration/__init__.py", "content": "" }, { "path": "ecommerce_analytics_bigquery/orchestration/orchestration/assets.py", "content": "import os\nfrom dagster import OpExecutionContext\nfrom dagster_dbt import DbtCliResource, dbt_assets\nfrom dagster_airbyte import AirbyteResource, load_assets_from_airbyte_instance\n\nfrom .constants import dbt_manifest_path\n\n@dbt_assets(manifest=dbt_manifest_path)\ndef dbt_project_dbt_assets(context: OpExecutionContext, dbt: DbtCliResource):\n yield from dbt.cli([\"build\"], context=context).stream()\n\nairbyte_instance = AirbyteResource(\n host=\"localhost\",\n port=\"8000\",\n # If using basic auth, include username and password:\n username=\"airbyte\",\n password=os.getenv(\"AIRBYTE_PASSWORD\")\n)\n\nairbyte_assets = load_assets_from_airbyte_instance(airbyte_instance, key_prefix=\"faker\")" }, { "path": "ecommerce_analytics_bigquery/orchestration/orchestration/constants.py", "content": "import os\nfrom pathlib import Path\n\nfrom dagster_dbt import DbtCliResource\n\ndbt_project_dir = Path(__file__).joinpath(\"..\", \"..\", \"..\", \"dbt_project\").resolve()\ndbt = DbtCliResource(project_dir=os.fspath(dbt_project_dir))\n\n# If DAGSTER_DBT_PARSE_PROJECT_ON_LOAD is set, a manifest will be created at runtime.\n# Otherwise, we expect a manifest to be present in the project's target directory.\nif os.getenv(\"DAGSTER_DBT_PARSE_PROJECT_ON_LOAD\"):\n dbt_parse_invocation = dbt.cli([\"parse\"], manifest={}).wait()\n dbt_manifest_path = dbt_parse_invocation.target_path.joinpath(\"manifest.json\")\nelse:\n dbt_manifest_path = dbt_project_dir.joinpath(\"target\", \"manifest.json\")" }, { "path": "ecommerce_analytics_bigquery/orchestration/orchestration/definitions.py", "content": "import os\n\nfrom dagster import Definitions\nfrom dagster_dbt import DbtCliResource\n\nfrom .assets import dbt_project_dbt_assets, airbyte_assets\nfrom .constants import dbt_project_dir\nfrom .schedules import schedules\n\ndefs = Definitions(\n assets=[dbt_project_dbt_assets, airbyte_assets],\n schedules=schedules,\n resources={\n \"dbt\": DbtCliResource(project_dir=os.fspath(dbt_project_dir)),\n },\n)" }, { "path": "ecommerce_analytics_bigquery/orchestration/orchestration/schedules.py", "content": "\"\"\"\nTo add a daily schedule that materializes your dbt assets, uncomment the following lines.\n\"\"\"\nfrom dagster_dbt import build_schedule_from_dbt_selection\n\nfrom .assets import dbt_project_dbt_assets\n\nschedules = [\n# build_schedule_from_dbt_selection(\n# [dbt_project_dbt_assets],\n# job_name=\"materialize_dbt_models\",\n# cron_schedule=\"0 0 * * *\",\n# dbt_select=\"fqn:*\",\n# ),\n]" }, { "path": "ecommerce_analytics_bigquery/orchestration/pyproject.toml", "content": "[build-system]\nrequires = [\"setuptools\"]\nbuild-backend = \"setuptools.build_meta\"\n\n[tool.dagster]\nmodule_name = \"orchestration.definitions\"\ncode_location_name = \"orchestration\"" }, { "path": "ecommerce_analytics_bigquery/orchestration/setup.py", "content": "from setuptools import find_packages, setup\n\nsetup(\n name=\"orchestration\",\n version=\"0.0.1\",\n packages=find_packages(),\n install_requires=[\n \"dagster\",\n \"dagster-cloud\",\n \"dagster-dbt\",\n \"dbt-core>=1.4.0\",\n \"dbt-bigquery\",\n ],\n extras_require={\n \"dev\": [\n \"dagster-webserver\",\n ]\n },\n)" }, { "path": "ecommerce_analytics_bigquery/setup.py", "content": "from setuptools import find_packages, setup\n\nsetup(\n name=\"airbyte-dbt-dagster\",\n packages=find_packages(),\n install_requires=[\n \"dbt-bigquery\",\n \"dagster\",\n \"dagster-cloud\",\n \"dagster-dbt\",\n \"dagster-airbyte\",\n ],\n extras_require={\"dev\": [\"dagit\", \"pytest\"]},\n)" }, { "path": "elt_simplified_stack/.gitignore", "content": "# Byte-compiled / optimized / DLL files\n__pycache__/\n*.py[cod]\n*$py.class\n\n# C extensions\n*.so\n\n# Distribution / packaging\n.Python\nbuild/\ndevelop-eggs/\ndist/\ndownloads/\neggs/\n.eggs/\nlib/\nlib64/\nparts/\nsdist/\nvar/\nwheels/\nshare/python-wheels/\n*.egg-info/\n.installed.cfg\n*.egg\nMANIFEST\n\n# PyInstaller\n# Usually these files are written by a python script from a template\n# before PyInstaller builds the exe, so as to inject date/other infos into it.\n*.manifest\n*.spec\n\n# Installer logs\npip-log.txt\npip-delete-this-directory.txt\n\n# Unit test / coverage reports\nhtmlcov/\n.tox/\n.nox/\n.coverage\n.coverage.*\n.cache\nnosetests.xml\ncoverage.xml\n*.cover\n*.py,cover\n.hypothesis/\n.pytest_cache/\ncover/\n\n# Translations\n*.mo\n*.pot\n\n# Django stuff:\n*.log\nlocal_settings.py\ndb.sqlite3\ndb.sqlite3-journal\n\n# Flask stuff:\ninstance/\n.webassets-cache\n\n# Scrapy stuff:\n.scrapy\n\n# Sphinx documentation\ndocs/_build/\n\n# PyBuilder\n.pybuilder/\ntarget/\n\n# Jupyter Notebook\n.ipynb_checkpoints\n\n# IPython\nprofile_default/\nipython_config.py\n\n# pyenv\n# For a library or package, you might want to ignore these files since the code is\n# intended to run in multiple environments; otherwise, check them in:\n# .python-version\n\n# pipenv\n# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.\n# However, in case of collaboration, if having platform-specific dependencies or dependencies\n# having no cross-platform support, pipenv may install dependencies that don't work, or not\n# install all needed dependencies.\n#Pipfile.lock\n\n# poetry\n# Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.\n# This is especially recommended for binary packages to ensure reproducibility, and is more\n# commonly ignored for libraries.\n# https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control\n#poetry.lock\n\n# pdm\n# Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control.\n#pdm.lock\n# pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it\n# in version control.\n# https://pdm.fming.dev/#use-with-ide\n.pdm.toml\n\n# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm\n__pypackages__/\n\n# Celery stuff\ncelerybeat-schedule\ncelerybeat.pid\n\n# SageMath parsed files\n*.sage.py\n\n# Environments\n.env\n.venv\nenv/\nvenv/\nENV/\nenv.bak/\nvenv.bak/\n\n# Spyder project settings\n.spyderproject\n.spyproject\n\n# Rope project settings\n.ropeproject\n\n# mkdocs documentation\n/site\n\n# mypy\n.mypy_cache/\n.dmypy.json\ndmypy.json\n\n# Pyre type checker\n.pyre/\n\n# pytype static type analyzer\n.pytype/\n\n# Cython debug symbols\ncython_debug/\n\n# PyCharm\n# JetBrains specific template is maintained in a separate JetBrains.gitignore that can\n# be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore\n# and can be added to the global gitignore or merged into this file. For a more nuclear\n# option (not recommended) you can uncomment the following to ignore the entire idea folder.\n#.idea/\n\n#Desktop Services Store\n.DS_Store" }, { "path": "elt_simplified_stack/README.md", "content": "# ELT simplified Stack With Github, Airbyte, DBT, Prefect and BigQuery\n\nWelcome to the \"ELT simplified Stack\" repository! ✨ For extracting from source - and travel towards destination with some intermediate transformations with Airbyte - Github, DBT, BigQuery, and Prefect. With this setup, you can pull Github data, extract it using Airbyte, put it into BigQuery, and play around with it using dbt and Prefect.\n\nThis Quickstart is all about making things easy, getting you started quickly and showing you how smoothly all these tools can work together!\n\n## Table of Contents\n\n- [Infrastructure Layout](#infrastructure-layout)\n- [Prerequisites](#prerequisites)\n- [Setting an environment for your project](#1-setting-an-environment-for-your-project)\n- [Setting Up BigQuery to work with Airbyte and dbt](#2-setting-up-bigquery)\n- [Setting Up Airbyte Connectors with Terraform](#3-setting-up-airbyte-connectors-with-terraform)\n- [Setting Up the dbt Project](#4-setting-up-the-dbt-project)\n- [Orchestrating with Prefect](#5-orchestrating-with-prefect)\n- [Next Steps](#next-steps)\n\n## Infrastructure Layout\n![insfrastructure layout](images/infrastructure.png)\n\n## Prerequisites\n\nBefore you embark on this integration, ensure you have the following set up and ready:\n\n1. **Python 3.10 or later**: If not installed, download and install it from [Python's official website](https://www.python.org/downloads/).\n\n2. **Docker and Docker Compose (Docker Desktop)**: Install [Docker](https://docs.docker.com/get-docker/) following the official documentation for your specific OS.\n\n3. **Airbyte OSS version**: Deploy the open-source version of Airbyte. Follow the installation instructions from the [Airbyte Documentation](https://docs.airbyte.com/quickstart/deploy-airbyte/).\n\n4. **Terraform**: Terraform will help you provision and manage the Airbyte resources. If you haven't installed it, follow the [official Terraform installation guide](https://developer.hashicorp.com/terraform/tutorials/aws-get-started/install-cli).\n\n5. **Google Cloud account with BigQuery**: You will also need to add the necessary permissions to allow Airbyte and dbt to access the data in BigQuery. A step-by-step guide is provided [below](#2-setting-up-bigquery).\n\n## 1. Setting an environment for your project\n\nGet the project up and running on your local machine by following these steps:\n\n1. **Clone the repository (Clone only this quickstart)**:\n\n ```bash\n git clone --filter=blob:none --sparse https://github.com/airbytehq/quickstarts.git\n ```\n\n ```bash\n cd quickstarts\n ```\n\n ```bash\n git sparse-checkout add elt_simplified\n ```\n\n2. **Navigate to the directory**:\n\n ```bash\n cd elt_simplified\n ```\n\n3. **Set Up a Virtual Environment**:\n\n - For Mac:\n ```bash\n python3 -m venv venv\n source venv/bin/activate\n ```\n - For Windows:\n ```bash\n python -m venv venv\n .\\venv\\Scripts\\activate\n ```\n4. **Install Dependencies**:\n ```bash\n pip install -e \".[dev]\"\n ```\n\n## 2. Setting Up BigQuery\n\n#### 1. **Create a Google Cloud Project**\n\n- If you have a Google Cloud project, you can skip this step.\n- Go to the [Google Cloud Console](https://console.cloud.google.com/).\n- Click on the \"Select a project\" dropdown at the top right and select \"New Project\".\n- Give your project a name and follow the steps to create it.\n\n#### 2. **Create BigQuery Datasets**\n\n- In the Google Cloud Console, go to BigQuery.\n- Make two new datasets: `raw_data` for Airbyte and `transformed_data` for dbt.\n - If you pick different names, remember to change the names in the code too.\n\n**How to create a dataset:**\n\n- In the left sidebar, click on your project name.\n- Click “Create Dataset”.\n- Enter the dataset ID (either `raw_data` or `transformed_data`).\n- Click \"Create Dataset\".\n\n#### 3. **Create Service Accounts and Assign Roles**\n\n- Go to “IAM & Admin” > “Service accounts” in the Google Cloud Console.\n- Click “Create Service Account”.\n- Name your service account (like `airbyte-service-account`).\n- Assign the “BigQuery Data Editor” and “BigQuery Job User” roles to the service account.\n- Follow the same steps to make another service account for dbt (like `dbt-service-account`) and assign the roles.\n\n**How to create a service account and assign roles:**\n\n- While creating the service account, under the “Grant this service account access to project” section, click the “Role” dropdown.\n- Choose the “BigQuery Data Editor” and “BigQuery Job User” roles.\n- Finish the creation process.\n\n#### 4. **Generate JSON Keys for Service Accounts**\n\n- For both service accounts, make a JSON key to let the service accounts sign in.\n\n**How to generate JSON key:**\n\n- Find the service account in the “Service accounts” list.\n- Click on the service account name.\n- In the “Keys” section, click “Add Key” and pick JSON.\n- The key will download automatically. Keep it safe and don’t share it.\n- Do this for the other service account too.\n\n## 3. Setting Up Airbyte Connectors with Terraform\n\nAirbyte allows you to create connectors for sources and destinations, facilitating data synchronization between various platforms. In this project, we're harnessing the power of Terraform to automate the creation of these connectors and the connections between them. Here's how you can set this up:\n\n1. **Navigate to the Airbyte Configuration Directory**:\n\n Change to the relevant directory containing the Terraform configuration for Airbyte:\n\n ```bash\n cd infra/airbyte\n ```\n\n2. **Modify Configuration Files**:\n\n Within the `infra/airbyte` directory, you'll find three crucial Terraform files:\n\n - `provider.tf`: Defines the Airbyte provider.\n - `main.tf`: Contains the main configuration for creating Airbyte resources.\n - `variables.tf`: Holds various variables, including credentials.\n\n Adjust the configurations in these files to suit your project's needs. Specifically, provide credentials for your BigQuery connection. You can utilize the `variables.tf` file to manage these credentials.\n\n3. **Initialize Terraform**:\n\n This step prepares Terraform to create the resources defined in your configuration files.\n\n ```bash\n terraform init\n ```\n\n4. **Review the Plan**:\n\n Before applying any changes, review the plan to understand what Terraform will do.\n\n ```bash\n terraform plan\n ```\n\n5. **Apply Configuration**:\n\n After reviewing and confirming the plan, apply the Terraform configurations to create the necessary Airbyte resources.\n\n ```bash\n terraform apply\n ```\n\n6. **Verify in Airbyte UI**:\n\n Once Terraform completes its tasks, navigate to the [Airbyte UI](http://localhost:8000/). Here, you should see your source and destination connectors, as well as the connection between them, set up and ready to go.\n\n## 4. Setting Up the dbt Project\n\n[dbt (data build tool)](https://www.getdbt.com/) allows you to transform your data by writing, documenting, and executing SQL workflows. Setting up the dbt project requires specifying connection details for your data platform, in this case, BigQuery. Here’s a step-by-step guide to help you set this up:\n\n1. **Navigate to the dbt Project Directory**:\n\n Change to the directory containing the dbt configuration:\n\n ```bash\n cd dbt_project\n ```\n\n2. **Update Connection Details**:\n\n You'll find a `profiles.yml` file within the directory. This file contains configurations for dbt to connect with your data platform. Update this file with your BigQuery connection details.\n\n3. **Utilize Environment Variables (Optional but Recommended)**:\n\n To keep your credentials secure, you can leverage environment variables. An example is provided within the `profiles.yml` file.\n\n4. **Test the Connection**:\n\n Once you’ve updated the connection details, you can test the connection to your BigQuery instance using:\n\n ```bash\n dbt debug\n ```\n\n If everything is set up correctly, this command should report a successful connection to BigQuery.\n\n5. **Run the Models**:\n\n If you would like to run the dbt models manually at this point, you can do so by executing:\n\n ```bash\n dbt run\n ```\n\n You can verify the data has been transformed by going to BigQuery and checking the `transformed_data` dataset.\n\n## 5. Orchestrating with Prefect\n\n[Prefect](https://prefect.io/) is an orchestration workflow tool that makes it easy to build, run, and monitor data workflows by writing Python code. In this section, we'll walk you through creating a Prefect flow to orchestrate both Airbyte extract and load operations, and dbt transformations with Python:\n\n1. **Navigate to the Orchestration Directory**:\n\n Switch to the directory containing the Prefect orchestration configurations:\n ```bash\n cd ../orchestration\n ```\n\n2. **Set Environment Variables**:\n\n Prefect requires certain environment variables to be set to interact with other tools like dbt and Airbyte. Set the following variables:\n\n ```bash\n export AIRBYTE_PASSWORD=password\n ```\n\n3. **Connect to Prefect's API**:\n\n Open a new terminal window. Start a local Prefect server instance in your virtual environment:\n\n ```bash\n prefect server start\n ```\n\n4. **Deploy the Flow**:\n\n When we run the flow script, Prefect will automatically create a flow deployment that you can interact with via the UI and API. The script will stay running so that it can listen for scheduled or triggered runs of this flow; once a run is found, it will be executed within a subprocess.\n\n ```bash\n python my_elt_flow.py\n ```\n\n5. **Access Prefect UI in Your Browser**:\n\n Open your browser and navigate to:\n ```\n http://127.0.0.1:4200\n ```\n You can now begin interacting with your newly created deployment!\n\n## Next Steps\n\nCongratulations on deploying and running the elt_simplified quickstart! 🎉 Here are some suggestions on what you can explore next to dive deeper and get more out of your project:\n\n### 1. **Explore the Data and Insights**\n - Your raw data extracted via Airbyte can be represented as sources in dbt. Start by [creating new dbt sources](https://docs.getdbt.com/docs/build/sources) to represent this data, allowing for structured transformations down the line.\n\n### 2. **Optimize Your dbt Models**\n - Review the transformations you’ve applied using dbt. Try optimizing the models or create new ones based on your evolving needs and insights you want to extract.\n\n### 3. **Extend the Prefect Pipeline**:\n\n - You can create flow runs from this deployment via API calls to be triggered by new data sync in Airbyte rather than on a schedule. You can customize your dbt runs based on the results got from AirbyteSyncResult. You can also migrate the deployment to the Prefect cloud.\n\n### 4. **Extend the Project**:\n\n - The real beauty of this integration is its extensibility. Whether you want to add more data sources, integrate additional tools, or enhance your transformation logic – the floor is yours. With the foundation set, sky's the limit for how you want to extend and refine your data processes.\n" }, { "path": "elt_simplified_stack/dbt_project/.gitignore", "content": "\ntarget/\ndbt_packages/\nlogs/\n\n#Desktop Services Store\n.DS_Store\n\n#User cookie\n.user.yml\n" }, { "path": "elt_simplified_stack/dbt_project/dbt_project.yml", "content": "\n# Name your project! Project names should contain only lowercase characters\n# and underscores. A good package name should reflect your organization's\n# name or the intended use of these models\nname: 'dbt_project'\nversion: '1.0.0'\nconfig-version: 2\n\n# This setting configures which \"profile\" dbt uses for this project.\nprofile: 'dbt_project'\n\n# These configurations specify where dbt should look for different types of files.\n# The `model-paths` config, for example, states that models in this project can be\n# found in the \"models/\" directory. You probably won't need to change these!\nmodel-paths: [\"models\"]\nanalysis-paths: [\"analyses\"]\ntest-paths: [\"tests\"]\nseed-paths: [\"seeds\"]\nmacro-paths: [\"macros\"]\nsnapshot-paths: [\"snapshots\"]\n\nclean-targets: # directories to be removed by `dbt clean`\n - \"target\"\n - \"dbt_packages\"\n\n\n# Configuring models\n# Full documentation: https://docs.getdbt.com/docs/configuring-models\n\n# In this example config, we tell dbt to build all models in the example/\n# directory as views. These settings can be overridden in the individual model\n# files using the `{{ config(...) }}` macro.\nmodels:\n dbt_project:\n # Config indicated by + and applies to all files under models/example/\n staging:\n +materialized: view\n marts:\n +materialized: view\n" }, { "path": "elt_simplified_stack/dbt_project/models/marts/commits-per-repo.sql", "content": "SELECT\n JSON_EXTRACT_SCALAR(author, '$.login') AS developer_username,\n repository,\n COUNT(*) AS num_commits\nFROM\n transformed_data.stg_commits\nGROUP BY\n developer_username, repository\n" }, { "path": "elt_simplified_stack/dbt_project/models/marts/pr-per-dev.sql", "content": "SELECT\n JSON_EXTRACT_SCALAR(user, '$.login') AS developer_username,\n COUNT(*) AS num_pull_requests_opened\nFROM\n transformed_data.stg_pull_requests\nGROUP BY\n developer_username\n\n" }, { "path": "elt_simplified_stack/dbt_project/models/marts/pr-per-status.sql", "content": "SELECT\n JSON_EXTRACT_SCALAR(user, '$.login') AS username,\n SUM(CASE WHEN state = 'opened' THEN 1 ELSE 0 END) AS opened_prs,\n SUM(CASE WHEN state = 'closed' THEN 1 ELSE 0 END) AS closed_prs\nFROM transformed_data.stg_pull_requests\nGROUP BY username\n" }, { "path": "elt_simplified_stack/dbt_project/models/sources/github_source.yml", "content": "version: 2\n\nsources:\n - name: github\n # Use your BigQuery project ID\n database: \"{{ env_var('BIGQUERY_PROJECT_ID', '') }}\"\n # Use your BigQuery dataset name\n schema: github_airbyte\n \n tables: \n\n - name: pull_requests\n description: \"Simulated pull_request data from the Github connector.\"\n columns:\n - name: id \n description: \"Unique identifier for the pull_requests.\"\n - name: active_lock_reason \n - name: assignee \n - name: assignees\n - name: author_association\n - name: auto_merge\n - name: base\n - name: body\n - name: closed_at\n - name: comments_url\n - name: commits_url\n - name: created_at\n - name: diff_url\n - name: draft\n - name: head\n - name: html_url\n - name: issue_url\n - name: labels\n - name: locked\n - name: merge_commit_sha\n - name: merged_at\n - name: milestone\n - name: node_id\n - name: number\n - name: patch_url\n - name: repository\n - name: requested_reviewers\n - name: requested_teams\n - name: review_comment_url\n - name: review_comments_url\n - name: state\n - name: statuses_url\n - name: title\n - name: updated_at\n - name: url\n - name: user\n\n - name: commits\n description: \"Simulated commit data from the Github connector.\"\n columns:\n - name: author \n - name: branch\n - name: comments_url\n - name: commit\n - name: committer\n - name: created_at\n - name: html_url\n - name: node_id\n - name: parents\n - name: repository\n - name: sha\n - name: url\n" }, { "path": "elt_simplified_stack/dbt_project/models/staging/stg_commits.sql", "content": "select\n *\nfrom {{ source('github', 'commits') }}\n" }, { "path": "elt_simplified_stack/dbt_project/models/staging/stg_pull_requests.sql", "content": "select\n *\nfrom {{ source('github', 'pull_requests') }}\n" }, { "path": "elt_simplified_stack/dbt_project/profiles.yml", "content": "dbt_project:\n outputs:\n dev:\n dataset: transformed_data\n job_execution_timeout_seconds: 300\n job_retries: 1\n # Use an env variable to indicate your JSON key file path\n keyfile: \"{{ env_var('DBT_BIGQUERY_KEYFILE_PATH', '') }}\"\n location: US\n method: service-account\n priority: interactive\n # Indicate your BigQuery project ID\n project: \"{{ env_var('BIGQUERY_PROJECT_ID', '') }}\"\n threads: 1\n type: bigquery\n target: dev\n" }, { "path": "elt_simplified_stack/dbt_project/snapshots/.gitkeep", "content": "" }, { "path": "elt_simplified_stack/dbt_project/tests/.gitkeep", "content": "" }, { "path": "elt_simplified_stack/infra/.gitignore", "content": "# Local .terraform directories\n**/.terraform/*\n\n# .tfstate files\n*.tfstate\n*.tfstate.*\n\n# Crash log files\ncrash.log\ncrash.*.log\n\n# Exclude all .tfvars files, which are likely to contain sensitive data, such as\n# password, private keys, and other secrets. These should not be part of version \n# control as they are data points which are potentially sensitive and subject \n# to change depending on the environment.\n*.tfvars\n*.tfvars.json\n\n# Ignore override files as they are usually used to override resources locally and so\n# are not checked in\noverride.tf\noverride.tf.json\n*_override.tf\n*_override.tf.json\n\n# Include override files you do wish to add to version control using negated pattern\n# !example_override.tf\n\n# Include tfplan files to ignore the plan output of command: terraform plan -out=tfplan\n# example: *tfplan*\n\n# Ignore CLI configuration files\n.terraformrc\nterraform.rc\n" }, { "path": "elt_simplified_stack/infra/airbyte/.terraform.lock.hcl", "content": "# This file is maintained automatically by \"terraform init\".\n# Manual edits may be lost in future updates.\n\nprovider \"registry.terraform.io/airbytehq/airbyte\" {\n version = \"0.3.3\"\n constraints = \"0.3.3\"\n hashes = [\n \"h1:0LmuAc5LvlMuOUPtNEaCAh9FHrV/C877bDJhm9Lz8MU=\",\n \"zh:0efa470b34d9b912b47efe4469c51713bfc3c2413e52c17e1e903f2a3cddb2f6\",\n \"zh:1bddd69fa2c2d4f3e239d60555446df9bc4ce0c0cabbe7e092fe1d44989ab004\",\n \"zh:2e20540403a0010007b53456663fb037b24e30f6c8943f65da1bcf7fa4dfc8a6\",\n \"zh:2f415369ad884e8b7115a5c5ff229d052f7af1fca27abbfc8ebef379ed11aec4\",\n \"zh:46fd9a906f4b6461112dcc5a5aa01a3fcd7a19a72d4ad0b2e37790da37701fe1\",\n \"zh:83503ebb77bb6d6941c42ba323cf22380d08a1506554a2dcc8ac54e74c0886a1\",\n \"zh:890df766e9b839623b1f0437355032a3c006226a6c200cd911e15ee1a9014e9f\",\n \"zh:8fd770eff726826d3a63b9e3733c5455b5cde004027b04ee3f75888eb8538c90\",\n \"zh:b0fc890ed4f9b077bf70ed121cc3550e7a07d16e7798ad517623274aa62ad7b0\",\n \"zh:c2a01612362da9b73cd5958f281e1aa7ff09af42182e463097d11ed78e778e72\",\n \"zh:c64b2bb1887a0367d64ba3393d4b3a16c418cf5b1792e2e7aae7c0b5413eb334\",\n \"zh:ce14ebbf0ed91913ec62655a511763dec62b5779de9a209bd6f1c336640cddc0\",\n \"zh:e0662ca837eee10f7733ea9a501d995281f56bd9b410ae13ad03eb106011db14\",\n \"zh:e103d480fc6066004bc98e9e04a141a1f55b918cc2912716beebcc6fc4c872fb\",\n \"zh:e2507049098f0f1b21cb56870f4a5ef624bcf6d3959e5612eada1f8117341648\",\n ]\n}\n" }, { "path": "elt_simplified_stack/infra/airbyte/main.tf", "content": "\n// source\nresource \"airbyte_source_github\" \"my_source_github\" {\n configuration = {\n credentials = {\n source_github_authentication_personal_access_token = {\n personal_access_token = var.personal_access_token\n }\n }\n repository = var.repository\n source_type = \"github\"\n start_date = \"2023-09-01T00:00:00Z\"\n }\n name = \"your_name\"\n workspace_id = var.workspace_id\n}\n\n\n// destination\nresource \"airbyte_destination_bigquery\" \"bigquery\" {\n configuration = {\n dataset_id = var.dataset_id\n dataset_location = \"US\"\n destination_type = \"bigquery\"\n project_id = var.project_id\n credentials_json = var.credentials_json\n loading_method = {\n destination_bigquery_loading_method_standard_inserts = {\n method = \"Standard\"\n }\n }\n }\n name = \"BigQuery\"\n workspace_id = var.workspace_id\n}\n\n\n// connection\nresource \"airbyte_connection\" \"github_bigquery\" {\n name = \"Github to bigquery\"\n source_id = airbyte_source_github.my_source_github.source_id\n destination_id = airbyte_destination_bigquery.bigquery.destination_id\n configurations = {\n streams = [\n {\n name = \"pull_requests\"\n },\n {\n name = \"commits\"\n }\n ]\n }\n}\n" }, { "path": "elt_simplified_stack/infra/airbyte/provider.tf", "content": "// Airbyte Terraform provider documentation: https://registry.terraform.io/providers/airbytehq/airbyte/latest/docs\n\nterraform {\n required_providers {\n airbyte = {\n source = \"airbytehq/airbyte\"\n version = \"0.3.3\"\n }\n }\n}\n\nprovider \"airbyte\" {\n // If running locally (Airbyte OSS) with docker-compose using the airbyte-proxy, \n // include the actual password/username you've set up (or use the defaults below)\n username = \"airbyte\"\n password = \"password\"\n\n // if running locally (Airbyte OSS), include the server url to the airbyte-api-server\n server_url = \"http://localhost:8006/v1/\"\n}\n" }, { "path": "elt_simplified_stack/infra/airbyte/variables.tf", "content": "variable \"personal_access_token\" {\n type = string\n}\n\nvariable \"project_id\" {\n type = string\n}\n\nvariable \"workspace_id\" {\n type = string\n}\n\nvariable \"dataset_id\" {\n type = string\n}\n\nvariable \"credentials_json\" {\n type = string\n}\n\nvariable \"repository\" {\n type = string\n}\n" }, { "path": "elt_simplified_stack/orchestration/my_elt_flow.py", "content": "import os\n\nfrom prefect import flow, task\n\nfrom prefect_airbyte.server import AirbyteServer\nfrom prefect_airbyte.connections import AirbyteConnection, AirbyteSyncResult\nfrom prefect_airbyte.flows import run_connection_sync\n\nfrom prefect_dbt.cli.commands import DbtCoreOperation\n\n\n\nremote_airbyte_server = AirbyteServer(\n username=\"airbyte\",\n password=os.getenv(\"AIRBYTE_PASSWORD\"),\n server_host=\"localhost\",\n server_port=\"8000\"\n)\n\nremote_airbyte_server.save(\"my-remote-airbyte-server\", overwrite=True)\n\nairbyte_connection = AirbyteConnection(\n airbyte_server=remote_airbyte_server,\n connection_id=\"...my_airbyte_connection_id...\",\n status_updates=True,\n)\n\n@task(name=\"Extract, Load with Airbyte\")\ndef run_airbyte_sync(connection: AirbyteConnection) -> AirbyteSyncResult:\n job_run = connection.trigger()\n job_run.wait_for_completion()\n return job_run.fetch_result()\n\ndef run_dbt_commands(commands, prev_task_result):\n dbt_task = DbtCoreOperation(\n commands=commands,\n project_dir=\"../dbt_project\",\n profiles_dir=\"../dbt_project\",\n wait_for=prev_task_result\n )\n return dbt_task\n\n@flow(log_prints=True)\ndef my_elt_flow():\n\n # run Airbyte sync\n # airbyte_sync_result: AirbyteSyncResult = run_connection_sync(\n # airbyte_connection=airbyte_connection,\n # )\n airbyte_sync_result = run_airbyte_sync(airbyte_connection)\n\n # run dbt precheck \n dbt_init_task = task(name=\"dbt Precheck\")(run_dbt_commands)(\n commands=[\"pwd\", \"dbt debug\", \"dbt list\"], \n prev_task_result=airbyte_sync_result\n )\n dbt_init_task.run()\n\n # run dbt models\n dbt_run_task = task(name=\"Transform with dbt\")(run_dbt_commands)(\n commands=[\"dbt run\"], \n prev_task_result=dbt_init_task\n )\n dbt_run_task.run()\n\n\n\nif __name__ == \"__main__\":\n # my_elt_flow.visualize()\n my_elt_flow()\n" }, { "path": "elt_simplified_stack/setup.py", "content": "from setuptools import find_packages, setup\n\nsetup(\n name=\"elt-simplified\"\n packages=find_packages(),\n install_requires=[\n \"prefect\",\n \"prefect-airbyte\",\n \"prefect-dbt\",\n \"dbt-core>=1.4.0\",\n \"dbt-bigquery\",\n ],\n extras_require={\"dev\": [\"pytest\"]},\n)\n" }, { "path": "error_analysis_stack_sentry/Readme.md", "content": "# Optimizing error resolution with Sentry, Airbyte, dbt, dagster and Snowflake\n\nWelcome to the \"Optimizing Error Resolution Processes with Sentry Stack\" repository. This quickstart guide is designed to assist you in configuring an error analysis stack utilizing Sentry, Airbyte, Snowflake, dbt, and Dagster. Within this framework, error data extracted from Sentry is ingested into Snowflake through the use of Airbyte. Subsequently, data transformations are performed using dbt, and the results can be visually presented through Dagster.\n\nPlease find below the detailed steps for setting up the quickstart. \n\n\n## Table of Contents\n\n- [Data flow Diagram](#data-flow-diagram)\n- [Infrastructure Layout](#infrastructure-layout)\n- [Prerequisites](#prerequisites)\n- [Setting an environment for your project](#setting-an-environment-for-your-project)\n - There are two ways to setup the connectors of airbyte.\n - [1. Using Airbyte UI](#1-using-airbyte-ui)\n - [2. Using Terraform to Setup the Connector](#2-using-terraform-to-setup-the-connector)\n- [Setting Up the dbt Project](#4-setting-up-the-dbt-project)\n- [Orchestrating with Dagster](#5-orchestrating-with-dagster)\n- [Next Steps](#next-steps)\n\n## Data Flow Diagram\n\n![Data flow Diagram](assets/orchestration_lineage.png)\n\n## Infrastructure Layout\n\n![Infrastructure Layout](assets/Infrastructure_layout.jpg)\n\n## Prerequisites\n\nBefore you embark on this integration, ensure you have the following set up and ready:\n\n1. **Python 3.10 or later**: If not installed, download and install it from [Python's official website](https://www.python.org/downloads/).\n\n2. **Docker and Docker Compose (Docker Desktop)**: Install [Docker](https://docs.docker.com/get-docker/) following the official documentation for your specific OS.\n\n3. **Airbyte OSS version**: Deploy the open-source version of Airbyte. Follow the installation instructions from the [Airbyte Documentation](https://docs.airbyte.com/quickstart/deploy-airbyte/).\n\n4. **Terraform**: Terraform will help you provision and manage the Airbyte resources. If you haven't installed it, follow the [official Terraform installation guide](https://developer.hashicorp.com/terraform/tutorials/aws-get-started/install-cli).\n\n5. **Snowflake account creation**: You will also need to add the necessary permissions to allow Airbyte and dbt to access the data in Snowflake. A step-by-step guide is provided [below](#2-setting-up-Snowflake).\n\n\n\n## Setting an environment for your project\n\nGet the project up and running on your local machine by following these steps:\n\n1. **Clone the repository (Clone only this quickstart)**: \n ```bash\n git clone --filter=blob:none --sparse https://github.com/airbytehq/quickstarts.git\n ```\n\n ```bash\n cd quickstarts\n ```\n\n ```bash\n git sparse-checkout add error_analysis_stack_sentry\n ```\n\n\n2. **Navigate to the directory**: \n ```bash\n cd error_analysis_stack_sentry\n ```\n\n3. **Set Up a Virtual Environment**: \n - For Mac:\n ```bash\n python3 -m venv venv\n source venv/bin/activate\n ```\n - For Windows:\n ```bash\n python -m venv venv\n .\\venv\\Scripts\\activate\n ```\n\n4. **Install Dependencies**: \n ```bash\n pip install -e \".[dev]\"\n ```\n\n## 1. Using Airbyte UI\n\nTo establish the connection and import data from the Sentry into the Snowflake warehouse, kindly proceed by utilizing the Airbyte user interface. The following steps should be adhered to:\n\n1. Run the Airbyte OSS version by following the [documentation](https://docs.airbyte.com/quickstart/deploy-airbyte).\n\n2. Setup the Sentry as source by following [these steps](https://docs.airbyte.com/integrations/sources/sentry).\n\n3. Setup the Snowflake as destination by following [these steps](https://docs.airbyte.com/integrations/destinations/snowflake)\n\n4. Please proceed to configure the synchronization time and select the specific tables you wish to load into Snowflake from Sentry. You can make your selection from the list of available streams.\n\n5. Enjoy :smile:, your data loaded into Snowflake data warehouse from Sentry.\n\n\n## 2. Using Terraform to Setup the Connector\n\nAirbyte enables you to make connections between different platforms by creating connectors for sources and destinations. In this project, we're using Terraform to automate the setup of these connectors and their connections. Here's how you can do it:\n\n1. **Navigate to the Airbyte Configuration Directory**:\n \n Change to the relevant directory containing the Terraform configuration for Airbyte:\n ```bash\n cd infra/airbyte\n ```\n\n2. **Modify Configuration Files**:\n\n Within the `infra/airbyte` directory, you'll find three crucial Terraform files:\n - `provider.tf`: Defines the Airbyte provider.\n - `main.tf`: Contains the main configuration for creating Airbyte resources.\n - `variables.tf`: Holds various variables, including credentials.\n\n Adjust the configurations in these files to suit your project's needs. Specifically, provide credentials for your Sentry and Snowflake connections. You can utilize the `variables.tf` file to manage these credentials.\n\n3. **Initialize Terraform**:\n \n This step prepares Terraform to create the resources defined in your configuration files.\n ```bash\n terraform init\n ```\n\n4. **Review the Plan**:\n\n Before applying any changes, review the plan to understand what Terraform will do.\n ```bash\n terraform plan\n ```\n\n5. **Apply Configuration**:\n\n After reviewing and confirming the plan, apply the Terraform configurations to create the necessary Airbyte resources.\n ```bash\n terraform apply\n ```\n\n6. **Verify in Airbyte UI**:\n\n After Terraform finishes its tasks, go to the Airbyte user interface. You will find your source and destination connectors already set up, along with the connection between them, all ready to use.\n\n\n\n## 4. Setting Up the dbt Project\n\n[dbt (data build tool)](https://www.getdbt.com/) allows you to transform your data by writing, documenting, and executing SQL workflows. Setting up the dbt project requires specifying connection details for your data platform, in this case, Snowflake. Here’s a step-by-step guide to help you set this up:\n\nFirst set up the dbt into your local machine by following steps from this [Link](https://docs.getdbt.com/docs/core/installation).\n\n1. **Navigate to the dbt Project Directory**:\n\n Change to the directory containing the dbt configuration:\n ```bash\n cd dbt_project\n ```\n\n2. **Update Connection Details**:\n\n You'll find a `profiles.yml` file within the directory. This file contains configurations for dbt to connect with your data platform. Update this file with your Snowflake connection details.\n\n3. **Utilize Environment Variables (Optional but Recommended)**:\n\n To keep your credentials secure, you can leverage environment variables. An example is provided within the `profiles.yml` file.\n\n4. **Test the Connection**:\n\n Once you’ve updated the connection details, you can test the connection to your Snowflake instance using:\n ```bash\n dbt debug\n ```\n\n If everything is set up correctly, this command should report a successful connection to Snowflake.\n\n5. **Run the Models**:\n\n If you would like to run the dbt models manually at this point, you can do so by executing:\n ```bash\n dbt run\n ```\n\n You can verify the data has been transformed by going to Snowflake and checking the dataset name that you provided.\n\n## 5. Orchestrating with Dagster\n\n[Dagster](https://dagster.io/) is a modern data orchestrator designed to help you build, test, and monitor your data workflows. In this section, we'll walk you through setting up Dagster to oversee both the Airbyte and dbt workflows:\n\n1. **Navigate to the Orchestration Directory**:\n\n Switch to the directory containing the Dagster orchestration configurations:\n ```bash\n cd orchestration\n ```\n\n2. **Set Environment Variables**:\n\n Dagster requires certain environment variables to be set to interact with other tools like dbt and Airbyte. Set the following variables:\n\n ```bash\n export DAGSTER_DBT_PARSE_PROJECT_ON_LOAD=1\n export AIRBYTE_PASSWORD=password\n ```\n \n Note: The `AIRBYTE_PASSWORD` is set to `password` as a default for local Airbyte instances. If you've changed this during your Airbyte setup, ensure you use the appropriate password here.\n\n\n3. **Launch the Dagster UI**:\n\n With the environment variables in place, kick-start the Dagster UI:\n ```bash\n dagster dev\n ```\n\n4. **Access Dagster in Your Browser**:\n\n Open your browser and navigate to:\n ```\n http://127.0.0.1:3000\n ```\n\n Here, you should see assets for both Airbyte and dbt. To get an overview of how these assets interrelate, click on \"view global asset lineage\". This will give you a clear picture of the data lineage, visualizing how data flows between the tools.\n\n5. **Materialize Dagster Assets**:\n In the Dagster UI, click on \"Materialize all\". This should trigger the full pipeline. First the Airbyte sync to extract data from Faker and load it into Snowflake, and then dbt to transform the raw data, materializing the `staging` and `marts` models.\n\n## Next Steps\n\nCongratulations on deploying and running the Error analysis Quickstart! 🎉 Here are some suggestions on what you can explore next to dive deeper and get more out of your project:\n\n### **Explore the Data and Insights**\n - Dive into the datasets in Snowflake, run some queries, and explore the data you've collected and transformed. This is your chance to uncover insights and understand the data better!\n\n### **Optimize Your dbt Models**\n - Review the transformations you’ve applied using dbt. Try optimizing the models or create new ones based on your evolving needs and insights you want to extract.\n\n\n\n" }, { "path": "error_analysis_stack_sentry/dbt_project/.gitignore", "content": "\ntarget/\ndbt_packages/\nlogs/\n\n.user.yml" }, { "path": "error_analysis_stack_sentry/dbt_project/README.md", "content": "Welcome to your new dbt project!\n\n### Using the starter project\n\nTry running the following commands:\n- dbt run\n- dbt test\n\n\n### Resources:\n- Learn more about dbt [in the docs](https://docs.getdbt.com/docs/introduction)\n- Check out [Discourse](https://discourse.getdbt.com/) for commonly asked questions and answers\n- Join the [chat](https://community.getdbt.com/) on Slack for live discussions and support\n- Find [dbt events](https://events.getdbt.com) near you\n- Check out [the blog](https://blog.getdbt.com/) for the latest news on dbt's development and best practices\n" }, { "path": "error_analysis_stack_sentry/dbt_project/analyses/.gitkeep", "content": "" }, { "path": "error_analysis_stack_sentry/dbt_project/dbt_project.yml", "content": "\n# Name your project! Project names should contain only lowercase characters\n# and underscores. A good package name should reflect your organization's\n# name or the intended use of these models\nname: 'dbt_sentry_to_snowflake'\nversion: '1.0.0'\nconfig-version: 2\n\n# This setting configures which \"profile\" dbt uses for this project.\nprofile: 'dbt_sentry_to_snowflake'\n\n# These configurations specify where dbt should look for different types of files.\n# The `model-paths` config, for example, states that models in this project can be\n# found in the \"models/\" directory. You probably won't need to change these!\nmodel-paths: [\"models\"]\nanalysis-paths: [\"analyses\"]\ntest-paths: [\"tests\"]\nseed-paths: [\"seeds\"]\nmacro-paths: [\"macros\"]\nsnapshot-paths: [\"snapshots\"]\n\ntarget-path: \"target\"\nclean-targets:\n - \"target\"\n - \"dbt_modules\"\n - \"logs\"\n\n\n# Configuring models\n# Full documentation: https://docs.getdbt.com/docs/configuring-models\n\n# In this example config, we tell dbt to build all models in the example/\n# directory as views. These settings can be overridden in the individual model\n# files using the `{{ config(...) }}` macro.\nmodels:\n dbt_sentry_to_snowflake:\n # Config indicated by + and applies to all files under models/example/\n example:\n +materialized: view\n" }, { "path": "error_analysis_stack_sentry/dbt_project/macros/.gitkeep", "content": "" }, { "path": "error_analysis_stack_sentry/dbt_project/models/example/Insight_Table.sql", "content": "\n/*\n Welcome to your first dbt model!\n Did you know that you can also configure models directly within SQL files?\n This will override configurations stated in dbt_project.yml\n\n Try changing \"table\" to \"view\" below\n*/\n\n{{ config(materialized='table') }}\n\nwith insight_table as (\n SELECT t1.METADATA as METADATA, t1.TITLE as TITLE, t1.LEVEL as LEVEL, t1.CULPRIT as CULPRIT, t2.USER as USER\n FROM {{ source('snowflake', 'issues') }} AS t1\n INNER JOIN {{ source('snowflake', 'events') }} AS t2\n ON t1.ID = t2.GROUPID\n)\n\nselect *\nfrom insight_table\n\n/*\n Uncomment the line below to remove records with null `id` values\n*/\n\n-- where id is not null\n" }, { "path": "error_analysis_stack_sentry/dbt_project/models/example/schema.yml", "content": "\nversion: 2\n\nmodels:\n - name: Insight_Table\n description: \"A starter dbt model\"\n columns:\n - name: id\n description: \"The primary key for this table\"\n tests:\n - unique\n - not_null\n\n" }, { "path": "error_analysis_stack_sentry/dbt_project/models/sources.yml", "content": "sources:\n - name: snowflake\n # Use your snowflake project ID\n database: sentry_to_snowflake\n # Use your snowflake dataset name\n schema: raw_data\n\n tables:\n - name: issues\n columns:\n - name: ANNOTATIONS\n - name: ASSIGNEDTO\n - name: COUNT\n - name: CULPRIT\n - name: FIRSTSEEN\n - name: HASSEEN\n - name: ID\n - name: ISBOOKMARKED\n - name: ISPUBLIC\n - name: ISSUBSCRIBED\n - name: LASTSEEN\n - name: LEVEL\n - name: LOGGER\n - name: METADATA\n - name: NUMCOMMENTS\n - name: PERMALINK\n - name: PROJECT\n - name: SHAREID\n - name: SHORTID\n - name: STATS\n - name: STATUS\n - name: STATUSDETAILS\n - name: SUBSCRIPTIONDETAILS\n - name: TITLE\n - name: TYPE\n - name: USERCOUNT\n - name: _AIRBYTE_EXTRACTED_AT\n - name: _AIRBYTE_META\n - name: _AIRBYTE_RAW_ID\n\n\n - name: events\n columns:\n - name: DATECREATED\n - name: EVENT.TYPE\n - name: EVENTID\n - name: GROUPID\n - name: ID\n - name: MESSAGE\n - name: PLATFORM\n - name: TAGS\n - name: TITLE\n - name: USER\n - name: _AIRBYTE_EXTRACTED_AT\n - name: _AIRBYTE_META\n - name: _AIRBYTE_RAW_ID\n\n " }, { "path": "error_analysis_stack_sentry/dbt_project/profiles.yml", "content": "dbt_sentry_to_snowflake:\n outputs:\n dev:\n account: YOUR_SNOWFLAKE_ACCOUNT_ID\n\n # User/password auth\n user: AIRBYTE_USER\n password: password\n\n role: AIRBYTE_ROLE\n database: AIRBYTE_DATABASE\n warehouse: AIRBYTE_WAREHOUSE\n schema: AIRBYTE_SCHEMA\n threads: 1\n client_session_keep_alive: False\n \n\n # optional\n connect_retries: 0 # default 0\n connect_timeout: 10 # default: 10\n retry_on_database_errors: False # default: false\n retry_all: False # default: false\n reuse_connections: False # default: false (available v1.4+)\n type: snowflake\n target: dev" }, { "path": "error_analysis_stack_sentry/dbt_project/seeds/.gitkeep", "content": "" }, { "path": "error_analysis_stack_sentry/dbt_project/snapshots/.gitkeep", "content": "" }, { "path": "error_analysis_stack_sentry/dbt_project/tests/.gitkeep", "content": "" }, { "path": "error_analysis_stack_sentry/error_analysis_stack.egg-info/PKG-INFO", "content": "Metadata-Version: 2.1\nName: error-analysis-stack\nVersion: 0.0.1\nProvides-Extra: dev\n" }, { "path": "error_analysis_stack_sentry/error_analysis_stack.egg-info/SOURCES.txt", "content": "setup.py\nerror_analysis_stack.egg-info/PKG-INFO\nerror_analysis_stack.egg-info/SOURCES.txt\nerror_analysis_stack.egg-info/dependency_links.txt\nerror_analysis_stack.egg-info/requires.txt\nerror_analysis_stack.egg-info/top_level.txt" }, { "path": "error_analysis_stack_sentry/error_analysis_stack.egg-info/dependency_links.txt", "content": "\n" }, { "path": "error_analysis_stack_sentry/error_analysis_stack.egg-info/requires.txt", "content": "dagster\ndagster-cloud\ndagster-airbyte\ndagster-dbt\ndbt-core>=1.4.0\ndbt-snowflake\n\n[dev]\ndagster-webserver\npytest\n" }, { "path": "error_analysis_stack_sentry/error_analysis_stack.egg-info/top_level.txt", "content": "\n" }, { "path": "error_analysis_stack_sentry/infra/.gitignore", "content": "# Local .terraform directories\n**/.terraform/*\n\n# .tfstate files\n*.tfstate\n*.tfstate.*\n\n# Crash log files\ncrash.log\ncrash.*.log\n\n# Exclude all .tfvars files, which are likely to contain sensitive data, such as\n# password, private keys, and other secrets. These should not be part of version \n# control as they are data points which are potentially sensitive and subject \n# to change depending on the environment.\n*.tfvars\n*.tfvars.json\n\n# Ignore override files as they are usually used to override resources locally and so\n# are not checked in\noverride.tf\noverride.tf.json\n*_override.tf\n*_override.tf.json\n\n# Include override files you do wish to add to version control using negated pattern\n# !example_override.tf\n\n# Include tfplan files to ignore the plan output of command: terraform plan -out=tfplan\n# example: *tfplan*\n\n# Ignore CLI configuration files\n.terraformrc\nterraform.rc" }, { "path": "error_analysis_stack_sentry/infra/airbyte/.terraform.lock.hcl", "content": "# This file is maintained automatically by \"terraform init\".\n# Manual edits may be lost in future updates.\n\nprovider \"registry.terraform.io/airbytehq/airbyte\" {\n version = \"0.3.4\"\n constraints = \"0.3.4\"\n hashes = [\n \"h1:0AHJKsRTlX6BCJZCJw5/oHsN97zi1AP33JeuPMwoX6U=\",\n \"zh:02167e00f7e89b6f09ae8796b9ee0ac2d8702b5cb295cb27d7a79266ffafe196\",\n \"zh:1ddad39354af090e830caf1e5cce845f24ff0bcef61b73e77ebc7703c2ecf90d\",\n \"zh:223a0a46d354ad0709d5f28d60accb3448ba5f256b84438238fb05235d1e5b34\",\n \"zh:29efd8848b9560456ec3d90f54984670e9d5b7e36f1edd2adb15c5fec3f57166\",\n \"zh:33d31310ba7ec699b5bd64edbb63b0a89bd55d87fae0f55409bbfa5fd7dd4d90\",\n \"zh:35ed0e2894e28ec7762406a18510b789b76b0649ace309eec22acaf10c982f08\",\n \"zh:4ba860918b65c00cc596d0b5b40068b89a72a300604a62bca7d286073779e684\",\n \"zh:59a0d1128477e587d9dac71f93598bae6050d176d29c840b6ad1bf95529d61e8\",\n \"zh:890df766e9b839623b1f0437355032a3c006226a6c200cd911e15ee1a9014e9f\",\n \"zh:8d9bb37e9094eba02acf8d08cf9f3331cd7c26478441d70e74e8d1ec9cb33aaa\",\n \"zh:9e5243eac43950889781a88d4e6186aea240898045e0e3c8fffd3291c5e74b6f\",\n \"zh:a0c31a5bc0cbc4a7341a0d185806a1c6797508580bede71a5009ad7b078d68c2\",\n \"zh:af341259999c6639a1c27e8f116a40b088dd192a3057096dc23a42affc97113f\",\n \"zh:b9779f8f695b4fab56e062abab61eaa58853f20c6411d53b2bd82a66d79a8b49\",\n \"zh:e284d898e5a30e507f1292635542dafe0e95ea8a5a215103a9d96d699aed9e75\",\n ]\n}\n" }, { "path": "error_analysis_stack_sentry/infra/airbyte/main.tf", "content": "// Airbyte Terraform provider documentation: https://registry.terraform.io/providers/airbytehq/airbyte/latest/docs\n\n// Sources - Documentation of Source : https://registry.terraform.io/providers/airbytehq/airbyte/latest/docs/resources/source_sentry\nresource \"airbyte_source_sentry\" \"my_source_sentry\" {\n configuration = {\n auth_token = \"\"\n # discover_fields = [\n # \"{ \\\"see\\\": \\\"documentation\\\" }\",\n # ]\n # hostname = \"muted-ingredient.biz\"\n organization = \"\"\n project = \"\"\n source_type = \"sentry\"\n }\n name = \"Sentry Source\"\n # secret_id = \"...my_secret_id...\"\n# secret_id = \"...my_secret_id...\"\n workspace_id = var.workspace_id\n}\n\n// Destinations - Documentation of Destination: https://registry.terraform.io/providers/airbytehq/airbyte/latest/docs/resources/destination_snowflake\nresource \"airbyte_destination_snowflake\" \"my_destination_snowflake\" {\n configuration = {\n credentials = {\n destination_snowflake_authorization_method_username_and_password = {\n password = \"\"\n }\n }\n database = \"\"\n destination_type = \"snowflake\"\n host = \"\"\n # jdbc_url_params = \"...my_jdbc_url_params...\"\n # raw_data_schema = \"...my_raw_data_schema...\"\n role = \"\"\n schema = \"\"\n username = \"\"\n warehouse = \"\"\n }\n name = \"Snowflake Warehouse\"\n workspace_id = var.workspace_id\n}\n\n// Connections\nresource \"airbyte_connection\" \"sentry_to_snowflake\" {\n name = \"Sentry to Snowflake Data Warehouse\"\n source_id = airbyte_source_sentry.my_source_sentry.source_id\n destination_id = airbyte_destination_snowflake.my_destination_snowflake.destination_id\n status = \"active\"\n configurations = {\n\n // Available Streams = Comments, Commit comment reactions, Commit comments,\n # Commits, Deployments, Events, Issue comment reactions, Issue events, Issue milestones,\n # Issue reactions, Issues, Project cards, Project columns, Projects, Pull request comment reactions,\n # Pull requests, Pull request stats, Releases, Review comments, Reviews, Stargazers, Workflow runs, \n # Workflows\n streams = [\n {\n name = \"events\"\n },\n {\n name = \"issues\"\n },\n {\n name=\"project_detail\"\n },\n {\n name=\"projects\"\n },\n {\n name=\"releases\"\n }\n ]\n sync_mode = \"full_refresh_overwrite\"\n }\n schedule = {\n cron_expression = \"\"\n schedule_type = \"cron\"\n }\n}" }, { "path": "error_analysis_stack_sentry/infra/airbyte/provider.tf", "content": "// Airbyte Terraform provider documentation: https://registry.terraform.io/providers/airbytehq/airbyte/latest/docs\n\nterraform {\n required_providers {\n airbyte = {\n source = \"airbytehq/airbyte\"\n version = \"0.3.4\"\n }\n }\n}\n\nprovider \"airbyte\" {\n // If running locally (Airbyte OSS) with docker-compose using the airbyte-proxy, \n // include the actual password/username you've set up (or use the defaults below)\n username = \"airbyte\"\n password = \"password\"\n \n // if running locally (Airbyte OSS), include the server url to the airbyte-api-server\n server_url = \"http://localhost:8006/v1\"\n}" }, { "path": "error_analysis_stack_sentry/infra/airbyte/variables.tf", "content": "\nvariable \"workspace_id\" {\n type = string\n}\n\n\n\n\n\n" }, { "path": "error_analysis_stack_sentry/orchestration/orchestration/__init__.py", "content": "" }, { "path": "error_analysis_stack_sentry/orchestration/orchestration/assets.py", "content": "import os\nfrom dagster import OpExecutionContext\nfrom dagster_dbt import DbtCliResource, dbt_assets\nfrom dagster_airbyte import AirbyteResource, load_assets_from_airbyte_instance\n\nfrom .constants import dbt_manifest_path\n\n@dbt_assets(manifest=dbt_manifest_path)\ndef dbt_project_dbt_assets(context: OpExecutionContext, dbt: DbtCliResource):\n yield from dbt.cli([\"build\"], context=context).stream()\n\nairbyte_instance = AirbyteResource(\n host=\"localhost\",\n port=\"8000\",\n # If using basic auth, include username and password:\n username=\"airbyte\",\n password=os.getenv(\"AIRBYTE_PASSWORD\")\n)\n\nairbyte_assets = load_assets_from_airbyte_instance(airbyte_instance,key_prefix=\"snowflake\")" }, { "path": "error_analysis_stack_sentry/orchestration/orchestration/constants.py", "content": "import os\nfrom pathlib import Path\n\nfrom dagster_dbt import DbtCliResource\n\ndbt_project_dir = Path(__file__).joinpath(\"..\", \"..\", \"..\", \"dbt_project\").resolve()\ndbt = DbtCliResource(project_dir=os.fspath(dbt_project_dir))\n\n# If DAGSTER_DBT_PARSE_PROJECT_ON_LOAD is set, a manifest will be created at runtime.\n# Otherwise, we expect a manifest to be present in the project's target directory.\nif os.getenv(\"DAGSTER_DBT_PARSE_PROJECT_ON_LOAD\"):\n dbt_parse_invocation = dbt.cli([\"parse\"], manifest={}).wait()\n dbt_manifest_path = dbt_parse_invocation.target_path.joinpath(\"manifest.json\")\nelse:\n dbt_manifest_path = dbt_project_dir.joinpath(\"target\", \"manifest.json\")" }, { "path": "error_analysis_stack_sentry/orchestration/orchestration/definitions.py", "content": "import os\n\nfrom dagster import Definitions\nfrom dagster_dbt import DbtCliResource\n\nfrom .assets import dbt_project_dbt_assets, airbyte_assets\nfrom .constants import dbt_project_dir\nfrom .schedules import schedules\n\ndefs = Definitions(\n assets=[dbt_project_dbt_assets, airbyte_assets],\n schedules=schedules,\n resources={\n \"dbt\": DbtCliResource(project_dir=os.fspath(dbt_project_dir)),\n },\n)" }, { "path": "error_analysis_stack_sentry/orchestration/orchestration/schedules.py", "content": "\"\"\"\nTo add a daily schedule that materializes your dbt assets, uncomment the following lines.\n\"\"\"\nfrom dagster_dbt import build_schedule_from_dbt_selection\n\nfrom .assets import dbt_project_dbt_assets\n\nschedules = [\n# build_schedule_from_dbt_selection(\n# [dbt_project_dbt_assets],\n# job_name=\"materialize_dbt_models\",\n# cron_schedule=\"0 0 * * *\",\n# dbt_select=\"fqn:*\",\n# ),\n]" }, { "path": "error_analysis_stack_sentry/orchestration/orchestration.egg-info/PKG-INFO", "content": "Metadata-Version: 2.1\nName: orchestration\nVersion: 0.0.1\nRequires-Dist: dagster\nRequires-Dist: dagster-cloud\nRequires-Dist: dagster-dbt\nRequires-Dist: dbt-core>=1.4.0\nRequires-Dist: dbt-snowflake\nProvides-Extra: dev\nRequires-Dist: dagster-webserver; extra == \"dev\"\n" }, { "path": "error_analysis_stack_sentry/orchestration/orchestration.egg-info/SOURCES.txt", "content": "pyproject.toml\nsetup.py\norchestration/__init__.py\norchestration/assets.py\norchestration/constants.py\norchestration/definitions.py\norchestration/schedules.py\norchestration.egg-info/PKG-INFO\norchestration.egg-info/SOURCES.txt\norchestration.egg-info/dependency_links.txt\norchestration.egg-info/requires.txt\norchestration.egg-info/top_level.txt" }, { "path": "error_analysis_stack_sentry/orchestration/orchestration.egg-info/dependency_links.txt", "content": "\n" }, { "path": "error_analysis_stack_sentry/orchestration/orchestration.egg-info/requires.txt", "content": "dagster\ndagster-cloud\ndagster-dbt\ndbt-core>=1.4.0\ndbt-snowflake\n\n[dev]\ndagster-webserver\n" }, { "path": "error_analysis_stack_sentry/orchestration/orchestration.egg-info/top_level.txt", "content": "orchestration\n" }, { "path": "error_analysis_stack_sentry/orchestration/pyproject.toml", "content": "[build-system]\nrequires = [\"setuptools\"]\nbuild-backend = \"setuptools.build_meta\"\n\n[tool.dagster]\nmodule_name = \"orchestration.definitions\"\ncode_location_name = \"orchestration\"" }, { "path": "error_analysis_stack_sentry/orchestration/setup.py", "content": "from setuptools import find_packages, setup\n\nsetup(\n name=\"orchestration\",\n version=\"0.0.1\",\n packages=find_packages(),\n install_requires=[\n \"dagster\",\n \"dagster-cloud\",\n \"dagster-dbt\",\n \"dbt-core>=1.4.0\",\n \"dbt-snowflake\",\n ],\n extras_require={\n \"dev\": [\n \"dagster-webserver\",\n ]\n },\n)" }, { "path": "error_analysis_stack_sentry/setup.py", "content": "from setuptools import find_packages, setup\n\nsetup(\n name=\"error-analysis-stack\",\n version=\"0.0.1\",\n packages=find_packages(),\n install_requires=[\n \"dagster\",\n \"dagster-cloud\",\n \"dagster-airbyte\",\n \"dagster-dbt\",\n \"dbt-core>=1.4.0\",\n \"dbt-snowflake\",\n ],\n extras_require={\n \"dev\": [\n \"dagster-webserver\",\n \"pytest\",\n ]\n },\n)" }, { "path": "github_insight_stack/.gitignore", "content": "# Byte-compiled / optimized / DLL files\n__pycache__/\n*.py[cod]\n*$py.class\n\n# C extensions\n*.so\n\n# Distribution / packaging\n.Python\nbuild/\ndevelop-eggs/\ndist/\ndownloads/\neggs/\n.eggs/\nlib/\nlib64/\nparts/\nsdist/\nvar/\nwheels/\nshare/python-wheels/\n*.egg-info/\n.installed.cfg\n*.egg\nMANIFEST\n\n# PyInstaller\n# Usually these files are written by a python script from a template\n# before PyInstaller builds the exe, so as to inject date/other infos into it.\n*.manifest\n*.spec\n\n# Installer logs\npip-log.txt\npip-delete-this-directory.txt\n\n# Unit test / coverage reports\nhtmlcov/\n.tox/\n.nox/\n.coverage\n.coverage.*\n.cache\nnosetests.xml\ncoverage.xml\n*.cover\n*.py,cover\n.hypothesis/\n.pytest_cache/\ncover/\n\n# Translations\n*.mo\n*.pot\n\n# Django stuff:\n*.log\nlocal_settings.py\ndb.sqlite3\ndb.sqlite3-journal\n\n# Flask stuff:\ninstance/\n.webassets-cache\n\n# Scrapy stuff:\n.scrapy\n\n# Sphinx documentation\ndocs/_build/\n\n# PyBuilder\n.pybuilder/\ntarget/\n\n# Jupyter Notebook\n.ipynb_checkpoints\n\n# IPython\nprofile_default/\nipython_config.py\n\n# pyenv\n# For a library or package, you might want to ignore these files since the code is\n# intended to run in multiple environments; otherwise, check them in:\n# .python-version\n\n# pipenv\n# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.\n# However, in case of collaboration, if having platform-specific dependencies or dependencies\n# having no cross-platform support, pipenv may install dependencies that don't work, or not\n# install all needed dependencies.\n#Pipfile.lock\n\n# poetry\n# Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.\n# This is especially recommended for binary packages to ensure reproducibility, and is more\n# commonly ignored for libraries.\n# https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control\n#poetry.lock\n\n# pdm\n# Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control.\n#pdm.lock\n# pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it\n# in version control.\n# https://pdm.fming.dev/#use-with-ide\n.pdm.toml\n\n# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm\n__pypackages__/\n\n# Celery stuff\ncelerybeat-schedule\ncelerybeat.pid\n\n# SageMath parsed files\n*.sage.py\n\n# Environments\n.env\n.venv\nenv/\nvenv/\nENV/\nenv.bak/\nvenv.bak/\n\n# Spyder project settings\n.spyderproject\n.spyproject\n\n# Rope project settings\n.ropeproject\n\n# mkdocs documentation\n/site\n\n# mypy\n.mypy_cache/\n.dmypy.json\ndmypy.json\n\n# Pyre type checker\n.pyre/\n\n# pytype static type analyzer\n.pytype/\n\n# Cython debug symbols\ncython_debug/\n\n# PyCharm\n# JetBrains specific template is maintained in a separate JetBrains.gitignore that can\n# be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore\n# and can be added to the global gitignore or merged into this file. For a more nuclear\n# option (not recommended) you can uncomment the following to ignore the entire idea folder.\n#.idea/\n\n#Desktop Services Store\n.DS_Store" }, { "path": "github_insight_stack/README.md", "content": "# Github Insight Stack with Airbyte, dbt, Dagster and BigQuery\n\nWelcome to the \"GitHub Analytics Stack\" repository! 🌟 This is your ultimate destination to seamlessly set up a data stack using Airbyte, dbt, and GitHub API. With this configuration, you can extract repository, commit, and pull request data from GitHub, transform it, and glean insights into code quality, collaboration patterns, and project health.\n\nDive in and discover how smoothly these tools integrate for an enriched data analytics experience!\n\n## Table of Contents\n\n- [Prerequisites](#prerequisites)\n- [Architecture Layout](#architecture-layout)\n- [Setting an environment for your project](#1-setting-an-environment-for-your-project)\n- [Setting Up BigQuery to work with Airbyte and dbt](#2-setting-up-bigquery)\n- [Setting Up Airbyte Connectors with Terraform](#3-setting-up-airbyte-connectors-with-terraform)\n- [Setting Up the dbt Project](#4-setting-up-the-dbt-project)\n- [Next Steps](#next-steps)\n\n## Prerequisites\n\nBefore you embark on this integration, ensure you have the following set up and ready:\n\n1. **Python 3.10 or later**: If not installed, download and install it from [Python's official website](https://www.python.org/downloads/).\n\n2. **Docker and Docker Compose (Docker Desktop)**: Install [Docker](https://docs.docker.com/get-docker/) following the official documentation for your specific OS.\n\n3. **Airbyte OSS version**: Deploy the open-source version of Airbyte. Follow the installation instructions from the [Airbyte Documentation](https://docs.airbyte.com/quickstart/deploy-airbyte/).\n\n4. **Terraform**: Terraform will help you provision and manage the Airbyte resources. If you haven't installed it, follow the [official Terraform installation guide](https://developer.hashicorp.com/terraform/tutorials/aws-get-started/install-cli).\n\n5. **Google Cloud account with BigQuery**: You will also need to add the necessary permissions to allow Airbyte and dbt to access the data in BigQuery. A step-by-step guide is provided [below](#2-setting-up-bigquery).\n\n## Architecture Layout\n![img](architecture.png)\n\n## 1. Setting an environment for your project\n\nGet the project up and running on your local machine by following these steps:\n\n1. **Clone the repository (Clone only this quickstart)**: \n ```bash\n git clone --filter=blob:none --sparse https://github.com/airbytehq/quickstarts.git\n ```\n\n ```bash\n cd quickstarts\n ```\n\n ```bash\n git sparse-checkout add github_insight_stack\n ```\n\n\n2. **Navigate to the directory**: \n ```bash\n cd github_insight_stack\n ```\n\n3. **Set Up a Virtual Environment**: \n - For Mac:\n ```bash\n python3 -m venv venv\n source venv/bin/activate\n ```\n - For Windows:\n ```bash\n python -m venv venv\n .\\venv\\Scripts\\activate\n ```\n\n4. **Install Dependencies**: \n ```bash\n pip install -e \".[dev]\"\n ```\n\n## 2. Setting Up BigQuery\n\n#### 1. **Create a Google Cloud Project**\n - If you have a Google Cloud project, you can skip this step.\n - Go to the [Google Cloud Console](https://console.cloud.google.com/).\n - Click on the \"Select a project\" dropdown at the top right and select \"New Project\".\n - Give your project a name and follow the steps to create it.\n\n#### 2. **Create BigQuery Datasets**\n - In the Google Cloud Console, go to BigQuery.\n - Make two new datasets: `raw_data` for Airbyte and `transformed_data` for dbt.\n - If you pick different names, remember to change the names in the code too.\n \n **How to create a dataset:**\n - In the left sidebar, click on your project name.\n - Click “Create Dataset”.\n - Enter the dataset ID (either `raw_data` or `transformed_data`).\n - Click \"Create Dataset\".\n\n#### 3. **Create Service Accounts and Assign Roles**\n - Go to “IAM & Admin” > “Service accounts” in the Google Cloud Console.\n - Click “Create Service Account”.\n - Name your service account (like `airbyte-service-account`).\n - Assign the “BigQuery Data Editor” and “BigQuery Job User” roles to the service account.\n - Follow the same steps to make another service account for dbt (like `dbt-service-account`) and assign the roles.\n\n **How to create a service account and assign roles:**\n - While creating the service account, under the “Grant this service account access to project” section, click the “Role” dropdown.\n - Choose the “BigQuery Data Editor” and “BigQuery Job User” roles.\n - Finish the creation process.\n \n#### 4. **Generate JSON Keys for Service Accounts**\n - For both service accounts, make a JSON key to let the service accounts sign in.\n \n **How to generate JSON key:**\n - Find the service account in the “Service accounts” list.\n - Click on the service account name.\n - In the “Keys” section, click “Add Key” and pick JSON.\n - The key will download automatically. Keep it safe and don’t share it.\n - Do this for the other service account too.\n\n## 3. Setting Up Airbyte Connectors with Terraform\n\nAirbyte allows you to create connectors for sources and destinations, facilitating data synchronization between various platforms. In this project, we're harnessing the power of Terraform to automate the creation of these connectors and the connections between them. Here's how you can set this up:\n\n1. **Navigate to the Airbyte Configuration Directory**:\n \n Change to the relevant directory containing the Terraform configuration for Airbyte:\n ```bash\n cd infra/airbyte\n ```\n\n2. **Modify Configuration Files**:\n\n Within the `infra/airbyte` directory, you'll find three crucial Terraform files:\n - `provider.tf`: Defines the Airbyte provider.\n - `main.tf`: Contains the main configuration for creating Airbyte resources.\n - `variables.tf`: Holds various variables, including credentials.\n\n Adjust the configurations in these files to suit your project's needs. Specifically, provide credentials for your BigQuery connection. You can utilize the `variables.tf` file to manage these credentials.\n\n3. **Initialize Terraform**:\n \n This step prepares Terraform to create the resources defined in your configuration files.\n ```bash\n terraform init\n ```\n\n4. **Review the Plan**:\n\n Before applying any changes, review the plan to understand what Terraform will do.\n ```bash\n terraform plan\n ```\n\n5. **Apply Configuration**:\n\n After reviewing and confirming the plan, apply the Terraform configurations to create the necessary Airbyte resources.\n ```bash\n terraform apply\n ```\n\n6. **Verify in Airbyte UI**:\n\n Once Terraform completes its tasks, navigate to the [Airbyte UI](http://localhost:8000/). Here, you should see your source and destination connectors, as well as the connection between them, set up and ready to go.\n\n7. **Add Normalization to the Airbyte Connection**: \n\n At the moment of creating this Quickstart, it's not possible to select normalization via Terraform, so you need to select that manually. In the Airbyte UI, go to the \"Connections\" tab, select the \"Faker to BigQuery\" connection, go to the \"Transformation\" tab and select \"Normalized tabular data\" and save your changes.\n\n## 4. Setting Up the dbt Project\n\n[dbt (data build tool)](https://www.getdbt.com/) allows you to transform your data by writing, documenting, and executing SQL workflows. Setting up the dbt project requires specifying connection details for your data platform, in this case, BigQuery. Here’s a step-by-step guide to help you set this up:\n\n1. **Navigate to the dbt Project Directory**:\n\n Change to the directory containing the dbt configuration:\n ```bash\n cd dbt_project\n ```\n\n2. **Update Connection Details**:\n\n You'll find a `profiles.yml` file within the directory. This file contains configurations for dbt to connect with your data platform. Update this file with your BigQuery connection details.\n\n3. **Utilize Environment Variables (Optional but Recommended)**:\n\n To keep your credentials secure, you can leverage environment variables. An example is provided within the `profiles.yml` file.\n\n4. **Test the Connection**:\n\n Once you’ve updated the connection details, you can test the connection to your BigQuery instance using:\n ```bash\n dbt debug\n ```\n\n If everything is set up correctly, this command should report a successful connection to BigQuery.\n\n5. **Run the Models**:\n\n If you would like to run the dbt models manually at this point, you can do so by executing:\n ```bash\n dbt run\n ```\n\n You can verify the data has been transformed by going to BigQuery and checking the `transformed_data` dataset.\n\n## 5. Orchestrating with Dagster\n\n[Dagster](https://dagster.io/) is a modern data orchestrator designed to help you build, test, and monitor your data workflows. In this section, we'll walk you through setting up Dagster to oversee both the Airbyte and dbt workflows:\n\n1. **Navigate to the Orchestration Directory**:\n\n Switch to the directory containing the Dagster orchestration configurations:\n ```bash\n cd orchestration\n ```\n\n2. **Set Environment Variables**:\n\n Dagster requires certain environment variables to be set to interact with other tools like dbt and Airbyte. Set the following variables:\n\n ```bash\n export DAGSTER_DBT_PARSE_PROJECT_ON_LOAD=1\n export AIRBYTE_PASSWORD=password\n ```\n \n Note: The `AIRBYTE_PASSWORD` is set to `password` as a default for local Airbyte instances. If you've changed this during your Airbyte setup, ensure you use the appropriate password here.\n\n3. **Launch the Dagster UI**:\n\n With the environment variables in place, kick-start the Dagster UI:\n ```bash\n dagster dev\n ```\n\n4. **Access Dagster in Your Browser**:\n\n Open your browser and navigate to:\n ```\n http://127.0.0.1:3000\n ```\n\n Here, you should see assets for both Airbyte and dbt. To get an overview of how these assets interrelate, click on \"view global asset lineage\". This will give you a clear picture of the data lineage, visualizing how data flows between the tools.\n\n5. **Materialize Dagster Assets**:\n In the Dagster UI, you can initiate your data workflows:\n Begin with the Airbyte sync to extract data from GitHub and send it to BigQuery.\n Next, execute dbt transformations based on the specific models you've developed:\n \n - code_quality\n - collaboration_patterns\n - project_health\n\nIn the Dagster UI, these models should be materialized, showing the transformed GitHub data according to the logic you've implemented in dbt.\n\nWhen you build custom pipelines in Dagster, make sure they reflect your dbt model names and dependencies. For example, if one model depends on the output of another, ensure that your Dagster pipeline respects this order of execution.\n\nThe central idea is to adjust the orchestration setup so it maps directly to your dbt model names and the dependencies among them. Remember, the success of the orchestration will also depend on the correct setup and error-free state of the individual dbt models and Airbyte configurations.\n\n\n## Next Steps\n\nKudos on setting up the GitHub Insight Stack! 🎉 Here’s how you can amplify your analytics capabilities:\n\n### 1. Analyze Collaboration Patterns:\n\n Examine the frequency, nature, and spread of commits and pull requests across repositories.\n\n### 2. Evaluate Code Quality:\n\n Monitor pull request acceptance rates, time to merge, and frequency of commits to assess project health and contributor efficiency.\n\n### 3. Expand Data Horizons:\n\n Bring in more GitHub data or integrate other platforms using Airbyte to enrich your dataset.\n\n### 4. Improve Transformations:\n\n Refine your dbt models to get more nuanced insights.\n\n### 5. Scale and Monitor:\n\n Optimize configurations for larger datasets and establish monitoring to ensure data flow remains smooth.\n\n### 6. Engage with the Community:\n\n Share findings, models, or new configurations with the community. Contribute to repositories and help foster collective growth.\n" }, { "path": "github_insight_stack/dbt_project/.gitignore", "content": "\ntarget/\ndbt_packages/\nlogs/\n\n.user.yml\n" }, { "path": "github_insight_stack/dbt_project/README.md", "content": "Welcome to your new dbt project!\n\n### Using the starter project\n\nTry running the following commands:\n- dbt run\n- dbt test\n\n\n### Resources:\n- Learn more about dbt [in the docs](https://docs.getdbt.com/docs/introduction)\n- Check out [Discourse](https://discourse.getdbt.com/) for commonly asked questions and answers\n- Join the [chat](https://community.getdbt.com/) on Slack for live discussions and support\n- Find [dbt events](https://events.getdbt.com) near you\n- Check out [the blog](https://blog.getdbt.com/) for the latest news on dbt's development and best practices\n" }, { "path": "github_insight_stack/dbt_project/analyses/.gitkeep", "content": "" }, { "path": "github_insight_stack/dbt_project/dbt_project.yml", "content": "\n# Name your project! Project names should contain only lowercase characters\n# and underscores. A good package name should reflect your organization's\n# name or the intended use of these models\nname: 'dbt_project'\nversion: '1.0.0'\nconfig-version: 2\n\n# This setting configures which \"profile\" dbt uses for this project.\nprofile: 'dbt_project'\n\n# These configurations specify where dbt should look for different types of files.\n# The `model-paths` config, for example, states that models in this project can be\n# found in the \"models/\" directory. You probably won't need to change these!\nmodel-paths: [\"models\"]\nanalysis-paths: [\"analyses\"]\ntest-paths: [\"tests\"]\nseed-paths: [\"seeds\"]\nmacro-paths: [\"macros\"]\nsnapshot-paths: [\"snapshots\"]\n\nclean-targets: # directories to be removed by `dbt clean`\n - \"target\"\n - \"dbt_packages\"\n\n\n# Configuring models\n# Full documentation: https://docs.getdbt.com/docs/configuring-models\n\n# In this example config, we tell dbt to build all models in the example/\n# directory as views. These settings can be overridden in the individual model\n# files using the `{{ config(...) }}` macro.\nmodels:\n dbt_project:\n # Config indicated by + and applies to all files under models/example/\n staging:\n +materialized: view\n marts:\n +materialized: view\n" }, { "path": "github_insight_stack/dbt_project/macros/.gitkeep", "content": "" }, { "path": "github_insight_stack/dbt_project/models/Readme.md", "content": "\n# Notes\n\n- These models are basic starting points. You can further refine and expand on them based on the insights you\nwant and the intricacies of your GitHub data.\n\n- Make sure to adjust field names if they don't match the schema of the data you've extracted from GitHub into BigQuery.\n\n- It might also be beneficial to integrate other dbt packages or custom macros to handle more advanced transformations or aggregations as needed.\n\n- Always remember to test your models with dbt to ensure they run correctly and produce the expected results." }, { "path": "github_insight_stack/dbt_project/models/sources.yml", "content": "version: 2\n\nsources:\n - name: github_raw\n database: your_bigquery_project_id\n schema: github_raw_data\n\n tables:\n - name: repositories\n - name: commits\n - name: pull_requests\n" }, { "path": "github_insight_stack/dbt_project/models/test-models/code_quality.sql", "content": "WITH commit_analysis AS (\n SELECT \n committer_name,\n COUNT(*) AS number_of_commits,\n AVG(LENGTH(commit_message)) AS avg_commit_message_length\n FROM {{ source('github_raw', 'commits') }}\n GROUP BY 1\n)\n\nSELECT * FROM commit_analysis" }, { "path": "github_insight_stack/dbt_project/models/test-models/collaboration_patterns.sql", "content": "WITH pr_analysis AS (\n SELECT \n author_name,\n COUNT(*) AS number_of_prs,\n AVG(LENGTH(pr_title)) AS avg_pr_title_length\n FROM {{ source('github_raw', 'pull_requests') }}\n GROUP BY 1\n)\n\nSELECT * FROM pr_analysis\n" }, { "path": "github_insight_stack/dbt_project/models/test-models/project_health.sql", "content": "WITH contributor_analysis AS (\n SELECT \n committer_name,\n COUNT(DISTINCT repo_name) AS repos_contributed_to,\n COUNT(*) AS number_of_commits\n FROM {{ source('github_raw', 'commits') }}\n GROUP BY 1\n)\n\nSELECT * FROM contributor_analysis\n" }, { "path": "github_insight_stack/dbt_project/profiles.yml", "content": "dbt_project:\n outputs:\n dev:\n dataset: github_transformed_data\n job_execution_timeout_seconds: 300\n job_retries: 1\n # Use an env variable to indicate your JSON key file path\n keyfile: \"{{ env_var('DBT_BIGQUERY_KEYFILE_PATH', '') }}\"\n location: US\n method: service-account\n priority: interactive\n # Indicate your BigQuery project ID\n project: your_project_id\n threads: 1\n type: bigquery\n target: dev" }, { "path": "github_insight_stack/dbt_project/seeds/.gitkeep", "content": "" }, { "path": "github_insight_stack/dbt_project/snapshots/.gitkeep", "content": "" }, { "path": "github_insight_stack/dbt_project/tests/.gitkeep", "content": "" }, { "path": "github_insight_stack/infra/.gitignore", "content": "# Local .terraform directories\n**/.terraform/*\n\n# .tfstate files\n*.tfstate\n*.tfstate.*\n\n# Crash log files\ncrash.log\ncrash.*.log\n\n# Exclude all .tfvars files, which are likely to contain sensitive data, such as\n# password, private keys, and other secrets. These should not be part of version \n# control as they are data points which are potentially sensitive and subject \n# to change depending on the environment.\n*.tfvars\n*.tfvars.json\n\n# Ignore override files as they are usually used to override resources locally and so\n# are not checked in\noverride.tf\noverride.tf.json\n*_override.tf\n*_override.tf.json\n\n# Include override files you do wish to add to version control using negated pattern\n# !example_override.tf\n\n# Include tfplan files to ignore the plan output of command: terraform plan -out=tfplan\n# example: *tfplan*\n\n# Ignore CLI configuration files\n.terraformrc\nterraform.rc" }, { "path": "github_insight_stack/infra/airbyte/.terraform.lock.hcl", "content": "# This file is maintained automatically by \"terraform init\".\n# Manual edits may be lost in future updates.\n\nprovider \"registry.terraform.io/airbytehq/airbyte\" {\n version = \"0.3.3\"\n constraints = \"0.3.3\"\n hashes = [\n \"h1:a6g5uWP/pt1/popVNlKwnTssWNfdYY4KVFPMisN/yvU=\",\n \"zh:0efa470b34d9b912b47efe4469c51713bfc3c2413e52c17e1e903f2a3cddb2f6\",\n \"zh:1bddd69fa2c2d4f3e239d60555446df9bc4ce0c0cabbe7e092fe1d44989ab004\",\n \"zh:2e20540403a0010007b53456663fb037b24e30f6c8943f65da1bcf7fa4dfc8a6\",\n \"zh:2f415369ad884e8b7115a5c5ff229d052f7af1fca27abbfc8ebef379ed11aec4\",\n \"zh:46fd9a906f4b6461112dcc5a5aa01a3fcd7a19a72d4ad0b2e37790da37701fe1\",\n \"zh:83503ebb77bb6d6941c42ba323cf22380d08a1506554a2dcc8ac54e74c0886a1\",\n \"zh:890df766e9b839623b1f0437355032a3c006226a6c200cd911e15ee1a9014e9f\",\n \"zh:8fd770eff726826d3a63b9e3733c5455b5cde004027b04ee3f75888eb8538c90\",\n \"zh:b0fc890ed4f9b077bf70ed121cc3550e7a07d16e7798ad517623274aa62ad7b0\",\n \"zh:c2a01612362da9b73cd5958f281e1aa7ff09af42182e463097d11ed78e778e72\",\n \"zh:c64b2bb1887a0367d64ba3393d4b3a16c418cf5b1792e2e7aae7c0b5413eb334\",\n \"zh:ce14ebbf0ed91913ec62655a511763dec62b5779de9a209bd6f1c336640cddc0\",\n \"zh:e0662ca837eee10f7733ea9a501d995281f56bd9b410ae13ad03eb106011db14\",\n \"zh:e103d480fc6066004bc98e9e04a141a1f55b918cc2912716beebcc6fc4c872fb\",\n \"zh:e2507049098f0f1b21cb56870f4a5ef624bcf6d3959e5612eada1f8117341648\",\n ]\n}\n" }, { "path": "github_insight_stack/infra/airbyte/main.tf", "content": "// Airbyte Terraform provider documentation: https://registry.terraform.io/providers/airbytehq/airbyte/latest/docs\n\n// Sources\nresource \"airbyte_source_github\" \"github\" {\n configuration = {\n access_token = var.github_access_token // You'll need to provide the GitHub access token as a variable.\n repository = var.github_repository // The GitHub repository you want to pull data from.\n start_date = \"2023-01-01\" // Starting date from which data should be pulled. Modify as needed.\n source_type = \"github\"\n }\n name = \"GitHub\"\n workspace_id = var.workspace_id\n}\n\n// Destinations\nresource \"airbyte_destination_bigquery\" \"bigquery\" {\n configuration = {\n dataset_id = var.dataset_id\n dataset_location = \"US\"\n destination_type = \"bigquery\"\n project_id = var.project_id\n credentials_json = var.credentials_json\n loading_method = {\n destination_bigquery_loading_method_standard_inserts = {\n method = \"Standard\"\n }\n }\n }\n name = \"BigQuery\"\n workspace_id = var.workspace_id\n}\n\n// Connections\nresource \"airbyte_connection\" \"github_to_bigquery\" {\n name = \"GitHub to BigQuery\"\n source_id = airbyte_source_github.github.source_id\n destination_id = airbyte_destination_bigquery.bigquery.destination_id\n configurations = {\n streams = [\n {\n name = \"commits\" // Modify to match the GitHub streams you want to sync.\n },\n {\n name = \"issues\"\n },\n {\n name = \"pull_requests\"\n },\n // Add or remove any other streams as per your needs.\n ]\n }\n}" }, { "path": "github_insight_stack/infra/airbyte/provider.tf", "content": "// Airbyte Terraform provider documentation: https://registry.terraform.io/providers/airbytehq/airbyte/latest/docs\n\nterraform {\n required_providers {\n airbyte = {\n source = \"airbytehq/airbyte\"\n version = \"0.3.3\"\n }\n }\n}\n\nprovider \"airbyte\" {\n // If running locally (Airbyte OSS) with docker-compose using the airbyte-proxy, \n // include the actual password/username you've set up (or use the defaults below)\n username = \"airbyte\"\n password = \"password\"\n \n // if running locally (Airbyte OSS), include the server url to the airbyte-api-server\n server_url = \"http://localhost:8006/v1\"\n}" }, { "path": "github_insight_stack/infra/airbyte/variables.tf", "content": "variable \"workspace_id\" {\n type = string\n}\n\nvariable \"dataset_id\" {\n type = string\n}\n\nvariable \"project_id\" {\n type = string\n}\n\nvariable \"credentials_json\" {\n type = string\n}\n\n\n\n\n" }, { "path": "github_insight_stack/orchestration/orchestration/__init__.py", "content": "" }, { "path": "github_insight_stack/orchestration/orchestration/assets.py", "content": "import os\nfrom dagster import OpExecutionContext\nfrom dagster_dbt import DbtCliResource, dbt_assets\nfrom dagster_airbyte import AirbyteResource, load_assets_from_airbyte_instance\n\nfrom .constants import dbt_manifest_path\n\n@dbt_assets(manifest=dbt_manifest_path)\ndef dbt_project_dbt_assets(context: OpExecutionContext, dbt: DbtCliResource):\n yield from dbt.cli([\"build\"], context=context).stream()\n\nairbyte_instance = AirbyteResource(\n host=\"localhost\",\n port=\"8000\",\n # If using basic auth, include username and password:\n username=\"airbyte\",\n password=os.getenv(\"AIRBYTE_PASSWORD\")\n)\n\nairbyte_assets = load_assets_from_airbyte_instance(airbyte_instance, key_prefix=\"github\")" }, { "path": "github_insight_stack/orchestration/orchestration/constants.py", "content": "import os\nfrom pathlib import Path\n\nfrom dagster_dbt import DbtCliResource\n\ndbt_project_dir = Path(__file__).joinpath(\"..\", \"..\", \"..\", \"dbt_project\").resolve()\ndbt = DbtCliResource(project_dir=os.fspath(dbt_project_dir))\n\n# If DAGSTER_DBT_PARSE_PROJECT_ON_LOAD is set, a manifest will be created at runtime.\n# Otherwise, we expect a manifest to be present in the project's target directory.\nif os.getenv(\"DAGSTER_DBT_PARSE_PROJECT_ON_LOAD\"):\n dbt_parse_invocation = dbt.cli([\"parse\"], manifest={}).wait()\n dbt_manifest_path = dbt_parse_invocation.target_path.joinpath(\"manifest.json\")\nelse:\n dbt_manifest_path = dbt_project_dir.joinpath(\"target\", \"manifest.json\")" }, { "path": "github_insight_stack/orchestration/orchestration/definitions.py", "content": "import os\n\nfrom dagster import Definitions\nfrom dagster_dbt import DbtCliResource\n\nfrom .assets import dbt_project_dbt_assets, airbyte_assets\nfrom .constants import dbt_project_dir\nfrom .schedules import schedules\n\ndefs = Definitions(\n assets=[dbt_project_dbt_assets, airbyte_assets],\n schedules=schedules,\n resources={\n \"dbt\": DbtCliResource(project_dir=os.fspath(dbt_project_dir)),\n },\n)" }, { "path": "github_insight_stack/orchestration/orchestration/schedules.py", "content": "\"\"\"\nTo add a daily schedule that materializes your dbt assets, uncomment the following lines.\n\"\"\"\nfrom dagster_dbt import build_schedule_from_dbt_selection\n\nfrom .assets import dbt_project_dbt_assets\n\nschedules = [\n# build_schedule_from_dbt_selection(\n# [dbt_project_dbt_assets],\n# job_name=\"materialize_dbt_models\",\n# cron_schedule=\"0 0 * * *\",\n# dbt_select=\"fqn:*\",\n# ),\n]" }, { "path": "github_insight_stack/orchestration/pyproject.toml", "content": "[build-system]\nrequires = [\"setuptools\"]\nbuild-backend = \"setuptools.build_meta\"\n\n[tool.dagster]\nmodule_name = \"orchestration.definitions\"\ncode_location_name = \"orchestration\"" }, { "path": "github_insight_stack/orchestration/setup.py", "content": "from setuptools import find_packages, setup\n\nsetup(\n name=\"orchestration\",\n version=\"0.0.1\",\n packages=find_packages(),\n install_requires=[\n \"dagster\",\n \"dagster-cloud\",\n \"dagster-dbt\",\n \"dbt-core>=1.4.0\",\n \"dbt-bigquery\",\n ],\n extras_require={\n \"dev\": [\n \"dagster-webserver\",\n ]\n },\n)" }, { "path": "github_insight_stack/setup.py", "content": "from setuptools import find_packages, setup\n\nsetup(\n name=\"airbyte-dbt-dagster\",\n packages=find_packages(),\n install_requires=[\n \"dbt-bigquery\",\n \"dagster\",\n \"dagster-cloud\",\n \"dagster-dbt\",\n \"dagster-airbyte\",\n ],\n extras_require={\"dev\": [\"dagit\", \"pytest\"]},\n)" }, { "path": "low_latency_data_availability/.gitignore", "content": "# Byte-compiled / optimized / DLL files\n__pycache__/\n*.py[cod]\n*$py.class\n\n# C extensions\n*.so\n\n# Distribution / packaging\n.Python\nbuild/\ndevelop-eggs/\ndist/\ndownloads/\neggs/\n.eggs/\nlib/\nlib64/\nparts/\nsdist/\nvar/\nwheels/\nshare/python-wheels/\n*.egg-info/\n.installed.cfg\n*.egg\nMANIFEST\n\n# PyInstaller\n# Usually these files are written by a python script from a template\n# before PyInstaller builds the exe, so as to inject date/other infos into it.\n*.manifest\n*.spec\n\n# Installer logs\npip-log.txt\npip-delete-this-directory.txt\n\n# Unit test / coverage reports\nhtmlcov/\n.tox/\n.nox/\n.coverage\n.coverage.*\n.cache\nnosetests.xml\ncoverage.xml\n*.cover\n*.py,cover\n.hypothesis/\n.pytest_cache/\ncover/\n\n# Translations\n*.mo\n*.pot\n\n# Django stuff:\n*.log\nlocal_settings.py\ndb.sqlite3\ndb.sqlite3-journal\n\n# Flask stuff:\ninstance/\n.webassets-cache\n\n# Scrapy stuff:\n.scrapy\n\n# Sphinx documentation\ndocs/_build/\n\n# PyBuilder\n.pybuilder/\ntarget/\n\n# Jupyter Notebook\n.ipynb_checkpoints\n\n# IPython\nprofile_default/\nipython_config.py\n\n# pyenv\n# For a library or package, you might want to ignore these files since the code is\n# intended to run in multiple environments; otherwise, check them in:\n# .python-version\n\n# pipenv\n# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.\n# However, in case of collaboration, if having platform-specific dependencies or dependencies\n# having no cross-platform support, pipenv may install dependencies that don't work, or not\n# install all needed dependencies.\n#Pipfile.lock\n\n# poetry\n# Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.\n# This is especially recommended for binary packages to ensure reproducibility, and is more\n# commonly ignored for libraries.\n# https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control\n#poetry.lock\n\n# pdm\n# Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control.\n#pdm.lock\n# pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it\n# in version control.\n# https://pdm.fming.dev/#use-with-ide\n.pdm.toml\n\n# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm\n__pypackages__/\n\n# Celery stuff\ncelerybeat-schedule\ncelerybeat.pid\n\n# SageMath parsed files\n*.sage.py\n\n# Environments\n.env\n.venv\nenv/\nvenv/\nENV/\nenv.bak/\nvenv.bak/\n\n# Spyder project settings\n.spyderproject\n.spyproject\n\n# Rope project settings\n.ropeproject\n\n# mkdocs documentation\n/site\n\n# mypy\n.mypy_cache/\n.dmypy.json\ndmypy.json\n\n# Pyre type checker\n.pyre/\n\n# pytype static type analyzer\n.pytype/\n\n# Cython debug symbols\ncython_debug/\n\n# PyCharm\n# JetBrains specific template is maintained in a separate JetBrains.gitignore that can\n# be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore\n# and can be added to the global gitignore or merged into this file. For a more nuclear\n# option (not recommended) you can uncomment the following to ignore the entire idea folder.\n#.idea/\n\n#Desktop Services Store\n.DS_Store" }, { "path": "low_latency_data_availability/README.md", "content": "# Low-Latency Data Availability Stack\n\nWelcome to the \"Low-Latency Data Availability Stack\" repository! This repo provides a quickstart template for building a Low-Latency Data Availability solution that syncs data from an existing Postgres database to a BigQuery dataset using Airbyte. We will easily replicate the tables and data from the Postgres database to BigQuery with Airbyte using Change Data Capture (CDC) and Postgres Write Ahead Log (WAL). This quickstart also explores using Airbyte Postgres features to ensure near real-time data availability and access. While this template doesn't delve into specific data, its goal is to showcase how the low latency data solution can be achieved with Airbyte.\n\nJust like other Airbyte quickstarts, this quickstart is designed to minimize setup hassles and propel you forward.\n\n## Table of Contents\n\n- [Low-Latency Data Availability Stack](#low-latency-data-availability-stack)\n - [Table of Contents](#table-of-contents)\n - [Infrastructure Layout](#infrastructure-layout)\n - [Prerequisites](#prerequisites)\n - [1. Setting an environment for your project](#1-setting-an-environment-for-your-project)\n - [2. Setting Up Airbyte Connectors with Terraform](#2-setting-up-airbyte-connectors-with-terraform)\n - [3. Achieving Low Latency with Airbyte](#3-achieving-low-latency-with-airbyte)\n - [Next Steps](#next-steps)\n\n\n## Infrastructure Layout\n\n![infrastructure layout](images/infrastructure_layout.png)\n\n## Prerequisites\n\nBefore you embark on this integration, ensure you have the following set up and ready:\n\n1. **Python 3.10 or later**: If not installed, download and install it from [Python's official website](https://www.python.org/downloads/).\n\n2. **Docker and Docker Compose (Docker Desktop)**: Install [Docker](https://docs.docker.com/get-docker/) following the official documentation for your specific OS.\n\n3. **Airbyte OSS version**: Deploy the open-source version of Airbyte. Follow the installation instructions from the [Airbyte Documentation](https://docs.airbyte.com/quickstart/deploy-airbyte/).\n\n4. **Terraform**: Terraform will help you provision and manage the Airbyte resources. If you haven't installed it, follow the [official Terraform installation guide](https://developer.hashicorp.com/terraform/tutorials/aws-get-started/install-cli).\n\n\n## 1. Setting an environment for your project\n\nGet the project up and running on your local machine by following these steps:\n\n1. **Clone the repository (Clone only this quickstart)**: \n ```bash\n git clone --filter=blob:none --sparse https://github.com/airbytehq/quickstarts.git\n ```\n\n ```bash\n cd quickstarts\n ```\n\n ```bash\n git sparse-checkout add low_latency_data_availability\n ```\n\n \n2. **Navigate to the directory**: \n ```bash\n cd low_latency_data_availability\n ```\n\n3. **Set Up a Virtual Environment**: \n - For Mac:\n ```bash\n python3 -m venv venv\n source venv/bin/activate\n ```\n - For Windows:\n ```bash\n python -m venv venv\n .\\venv\\Scripts\\activate\n ```\n\n4. **Install Dependencies**: \n ```bash\n pip install -e \".[dev]\"\n ```\n\n## 2. Setting Up Airbyte Connectors with Terraform\n\nAirbyte allows you to create connectors for sources and destinations, facilitating data synchronization between various platforms. In this project, we're harnessing the power of Terraform to automate the creation of these connectors and the connections between them. Here's how you can set this up:\n\n1. **Navigate to the Airbyte Configuration Directory**:\n \n Change to the relevant directory containing the Terraform configuration for Airbyte:\n ```bash\n cd infra/airbyte\n ```\n\n2. **Modify Configuration Files**:\n\n Within the `infra/airbyte` directory, you'll find three crucial Terraform files:\n - `provider.tf`: Defines the Airbyte provider.\n - `main.tf`: Contains the main configuration for creating Airbyte resources.\n - `variables.tf`: Holds various variables, including credentials.\n\n Adjust the configurations in these files to suit your project's needs. Specifically, provide credentials for your Postgres and BigQuery connections. You can utilize the `variables.tf` file to manage these credentials.\n\n3. **Initialize Terraform**:\n \n This step prepares Terraform to create the resources defined in your configuration files.\n ```bash\n terraform init\n ```\n\n4. **Review the Plan**:\n\n Before applying any changes, review the plan to understand what Terraform will do.\n ```bash\n terraform plan\n ```\n\n5. **Apply Configuration**:\n\n After reviewing and confirming the plan, apply the Terraform configurations to create the necessary Airbyte resources.\n ```bash\n terraform apply\n ```\n\n6. **Verify in Airbyte UI**:\n\n Once Terraform completes its tasks, navigate to the Airbyte UI. Here, you should see your Postgres source and BigQuery destination connectors, as well as the connection between them, set up and ready to go.\n\n## 3. Achieving Low Latency with Airbyte\n\nUsing Airbyte for the data replication offers the following for achieving low latency;\n\n- Throughput performances for the Postgres source connector are about 11 MB per second. This offers users the capability to sync Terabytes of tables is a fast, reliable manner.\n- For very large database tables, the data are read in chunks. This caters for reliability issues due to the strain on the server or network issues. These chunks are either read successively or even concurrently.\n- [Checkpointing](https://docs.airbyte.com/understanding-airbyte/airbyte-protocol/#state--checkpointing). This happens when there is a network error or a server going down for maintenance during a sync operation. Airbyte stores the state of a sync such that we can restart from a known point. This is known as the [CTID](https://enterprisedb.com/postgres-tutorials/what-equivalent-rowid-postgresql#:~:text=The%20ctid%20field%20is%20a,the%20location%20of%20the%20tuple.) markers. Thus, if there is an error, we can restart our read from a last known saved checkpoint.\n- Once an initial sync is done, for subsequent incremental syncs, Airbyte can use either of 3 [options](https://docs.airbyte.com/integrations/sources/postgres#postgres-replication-methods) that depend on a reliable cursor to be able to find data that has changed. These options are;\n - [CDC](https://docs.airbyte.com/integrations/sources/postgres#cdc),\n - [xmin](https://docs.airbyte.com/integrations/sources/postgres#xmin), or \n - a user column.\n\n For this quickstart, we will be using the CDC option as it offers the least latency for high volume data sync. If your data is less than 500GB, you can go for the xmin option.\n\n## Next Steps\n\nOnce you've set up and launched this initial integration, you can proceed to sync the connection to trigger the data sync. The real power of this quickstart lies in its adaptability and extensibility. Here’s a roadmap to help you customize and harness this project tailored to your specific data needs:\n\n1. **Plan your Data Replication**:\n\n Ideally, database replication should be a planned activity. Do not run a data replication job during a production peak. Data latency depends on factors such as the size of data to be moved, geographic location of the source and destination, and other parameters. Ensure you test thoroughly before deploying to production.\n \n2. **Extend the Project**:\n\n The real beauty of this integration is its extensibility. Whether you want to add more Postgres sources, integrate additional tools, or modify the sync schedule – the floor is yours. The granularity of the replication can also be set by selecting the correct sync mode for each stream (table). Read [sync mode](https://docs.airbyte.com/understanding-airbyte/connections/) for more details. With the foundation set, sky's the limit for how you want to extend and refine your data processes.\n" }, { "path": "low_latency_data_availability/infra/.gitignore", "content": "# Local .terraform directories\n**/.terraform/*\n\n# .tfstate files\n*.tfstate\n*.tfstate.*\n\n# Crash log files\ncrash.log\ncrash.*.log\n\n# Exclude all .tfvars files, which are likely to contain sensitive data, such as\n# password, private keys, and other secrets. These should not be part of version \n# control as they are data points which are potentially sensitive and subject \n# to change depending on the environment.\n*.tfvars\n*.tfvars.json\n\n# Ignore override files as they are usually used to override resources locally and so\n# are not checked in\noverride.tf\noverride.tf.json\n*_override.tf\n*_override.tf.json\n\n# Include override files you do wish to add to version control using negated pattern\n# !example_override.tf\n\n# Include tfplan files to ignore the plan output of command: terraform plan -out=tfplan\n# example: *tfplan*\n\n# Ignore CLI configuration files\n.terraformrc\nterraform.rc" }, { "path": "low_latency_data_availability/infra/airbyte/main.tf", "content": "// Airbyte Terraform provider documentation: https://registry.terraform.io/providers/airbytehq/airbyte/latest/docs\n\n// Sources\nresource \"airbyte_source_postgres\" \"postgres\" {\n configuration = {\n database = \"...my_database...\"\n host = \"...my_host...\"\n username = \"...my_username...\"\n password = \"...my_password...\"\n port = 5432\n source_type = \"postgres\"\n schemas = [\n \"...my_schema...\"\n ]\n ssl_mode = {\n allow = {}\n }\n tunnel_method = {\n no_tunnel = {}\n }\n replication_method = {\n read_changes_using_write_ahead_log_cdc = {\n publication = \"...pub...\"\n replication_slot = \"...slot...\"\n }\n }\n }\n name = \"Postgres\"\n workspace_id = var.workspace_id\n}\n\n// Destinations\nresource \"airbyte_destination_bigquery\" \"bigquery\" {\n configuration = {\n dataset_id = \"...my_dataset_id...\"\n dataset_location = \"...my_dataset_location...\"\n destination_type = \"bigquery\"\n project_id = \"...my_project_id...\"\n credentials_json = \"...my_credentials_json_file_path...\"\n loading_method = {\n destination_bigquery_loading_method_standard_inserts = {\n method = \"Standard\"\n }\n }\n }\n name = \"BigQuery\"\n workspace_id = var.workspace_id\n}\n\n// Connections\nresource \"airbyte_connection\" \"postgres_to_bigquery\" {\n name = \"Postgres to BigQuery [Low Latency Data]\"\n source_id = airbyte_source_postgres.postgres.source_id\n destination_id = airbyte_destination_bigquery.bigquery.destination_id\n configurations = {\n streams = [\n {\n cursor_field = [\"...\",]\n name = \"...my_table_name_1...\"\n primary_key = [[\"...\",],]\n sync_mode = \"incremental_deduped_history\"\n },\n {\n cursor_field = [\"...\",]\n name = \"...my_table_name_2...\"\n primary_key = [[\"...\",],]\n sync_mode = \"incremental_deduped_history\"\n },\n ]\n }\n}" }, { "path": "low_latency_data_availability/infra/airbyte/provider.tf", "content": "// Airbyte Terraform provider documentation: https://registry.terraform.io/providers/airbytehq/airbyte/latest/docs\n\nterraform {\n required_providers {\n airbyte = {\n source = \"airbytehq/airbyte\"\n version = \"0.3.4\"\n }\n }\n}\n\nprovider \"airbyte\" {\n // If running locally (Airbyte OSS) with docker-compose using the airbyte-proxy, \n // include the actual password/username you've set up (or use the defaults below)\n username = \"airbyte\"\n password = \"password\"\n \n // if running locally (Airbyte OSS), include the server url to the airbyte-api-server\n server_url = \"http://localhost:8006/v1\"\n}" }, { "path": "low_latency_data_availability/infra/airbyte/variables.tf", "content": "variable \"workspace_id\" {\n type = string\n}\n\n\n\n\n\n\n" }, { "path": "low_latency_data_availability/setup.py", "content": "from setuptools import find_packages, setup\n\nsetup(\n name=\"low-latency-data-availability\",\n packages=find_packages(),\n install_requires=[\n ],\n extras_require={\"dev\": [\"pytest\"]},\n)" }, { "path": "mongodb_mysql_integration/.gitignore", "content": "# Byte-compiled / optimized / DLL files\n__pycache__/\n*.py[cod]\n*$py.class\n\n# C extensions\n*.so\n\n# Distribution / packaging\n.Python\nbuild/\ndevelop-eggs/\ndist/\ndownloads/\neggs/\n.eggs/\nlib/\nlib64/\nparts/\nsdist/\nvar/\nwheels/\nshare/python-wheels/\n*.egg-info/\n.installed.cfg\n*.egg\nMANIFEST\n\n# PyInstaller\n# Usually these files are written by a python script from a template\n# before PyInstaller builds the exe, so as to inject date/other infos into it.\n*.manifest\n*.spec\n\n# Installer logs\npip-log.txt\npip-delete-this-directory.txt\n\n# Unit test / coverage reports\nhtmlcov/\n.tox/\n.nox/\n.coverage\n.coverage.*\n.cache\nnosetests.xml\ncoverage.xml\n*.cover\n*.py,cover\n.hypothesis/\n.pytest_cache/\ncover/\n\n# Translations\n*.mo\n*.pot\n\n# Django stuff:\n*.log\nlocal_settings.py\ndb.sqlite3\ndb.sqlite3-journal\n\n# Flask stuff:\ninstance/\n.webassets-cache\n\n# Scrapy stuff:\n.scrapy\n\n# Sphinx documentation\ndocs/_build/\n\n# PyBuilder\n.pybuilder/\ntarget/\n\n# Jupyter Notebook\n.ipynb_checkpoints\n\n# IPython\nprofile_default/\nipython_config.py\n\n# pyenv\n# For a library or package, you might want to ignore these files since the code is\n# intended to run in multiple environments; otherwise, check them in:\n# .python-version\n\n# pipenv\n# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.\n# However, in case of collaboration, if having platform-specific dependencies or dependencies\n# having no cross-platform support, pipenv may install dependencies that don't work, or not\n# install all needed dependencies.\n#Pipfile.lock\n\n# poetry\n# Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.\n# This is especially recommended for binary packages to ensure reproducibility, and is more\n# commonly ignored for libraries.\n# https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control\n#poetry.lock\n\n# pdm\n# Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control.\n#pdm.lock\n# pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it\n# in version control.\n# https://pdm.fming.dev/#use-with-ide\n.pdm.toml\n\n# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm\n__pypackages__/\n\n# Celery stuff\ncelerybeat-schedule\ncelerybeat.pid\n\n# SageMath parsed files\n*.sage.py\n\n# Environments\n.env\n.venv\nenv/\nvenv/\nENV/\nenv.bak/\nvenv.bak/\n\n# Spyder project settings\n.spyderproject\n.spyproject\n\n# Rope project settings\n.ropeproject\n\n# mkdocs documentation\n/site\n\n# mypy\n.mypy_cache/\n.dmypy.json\ndmypy.json\n\n# Pyre type checker\n.pyre/\n\n# pytype static type analyzer\n.pytype/\n\n# Cython debug symbols\ncython_debug/\n\n# PyCharm\n# JetBrains specific template is maintained in a separate JetBrains.gitignore that can\n# be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore\n# and can be added to the global gitignore or merged into this file. For a more nuclear\n# option (not recommended) you can uncomment the following to ignore the entire idea folder.\n#.idea/\n\n#Desktop Services Store\n.DS_Store\n" }, { "path": "mongodb_mysql_integration/README.md", "content": "# MongoDB MySQL integration Stack\n\nWelcome to the \"MongoDB to MySQL Stack\" repository! This repo provides a quickstart template for building a MongoDB data integration solution using Airbyte. We will easily synchronize the NoSQL mongoDB data to SQL type MySQL databases with Airbyte terraform airbyte provider. Also this template involves flexibility and use cases for integrating new sources and connections.\n\nThis quickstart is designed to minimize setup hassles and propel you forward.\n\n## Table of Contents\n\n- [MongoDB MySQL integration Stack](#mongodb-mysql-integration-stack)\n - [Table of Contents](#table-of-contents)\n - [Infrastructure Layout](#infrastructure-layout)\n - [Prerequisites](#prerequisites)\n - [1. Setting an environment for your project](#1-setting-an-environment-for-your-project)\n - [2. Setting Up Airbyte Connectors with Terraform](#2-setting-up-airbyte-connectors-with-terraform)\n - [Next Steps](#next-steps)\n\n\n## Infrastructure Layout\n\n![infrastructure layout](images/infrastructure_layout.png)\n\n## Prerequisites\n\nBefore you embark on this integration, ensure you have the following set up and ready:\n\n1. **Python 3.10 or later**: If not installed, download and install it from [Python's official website](https://www.python.org/downloads/).\n\n2. **Docker and Docker Compose (Docker Desktop)**: Install [Docker](https://docs.docker.com/get-docker/) following the official documentation for your specific OS.\n\n3. **Airbyte OSS version**: Deploy the open-source version of Airbyte. Follow the installation instructions from the [Airbyte Documentation](https://docs.airbyte.com/quickstart/deploy-airbyte/).\n\n4. **Terraform**: Terraform will help you provision and manage the Airbyte resources. If you haven't installed it, follow the [official Terraform installation guide](https://developer.hashicorp.com/terraform/tutorials/aws-get-started/install-cli).\n\n\n## 1. Setting an environment for your project\n\nGet the project up and running on your local machine by following these steps:\n\n1. **Clone the repository (Clone only this quickstart)**: \n ```bash\n git clone --filter=blob:none --sparse https://github.com/airbytehq/quickstarts.git\n ```\n\n ```bash\n cd quickstarts\n ```\n\n ```bash\n git sparse-checkout add mongodb_mysql_integration\n ```\n\n \n2. **Navigate to the directory**: \n ```bash\n cd mongodb_mysql_integration\n ```\n\n3. **Set Up a Virtual Environment**: \n - For Mac:\n ```bash\n python3 -m venv venv\n source venv/bin/activate\n ```\n - For Windows:\n ```bash\n python -m venv venv\n .\\venv\\Scripts\\activate\n ```\n\n4. **Install Dependencies**: \n ```bash\n pip install -e \".[dev]\"\n ```\n\n## 2. Setting Up Airbyte Connectors with Terraform\n\nAirbyte allows you to create connectors for sources and destinations, facilitating data synchronization between various platforms. In this project, we're harnessing the power of Terraform to automate the creation of these connectors and the connections between them. Here's how you can set this up:\n\n1. **Navigate to the Airbyte Configuration Directory**:\n \n Change to the relevant directory containing the Terraform configuration for Airbyte:\n ```bash\n cd infra/airbyte\n ```\n\n2. **Modify Configuration Files**:\n\n Within the `infra/airbyte` directory, you'll find three crucial Terraform files:\n - `provider.tf`: Defines the Airbyte provider.\n - `main.tf`: Contains the main configuration for creating Airbyte resources.\n - `variables.tf`: Holds various variables, including credentials.\n\n Adjust the configurations in these files to suit your project's needs. Specifically, provide credentials for your Postgres connections. You can utilize the `variables.tf` file to manage these credentials.\n\n3. **Initialize Terraform**:\n \n This step prepares Terraform to create the resources defined in your configuration files.\n ```bash\n terraform init\n ```\n\n4. **Review the Plan**:\n\n Before applying any changes, review the plan to understand what Terraform will do.\n ```bash\n terraform plan\n ```\n\n5. **Apply Configuration**:\n\n After reviewing and confirming the plan, apply the Terraform configurations to create the necessary Airbyte resources.\n ```bash\n terraform apply\n ```\n\n6. **Verify in Airbyte UI**:\n\n Once Terraform completes its tasks, navigate to the Airbyte UI. Here, you should see your source and destination connectors, as well as the connection between them, set up and ready to go.\n\n## Next Steps\n\nOnce you've set up and launched this initial integration, the real power lies in its adaptability and extensibility. Here’s a roadmap to help you customize and harness this project tailored to your specific data needs:\n\n\n1. **Extend the Project**:\n\n The real beauty of this integration is its extensibility. Whether you want to add more data sources, integrate additional tools, or add some transformation logic – the floor is yours. With the foundation set, sky's the limit for how you want to extend and refine your data processes." }, { "path": "mongodb_mysql_integration/infra/.gitignore", "content": "# Local .terraform directories\n**/.terraform/*\n\n# .tfstate files\n*.tfstate\n*.tfstate.*\n\n# Crash log files\ncrash.log\ncrash.*.log\n\n# Exclude all .tfvars files, which are likely to contain sensitive data, such as\n# password, private keys, and other secrets. These should not be part of version \n# control as they are data points which are potentially sensitive and subject \n# to change depending on the environment.\n*.tfvars\n*.tfvars.json\n\n# Ignore override files as they are usually used to override resources locally and so\n# are not checked in\noverride.tf\noverride.tf.json\n*_override.tf\n*_override.tf.json\n\n# Include override files you do wish to add to version control using negated pattern\n# !example_override.tf\n\n# Include tfplan files to ignore the plan output of command: terraform plan -out=tfplan\n# example: *tfplan*\n\n# Ignore CLI configuration files\n.terraformrc\nterraform.rc\n" }, { "path": "mongodb_mysql_integration/infra/airbyte/main.tf", "content": "// Airbyte Terraform provider documentation: https://registry.terraform.io/providers/airbytehq/airbyte/latest/docs\n\n// Sources\nresource \"airbyte_source_mongodb\" \"mongodb\" {\n configuration = {\n auth_source = \"admin\"\n database = \"...my_database...\"\n instance_type = {\n source_mongodb_mongo_db_instance_type_mongo_db_atlas = {\n cluster_url = \"...my_cluster_url...\"\n instance = \"atlas\"\n }\n }\n password = \"...my_password...\"\n source_type = \"mongodb\"\n user = \"...my_user...\"\n }\n name = \"MongoDB-Source\"\n secret_id = \"...my_secret_id...\"\n workspace_id = var.workspace_id\n}\n\n// Destinations\nresource \"airbyte_destination_mysql\" \"mysql\" {\n configuration = {\n database = \"...my_database...\"\n destination_type = \"mysql\"\n host = \"...my_host...\"\n jdbc_url_params = \"...my_jdbc_url_params...\"\n password = \"...my_password...\"\n port = 3306\n tunnel_method = {\n destination_mysql_ssh_tunnel_method_no_tunnel = {\n tunnel_method = \"NO_TUNNEL\"\n }\n }\n username = \"...my_username...\"\n }\n name = \"MySql-Destination\"\n workspace_id = var.workspace_id\n}\n\n// Connections\nresource \"airbyte_connection\" \"mongodb_to_mysql\" {\n name = \"MongoDB to MySQL\"\n source_id = airbyte_source_mongodb.mongodb.source_id\n destination_id = airbyte_destination_mysql.mysql.destination_id\n configurations = {\n streams = [\n {\n name = \"...my_table_name_1...\"\n },\n {\n name = \"...my_table_name_2...\"\n },\n ]\n }\n schedule = {\n cron_expression = \"...my_cron_expression...\"\n schedule_type = \"cron\"\n }\n}\n" }, { "path": "mongodb_mysql_integration/infra/airbyte/provider.tf", "content": "// Airbyte Terraform provider documentation: https://registry.terraform.io/providers/airbytehq/airbyte/latest/docs\n\nterraform {\n required_providers {\n airbyte = {\n source = \"airbytehq/airbyte\"\n version = \"0.3.4\"\n }\n }\n}\n\nprovider \"airbyte\" {\n // If running locally (Airbyte OSS) with docker-compose using the airbyte-proxy, \n // include the actual password/username you've set up (or use the defaults below)\n username = \"airbyte\"\n password = \"password\"\n \n // if running locally (Airbyte OSS), include the server url to the airbyte-api-server\n server_url = \"http://localhost:8006/v1\"\n}\n" }, { "path": "mongodb_mysql_integration/infra/airbyte/variables.tf", "content": "variable \"workspace_id\" {\n type = string\n}\n\n\n\n\n\n\n" }, { "path": "mongodb_mysql_integration/setup.py", "content": "from setuptools import find_packages, setup\n\nsetup(\n name=\"postgres-data-replication\",\n packages=find_packages(),\n install_requires=[\n ],\n extras_require={\"dev\": [\"pytest\"]},\n)\n" }, { "path": "multisource_aggregation/.gitignore", "content": "# Byte-compiled / optimized / DLL files\n__pycache__/\n*.py[cod]\n*$py.class\n\n# C extensions\n*.so\n\n# Distribution / packaging\n.Python\nbuild/\ndevelop-eggs/\ndist/\ndownloads/\neggs/\n.eggs/\nlib/\nlib64/\nparts/\nsdist/\nvar/\nwheels/\nshare/python-wheels/\n*.egg-info/\n.installed.cfg\n*.egg\nMANIFEST\n\n# PyInstaller\n# Usually these files are written by a python script from a template\n# before PyInstaller builds the exe, so as to inject date/other infos into it.\n*.manifest\n*.spec\n\n# Installer logs\npip-log.txt\npip-delete-this-directory.txt\n\n# Unit test / coverage reports\nhtmlcov/\n.tox/\n.nox/\n.coverage\n.coverage.*\n.cache\nnosetests.xml\ncoverage.xml\n*.cover\n*.py,cover\n.hypothesis/\n.pytest_cache/\ncover/\n\n# Translations\n*.mo\n*.pot\n\n# Django stuff:\n*.log\nlocal_settings.py\ndb.sqlite3\ndb.sqlite3-journal\n\n# Flask stuff:\ninstance/\n.webassets-cache\n\n# Scrapy stuff:\n.scrapy\n\n# Sphinx documentation\ndocs/_build/\n\n# PyBuilder\n.pybuilder/\ntarget/\n\n# Jupyter Notebook\n.ipynb_checkpoints\n\n# IPython\nprofile_default/\nipython_config.py\n\n# pyenv\n# For a library or package, you might want to ignore these files since the code is\n# intended to run in multiple environments; otherwise, check them in:\n# .python-version\n\n# pipenv\n# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.\n# However, in case of collaboration, if having platform-specific dependencies or dependencies\n# having no cross-platform support, pipenv may install dependencies that don't work, or not\n# install all needed dependencies.\n#Pipfile.lock\n\n# poetry\n# Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.\n# This is especially recommended for binary packages to ensure reproducibility, and is more\n# commonly ignored for libraries.\n# https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control\n#poetry.lock\n\n# pdm\n# Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control.\n#pdm.lock\n# pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it\n# in version control.\n# https://pdm.fming.dev/#use-with-ide\n.pdm.toml\n\n# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm\n__pypackages__/\n\n# Celery stuff\ncelerybeat-schedule\ncelerybeat.pid\n\n# SageMath parsed files\n*.sage.py\n\n# Environments\n.env\n.venv\nenv/\nvenv/\nENV/\nenv.bak/\nvenv.bak/\n\n# Spyder project settings\n.spyderproject\n.spyproject\n\n# Rope project settings\n.ropeproject\n\n# mkdocs documentation\n/site\n\n# mypy\n.mypy_cache/\n.dmypy.json\ndmypy.json\n\n# Pyre type checker\n.pyre/\n\n# pytype static type analyzer\n.pytype/\n\n# Cython debug symbols\ncython_debug/\n\n# PyCharm\n# JetBrains specific template is maintained in a separate JetBrains.gitignore that can\n# be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore\n# and can be added to the global gitignore or merged into this file. For a more nuclear\n# option (not recommended) you can uncomment the following to ignore the entire idea folder.\n#.idea/\n\n#Desktop Services Store\n.DS_Store" }, { "path": "multisource_aggregation/README.md", "content": "# Multisource Database Aggregation\n\nWelcome to the \"Multisource Database Aggregation\" repository! This repo provides a quickstart template for building a full data stack that aggregates data from multiple databases and data sources using Airbyte and loads the aggregated data in a preferred data warehouse. In this quickstart, we will easily extract data from Postgres and MySQL tables, load it into BigQuery, and apply necessary transformations in the BigQuery dataset using dbt. The data aggregation with Airbyte and transformations with dbt are orchestrated seamlessly with Dagster. While this template doesn't delve into specific data or transformations, its goal is to showcase the synergy of these tools.\n\nLike other quickstarts, this is designed to minimize setup hassles and propel you forward.\n\n## Table of Contents\n\n- [Multisource Database Aggregation](#multisource-database-aggregation)\n - [Table of Contents](#table-of-contents)\n - [Infrastructure Layout](#infrastructure-layout)\n - [Dagster Pipeline DAG](#dagster-pipeline-dag)\n - [Prerequisites](#prerequisites)\n - [1. Setting an environment for your project](#1-setting-an-environment-for-your-project)\n - [2. Setting Up Airbyte Connectors with Terraform](#2-setting-up-airbyte-connectors-with-terraform)\n - [3. Setting Up the dbt Project](#3-setting-up-the-dbt-project)\n - [4. Orchestrating with Dagster](#4-orchestrating-with-dagster)\n - [Next Steps](#next-steps)\n\n## Infrastructure Layout\n![insfrastructure layout](images/layout.png)\n\n## Dagster Pipeline DAG\n![pipeline dag](images/dag.svg)\n\n## Prerequisites\n\nBefore you embark on this integration, ensure you have the following set up and ready:\n\n1. **Python 3.10 or later**: If not installed, download and install it from [Python's official website](https://www.python.org/downloads/).\n\n2. **Docker and Docker Compose (Docker Desktop)**: Install [Docker](https://docs.docker.com/get-docker/) following the official documentation for your specific OS.\n\n3. **Airbyte OSS version**: Deploy the open-source version of Airbyte. Follow the installation instructions from the [Airbyte Documentation](https://docs.airbyte.com/quickstart/deploy-airbyte/).\n\n4. **Terraform**: Terraform will help you provision and manage the Airbyte resources. If you haven't installed it, follow the [official Terraform installation guide](https://developer.hashicorp.com/terraform/tutorials/aws-get-started/install-cli).\n\n\n## 1. Setting an environment for your project\n\nGet the project up and running on your local machine by following these steps:\n\n1. **Clone the repository (Clone only this quickstart)**: \n ```bash\n git clone --filter=blob:none --sparse https://github.com/airbytehq/quickstarts.git\n ```\n\n ```bash\n cd quickstarts\n ```\n\n ```bash\n git sparse-checkout add multisource_aggregation\n ```\n\n \n2. **Navigate to the directory**: \n ```bash\n cd multisource_aggregation\n ```\n\n3. **Set Up a Virtual Environment**: \n - For Mac:\n ```bash\n python3 -m venv venv\n source venv/bin/activate\n ```\n - For Windows:\n ```bash\n python -m venv venv\n .\\venv\\Scripts\\activate\n ```\n\n4. **Install Dependencies**: \n ```bash\n pip install -e \".[dev]\"\n ```\n\n## 2. Setting Up Airbyte Connectors with Terraform\n\nAirbyte allows you to create connectors for sources and destinations, facilitating data synchronization between various platforms. In this project, we're harnessing the power of Terraform to automate the creation of these connectors and the connections between them. Here's how you can set this up:\n\n1. **Navigate to the Airbyte Configuration Directory**:\n \n Change to the relevant directory containing the Terraform configuration for Airbyte:\n ```bash\n cd infra/airbyte\n ```\n\n2. **Modify Configuration Files**:\n\n Within the `infra/airbyte` directory, you'll find three crucial Terraform files:\n - `provider.tf`: Defines the Airbyte provider.\n - `main.tf`: Contains the main configuration for creating Airbyte resources.\n - `variables.tf`: Holds various variables, including credentials.\n\n You'll also find three crucial Terraform modules:\n - `connections`: Contains the configuration files for the Airbyte connections.\n - `destination_warehouse`: Contains the configuration files for the Airbyte destination connector(s).\n - `source_databases`: Contains the configuration files for the Airbyte source connector(s).\n\n In each terraform module, you will find the following Terraform configuration files:\n - `provider.tf`: Defines the Airbyte provider.\n - `main.tf`: Contains the main configuration for creating Airbyte resources.\n - `variables.tf`: Holds various variables, including credentials.\n - `outputs.tf`: Defines exported data or metadata about your resources.\n\n Adjust the configurations in these files to suit your project's needs. Specifically, provide credentials for your Postgres, MySQL and BigQuery connections. You can utilize the `variables.tf` files to manage these credentials.\n\n3. **Initialize Terraform**:\n \n This step prepares Terraform to create the resources defined in your configuration files.\n ```bash\n terraform init\n ```\n\n4. **Review the Plan**:\n\n Before applying any changes, review the plan to understand what Terraform will do.\n ```bash\n terraform plan\n ```\n\n5. **Apply Configuration**:\n\n After reviewing and confirming the plan, apply the Terraform configurations to create the necessary Airbyte resources.\n ```bash\n terraform apply\n ```\n\n6. **Verify in Airbyte UI**:\n\n Once Terraform completes its tasks, navigate to the Airbyte UI. Here, you should see your source and destination connectors, as well as the connection between them, set up and ready to go.\n\n## 3. Setting Up the dbt Project\n\n[dbt (data build tool)](https://www.getdbt.com/) allows you to transform your data by writing, documenting, and executing SQL workflows. Setting up the dbt project requires specifying connection details for your data platform, in this case, BigQuery. Here’s a step-by-step guide to help you set this up:\n\n1. **Navigate to the dbt Project Directory**:\n\n Change to the directory containing the dbt configuration:\n ```bash\n cd ../../dbt_project\n ```\n\n2. **Update Connection Details**:\n\n You'll find a `profiles.yml` file within the directory. This file contains configurations for dbt to connect with your data platform. Update this file with your BigQuery connection details.\n\n3. **Utilize Environment Variables (Optional but Recommended)**:\n\n To keep your credentials secure, you can leverage environment variables. An example is provided within the `profiles.yml` file.\n\n4. **Test the Connection**:\n\n Once you’ve updated the connection details, you can test the connection to your BigQuery instance using:\n ```bash\n dbt debug\n ```\n\n If everything is set up correctly, this command should report a successful connection to BigQuery.\n\n## 4. Orchestrating with Dagster\n\n[Dagster](https://dagster.io/) is a modern data orchestrator designed to help you build, test, and monitor your data workflows. In this section, we'll walk you through setting up Dagster to oversee both the Airbyte and dbt workflows:\n\n1. **Navigate to the Orchestration Directory**:\n\n Switch to the directory containing the Dagster orchestration configurations:\n ```bash\n cd ../orchestration\n ```\n\n2. **Set Environment Variables**:\n\n Dagster requires certain environment variables to be set to interact with other tools like dbt and Airbyte. Set the following variables:\n\n ```bash\n export DAGSTER_DBT_PARSE_PROJECT_ON_LOAD=1\n export AIRBYTE_PASSWORD=password\n ```\n \n Note: The `AIRBYTE_PASSWORD` is set to `password` as a default for local Airbyte instances. If you've changed this during your Airbyte setup, ensure you use the appropriate password here.\n\n3. **Launch the Dagster UI**:\n\n With the environment variables in place, kick-start the Dagster UI:\n ```bash\n dagster dev\n ```\n\n4. **Access Dagster in Your Browser**:\n\n Open your browser and navigate to:\n ```\n http://127.0.0.1:3000\n ```\n\n Here, you should see assets for both Airbyte and dbt. To get an overview of how these assets interrelate, click on \"view global asset lineage\". This will give you a clear picture of the data lineage, visualizing how data flows between the tools.\n\n## Next Steps\n\nOnce you've set up and launched this initial integration, the real power lies in its adaptability and extensibility. Here’s a roadmap to help you customize and harness this project tailored to your specific data needs:\n\n1. **Add more Data(base) sources**:\n\n You can add more databases or data sources from Airbyte's [source catalogue](https://docs.airbyte.com/category/sources). To do this, edit the Terraform `source_databases` module and create a new connection in the `connections` module for each source added.\n\n2. **Create dbt Sources for Airbyte Data**:\n\n Your raw data extracted via Airbyte can be represented as sources in dbt. Start by [creating new dbt sources](https://docs.getdbt.com/docs/build/sources) to represent this data, allowing for structured transformations down the line.\n\n3. **Add Your dbt Transformations**:\n\n With your dbt sources in place, you can now build upon them. Add your custom SQL transformations in dbt, ensuring that you treat the sources as an upstream dependency. This ensures that your transformations work on the most up-to-date raw data.\n\n4. **Execute the Pipeline in Dagster**:\n\n Navigate to the Dagster UI and click on \"Materialize all\". This triggers the entire pipeline, encompassing the extraction via Airbyte, transformations via dbt, and any other subsequent steps.\n\n5. **Extend the Project**:\n\n The real beauty of this integration is its extensibility. Whether you want to add more data sources, integrate additional tools, or enhance your transformation logic – the floor is yours. With the foundation set, sky's the limit for how you want to extend and refine your data processes." }, { "path": "multisource_aggregation/dbt_project/.gitignore", "content": "\ntarget/\ndbt_packages/\nlogs/\n\n#Desktop Services Store\n.DS_Store\n\n#User cookie\n.user.yml" }, { "path": "multisource_aggregation/dbt_project/README.md", "content": "Welcome to your new dbt project!\n\n### Using the starter project\n\nTry running the following commands:\n- dbt run\n- dbt test\n\n\n### Resources:\n- Learn more about dbt [in the docs](https://docs.getdbt.com/docs/introduction)\n- Check out [Discourse](https://discourse.getdbt.com/) for commonly asked questions and answers\n- Join the [chat](https://community.getdbt.com/) on Slack for live discussions and support\n- Find [dbt events](https://events.getdbt.com) near you\n- Check out [the blog](https://blog.getdbt.com/) for the latest news on dbt's development and best practices\n" }, { "path": "multisource_aggregation/dbt_project/analyses/.gitkeep", "content": "" }, { "path": "multisource_aggregation/dbt_project/dbt_project.yml", "content": "\n# Name your project! Project names should contain only lowercase characters\n# and underscores. A good package name should reflect your organization's\n# name or the intended use of these models\nname: 'dbt_project'\nversion: '1.0.0'\nconfig-version: 2\n\n# This setting configures which \"profile\" dbt uses for this project.\nprofile: 'dbt_project'\n\n# These configurations specify where dbt should look for different types of files.\n# The `model-paths` config, for example, states that models in this project can be\n# found in the \"models/\" directory. You probably won't need to change these!\nmodel-paths: [\"models\"]\nanalysis-paths: [\"analyses\"]\ntest-paths: [\"tests\"]\nseed-paths: [\"seeds\"]\nmacro-paths: [\"macros\"]\nsnapshot-paths: [\"snapshots\"]\n\nclean-targets: # directories to be removed by `dbt clean`\n - \"target\"\n - \"dbt_packages\"\n\n\n# Configuring models\n# Full documentation: https://docs.getdbt.com/docs/configuring-models\n\n# In this example config, we tell dbt to build all models in the example/\n# directory as views. These settings can be overridden in the individual model\n# files using the `{{ config(...) }}` macro.\nmodels:\n dbt_project:\n # Config indicated by + and applies to all files under models/example/\n example:\n +materialized: view\n" }, { "path": "multisource_aggregation/dbt_project/macros/.gitkeep", "content": "" }, { "path": "multisource_aggregation/dbt_project/models/example/my_first_dbt_model.sql", "content": "\n/*\n Welcome to your first dbt model!\n Did you know that you can also configure models directly within SQL files?\n This will override configurations stated in dbt_project.yml\n\n Try changing \"table\" to \"view\" below\n*/\n\n{{ config(materialized='table') }}\n\nwith pg_table as (\n\n select * from {{ source('bigquery', 'sample_table') }}\n\n)\n\nwith mysql_table as (\n\n select * from {{ source('bigquery', 'test_table') }}\n\n)\n\nselect *\nfrom pg_table\n\nunion\n\nselect *\nfrom mysql_table\n\n/*\n Uncomment the line below to remove records with null `id` values\n*/\n\n-- where id is not null\n" }, { "path": "multisource_aggregation/dbt_project/models/example/my_second_dbt_model.sql", "content": "\n-- Use the `ref` function to select from other models\n\nselect *\nfrom {{ ref('my_first_dbt_model') }}\nwhere id = 1\n" }, { "path": "multisource_aggregation/dbt_project/models/example/schema.yml", "content": "\nversion: 2\n\nmodels:\n - name: my_first_dbt_model\n description: \"A starter dbt model\"\n columns:\n - name: id\n description: \"The primary key for this table\"\n tests:\n - unique\n - not_null\n\n - name: my_second_dbt_model\n description: \"A starter dbt model\"\n columns:\n - name: id\n description: \"The primary key for this table\"\n tests:\n - unique\n - not_null\n" }, { "path": "multisource_aggregation/dbt_project/models/sources.yml", "content": "version: 2\n\nsources:\n - name: bigquery\n tables:\n - name: sample_table\n meta:\n dagster:\n asset_key: [\"pg_sample_table\"] # This metadata specifies the corresponding Dagster asset for this dbt source.\n\n - name: test_table\n meta:\n dagster:\n asset_key: [\"mysql_test_table\"] # This metadata specifies the corresponding Dagster asset for this dbt source." }, { "path": "multisource_aggregation/dbt_project/profiles.yml", "content": "dbt_project:\n outputs:\n dev:\n dataset: my_dataset\n job_execution_timeout_seconds: 300\n job_retries: 1\n keyfile: \"{{ env_var('DBT_BIGQUERY_KEYFILE_PATH', '') }}\"\n location: my_dataset_location\n method: service-account\n priority: interactive\n project: my_project_id\n threads: 1\n type: bigquery\n target: dev" }, { "path": "multisource_aggregation/dbt_project/seeds/.gitkeep", "content": "" }, { "path": "multisource_aggregation/dbt_project/snapshots/.gitkeep", "content": "" }, { "path": "multisource_aggregation/dbt_project/tests/.gitkeep", "content": "" }, { "path": "multisource_aggregation/infra/.gitignore", "content": "# Local .terraform directories\n**/.terraform/*\n\n# .tfstate files\n*.tfstate\n*.tfstate.*\n\n# Crash log files\ncrash.log\ncrash.*.log\n\n# Exclude all .tfvars files, which are likely to contain sensitive data, such as\n# password, private keys, and other secrets. These should not be part of version \n# control as they are data points which are potentially sensitive and subject \n# to change depending on the environment.\n*.tfvars\n*.tfvars.json\n\n# Ignore override files as they are usually used to override resources locally and so\n# are not checked in\noverride.tf\noverride.tf.json\n*_override.tf\n*_override.tf.json\n\n# Include override files you do wish to add to version control using negated pattern\n# !example_override.tf\n\n# Include tfplan files to ignore the plan output of command: terraform plan -out=tfplan\n# example: *tfplan*\n\n# Ignore CLI configuration files\n.terraformrc\nterraform.rc" }, { "path": "multisource_aggregation/infra/airbyte/connections/main.tf", "content": "// Airbyte Terraform provider documentation: https://registry.terraform.io/providers/airbytehq/airbyte/latest/docs\n\n// Connections\nresource \"airbyte_connection\" \"postgres_to_bigquery\" {\n name = \"Postgres to BigQuery\"\n source_id = var.postgres_id\n destination_id = var.bigquery_id\n prefix = \"pg_\"\n schedule = {\n schedule_type = \"manual\"\n }\n configurations = {\n streams = [\n {\n cursor_field = [\"...\",]\n name = \"...my_table_name_1...\"\n primary_key = [[\"...\",],]\n sync_mode = \"full_refresh_append\"\n },\n {\n cursor_field = [\"...\",]\n name = \"...my_table_name_2...\"\n primary_key = [[\"...\",],]\n sync_mode = \"full_refresh_append\"\n },\n ]\n }\n}\n\nresource \"airbyte_connection\" \"mysql_to_bigquery\" {\n name = \"MySQL to BigQuery\"\n source_id = var.mysql_id\n destination_id = var.bigquery_id\n prefix = \"mysql_\"\n schedule = {\n schedule_type = \"manual\"\n }\n configurations = {\n streams = [\n {\n cursor_field = [\"...\",]\n name = \"...my_table_name_1...\"\n primary_key = [[\"...\",],]\n sync_mode = \"full_refresh_append\"\n },\n {\n cursor_field = [\"...\",]\n name = \"...my_table_name_2...\"\n primary_key = [[\"...\",],]\n sync_mode = \"full_refresh_append\"\n },\n ]\n }\n}" }, { "path": "multisource_aggregation/infra/airbyte/connections/provider.tf", "content": "// Airbyte Terraform provider documentation: https://registry.terraform.io/providers/airbytehq/airbyte/latest/docs\n\nterraform {\n required_providers {\n airbyte = {\n source = \"airbytehq/airbyte\"\n version = \"0.3.3\"\n }\n }\n}\n\nprovider \"airbyte\" {\n // If running locally (Airbyte OSS) with docker-compose using the airbyte-proxy, \n // include the actual password/username you've set up (or use the defaults below)\n username = \"airbyte\"\n password = var.airbyte_password\n \n // if running locally (Airbyte OSS), include the server url to the airbyte-api-server\n server_url = \"http://localhost:8006/v1\"\n}" }, { "path": "multisource_aggregation/infra/airbyte/connections/variables.tf", "content": "variable \"mysql_id\" {\n type = string\n}\n\nvariable \"postgres_id\" {\n type = string\n}\n\nvariable \"bigquery_id\" {\n type = string\n}\n\nvariable \"airbyte_password\" {\n type = string\n default = \"password\"\n}\n" }, { "path": "multisource_aggregation/infra/airbyte/destination_warehouse/main.tf", "content": "// Airbyte Terraform provider documentation: https://registry.terraform.io/providers/airbytehq/airbyte/latest/docs\n\n// Destinations\nresource \"airbyte_destination_bigquery\" \"bigquery\" {\n configuration = {\n dataset_id = \"...my_dataset_id...\"\n dataset_location = \"...my_dataset_location...\"\n destination_type = \"bigquery\"\n project_id = \"...my_project_id...\"\n credentials_json = \"...my_credentials_json_file_path...\"\n loading_method = {\n destination_bigquery_loading_method_standard_inserts = {\n method = \"Standard\"\n }\n }\n }\n name = \"BigQuery\"\n workspace_id = var.workspace_id\n}\n" }, { "path": "multisource_aggregation/infra/airbyte/destination_warehouse/outputs.tf", "content": "output \"bigquery_id\" {\n value = airbyte_destination_bigquery.bigquery.destination_id\n}" }, { "path": "multisource_aggregation/infra/airbyte/destination_warehouse/provider.tf", "content": "// Airbyte Terraform provider documentation: https://registry.terraform.io/providers/airbytehq/airbyte/latest/docs\n\nterraform {\n required_providers {\n airbyte = {\n source = \"airbytehq/airbyte\"\n version = \"0.3.3\"\n }\n }\n}\n\nprovider \"airbyte\" {\n // If running locally (Airbyte OSS) with docker-compose using the airbyte-proxy, \n // include the actual password/username you've set up (or use the defaults below)\n username = \"airbyte\"\n password = var.airbyte_password\n \n // if running locally (Airbyte OSS), include the server url to the airbyte-api-server\n server_url = \"http://localhost:8006/v1\"\n}" }, { "path": "multisource_aggregation/infra/airbyte/destination_warehouse/variables.tf", "content": "variable \"workspace_id\" {\n type = string\n}\n\nvariable \"airbyte_password\" {\n type = string\n default = \"password\"\n}\n" }, { "path": "multisource_aggregation/infra/airbyte/main.tf", "content": "// Airbyte Terraform provider documentation: https://registry.terraform.io/providers/airbytehq/airbyte/latest/docs\n\nmodule \"sources\" {\n source = \"./source_databases\"\n workspace_id = \"...my_workspace_id...\"\n}\n\nmodule \"destination\" {\n source = \"./destination_warehouse\"\n workspace_id = \"...my_workspace_id...\"\n}\n\nmodule \"connections\" {\n source = \"./connections\"\n mysql_id = module.sources.mysql_id\n postgres_id = module.sources.postgres_id\n bigquery_id = module.destination.bigquery_id\n}\n" }, { "path": "multisource_aggregation/infra/airbyte/provider.tf", "content": "// Airbyte Terraform provider documentation: https://registry.terraform.io/providers/airbytehq/airbyte/latest/docs\n\nterraform {\n required_providers {\n airbyte = {\n source = \"airbytehq/airbyte\"\n version = \"0.3.3\"\n }\n }\n}\n\nprovider \"airbyte\" {\n // If running locally (Airbyte OSS) with docker-compose using the airbyte-proxy, \n // include the actual password/username you've set up (or use the defaults below)\n username = \"airbyte\"\n password = var.airbyte_password\n \n // if running locally (Airbyte OSS), include the server url to the airbyte-api-server\n server_url = \"http://localhost:8006/v1\"\n}" }, { "path": "multisource_aggregation/infra/airbyte/source_databases/main.tf", "content": "// Airbyte Terraform provider documentation: https://registry.terraform.io/providers/airbytehq/airbyte/latest/docs\n\n// Sources\nresource \"airbyte_source_mysql\" \"mysql\" {\n configuration = {\n database = \"...my_database...\"\n host = \"...my_host...\"\n jdbc_url_params = \"...my_jdbc_url_params...\"\n password = \"...my_password...\"\n port = 3306\n source_type = \"mysql\"\n username = \"...my_username...\"\n replication_method = {\n source_mysql_update_method_scan_changes_with_user_defined_cursor = {\n method = \"STANDARD\"\n }\n }\n ssl_mode = {\n source_mysql_ssl_modes_preferred = {\n mode = \"preferred\"\n }\n }\n tunnel_method = {\n source_mysql_ssh_tunnel_method_no_tunnel = {\n tunnel_method = \"NO_TUNNEL\"\n }\n }\n }\n name = \"MySQL\"\n secret_id = \"...my_secret_id...\"\n workspace_id = var.workspace_id\n}\n\nresource \"airbyte_source_postgres\" \"postgres\" {\n configuration = {\n database = \"...my_database...\"\n host = \"...my_host...\"\n username = \"...my_username...\"\n password = \"...my_password...\"\n port = 5432\n source_type = \"postgres\"\n schemas = [\n \"...my_schema...\"\n ]\n ssl_mode = {\n source_postgres_ssl_modes_allow = {\n mode = \"allow\"\n }\n }\n tunnel_method = {\n source_postgres_ssh_tunnel_method_no_tunnel = {\n tunnel_method = \"NO_TUNNEL\"\n }\n }\n replication_method = {\n source_postgres_replication_method_standard = {\n method = \"Standard\"\n }\n }\n }\n name = \"Postgres\"\n workspace_id = var.workspace_id\n}\n" }, { "path": "multisource_aggregation/infra/airbyte/source_databases/outputs.tf", "content": "\noutput \"postgres_id\" {\n value = airbyte_source_postgres.postgres.source_id\n}\n\noutput \"mysql_id\" {\n value = airbyte_source_mysql.mysql.source_id\n}" }, { "path": "multisource_aggregation/infra/airbyte/source_databases/provider.tf", "content": "// Airbyte Terraform provider documentation: https://registry.terraform.io/providers/airbytehq/airbyte/latest/docs\n\nterraform {\n required_providers {\n airbyte = {\n source = \"airbytehq/airbyte\"\n version = \"0.3.3\"\n }\n }\n}\n\nprovider \"airbyte\" {\n // If running locally (Airbyte OSS) with docker-compose using the airbyte-proxy, \n // include the actual password/username you've set up (or use the defaults below)\n username = \"airbyte\"\n password = var.airbyte_password\n \n // if running locally (Airbyte OSS), include the server url to the airbyte-api-server\n server_url = \"http://localhost:8006/v1\"\n}" }, { "path": "multisource_aggregation/infra/airbyte/source_databases/variables.tf", "content": "variable \"workspace_id\" {\n type = string\n}\n\nvariable \"airbyte_password\" {\n type = string\n default = \"password\"\n}\n" }, { "path": "multisource_aggregation/infra/airbyte/variables.tf", "content": "variable \"workspace_id\" {\n type = string\n}\n\nvariable \"airbyte_password\" {\n type = string\n default = \"password\"\n}\n" }, { "path": "multisource_aggregation/orchestration/orchestration/__init__.py", "content": "" }, { "path": "multisource_aggregation/orchestration/orchestration/assets.py", "content": "import os\nfrom dagster import OpExecutionContext\nfrom dagster_dbt import DbtCliResource, dbt_assets\nfrom dagster_airbyte import AirbyteResource, load_assets_from_airbyte_instance\n\nfrom .constants import dbt_manifest_path\n\n@dbt_assets(manifest=dbt_manifest_path)\ndef dbt_project_dbt_assets(context: OpExecutionContext, dbt: DbtCliResource):\n yield from dbt.cli([\"build\"], context=context).stream()\n\nairbyte_instance = AirbyteResource(\n host=\"localhost\",\n port=\"8000\",\n # If using basic auth, include username and password:\n username=\"airbyte\",\n password=os.getenv(\"AIRBYTE_PASSWORD\")\n)\n\nairbyte_assets = load_assets_from_airbyte_instance(airbyte_instance)" }, { "path": "multisource_aggregation/orchestration/orchestration/constants.py", "content": "import os\nfrom pathlib import Path\n\nfrom dagster_dbt import DbtCliResource\n\ndbt_project_dir = Path(__file__).joinpath(\"..\", \"..\", \"..\", \"dbt_project\").resolve()\ndbt = DbtCliResource(project_dir=os.fspath(dbt_project_dir))\n\n# If DAGSTER_DBT_PARSE_PROJECT_ON_LOAD is set, a manifest will be created at runtime.\n# Otherwise, we expect a manifest to be present in the project's target directory.\nif os.getenv(\"DAGSTER_DBT_PARSE_PROJECT_ON_LOAD\"):\n dbt_parse_invocation = dbt.cli([\"parse\"], manifest={}).wait()\n dbt_manifest_path = dbt_parse_invocation.target_path.joinpath(\"manifest.json\")\nelse:\n dbt_manifest_path = dbt_project_dir.joinpath(\"target\", \"manifest.json\")" }, { "path": "multisource_aggregation/orchestration/orchestration/definitions.py", "content": "import os\n\nfrom dagster import Definitions\nfrom dagster_dbt import DbtCliResource\n\nfrom .assets import dbt_project_dbt_assets, airbyte_assets\nfrom .constants import dbt_project_dir\nfrom .schedules import schedules\n\ndefs = Definitions(\n assets=[dbt_project_dbt_assets, airbyte_assets],\n schedules=schedules,\n resources={\n \"dbt\": DbtCliResource(project_dir=os.fspath(dbt_project_dir)),\n },\n)" }, { "path": "multisource_aggregation/orchestration/orchestration/schedules.py", "content": "\"\"\"\nTo add a daily schedule that materializes your dbt assets, uncomment the following lines.\n\"\"\"\nfrom dagster_dbt import build_schedule_from_dbt_selection\n\nfrom .assets import dbt_project_dbt_assets\n\nschedules = [\n# build_schedule_from_dbt_selection(\n# [dbt_project_dbt_assets],\n# job_name=\"materialize_dbt_models\",\n# cron_schedule=\"0 0 * * *\",\n# dbt_select=\"fqn:*\",\n# ),\n]" }, { "path": "multisource_aggregation/orchestration/pyproject.toml", "content": "[build-system]\nrequires = [\"setuptools\"]\nbuild-backend = \"setuptools.build_meta\"\n\n[tool.dagster]\nmodule_name = \"orchestration.definitions\"\ncode_location_name = \"orchestration\"" }, { "path": "multisource_aggregation/orchestration/setup.py", "content": "from setuptools import find_packages, setup\n\nsetup(\n name=\"orchestration\",\n version=\"0.0.1\",\n packages=find_packages(),\n install_requires=[\n \"dagster\",\n \"dagster-cloud\",\n \"dagster-dbt\",\n \"dbt-core>=1.4.0\",\n \"dbt-bigquery\",\n ],\n extras_require={\n \"dev\": [\n \"dagster-webserver\",\n ]\n },\n)" }, { "path": "multisource_aggregation/setup.py", "content": "from setuptools import find_packages, setup\n\nsetup(\n name=\"multisource-database-aggregation\",\n packages=find_packages(),\n install_requires=[\n \"dbt-bigquery\",\n \"dagster\",\n \"dagster-cloud\",\n \"dagster-dbt\",\n \"dagster-airbyte\",\n ],\n extras_require={\"dev\": [\"dagit\", \"pytest\"]},\n)" }, { "path": "mysql_to_postgres_incremental_stack/.gitignore", "content": "# Byte-compiled / optimized / DLL files\n__pycache__/\n*.py[cod]\n*$py.class\n\n# C extensions\n*.so\n\n# Distribution / packaging\n.Python\nbuild/\ndevelop-eggs/\ndist/\ndownloads/\neggs/\n.eggs/\nlib/\nlib64/\nparts/\nsdist/\nvar/\nwheels/\nshare/python-wheels/\n*.egg-info/\n.installed.cfg\n*.egg\nMANIFEST\n\n# PyInstaller\n# Usually these files are written by a python script from a template\n# before PyInstaller builds the exe, so as to inject date/other infos into it.\n*.manifest\n*.spec\n\n# Installer logs\npip-log.txt\npip-delete-this-directory.txt\n\n# Unit test / coverage reports\nhtmlcov/\n.tox/\n.nox/\n.coverage\n.coverage.*\n.cache\nnosetests.xml\ncoverage.xml\n*.cover\n*.py,cover\n.hypothesis/\n.pytest_cache/\ncover/\n\n# Translations\n*.mo\n*.pot\n\n# Django stuff:\n*.log\nlocal_settings.py\ndb.sqlite3\ndb.sqlite3-journal\n\n# Flask stuff:\ninstance/\n.webassets-cache\n\n# Scrapy stuff:\n.scrapy\n\n# Sphinx documentation\ndocs/_build/\n\n# PyBuilder\n.pybuilder/\ntarget/\n\n# Jupyter Notebook\n.ipynb_checkpoints\n\n# IPython\nprofile_default/\nipython_config.py\n\n# pyenv\n# For a library or package, you might want to ignore these files since the code is\n# intended to run in multiple environments; otherwise, check them in:\n# .python-version\n\n# pipenv\n# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.\n# However, in case of collaboration, if having platform-specific dependencies or dependencies\n# having no cross-platform support, pipenv may install dependencies that don't work, or not\n# install all needed dependencies.\n#Pipfile.lock\n\n# poetry\n# Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.\n# This is especially recommended for binary packages to ensure reproducibility, and is more\n# commonly ignored for libraries.\n# https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control\n#poetry.lock\n\n# pdm\n# Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control.\n#pdm.lock\n# pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it\n# in version control.\n# https://pdm.fming.dev/#use-with-ide\n.pdm.toml\n\n# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm\n__pypackages__/\n\n# Celery stuff\ncelerybeat-schedule\ncelerybeat.pid\n\n# SageMath parsed files\n*.sage.py\n\n# Environments\n.env\n.venv\nenv/\nvenv/\nENV/\nenv.bak/\nvenv.bak/\n\n# Spyder project settings\n.spyderproject\n.spyproject\n\n# Rope project settings\n.ropeproject\n\n# mkdocs documentation\n/site\n\n# mypy\n.mypy_cache/\n.dmypy.json\ndmypy.json\n\n# Pyre type checker\n.pyre/\n\n# pytype static type analyzer\n.pytype/\n\n# Cython debug symbols\ncython_debug/\n\n# PyCharm\n# JetBrains specific template is maintained in a separate JetBrains.gitignore that can\n# be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore\n# and can be added to the global gitignore or merged into this file. For a more nuclear\n# option (not recommended) you can uncomment the following to ignore the entire idea folder.\n#.idea/\n\n#Desktop Services Store\n.DS_Store" }, { "path": "mysql_to_postgres_incremental_stack/README.md", "content": "# Mysql to Postgres Incremental Stack\n\nWelcome to the \"Mysql to Postgres Incremental Stack\" repository! This repo provides a quickstart template for building a one-off database migration solution from an existing MySQL database to a Postgres database with incremental sync using Airbyte. We will easily migrate the tables and data from the MySQL database to the Postgres database with Airbyte using Change Data Capture (CDC). While this template doesn't delve into specific data, its goal is to showcase how the database migration solution can be achieved with Airbyte.\n\nJust like other Airbyte quickstarts, this quickstart is designed to minimize setup hassles and propel you forward.\n\n## Table of Contents\n\n- [Mysql to Postgres Incremental Stack](#mysql-to-postgres-incremental-stack)\n - [Table of Contents](#table-of-contents)\n - [Infrastructure Layout](#infrastructure-layout)\n - [Prerequisites](#prerequisites)\n - [1. Setting an environment for your project](#1-setting-an-environment-for-your-project)\n - [2. Setting Up Airbyte Connectors with Terraform](#2-setting-up-airbyte-connectors-with-terraform)\n - [Next Steps](#next-steps)\n\n\n## Infrastructure Layout\n\n![infrastructure layout](image/sample.png)\n\n## Prerequisites\n\nBefore you embark on this integration, ensure you have the following set up and ready:\n\n1. **Python 3.10 or later**: If not installed, download and install it from [Python's official website](https://www.python.org/downloads/).\n\n2. **Docker and Docker Compose (Docker Desktop)**: Install [Docker](https://docs.docker.com/get-docker/) following the official documentation for your specific OS.\n\n3. **Airbyte OSS version**: Deploy the open-source version of Airbyte. Follow the installation instructions from the [Airbyte Documentation](https://docs.airbyte.com/quickstart/deploy-airbyte/).\n\n4. **Terraform**: Terraform will help you provision and manage the Airbyte resources. If you haven't installed it, follow the [official Terraform installation guide](https://developer.hashicorp.com/terraform/tutorials/aws-get-started/install-cli).\n\n\n## 1. Setting an environment for your project\n\nGet the project up and running on your local machine by following these steps:\n\n1. **Clone the repository (Clone only this quickstart)**: \n ```bash\n git clone --filter=blob:none --sparse https://github.com/airbytehq/quickstarts.git\n ```\n\n ```bash\n cd quickstarts\n ```\n\n ```bash\n git sparse-checkout add mysql_to_postgres_incremental_stack\n ```\n\n \n2. **Navigate to the directory**: \n ```bash\n cd mysql_to_postgres_incremental_stack\n ```\n\n3. **Set Up a Virtual Environment**: \n - For Mac:\n ```bash\n python3 -m venv venv\n source venv/bin/activate\n ```\n - For Windows:\n ```bash\n python -m venv venv\n .\\venv\\Scripts\\activate\n ```\n\n4. **Install Dependencies**: \n ```bash\n pip install -e \".[dev]\"\n ```\n\n## 2. Setting Up Airbyte Connectors with Terraform\n\nAirbyte allows you to create connectors for sources and destinations, facilitating data synchronization between various platforms. In this project, we're harnessing the power of Terraform to automate the creation of these connectors and the connections between them. Here's how you can set this up:\n\n1. **Navigate to the Airbyte Configuration Directory**:\n \n Change to the relevant directory containing the Terraform configuration for Airbyte:\n ```bash\n cd infra/airbyte\n ```\n\n2. **Modify Configuration Files**:\n\n Within the `infra/airbyte` directory, you'll find three crucial Terraform files:\n - `provider.tf`: Defines the Airbyte provider.\n - `main.tf`: Contains the main configuration for creating Airbyte resources.\n - `variables.tf`: Holds various variables, including credentials.\n\n Adjust the configurations in these files to suit your project's needs. Specifically, provide credentials for your Postgres and MySQL connections. You can utilize the `variables.tf` file to manage these credentials.\n\n3. **Initialize Terraform**:\n \n This step prepares Terraform to create the resources defined in your configuration files.\n ```bash\n terraform init\n ```\n\n4. **Review the Plan**:\n\n Before applying any changes, review the plan to understand what Terraform will do.\n ```bash\n terraform plan\n ```\n\n5. **Apply Configuration**:\n\n After reviewing and confirming the plan, apply the Terraform configurations to create the necessary Airbyte resources.\n ```bash\n terraform apply\n ```\n\n6. **Verify in Airbyte UI**:\n\n Once Terraform completes its tasks, navigate to the Airbyte UI. Here, you should see your Postgres source and MySQL destination connectors, as well as the connection between them, set up and ready to go.\n\n## Next Steps\n\nOnce you've set up and launched this initial integration, you can proceed to sync the connection to trigger a one-off migration. The real power of this quickstart lies in its adaptability and extensibility. Here’s a roadmap to help you customize and harness this project tailored to your specific data needs:\n\n\n\n\n1. **Extend the Project**:\n\n The real beauty of this integration is its extensibility. Whether you want to add more MySQL sources, migrate to more than one Postgres databases, integrate additional tools, or modify the sync schedule – the floor is yours. The granularity of the migration can also be set by selecting the correct sync mode for each stream (table). Read [sync mode](https://docs.airbyte.com/understanding-airbyte/connections/) for more details. With the foundation set, sky's the limit for how you want to extend and refine your data processes.\n" }, { "path": "mysql_to_postgres_incremental_stack/infra/.gitignore", "content": "# Local .terraform directories\n**/.terraform/*\n\n# .tfstate files\n*.tfstate\n*.tfstate.*\n\n# Crash log files\ncrash.log\ncrash.*.log\n\n# Exclude all .tfvars files, which are likely to contain sensitive data, such as\n# password, private keys, and other secrets. These should not be part of version \n# control as they are data points which are potentially sensitive and subject \n# to change depending on the environment.\n*.tfvars\n*.tfvars.json\n\n# Ignore override files as they are usually used to override resources locally and so\n# are not checked in\noverride.tf\noverride.tf.json\n*_override.tf\n*_override.tf.json\n\n# Include override files you do wish to add to version control using negated pattern\n# !example_override.tf\n\n# Include tfplan files to ignore the plan output of command: terraform plan -out=tfplan\n# example: *tfplan*\n\n# Ignore CLI configuration files\n.terraformrc\nterraform.rc" }, { "path": "mysql_to_postgres_incremental_stack/infra/airbyte/main.tf", "content": "\n// Source\nresource \"airbyte_source_mysql\" \"my_source_mysql\" {\n configuration = {\n database = \"...my_database...\"\n host = \"...my_host...\"\n port = 3306\n replication_method = {\n source_mysql_update_method_read_changes_using_binary_log_cdc_ = {\n initial_waiting_seconds = 10\n method = \"CDC\"\n server_time_zone = \"...my_server_time_zone...\"\n }\n }\n source_type = \"mysql\"\n tunnel_method = {\n source_mysql_ssh_tunnel_method_no_tunnel = {\n tunnel_method = \"NO_TUNNEL\"\n }\n }\n username = \"...my_username...\"\n }\n name = \"MySQL\"\n workspace_id = var.workspace_id\n}\n\n// Destination\nresource \"airbyte_destination_postgres\" \"my_destination_postgres\" {\n configuration = {\n database = \"...my_database...\"\n destination_type = \"postgres\"\n host = \"...my_host...\"\n password = \"...my_password...\"\n port = 5432\n schema = \"public\"\n ssl = true\n ssl_mode = {\n allow = {}\n }\n tunnel_method = {\n no_tunnel = {}\n }\n username = \"...my_username...\"\n }\n name = \"Postgres\"\n workspace_id = var.workspace_id\n}\n\n// Connection\nresource \"airbyte_connection\" \"mysql_to_postgres\" {\n name = \"Mysql to Postgres\"\n source_id = airbyte_source_mysql.my_source_mysql.source_id\n destination_id = airbyte_destination_postgres.my_destination_postgres.destination_id\n configurations = {\n streams = [{\n cursor_field = [\"...\", ]\n name = \"...my_table_name_1...\"\n primary_key = [[\"...\", ], ]\n sync_mode = \"incremental_append\"\n }]\n }\n}\n" }, { "path": "mysql_to_postgres_incremental_stack/infra/airbyte/provider.tf", "content": "// Airbyte Terraform provider documentation: https://registry.terraform.io/providers/airbytehq/airbyte/latest/docs\n\nterraform {\n required_providers {\n airbyte = {\n source = \"airbytehq/airbyte\"\n version = \"0.3.4\"\n }\n }\n}\n\nprovider \"airbyte\" {\n // If running locally (Airbyte OSS) with docker-compose using the airbyte-proxy, \n // include the actual password/username you've set up (or use the defaults below)\n username = \"airbyte\"\n password = \"password\"\n \n // if running locally (Airbyte OSS), include the server url to the airbyte-api-server\n server_url = \"http://localhost:8006/v1\"\n}" }, { "path": "mysql_to_postgres_incremental_stack/infra/airbyte/variables.tf", "content": "variable \"workspace_id\" {\n type = string\n}\n\n\n\n\n\n\n" }, { "path": "mysql_to_postgres_incremental_stack/setup.py", "content": "from setuptools import find_packages, setup\n\nsetup(\n name=\"postgres-to-mysql-migration\",\n packages=find_packages(),\n install_requires=[\n ],\n extras_require={\"dev\": [\"pytest\"]},\n)" }, { "path": "outdoor_activity_analytics_recreation/.gitignore", "content": "# Byte-compiled / optimized / DLL files\n__pycache__/\n*.py[cod]\n*$py.class\n\n# C extensions\n*.so\n\n# Distribution / packaging\n.Python\nbuild/\ndevelop-eggs/\ndist/\ndownloads/\neggs/\n.eggs/\nlib/\nlib64/\nparts/\nsdist/\nvar/\nwheels/\nshare/python-wheels/\n*.egg-info/\n.installed.cfg\n*.egg\nMANIFEST\n\n# PyInstaller\n# Usually these files are written by a python script from a template\n# before PyInstaller builds the exe, so as to inject date/other infos into it.\n*.manifest\n*.spec\n\n# Installer logs\npip-log.txt\npip-delete-this-directory.txt\n\n# Unit test / coverage reports\nhtmlcov/\n.tox/\n.nox/\n.coverage\n.coverage.*\n.cache\nnosetests.xml\ncoverage.xml\n*.cover\n*.py,cover\n.hypothesis/\n.pytest_cache/\ncover/\n\n# Translations\n*.mo\n*.pot\n\n# Django stuff:\n*.log\nlocal_settings.py\ndb.sqlite3\ndb.sqlite3-journal\n\n# Flask stuff:\ninstance/\n.webassets-cache\n\n# Scrapy stuff:\n.scrapy\n\n# Sphinx documentation\ndocs/_build/\n\n# PyBuilder\n.pybuilder/\ntarget/\n\n# Jupyter Notebook\n.ipynb_checkpoints\n\n# IPython\nprofile_default/\nipython_config.py\n\n# pyenv\n# For a library or package, you might want to ignore these files since the code is\n# intended to run in multiple environments; otherwise, check them in:\n# .python-version\n\n# pipenv\n# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.\n# However, in case of collaboration, if having platform-specific dependencies or dependencies\n# having no cross-platform support, pipenv may install dependencies that don't work, or not\n# install all needed dependencies.\n#Pipfile.lock\n\n# poetry\n# Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.\n# This is especially recommended for binary packages to ensure reproducibility, and is more\n# commonly ignored for libraries.\n# https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control\n#poetry.lock\n\n# pdm\n# Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control.\n#pdm.lock\n# pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it\n# in version control.\n# https://pdm.fming.dev/#use-with-ide\n.pdm.toml\n\n# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm\n__pypackages__/\n\n# Celery stuff\ncelerybeat-schedule\ncelerybeat.pid\n\n# SageMath parsed files\n*.sage.py\n\n# Environments\n.env\n.venv\nenv/\nvenv/\nENV/\nenv.bak/\nvenv.bak/\n\n# Spyder project settings\n.spyderproject\n.spyproject\n\n# Rope project settings\n.ropeproject\n\n# mkdocs documentation\n/site\n\n# mypy\n.mypy_cache/\n.dmypy.json\ndmypy.json\n\n# Pyre type checker\n.pyre/\n\n# pytype static type analyzer\n.pytype/\n\n# Cython debug symbols\ncython_debug/\n\n# PyCharm\n# JetBrains specific template is maintained in a separate JetBrains.gitignore that can\n# be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore\n# and can be added to the global gitignore or merged into this file. For a more nuclear\n# option (not recommended) you can uncomment the following to ignore the entire idea folder.\n#.idea/\n\n#Desktop Services Store\n.DS_Store" }, { "path": "outdoor_activity_analytics_recreation/README.md", "content": "# Outdoor Activity Analytics Stack With Recreation Api, Airbyte, Dbt, Dagster and BigQuery\n\nWelcome to the \"Outdoor Activity Analytics Stack\" repository! ✨ This is your go-to place to easily set up a data stack using Recreation Api, Airbyte, Dbt, BigQuery, and Dagster. With this setup, you can pull Recreation Api data, extract it using Airbyte, put it into BigQuery, and play around with it using dbt and Dagster.\n\nThis Quickstart is all about making things easy, getting you started quickly and showing you how smoothly all these tools can work together!\n\nBelow is a visual representation of how data flows through our integrated tools in this Quickstart. This comes from Dagster's global asset lineage view:\n\n![Global Asset Lineage](<./assets/Global_Asset_Lineage%20(6).svg>)\n\n## Table of Contents\n\n- [Prerequisites](#prerequisites)\n- [Setting an environment for your project](#1-setting-an-environment-for-your-project)\n- [Setting Up BigQuery to work with Airbyte and dbt](#2-setting-up-bigquery)\n- [Setting Up Airbyte Connectors with Terraform](#3-setting-up-airbyte-connectors-with-terraform)\n- [Setting Up the dbt Project](#4-setting-up-the-dbt-project)\n- [Orchestrating with Dagster](#5-orchestrating-with-dagster)\n- [Next Steps](#next-steps)\n\n## Prerequisites\n\nBefore you embark on this integration, ensure you have the following set up and ready:\n\n1. **Python 3.10 or later**: If not installed, download and install it from [Python's official website](https://www.python.org/downloads/).\n\n2. **Docker and Docker Compose (Docker Desktop)**: Install [Docker](https://docs.docker.com/get-docker/) following the official documentation for your specific OS.\n\n3. **Airbyte OSS version**: Deploy the open-source version of Airbyte. Follow the installation instructions from the [Airbyte Documentation](https://docs.airbyte.com/quickstart/deploy-airbyte/).\n\n4. **Terraform**: Terraform will help you provision and manage the Airbyte resources. If you haven't installed it, follow the [official Terraform installation guide](https://developer.hashicorp.com/terraform/tutorials/aws-get-started/install-cli).\n\n5. **Google Cloud account with BigQuery**: You will also need to add the necessary permissions to allow Airbyte and dbt to access the data in BigQuery. A step-by-step guide is provided [below](#2-setting-up-bigquery).\n\n## 1. Setting an environment for your project\n\nGet the project up and running on your local machine by following these steps:\n\n1. **Clone the repository (Clone only this quickstart)**:\n\n ```bash\n git clone --filter=blob:none --sparse https://github.com/airbytehq/quickstarts.git\n ```\n\n ```bash\n cd quickstarts\n ```\n\n ```bash\n git sparse-checkout add outdoor_activity_analytics_recreation\n ```\n\n2. **Navigate to the directory**:\n\n ```bash\n cd outdoor_activity_analytics_recreation\n ```\n\n3. **Set Up a Virtual Environment**:\n\n - For Mac:\n ```bash\n python3 -m venv venv\n source venv/bin/activate\n ```\n - For Windows:\n ```bash\n python -m venv venv\n .\\venv\\Scripts\\activate\n ```\n\n4. **Install Dependencies**:\n ```bash\n pip install -e \".[dev]\"\n ```\n\n## 2. Setting Up BigQuery\n\n#### 1. **Create a Google Cloud Project**\n\n- If you have a Google Cloud project, you can skip this step.\n- Go to the [Google Cloud Console](https://console.cloud.google.com/).\n- Click on the \"Select a project\" dropdown at the top right and select \"New Project\".\n- Give your project a name and follow the steps to create it.\n\n#### 2. **Create BigQuery Datasets**\n\n- In the Google Cloud Console, go to BigQuery.\n- Make two new datasets: `raw_data` for Airbyte and `transformed_data` for dbt.\n - If you pick different names, remember to change the names in the code too.\n\n**How to create a dataset:**\n\n- In the left sidebar, click on your project name.\n- Click “Create Dataset”.\n- Enter the dataset ID (either `raw_data` or `transformed_data`).\n- Click \"Create Dataset\".\n\n#### 3. **Create Service Accounts and Assign Roles**\n\n- Go to “IAM & Admin” > “Service accounts” in the Google Cloud Console.\n- Click “Create Service Account”.\n- Name your service account (like `airbyte-service-account`).\n- Assign the “BigQuery Data Editor” and “BigQuery Job User” roles to the service account.\n- Follow the same steps to make another service account for dbt (like `dbt-service-account`) and assign the roles.\n\n**How to create a service account and assign roles:**\n\n- While creating the service account, under the “Grant this service account access to project” section, click the “Role” dropdown.\n- Choose the “BigQuery Data Editor” and “BigQuery Job User” roles.\n- Finish the creation process.\n\n#### 4. **Generate JSON Keys for Service Accounts**\n\n- For both service accounts, make a JSON key to let the service accounts sign in.\n\n**How to generate JSON key:**\n\n- Find the service account in the “Service accounts” list.\n- Click on the service account name.\n- In the “Keys” section, click “Add Key” and pick JSON.\n- The key will download automatically. Keep it safe and don’t share it.\n- Do this for the other service account too.\n\n## 3. Setting Up Airbyte Connectors with Terraform\n\nAirbyte allows you to create connectors for sources and destinations, facilitating data synchronization between various platforms. In this project, we're harnessing the power of Terraform to automate the creation of these connectors and the connections between them. Here's how you can set this up:\n\n1. **Navigate to the Airbyte Configuration Directory**:\n\n Change to the relevant directory containing the Terraform configuration for Airbyte:\n\n ```bash\n cd infra/airbyte\n ```\n\n2. **Modify Configuration Files**:\n\n Within the `infra/airbyte` directory, you'll find three crucial Terraform files:\n\n - `provider.tf`: Defines the Airbyte provider.\n - `main.tf`: Contains the main configuration for creating Airbyte resources.\n - `variables.tf`: Holds various variables, including credentials.\n\n Adjust the configurations in these files to suit your project's needs. Specifically, provide credentials for your BigQuery connection. You can utilize the `variables.tf` file to manage these credentials.\n\n3. **Initialize Terraform**:\n\n This step prepares Terraform to create the resources defined in your configuration files.\n\n ```bash\n terraform init\n ```\n\n4. **Review the Plan**:\n\n Before applying any changes, review the plan to understand what Terraform will do.\n\n ```bash\n terraform plan\n ```\n\n5. **Apply Configuration**:\n\n After reviewing and confirming the plan, apply the Terraform configurations to create the necessary Airbyte resources.\n\n ```bash\n terraform apply\n ```\n\n6. **Verify in Airbyte UI**:\n\n Once Terraform completes its tasks, navigate to the [Airbyte UI](http://localhost:8000/). Here, you should see your source and destination connectors, as well as the connection between them, set up and ready to go.\n\n## 4. Setting Up the dbt Project\n\n[dbt (data build tool)](https://www.getdbt.com/) allows you to transform your data by writing, documenting, and executing SQL workflows. Setting up the dbt project requires specifying connection details for your data platform, in this case, BigQuery. Here’s a step-by-step guide to help you set this up:\n\n1. **Navigate to the dbt Project Directory**:\n\n Change to the directory containing the dbt configuration:\n\n ```bash\n cd dbt_project\n ```\n\n2. **Update Connection Details**:\n\n You'll find a `profiles.yml` file within the directory. This file contains configurations for dbt to connect with your data platform. Update this file with your BigQuery connection details.\n\n3. **Utilize Environment Variables (Optional but Recommended)**:\n\n To keep your credentials secure, you can leverage environment variables. An example is provided within the `profiles.yml` file.\n\n4. **Test the Connection**:\n\n Once you’ve updated the connection details, you can test the connection to your BigQuery instance using:\n\n ```bash\n dbt debug\n ```\n\n If everything is set up correctly, this command should report a successful connection to BigQuery.\n\n5. **Run the Models**:\n\n If you would like to run the dbt models manually at this point, you can do so by executing:\n\n ```bash\n dbt run\n ```\n\n You can verify the data has been transformed by going to BigQuery and checking the `transformed_data` dataset.\n\n6. **Visualise the Data(optional)**:\n\n This is totally an optional step to visualise the data. We will be using python and matplotlib you can use any of your choice. First we need to install the necessary dependencies and we can do this by the following command.\n\n ```bash\n pip install google-cloud-bigquery matplotlib\n ``` \n\n Now create a folder named \"analyses\" under the dbt_project directory. Make sure to name the folder exactly the same as you've mentioned in the `dbt_project.yml` file otherwise it will throw error. Next, create python file under the \"analyses\" folder with appropriate name like `most_common_activities_in_recareas_analysis.py`. Now write down your python script for the analysis. Make sure to set your BigQuery service account json file path as environment variables and use it to authenticate with BigQuery. \n\n Now after you are done writing your python script go to \"analyses\" folder.\n\n ```bash\n cd analyses\n ```\n\n Now run the following command to run the python file. Make sure to replace `most_common_activities_in_recareas_analysis.py` with your actual file name. \n\n ```bash\n python most_common_activities_in_recareas_analysis.py\n ```\n\n You should then see a window displaying a beautiful chart.\n\n\n\n## 5. Orchestrating with Dagster\n\n[Dagster](https://dagster.io/) is a modern data orchestrator designed to help you build, test, and monitor your data workflows. In this section, we'll walk you through setting up Dagster to oversee both the Airbyte and dbt workflows:\n\n1. **Navigate to the Orchestration Directory**:\n\n Switch to the directory containing the Dagster orchestration configurations:\n\n ```bash\n cd orchestration\n ```\n\n2. **Set Environment Variables**:\n\n Dagster requires certain environment variables to be set to interact with other tools like dbt and Airbyte. Set the following variables:\n\n ```bash\n export DAGSTER_DBT_PARSE_PROJECT_ON_LOAD=1\n export AIRBYTE_PASSWORD=password\n ```\n\n Note: The `AIRBYTE_PASSWORD` is set to `password` as a default for local Airbyte instances. If you've changed this during your Airbyte setup, ensure you use the appropriate password here.\n\n3. **Launch the Dagster UI**:\n\n With the environment variables in place, kick-start the Dagster UI:\n\n ```bash\n dagster dev\n ```\n\n4. **Access Dagster in Your Browser**:\n\n Open your browser and navigate to:\n\n ```\n http://127.0.0.1:3000\n ```\n\n Here, you should see assets for both Airbyte and dbt. To get an overview of how these assets interrelate, click on \"view global asset lineage\". This will give you a clear picture of the data lineage, visualizing how data flows between the tools.\n\n5. **Materialize Dagster Assets**:\n In the Dagster UI, click on \"Materialize all\". This should trigger the full pipeline. First the Airbyte sync to extract data from Faker and load it into BigQuery, and then dbt to transform the raw data, materializing the `staging` and `marts` models.\n\n## Next Steps\n\nCongratulations on deploying and running the Customer Satisfaction Analytics Quistart! 🎉 Here are some suggestions on what you can explore next to dive deeper and get more out of your project:\n\n### 1. **Explore the Data and Insights**\n - Dive into the datasets in BigQuery, run some queries, and explore the data you've collected and transformed. This is your chance to uncover insights and understand the data better!\n\n### 2. **Optimize Your dbt Models**\n - Review the transformations you’ve applied using dbt. Try optimizing the models or create new ones based on your evolving needs and insights you want to extract.\n\n### 3. **Automate and Monitor Your Pipelines**\n - Explore more advanced Dagster configurations and setups to automate your pipelines further and set up monitoring and alerting to be informed of any issues immediately." }, { "path": "outdoor_activity_analytics_recreation/dbt_project/.gitignore", "content": "\ntarget/\ndbt_packages/\nlogs/\n\n.user.yml\n" }, { "path": "outdoor_activity_analytics_recreation/dbt_project/README.md", "content": "Welcome to your new dbt project!\n\n### Using the starter project\n\nTry running the following commands:\n\n- dbt run\n- dbt test\n\n### Resources:\n\n- Learn more about dbt [in the docs](https://docs.getdbt.com/docs/introduction)\n- Check out [Discourse](https://discourse.getdbt.com/) for commonly asked questions and answers\n- Join the [chat](https://community.getdbt.com/) on Slack for live discussions and support\n- Find [dbt events](https://events.getdbt.com) near you\n- Check out [the blog](https://blog.getdbt.com/) for the latest news on dbt's development and best practices\n" }, { "path": "outdoor_activity_analytics_recreation/dbt_project/analyses/campsite_availability_analysis.py", "content": "from google.cloud import bigquery\nimport os\n\nfrom matplotlib import pyplot as plt\n\nservice_account_key_path = os.environ.get('DBT_BIGQUERY_KEYFILE_PATH')\n\nclient = bigquery.Client.from_service_account_json(service_account_key_path)\n\nquery = \"\"\"\nSELECT\n DATE(CreatedDate) AS date,\n COUNT(*) AS campsite_count\nFROM\n transformed_data.stg_campsites\nGROUP BY\n date\nORDER BY\n date\n\"\"\"\n\nquery_job = client.query(query)\n\nresults = list(query_job.result())\n\ndates = [row.date for row in results]\ncampsite_counts = [row.campsite_count for row in results]\n\nplt.figure(figsize=(10, 6))\nplt.plot(dates, campsite_counts, marker='o')\nplt.xlabel('Date')\nplt.ylabel('Campsite Count')\nplt.title('Campsite Availability Over Time')\nplt.show()" }, { "path": "outdoor_activity_analytics_recreation/dbt_project/analyses/campsite_type_analysis.py", "content": "from google.cloud import bigquery\nimport os\n\nfrom matplotlib import pyplot as plt\n\nservice_account_key_path = os.environ.get('DBT_BIGQUERY_KEYFILE_PATH')\n\nclient = bigquery.Client.from_service_account_json(service_account_key_path)\n\nquery = \"\"\"\nSELECT\n CampsiteType,\n COUNT(*) AS campsite_count\nFROM\n transformed_data.stg_campsites\nGROUP BY\n CampsiteType\n\"\"\"\n\nquery_job = client.query(query)\n\nresults = list(query_job.result())\n\ncampsite_types = [row.CampsiteType for row in results]\ncampsite_counts = [row.campsite_count for row in results]\n\nplt.figure(figsize=(10, 6))\nplt.barh(campsite_types, campsite_counts)\nplt.xlabel('Campsite Count')\nplt.ylabel('Campsite Type')\nplt.title('Campsite Count by Campsite Type')\nplt.show()\n" }, { "path": "outdoor_activity_analytics_recreation/dbt_project/analyses/count_recareas_by_activity_analysis.py", "content": "from google.cloud import bigquery\nimport os\n\nfrom matplotlib import pyplot as plt\n\nservice_account_key_path = os.environ.get('DBT_BIGQUERY_KEYFILE_PATH')\n\nclient = bigquery.Client.from_service_account_json(service_account_key_path)\n\nquery = \"\"\"\nSELECT\n a.ActivityName,\n COUNT(r.RecAreaID) AS rec_area_count\nFROM\n transformed_data.stg_activities AS a\nLEFT JOIN\n transformed_data.stg_recreationareas AS r\nON\n a.ActivityID = CAST(JSON_EXTRACT_SCALAR(r.ACTIVITY, '$.ActivityID') AS INT64)\nGROUP BY\n a.ActivityName\n\"\"\"\n\nquery_job = client.query(query)\n\nresults = list(query_job.result())\n\nactivity_names = [row.ActivityName for row in results]\nrec_area_counts = [row.rec_area_count for row in results]\n\nplt.figure(figsize=(10, 6))\nplt.barh(activity_names, rec_area_counts)\nplt.xlabel('Recreational Area Count')\nplt.ylabel('Activity Name')\nplt.title('Recreational Area Count by Activity')\nplt.show()" }, { "path": "outdoor_activity_analytics_recreation/dbt_project/analyses/most_common_activities_in_recareas_analysis.py", "content": "from google.cloud import bigquery\nimport os\n\nfrom matplotlib import pyplot as plt\n\nservice_account_key_path = os.environ.get('DBT_BIGQUERY_KEYFILE_PATH')\n\nclient = bigquery.Client.from_service_account_json(service_account_key_path)\n\nquery = \"\"\"\nWITH ActivityCounts AS (\n SELECT\n ra.RecAreaName,\n a.ActivityName,\n COUNT(*) AS activity_count\n FROM\n transformed_data.stg_recreationareas AS ra\n LEFT JOIN\n transformed_data.stg_activities AS a\n ON\n CAST(ra.RecAreaID AS INT64) = a.ActivityParentID\n GROUP BY\n ra.RecAreaName, a.ActivityName\n)\nSELECT\n RecAreaName,\n ActivityName,\n activity_count\nFROM (\n SELECT\n RecAreaName,\n ActivityName,\n activity_count,\n ROW_NUMBER() OVER (PARTITION BY RecAreaName ORDER BY activity_count DESC) AS rn\n FROM ActivityCounts\n)\nWHERE rn = 1\n\"\"\"\n\nquery_job = client.query(query)\n\n# Get the results\nresults = list(query_job.result())\n\n# Plot the data\nrec_areas = [row.RecAreaName for row in results]\ncommon_activities = [row.ActivityName for row in results]\n\nplt.figure(figsize=(10, 6))\nplt.barh(rec_areas, common_activities)\nplt.xlabel('Most Common Activity')\nplt.ylabel('RecArea Name')\nplt.title('Most Common Activity in RecAreas')\nplt.show()" }, { "path": "outdoor_activity_analytics_recreation/dbt_project/dbt_project.yml", "content": "\n# Name your project! Project names should contain only lowercase characters\n# and underscores. A good package name should reflect your organization's\n# name or the intended use of these models\nname: 'dbt_project'\nversion: '1.0.0'\nconfig-version: 2\n\n# This setting configures which \"profile\" dbt uses for this project.\nprofile: 'dbt_project'\n\n# These configurations specify where dbt should look for different types of files.\n# The `model-paths` config, for example, states that models in this project can be\n# found in the \"models/\" directory. You probably won't need to change these!\nmodel-paths: [\"models\"]\nanalysis-paths: [\"analyses\"]\ntest-paths: [\"tests\"]\nseed-paths: [\"seeds\"]\nmacro-paths: [\"macros\"]\nsnapshot-paths: [\"snapshots\"]\n\nclean-targets: # directories to be removed by `dbt clean`\n - \"target\"\n - \"dbt_packages\"\n\n\n# Configuring models\n# Full documentation: https://docs.getdbt.com/docs/configuring-models\n\n# In this example config, we tell dbt to build all models in the example/\n# directory as views. These settings can be overridden in the individual model\n# files using the `{{ config(...) }}` macro.\nmodels:\n dbt_project:\n # Config indicated by + and applies to all files under models/example/\n staging:\n +materialized: view\n marts:\n +materialized: view\n" }, { "path": "outdoor_activity_analytics_recreation/dbt_project/models/marts/campsite_availability_over_time.sql", "content": "SELECT\n DATE(CreatedDate) AS date,\n COUNT(*) AS campsite_count\nFROM\n transformed_data.stg_campsites\nGROUP BY\n date\nORDER BY\n date" }, { "path": "outdoor_activity_analytics_recreation/dbt_project/models/marts/campsite_type_counts.sql", "content": "WITH campsite_type_counts AS (\n SELECT\n CampsiteType,\n COUNT(*) AS campsite_count\n FROM\n {{ ref('stg_campsites') }}\n GROUP BY\n CampsiteType\n)\n\nSELECT\n *\nFROM\n campsite_type_counts" }, { "path": "outdoor_activity_analytics_recreation/dbt_project/models/marts/count_recarea_by_activity_analysis.sql", "content": "SELECT\n a.ActivityName,\n COUNT(r.RecAreaID) AS rec_area_count\nFROM\n transformed_data.stg_activities AS a\nLEFT JOIN\n transformed_data.stg_recreationareas AS r\nON\n a.ActivityID = CAST(JSON_EXTRACT_SCALAR(r.ACTIVITY, '$.ActivityID') AS INT64)\nGROUP BY\n a.ActivityName\n" }, { "path": "outdoor_activity_analytics_recreation/dbt_project/models/marts/most_common_activities_in_recareas.sql", "content": "WITH ActivityCounts AS (\n SELECT\n ra.RecAreaName,\n a.ActivityName,\n COUNT(*) AS activity_count\n FROM\n transformed_data.stg_recreationareas AS ra\n LEFT JOIN\n transformed_data.stg_activities AS a\n ON\n CAST(ra.RecAreaID AS INT64) = a.ActivityParentID\n GROUP BY\n ra.RecAreaName, a.ActivityName\n)\nSELECT\n RecAreaName,\n ActivityName,\n activity_count\nFROM (\n SELECT\n RecAreaName,\n ActivityName,\n activity_count,\n ROW_NUMBER() OVER (PARTITION BY RecAreaName ORDER BY activity_count DESC) AS rn\n FROM ActivityCounts\n)\nWHERE rn = 1\n" }, { "path": "outdoor_activity_analytics_recreation/dbt_project/models/sources/recreation_source.yml", "content": "version: 2\n\nsources:\n - name: recreation\n # Use your BigQuery project ID\n database: \"{{ env_var('BIGQUERY_PROJECT_ID', '') }}\"\n # Use your BigQuery dataset name\n schema: recreation_airbyte\n \n tables:\n - name: recreationareas\n description: \"Simulated recreationareas data from the Recreation connector.\"\n columns:\n - name: RecAreaID\n description: \"Unique identifier for the recreationareas.\"\n - name: ACTIVITY\n - name: Enabled\n - name: EVENT\n - name: FACILITY\n - name: GEOJSON\n - name: Keywords\n - name: LastUpdatedDate\n - name: LINK\n - name: MEDIA\n - name: ORGANIZATION\n - name: OrgRecAreaID\n - name: ParentOrgID\n - name: RECAREAADDRESS\n - name: RecAreaDescription\n - name: RecAreaDirections\n - name: RecAreaEmail\n - name: RecAreaFeeDescription\n - name: RecAreaLatitude\n - name: RecAreaLongitude\n - name: RecAreaMapURL\n - name: RecAreaName\n - name: RecAreaPhone\n - name: RecAreaReservationURL\n - name: Reservable\n - name: StayLimit\n\n - name: activities\n description: \"Simulated activities data from the Recreation connector.\"\n columns:\n - name: ActivityID\n description: \"Unique identifier for the activities.\"\n - name: ActivityLevel\n - name: ActivityName\n - name: ActivityParentID\n\n - name: campsites\n description: \"Simulated campsites data from the Recreation connector.\"\n columns:\n - name: CampsiteID\n description: \"Unique identifier for the campsites.\"\n - name: ATTRIBUTES\n - name: CampsiteAccessible\n - name: CampsiteLatitude\n - name: CampsiteLongitude\n - name: CampsiteName\n - name: CampsiteType\n - name: CreatedDate\n - name: ENTITYMEDIA\n - name: FacilityID\n - name: LastUpdatedDate\n - name: Loop\n - name: PERMITTEDEQUIPMENT\n - name: TypeOfUse\n\n - name: facilities\n description: \"Simulated facilities data from the Recreation connector.\"\n columns:\n - name: FacilityID\n description: \"Unique identifier for the facilities.\"\n - name: ACTIVITY\n - name: CAMPSITE\n - name: Enabled\n - name: EVENT\n - name: FacilityAdaAccess\n - name: FACILITYADDRESS\n - name: FacilityDescription\n - name: FacilityDirections\n - name: FacilityEmail\n - name: FacilityLatitude\n - name: FacilityLongitude\n - name: FacilityMapURL\n - name: FacilityName\n - name: FacilityPhone\n - name: FacilityReservationURL\n - name: FacilityTypeDescription\n - name: FacilityUseFeeDescription\n - name: GEOJSON\n - name: Keywords\n - name: LastUpdatedDate\n - name: LegacyFacilityID\n - name: LINK\n - name: MEDIA\n - name: ORGANIZATION\n - name: OrgFacilityID\n - name: ParentOrgID\n - name: ParentRecAreaID\n - name: PERMITENTRANCE\n - name: RECAREA\n - name: Reservable\n - name: StayLimit\n - name: TOUR\n\n\n\n\n \n\n\n\n" }, { "path": "outdoor_activity_analytics_recreation/dbt_project/models/staging/stg_activities.sql", "content": "select\n *\nfrom {{ source('recreation', 'activities') }}" }, { "path": "outdoor_activity_analytics_recreation/dbt_project/models/staging/stg_campsites.sql", "content": "select\n *\nfrom {{ source('recreation', 'campsites') }}" }, { "path": "outdoor_activity_analytics_recreation/dbt_project/models/staging/stg_facilities.sql", "content": "select\n *\nfrom {{ source('recreation', 'facilities') }}" }, { "path": "outdoor_activity_analytics_recreation/dbt_project/models/staging/stg_recreationareas.sql", "content": "select\n *\nfrom {{ source('recreation', 'recreationareas') }}" }, { "path": "outdoor_activity_analytics_recreation/dbt_project/profiles.yml", "content": "dbt_project:\n outputs:\n dev:\n dataset: transformed_data\n job_execution_timeout_seconds: 300\n job_retries: 1\n # Use an env variable to indicate your JSON key file path\n keyfile: \"{{ env_var('DBT_BIGQUERY_KEYFILE_PATH', '') }}\"\n location: US\n method: service-account\n priority: interactive\n # Indicate your BigQuery project ID\n project: \"{{ env_var('BIGQUERY_PROJECT_ID', '') }}\"\n threads: 1\n type: bigquery\n target: dev" }, { "path": "outdoor_activity_analytics_recreation/infra/.gitignore", "content": "# Local .terraform directories\n**/.terraform/*\n\n# .tfstate files\n*.tfstate\n*.tfstate.*\n\n# Crash log files\ncrash.log\ncrash.*.log\n\n# Exclude all .tfvars files, which are likely to contain sensitive data, such as\n# password, private keys, and other secrets. These should not be part of version \n# control as they are data points which are potentially sensitive and subject \n# to change depending on the environment.\n*.tfvars\n*.tfvars.json\n\n# Ignore override files as they are usually used to override resources locally and so\n# are not checked in\noverride.tf\noverride.tf.json\n*_override.tf\n*_override.tf.json\n\n# Include override files you do wish to add to version control using negated pattern\n# !example_override.tf\n\n# Include tfplan files to ignore the plan output of command: terraform plan -out=tfplan\n# example: *tfplan*\n\n# Ignore CLI configuration files\n.terraformrc\nterraform.rc" }, { "path": "outdoor_activity_analytics_recreation/infra/airbyte/.terraform.lock.hcl", "content": "# This file is maintained automatically by \"terraform init\".\n# Manual edits may be lost in future updates.\n\nprovider \"registry.terraform.io/airbytehq/airbyte\" {\n version = \"0.3.3\"\n constraints = \"0.3.3\"\n hashes = [\n \"h1:0LmuAc5LvlMuOUPtNEaCAh9FHrV/C877bDJhm9Lz8MU=\",\n \"zh:0efa470b34d9b912b47efe4469c51713bfc3c2413e52c17e1e903f2a3cddb2f6\",\n \"zh:1bddd69fa2c2d4f3e239d60555446df9bc4ce0c0cabbe7e092fe1d44989ab004\",\n \"zh:2e20540403a0010007b53456663fb037b24e30f6c8943f65da1bcf7fa4dfc8a6\",\n \"zh:2f415369ad884e8b7115a5c5ff229d052f7af1fca27abbfc8ebef379ed11aec4\",\n \"zh:46fd9a906f4b6461112dcc5a5aa01a3fcd7a19a72d4ad0b2e37790da37701fe1\",\n \"zh:83503ebb77bb6d6941c42ba323cf22380d08a1506554a2dcc8ac54e74c0886a1\",\n \"zh:890df766e9b839623b1f0437355032a3c006226a6c200cd911e15ee1a9014e9f\",\n \"zh:8fd770eff726826d3a63b9e3733c5455b5cde004027b04ee3f75888eb8538c90\",\n \"zh:b0fc890ed4f9b077bf70ed121cc3550e7a07d16e7798ad517623274aa62ad7b0\",\n \"zh:c2a01612362da9b73cd5958f281e1aa7ff09af42182e463097d11ed78e778e72\",\n \"zh:c64b2bb1887a0367d64ba3393d4b3a16c418cf5b1792e2e7aae7c0b5413eb334\",\n \"zh:ce14ebbf0ed91913ec62655a511763dec62b5779de9a209bd6f1c336640cddc0\",\n \"zh:e0662ca837eee10f7733ea9a501d995281f56bd9b410ae13ad03eb106011db14\",\n \"zh:e103d480fc6066004bc98e9e04a141a1f55b918cc2912716beebcc6fc4c872fb\",\n \"zh:e2507049098f0f1b21cb56870f4a5ef624bcf6d3959e5612eada1f8117341648\",\n ]\n}\n" }, { "path": "outdoor_activity_analytics_recreation/infra/airbyte/main.tf", "content": "// Source\nresource \"airbyte_source_recreation\" \"my_source_recreation\" {\n configuration = {\n apikey = var.api_key\n source_type = \"recreation\"\n }\n name = \"Recreation\"\n workspace_id = var.workspace_id\n}\n\n// Destinations\nresource \"airbyte_destination_bigquery\" \"bigquery\" {\n configuration = {\n dataset_id = var.dataset_id\n dataset_location = \"US\"\n destination_type = \"bigquery\"\n project_id = var.project_id\n credentials_json = var.credentials_json\n loading_method = {\n destination_bigquery_loading_method_standard_inserts = {\n method = \"Standard\"\n }\n }\n }\n name = \"BigQuery\"\n workspace_id = var.workspace_id\n}\n\n// Connections\nresource \"airbyte_connection\" \"recreation_bigquery\" {\n name = \"Recreation to BigQuery\"\n source_id = airbyte_source_recreation.my_source_recreation.source_id\n destination_id = airbyte_destination_bigquery.bigquery.destination_id\n configurations = {\n streams = [{\n name = \"recreationareas\"\n },\n {\n name = \"facilities\"\n },\n {\n name = \"activities\"\n },\n {\n name = \"campsites\"\n }]\n }\n}\n" }, { "path": "outdoor_activity_analytics_recreation/infra/airbyte/provider.tf", "content": "// Airbyte Terraform provider documentation: https://registry.terraform.io/providers/airbytehq/airbyte/latest/docs\n\nterraform {\n required_providers {\n airbyte = {\n source = \"airbytehq/airbyte\"\n version = \"0.3.3\"\n }\n }\n}\n\nprovider \"airbyte\" {\n // If running locally (Airbyte OSS) with docker-compose using the airbyte-proxy, \n // include the actual password/username you've set up (or use the defaults below)\n username = \"airbyte\"\n password = \"password\"\n\n // if running locally (Airbyte OSS), include the server url to the airbyte-api-server\n server_url = \"http://localhost:8006/v1/\"\n}\n" }, { "path": "outdoor_activity_analytics_recreation/infra/airbyte/variables.tf", "content": "variable \"api_key\" {\n type = string\n}\n\nvariable \"workspace_id\" {\n type = string\n}\n\nvariable \"dataset_id\" {\n type = string\n}\n\nvariable \"project_id\" {\n type = string\n}\n\nvariable \"credentials_json\" {\n type = string\n}\n\n\n\n\n" }, { "path": "outdoor_activity_analytics_recreation/orchestration/orchestration/__init__.py", "content": "" }, { "path": "outdoor_activity_analytics_recreation/orchestration/orchestration/assets.py", "content": "import os\nfrom dagster import OpExecutionContext\nfrom dagster_dbt import DbtCliResource, dbt_assets\nfrom dagster_airbyte import AirbyteResource, load_assets_from_airbyte_instance\n\nfrom .constants import dbt_manifest_path\n\n\n@dbt_assets(manifest=dbt_manifest_path)\ndef dbt_project_dbt_assets(context: OpExecutionContext, dbt: DbtCliResource):\n yield from dbt.cli([\"build\"], context=context).stream()\n\n\nairbyte_instance = AirbyteResource(\n host=\"localhost\",\n port=\"8000\",\n # If using basic auth, include username and password:\n username=\"airbyte\",\n password=\"password\"\n)\n\nairbyte_assets = load_assets_from_airbyte_instance(airbyte_instance,)\n" }, { "path": "outdoor_activity_analytics_recreation/orchestration/orchestration/constants.py", "content": "import os\nfrom pathlib import Path\n\nfrom dagster_dbt import DbtCliResource\n\ndbt_project_dir = Path(__file__).joinpath(\"..\", \"..\", \"..\", \"dbt_project\").resolve()\ndbt = DbtCliResource(project_dir=os.fspath(dbt_project_dir))\n\n# If DAGSTER_DBT_PARSE_PROJECT_ON_LOAD is set, a manifest will be created at runtime.\n# Otherwise, we expect a manifest to be present in the project's target directory.\nif os.getenv(\"DAGSTER_DBT_PARSE_PROJECT_ON_LOAD\"):\n dbt_parse_invocation = dbt.cli([\"parse\"], manifest={}).wait()\n dbt_manifest_path = dbt_parse_invocation.target_path.joinpath(\"manifest.json\")\nelse:\n dbt_manifest_path = dbt_project_dir.joinpath(\"target\", \"manifest.json\")" }, { "path": "outdoor_activity_analytics_recreation/orchestration/orchestration/definitions.py", "content": "import os\n\nfrom dagster import Definitions\nfrom dagster_dbt import DbtCliResource\n\nfrom .assets import dbt_project_dbt_assets, airbyte_assets\nfrom .constants import dbt_project_dir\nfrom .schedules import schedules\n\ndefs = Definitions(\n assets=[dbt_project_dbt_assets, airbyte_assets],\n schedules=schedules,\n resources={\n \"dbt\": DbtCliResource(project_dir=os.fspath(dbt_project_dir)),\n },\n)\n" }, { "path": "outdoor_activity_analytics_recreation/orchestration/orchestration/schedules.py", "content": "\"\"\"\nTo add a daily schedule that materializes your dbt assets, uncomment the following lines.\n\"\"\"\nfrom dagster_dbt import build_schedule_from_dbt_selection\n\nfrom .assets import dbt_project_dbt_assets\n\nschedules = [\n # build_schedule_from_dbt_selection(\n # [dbt_project_dbt_assets],\n # job_name=\"materialize_dbt_models\",\n # cron_schedule=\"0 0 * * *\",\n # dbt_select=\"fqn:*\",\n # ),\n]\n" }, { "path": "outdoor_activity_analytics_recreation/orchestration/pyproject.toml", "content": "[build-system]\nrequires = [\"setuptools\"]\nbuild-backend = \"setuptools.build_meta\"\n\n[tool.dagster]\nmodule_name = \"orchestration.definitions\"\ncode_location_name = \"orchestration\"" }, { "path": "outdoor_activity_analytics_recreation/orchestration/setup.py", "content": "from setuptools import find_packages, setup\n\nsetup(\n name=\"orchestration\",\n version=\"0.0.1\",\n packages=find_packages(),\n install_requires=[\n \"dagster\",\n \"dagster-cloud\",\n \"dagster-dbt\",\n \"dbt-core>=1.4.0\",\n \"dbt-bigquery\",\n ],\n extras_require={\n \"dev\": [\n \"dagster-webserver\",\n ]\n },\n)" }, { "path": "outdoor_activity_analytics_recreation/setup.py", "content": "from setuptools import find_packages, setup\n\nsetup(\n name=\"airbyte-dbt-dagster\",\n packages=find_packages(),\n install_requires=[\n \"dbt-bigquery\",\n \"dagster\",\n \"dagster-cloud\",\n \"dagster-dbt\",\n \"dagster-airbyte\",\n ],\n extras_require={\"dev\": [\"dagit\", \"pytest\"]},\n)" }, { "path": "postgres_data_replication/.gitignore", "content": "# Byte-compiled / optimized / DLL files\n__pycache__/\n*.py[cod]\n*$py.class\n\n# C extensions\n*.so\n\n# Distribution / packaging\n.Python\nbuild/\ndevelop-eggs/\ndist/\ndownloads/\neggs/\n.eggs/\nlib/\nlib64/\nparts/\nsdist/\nvar/\nwheels/\nshare/python-wheels/\n*.egg-info/\n.installed.cfg\n*.egg\nMANIFEST\n\n# PyInstaller\n# Usually these files are written by a python script from a template\n# before PyInstaller builds the exe, so as to inject date/other infos into it.\n*.manifest\n*.spec\n\n# Installer logs\npip-log.txt\npip-delete-this-directory.txt\n\n# Unit test / coverage reports\nhtmlcov/\n.tox/\n.nox/\n.coverage\n.coverage.*\n.cache\nnosetests.xml\ncoverage.xml\n*.cover\n*.py,cover\n.hypothesis/\n.pytest_cache/\ncover/\n\n# Translations\n*.mo\n*.pot\n\n# Django stuff:\n*.log\nlocal_settings.py\ndb.sqlite3\ndb.sqlite3-journal\n\n# Flask stuff:\ninstance/\n.webassets-cache\n\n# Scrapy stuff:\n.scrapy\n\n# Sphinx documentation\ndocs/_build/\n\n# PyBuilder\n.pybuilder/\ntarget/\n\n# Jupyter Notebook\n.ipynb_checkpoints\n\n# IPython\nprofile_default/\nipython_config.py\n\n# pyenv\n# For a library or package, you might want to ignore these files since the code is\n# intended to run in multiple environments; otherwise, check them in:\n# .python-version\n\n# pipenv\n# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.\n# However, in case of collaboration, if having platform-specific dependencies or dependencies\n# having no cross-platform support, pipenv may install dependencies that don't work, or not\n# install all needed dependencies.\n#Pipfile.lock\n\n# poetry\n# Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.\n# This is especially recommended for binary packages to ensure reproducibility, and is more\n# commonly ignored for libraries.\n# https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control\n#poetry.lock\n\n# pdm\n# Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control.\n#pdm.lock\n# pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it\n# in version control.\n# https://pdm.fming.dev/#use-with-ide\n.pdm.toml\n\n# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm\n__pypackages__/\n\n# Celery stuff\ncelerybeat-schedule\ncelerybeat.pid\n\n# SageMath parsed files\n*.sage.py\n\n# Environments\n.env\n.venv\nenv/\nvenv/\nENV/\nenv.bak/\nvenv.bak/\n\n# Spyder project settings\n.spyderproject\n.spyproject\n\n# Rope project settings\n.ropeproject\n\n# mkdocs documentation\n/site\n\n# mypy\n.mypy_cache/\n.dmypy.json\ndmypy.json\n\n# Pyre type checker\n.pyre/\n\n# pytype static type analyzer\n.pytype/\n\n# Cython debug symbols\ncython_debug/\n\n# PyCharm\n# JetBrains specific template is maintained in a separate JetBrains.gitignore that can\n# be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore\n# and can be added to the global gitignore or merged into this file. For a more nuclear\n# option (not recommended) you can uncomment the following to ignore the entire idea folder.\n#.idea/\n\n#Desktop Services Store\n.DS_Store" }, { "path": "postgres_data_replication/README.md", "content": "# Postgres Data Replication Stack\n\nWelcome to the \"Postgres Data Replication Stack\" repository! This repo provides a quickstart template for building a postgres data replication solution using Airbyte. We will easily synchronize two Postgres databases with Airbyte using Change Data Capture (CDC) and Postgres Write Ahead Log (WAL). While this template doesn't delve into specific data, its goal is to showcase how the data replication solution can be achieved.\n\nThis quickstart is designed to minimize setup hassles and propel you forward.\n\n## Table of Contents\n\n- [Postgres Data Replication Stack](#postgres-data-replication-stack)\n - [Table of Contents](#table-of-contents)\n - [Infrastructure Layout](#infrastructure-layout)\n - [Prerequisites](#prerequisites)\n - [1. Setting an environment for your project](#1-setting-an-environment-for-your-project)\n - [2. Setting Up Airbyte Connectors with Terraform](#2-setting-up-airbyte-connectors-with-terraform)\n - [Next Steps](#next-steps)\n\n\n## Infrastructure Layout\n\n![infrastructure layout](images/infrastructure_layout.png)\n\n## Prerequisites\n\nBefore you embark on this integration, ensure you have the following set up and ready:\n\n1. **Python 3.10 or later**: If not installed, download and install it from [Python's official website](https://www.python.org/downloads/).\n\n2. **Docker and Docker Compose (Docker Desktop)**: Install [Docker](https://docs.docker.com/get-docker/) following the official documentation for your specific OS.\n\n3. **Airbyte OSS version**: Deploy the open-source version of Airbyte. Follow the installation instructions from the [Airbyte Documentation](https://docs.airbyte.com/quickstart/deploy-airbyte/).\n\n4. **Terraform**: Terraform will help you provision and manage the Airbyte resources. If you haven't installed it, follow the [official Terraform installation guide](https://developer.hashicorp.com/terraform/tutorials/aws-get-started/install-cli).\n\n\n## 1. Setting an environment for your project\n\nGet the project up and running on your local machine by following these steps:\n\n1. **Clone the repository (Clone only this quickstart)**: \n ```bash\n git clone --filter=blob:none --sparse https://github.com/airbytehq/quickstarts.git\n ```\n\n ```bash\n cd quickstarts\n ```\n\n ```bash\n git sparse-checkout add postgres_data_replication\n ```\n\n \n2. **Navigate to the directory**: \n ```bash\n cd postgres_data_replication\n ```\n\n3. **Set Up a Virtual Environment**: \n - For Mac:\n ```bash\n python3 -m venv venv\n source venv/bin/activate\n ```\n - For Windows:\n ```bash\n python -m venv venv\n .\\venv\\Scripts\\activate\n ```\n\n4. **Install Dependencies**: \n ```bash\n pip install -e \".[dev]\"\n ```\n\n## 2. Setting Up Airbyte Connectors with Terraform\n\nAirbyte allows you to create connectors for sources and destinations, facilitating data synchronization between various platforms. In this project, we're harnessing the power of Terraform to automate the creation of these connectors and the connections between them. Here's how you can set this up:\n\n1. **Navigate to the Airbyte Configuration Directory**:\n \n Change to the relevant directory containing the Terraform configuration for Airbyte:\n ```bash\n cd infra/airbyte\n ```\n\n2. **Modify Configuration Files**:\n\n Within the `infra/airbyte` directory, you'll find three crucial Terraform files:\n - `provider.tf`: Defines the Airbyte provider.\n - `main.tf`: Contains the main configuration for creating Airbyte resources.\n - `variables.tf`: Holds various variables, including credentials.\n\n Adjust the configurations in these files to suit your project's needs. Specifically, provide credentials for your Postgres connections. You can utilize the `variables.tf` file to manage these credentials.\n\n3. **Initialize Terraform**:\n \n This step prepares Terraform to create the resources defined in your configuration files.\n ```bash\n terraform init\n ```\n\n4. **Review the Plan**:\n\n Before applying any changes, review the plan to understand what Terraform will do.\n ```bash\n terraform plan\n ```\n\n5. **Apply Configuration**:\n\n After reviewing and confirming the plan, apply the Terraform configurations to create the necessary Airbyte resources.\n ```bash\n terraform apply\n ```\n\n6. **Verify in Airbyte UI**:\n\n Once Terraform completes its tasks, navigate to the Airbyte UI. Here, you should see your source and destination connectors, as well as the connection between them, set up and ready to go.\n\n## Next Steps\n\nOnce you've set up and launched this initial integration, the real power lies in its adaptability and extensibility. Here’s a roadmap to help you customize and harness this project tailored to your specific data needs:\n\n\n1. **Extend the Project**:\n\n The real beauty of this integration is its extensibility. Whether you want to add more data sources, integrate additional tools, or add some transformation logic – the floor is yours. With the foundation set, sky's the limit for how you want to extend and refine your data processes." }, { "path": "postgres_data_replication/infra/.gitignore", "content": "# Local .terraform directories\n**/.terraform/*\n\n# .tfstate files\n*.tfstate\n*.tfstate.*\n\n# Crash log files\ncrash.log\ncrash.*.log\n\n# Exclude all .tfvars files, which are likely to contain sensitive data, such as\n# password, private keys, and other secrets. These should not be part of version \n# control as they are data points which are potentially sensitive and subject \n# to change depending on the environment.\n*.tfvars\n*.tfvars.json\n\n# Ignore override files as they are usually used to override resources locally and so\n# are not checked in\noverride.tf\noverride.tf.json\n*_override.tf\n*_override.tf.json\n\n# Include override files you do wish to add to version control using negated pattern\n# !example_override.tf\n\n# Include tfplan files to ignore the plan output of command: terraform plan -out=tfplan\n# example: *tfplan*\n\n# Ignore CLI configuration files\n.terraformrc\nterraform.rc" }, { "path": "postgres_data_replication/infra/airbyte/main.tf", "content": "// Airbyte Terraform provider documentation: https://registry.terraform.io/providers/airbytehq/airbyte/latest/docs\n\n// Sources\nresource \"airbyte_source_postgres\" \"postgres\" {\n configuration = {\n database = \"...my_database...\"\n host = \"...my_host...\"\n username = \"...my_username...\"\n password = \"...my_password...\"\n port = 5432\n source_type = \"postgres\"\n schemas = [\n \"...my_schema...\"\n ]\n ssl_mode = {\n allow = {}\n }\n tunnel_method = {\n no_tunnel = {}\n }\n replication_method = {\n read_changes_using_write_ahead_log_cdc = {\n publication = \"...pub...\"\n replication_slot = \"...slot...\"\n }\n }\n }\n name = \"Postgres-Primary\"\n workspace_id = var.workspace_id\n}\n\n// Destinations\nresource \"airbyte_destination_postgres\" \"postgres\" {\n configuration = {\n database = \"...my_database...\"\n destination_type = \"postgres\"\n host = \"...my_host...\"\n jdbc_url_params = \"...my_jdbc_url_params...\"\n password = \"...my_password...\"\n port = 5432\n schema = \"public\"\n ssl_mode = {\n destination_postgres_ssl_modes_allow = {\n mode = \"allow\"\n }\n }\n tunnel_method = {\n destination_postgres_ssh_tunnel_method_no_tunnel = {\n tunnel_method = \"NO_TUNNEL\"\n }\n }\n username = \"...my_username...\"\n }\n name = \"Postgres-Secondary\"\n workspace_id = var.workspace_id\n}\n\n// Connections\nresource \"airbyte_connection\" \"postgres_to_postgres\" {\n name = \"Postgres to Postgres\"\n source_id = airbyte_source_postgres.postgres.source_id\n destination_id = airbyte_destination_postgres.postgres.destination_id\n configurations = {\n streams = [\n {\n name = \"...my_table_name_1...\"\n },\n {\n name = \"...my_table_name_2...\"\n },\n ]\n }\n schedule = {\n cron_expression = \"...my_cron_expression...\"\n schedule_type = \"cron\"\n }\n}" }, { "path": "postgres_data_replication/infra/airbyte/provider.tf", "content": "// Airbyte Terraform provider documentation: https://registry.terraform.io/providers/airbytehq/airbyte/latest/docs\n\nterraform {\n required_providers {\n airbyte = {\n source = \"airbytehq/airbyte\"\n version = \"0.3.4\"\n }\n }\n}\n\nprovider \"airbyte\" {\n // If running locally (Airbyte OSS) with docker-compose using the airbyte-proxy, \n // include the actual password/username you've set up (or use the defaults below)\n username = \"airbyte\"\n password = \"password\"\n \n // if running locally (Airbyte OSS), include the server url to the airbyte-api-server\n server_url = \"http://localhost:8006/v1\"\n}" }, { "path": "postgres_data_replication/infra/airbyte/variables.tf", "content": "variable \"workspace_id\" {\n type = string\n}\n\n\n\n\n\n\n" }, { "path": "postgres_data_replication/setup.py", "content": "from setuptools import find_packages, setup\n\nsetup(\n name=\"postgres-data-replication\",\n packages=find_packages(),\n install_requires=[\n ],\n extras_require={\"dev\": [\"pytest\"]},\n)" }, { "path": "postgres_snowflake_integration/.gitignore", "content": "# Byte-compiled / optimized / DLL files\n__pycache__/\n*.py[cod]\n*$py.class\n\n# C extensions\n*.so\n\n# Distribution / packaging\n.Python\nbuild/\ndevelop-eggs/\ndist/\ndownloads/\neggs/\n.eggs/\nlib/\nlib64/\nparts/\nsdist/\nvar/\nwheels/\nshare/python-wheels/\n*.egg-info/\n.installed.cfg\n*.egg\nMANIFEST\n\n# PyInstaller\n# Usually these files are written by a python script from a template\n# before PyInstaller builds the exe, so as to inject date/other infos into it.\n*.manifest\n*.spec\n\n# Installer logs\npip-log.txt\npip-delete-this-directory.txt\n\n# Unit test / coverage reports\nhtmlcov/\n.tox/\n.nox/\n.coverage\n.coverage.*\n.cache\nnosetests.xml\ncoverage.xml\n*.cover\n*.py,cover\n.hypothesis/\n.pytest_cache/\ncover/\n\n# Translations\n*.mo\n*.pot\n\n# Django stuff:\n*.log\nlocal_settings.py\ndb.sqlite3\ndb.sqlite3-journal\n\n# Flask stuff:\ninstance/\n.webassets-cache\n\n# Scrapy stuff:\n.scrapy\n\n# Sphinx documentation\ndocs/_build/\n\n# PyBuilder\n.pybuilder/\ntarget/\n\n# Jupyter Notebook\n.ipynb_checkpoints\n\n# IPython\nprofile_default/\nipython_config.py\n\n# pyenv\n# For a library or package, you might want to ignore these files since the code is\n# intended to run in multiple environments; otherwise, check them in:\n# .python-version\n\n# pipenv\n# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.\n# However, in case of collaboration, if having platform-specific dependencies or dependencies\n# having no cross-platform support, pipenv may install dependencies that don't work, or not\n# install all needed dependencies.\n#Pipfile.lock\n\n# poetry\n# Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.\n# This is especially recommended for binary packages to ensure reproducibility, and is more\n# commonly ignored for libraries.\n# https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control\n#poetry.lock\n\n# pdm\n# Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control.\n#pdm.lock\n# pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it\n# in version control.\n# https://pdm.fming.dev/#use-with-ide\n.pdm.toml\n\n# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm\n__pypackages__/\n\n# Celery stuff\ncelerybeat-schedule\ncelerybeat.pid\n\n# SageMath parsed files\n*.sage.py\n\n# Environments\n.env\n.venv\nenv/\nvenv/\nENV/\nenv.bak/\nvenv.bak/\n\n# Spyder project settings\n.spyderproject\n.spyproject\n\n# Rope project settings\n.ropeproject\n\n# mkdocs documentation\n/site\n\n# mypy\n.mypy_cache/\n.dmypy.json\ndmypy.json\n\n# Pyre type checker\n.pyre/\n\n# pytype static type analyzer\n.pytype/\n\n# Cython debug symbols\ncython_debug/\n\n# PyCharm\n# JetBrains specific template is maintained in a separate JetBrains.gitignore that can\n# be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore\n# and can be added to the global gitignore or merged into this file. For a more nuclear\n# option (not recommended) you can uncomment the following to ignore the entire idea folder.\n#.idea/\n\n#Desktop Services Store\n.DS_Store" }, { "path": "postgres_snowflake_integration/README.md", "content": "# Postgres Snowflake Data Integration Stack\n\nWelcome to the \"Postgres Snowflake Data Integration Stack\" repository! This repo provides a quickstart template for integrating postgres data to snowflake warehouses using Airbyte powering terraform. We will easily integrate data from Postgres databases with Airbyte using terraform airbyte provider. This template could be act as a starter for integrating and also adding new sources, etc... the limits are endless.\n\nThis quickstart is designed to minimize setup hassles and propel you forward.\n\n## Table of Contents\n\n- [Postgres SnowFlake Data integration Stack](#postgres-snowflake-integration-stack)\n - [Table of Contents](#table-of-contents)\n - [Infrastructure Layout](#infrastructure-layout)\n - [Prerequisites](#prerequisites)\n - [1. Setting an environment for your project](#1-setting-an-environment-for-your-project)\n - [2. Setting Up Airbyte Connectors with Terraform](#2-setting-up-airbyte-connectors-with-terraform)\n - [Next Steps](#next-steps)\n\n\n## Infrastructure Layout\n\n![infrastructure layout](images/infrastructure_layout.png)\n\n## Prerequisites\n\nBefore you embark on this integration, ensure you have the following set up and ready:\n\n1. **Python 3.10 or later**: If not installed, download and install it from [Python's official website](https://www.python.org/downloads/).\n\n2. **Docker and Docker Compose (Docker Desktop)**: Install [Docker](https://docs.docker.com/get-docker/) following the official documentation for your specific OS.\n\n3. **Airbyte OSS version**: Deploy the open-source version of Airbyte. Follow the installation instructions from the [Airbyte Documentation](https://docs.airbyte.com/quickstart/deploy-airbyte/).\n\n4. **Terraform**: Terraform will help you provision and manage the Airbyte resources. If you haven't installed it, follow the [official Terraform installation guide](https://developer.hashicorp.com/terraform/tutorials/aws-get-started/install-cli).\n\n\n## 1. Setting an environment for your project\n\nGet the project up and running on your local machine by following these steps:\n\n1. **Clone the repository (Clone only this quickstart)**: \n ```bash\n git clone --filter=blob:none --sparse https://github.com/airbytehq/quickstarts.git\n ```\n\n ```bash\n cd quickstarts\n ```\n\n ```bash\n git sparse-checkout add postgres_snowflake_integration\n ```\n\n \n2. **Navigate to the directory**: \n ```bash\n cd postgres_snowflake_integration\n ```\n\n3. **Set Up a Virtual Environment**: \n - For Mac:\n ```bash\n python3 -m venv venv\n source venv/bin/activate\n ```\n - For Windows:\n ```bash\n python -m venv venv\n .\\venv\\Scripts\\activate\n ```\n\n4. **Install Dependencies**: \n ```bash\n pip install -e \".[dev]\"\n ```\n\n## 2. Setting Up Airbyte Connectors with Terraform\n\nAirbyte allows you to create connectors for sources and destinations, facilitating data synchronization between various platforms. In this project, we're harnessing the power of Terraform to automate the creation of these connectors and the connections between them. Here's how you can set this up:\n\n1. **Navigate to the Airbyte Configuration Directory**:\n \n Change to the relevant directory containing the Terraform configuration for Airbyte:\n ```bash\n cd infra/airbyte\n ```\n\n2. **Modify Configuration Files**:\n\n Within the `infra/airbyte` directory, you'll find three crucial Terraform files:\n - `provider.tf`: Defines the Airbyte provider.\n - `main.tf`: Contains the main configuration for creating Airbyte resources.\n - `variables.tf`: Holds various variables, including credentials.\n\n Adjust the configurations in these files to suit your project's needs. Specifically, provide credentials for your Postgres connections. You can utilize the `variables.tf` file to manage these credentials.\n\n3. **Initialize Terraform**:\n \n This step prepares Terraform to create the resources defined in your configuration files.\n ```bash\n terraform init\n ```\n\n4. **Review the Plan**:\n\n Before applying any changes, review the plan to understand what Terraform will do.\n ```bash\n terraform plan\n ```\n\n5. **Apply Configuration**:\n\n After reviewing and confirming the plan, apply the Terraform configurations to create the necessary Airbyte resources.\n ```bash\n terraform apply\n ```\n\n6. **Verify in Airbyte UI**:\n\n Once Terraform completes its tasks, navigate to the Airbyte UI. Here, you should see your source and destination connectors, as well as the connection between them, set up and ready to go.\n\n## Next Steps\n\nOnce you've set up and launched this initial integration, the real power lies in its adaptability and extensibility. Here’s a roadmap to help you customize and harness this project tailored to your specific data needs:\n\n\n1. **Extend the Project**:\n\n The real beauty of this integration is its extensibility. Whether you want to add more data sources, integrate additional tools, or add some transformation logic – the floor is yours. With the foundation set, sky's the limit for how you want to extend and refine your data processes." }, { "path": "postgres_snowflake_integration/infra/.gitignore", "content": "# Local .terraform directories\n**/.terraform/*\n\n# .tfstate files\n*.tfstate\n*.tfstate.*\n\n# Crash log files\ncrash.log\ncrash.*.log\n\n# Exclude all .tfvars files, which are likely to contain sensitive data, such as\n# password, private keys, and other secrets. These should not be part of version \n# control as they are data points which are potentially sensitive and subject \n# to change depending on the environment.\n*.tfvars\n*.tfvars.json\n\n# Ignore override files as they are usually used to override resources locally and so\n# are not checked in\noverride.tf\noverride.tf.json\n*_override.tf\n*_override.tf.json\n\n# Include override files you do wish to add to version control using negated pattern\n# !example_override.tf\n\n# Include tfplan files to ignore the plan output of command: terraform plan -out=tfplan\n# example: *tfplan*\n\n# Ignore CLI configuration files\n.terraformrc\nterraform.rc" }, { "path": "postgres_snowflake_integration/infra/airbyte/main.tf", "content": "// Airbyte Terraform provider documentation: https://registry.terraform.io/providers/airbytehq/airbyte/latest/docs\n\n// Sources\nresource \"airbyte_source_postgres\" \"postgres\" {\n configuration = {\n database = \"...my_database...\"\n host = \"...my_host...\"\n username = \"...my_username...\"\n password = \"...my_password...\"\n port = 5432\n source_type = \"postgres\"\n schemas = [\n \"...my_schema...\"\n ]\n ssl_mode = {\n allow = {}\n }\n tunnel_method = {\n no_tunnel = {}\n }\n replication_method = {\n read_changes_using_write_ahead_log_cdc = {\n publication = \"...pub...\"\n replication_slot = \"...slot...\"\n }\n }\n }\n name = \"Postgres\"\n workspace_id = var.workspace_id\n}\n\n// Destinations\nresource \"airbyte_destination_snowflake\" \"snowflake\" {\n configuration = {\n credentials = {\n destination_snowflake_authorization_method_key_pair_authentication = {\n auth_type = \"Key Pair Authentication\"\n private_key = \"...my_private_key...\"\n private_key_password = \"...my_private_key_password...\"\n }\n }\n database = \"AIRBYTE_DATABASE\"\n destination_type = \"snowflake\"\n host = \"accountname.us-east-2.aws.snowflakecomputing.com\"\n jdbc_url_params = \"...my_jdbc_url_params...\"\n raw_data_schema = \"...my_raw_data_schema...\"\n role = \"AIRBYTE_ROLE\"\n schema = \"AIRBYTE_SCHEMA\"\n username = \"AIRBYTE_USER\"\n warehouse = \"AIRBYTE_WAREHOUSE\"\n }\n name = \"Snowflake\"\n workspace_id = var.workspace_id\n} \n\n// Connections\nresource \"airbyte_connection\" \"postgres_to_snowflake\" {\n name = \"Postgres to Snowflake\"\n source_id = airbyte_source_postgres.postgres.source_id\n destination_id = airbyte_destination_snowflake.snowflake.destination_id\n configurations = {\n streams = [\n {\n name = \"...my_table_name_1...\"\n },\n {\n name = \"...my_table_name_2...\"\n },\n ]\n }\n}\n" }, { "path": "postgres_snowflake_integration/infra/airbyte/provider.tf", "content": "// Airbyte Terraform provider documentation: https://registry.terraform.io/providers/airbytehq/airbyte/latest/docs\n\nterraform {\n required_providers {\n airbyte = {\n source = \"airbytehq/airbyte\"\n version = \"0.3.4\"\n }\n }\n}\n\nprovider \"airbyte\" {\n // If running locally (Airbyte OSS) with docker-compose using the airbyte-proxy, \n // include the actual password/username you've set up (or use the defaults below)\n username = \"airbyte\"\n password = \"password\"\n \n // if running locally (Airbyte OSS), include the server url to the airbyte-api-server\n server_url = \"http://localhost:8006/v1\"\n}\n" }, { "path": "postgres_snowflake_integration/infra/airbyte/variables.tf", "content": "variable \"workspace_id\" {\n type = string\n}\n\n\n\n\n\n\n" }, { "path": "postgres_snowflake_integration/setup.py", "content": "from setuptools import find_packages, setup\n\nsetup(\n name=\"postgres-snowflake-integration\",\n packages=find_packages(),\n install_requires=[\n ],\n extras_require={\"dev\": [\"pytest\"]},\n)\n" }, { "path": "postgres_to_mysql_migration/.gitignore", "content": "# Byte-compiled / optimized / DLL files\n__pycache__/\n*.py[cod]\n*$py.class\n\n# C extensions\n*.so\n\n# Distribution / packaging\n.Python\nbuild/\ndevelop-eggs/\ndist/\ndownloads/\neggs/\n.eggs/\nlib/\nlib64/\nparts/\nsdist/\nvar/\nwheels/\nshare/python-wheels/\n*.egg-info/\n.installed.cfg\n*.egg\nMANIFEST\n\n# PyInstaller\n# Usually these files are written by a python script from a template\n# before PyInstaller builds the exe, so as to inject date/other infos into it.\n*.manifest\n*.spec\n\n# Installer logs\npip-log.txt\npip-delete-this-directory.txt\n\n# Unit test / coverage reports\nhtmlcov/\n.tox/\n.nox/\n.coverage\n.coverage.*\n.cache\nnosetests.xml\ncoverage.xml\n*.cover\n*.py,cover\n.hypothesis/\n.pytest_cache/\ncover/\n\n# Translations\n*.mo\n*.pot\n\n# Django stuff:\n*.log\nlocal_settings.py\ndb.sqlite3\ndb.sqlite3-journal\n\n# Flask stuff:\ninstance/\n.webassets-cache\n\n# Scrapy stuff:\n.scrapy\n\n# Sphinx documentation\ndocs/_build/\n\n# PyBuilder\n.pybuilder/\ntarget/\n\n# Jupyter Notebook\n.ipynb_checkpoints\n\n# IPython\nprofile_default/\nipython_config.py\n\n# pyenv\n# For a library or package, you might want to ignore these files since the code is\n# intended to run in multiple environments; otherwise, check them in:\n# .python-version\n\n# pipenv\n# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.\n# However, in case of collaboration, if having platform-specific dependencies or dependencies\n# having no cross-platform support, pipenv may install dependencies that don't work, or not\n# install all needed dependencies.\n#Pipfile.lock\n\n# poetry\n# Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.\n# This is especially recommended for binary packages to ensure reproducibility, and is more\n# commonly ignored for libraries.\n# https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control\n#poetry.lock\n\n# pdm\n# Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control.\n#pdm.lock\n# pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it\n# in version control.\n# https://pdm.fming.dev/#use-with-ide\n.pdm.toml\n\n# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm\n__pypackages__/\n\n# Celery stuff\ncelerybeat-schedule\ncelerybeat.pid\n\n# SageMath parsed files\n*.sage.py\n\n# Environments\n.env\n.venv\nenv/\nvenv/\nENV/\nenv.bak/\nvenv.bak/\n\n# Spyder project settings\n.spyderproject\n.spyproject\n\n# Rope project settings\n.ropeproject\n\n# mkdocs documentation\n/site\n\n# mypy\n.mypy_cache/\n.dmypy.json\ndmypy.json\n\n# Pyre type checker\n.pyre/\n\n# pytype static type analyzer\n.pytype/\n\n# Cython debug symbols\ncython_debug/\n\n# PyCharm\n# JetBrains specific template is maintained in a separate JetBrains.gitignore that can\n# be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore\n# and can be added to the global gitignore or merged into this file. For a more nuclear\n# option (not recommended) you can uncomment the following to ignore the entire idea folder.\n#.idea/\n\n#Desktop Services Store\n.DS_Store" }, { "path": "postgres_to_mysql_migration/README.md", "content": "# Postgres to MySQL Database Migration Stack\n\nWelcome to the \"Postgres to MySQL Database Migration Stack\" repository! This repo provides a quickstart template for building a one-off database migration solution from an existing Postgres database to a MySQL database using Airbyte. We will easily migrate the tables and data from the Postgres database to the MySQL database with Airbyte using Change Data Capture (CDC) and Postgres Write Ahead Log (WAL). While this template doesn't delve into specific data, its goal is to showcase how the database migration solution can be achieved with Airbyte.\n\nJust like other Airbyte quickstarts, this quickstart is designed to minimize setup hassles and propel you forward.\n\n## Table of Contents\n\n- [Postgres to MySQL Database Migration Stack](#postgres-to-mysql-database-migration-stack)\n - [Table of Contents](#table-of-contents)\n - [Infrastructure Layout](#infrastructure-layout)\n - [Prerequisites](#prerequisites)\n - [1. Setting an environment for your project](#1-setting-an-environment-for-your-project)\n - [2. Setting Up Airbyte Connectors with Terraform](#2-setting-up-airbyte-connectors-with-terraform)\n - [Next Steps](#next-steps)\n\n\n## Infrastructure Layout\n\n![infrastructure layout](images/infrastructure_layout.png)\n\n## Prerequisites\n\nBefore you embark on this integration, ensure you have the following set up and ready:\n\n1. **Python 3.10 or later**: If not installed, download and install it from [Python's official website](https://www.python.org/downloads/).\n\n2. **Docker and Docker Compose (Docker Desktop)**: Install [Docker](https://docs.docker.com/get-docker/) following the official documentation for your specific OS.\n\n3. **Airbyte OSS version**: Deploy the open-source version of Airbyte. Follow the installation instructions from the [Airbyte Documentation](https://docs.airbyte.com/quickstart/deploy-airbyte/).\n\n4. **Terraform**: Terraform will help you provision and manage the Airbyte resources. If you haven't installed it, follow the [official Terraform installation guide](https://developer.hashicorp.com/terraform/tutorials/aws-get-started/install-cli).\n\n\n## 1. Setting an environment for your project\n\nGet the project up and running on your local machine by following these steps:\n\n1. **Clone the repository (Clone only this quickstart)**: \n ```bash\n git clone --filter=blob:none --sparse https://github.com/airbytehq/quickstarts.git\n ```\n\n ```bash\n cd quickstarts\n ```\n\n ```bash\n git sparse-checkout add postgres_to_mysql_migration\n ```\n\n \n2. **Navigate to the directory**: \n ```bash\n cd postgres_to_mysql_migration\n ```\n\n3. **Set Up a Virtual Environment**: \n - For Mac:\n ```bash\n python3 -m venv venv\n source venv/bin/activate\n ```\n - For Windows:\n ```bash\n python -m venv venv\n .\\venv\\Scripts\\activate\n ```\n\n4. **Install Dependencies**: \n ```bash\n pip install -e \".[dev]\"\n ```\n\n## 2. Setting Up Airbyte Connectors with Terraform\n\nAirbyte allows you to create connectors for sources and destinations, facilitating data synchronization between various platforms. In this project, we're harnessing the power of Terraform to automate the creation of these connectors and the connections between them. Here's how you can set this up:\n\n1. **Navigate to the Airbyte Configuration Directory**:\n \n Change to the relevant directory containing the Terraform configuration for Airbyte:\n ```bash\n cd infra/airbyte\n ```\n\n2. **Modify Configuration Files**:\n\n Within the `infra/airbyte` directory, you'll find three crucial Terraform files:\n - `provider.tf`: Defines the Airbyte provider.\n - `main.tf`: Contains the main configuration for creating Airbyte resources.\n - `variables.tf`: Holds various variables, including credentials.\n\n Adjust the configurations in these files to suit your project's needs. Specifically, provide credentials for your Postgres and MySQL connections. You can utilize the `variables.tf` file to manage these credentials.\n\n3. **Initialize Terraform**:\n \n This step prepares Terraform to create the resources defined in your configuration files.\n ```bash\n terraform init\n ```\n\n4. **Review the Plan**:\n\n Before applying any changes, review the plan to understand what Terraform will do.\n ```bash\n terraform plan\n ```\n\n5. **Apply Configuration**:\n\n After reviewing and confirming the plan, apply the Terraform configurations to create the necessary Airbyte resources.\n ```bash\n terraform apply\n ```\n\n6. **Verify in Airbyte UI**:\n\n Once Terraform completes its tasks, navigate to the Airbyte UI. Here, you should see your Postgres source and MySQL destination connectors, as well as the connection between them, set up and ready to go.\n\n## Next Steps\n\nOnce you've set up and launched this initial integration, you can proceed to sync the connection to trigger a one-off migration. The real power of this quickstart lies in its adaptability and extensibility. Here’s a roadmap to help you customize and harness this project tailored to your specific data needs:\n\n\n\n1. **Plan your Migration**:\n\n Ideally, database migration should be a planned activity. Do not run a migration job during a production peak. Migration latency depends on factors such as the size of data to be moved, geographic location of the source and destination, and other parameters. Ensure you test thoroughly before deploying to production.\n \n2. **Extend the Project**:\n\n The real beauty of this integration is its extensibility. Whether you want to add more Postgres sources, migrate to more than one MySQL databases, integrate additional tools, or modify the sync schedule – the floor is yours. The granularity of the migration can also be set by selecting the correct sync mode for each stream (table). Read [sync mode](https://docs.airbyte.com/understanding-airbyte/connections/) for more details. With the foundation set, sky's the limit for how you want to extend and refine your data processes.\n" }, { "path": "postgres_to_mysql_migration/infra/.gitignore", "content": "# Local .terraform directories\n**/.terraform/*\n\n# .tfstate files\n*.tfstate\n*.tfstate.*\n\n# Crash log files\ncrash.log\ncrash.*.log\n\n# Exclude all .tfvars files, which are likely to contain sensitive data, such as\n# password, private keys, and other secrets. These should not be part of version \n# control as they are data points which are potentially sensitive and subject \n# to change depending on the environment.\n*.tfvars\n*.tfvars.json\n\n# Ignore override files as they are usually used to override resources locally and so\n# are not checked in\noverride.tf\noverride.tf.json\n*_override.tf\n*_override.tf.json\n\n# Include override files you do wish to add to version control using negated pattern\n# !example_override.tf\n\n# Include tfplan files to ignore the plan output of command: terraform plan -out=tfplan\n# example: *tfplan*\n\n# Ignore CLI configuration files\n.terraformrc\nterraform.rc" }, { "path": "postgres_to_mysql_migration/infra/airbyte/main.tf", "content": "// Airbyte Terraform provider documentation: https://registry.terraform.io/providers/airbytehq/airbyte/latest/docs\n\n// Sources\nresource \"airbyte_source_postgres\" \"postgres\" {\n configuration = {\n database = \"...my_database...\"\n host = \"...my_host...\"\n username = \"...my_username...\"\n password = \"...my_password...\"\n port = 5432\n source_type = \"postgres\"\n schemas = [\n \"...my_schema...\"\n ]\n ssl_mode = {\n allow = {}\n }\n tunnel_method = {\n no_tunnel = {}\n }\n replication_method = {\n read_changes_using_write_ahead_log_cdc = {\n publication = \"...pub...\"\n replication_slot = \"...slot...\"\n }\n }\n }\n name = \"Postgres\"\n workspace_id = var.workspace_id\n}\n\n// Destinations\nresource \"airbyte_destination_mysql\" \"mysql\" {\n configuration = {\n database = \"...my_database...\"\n destination_type = \"mysql\"\n host = \"...my_host...\"\n jdbc_url_params = \"...my_jdbc_url_params...\"\n password = \"...my_password...\"\n port = 3306\n tunnel_method = {\n destination_mysql_ssh_tunnel_method_no_tunnel = {\n tunnel_method = \"NO_TUNNEL\"\n }\n }\n username = \"...my_username...\"\n }\n name = \"MySQL\"\n workspace_id = var.workspace_id\n}\n\n// Connections\nresource \"airbyte_connection\" \"postgres_to_mysql\" {\n name = \"Postgres to MySQL\"\n source_id = airbyte_source_postgres.postgres.source_id\n destination_id = airbyte_destination_mysql.mysql.destination_id\n configurations = {\n streams = [\n {\n cursor_field = [\"...\",]\n name = \"...my_table_name_1...\"\n primary_key = [[\"...\",],]\n sync_mode = \"full_refresh_append\"\n },\n {\n cursor_field = [\"...\",]\n name = \"...my_table_name_2...\"\n primary_key = [[\"...\",],]\n sync_mode = \"incremental_deduped_history\"\n },\n ]\n }\n}" }, { "path": "postgres_to_mysql_migration/infra/airbyte/provider.tf", "content": "// Airbyte Terraform provider documentation: https://registry.terraform.io/providers/airbytehq/airbyte/latest/docs\n\nterraform {\n required_providers {\n airbyte = {\n source = \"airbytehq/airbyte\"\n version = \"0.3.4\"\n }\n }\n}\n\nprovider \"airbyte\" {\n // If running locally (Airbyte OSS) with docker-compose using the airbyte-proxy, \n // include the actual password/username you've set up (or use the defaults below)\n username = \"airbyte\"\n password = \"password\"\n \n // if running locally (Airbyte OSS), include the server url to the airbyte-api-server\n server_url = \"http://localhost:8006/v1\"\n}" }, { "path": "postgres_to_mysql_migration/infra/airbyte/variables.tf", "content": "variable \"workspace_id\" {\n type = string\n}\n\n\n\n\n\n\n" }, { "path": "postgres_to_mysql_migration/setup.py", "content": "from setuptools import find_packages, setup\n\nsetup(\n name=\"postgres-to-mysql-migration\",\n packages=find_packages(),\n install_requires=[\n ],\n extras_require={\"dev\": [\"pytest\"]},\n)" }, { "path": "pyairbyte_notebooks/AI ChatBot - 1.0 Launch Demo.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"view-in-github\",\n \"colab_type\": \"text\"\n },\n \"source\": [\n \"\\\"Open\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"source\": [\n \"▶️ _View our 1.0 Launch Demo [on YouTube](https://www.youtube.com/watch?v=xhui_QDN8Ck)!_\"\n ],\n \"metadata\": {\n \"id\": \"Ctm_l6ZWHMuE\"\n }\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"BeGeIDzAxbPS\"\n },\n \"source\": [\n \"# Airbyte 1.0 Demo: AI ChatBot with PyAirbyte\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"S-54_kh5IBok\"\n },\n \"source\": [\n \"This demo shows how **anyone** can quickly build a RAG ChatBot app using PyAirbyte.\\n\",\n \"\\n\",\n \"\\n\",\n \"\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"v7Phm4FxnVF5\"\n },\n \"source\": [\n \"**Definitions:**\\n\",\n \"\\n\",\n \"- **Chunking** - Breaking a long text document into smaller \\\"chunks\\\".\\n\",\n \"- **Embedding** - The process of creating vectors that summarize text or other content.\\n\",\n \"- **Vector** - A list of numbers that represent a block of text or other content.\\n\",\n \"- **Large language model (LLM)** - An AI model that can predict the best response to a question, given a set of user-provided inputs, called a \\\"prompt\\\".\\n\",\n \"- **Retrieval-Augmented Generation (RAG)** - The strategy of \\\"augmenting\\\" questions sent to the AI by adding relevant context from a vector store lookup.\\n\",\n \"- **PGVector** - A popular extension for Postgres, which allows Postgres to store and vector emdeddings and query them.\\n\",\n \"\\n\",\n \"_Don't worry if any of these are new to you. Airbyte makes building AI pipelines simple so you can focus on what matters._\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"an3tbYCI5eDl\"\n },\n \"source\": [\n \"# ⚙ Prereqs: Setup the Environment\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"VGGynBZnxbPT\"\n },\n \"source\": [\n \"## Install Python Libraries\\n\",\n \"\\n\",\n \"_Installing PyAirbyte is as easy as **`pip install airbyte`!**_\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 36,\n \"metadata\": {\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\"\n },\n \"collapsed\": true,\n \"id\": \"rWeXNxcHxbPT\",\n \"outputId\": \"ec6d3c56-730a-459e-ec1b-401a7ce14bdb\"\n },\n \"outputs\": [\n {\n \"output_type\": \"stream\",\n \"name\": \"stdout\",\n \"text\": [\n \"\\r0% [Working]\\r \\rHit:1 https://cloud.r-project.org/bin/linux/ubuntu jammy-cran40/ InRelease\\n\",\n \"Hit:2 https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2204/x86_64 InRelease\\n\",\n \"Hit:3 http://security.ubuntu.com/ubuntu jammy-security InRelease\\n\",\n \"Hit:4 http://archive.ubuntu.com/ubuntu jammy InRelease\\n\",\n \"Ign:5 https://r2u.stat.illinois.edu/ubuntu jammy InRelease\\n\",\n \"Hit:6 https://r2u.stat.illinois.edu/ubuntu jammy Release\\n\",\n \"Hit:7 http://archive.ubuntu.com/ubuntu jammy-updates InRelease\\n\",\n \"Hit:8 http://archive.ubuntu.com/ubuntu jammy-backports InRelease\\n\",\n \"Hit:10 https://ppa.launchpadcontent.net/deadsnakes/ppa/ubuntu jammy InRelease\\n\",\n \"Hit:11 https://ppa.launchpadcontent.net/graphics-drivers/ppa/ubuntu jammy InRelease\\n\",\n \"Hit:12 https://ppa.launchpadcontent.net/ubuntugis/ppa/ubuntu jammy InRelease\\n\",\n \"Reading package lists... Done\\n\",\n \"W: Skipping acquire of configured file 'main/source/Sources' as repository 'https://r2u.stat.illinois.edu/ubuntu jammy InRelease' does not seem to provide it (sources.list entry misspelt?)\\n\",\n \"Requirement already satisfied: uv in /usr/local/lib/python3.10/dist-packages (0.4.16)\\n\"\n ]\n }\n ],\n \"source\": [\n \"# Add virtual environment support for running in Google Colab:\\n\",\n \"!apt-get update && apt-get install -qq python3.10-venv\\n\",\n \"# Install `uv` to speed up pip installs\\n\",\n \"%pip install uv\\n\",\n \"\\n\",\n \"# Install PyAirbyte and OpenAI\\n\",\n \"!uv pip install --system --quiet airbyte\\n\",\n \"!uv pip install --system --quiet openai\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"sSaek4Hs7GlC\"\n },\n \"source\": [\n \"## Import Python libraries\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 38,\n \"metadata\": {\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\"\n },\n \"id\": \"BCwiP6Tg7Dcc\",\n \"outputId\": \"7ca68881-f255-4fbc-9eae-c64e6dccaa9d\"\n },\n \"outputs\": [\n {\n \"output_type\": \"stream\",\n \"name\": \"stdout\",\n \"text\": [\n \"Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\\\"/content/drive\\\", force_remount=True).\\n\",\n \"Using persistent PyAirbyte cache in Google Drive: `/content/drive/Shareddrives/Company/20_Demos/PyAirbyte Demo/Cache/default_cache.duckdb`.\\n\"\n ]\n }\n ],\n \"source\": [\n \"# Import PyAirbyte and get the cache from Google Drive:\\n\",\n \"import airbyte as ab\\n\",\n \"colab_cache = ab.get_colab_cache(\\n\",\n \" drive_name=\\\"Company\\\",\\n\",\n \" sub_dir=\\\"20_Demos/PyAirbyte Demo/Cache\\\",\\n\",\n \")\\n\",\n \"\\n\",\n \"# Standard inputs:\\n\",\n \"from pathlib import Path\\n\",\n \"from textwrap import dedent\\n\",\n \"\\n\",\n \"# For accessing Google Drive:\\n\",\n \"from google.colab import drive\\n\",\n \"\\n\",\n \"# For printing Markdown:\\n\",\n \"from rich.markdown import Markdown\\n\",\n \"from rich import print\\n\",\n \"\\n\",\n \"# Import OpenAI and setup connection\\n\",\n \"import openai\\n\",\n \"openai_client = openai.OpenAI(\\n\",\n \" api_key=ab.get_secret(\\\"OPENAI_API_KEY\\\"),\\n\",\n \")\\n\",\n \"\\n\",\n \"# For working with SQL:\\n\",\n \"from sqlalchemy import create_engine, text, URL\\n\",\n \"\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"cRTjmG8tXIV6\"\n },\n \"source\": [\n \"## Connect to Postgres\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"metadata\": {\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\"\n },\n \"id\": \"KXF3Bbs-p4US\",\n \"outputId\": \"4e784dd5-e7fd-486e-c624-76459c13658c\"\n },\n \"outputs\": [\n {\n \"output_type\": \"stream\",\n \"name\": \"stdout\",\n \"text\": [\n \"The sql extension is already loaded. To reload it, use:\\n\",\n \" %reload_ext sql\\n\"\n ]\n }\n ],\n \"source\": [\n \"# Install JupySQL to enable SQL cell magics (https://jupysql.ploomber.io)\\n\",\n \"%pip install --quiet jupysql\\n\",\n \"# Load JupySQL extension\\n\",\n \"%load_ext sql\\n\",\n \"# Configure max row limit (optional)\\n\",\n \"%config SqlMagic.displaylimit = 200\\n\",\n \"\\n\",\n \"# Connect to the Postgres instance with PGVector installed\\n\",\n \"# Get the SQLAlchemy 'engine' object for the cache\\n\",\n \"engine = create_engine(\\n\",\n \" URL.create(\\n\",\n \" \\\"postgresql\\\",\\n\",\n \" host=ab.get_secret(\\\"POSTGRES_HOST\\\"),\\n\",\n \" username=ab.get_secret(\\\"POSTGRES_USERNAME\\\"),\\n\",\n \" password=ab.get_secret(\\\"POSTGRES_PASSWORD\\\"),\\n\",\n \" database=\\\"ai_db\\\",\\n\",\n \" )\\n\",\n \")\\n\",\n \"\\n\",\n \"# Pass the engine to JupySQL\\n\",\n \"%sql engine\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"muYWzVjNXV7i\"\n },\n \"source\": [\n \"## Install PGVector\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"metadata\": {\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\",\n \"height\": 137\n },\n \"id\": \"V68iVoc-sWw-\",\n \"outputId\": \"11e0a9d5-ebd0-4869-dc80-ca4fc5bc1e6c\"\n },\n \"outputs\": [\n {\n \"output_type\": \"display_data\",\n \"data\": {\n \"text/plain\": [\n \"Running query in 'postgresql://postgres:***@airbyte-ai-demo-01.c7qksqsykgn8.us-east-1.rds.amazonaws.com/ai_db'\"\n ],\n \"text/html\": [\n \"Running query in 'postgresql://postgres:***@airbyte-ai-demo-01.c7qksqsykgn8.us-east-1.rds.amazonaws.com/ai_db'\"\n ]\n },\n \"metadata\": {}\n },\n {\n \"output_type\": \"display_data\",\n \"data\": {\n \"text/plain\": [\n \"3 rows affected.\"\n ],\n \"text/html\": [\n \"3 rows affected.\"\n ]\n },\n \"metadata\": {}\n },\n {\n \"output_type\": \"execute_result\",\n \"data\": {\n \"text/plain\": [\n \"+--------------------+\\n\",\n \"| schema_name |\\n\",\n \"+--------------------+\\n\",\n \"| public |\\n\",\n \"| information_schema |\\n\",\n \"| pg_catalog |\\n\",\n \"+--------------------+\"\n ],\n \"text/html\": [\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
schema_name
public
information_schema
pg_catalog
\"\n ]\n },\n \"metadata\": {},\n \"execution_count\": 7\n }\n ],\n \"source\": [\n \"%%sql\\n\",\n \"\\n\",\n \"# Confirm we can run queries in Postgres:\\n\",\n \"SELECT schema_name\\n\",\n \"FROM information_schema.schemata;\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"metadata\": {\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\",\n \"height\": 34\n },\n \"id\": \"neqQLUh6sVHu\",\n \"outputId\": \"02b023a5-fd7d-48aa-cb0d-b612875e6f50\"\n },\n \"outputs\": [\n {\n \"output_type\": \"display_data\",\n \"data\": {\n \"text/plain\": [\n \"Running query in 'postgresql://postgres:***@airbyte-ai-demo-01.c7qksqsykgn8.us-east-1.rds.amazonaws.com/ai_db'\"\n ],\n \"text/html\": [\n \"Running query in 'postgresql://postgres:***@airbyte-ai-demo-01.c7qksqsykgn8.us-east-1.rds.amazonaws.com/ai_db'\"\n ]\n },\n \"metadata\": {}\n },\n {\n \"output_type\": \"execute_result\",\n \"data\": {\n \"text/plain\": [\n \"++\\n\",\n \"||\\n\",\n \"++\\n\",\n \"++\"\n ],\n \"text/html\": [\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
\"\n ]\n },\n \"metadata\": {},\n \"execution_count\": 8\n }\n ],\n \"source\": [\n \"%%sql\\n\",\n \"\\n\",\n \"# Install the PGVector extension:\\n\",\n \"CREATE EXTENSION IF NOT EXISTS vector;\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"GLu1YD7NxbPU\"\n },\n \"source\": [\n \"# ⬇ Extract Data from the Source\\n\",\n \"\\n\",\n \"\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"6UcTsY6SxbPU\"\n },\n \"source\": [\n \"In this step, we'll connect to `source-github` in order to fetch issues and pull requests.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"ollrBnVVVV7U\"\n },\n \"source\": [\n \"### Introducing the GitHub Source\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"rNjxPxmsV1bf\"\n },\n \"source\": [\n \"> 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)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"QYWcfO5-sfUe\"\n },\n \"source\": [\n \"### Creating the GitHub Source in PyAirbyte\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 10,\n \"metadata\": {\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\",\n \"height\": 476\n },\n \"id\": \"w3BU0d6dxbPU\",\n \"outputId\": \"67d0fe5c-3d4c-43d2-984d-c7bd2cd02a87\"\n },\n \"outputs\": [\n {\n \"output_type\": \"display_data\",\n \"data\": {\n \"text/plain\": [\n \"\"\n ],\n \"text/markdown\": \"------------------------------------------------\\n\\n### Sync Progress: `source-github -> DuckDBCache`\\n\\n**Started reading from source at `17:51:16`:**\\n\\n- Read **1,909** records over **1min 11s** (27.0 records/s, 0.13 MB/s).\\n\\n- Received records for 1 streams:\\n - 1,909 issues\\n\\n\\n\\n\\n------------------------------------------------\\n\"\n },\n \"metadata\": {}\n },\n {\n \"output_type\": \"error\",\n \"ename\": \"KeyboardInterrupt\",\n \"evalue\": \"\",\n \"traceback\": [\n \"\\u001b[0;31m---------------------------------------------------------------------------\\u001b[0m\",\n \"\\u001b[0;31mKeyboardInterrupt\\u001b[0m Traceback (most recent call last)\",\n \"\\u001b[0;32m\\u001b[0m in \\u001b[0;36m\\u001b[0;34m()\\u001b[0m\\n\\u001b[1;32m 11\\u001b[0m )\\n\\u001b[1;32m 12\\u001b[0m \\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0;32m---> 13\\u001b[0;31m \\u001b[0mread_result\\u001b[0m \\u001b[0;34m=\\u001b[0m \\u001b[0msource\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mread\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0mcache\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0mab\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mget_colab_cache\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0mdrive_name\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0;34m\\\"Company\\\"\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0msub_dir\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0;34m\\\"Demos/PyAirbyte Demo/Cache\\\"\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0m\",\n \"\\u001b[0;32m/usr/local/lib/python3.10/dist-packages/airbyte/sources/base.py\\u001b[0m in \\u001b[0;36mread\\u001b[0;34m(self, cache, streams, write_strategy, force_full_refresh, skip_validation)\\u001b[0m\\n\\u001b[1;32m 658\\u001b[0m \\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m 659\\u001b[0m \\u001b[0;32mtry\\u001b[0m\\u001b[0;34m:\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0;32m--> 660\\u001b[0;31m result = self._read_to_cache(\\n\\u001b[0m\\u001b[1;32m 661\\u001b[0m \\u001b[0mcache\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0mcache\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m 662\\u001b[0m \\u001b[0mcatalog_provider\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0mCatalogProvider\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0mself\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mconfigured_catalog\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\",\n \"\\u001b[0;32m/usr/local/lib/python3.10/dist-packages/airbyte/sources/base.py\\u001b[0m in \\u001b[0;36m_read_to_cache\\u001b[0;34m(self, cache, catalog_provider, stream_names, state_provider, state_writer, write_strategy, force_full_refresh, skip_validation, progress_tracker)\\u001b[0m\\n\\u001b[1;32m 742\\u001b[0m )\\n\\u001b[1;32m 743\\u001b[0m )\\n\\u001b[0;32m--> 744\\u001b[0;31m cache._write_airbyte_message_stream( # noqa: SLF001 # Non-public API\\n\\u001b[0m\\u001b[1;32m 745\\u001b[0m \\u001b[0mstdin\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0mairbyte_message_iterator\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m 746\\u001b[0m \\u001b[0mcatalog_provider\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0mcatalog_provider\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\",\n \"\\u001b[0;32m/usr/local/lib/python3.10/dist-packages/airbyte/caches/base.py\\u001b[0m in \\u001b[0;36m_write_airbyte_message_stream\\u001b[0;34m(self, stdin, catalog_provider, write_strategy, state_writer, progress_tracker)\\u001b[0m\\n\\u001b[1;32m 282\\u001b[0m \\u001b[0mstate_writer\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0mstate_writer\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m 283\\u001b[0m )\\n\\u001b[0;32m--> 284\\u001b[0;31m cache_processor.process_airbyte_messages(\\n\\u001b[0m\\u001b[1;32m 285\\u001b[0m \\u001b[0mmessages\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0mstdin\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m 286\\u001b[0m \\u001b[0mwrite_strategy\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0mwrite_strategy\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\",\n \"\\u001b[0;32m/usr/local/lib/python3.10/dist-packages/airbyte/shared/sql_processor.py\\u001b[0m in \\u001b[0;36mprocess_airbyte_messages\\u001b[0;34m(self, messages, write_strategy, progress_tracker)\\u001b[0m\\n\\u001b[1;32m 262\\u001b[0m \\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m 263\\u001b[0m \\u001b[0;31m# Process messages, writing to batches as we go\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0;32m--> 264\\u001b[0;31m \\u001b[0;32mfor\\u001b[0m \\u001b[0mmessage\\u001b[0m \\u001b[0;32min\\u001b[0m \\u001b[0mmessages\\u001b[0m\\u001b[0;34m:\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0m\\u001b[1;32m 265\\u001b[0m \\u001b[0;32mif\\u001b[0m \\u001b[0mmessage\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mtype\\u001b[0m \\u001b[0;32mis\\u001b[0m \\u001b[0mType\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mRECORD\\u001b[0m\\u001b[0;34m:\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m 266\\u001b[0m \\u001b[0mrecord_msg\\u001b[0m \\u001b[0;34m=\\u001b[0m \\u001b[0mcast\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0mAirbyteRecordMessage\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0mmessage\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mrecord\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\",\n \"\\u001b[0;32m/usr/local/lib/python3.10/dist-packages/airbyte/sources/base.py\\u001b[0m in \\u001b[0;36m_read_with_catalog\\u001b[0;34m(self, catalog, progress_tracker, state)\\u001b[0m\\n\\u001b[1;32m 571\\u001b[0m \\u001b[0mprogress_tracker\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0mprogress_tracker\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m 572\\u001b[0m )\\n\\u001b[0;32m--> 573\\u001b[0;31m \\u001b[0;32myield\\u001b[0m \\u001b[0;32mfrom\\u001b[0m \\u001b[0mprogress_tracker\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mtally_records_read\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0mmessage_generator\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0m\\u001b[1;32m 574\\u001b[0m \\u001b[0mprogress_tracker\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mlog_read_complete\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m 575\\u001b[0m \\u001b[0;34m\\u001b[0m\\u001b[0m\\n\",\n \"\\u001b[0;32m/usr/local/lib/python3.10/dist-packages/airbyte/progress.py\\u001b[0m in \\u001b[0;36mtally_records_read\\u001b[0;34m(self, messages, auto_close_streams)\\u001b[0m\\n\\u001b[1;32m 259\\u001b[0m \\u001b[0mupdate_period\\u001b[0m \\u001b[0;34m=\\u001b[0m \\u001b[0;36m1\\u001b[0m \\u001b[0;31m# Reset the update period to 1 before start.\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m 260\\u001b[0m \\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0;32m--> 261\\u001b[0;31m \\u001b[0;32mfor\\u001b[0m \\u001b[0mcount\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0mmessage\\u001b[0m \\u001b[0;32min\\u001b[0m \\u001b[0menumerate\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0mmessages\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0mstart\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0;36m1\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m:\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0m\\u001b[1;32m 262\\u001b[0m \\u001b[0;31m# Yield the message immediately.\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m 263\\u001b[0m \\u001b[0;32myield\\u001b[0m \\u001b[0mmessage\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\",\n \"\\u001b[0;32m/usr/local/lib/python3.10/dist-packages/airbyte/_connector_base.py\\u001b[0m in \\u001b[0;36m_execute\\u001b[0;34m(self, args, stdin, progress_tracker)\\u001b[0m\\n\\u001b[1;32m 420\\u001b[0m \\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m 421\\u001b[0m \\u001b[0;32mtry\\u001b[0m\\u001b[0;34m:\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0;32m--> 422\\u001b[0;31m \\u001b[0;32mfor\\u001b[0m \\u001b[0mline\\u001b[0m \\u001b[0;32min\\u001b[0m \\u001b[0mself\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mexecutor\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mexecute\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0margs\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0mstdin\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0mstdin\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m:\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0m\\u001b[1;32m 423\\u001b[0m \\u001b[0;32mtry\\u001b[0m\\u001b[0;34m:\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m 424\\u001b[0m \\u001b[0mmessage\\u001b[0m\\u001b[0;34m:\\u001b[0m \\u001b[0mAirbyteMessage\\u001b[0m \\u001b[0;34m=\\u001b[0m \\u001b[0mAirbyteMessage\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mmodel_validate_json\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0mjson_data\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0mline\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\",\n \"\\u001b[0;32m/usr/local/lib/python3.10/dist-packages/airbyte/_executors/base.py\\u001b[0m in \\u001b[0;36mexecute\\u001b[0;34m(self, args, stdin)\\u001b[0m\\n\\u001b[1;32m 197\\u001b[0m \\u001b[0mstdin\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0mstdin\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m 198\\u001b[0m ) as stream_lines:\\n\\u001b[0;32m--> 199\\u001b[0;31m \\u001b[0;32myield\\u001b[0m \\u001b[0;32mfrom\\u001b[0m \\u001b[0mstream_lines\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0m\\u001b[1;32m 200\\u001b[0m \\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m 201\\u001b[0m \\u001b[0;34m@\\u001b[0m\\u001b[0mabstractmethod\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\",\n \"\\u001b[0;32m/usr/local/lib/python3.10/dist-packages/airbyte/_executors/base.py\\u001b[0m in \\u001b[0;36m_stream_from_file\\u001b[0;34m(file)\\u001b[0m\\n\\u001b[1;32m 50\\u001b[0m \\u001b[0;34m\\\"\\\"\\\"Stream lines from a file.\\\"\\\"\\\"\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m 51\\u001b[0m \\u001b[0;32mwhile\\u001b[0m \\u001b[0;32mTrue\\u001b[0m\\u001b[0;34m:\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0;32m---> 52\\u001b[0;31m \\u001b[0mline\\u001b[0m \\u001b[0;34m=\\u001b[0m \\u001b[0mfile\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mreadline\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0m\\u001b[1;32m 53\\u001b[0m \\u001b[0;32mif\\u001b[0m \\u001b[0;32mnot\\u001b[0m \\u001b[0mline\\u001b[0m\\u001b[0;34m:\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m 54\\u001b[0m \\u001b[0;32mbreak\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\",\n \"\\u001b[0;31mKeyboardInterrupt\\u001b[0m: \"\n ]\n }\n ],\n \"source\": [\n \"import airbyte as ab\\n\",\n \"source = ab.get_source(\\n\",\n \" \\\"source-github\\\",\\n\",\n \" config={\\n\",\n \" \\\"repositories\\\": [\\\"airbytehq/quickstarts\\\"],\\n\",\n \" \\\"credentials\\\": {\\n\",\n \" \\\"personal_access_token\\\": ab.get_secret(\\\"GITHUB_PERSONAL_ACCESS_TOKEN\\\")\\n\",\n \" },\\n\",\n \" },\\n\",\n \" streams=[\\\"issues\\\"],\\n\",\n \")\\n\",\n \"\\n\",\n \"read_result = source.read(cache=colab_cache)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"id\": \"oL7Du44_jtd7\"\n },\n \"outputs\": [],\n \"source\": []\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"aAjxYAOBY75u\"\n },\n \"source\": [\n \"# **↗** Publish Data to the Vector Store\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"32I4Kn4tJ8fO\"\n },\n \"source\": [\n \"## Introducing the PGVector Destination\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"zLqL0cA2J8C6\"\n },\n \"source\": [\n \"\\n\",\n \"\\n\",\n \"> \\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"q_rCb2vPJ3qb\"\n },\n \"source\": [\n \"## Creating the PGVector Destination in PyAirbyte\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 34,\n \"metadata\": {\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\",\n \"height\": 476\n },\n \"id\": \"JBFAFmewY_1p\",\n \"outputId\": \"49e8758f-d362-4587-abcb-80152acca509\"\n },\n \"outputs\": [\n {\n \"output_type\": \"display_data\",\n \"data\": {\n \"text/plain\": [\n \"\"\n ],\n \"text/markdown\": \"------------------------------------------------\\n\\n### Sync Progress: `DuckDBCache -> destination-pgvector`\\n\\n\\n\\n**Started writing to destination at `18:05:38`:**\\n\\n- Sent **115 records** to destination over **33 seconds** (3.5 records/s).\\n\\n- Stream records delivered:\\n - 115 issues\\n\\n\\n------------------------------------------------\\n\"\n },\n \"metadata\": {}\n },\n {\n \"output_type\": \"error\",\n \"ename\": \"KeyboardInterrupt\",\n \"evalue\": \"\",\n \"traceback\": [\n \"\\u001b[0;31m---------------------------------------------------------------------------\\u001b[0m\",\n \"\\u001b[0;31mKeyboardInterrupt\\u001b[0m Traceback (most recent call last)\",\n \"\\u001b[0;32m\\u001b[0m in \\u001b[0;36m\\u001b[0;34m()\\u001b[0m\\n\\u001b[1;32m 38\\u001b[0m \\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m 39\\u001b[0m \\u001b[0;31m# Write data to PGVector\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0;32m---> 40\\u001b[0;31m \\u001b[0mwrite_result\\u001b[0m \\u001b[0;34m=\\u001b[0m \\u001b[0mdestination\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mwrite\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0mread_result\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0m\",\n \"\\u001b[0;32m/usr/local/lib/python3.10/dist-packages/airbyte/destinations/base.py\\u001b[0m in \\u001b[0;36mwrite\\u001b[0;34m(self, source_data, streams, cache, state_cache, write_strategy, force_full_refresh)\\u001b[0m\\n\\u001b[1;32m 225\\u001b[0m \\u001b[0;31m# Write the data to the destination\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m 226\\u001b[0m \\u001b[0;32mtry\\u001b[0m\\u001b[0;34m:\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0;32m--> 227\\u001b[0;31m self._write_airbyte_message_stream(\\n\\u001b[0m\\u001b[1;32m 228\\u001b[0m \\u001b[0mstdin\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0mmessage_iterator\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m 229\\u001b[0m \\u001b[0mcatalog_provider\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0mcatalog_provider\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\",\n \"\\u001b[0;32m/usr/local/lib/python3.10/dist-packages/airbyte/destinations/base.py\\u001b[0m in \\u001b[0;36m_write_airbyte_message_stream\\u001b[0;34m(self, stdin, catalog_provider, write_strategy, state_writer, progress_tracker)\\u001b[0m\\n\\u001b[1;32m 275\\u001b[0m \\u001b[0;32mtry\\u001b[0m\\u001b[0;34m:\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m 276\\u001b[0m \\u001b[0;31m# We call the connector to write the data, tallying the inputs and outputs\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0;32m--> 277\\u001b[0;31m for destination_message in progress_tracker.tally_confirmed_writes(\\n\\u001b[0m\\u001b[1;32m 278\\u001b[0m messages=self._execute(\\n\\u001b[1;32m 279\\u001b[0m args=[\\n\",\n \"\\u001b[0;32m/usr/local/lib/python3.10/dist-packages/airbyte/progress.py\\u001b[0m in \\u001b[0;36mtally_confirmed_writes\\u001b[0;34m(self, messages)\\u001b[0m\\n\\u001b[1;32m 345\\u001b[0m \\\"\\\"\\\"\\n\\u001b[1;32m 346\\u001b[0m \\u001b[0mself\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0m_start_rich_view\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0;34m)\\u001b[0m \\u001b[0;31m# Start Rich's live view if not already running.\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0;32m--> 347\\u001b[0;31m \\u001b[0;32mfor\\u001b[0m \\u001b[0mmessage\\u001b[0m \\u001b[0;32min\\u001b[0m \\u001b[0mmessages\\u001b[0m\\u001b[0;34m:\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0m\\u001b[1;32m 348\\u001b[0m \\u001b[0;32mif\\u001b[0m \\u001b[0mmessage\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mstate\\u001b[0m\\u001b[0;34m:\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m 349\\u001b[0m \\u001b[0;31m# This is a state message from the destination. Tally the records written.\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\",\n \"\\u001b[0;32m/usr/local/lib/python3.10/dist-packages/airbyte/_connector_base.py\\u001b[0m in \\u001b[0;36m_execute\\u001b[0;34m(self, args, stdin, progress_tracker)\\u001b[0m\\n\\u001b[1;32m 420\\u001b[0m \\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m 421\\u001b[0m \\u001b[0;32mtry\\u001b[0m\\u001b[0;34m:\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0;32m--> 422\\u001b[0;31m \\u001b[0;32mfor\\u001b[0m \\u001b[0mline\\u001b[0m \\u001b[0;32min\\u001b[0m \\u001b[0mself\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mexecutor\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mexecute\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0margs\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0mstdin\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0mstdin\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m:\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0m\\u001b[1;32m 423\\u001b[0m \\u001b[0;32mtry\\u001b[0m\\u001b[0;34m:\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m 424\\u001b[0m \\u001b[0mmessage\\u001b[0m\\u001b[0;34m:\\u001b[0m \\u001b[0mAirbyteMessage\\u001b[0m \\u001b[0;34m=\\u001b[0m \\u001b[0mAirbyteMessage\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mmodel_validate_json\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0mjson_data\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0mline\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\",\n \"\\u001b[0;32m/usr/local/lib/python3.10/dist-packages/airbyte/_executors/base.py\\u001b[0m in \\u001b[0;36mexecute\\u001b[0;34m(self, args, stdin)\\u001b[0m\\n\\u001b[1;32m 193\\u001b[0m \\u001b[0mIf\\u001b[0m \\u001b[0mstdin\\u001b[0m \\u001b[0;32mis\\u001b[0m \\u001b[0mprovided\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0mit\\u001b[0m \\u001b[0mwill\\u001b[0m \\u001b[0mbe\\u001b[0m \\u001b[0mpassed\\u001b[0m \\u001b[0mto\\u001b[0m \\u001b[0mthe\\u001b[0m \\u001b[0msubprocess\\u001b[0m \\u001b[0;32mas\\u001b[0m \\u001b[0mSTDIN\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m 194\\u001b[0m \\\"\\\"\\\"\\n\\u001b[0;32m--> 195\\u001b[0;31m with _stream_from_subprocess(\\n\\u001b[0m\\u001b[1;32m 196\\u001b[0m \\u001b[0;34m[\\u001b[0m\\u001b[0;34m*\\u001b[0m\\u001b[0mself\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0m_cli\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0;34m*\\u001b[0m\\u001b[0margs\\u001b[0m\\u001b[0;34m]\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m 197\\u001b[0m \\u001b[0mstdin\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0mstdin\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\",\n \"\\u001b[0;32m/usr/lib/python3.10/contextlib.py\\u001b[0m in \\u001b[0;36m__enter__\\u001b[0;34m(self)\\u001b[0m\\n\\u001b[1;32m 133\\u001b[0m \\u001b[0;32mdel\\u001b[0m \\u001b[0mself\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0margs\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0mself\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mkwds\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0mself\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mfunc\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m 134\\u001b[0m \\u001b[0;32mtry\\u001b[0m\\u001b[0;34m:\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0;32m--> 135\\u001b[0;31m \\u001b[0;32mreturn\\u001b[0m \\u001b[0mnext\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0mself\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mgen\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0m\\u001b[1;32m 136\\u001b[0m \\u001b[0;32mexcept\\u001b[0m \\u001b[0mStopIteration\\u001b[0m\\u001b[0;34m:\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m 137\\u001b[0m \\u001b[0;32mraise\\u001b[0m \\u001b[0mRuntimeError\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0;34m\\\"generator didn't yield\\\"\\u001b[0m\\u001b[0;34m)\\u001b[0m \\u001b[0;32mfrom\\u001b[0m \\u001b[0;32mNone\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\",\n \"\\u001b[0;32m/usr/local/lib/python3.10/dist-packages/airbyte/_executors/base.py\\u001b[0m in \\u001b[0;36m_stream_from_subprocess\\u001b[0;34m(args, stdin, log_file)\\u001b[0m\\n\\u001b[1;32m 84\\u001b[0m )\\n\\u001b[1;32m 85\\u001b[0m \\u001b[0minput_thread\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mstart\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0;32m---> 86\\u001b[0;31m \\u001b[0minput_thread\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mjoin\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0;34m)\\u001b[0m \\u001b[0;31m# Ensure the input thread has finished\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0m\\u001b[1;32m 87\\u001b[0m \\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m 88\\u001b[0m \\u001b[0;31m# Don't bother raising broken pipe errors, as they only\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\",\n \"\\u001b[0;32m/usr/lib/python3.10/threading.py\\u001b[0m in \\u001b[0;36mjoin\\u001b[0;34m(self, timeout)\\u001b[0m\\n\\u001b[1;32m 1094\\u001b[0m \\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m 1095\\u001b[0m \\u001b[0;32mif\\u001b[0m \\u001b[0mtimeout\\u001b[0m \\u001b[0;32mis\\u001b[0m \\u001b[0;32mNone\\u001b[0m\\u001b[0;34m:\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0;32m-> 1096\\u001b[0;31m \\u001b[0mself\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0m_wait_for_tstate_lock\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0m\\u001b[1;32m 1097\\u001b[0m \\u001b[0;32melse\\u001b[0m\\u001b[0;34m:\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m 1098\\u001b[0m \\u001b[0;31m# the behavior of a negative timeout isn't documented, but\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\",\n \"\\u001b[0;32m/usr/lib/python3.10/threading.py\\u001b[0m in \\u001b[0;36m_wait_for_tstate_lock\\u001b[0;34m(self, block, timeout)\\u001b[0m\\n\\u001b[1;32m 1114\\u001b[0m \\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m 1115\\u001b[0m \\u001b[0;32mtry\\u001b[0m\\u001b[0;34m:\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0;32m-> 1116\\u001b[0;31m \\u001b[0;32mif\\u001b[0m \\u001b[0mlock\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0macquire\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0mblock\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0mtimeout\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m:\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0m\\u001b[1;32m 1117\\u001b[0m \\u001b[0mlock\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mrelease\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m 1118\\u001b[0m \\u001b[0mself\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0m_stop\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\",\n \"\\u001b[0;31mKeyboardInterrupt\\u001b[0m: \"\n ]\n }\n ],\n \"source\": [\n \"destination = ab.get_destination(\\n\",\n \" \\\"destination-pgvector\\\",\\n\",\n \" # Configure the destination:\\n\",\n \" config={\\n\",\n \" \\\"indexing\\\": {\\n\",\n \" # Connection info for Postgres:\\n\",\n \" \\\"host\\\": ab.get_secret(\\\"POSTGRES_HOST\\\"),\\n\",\n \" \\\"database\\\": \\\"ai_db\\\",\\n\",\n \" \\\"default_schema\\\": \\\"public\\\",\\n\",\n \" \\\"port\\\": 5432,\\n\",\n \" # Postgres credentials:\\n\",\n \" \\\"username\\\": ab.get_secret(\\\"POSTGRES_USERNAME\\\"),\\n\",\n \" \\\"credentials\\\": {\\\"password\\\": ab.get_secret(\\\"POSTGRES_PASSWORD\\\")},\\n\",\n \" },\\n\",\n \" \\\"embedding\\\": {\\n\",\n \" # Configure how to perform embeddings:\\n\",\n \" \\\"mode\\\": \\\"openai\\\",\\n\",\n \" \\\"model\\\": \\\"text-embedding-ada-002\\\",\\n\",\n \" \\\"openai_key\\\": ab.get_secret(\\\"OPENAI_API_KEY\\\"),\\n\",\n \" },\\n\",\n \" \\\"processing\\\": {\\n\",\n \" # Which fields to use when mapping from\\n\",\n \" # records to documents:\\n\",\n \" \\\"text_fields\\\": [\\n\",\n \" \\\"title\\\",\\n\",\n \" \\\"body\\\",\\n\",\n \" ],\\n\",\n \"\\n\",\n \" # Default to keeping all fields as metadata:\\n\",\n \" # \\\"metadata_fields\\\": [],\\n\",\n \"\\n\",\n \" # Text splitting logic, aka \\\"chunking\\\":\\n\",\n \" \\\"chunk_size\\\": 600, # each chunk will be 600 tokens at max\\n\",\n \" \\\"chunk_overlap\\\": 60, # use a 60 character overlap\\n\",\n \" },\\n\",\n \" },\\n\",\n \")\\n\",\n \"\\n\",\n \"# Write data to PGVector\\n\",\n \"write_result = destination.write(read_result)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"QbYKUyRjJE72\"\n },\n \"source\": [\n \"# 🦾 Build your Custom AI ChatBot\\n\",\n \"\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"ujAoSh1BBglg\"\n },\n \"source\": [\n \"We'll create our chatbot by building **three simple functions**:\\n\",\n \"- **`get_vector(question)`** gets a **numeric representation** of the question\\n\",\n \"- **`get_related_context(question)`** -> gets **text chunks** related to the question\\n\",\n \"- **`ask_question(question)`** -> gets an **answer** from our AI chatbot\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"9iQowrUZxbPV\"\n },\n \"source\": [\n \"## A. Calculate the **vector** of our specific question\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 12,\n \"metadata\": {\n \"id\": \"eJ0ZT7g_17gR\"\n },\n \"outputs\": [],\n \"source\": [\n \"# This function calculates a vector (list of numbers) that is a\\n\",\n \"# numeric represention of the original question.\\n\",\n \"\\n\",\n \"def get_vector(question) -> list[float]:\\n\",\n \" return openai_client.embeddings.create(\\n\",\n \" input=question,\\n\",\n \" model=\\\"text-embedding-ada-002\\\",\\n\",\n \" ).data[0].embedding\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 20,\n \"metadata\": {\n \"cellView\": \"form\",\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\",\n \"height\": 461\n },\n \"id\": \"RPafAHXvcgXu\",\n \"outputId\": \"49d26cb9-7029-4aac-ddad-a0e2e2b1212e\"\n },\n \"outputs\": [\n {\n \"output_type\": \"display_data\",\n \"data\": {\n \"text/plain\": [\n \"\\n\",\n \"Question:\\n\",\n \"\\u001b[32m\\\"How are you\\\"\\u001b[0m\\n\",\n \"\\n\",\n \"Calculated Vector \\u001b[1m(\\u001b[0m\\u001b[33mlen\\u001b[0m=\\u001b[1;36m1536\\u001b[0m\\u001b[1m)\\u001b[0m:\\n\",\n \"\\n\"\n ],\n \"text/html\": [\n \"
\\n\",\n              \"Question:\\n\",\n              \"\\\"How are you\\\"\\n\",\n              \"\\n\",\n              \"Calculated Vector (len=1536):\\n\",\n              \"\\n\",\n              \"
\\n\"\n ]\n },\n \"metadata\": {}\n },\n {\n \"output_type\": \"execute_result\",\n \"data\": {\n \"text/plain\": [\n \"[-0.016069870442152023,\\n\",\n \" -0.0027824214193969965,\\n\",\n \" -0.00472139660269022,\\n\",\n \" -0.036299899220466614,\\n\",\n \" -0.023806747049093246,\\n\",\n \" 0.016577206552028656,\\n\",\n \" -0.029070358723402023,\\n\",\n \" -0.02003977634012699,\\n\",\n \" -0.023337461054325104,\\n\",\n \" -0.005200195126235485,\\n\",\n \" 0.025303388014435768,\\n\",\n \" 0.005558501463383436,\\n\",\n \" -0.01047649048268795,\\n\",\n \" 0.003928684163838625,\\n\",\n \" -0.010134038515388966,\\n\",\n \" -0.015499117784202099,\\n\",\n \" 0.04373237118124962,\\n\",\n \" -0.009227175265550613,\\n\",\n \" 0.008003227412700653,\\n\",\n \" -0.014649329707026482,\\n\",\n \" '...']\"\n ]\n },\n \"metadata\": {},\n \"execution_count\": 20\n }\n ],\n \"source\": [\n \"# @title Test the vector embeddings function {\\\"run\\\":\\\"auto\\\",\\\"vertical-output\\\":true}\\n\",\n \"question = \\\"How are you?\\\" # @param [\\\"What new features have users requested for postgres?\\\",\\\"What features have been added recently for HubSpot?\\\",\\\"Tell me about PGVector.\\\"] {\\\"allow-input\\\":true}\\n\",\n \"question_vector = get_vector(question)\\n\",\n \"\\n\",\n \"print(f\\\"\\\"\\\"\\n\",\n \"Question:\\n\",\n \"\\\"{question}\\\"\\n\",\n \"\\n\",\n \"Calculated Vector (len={len(question_vector)}):\\n\",\n \"\\\"\\\"\\\")\\n\",\n \"question_vector[:20]+[\\\"...\\\"]\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"RgKK1feQJmKh\"\n },\n \"source\": [\n \"## B. Find relevant **document chunks** in our knowlege base\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 29,\n \"metadata\": {\n \"id\": \"6xTZ1hRTxbPV\"\n },\n \"outputs\": [],\n \"source\": [\n \"RAG_SQL_QUERY = \\\"\\\"\\\"\\n\",\n \"SELECT document_content, metadata->'number' as number\\n\",\n \"FROM issues\\n\",\n \"ORDER BY embedding <-> '{question_vector}'\\n\",\n \"LIMIT 15\\n\",\n \"\\\"\\\"\\\"\\n\",\n \"\\n\",\n \"# Markdown divider\\n\",\n \"HORIZONTAL_DIV = \\\"\\\\n\\\\n-------\\\\n\\\\n\\\"\\n\",\n \"\\n\",\n \"def get_related_context(question) -> str:\\n\",\n \" # Create a SQL query with our question's vector:\\n\",\n \" sql_query = RAG_SQL_QUERY.format(\\n\",\n \" question_vector=get_vector(question)\\n\",\n \" )\\n\",\n \" with engine.begin() as connection:\\n\",\n \" # Run the query and return results as a single string:\\n\",\n \" return (HORIZONTAL_DIV).join(\\n\",\n \" [\\n\",\n \" f\\\"Issue {row.number}: {row.document_content}\\\"\\n\",\n \" for row in connection.execute(text(sql_query))\\n\",\n \" ]\\n\",\n \" )\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 30,\n \"metadata\": {\n \"cellView\": \"form\",\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\",\n \"height\": 1000\n },\n \"id\": \"bIZKff1w9QUU\",\n \"outputId\": \"bf882128-de0a-4f19-b043-940474adba2a\"\n },\n \"outputs\": [\n {\n \"output_type\": \"display_data\",\n \"data\": {\n \"text/plain\": [\n \"\\u001b[1mQuestion: \\\"What features have been added recently for HubSpot?\\\"\\u001b[0m \\n\",\n \"\\n\",\n \"Related Context: \\n\",\n \"\\n\",\n \"\\u001b[33m───────────────────────────────────────────────────────────────────────────────────────────────────────────────────\\u001b[0m\\n\",\n \"Issue 43317: HubSpot recently began running a \\\"Batch Update\\\" process at the end of the month (to reset marketing \\n\",\n \"status, etc) which sets all affected records to the exact same timestamp. See example below: \\n\",\n \"\\n\",\n \"\\u001b[33m───────────────────────────────────────────────────────────────────────────────────────────────────────────────────\\u001b[0m\\n\",\n \"Issue 44481: title: ✨Source Hubspot: Add Leads Stream body: \\n\",\n \"\\n\",\n \"\\u001b[33m───────────────────────────────────────────────────────────────────────────────────────────────────────────────────\\u001b[0m\\n\",\n \"Issue 36435: title: ✨ Source Linkedin-ads: add new streams (Leads and Forms) body: ## Overview \\n\"\n ],\n \"text/html\": [\n \"
Question: \\\"What features have been added recently for HubSpot?\\\"                                                    \\n\",\n              \"\\n\",\n              \"Related Context:                                                                                                   \\n\",\n              \"\\n\",\n              \"───────────────────────────────────────────────────────────────────────────────────────────────────────────────────\\n\",\n              \"Issue 43317: HubSpot recently began running a \\\"Batch Update\\\" process at the end of the month (to reset marketing   \\n\",\n              \"status, etc) which sets all affected records to the exact same timestamp. See example below:                       \\n\",\n              \"\\n\",\n              \"───────────────────────────────────────────────────────────────────────────────────────────────────────────────────\\n\",\n              \"Issue 44481: title: ✨Source Hubspot: Add Leads Stream body: <!-- Thanks for your contribution! Before you submit  \\n\",\n              \"the pull request, I'd like to kindly remind you to take a moment and read through our guidelines to ensure that    \\n\",\n              \"your contribution aligns with the type of contributions our project accepts. All the information you need can be   \\n\",\n              \"found here:                                                                                                        \\n\",\n              \"\\n\",\n              \"───────────────────────────────────────────────────────────────────────────────────────────────────────────────────\\n\",\n              \"Issue 43317: Since HubSpot won't let you pull more than 10k search results at a time, the connector grabs the last \\n\",\n              \"updated time of the last 10k received and uses that timestamp as a starting point to pull the next 10k. For        \\n\",\n              \"example, if the first query                                                                                        \\n\",\n              \"\\n\",\n              \"───────────────────────────────────────────────────────────────────────────────────────────────────────────────────\\n\",\n              \"Issue 44916: I am a paid customer on airbyte cloud and load various data from hubspot/ga4/github into our own data \\n\",\n              \"platform: timeplus.com                                                                                             \\n\",\n              \"\\n\",\n              \"I wrote the destination-timeplus more than 1 year ago and released 0.1.0. After that it seems that airbyte         \\n\",\n              \"engineering team made some update. The latest version is 0.1.17. However in airbyte cloud, it remains 0.1.0        \\n\",\n              \"\\n\",\n              \"───────────────────────────────────────────────────────────────────────────────────────────────────────────────────\\n\",\n              \"Issue 36435: ## Changes Below is a detailed overview of the changes included in this pull request:                 \\n\",\n              \"\\n\",\n              \"                                                 Added Form Stream                                                 \\n\",\n              \"\\n\",\n              \" • Implemented a new stream for ingesting form data, enabling us to capture and integrate user submission data     \\n\",\n              \"   directly into our system. This addition is crucial for analytics and monitoring user interactions, providing    \\n\",\n              \"   valuable insights into user behavior and preferences.                                                           \\n\",\n              \"\\n\",\n              \"                                                 Added Lead Stream                                                 \\n\",\n              \"\\n\",\n              \"───────────────────────────────────────────────────────────────────────────────────────────────────────────────────\\n\",\n              \"Issue 45018: title: 🐙 source-hubspot: run up-to-date pipeline [2024-09-07] body: # Update source-hubspot          \\n\",\n              \"\\n\",\n              \"This PR was autogenerated by running airbyte-ci connectors --name=source-hubspot up_to_date --pull                 \\n\",\n              \"\\n\",\n              \"───────────────────────────────────────────────────────────────────────────────────────────────────────────────────\\n\",\n              \"Issue 44894: During the Sync                                                                                       \\n\",\n              \"\\n\",\n              \"                                               Relevant information                                                \\n\",\n              \"\\n\",\n              \"\\n\",\n              \"I have connect source from hubspot to S3 while I sync with S3 I got issue to upload data                           \\n\",\n              \"\\n\",\n              \"───────────────────────────────────────────────────────────────────────────────────────────────────────────────────\\n\",\n              \"Issue 44191: Currenly working with v 3.3.4 google ads connector.                                                   \\n\",\n              \"\\n\",\n              \"Thanks!                                                                                                            \\n\",\n              \"\\n\",\n              \"───────────────────────────────────────────────────────────────────────────────────────────────────────────────────\\n\",\n              \"Issue 45391: I would like to add two new streams in source zendesk support:                                        \\n\",\n              \"\\n\",\n              \" • Categories                                                                                                      \\n\",\n              \" • Sections                                                                                                        \\n\",\n              \"\\n\",\n              \"───────────────────────────────────────────────────────────────────────────────────────────────────────────────────\\n\",\n              \"Issue 37775: - Connector version is set to 0.1.1                                                                   \\n\",\n              \"\\n\",\n              \" • Documentation updated                                                                                           \\n\",\n              \"    • docs/integrations/source/partnerstack.md including changelog with an entry for the initial version.          \\n\",\n              \"    • docs/integrations/README.md                                                                                  \\n\",\n              \"\\n\",\n              \"\\n\",\n              \"───────────────────────────────────────────────────────────────────────────────────────────────────────────────────\\n\",\n              \"Issue 43712: - in OAuth mode, there is also a need for TenantId field                                              \\n\",\n              \"\\n\",\n              \"P.S. I am able to connect with V 1.1.0 but I would like to use refresh feature and incremental deduplication       \\n\",\n              \"features in V3.                                                                                                    \\n\",\n              \"\\n\",\n              \"───────────────────────────────────────────────────────────────────────────────────────────────────────────────────\\n\",\n              \"Issue 39114: 1. Connect Hubspot 2. Select contacts_form_submissions 3. Launch it as full refresh The table is      \\n\",\n              \"created in the DW with the right columns but it's empty. Our Hubspot is 6 years old and contain many form          \\n\",\n              \"submissions.                                                                                                       \\n\",\n              \"\\n\",\n              \"                                                Relevant log output                                                \\n\",\n              \"\\n\",\n              \"                                                                                                                   \\n\",\n              \"                                                                                                                   \\n\",\n              \"                                                                                                                   \\n\",\n              \"\\n\",\n              \"                                                    Contribute                                                     \\n\",\n              \"\\n\",\n              \"───────────────────────────────────────────────────────────────────────────────────────────────────────────────────\\n\",\n              \"Issue 44800: ### Relevant information                                                                              \\n\",\n              \"\\n\",\n              \"\\n\",\n              \"We can add webhook urls from the airbyte cloud. Can we also add that through powered by airbyte? Please update     \\n\",\n              \"powered by airbyte with this endpoint as well.                                                                     \\n\",\n              \"\\n\",\n              \"───────────────────────────────────────────────────────────────────────────────────────────────────────────────────\\n\",\n              \"Issue 21123: <!--- We accept contributions! Don't feel pressured, but if you want to contribute we can help you by \\n\",\n              \"giving some tips, highlighting the necessary code change or explaining any relevant point your feature will impact.\\n\",\n              \"You can also send questions on #dev Slack channel.                                                                 \\n\",\n              \"\\n\",\n              \"We understand if you can't submit a PR and we're tremendously grateful that you've already contributed by          \\n\",\n              \"suggesting a new feature. -->                                                                                      \\n\",\n              \"\\n\",\n              \"───────────────────────────────────────────────────────────────────────────────────────────────────────────────────\\n\",\n              \"Issue 36435: title: ✨ Source Linkedin-ads: add new streams (Leads and Forms) body: ## Overview                    \\n\",\n              \"
\\n\"\n ]\n },\n \"metadata\": {}\n }\n ],\n \"source\": [\n \"# @title Test the RAG lookup function {\\\"run\\\":\\\"auto\\\",\\\"vertical-output\\\":true}\\n\",\n \"question = \\\"What features have been added recently for HubSpot?\\\" # @param [\\\"What new features have users requested for postgres?\\\",\\\"What features have been added recently for HubSpot?\\\",\\\"Tell me about PGVector.\\\"] {\\\"allow-input\\\":true}\\n\",\n \"context = get_related_context(question)\\n\",\n \"\\n\",\n \"print(Markdown(f\\\"\\\"\\\"\\n\",\n \"**Question: \\\"{question}\\\"**\\n\",\n \"\\n\",\n \"Related Context:\\n\",\n \"\\n\",\n \"{HORIZONTAL_DIV}\\n\",\n \"\\n\",\n \"{context}\\n\",\n \"\\\"\\\"\\\"))\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"OKBTDbhCxbPV\"\n },\n \"source\": [\n \"## C. Call the **large language model** to answer our questions\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"NyOenZWrxbPW\"\n },\n \"source\": [\n \"The large language model (LLM) can answer questions if we write them out in english.\\n\",\n \"\\n\",\n \"When we send the question to the LLM we'll include the below instructions, along with any context we can find that might be helpful.\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 31,\n \"metadata\": {\n \"id\": \"7nSWX0m8nKZT\"\n },\n \"outputs\": [],\n \"source\": [\n \"# We specify our instructions to the LLM in plain English.\\n\",\n \"\\n\",\n \"PROMPT_TEMPLATE = \\\"\\\"\\\"\\n\",\n \"You are an AI assistant. You are able to find answers to the questions\\n\",\n \"from the contextual passage snippets provided.\\n\",\n \"\\n\",\n \"Use the following pieces of information enclosed in tags to provide an\\n\",\n \"answer to the question enclosed in tags.\\n\",\n \"\\n\",\n \"Please provide your answer using markdown, and bullets when appropriate. The\\n\",\n \"context will be a set of excerpts from GitHub issues and pull requests. In your\\n\",\n \"answer, you should list the issue number that was helpful in answering the\\n\",\n \"question.\\n\",\n \"\\n\",\n \"\\n\",\n \"{context}\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"{question}\\n\",\n \"\\n\",\n \"\\\"\\\"\\\"\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 32,\n \"metadata\": {\n \"id\": \"9jTp54NWxbPW\"\n },\n \"outputs\": [],\n \"source\": [\n \"# To get an answer we send our prompt to the LLM after\\n\",\n \"# populating it with the specific question and the context\\n\",\n \"# that we found.\\n\",\n \"\\n\",\n \"def get_answer(question) -> None:\\n\",\n \" answer_text = openai_client.chat.completions.create(\\n\",\n \" model=\\\"gpt-3.5-turbo\\\",\\n\",\n \" messages=[\\n\",\n \" {\\n\",\n \" \\\"role\\\": \\\"user\\\",\\n\",\n \" \\\"content\\\": PROMPT_TEMPLATE.format(\\n\",\n \" question=question,\\n\",\n \" context=get_related_context(question),\\n\",\n \" )\\n\",\n \" },\\n\",\n \" ],\\n\",\n \" ).choices[0].message.content\\n\",\n \"\\n\",\n \" print(Markdown(\\n\",\n \" f'Question: \\\"{question}\\\" {HORIZONTAL_DIV}' +\\n\",\n \" answer_text\\n\",\n \" ))\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 33,\n \"metadata\": {\n \"cellView\": \"form\",\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\",\n \"height\": 113\n },\n \"id\": \"Z__nLYZBxbPW\",\n \"outputId\": \"bb4efb93-c941-4a41-a3fc-05fe58b34148\"\n },\n \"outputs\": [\n {\n \"output_type\": \"display_data\",\n \"data\": {\n \"text/plain\": [\n \"Question: \\\"What features have been added recently for HubSpot?\\\" \\n\",\n \"\\n\",\n \"\\u001b[33m───────────────────────────────────────────────────────────────────────────────────────────────────────────────────\\u001b[0m\\n\",\n \"\\n\",\n \"\\u001b[1;33m • \\u001b[0mAdded Form Stream for ingesting form data for analytics and user interaction insights (Issue 36435) \\n\",\n \"\\u001b[1;33m • \\u001b[0mAdded Lead Stream (Issue 36435) \\n\"\n ],\n \"text/html\": [\n \"
Question: \\\"What features have been added recently for HubSpot?\\\"                                                    \\n\",\n              \"\\n\",\n              \"───────────────────────────────────────────────────────────────────────────────────────────────────────────────────\\n\",\n              \"\\n\",\n              \" • Added Form Stream for ingesting form data for analytics and user interaction insights (Issue 36435)             \\n\",\n              \" • Added Lead Stream (Issue 36435)                                                                                 \\n\",\n              \"
\\n\"\n ]\n },\n \"metadata\": {}\n }\n ],\n \"source\": [\n \"# @title Ask the AI Bot! 🤖 {\\\"run\\\":\\\"auto\\\",\\\"vertical-output\\\":true}\\n\",\n \"\\n\",\n \"question = \\\"What features have been added recently for HubSpot?\\\" # @param [\\\"What new features have users requested for postgres?\\\",\\\"What features have been added recently for HubSpot?\\\",\\\"Tell me about PGVector.\\\"] {\\\"allow-input\\\":true}\\n\",\n \"\\n\",\n \"get_answer(question)\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"5Cx1x0WqxbPW\"\n },\n \"source\": [\n \"# ⭐ RECAP and Next Steps\\n\",\n \"\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"x4jU6bm_UY4W\"\n },\n \"source\": [\n \"In this demo, we've shown how to build your own AI app with Airbyte. Using Airbyte and PyAirbyte, you can reach data from hundreds of data sources and load to any destination - whether a vector store, data lake, or a SQL warehouse.\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"hr6ICbeHRoVs\"\n },\n \"source\": [\n \"\\n\",\n \"\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"A4vnCILIaZ79\"\n },\n \"source\": [\n \"## Before we go...\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"SzviOha8Xtp8\"\n },\n \"source\": [\n \"👀 _A few things to know about **PyAirbyte** and our **Airbyte Connectors for AI**..._\\n\",\n \"\\n\",\n \"### Airbyte **Vector Store** Destinations\\n\",\n \"\\n\",\n \"- **Fully automated embedding, chunking, deduping.**\\n\",\n \"- Support for **all major embeddings providers**, including self-hosted embedding services.\\n\",\n \"- Support for **all major RAG vector store providers**, including purpose-built vector stores like **Pinecone**, **Chroma**, **Vectara**, and **Milvus**, as well as hybrid-SQL vector stores like **PGVector** and **Snowflake Cortex**.\\n\",\n \"\\n\",\n \"### Airbyte **Stuctured and Unstructured** Sources\\n\",\n \"\\n\",\n \"- Can **scrape text from unstructures** sources like PDFs, Google Docs, Word Docs, and many more.\\n\",\n \"- Support for **all major cloud storage providers**: S3, Azure, Google Drive, Google Cloud, SharePoint, and more.\\n\",\n \"- Support for **hundreds of REST APIs, web services, and databases**, as well as **custom connectors** with the **Airbyte Connector Builder** and **AI Assistant** ✨.\\n\",\n \"\\n\",\n \"### PyAirbyte Gives Control and Flexibility\\n\",\n \"\\n\",\n \"- Brings the **full set of Airbyte connectors to Python** so you can experiment and iterate **locally**, with full control over execution.\\n\",\n \"- Integrated with **popular AI and Analysis tools**: LangChain, LlamaIndex, Pandas, Apache Arrow, and more.\\n\",\n \"- Pipelines can be **easily deployed** to Python runtimes, or promoted to **Airbyte Cloud, Enterprise, or OSS!** 🚀\\n\"\n ]\n }\n ],\n \"metadata\": {\n \"colab\": {\n \"collapsed_sections\": [\n \"BeGeIDzAxbPS\",\n \"VGGynBZnxbPT\",\n \"sSaek4Hs7GlC\",\n \"cRTjmG8tXIV6\",\n \"muYWzVjNXV7i\",\n \"q_rCb2vPJ3qb\",\n \"SzviOha8Xtp8\"\n ],\n \"provenance\": [],\n \"include_colab_link\": true\n },\n \"kernelspec\": {\n \"display_name\": \"pyairbyte-hackathon\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.11.0\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 0\n}" }, { "path": "pyairbyte_notebooks/Chatoverpolygonstockdata_langchain.ipynb", "content": "{\n \"nbformat\": 4,\n \"nbformat_minor\": 0,\n \"metadata\": {\n \"colab\": {\n \"provenance\": []\n },\n \"kernelspec\": {\n \"name\": \"python3\",\n \"display_name\": \"Python 3\"\n },\n \"language_info\": {\n \"name\": \"python\"\n }\n },\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"source\": [\n \"## Install PyAirbyte and other dependencies\"\n ],\n \"metadata\": {\n \"id\": \"YKwX9XL1aHrY\"\n }\n },\n {\n \"cell_type\": \"code\",\n \"source\": [\n \"!pip3 install airbyte langchain_openai langchain-experimental\\n\"\n ],\n \"metadata\": {\n \"id\": \"9j9kjtEFaKxW\"\n },\n \"execution_count\": null,\n \"outputs\": []\n },\n {\n \"cell_type\": \"markdown\",\n \"source\": [\n \"## Polygon Airbyte Datasource\"\n ],\n \"metadata\": {\n \"id\": \"Q--tVuoMsGU5\"\n }\n },\n {\n \"cell_type\": \"code\",\n \"source\": [\n \"import airbyte as ab\\n\",\n \"\\n\",\n \"source = ab.get_source(\\n\",\n \" \\\"source-polygon-stock-api\\\",\\n\",\n \" install_if_missing=True,\\n\",\n \" config={\\n\",\n \" \\\"apiKey\\\": ab.get_secret(\\\"POLYGON_API_KEY\\\"),\\n\",\n \" \\\"stocksTicker\\\": \\\"AAPL\\\" ,\\n\",\n \" \\\"multiplier\\\": 1,\\n\",\n \" \\\"timespan\\\": \\\"day\\\",\\n\",\n \" \\\"start_date\\\": \\\"2023-01-01\\\",\\n\",\n \" \\\"end_date\\\": \\\"2023-12-31\\\",\\n\",\n \" \\\"adjusted\\\": \\\"true\\\"\\n\",\n \" },\\n\",\n \"\\n\",\n \")\\n\",\n \"\\n\",\n \"# Verify the config and creds by running `check`:\\n\",\n \"source.check()\"\n ],\n \"metadata\": {\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\",\n \"height\": 34\n },\n \"id\": \"6e5bD7A8cBEx\",\n \"outputId\": \"c7a57019-7623-401f-b529-aaa16a69a762\"\n },\n \"execution_count\": null,\n \"outputs\": [\n {\n \"output_type\": \"display_data\",\n \"data\": {\n \"text/plain\": [\n \"Connection check succeeded for `source-polygon-stock-api`.\\n\"\n ],\n \"text/html\": [\n \"
Connection check succeeded for `source-polygon-stock-api`.\\n\",\n              \"
\\n\"\n ]\n },\n \"metadata\": {}\n }\n ]\n },\n {\n \"cell_type\": \"code\",\n \"source\": [\n \"source.select_streams(['stock_api']) # Select only issues stream\\n\",\n \"read_result: ab.ReadResult = source.read()\"\n ],\n \"metadata\": {\n \"id\": \"EbFErEv8kgzw\",\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\",\n \"height\": 303\n },\n \"outputId\": \"54d919d4-c155-4dd3-b0c1-19bbb26e56f5\"\n },\n \"execution_count\": null,\n \"outputs\": [\n {\n \"output_type\": \"display_data\",\n \"data\": {\n \"text/plain\": [\n \"\"\n ],\n \"text/markdown\": \"## Read Progress\\n\\nStarted reading at 17:27:59.\\n\\nRead **120** records over **3 seconds** (40.0 records / second).\\n\\nWrote **120** records over 1 batches.\\n\\nFinished reading at 17:28:03.\\n\\nStarted finalizing streams at 17:28:03.\\n\\nFinalized **1** batches over 1 seconds.\\n\\nCompleted 1 out of 1 streams:\\n\\n - stock_api\\n\\n\\nCompleted writing at 17:28:05. Total time elapsed: 5 seconds\\n\\n\\n------------------------------------------------\\n\"\n },\n \"metadata\": {}\n },\n {\n \"output_type\": \"display_data\",\n \"data\": {\n \"text/plain\": [\n \"Completed `source-polygon-stock-api` read operation at \\u001b[1;92m17:28:05\\u001b[0m.\\n\"\n ],\n \"text/html\": [\n \"
Completed `source-polygon-stock-api` read operation at 17:28:05.\\n\",\n              \"
\\n\"\n ]\n },\n \"metadata\": {}\n }\n ]\n },\n {\n \"cell_type\": \"code\",\n \"source\": [\n \"stock_data = read_result[\\\"stock_api\\\"].to_pandas()\\n\",\n \"stock_data = stock_data.rename(columns={\\n\",\n \" 'c': 'Close',\\n\",\n \" 'h': 'High',\\n\",\n \" 'l': 'Low',\\n\",\n \" 'o': 'Open',\\n\",\n \" 't': 'Timestamp',\\n\",\n \" 'v': 'Volume',\\n\",\n \" 'vw': 'VWAP',\\n\",\n \"\\n\",\n \"})\\n\",\n \"\\n\",\n \"stock_data = stock_data[['Close', 'High', 'Low', 'Open', 'Timestamp', 'Volume', 'VWAP']]\\n\",\n \"\\n\",\n \"\\n\",\n \"display(stock_data)\"\n ],\n \"metadata\": {\n \"id\": \"SyFFmXBjkzcS\",\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\",\n \"height\": 424\n },\n \"outputId\": \"c14c5248-8786-415d-ccec-1a0bbc4c74d2\"\n },\n \"execution_count\": null,\n \"outputs\": [\n {\n \"output_type\": \"display_data\",\n \"data\": {\n \"text/plain\": [\n \" Close High Low Open Timestamp Volume VWAP\\n\",\n \"0 125.07 130.9000 124.1700 130.280 1672722000000 112117471.0 125.7250\\n\",\n \"1 126.36 128.6557 125.0800 126.890 1672808400000 89100633.0 126.6464\\n\",\n \"2 125.02 127.7700 124.7600 127.130 1672894800000 80716808.0 126.0883\\n\",\n \"3 129.62 130.2900 124.8900 126.010 1672981200000 87754715.0 128.1982\\n\",\n \"4 130.15 133.4100 129.8900 130.465 1673240400000 70790813.0 131.6292\\n\",\n \".. ... ... ... ... ... ... ...\\n\",\n \"115 185.01 186.1000 184.4100 184.410 1687233600000 49799092.0 185.2709\\n\",\n \"116 183.96 185.4100 182.5901 184.900 1687320000000 49515697.0 184.0809\\n\",\n \"117 187.00 187.0450 183.6700 183.740 1687406400000 51245327.0 186.1166\\n\",\n \"118 186.68 187.5600 185.0100 185.550 1687492800000 53112346.0 186.4952\\n\",\n \"119 185.27 188.0500 185.2300 186.830 1687752000000 48088661.0 186.3292\\n\",\n \"\\n\",\n \"[120 rows x 7 columns]\"\n ],\n \"text/html\": [\n \"\\n\",\n \"
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\\n\"\n ],\n \"application/vnd.google.colaboratory.intrinsic+json\": {\n \"type\": \"dataframe\",\n \"variable_name\": \"stock_data\",\n \"summary\": \"{\\n \\\"name\\\": \\\"stock_data\\\",\\n \\\"rows\\\": 120,\\n \\\"fields\\\": [\\n {\\n \\\"column\\\": \\\"Close\\\",\\n \\\"properties\\\": {\\n \\\"dtype\\\": \\\"number\\\",\\n \\\"std\\\": 15.539265956564279,\\n \\\"min\\\": 125.02,\\n \\\"max\\\": 187.0,\\n \\\"num_unique_values\\\": 118,\\n \\\"samples\\\": [\\n 160.25,\\n 172.57,\\n 130.15\\n ],\\n \\\"semantic_type\\\": \\\"\\\",\\n \\\"description\\\": \\\"\\\"\\n }\\n },\\n {\\n \\\"column\\\": \\\"High\\\",\\n \\\"properties\\\": {\\n \\\"dtype\\\": \\\"number\\\",\\n \\\"std\\\": 15.372639153874227,\\n \\\"min\\\": 127.77,\\n \\\"max\\\": 188.05,\\n \\\"num_unique_values\\\": 119,\\n \\\"samples\\\": [\\n 160.34,\\n 174.59,\\n 133.41\\n ],\\n \\\"semantic_type\\\": \\\"\\\",\\n \\\"description\\\": \\\"\\\"\\n }\\n },\\n {\\n \\\"column\\\": \\\"Low\\\",\\n \\\"properties\\\": {\\n \\\"dtype\\\": \\\"number\\\",\\n \\\"std\\\": 15.741200261457518,\\n \\\"min\\\": 124.17,\\n \\\"max\\\": 185.23,\\n \\\"num_unique_values\\\": 120,\\n \\\"samples\\\": [\\n 151.83,\\n 147.7,\\n 129.89\\n ],\\n \\\"semantic_type\\\": \\\"\\\",\\n \\\"description\\\": \\\"\\\"\\n }\\n },\\n {\\n \\\"column\\\": \\\"Open\\\",\\n \\\"properties\\\": {\\n \\\"dtype\\\": \\\"number\\\",\\n \\\"std\\\": 15.608952409500736,\\n \\\"min\\\": 126.01,\\n \\\"max\\\": 186.83,\\n \\\"num_unique_values\\\": 119,\\n \\\"samples\\\": [\\n 158.86,\\n 173.62,\\n 130.465\\n ],\\n \\\"semantic_type\\\": \\\"\\\",\\n \\\"description\\\": \\\"\\\"\\n }\\n },\\n {\\n \\\"column\\\": \\\"Timestamp\\\",\\n \\\"properties\\\": {\\n \\\"dtype\\\": \\\"number\\\",\\n \\\"std\\\": 4351425628,\\n \\\"min\\\": 1672722000000,\\n \\\"max\\\": 1687752000000,\\n \\\"num_unique_values\\\": 120,\\n \\\"samples\\\": [\\n 1678251600000,\\n 1678680000000,\\n 1673240400000\\n ],\\n \\\"semantic_type\\\": \\\"\\\",\\n \\\"description\\\": \\\"\\\"\\n }\\n },\\n {\\n \\\"column\\\": \\\"Volume\\\",\\n \\\"properties\\\": {\\n \\\"dtype\\\": \\\"number\\\",\\n \\\"std\\\": 18456132.44353353,\\n \\\"min\\\": 37264259.0,\\n \\\"max\\\": 154338835.0,\\n \\\"num_unique_values\\\": 120,\\n \\\"samples\\\": [\\n 47204791.0,\\n 84457122.0,\\n 70790813.0\\n ],\\n \\\"semantic_type\\\": \\\"\\\",\\n \\\"description\\\": \\\"\\\"\\n }\\n },\\n {\\n \\\"column\\\": \\\"VWAP\\\",\\n \\\"properties\\\": {\\n \\\"dtype\\\": \\\"number\\\",\\n \\\"std\\\": 15.589473557357758,\\n \\\"min\\\": 125.725,\\n \\\"max\\\": 186.4952,\\n \\\"num_unique_values\\\": 120,\\n \\\"samples\\\": [\\n 152.6973,\\n 151.1835,\\n 131.6292\\n ],\\n \\\"semantic_type\\\": \\\"\\\",\\n \\\"description\\\": \\\"\\\"\\n }\\n }\\n ]\\n}\"\n }\n },\n \"metadata\": {}\n }\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"source\": [\n \"## Chat with your data\"\n ],\n \"metadata\": {\n \"id\": \"b91ASLVSsKwx\"\n }\n },\n {\n \"cell_type\": \"code\",\n \"source\": [\n \"from langchain.agents.agent_types import AgentType\\n\",\n \"from langchain_experimental.agents.agent_toolkits import create_pandas_dataframe_agent\\n\",\n \"from langchain_openai import ChatOpenAI\\n\",\n \"from IPython.display import display\"\n ],\n \"metadata\": {\n \"id\": \"K_vft4losRiO\"\n },\n \"execution_count\": null,\n \"outputs\": []\n },\n {\n \"cell_type\": \"code\",\n \"source\": [\n \"import os\\n\",\n \"\\n\",\n \"os.environ['OPENAI_API_KEY'] = ab.get_secret(\\\"OPENAI_API_KEY\\\")\\n\",\n \"agent = create_pandas_dataframe_agent(\\n\",\n \" ChatOpenAI(model=\\\"gpt-3.5-turbo\\\"),\\n\",\n \" stock_data,\\n\",\n \" verbose=True,\\n\",\n \" agent_type=AgentType.OPENAI_FUNCTIONS,\\n\",\n \" )\"\n ],\n \"metadata\": {\n \"id\": \"VjP_PfhEscBk\"\n },\n \"execution_count\": null,\n \"outputs\": []\n },\n {\n \"cell_type\": \"code\",\n \"source\": [\n \"output = agent.invoke(\\\"what is the given data is about?\\\")\\n\",\n \"print(output['output'])\"\n ],\n \"metadata\": {\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\"\n },\n \"id\": \"K8lQaOretq5S\",\n \"outputId\": \"76ea1ddc-f3bb-4db6-bf7f-87b00747b804\"\n },\n \"execution_count\": null,\n \"outputs\": [\n {\n \"output_type\": \"stream\",\n \"name\": \"stdout\",\n \"text\": [\n \"\\n\",\n \"\\n\",\n \"\\u001b[1m> Entering new AgentExecutor chain...\\u001b[0m\\n\",\n \"\\u001b[32;1m\\u001b[1;3mBased on the columns in the dataframe `df`, the data appears to be financial market data related to trading. Here are the columns and their potential meanings:\\n\",\n \"\\n\",\n \"- `Close`: Closing price of a financial asset\\n\",\n \"- `High`: High price of a financial asset during a specific time period\\n\",\n \"- `Low`: Low price of a financial asset during a specific time period\\n\",\n \"- `Open`: Opening price of a financial asset\\n\",\n \"- `Timestamp`: Timestamp of the data point\\n\",\n \"- `Volume`: Volume of the financial asset traded\\n\",\n \"- `VWAP`: Volume Weighted Average Price\\n\",\n \"\\n\",\n \"Therefore, the data in the dataframe `df` seems to represent price and volume data of a financial asset over different timestamps.\\u001b[0m\\n\",\n \"\\n\",\n \"\\u001b[1m> Finished chain.\\u001b[0m\\n\",\n \"Based on the columns in the dataframe `df`, the data appears to be financial market data related to trading. Here are the columns and their potential meanings:\\n\",\n \"\\n\",\n \"- `Close`: Closing price of a financial asset\\n\",\n \"- `High`: High price of a financial asset during a specific time period\\n\",\n \"- `Low`: Low price of a financial asset during a specific time period\\n\",\n \"- `Open`: Opening price of a financial asset\\n\",\n \"- `Timestamp`: Timestamp of the data point\\n\",\n \"- `Volume`: Volume of the financial asset traded\\n\",\n \"- `VWAP`: Volume Weighted Average Price\\n\",\n \"\\n\",\n \"Therefore, the data in the dataframe `df` seems to represent price and volume data of a financial asset over different timestamps.\\n\"\n ]\n }\n ]\n },\n {\n \"cell_type\": \"code\",\n \"source\": [\n \"output = agent.invoke(\\\"what is the yearly returns of the given stock?\\\")\\n\",\n \"print(output['output'])\"\n ],\n \"metadata\": {\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\"\n },\n \"id\": \"zb9B_glfuYN0\",\n \"outputId\": \"4f91af29-f371-4c60-b9e5-92e44884b9df\"\n },\n \"execution_count\": null,\n \"outputs\": [\n {\n \"output_type\": \"stream\",\n \"name\": \"stdout\",\n \"text\": [\n \"\\n\",\n \"\\n\",\n \"\\u001b[1m> Entering new AgentExecutor chain...\\u001b[0m\\n\",\n \"\\u001b[32;1m\\u001b[1;3m\\n\",\n \"Invoking: `python_repl_ast` with `{'query': \\\"df['Year'] = pd.to_datetime(df['Timestamp'], unit='ms').dt.year\\\\nyearly_returns = df.groupby('Year')['Close'].last() / df.groupby('Year')['Close'].first() - 1\\\\nyearly_returns\\\"}`\\n\",\n \"\\n\",\n \"\\n\",\n \"\\u001b[0m\\u001b[36;1m\\u001b[1;3mYear\\n\",\n \"2023 0.48133\\n\",\n \"Name: Close, dtype: float64\\u001b[0m\\u001b[32;1m\\u001b[1;3mThe yearly returns of the given stock for the year 2023 are approximately 48.13%.\\u001b[0m\\n\",\n \"\\n\",\n \"\\u001b[1m> Finished chain.\\u001b[0m\\n\",\n \"The yearly returns of the given stock for the year 2023 are approximately 48.13%.\\n\"\n ]\n }\n ]\n },\n {\n \"cell_type\": \"code\",\n \"source\": [\n \"output = agent.invoke(\\\"what is the 50-day moving average of the entire data?\\\")\\n\",\n \"print(output['output'])\"\n ],\n \"metadata\": {\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\"\n },\n \"id\": \"pf41wXkuumnp\",\n \"outputId\": \"59088435-3391-4d64-ec84-87429e47eda5\"\n },\n \"execution_count\": null,\n \"outputs\": [\n {\n \"output_type\": \"stream\",\n \"name\": \"stdout\",\n \"text\": [\n \"\\n\",\n \"\\n\",\n \"\\u001b[1m> Entering new AgentExecutor chain...\\u001b[0m\\n\",\n \"\\u001b[32;1m\\u001b[1;3m\\n\",\n \"Invoking: `python_repl_ast` with `{'query': \\\"df['Close'].rolling(window=50).mean().iloc[-1]\\\"}`\\n\",\n \"\\n\",\n \"\\n\",\n \"\\u001b[0m\\u001b[36;1m\\u001b[1;3m174.67870000000002\\u001b[0m\\u001b[32;1m\\u001b[1;3mThe 50-day moving average of the entire data is approximately 174.68.\\u001b[0m\\n\",\n \"\\n\",\n \"\\u001b[1m> Finished chain.\\u001b[0m\\n\",\n \"The 50-day moving average of the entire data is approximately 174.68.\\n\"\n ]\n }\n ]\n }\n ]\n}" }, { "path": "pyairbyte_notebooks/PyAirbyte_Apify_Demo.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"3s3Jow7_BjQi\"\n },\n \"source\": [\n \"# Scraping Web Data from Apify Source into Airbyte for LangChain\\n\",\n \"\\n\",\n \"This tutorial will demonstrate how to scrape data from a website using Apify, load the scraped data using PyAirbyte, and store the data in a database using LangChain.\\n\",\n \"Integrating web data into LLMs can enhance their performance by providing up-to-date and relevant information. This process can be complex, and this guide aims to simplify it for users.\\n\",\n \"\\n\",\n \"## Prerequisites\\n\",\n \"\\n\",\n \"1. **Apify Account**:\\n\",\n \" - Follow the instructions in the [Apify](https://docs.airbyte.com/integrations/sources/apify-dataset) to set up your apify account and obtain the necessary access keys.\\n\",\n \"\\n\",\n \"2. **Pinecone Account**:\\n\",\n \" - **Create a Pinecone Account**: Sign up for an account on the [Pinecone website](https://www.pinecone.io/).\\n\",\n \" - **Obtain Pinecone API Key**: Generate a new API key from your Pinecone project settings. For detailed instructions, refer to the [Pinecone documentation](https://docs.pinecone.io/docs/quickstart).\\n\",\n \"\\n\",\n \"3. **OpenAI API Key**:\\n\",\n \" - **Create an OpenAI Account**: Sign up for an account on [OpenAI](https://platform.openai.com/docs/overview).\\n\",\n \" - **Generate an API Key**: Go to the API section and generate a new API key. For detailed instructions, refer to the [OpenAI documentation](https://platform.openai.com/api-keys).\\n\",\n \"\\n\",\n \"\\n\",\n \"## Install PyAirbyte and other dependencies\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"id\": \"ij3THvimBjQk\"\n },\n \"outputs\": [],\n \"source\": [\n \"# Add virtual environment support in Google Colab\\n\",\n \"!apt-get install -qq python3.10-venv\\n\",\n \"\\n\",\n \"# First, we need to install the necessary libraries.\\n\",\n \"!pip3 install airbyte openai langchain pinecone-client langchain-openai langchain-pinecone python-dotenv langchainhub\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"8jDsEZdiBjQl\"\n },\n \"source\": [\n \"## Setup Source Apify with PyAirbyte\\n\",\n \"\\n\",\n \"The provided code configures an Airbyte source to extract data from specific dataset in apify.\\n\",\n \"\\n\",\n \"To configure according to your requirements, you can refer to [this references](https://docs.airbyte.com/integrations/sources/apify-dataset#reference).\\n\",\n \"\\n\",\n \"Note: The credentials are retrieved securely using the get_secret() method. This will automatically locate a matching Google Colab secret or environment variable, ensuring they are not hard-coded into the notebook. Make sure to add your key to the Secrets section on the left.\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"id\": \"U7DxyLVUBjQl\"\n },\n \"outputs\": [],\n \"source\": [\n \"import airbyte as ab\\n\",\n \"\\n\",\n \"source = ab.get_source(\\n\",\n \" \\\"source-apify-dataset\\\",\\n\",\n \" config={\\n\",\n \" \\\"token\\\": ab.get_secret(\\\"API_TOKEN\\\"),\\n\",\n \" \\\"dataset_id\\\": ab.get_secret(\\\"DATASET_ID\\\"),\\n\",\n \" }\\n\",\n \")\\n\",\n \"source.check()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"rtSCjGcQBjQl\"\n },\n \"source\": [\n \"This is a basic process of fetching data from Apify dataset using Airbyte and converting it into a format suitable for further processing or analysis.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"id\": \"mlg7K8GUBjQm\"\n },\n \"outputs\": [],\n \"source\": [\n \"source.select_all_streams() # Select all streams\\n\",\n \"read_result = source.read() # Read the data\\n\",\n \"review_list = [doc for doc in read_result[\\\"item_collection\\\"].to_documents()] # We are only intrested in item_collection stream only\\n\",\n \"\\n\",\n \"print(str(review_list[10]))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"L6KM7KPMBjQm\"\n },\n \"source\": [\n \"# Use Langchain to build a RAG pipeline.\\n\",\n \"\\n\",\n \"The code uses RecursiveCharacterTextSplitter to break documents into smaller chunks. Metadata within these chunks is converted to strings. This facilitates efficient processing of large texts, enhancing analysis capabilities.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"id\": \"LXvSJoUSBjQm\",\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\"\n },\n \"outputId\": \"eb25babf-f917-4f1d-8970-5e8acd132776\"\n },\n \"outputs\": [\n {\n \"output_type\": \"stream\",\n \"name\": \"stdout\",\n \"text\": [\n \"Created 493 document chunks.\\n\"\n ]\n }\n ],\n \"source\": [\n \"\\n\",\n \"from langchain.text_splitter import RecursiveCharacterTextSplitter\\n\",\n \"\\n\",\n \"splitter = RecursiveCharacterTextSplitter(chunk_size=512, chunk_overlap=50)\\n\",\n \"chunked_docs = splitter.split_documents(review_list)\\n\",\n \"print(f\\\"Created {len(chunked_docs)} document chunks.\\\")\\n\",\n \"\\n\",\n \"for doc in chunked_docs:\\n\",\n \" for md in doc.metadata:\\n\",\n \" doc.metadata[md] = str(doc.metadata[md])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"id\": \"kCF7gZTMBjQm\"\n },\n \"outputs\": [],\n \"source\": [\n \"from langchain_openai import OpenAIEmbeddings\\n\",\n \"import os\\n\",\n \"\\n\",\n \"os.environ['OPENAI_API_KEY'] = ab.get_secret(\\\"OPENAI_API_KEY\\\")\\n\",\n \"## Embedding Technique Of OPENAI\\n\",\n \"embeddings=OpenAIEmbeddings()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"5TKCIAhYBjQm\"\n },\n \"source\": [\n \"## Setting up Pinecone\\n\",\n \"\\n\",\n \"Pinecone is a managed vector database service designed for storing, indexing, and querying high-dimensional vector data efficiently.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"id\": \"iXb5YPnhBjQn\"\n },\n \"outputs\": [],\n \"source\": [\n \"from pinecone import Pinecone, ServerlessSpec\\n\",\n \"from pinecone import Pinecone\\n\",\n \"\\n\",\n \"os.environ['PINECONE_API_KEY'] = ab.get_secret(\\\"PINECONE_API_KEY\\\")\\n\",\n \"pc = Pinecone()\\n\",\n \"index_name = \\\"apifyproductreview\\\" # Replace with your index name\\n\",\n \"\\n\",\n \"\\n\",\n \"# Uncomment this if you have not created a Pinecone index yet\\n\",\n \"\\n\",\n \"spec = ServerlessSpec(cloud=\\\"aws\\\", region=\\\"us-east-1\\\") # Replace with your cloud and region\\n\",\n \"pc.create_index(\\n\",\n \" name = index_name,\\n\",\n \" dimension=1536, # Replace with your model dimensions\\n\",\n \" metric='cosine', # Replace with your model metric\\n\",\n \" spec=spec\\n\",\n \")\\n\",\n \"\\n\",\n \"index = pc.Index(index_name)\\n\",\n \"\\n\",\n \"index.describe_index_stats()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"6kRv6s7zBjQn\"\n },\n \"source\": [\n \"PineconeVectorStore is a class provided by the LangChain library specifically designed for interacting with Pinecone vector stores.\\n\",\n \"from_documents method of PineconeVectorStore is used to create or update vectors in a Pinecone vector store based on the provided documents and their corresponding embeddings.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"id\": \"3hToKOPsBjQn\"\n },\n \"outputs\": [],\n \"source\": [\n \"from langchain_pinecone import PineconeVectorStore\\n\",\n \"\\n\",\n \"pinecone = PineconeVectorStore.from_documents(\\n\",\n \" chunked_docs, embeddings, index_name=index_name\\n\",\n \")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"vj0hSWo2BjQn\"\n },\n \"source\": [\n \"Now setting up a pipeline for RAG using LangChain, incorporating document retrieval from Pinecone, prompt configuration, and a chat model from OpenAI for response generation.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"id\": \"N0gE_LbmBjQn\"\n },\n \"outputs\": [],\n \"source\": [\n \"from langchain_openai import ChatOpenAI\\n\",\n \"from langchain import hub\\n\",\n \"from langchain_core.output_parsers import StrOutputParser\\n\",\n \"from langchain_core.runnables import RunnablePassthrough\\n\",\n \"\\n\",\n \"retriever = pinecone.as_retriever()\\n\",\n \"prompt = hub.pull(\\\"rlm/rag-prompt\\\")\\n\",\n \"llm = ChatOpenAI(model_name=\\\"gpt-3.5-turbo\\\", temperature=0)\\n\",\n \"\\n\",\n \"\\n\",\n \"def format_docs(docs):\\n\",\n \" return \\\"\\\\n\\\\n\\\".join(doc.page_content for doc in docs)\\n\",\n \"\\n\",\n \"rag_chain = (\\n\",\n \" {\\\"context\\\": retriever | format_docs, \\\"question\\\": RunnablePassthrough()}\\n\",\n \" | prompt\\n\",\n \" | llm\\n\",\n \" | StrOutputParser()\\n\",\n \")\\n\",\n \"print(\\\"Langchain RAG pipeline set up successfully.\\\")\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"id\": \"axbwi9j8BjQn\"\n },\n \"outputs\": [],\n \"source\": [\n \"print(rag_chain.invoke(\\\"What is overall review of products\\\"))\\n\"\n ]\n }\n ],\n \"metadata\": {\n \"colab\": {\n \"provenance\": []\n },\n \"kernelspec\": {\n \"display_name\": \"myenv\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.10.11\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 0\n}" }, { "path": "pyairbyte_notebooks/PyAirbyte_Basic_Features_Demo.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"colab_type\": \"text\",\n \"id\": \"view-in-github\"\n },\n \"source\": [\n \"\\\"Open\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"R8XtHKK4PujA\"\n },\n \"source\": [\n \"# PyAirbyte Demo\\n\",\n \"\\n\",\n \"Below is a pre-release demo of PyAirbyte.\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"Lyxh2NLuQJUf\"\n },\n \"source\": [\n \"## Install PyAirbyte\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"id\": \"9DEgu1WpQNt-\"\n },\n \"outputs\": [],\n \"source\": [\n \"# Add virtual environment support for running in Google Colab\\n\",\n \"!apt-get install -qq python3.10-venv\\n\",\n \"\\n\",\n \"# Install PyAirbyte\\n\",\n \"%pip install --quiet airbyte\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"cXJ_cRQV7nIb\"\n },\n \"source\": [\n \"## Locating your Data Source\\n\",\n \"\\n\",\n \"To see what data sources are available, you can check [our docs](https://docs.airbyte.com/using-airbyte/airbyte-lib/getting-started) or run the following:\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"id\": \"tfjct5EQ7nIb\"\n },\n \"outputs\": [],\n \"source\": [\n \"# Import PyAirbyte\\n\",\n \"import airbyte as ab\\n\",\n \"\\n\",\n \"# Show all available connectors\\n\",\n \"ab.get_available_connectors()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"JWWeEbTVEDFz\"\n },\n \"source\": [\n \"## Load the Source Data using PyAirbyte\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"1PhfWpS8QVzE\"\n },\n \"source\": [\n \"Create and install a source connector:\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\",\n \"height\": 129\n },\n \"id\": \"5BI9hIeUvxXE\",\n \"outputId\": \"4d674fb3-25f1-400e-f2e6-c4160cb245e4\"\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
Installing 'source-faker' into virtual environment '/content/.venv-source-faker'.\\n\",\n              \"Running 'pip install airbyte-source-faker'...\\n\",\n              \"\\n\",\n              \"
\\n\"\n ],\n \"text/plain\": [\n \"Installing \\u001b[32m'source-faker'\\u001b[0m into virtual environment \\u001b[32m'/content/.venv-source-faker'\\u001b[0m.\\n\",\n \"Running \\u001b[32m'pip install airbyte-source-faker'\\u001b[0m\\u001b[33m...\\u001b[0m\\n\",\n \"\\n\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n },\n {\n \"data\": {\n \"text/html\": [\n \"
Connector 'source-faker' installed successfully!\\n\",\n              \"For more information, see the source-faker documentation:\\n\",\n              \"https://docs.airbyte.com/integrations/sources/faker#reference\\n\",\n              \"\\n\",\n              \"
\\n\"\n ],\n \"text/plain\": [\n \"Connector \\u001b[32m'source-faker'\\u001b[0m installed successfully!\\n\",\n \"For more information, see the source-faker documentation:\\n\",\n \"\\u001b[4;94mhttps://docs.airbyte.com/integrations/sources/faker#reference\\u001b[0m\\n\",\n \"\\n\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"import airbyte as ab\\n\",\n \"\\n\",\n \"# Create and install the source:\\n\",\n \"source: ab.Source = ab.get_source(\\\"source-faker\\\")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\",\n \"height\": 33\n },\n \"id\": \"Ww7NA9cQQjQI\",\n \"outputId\": \"4c355b12-18a6-43ef-ecc7-e6986af6b73a\"\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
Connection check succeeded for `source-faker`.\\n\",\n              \"
\\n\"\n ],\n \"text/plain\": [\n \"Connection check succeeded for `source-faker`.\\n\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"# Configure the source\\n\",\n \"source.set_config(\\n\",\n \" config={\\n\",\n \" \\\"count\\\": 50_000, # Adjust this to get a larger or smaller dataset\\n\",\n \" \\\"seed\\\": 123,\\n\",\n \" },\\n\",\n \")\\n\",\n \"# Verify the config and creds by running `check`:\\n\",\n \"source.check()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"Que5DAqtEJu0\"\n },\n \"source\": [\n \"## Read Data from the PyAirbyte Cache\\n\",\n \"\\n\",\n \"Once data is read, we can do anything we want to with the resulting streams. This includes `to_pandas()` which registers a Pandas dataframe and `to_sql_table()` which gives us a SQLAlchemy `Table` boject, which we can use to run SQL queries.\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"metadata\": {\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\",\n \"height\": 337\n },\n \"id\": \"qQVRO69c2DoA\",\n \"outputId\": \"b94fa743-ed48-4242-9344-ee60cee2f225\"\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/markdown\": [\n \"## Read Progress\\n\",\n \"\\n\",\n \"Started reading at 20:10:41.\\n\",\n \"\\n\",\n \"Read **100,100** records over **1min 17s** (1,300.0 records / second).\\n\",\n \"\\n\",\n \"Wrote **100,100** records over 11 batches.\\n\",\n \"\\n\",\n \"Finished reading at 20:11:59.\\n\",\n \"\\n\",\n \"Started finalizing streams at 20:11:59.\\n\",\n \"\\n\",\n \"Finalized **11** batches over 1 seconds.\\n\",\n \"\\n\",\n \"Completed 3 out of 3 streams:\\n\",\n \"\\n\",\n \" - users\\n\",\n \" - purchases\\n\",\n \" - products\\n\",\n \"\\n\",\n \"\\n\",\n \"Completed writing at 20:12:01. Total time elapsed: 1min 19s\\n\",\n \"\\n\",\n \"\\n\",\n \"------------------------------------------------\\n\"\n ],\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n },\n {\n \"data\": {\n \"text/html\": [\n \"
Completed `source-faker` read operation at 20:12:01.\\n\",\n              \"
\\n\"\n ],\n \"text/plain\": [\n \"Completed `source-faker` read operation at \\u001b[1;92m20:12:01\\u001b[0m.\\n\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"# Select all of the source's streams and read data into the internal cache:\\n\",\n \"source.select_all_streams()\\n\",\n \"read_result: ab.ReadResult = source.read()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"metadata\": {\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\",\n \"height\": 424\n },\n \"id\": \"gRuGVOoDEw1R\",\n \"outputId\": \"8c221488-54b7-4eed-a882-dc6d776d7b8e\"\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"\\n\",\n \"
\\n\",\n \"
\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
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9798CadillacCTS-V200719914.02021-09-02 15:38:462024-02-12 20:10:42
9899GMC1500 Club Coupe199782288.02021-04-20 18:58:152024-02-12 20:10:42
99100BuickSomerset198664148.02021-06-10 19:07:382024-02-12 20:10:42
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\\n\"\n ],\n \"text/plain\": [\n \" id make model year price created_at \\\\\\n\",\n \"0 1 Mazda MX-5 2008 2869.0 2022-02-01 17:02:19 \\n\",\n \"1 2 Mercedes-Benz C-Class 2009 42397.0 2021-01-25 14:31:33 \\n\",\n \"2 3 Honda Accord Crosstour 2011 63293.0 2021-02-11 05:36:03 \\n\",\n \"3 4 GMC Jimmy 1998 34079.0 2022-01-24 03:00:03 \\n\",\n \"4 5 Infiniti FX 2004 17036.0 2021-10-02 03:55:44 \\n\",\n \".. ... ... ... ... ... ... \\n\",\n \"95 96 BMW 330 2006 14494.0 2021-09-17 20:52:48 \\n\",\n \"96 97 Audi R8 2008 17642.0 2021-09-21 11:56:24 \\n\",\n \"97 98 Cadillac CTS-V 2007 19914.0 2021-09-02 15:38:46 \\n\",\n \"98 99 GMC 1500 Club Coupe 1997 82288.0 2021-04-20 18:58:15 \\n\",\n \"99 100 Buick Somerset 1986 64148.0 2021-06-10 19:07:38 \\n\",\n \"\\n\",\n \" updated_at \\n\",\n \"0 2024-02-12 20:10:42 \\n\",\n \"1 2024-02-12 20:10:42 \\n\",\n \"2 2024-02-12 20:10:42 \\n\",\n \"3 2024-02-12 20:10:42 \\n\",\n \"4 2024-02-12 20:10:42 \\n\",\n \".. ... \\n\",\n \"95 2024-02-12 20:10:42 \\n\",\n \"96 2024-02-12 20:10:42 \\n\",\n \"97 2024-02-12 20:10:42 \\n\",\n \"98 2024-02-12 20:10:42 \\n\",\n \"99 2024-02-12 20:10:42 \\n\",\n \"\\n\",\n \"[100 rows x 7 columns]\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"# Display or transform the loaded data\\n\",\n \"products_df = read_result[\\\"products\\\"].to_pandas()\\n\",\n \"display(products_df)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"VJgl59hBF1rJ\"\n },\n \"source\": [\n \"## Creating graphs\\n\",\n \"\\n\",\n \"PyAirbyte integrates with Pandas, which integrates with `matplotlib` as well as many other popular libraries. We can use this as a means of quickly creating graphs.\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"metadata\": {\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\",\n \"height\": 718\n },\n \"id\": \"XcQcBlYjF3oO\",\n \"outputId\": \"b8b2ae5f-605d-485a-a3c7-8bf72fd826a9\"\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Requirement already satisfied: matplotlib in /usr/local/lib/python3.10/dist-packages (3.7.1)\\n\",\n \"Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib) (1.2.0)\\n\",\n \"Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.10/dist-packages (from matplotlib) (0.12.1)\\n\",\n \"Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib) (4.48.1)\\n\",\n \"Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib) (1.4.5)\\n\",\n \"Requirement already satisfied: numpy>=1.20 in /usr/local/lib/python3.10/dist-packages (from matplotlib) (1.23.5)\\n\",\n \"Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib) (23.2)\\n\",\n \"Requirement already satisfied: pillow>=6.2.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib) (9.4.0)\\n\",\n \"Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib) (3.1.1)\\n\",\n \"Requirement already satisfied: python-dateutil>=2.7 in /usr/local/lib/python3.10/dist-packages (from matplotlib) (2.8.2)\\n\",\n \"Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.7->matplotlib) (1.16.0)\\n\"\n ]\n },\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"/usr/local/lib/python3.10/dist-packages/duckdb_engine/__init__.py:178: DuckDBEngineWarning: duckdb-engine doesn't yet support reflection on indices\\n\",\n \" warnings.warn(\\n\"\n ]\n },\n {\n \"data\": {\n \"image/png\": 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\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"%pip install matplotlib\\n\",\n \"\\n\",\n \"import matplotlib.pyplot as plt\\n\",\n \"\\n\",\n \"users_df = read_result[\\\"users\\\"].to_pandas()\\n\",\n \"\\n\",\n \"plt.hist(users_df[\\\"age\\\"], bins=10, edgecolor=\\\"black\\\")\\n\",\n \"plt.title(\\\"Histogram of Ages\\\")\\n\",\n \"plt.xlabel(\\\"Ages\\\")\\n\",\n \"plt.ylabel(\\\"Frequency\\\")\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"wSru-wGpHZrh\"\n },\n \"source\": [\n \"## Working in SQL\\n\",\n \"\\n\",\n \"Since data is cached in a local DuckDB database, we can query the data with SQL.\\n\",\n \"\\n\",\n \"We can do this in multiple ways. One way is to use the [JupySQL Extension](https://jupysql.ploomber.io/en/latest/user-guide/template.html), which we'll use below.\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"metadata\": {\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\"\n },\n \"id\": \"xdotIOg70nuL\",\n \"outputId\": \"b4ca91ec-0b36-42fd-d19e-16b8d1f7ae45\"\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"\\u001b[?25l \\u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\\u001b[0m \\u001b[32m0.0/95.7 kB\\u001b[0m \\u001b[31m?\\u001b[0m eta \\u001b[36m-:--:--\\u001b[0m\\r\\u001b[2K \\u001b[91m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\\u001b[0m\\u001b[91m╸\\u001b[0m\\u001b[90m━\\u001b[0m \\u001b[32m92.2/95.7 kB\\u001b[0m \\u001b[31m2.9 MB/s\\u001b[0m eta \\u001b[36m0:00:01\\u001b[0m\\r\\u001b[2K \\u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\\u001b[0m \\u001b[32m95.7/95.7 kB\\u001b[0m \\u001b[31m2.3 MB/s\\u001b[0m eta \\u001b[36m0:00:00\\u001b[0m\\n\",\n \"\\u001b[2K \\u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\\u001b[0m \\u001b[32m250.9/250.9 kB\\u001b[0m \\u001b[31m13.9 MB/s\\u001b[0m eta \\u001b[36m0:00:00\\u001b[0m\\n\",\n \"\\u001b[2K \\u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\\u001b[0m \\u001b[32m193.0/193.0 kB\\u001b[0m \\u001b[31m20.5 MB/s\\u001b[0m eta \\u001b[36m0:00:00\\u001b[0m\\n\",\n \"\\u001b[2K \\u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\\u001b[0m \\u001b[32m41.1/41.1 kB\\u001b[0m \\u001b[31m5.2 MB/s\\u001b[0m eta \\u001b[36m0:00:00\\u001b[0m\\n\",\n \"\\u001b[?25hDeploy Panel apps for free on Ploomber Cloud! Learn more: https://ploomber.io/s/signup\\n\"\n ]\n }\n ],\n \"source\": [\n \"# Install JupySQL to enable SQL cell magics\\n\",\n \"%pip install --quiet jupysql\\n\",\n \"# Load JupySQL extension\\n\",\n \"%load_ext sql\\n\",\n \"# Configure max row limit (optional)\\n\",\n \"%config SqlMagic.displaylimit = 200\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 10,\n \"metadata\": {\n \"id\": \"2tA6L1dHZ2w0\"\n },\n \"outputs\": [],\n \"source\": [\n \"# Get the SQLAlchemy 'engine' object for the cache\\n\",\n \"engine = read_result.cache.get_sql_engine()\\n\",\n \"# Pass the engine to JupySQL\\n\",\n \"%sql engine\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 14,\n \"metadata\": {\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\",\n \"height\": 34\n },\n \"id\": \"0eaYnErPaFsH\",\n \"outputId\": \"3c017bfc-adf3-40bc-81af-e6b1478d229f\"\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"['main.users', 'main.purchases']\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"# Get table objects for the 'users' and 'purchases' streams\\n\",\n \"users_table = read_result.cache[\\\"users\\\"].to_sql_table()\\n\",\n \"purchases_table = read_result.cache[\\\"purchases\\\"].to_sql_table()\\n\",\n \"display([users_table.fullname, purchases_table.fullname])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 18,\n \"metadata\": {\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\",\n \"height\": 271\n },\n \"id\": \"VjeTOtKHHiA5\",\n \"outputId\": \"a4bef98d-0572-4535-9b63-a37dba00723d\"\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"Running query in 'duckdb:///.cache/default_cache_db.duckdb'\"\n ],\n \"text/plain\": [\n \"Running query in 'duckdb:///.cache/default_cache_db.duckdb'\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n },\n {\n \"data\": {\n \"text/html\": [\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
idnameproduct_idpurchased_at
21589Torie482024-12-10 20:32:08
39842Kareen452024-11-28 11:26:13
19248Jerry412024-11-05 00:59:42
30780Dwana822024-10-20 20:09:11
4669Frankie1002024-10-15 16:23:02
42204Lashaun92024-10-06 08:06:59
13251Charlie462024-09-19 17:55:52
47798Issac402024-09-13 05:13:17
34970Eleni392024-09-13 00:15:55
24782Jose402024-09-08 09:49:06
\"\n ],\n \"text/plain\": [\n \"+-------+---------+------------+---------------------+\\n\",\n \"| id | name | product_id | purchased_at |\\n\",\n \"+-------+---------+------------+---------------------+\\n\",\n \"| 21589 | Torie | 48 | 2024-12-10 20:32:08 |\\n\",\n \"| 39842 | Kareen | 45 | 2024-11-28 11:26:13 |\\n\",\n \"| 19248 | Jerry | 41 | 2024-11-05 00:59:42 |\\n\",\n \"| 30780 | Dwana | 82 | 2024-10-20 20:09:11 |\\n\",\n \"| 4669 | Frankie | 100 | 2024-10-15 16:23:02 |\\n\",\n \"| 42204 | Lashaun | 9 | 2024-10-06 08:06:59 |\\n\",\n \"| 13251 | Charlie | 46 | 2024-09-19 17:55:52 |\\n\",\n \"| 47798 | Issac | 40 | 2024-09-13 05:13:17 |\\n\",\n \"| 34970 | Eleni | 39 | 2024-09-13 00:15:55 |\\n\",\n \"| 24782 | Jose | 40 | 2024-09-08 09:49:06 |\\n\",\n \"+-------+---------+------------+---------------------+\"\n ]\n },\n \"execution_count\": 18,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"%%sql\\n\",\n \"# Show most recent purchases by purchase date:\\n\",\n \"SELECT users.id, users.name, purchases.product_id, purchases.purchased_at\\n\",\n \"FROM {{ users_table.fullname }} AS users\\n\",\n \"JOIN {{ purchases_table.fullname }} AS purchases\\n\",\n \"ON users.id = purchases.user_id\\n\",\n \"ORDER BY purchases.purchased_at DESC\\n\",\n \"LIMIT 10\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 19,\n \"metadata\": {\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\",\n \"height\": 147\n },\n \"id\": \"nSTpNpRHhk7F\",\n \"outputId\": \"a906b74d-9c4a-449e-fef5-08a0c5b8bf08\"\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
Name
products
purchases
users
_PyAirbyte_state
_PyAirbyte_streams
\"\n ],\n \"text/plain\": [\n \"+---------------------+\\n\",\n \"| Name |\\n\",\n \"+---------------------+\\n\",\n \"| products |\\n\",\n \"| purchases |\\n\",\n \"| users |\\n\",\n \"| _PyAirbyte_state |\\n\",\n \"| _PyAirbyte_streams |\\n\",\n \"+---------------------+\"\n ]\n },\n \"execution_count\": 19,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# Show tables for the other streams\\n\",\n \"%sqlcmd tables\"\n ]\n }\n ],\n \"metadata\": {\n \"colab\": {\n \"include_colab_link\": true,\n \"provenance\": []\n },\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.9.6\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 0\n}\n" }, { "path": "pyairbyte_notebooks/PyAirbyte_CoinAPI_Demo.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"colab_type\": \"text\",\n \"id\": \"view-in-github\"\n },\n \"source\": [\n \"\\\"Open\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"En5baAhvYE_y\"\n },\n \"source\": [\n \"In this demo, we use PyAirbyte to extract cryptocurrency data from [CoinAPI.io](https://www.coinapi.io/), followed by a series of transformations and analyses to derive meaningful insights from this data.\\n\",\n \"\\n\",\n \"The only prerequisite is a CoinAPI [API key](https://www.coinapi.io/get-free-api-key?product_id=market-data-api).\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"8awBDcLvRW2g\"\n },\n \"source\": [\n \"### Installing PyAirbyte\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"id\": \"xrhNw5kK5Lvx\"\n },\n \"outputs\": [],\n \"source\": [\n \"# Add virtual environment support in Google Colab\\n\",\n \"!apt-get install -qq python3.10-venv\\n\",\n \"\\n\",\n \"# Install PyAirbyte\\n\",\n \"%pip install --quiet airbyte\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"mYsTAS1wRgO_\"\n },\n \"source\": [\n \"### Load source data from CoinAPI.io to local cache\\n\",\n \"\\n\",\n \"In this section, we establish a connection to CoinAPI.io to access cryptocurrency data via PyAirbyte. The connector is configured with necessary parameters like the API key, environment setting, symbol ID for the specific cryptocurrency index (in this case, `COINBASE_SPOT_INDEX_USD`), and the data period we are interested in. [Check the docs](https://docs.airbyte.com/integrations/sources/coin-api) for more details.\\n\",\n \"\\n\",\n \"After configuring the source connector, we perform a check to ensure that the configuration is correct and the connection to the API is successful. Then, we proceed to read from the source into the internal DuckDB cache. The `read()` retrieves all available streams from the source.\\n\",\n \"\\n\",\n \"Note: The credentials are retrieved securely using the `get_secret()` method. This will automatically locate a matching Google Colab secret or environment variable, ensuring they are not hard-coded into the notebook. Make sure to add your key to the Secrets section on the left.\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"id\": \"9em82J2Q5WzN\"\n },\n \"outputs\": [],\n \"source\": [\n \"import airbyte as ab\\n\",\n \"\\n\",\n \"# Create and configure the source connector:\\n\",\n \"source = ab.get_source(\\n\",\n \" \\\"source-coin-api\\\",\\n\",\n \" config={\\n\",\n \" \\\"api_key\\\": ab.get_secret(\\\"COIN_API_KEY\\\"),\\n\",\n \" \\\"environment\\\": \\\"production\\\",\\n\",\n \" \\\"symbol_id\\\": \\\"COINBASE_SPOT_INDEX_USD\\\",\\n\",\n \" \\\"period\\\": \\\"1DAY\\\",\\n\",\n \" \\\"start_date\\\": \\\"2023-01-01T00:00:00\\\",\\n\",\n \" },\\n\",\n \" streams=\\\"*\\\",\\n\",\n \")\\n\",\n \"\\n\",\n \"# Verify the config and creds by running `check`:\\n\",\n \"source.check()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"id\": \"MxY9a8RlD-PY\"\n },\n \"outputs\": [],\n \"source\": [\n \"# Read data from the source into the default cache:\\n\",\n \"cache = ab.get_default_cache()\\n\",\n \"result = source.read(cache=cache)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"ZKBR6MilSJta\"\n },\n \"source\": [\n \"### Read data from the cache\\n\",\n \"\\n\",\n \"Read from the already-written DuckDB cache into a pandas Dataframe. After the data is in the cache, you can read it without re-configuring or re-creating the source object. You can also select a stream to read from.\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"id\": \"lFlveLjfGYof\"\n },\n \"outputs\": [],\n \"source\": [\n \"# Read from the cache into a pandas Dataframe:\\n\",\n \"ohlcv_df = cache[\\\"ohlcv_historical_data\\\"].to_pandas()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"IFBo4q26SVX_\"\n },\n \"source\": [\n \"### Run data transformations\\n\",\n \"\\n\",\n \"In this section, we're transforming our data for analysis:\\n\",\n \"\\n\",\n \"- Convert `time_period_start` to datetime for easy handling of dates.\\n\",\n \"- Convert numeric columns to numeric types for calculations.\\n\",\n \"- Calculate `daily_movement` to analyze daily price changes in the market.\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"id\": \"vr37o4AgB_o6\"\n },\n \"outputs\": [],\n \"source\": [\n \"import pandas as pd\\n\",\n \"\\n\",\n \"# Convert 'time_period_start' to datetime format and necessary columns to numeric\\n\",\n \"ohlcv_df[\\\"time_period_start\\\"] = pd.to_datetime(ohlcv_df[\\\"time_period_start\\\"])\\n\",\n \"numeric_columns = [\\n\",\n \" \\\"price_open\\\",\\n\",\n \" \\\"price_high\\\",\\n\",\n \" \\\"price_low\\\",\\n\",\n \" \\\"price_close\\\",\\n\",\n \" \\\"volume_traded\\\",\\n\",\n \" \\\"trades_count\\\",\\n\",\n \"]\\n\",\n \"ohlcv_df[numeric_columns] = ohlcv_df[numeric_columns].apply(\\n\",\n \" pd.to_numeric, errors=\\\"coerce\\\"\\n\",\n \")\\n\",\n \"\\n\",\n \"# Calculate daily price movement\\n\",\n \"ohlcv_df[\\\"daily_movement\\\"] = ohlcv_df[\\\"price_close\\\"] - ohlcv_df[\\\"price_open\\\"]\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"p7vX1_w4SYaF\"\n },\n \"source\": [\n \"### Analyze the data\\n\",\n \"\\n\",\n \"Now, we delve into visual analysis:\\n\",\n \"\\n\",\n \"- We plot the `daily_movement` to observe day-to-day price changes.\\n\",\n \"- A dual-axis plot is created to compare 'price_close' with `volume_traded`, helping us see the relation between price and trading volume.\\n\",\n \"- We calculate and plot 7-day and 30-day moving averages alongside the closing price to identify trends and potential crossovers.\\n\",\n \"\\n\",\n \"These visualizations help us understanding market behaviors and trends in the data.\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\",\n \"height\": 607\n },\n \"id\": \"VMRjR836LJxj\",\n \"outputId\": \"3a63df58-fd14-4fd4-8802-7a922d36622a\"\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"import matplotlib.pyplot as plt\\n\",\n \"\\n\",\n \"# Set the 'time_period_start' column as the index for plotting\\n\",\n \"ohlcv_df.set_index(\\\"time_period_start\\\", inplace=True)\\n\",\n \"\\n\",\n \"# Plotting the daily movement\\n\",\n \"plt.figure(figsize=(12, 6)) # Set the figure size\\n\",\n \"plt.plot(ohlcv_df[\\\"daily_movement\\\"], marker=\\\"o\\\", linestyle=\\\"-\\\")\\n\",\n \"plt.title(\\\"Daily Price Movement\\\")\\n\",\n \"plt.xlabel(\\\"Date\\\")\\n\",\n \"plt.ylabel(\\\"Price Movement\\\")\\n\",\n \"plt.grid(True)\\n\",\n \"plt.xticks(rotation=45) # Rotates the date labels for better readability\\n\",\n \"plt.tight_layout() # Adjusts the plot to ensure everything fits without overlapping\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\",\n \"height\": 607\n },\n \"id\": \"yRipqYxSMu3I\",\n \"outputId\": \"e2f770fe-3513-44db-efe3-2a36b5b54751\"\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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ozZ87k22+/5a233uKpp57imWeecQwPv/nmm4vNzrF07ty51PNv27aNCy64gHbt2jFu3DgaN25MSEgIM2bM4LXXXis2nLykfyswqzO8qaRKFVc/l4rw1r9Dt27daNeuHV988QWPP/44X3zxBYZheKzqzGazlbjm/Pz8Eo8v7fMu79/D+rf/9NNPqVevXrHjCldZlaR9+/asWrWK7OxsQkNDSzxm7dq1BAcHFwkbBw0axPPPP8+RI0eIjo7mxx9/5IYbbnBcz53vH29USUHF/63d/ZkgIiLi7xRKiYiIX2vZsiWzZs3i2LFjpVZLtWzZEsMwaN68OW3atHH5GpdeeinvvPMOixYtKrL1u7OaNm3K5s2bi92/adMmx+OWyMhIBg0axKBBg8jJyeHqq6/m+eefZ/To0cTHxxMdHU1+fj79+vVzeR3Tpk0jOzubH3/8sUgVjLPDuEvStGlTR2VMYSV9vp7k7Odi/dtu2bKFFi1aOO4/fPhwkYqvimratCkbNmzAMIwiFS9bt2516Tw33XQT//d//8fatWuZPHkyrVu3pkePHkWu4+zX0qlq1apVpOXQYlWTeYq1Y15CQoJbX6eXXnopixYt4uuvvy7SNmnZuXMnCxYsoF+/fkVCo0GDBvHMM8/w7bffkpiYSFpaWpGWuIp+//ij6vg5iYiIFKaZUiIi4teuueYaDMPgmWeeKfaYVU1w9dVXExgYyDPPPFOsUsQwDI4ePVrmNR555BEiIyO58847OXjwYLHHt23bxvjx40t9/sCBA1m6dCmLFi1y3Hfy5EneeecdmjVrRlJSEkCxdYSEhJCUlIRhGOTm5hIYGMg111zDt99+W2Ll1uHDh8v8PKyqi8L/BqmpqXz44YdlPq8sAwcOZPHixSxdurTIOj7//HO3z+kMZz+Xfv36ERwczBtvvFHk2Ndff92j6xkwYAB79+7lxx9/dNyXlZXFu+++69J5rKqop556itWrVxerknL2a6kkLVu2ZNOmTUW+TtasWcOff/7p0hrLM2DAAGJiYnjhhRfIzc0t9nh5X6d33303CQkJjBo1qliIlpWVxe23345hGDz11FNFHmvfvj2dOnViypQpTJkyhfr169OnTx/H4xX9/vFH1fFzEhERKUyVUiIi4tfOP/98brnlFv73v/+xZcsWLrroIux2OwsWLOD8889n+PDhtGzZkueee47Ro0ezc+dOrrzySqKjo9mxYwdTp07lX//6Fw8//HCp12jZsiWTJ09m0KBBtG/fnltvvZWOHTuSk5PDwoUL+frrr7nttttKff5jjz3GF198wcUXX8yIESOoXbs2H3/8MTt27ODbb791tCn279+fevXq0atXLxITE9m4cSMTJkzgkksucczOefHFF5k3bx5nnHEGd911F0lJSRw7doyVK1cyd+5cjh07Vuo6+vfvT0hICJdddhl33303J06c4N133yUhIYH9+/e79e//yCOP8Omnn3LRRRdx//33ExkZyTvvvEPTpk2LzFzyNGc/l/j4eB5++GHGjBnDpZdeysCBA1m1ahU///wzdevW9dh67r77biZMmMANN9zA/fffT/369fn8888JCwsDKDYvqDTNmzfn7LPP5ocffgAoFko5+7VUkjvuuINx48YxYMAAhg4dyqFDh5g0aRIdOnRwDAz3hJiYGCZOnMgtt9zC6aefzuDBg4mPjyc5OZmffvqJXr16MWHChFKfX6dOHb755hsuueQSTj/9dO68806SkpI4cOAAH330EVu3bmX8+PGcffbZxZ47aNAgnnrqKcLCwhg6dGixf4+KfP/4q+r4OYmIiFgUSomIiN/78MMP6dy5M++//z6jRo0iNjaW7t27F/ml9bHHHqNNmza89tprjqqqxo0b079//yI7ppXm8ssvZ+3atbzyyiv88MMPTJw4kdDQUDp37szYsWPL3GUtMTGRhQsX8uijj/LGG2+QlZVF586dmTZtGpdcconjuLvvvpvPP/+ccePGceLECRo1asSIESN48skni5xr6dKl/Pe//+W7777jrbfeok6dOnTo0IGXXnqpzM+hbdu2fPPNNzz55JM8/PDD1KtXj3vvvZf4+HjuuOOOcv8NSlK/fn3mzZvHfffdx4svvkidOnW45557aNCggWPgdGVw5XN57rnnCAsLY9KkSY5f3mfPnl3k376ioqKi+PXXX7nvvvsYP348UVFR3HrrrZx99tlcc801jnDKGTfddBMLFy50DLwvzNmvpZK0b9+eTz75hKeeeoqRI0eSlJTEp59+yuTJk5k/f747n3apbrzxRho0aMCLL77IK6+8QnZ2Ng0bNqR3797cfvvt5T6/d+/erF27lhdeeIGvv/6a/fv3Exsby9lnn80HH3xQZPfBwgYNGsSTTz5JRkaGY9e9wiry/eOvquPnJCIiYrEZ3p5mKiIiIlJNvP766zz44IPs2bOHhg0b+no5IiIiIlWKQikRERERJ2RmZhYZvJ2VlcVpp51Gfn4+f//9tw9XJiIiIlI1qX1PRERExAlXX301TZo0oWvXrqSmpvLZZ5+xadOmSh/6LiIiIlJdKZQSERERccKAAQN47733+Pzzz8nPzycpKYkvv/yyxNlGIiIiIlI+te+JiIiIiIiIiIjXlb6vsIiIiIiIiIiISCVRKCUiIiIiIiIiIl5X42ZK5eXlsWrVKhITEwkIUCYnIiIiIiIiIia73c7Bgwc57bTTCAqqcZGJ19W4f+FVq1bRs2dPXy9DRERERERERPzU0qVL6dGjh6+XUe3VuFAqMTERML/A6tev7+PViIiIiIiIiIi/2L9/Pz179nRkB1K5alwoZbXs1a9fn0aNGvl4NSIiIiIiIiLibzTuxzv0rywiIiIiIiIiIl6nUEpERERERERERLxOoZSIiIiIiIiIiHidQikREREREREREfE6hVIiIiIiIiIiIuJ1CqVERERERERERMTrFEqJiIiIiIiIiIjXKZQSERERERERERGvUyglIiIiIiIiIiJep1BKRERERERERES8TqGUiIiIiIiIiIh4nUIpERERERERERHxOoVSIiIiIiIiIiLidQqlRERERERERETE6xRKiYiIiIiIiIiI1ymUEhERERERERERr1MoJSIiIiIiIiIiXqdQSkREREREREREvE6hlIiIiIiIiIiIeJ1CKREREfGJxYvhqqtg2zZfr8T3xiwYw/AZwzEMw9dLEREREfEahVIiIiLiE++/D99/D5Mn+3olvpWWncYTvz7Bm8veZMuxLb5ejoiIiIjX+DSUGrNgDD3e7UH0mGgSXkngyi+vZPORzWU+57uN39H9ne7EvRhH5AuRdJ3UlU/XfOqlFYuIiIinZGaabw8f9u06fG35vuUYmBVSO47v8PFqRERERLwnyJcX/23XbwzrMYweDXqQZ8/j8V8fp/9n/dnw7w1EhkSW+Jza4bV5ovcTtKvbjpDAEKb/PZ3bf7idhMgEBrQa4OXPQERERNyVk2O+PXLEt+vwtaV7lzre3358uw9XIiIiIuJdPg2lZt48s8jHH13xEQmvJrBi/wr6NO1T4nPOa3ZekY/vP/N+Pl7zMX8k/6FQSkREpApRKGVatm+Z4/0dKaqUEhERkZrDr2ZKpWanAmY1lDMMw+CX7b+w+ejmUkMsERER8U8KpUyFK6UUSomIiEhN4tNKqcLshp0HZj5Ar8a96JjQscxjU7NSaTiuIdn52QTaAnnrkre4sOWFJR6bnZ1Ndna24+P09HSPrltERETck5trvq3JodS+9H3sSdvj+FgzpURERKQm8ZtQathPw1h3aB1/3PFHucdGh0az+p7VnMg5wS/bf2HkrJG0qNWiWGsfwJgxY3jmmWcqYcUiIiJSEaqUgmV7zda9iOAIMnIzVCklIiIiNYpftO8NnzGc6VumM2/IPBrFNCr3+ABbAK1qt6Jrva48dPZDXJt0LWP+GFPisaNHjyY1NdVx27Bhg6eXLyIiIm6wQqnMTMjI8O1afMVq3bu0zaUAHMs8Rlp2mi+XJCIiIuI1Pg2lDMNg+IzhTN00lV9v/ZXmtZq7dR67YSc7L7vEx0JDQ4mJiXHcoqOjK7JkERER8RArlIKaWy1lDTm/oPkF1I2oC6iFT0RERGoOn4ZSw2YM47O1nzH56slEh0Zz4MQBDpw4QGZupuOYW6feyui5ox0fj1kwhjnb5rD9+HY2Ht7I2IVj+XTtp9zc+WZffAoiIiLippoeStkNuyOU6tGgB83jzD/OqYVPREREagqfzpSauHwiAOd9fF6R+z+84kNu63obAMmpyQTYCrKzk7kn+feMf7MnbQ/hQeG0q9uOz676jEEdB3lp1SIiIuIJVT2U+iP5D/Lt+Zzb7Fy3nr/12FZSslIICwqjY0JHmtdqzrJ9y1QpJSIiIjWGT0Mp42mj3GPm3za/yMfP9X2O5/o+V0krEhEREW+pyqHUz1t+5tIvLiXQFkjyg8nUi6rn8jmseVKn1z+d4MBgR6XU9uPbPbpWEREREX/lF4PORUREpOapqqHU+kPrGfTNIOyGnVx7LrO3zXbrPFYo1bNBTwC174mIiEiNo1BKREREfCI3t+D9qhJKHT55mMu+uIz0nHRCA0MBmLVtllvncoRSDf8JpWoplBIREZGaRaGUiIiI+ERVq5TKzsvmqilXsSNlBy1qteDLa78EYNbWWeTb8106V05+DqsPrAagR8MeQEGl1M6UnRhG+SMORERERKo6hVIiIiLiE1UplDIMg7um3cWfu/8kNjSW6TdM55LWlxATGsPRzKOs3L/SpfP9dfAvsvOzqRVWi5a1WgLQJLYJNmxk5GZw6OShyvg0RERERPyKQikRERHxiaoUSr34x4t8uvZTAm2BfH3d17SPb09wYDD9WvQDYObWmS6dr3Drns1mAyA0KJRGMY0AtfCJiIhIzaBQSkRERLwuP9+8Wfw5lPpu43c8/uvjAPzv4v9xYcsLHY9d1PIiAGZuczGU2ld0npTFMVfquEIpERERqf4USomIiIjXFR5yDv4bSq3cv5Jbpt4CwH097+PfPf5d5PEBrQYAsHjPYo5nHnf6vKcOObdoBz4RERGpSRRKiYiIiNcVbt0DM5Tyt9nee9P2ctkXl5GRm8GAlgMYN2BcsWOaxDahfd322A07v+z4xanzpmens/HwRgB6NOhR5DFHKKVKKREREakBFEqJiIiI151aKZWbC+npvllLSTJyM7jiyyvYl76PpPgkplw7haCAoBKPvajVPy18Ts6VWrF/BQYGTWKbkBiVWOQxq31ve8r2CqxeREREpGpQKCUiIiJeZ1VKBQZCRIT5vr+08NkNO7dOvZUV+1dQN6Iu026YRmxYbKnHFw6lDCfKvUpr3QNVSomIiEjNolBKREREvM4KpUJCoG5d831/CaU+WPUB3278lpDAEKYOmkqLWi3KPL53k96EBYWxN30vGw5vKPf8jlCqQQmh1D+VUsmpyeTZ89xYvYiIiEjVoVBKREREvM6fQ6lfd/wKwKO9HuWcJueUe3x4cDjnNTsPcK6Fr6xKqQbRDQgJDCHfyGdP2h4XVi0iIiJS9SiUEhEREa+zQqngYP8LpdYfXg+UHBqV5qKW/7TwbSs7lDpw4gC703Zjw8bp9U8v9niALYCmsU0BtfCJiIhI9adQSkRERLzOXyul8ux5bDqyCYAO8R2cfp41V+r3Xb9zMudkqcct27sMgKT4JKJDo0s8xmoX3JGiUEpERESqN4VSIiIi4nX+GkptO7aNnPwcIoIjaBrX1OnntanThqaxTcnJz2H+zvmlHldW655Fw85FRESkplAoJSIiIl7nr6GU1bqXFJ9EgM35l0k2m81RLTVr26xSj1u6z4lQ6p9h56qUEhERkepOoZSIiIh4XW6u+dbvQqlDZijlSuuexQqlSht2bhiGa5VSCqVERESqnBdfBJsNHnig4L6sLBg2DOrUgagouOYaOHiw6POSk+GSSyAiAhISYNQoyDtlI9758+H00yE0FFq1go8+Kn79N9+EZs0gLAzOOAOWLi36uDNr8SaFUiIiIuJ1/l4p5U4o1bd5X4ICgthybAvbjm0r9vjWY1tJyUohNDCUTgmdSj2PVSm1/fh2l9cgIiIivrNsGbz9NnTuXPT+Bx+EadPg66/ht99g3z64+uqCx/PzzUAqJwcWLoSPPzYDp6eeKjhmxw7zmPPPh9WrzdDrzjthVqEC7SlTYORIePppWLkSunSBAQPg0CHn1+JtCqVERETE6/w1lFp3aB0AHRJcD6ViQmM4u/HZQMktfMv2mUPOT6t/GsGBwaWex6qUOnDiAJm5mS6vQ0RERLzvxAm46SZ4912oVavg/tRUeP99GDcO+vaFbt3gww/N8GnxYvOY2bNhwwb47DPo2hUuvhiefdaserJeM02aBM2bw9ix0L49DB8O114Lr71WcK1x4+Cuu+D22yEpyXxORAR88IHza/E2hVIiIiLidf4YSuXm5/L30b8B6JjQ0a1zXNSy9LlSjta9BqW37gHUDq9NdIi5M9/OlJ1urUNEREQqJj09nbS0NMctOzu7zOOHDTMrmfr1K3r/ihXm2ILC97drB02awKJF5seLFkGnTpCYWHDMgAGQlgbr1xccc+q5BwwoOEdOjnmtwscEBJgfW8c4sxZvUyglIiIiXmeFUsHBBaHU0aNgt/tuTVuObSHXnkt0SDSNYxq7dQ5rrtQv238hJz+nyGPOzJMCc2i6hp2LiIj4VlJSErGxsY7bmDFjSj32yy/NdrmSDjlwwPwjXFxc0fsTE83HrGMKB1LW49ZjZR2TlgaZmeYf9/LzSz6m8DnKW4u3BfnmsiIiIlKTFa6UqlPHfN9uh5QUqF3bN2uyhpwnxSdhs9ncOkeXel1IjEzk4MmD/Jn8J+c3Px8wq7BW7l8JlB9KAbSo1YK1B9ey47hCKREREV/YsGEDDRs2dHwcGhpa4nG7d8P998OcOeZwcXGNKqVERETE6wqHUiEhEBNjfuzLFr6KDDm3BNgCGNBqAFB0F76/Dv1Fdn42cWFxtKrdqtzzaAc+ERER34qOjiYmJsZxKy2UWrHCHCR++ukQFGTefvsN/vc/8/3ERPN1T0pK0ecdPAj16pnv16tXfAc86+PyjomJgfBws/I8MLDkYwqfo7y1eJtCKREREfG6wqEU+MdcKUco5caQ88IGtPwnlNpWEEot22sOOe/RoIdTVVgKpURERKqGCy6Av/4yd8Szbt27m0PPrfeDg+GXXwqes3kzJCfDWWeZH591lnmOwrvkzZljBk5JSQXHFD6HdYx1jpAQc3B54WPsdvNj65hu3cpfi7epfU9ERES8LjfXfFs4lNq+3ceh1KGKV0oBXNjiQmzYWHtwLfvS99EguoHT86QsjplSat8TERHxa9HR0PGU/VEiI83xBNb9Q4fCyJHmiIKYGLjvPjMEOvNM8/H+/c3w6ZZb4OWXzflOTz5pDk+3CrTuuQcmTIBHHoE77oBff4WvvoKffiq47siRMGSIGYT17Amvvw4nT5q78QHExpa/Fm9TKCUiIiJe52+VUjn5OWw5tgWoeKVUfGQ83Rt0Z9m+ZczeNpvbut7G0n0uhlL/VEptP769QmsRERER33vtNXMnvGuugexsc9e8t94qeDwwEKZPh3vvNQOiyEgzXPrvfwuOad7cDKAefBDGj4dGjeC998xzWQYNgsOH4amnzGCra1eYObPo8PPy1uJtCqVERETE6/wtlPr76N/k2fOICY2hYXTD8p9QjotaXcSyfcuYuXUm17S/xlGF1aNBD6ee3yyuGQCp2akczzxOrfBaFV6TiIiIeMf8+UU/DguDN980b6Vp2hRmzCj7vOedB6tWlX3M8OHmrTTOrMWbNFNKREREvM7fQqnCrXvu7rxXmDVXava22SzbtwwDg8YxjakfXd+p50eGRJIQmQBorpSIiIhUXwqlRERExOusUCo42Hzr81DKAzvvFXZGozOIDY3leNZxJi6fCECPhs5VSVkcw841V0pERESqKYVSIiIi4nV+VynloZ33LEEBQVzY8kIAvtnwDQA9Gzg3T8riGHauSikRERGpphRKiYiIiNf5XSjloZ33Cruo5UVFPnZ2yLmlRVwLQJVSIiIiUn0plBIRERGvy8013/pDKJWdl83WY1sBz1VKAQxoVbAdjg0b3Rp0c+n5qpQSERGR6k6hlIiIiHidP1VKbT66mXwjn7iwOOpHOTeI3BmNYho5Kq/ax7cnJjTGpec7ZkoplBIREZFqSqGUiIiIeF1podTx45CX5921rDu0DvDcznuFDWw9EICzGp3l8nOtSqmdKTuxG3aPrktERETEHwT5egEiIiJS85waStWqBTYbGAYcOwYJCd5bS2XMk7I82edJYkNjuf20211+buOYxgTYAsjKy+LAiQM0iG7g8fWJiIiI+JIqpURERMTrrFAqONh8GxRkBlPg/RY+T++8V1hMaAxP9HnCrUApODCYxjGNAQ07FxERkepJoZSIiIh43amVUuC7uVKOUKoSKqUqSsPORUREpDpTKCUiIiJe5y+hVGZuJtuObQMqp1KqohzDzlUpJSIiItWQQikRERHxOn8JpTYd2YSBQe3w2iRGJnrvwk7SDnwiIiJSnSmUEhEREa/LzTXf+jqUKty65+md9zyhRa0WgEIpERERqZ4USomIiIjX+UulVGXuvOcJjplSat8TERGRakihlIiIiHid34RSlbjznidY7Xu703aTm5/r49WIiIiIeJZCKREREfE6vwul/LRSql5UPcKCwrAbdnan7fb1ckREREQ8SqGUiIiIeJ0VSgUHF9zn7VAqIzfD0Rbnr5VSNpuNZnHNANh+fLtvFyMiIiLiYQqlRERExOv8oVJq4+GNGBjUjahLQmSCdy7qBscOfJorJSIiItWMQikRERHxOn8Ipfy9dc/iCKW0A5+IiIhUMz4NpcYsGEOPd3sQPSaahFcSuPLLK9l8ZHOZz3l3xbv0/rA3tV6qRa2XatHvk34s3bvUSysWERERTygrlEpPh+zsyl+DtfNex4SOlX+xCnDswKdQSkRERKoZn4ZSv+36jWE9hrF46GLm3DKHXHsu/T/rz8mck6U+Z/6u+dzQ8QbmDZnHoqGLaBzbmP6f9mdv2l4vrlxEREQqIvefjeQKh1KxsRAYaL5/9Gjlr6HKVUqpfU9ERESqmSBfXnzmzTOLfPzRFR+R8GoCK/avoE/TPiU+5/OrPy/y8XuXvce3G77llx2/cGuXWyttrSIiIuIZhlFypVRAANSpA4cOmS18DRpU7jocoZSfDjm3tKjVAlCllIiIiFQ/fjVTKjU7FYDa4bWdfk5Gbga59lyXniMiIiK+k59vBlNQNJQC782VOpFzgp0pO4EqUCn1T/veoZOHyqwmFxEREalq/CaUsht2Hpj5AL0a93JptsOjcx+lQXQD+rXoV+Lj2dnZpKWlOW7p6emeWrKIiIi4waqSAggOLvqYt0KpDYc3AJAYmUidiDqVe7EKiguLIy4sDsARpImIiIhUB34TSg37aRjrDq3jy2u/dPo5L/7xIl+u+5Kpg6YSFhRW4jFjxowhNjbWcUtKSvLUkkVERMQNhUMpX1VKWUPO/b11z6Id+ERERKQ68otQaviM4UzfMp15Q+bRKKaRU895deGrvPjHi8y+ZTadEzuXetzo0aNJTU113DZs2OCpZYuIiIgb/KFSqqoMObdYLXzbj2/38UpEREREPMeng84Nw+C+n+9j6qapzB8y3/GCqzwv//kyzy94nlk3z6J7g+5lHhsaGkpoaKjj47S0tAqtWURERCrGCqWCg8FmK/qYQqmSaQc+ERERqY58GkoNmzGMyX9N5ofBPxAdGs2BEwcAiA2NJTw4HIBbp95Kw+iGjOk3BoCX/niJp+Y/xeSrJ9MsrpnjOVEhUUSFRPnmExERERGn5eaab09t3QO175VG7XsiIiJSHfk0lJq4fCIA5318XpH7P7ziQ27rehsAyanJBNgCijwnJz+Ha7++tshznj73af5z3n8qcbUiIiLiCVallK9CqbTsNHan7QaqUKVULYVSIiIiUv34tn3vaaPcY+bfNr/Ixzsf2Fk5ixERERGv8HUoZe28Vz+qPrXCa1XehTyocPueYRjYTu17FBEREamC/GLQuYiIiNQcvg6lqlrrHkCzuGYApOekcyzzmG8XIyIiIuIhCqVERETEqwoPOj+VV0KpKjbkHCA8OJz6UfUBtfCJiIhI9aFQSkRERLzKmUqpzEzIyKic61fFUAoKzZXSDnwiIiJSTSiUEhEREa8qK5SKiiq4v7Kqpapi+x5oBz4RERGpfhRKiYiIiFeVFUrZbJXbwpeSlcLe9L0AJMUnef4ClajwsHMRERGR6kChlIiIiHhVbq75tqRQCio3lLJ23msY3ZC4sDjPX6AS1YuqB8CRzEocuCUiIiLiRQqlRERExKvKqpSCyg2lqmrrHuAI0VKyUny6DhERERFPUSglIiIiXuXTUKqKDjkHhVIiIiJS/SiUEhEREa+yQqng4JIfr8xQat2hdYBCKRERERF/oFBKREREvMovKqXUviciIiLicwqlRERExKt8FUqlZqVy4MQBoOrtvAdFQynDMHy7GBEREREPUCglIiIiXuWrUGp32m4AaoXVIiY0xrMn9wIrlMqz55GRm+HbxYiIiIh4gEIpERER8arcXPOt10OpVDOUahzb2LMn9pKI4AiCAoIAtfCJiIhI9aBQSkRERLzK15VSjWOqZihls9k0V0pERESqFYVSIiIi4lWuhFKeHJ20J20PAI1iGnnupF6mUEpERESqE4VSIiIi4lXlhVJ16phvc3MhPd1z163qlVKgUEpERESqF4VSIiIi4lVWKBUcXPLjERHmDTzbwlfVZ0qBQikRERGpXhRKiYiIiFeVVykFlTNXSpVSIiIiIv5FoZSIiIh4lS9CKcMwqsdMqdA4QKGUiIiIVA8KpURERMSrfBFKHc86TkZuBlC1Q6nYsFhAoZSIiIhUDwqlRERExKtyc8233gylrHlSdSPqEh4c7pmT+oDa90RERKQ6USglIiIiXuWLSqnq0LoHhUKp7BSfrkNERETEExRKiYiIiFf5IpSqDkPOQZVSIiIiUr0olBIRERGv8kkolapQSkRERMTfKJQSERERr7JCqeDg0o/xePteutm+1zi2eoRSqVmpvl2IiIiIiAcolBIRERGv8mWlVLWZKaVKKREREakGFEqJiIiIV2mmlPsKh1KGYfh2MSIiIiIVpFBKREREvMqVUOroUbDbK3Y9wzAcu+9Vl/a9XHsumXmZvl2MiIiISAUplBIRERGvys0135YVStWpY7612yElpWLXO5p5lKy8LAAaRjes2Ml8LDI4kkBbIKAWPhEREan6FEqJiIiIVzlTKRUSAjEx5vuHD1fsetY8qYTIBEKDQit2Mh+z2WyaKyUiIiLVhkIpERER8SpnQinw3Fyp6jJPyqJQSkRERKoLhVIiIiLiVVYoFRxc9nGeCqWqyzwpi0IpERERqS4USomIiIhXeb1S6p/2vUbRjSp2Ij+hUEpERESqC4VSIiIi4lU+a99TpZSIiIiIX1EoJSIiIl7l7VDK0b6nmVIiIiIifkWhlIiIiHiNYUBenvm+tyulGsWofU9ERETEnyiUEhEREa/JzS143xuhlN2wa9C5iIiIiJ9SKCUiIiJeY7XugXdCqcMnD5OTn4MNGw2jG7p/Ij+iUEpERESqC4VSIiIi4jXeDqWsKql6UfUIDgx2/0R+JDY0FlAoJSIiIlWfQikRERHxGiuUstkgMLDsYz0RSlW3eVKgSikRERGpPhRKiYiIiNc4u/MeFIRSKSlFZ1G5YneqGUpVl3lSoFBKREREqg+FUiIiIuI1roRStWqZFVUAx465dz3HkPMYhVIiIiIi/kahlIiIiHiNK6FUUJAZTIH7LXzVvX3PMAzfLkZERESkAhRKiYiIiNdYbXjOhFJQ8blSVihVHSulcu25ZOZl+nYxIiIiIhWgUEpERES8xpVKKfBAKFUNZ0pFhUQRYDNfwqVmpfp4NSIiIiLuUyglIiIiXuPNUMpu2NmbvheoXu17NptNc6VERESkWvBpKDVmwRh6vNuD6DHRJLySwJVfXsnmI5vLfM76Q+u55qtraPZ6M2zP2Hh98eveWayIiIhUmBVKBQc7d3xFQqmDJw6SZ88jwBZAg+gGrp/AjymUEhERkerAp6HUb7t+Y1iPYSweupg5t8wh155L/8/6czLnZKnPycjNoEVcC17s9yL1oup5cbUiIiJSUd6slLLmSdWPqk9QQJDrJ/BjCqVERESkOvDpK7SZN88s8vFHV3xEwqsJrNi/gj5N+5T4nB4Ne9CjYQ8AHpv7WKWvUURERDzHm6HUnrQ9QPWaJ2VRKCUiIiLVgV/92TA12xzWWTu8tsfOmZ2dTXZ2tuPj9PR0j51bREREXOPVSql/hpxXp3lSFoVSIiIiUh34zaBzu2HngZkP0KtxLzomdPTYeceMGUNsbKzjlpSU5LFzi4iIiGtyc8233mzfaxxTDSulQuMAhVIiIiJStflNKDXsp2GsO7SOL6/90qPnHT16NKmpqY7bhg0bPHp+ERERcZ5P2veqYyilSikRERGpBvyifW/4jOFM3zKd32/73eMl9qGhoYSGhjo+TktL8+j5RURExHm+GHReWe17P/0EmZlw7bWVcvoyKZQSERGR6sCnoZRhGNz3831M3TSV+UPm07xWc18uR0RERCqZq6FUXJz59sQJyM+HwEDnr2XNlKqMQed5eXDddWY74oABEB3t8UuUyRFKZad498IiIiIiHuTTUGrYjGFM/msyPwz+gejQaA6cOABAbGgs4cHhANw69VYaRjdkTL8xAOTk57Dh8AbH+3vT9rL6wGqiQqJoVbuVbz4RERERcYoVSgUHO3d8eHjB+1lZEBnp3PPy7fnsS98HVE77XmameQNIT/dhKKVKKREREanCfBpKTVw+EYDzPj6vyP0fXvEht3W9DYDk1GQCbAWjr/al7+O0t09zfPzqold5ddGrnNv0XObfNr+SVywiIiIV4WqlVFhYwfuuhFIHThwg38gn0BZIvah6ri3SCRkZBe8X2uTXaxRKiYiISHXg2/a9p41yjzk1aGoW18yp54mIiIj/cTWUCgoyb3l5BZVJzrDmSTWIbkBggAs9f04qvJasLI+fvlwKpURERKQ68Jvd90RERKT6czWUgoIWPpdCqUqcJ3XqWnxRKRUbFgsolBIREZGqTaGUiIiIeE1urvnWnVDKlYqkPWl7gMqZJwX+VSllGKogFxERkapJoZSIiIh4jTuVUtZcKXfa9xrFNHL+SS7wl1AqJz+HrDwfLEBERETEAxRKiYiIiNd4rX3vn1DKG5VSvmjfiwqJcmwEoxY+ERERqaoUSomIiIjXWKFUcLDzz6lQ+54XZkr5olIqwBZAbKjmSomIiEjVplBKREREvMZr7Xup3quU8kUoBdqBT0RERKo+hVIiIiLiNd5o38uz57H/xH6g8mZKZWQUvO+L9j1QKCUiIiJVn0IpERER8ZqKhFLOViTtS9+H3bATHBBMYlSiawt0kj9VSqVmp/pmASIiIiIVpFBKREREvMYb7XvWPKmGMQ0dw8A9zdeDzkGVUiIiIlL1KZQSERERr8nNNd9WZvueNU+qslr3Tl2LryulFEqJiIj4zsSJ0LkzxMSYt7POgp9/Lng8KwuGDYM6dSAqCq65Bg4eLHqO5GS45BKIiICEBBg1CvLyih4zfz6cfjqEhkKrVvDRR8XX8uab0KyZ+Qe9M86ApUuLPu7MWrxNoZSIiIh4jTdmSu1Oq9wh56euRaGUiIhIzdWoEbz4IqxYAcuXQ9++cMUVsH69+fiDD8K0afD11/Dbb7BvH1x9dcHz8/PNQConBxYuhI8/NgOnp54qOGbHDvOY88+H1avhgQfgzjth1qyCY6ZMgZEj4emnYeVK6NIFBgyAQ4cKjilvLb6gUEpERES8xhszpaz2PW+FUmrfExERqbkuuwwGDoTWraFNG3j+ebMKafFiSE2F99+HcePMsKpbN/jwQzN8WrzYfP7s2bBhA3z2GXTtChdfDM8+a1Y9Wa+bJk2C5s1h7Fho3x6GD4drr4XXXitYx7hxcNddcPvtkJRkPiciAj74wHzcmbX4gkIpERER8RrrxVVwsPPPcXWmlFUppfY9ERER8ab8fPjySzh50mzjW7HCHF3Qr1/BMe3aQZMmsGiR+fGiRdCpEyQW2ptlwABISyuotlq0qOg5rGOsc+TkmNcqfExAgPmxdYwza/GFIN9dWkRERGoar7Tv/TNTqnGsKqVERETEPenp6aSlpTk+Dg0NJTQ0tMRj//rLDKGysswqqalTzWql1avN1zxxcUWPT0yEAwfM9w8cKBpIWY9bj5V1TFqa+Zrk+HEzECvpmE2bCs5R3lp8QZVSIiIi4jXVpX0vI6PgfVVKiVSugycOcujkofIPFBHxoKSkJGJjYx23MWPGlHps27ZmALVkCdx7LwwZYrbkSflUKSUiIiJe404o5Ur7Xk5+DgdOmH/u81allEIpkcqTmZtJ50mdMQyD9f9eT3xkvK+XJCI1xIYNG2jYsKHj49KqpMB8XdOqlfl+t26wbBmMHw+DBpmvfVJSilYoHTwI9eqZ79erV3yXPGtHvMLHnLpL3sGD5m5/4eEQGGjeSjqm8DnKW4svqFJKREREvCY313xbWe17+9L3YWAQEhhC3Yi6ri/QSWrfE/GOtQfXcujkIQ5nHOaZ357x9XJEpAaJjo4mJibGcSsrlDqV3W6+PujWzZyj+csvBY9t3gzJyWa7H5hv//qr6C55c+aYgVNSUsExhc9hHWOdIyTEvFbhY+x282PrGGfW4guqlBIRERGvqez2PWueVKOYRgTYKu9vb6qUEvGO1QdWO96ftHwSw3sOp13ddr5bkIjIKUaPNnfMa9IE0tNh8mSYPx9mzYLYWBg6FEaOhNq1zaDpvvvMEOjMM83n9+9vhk+33AIvv2zOd3rySRg2DKwc7J57YMIEeOQRuOMO+PVX+Oor+OmngnWMHGm2DXbvDj17wuuvmwPXb7/dfNyZtfiCQikRERHxmspu3/PGPKlT1+LrSqns/Gyy8rIICwrzzUJEKpEVSgXYAsg38hk1ZxTTbpjm20WJiBRy6BDceivs328GP507m4HUhReaj7/2mrkT3jXXmK8ZBgyAt94qeH5gIEyfbs6iOussiIw0w6X//rfgmObNzQDqwQfNtsBGjeC998xzWQYNgsOH4amnzGCra1eYObPo8PPy1uILCqVERETEayp7973daQWVUpXJHyqlokKiCLAFYDfspGSlUC/KhwMhRCrJmoNrAPi/Pv/H8wueZ/rf0/ll+y9c0OICH69MRMT0/vtlPx4WBm++ad5K07QpzJhR9nnOOw9WrSr7mOHDzVtF1uJtmiklIiIiXmOFUsHBzj/HnfY9b1ZK+SqUCrAFEBMaA6iFT6qnfHs+aw+uBWBQh0Hc2/1eAB6a/RD59nxfLk1ERDxEoZSIiIh4TWVXSu1J/6d9rxJ33jt1Lb5q3wPNlZLqbdvxbZzMPUl4UDht6rTh6XOfJi4sjjUH1/DJmk98vTwREfEAhVIiIiLiFfn55k4wUHkzpbxVKZWRUfC+ryqlQKGUVG/WPKmOCR0JDAikTkQdnuz9JABP/PoEJ3JO+HB1IiLiCQqlRERExCusKinQTClPUSgl1ZkVSnWt19Vx3/Cew2lRqwX7T+zn1YWv+mZhIiLiMQqlRERExCtycwvedyeUKi/8yc7L5tDJQ0Dltu/l5Zk3x3XVvidSKUoKpUKDQnmp30sAvPzny+xN2+uDlYmIiKcolBIRERGvKFwp5cqg88Lte4ZR+nF7081fTsOCwqgTXseNFTrn1IotVUqJVA5r573CoRTANe2voVfjXmTmZfLkvCd9sDIREfEUhVIiIiLiFVYoFRQEAS68ArEqpQyjaLB1KmueVKOYRthsNjdXWb5TQymfVkqFxgGQmpXqu0WIVIJDJw+xL30fNmx0SuhU5DGbzcbY/mMB+Hj1x6zaX84e6SIi4rcUSomIiIhXWIGSK1VSUBBKQdlVSdY8qcoecl5SpVRZFVyVSZVSUl2tOWBWSbWq3Yro0Ohij5/R6Axu6HgDBgYPzX4Iw1ffhCIiUiEKpURERMQrrFDKlXlS1vFW4VNZw873pO0BKneeVOE1WNVehlF0XpY3OUKp7BTfLECkkljzpLrU61LqMWMuGENoYCjzds5j2t/TvLQyERHxJIVSIiIi4hXuhlI2W9G5UqWx2ve8VSkVG1twn69a+FQpJdXV6oOrAeia2LXUY5rGNeXBMx8EYNScUeTm+ygdFhERtymUEhEREa9wN5QC53bgs9r3GsU0cv0CLsjIMN/GxRXc56th5wqlpLqy2vdOHXJ+qtG9RxMfEc/fR/9m0vJJXliZiIh4kkIpERER8Qqrxc2dUMqZSilH+56XKqWiogrmY6lSSsRzMnMz2XRkE1B+KBUTGsN/z/8vAM/89gzHM49X9vJERMSDFEqJiIiIV3iiUqrM9j1r0LmXZkqFhxeEZaqUEvGc9YfXk2/kUzeiLg2iG5R7/J2n30lSfBJHM48y/OfhGnouIlKFKJQSERERr6jMUCozN5MjGUeAym/fKxxKhYaa7yuUEvEca8h513pdsVm7HJQhKCCItwa+RaAtkMl/TeaFBS9U8gpFRMRTFEqJiIiIV1TmTKm96XsBiAiOoFZYLTdW57ySKqV83b6XlZdFVp6PkjERD3PsvJdY+s57pzq32blMGDgBgCfnPck3G76pjKWJiIiHKZQSERERr7BCKWsOkyvKmylVeOc9ZyorKsKf2veiQ6OxYX6+qVmpvlmEiIcVrpRyxT3d72FEzxEA3Dr1VpbvW+7hlYmIiKcplBIRERGvqMz2PW/tvFd4DYXb93xVKRVgCyA2LBZQC59UD3bDztqDawHXQymAsQPGcnGri8nMy+TyLy53bIAgIiL+SaGUiIiIeEVltu85KqUqecg5+FelFGiulFQvO47vID0nndDAUNrWaevy84MCgvjy2i/pEN+B/Sf2c/kXl3My52QlrFRERDxBoZSIiIh4RUVCqbLa9wzDYMbWGQC0iGvh5uqc50+DzkGhlFQvVutex4SOBAe60esLxITGMO2GadSNqMuqA6u49ftbsRt2D65SREQ8RaGUiIiIeEVurvnW0+173278loW7FxIeFM4dp93h/gKdlJFhvo2I8P2gc4DYULXvSfXh7jypUzWv1ZzvB31PSGAI3238jid/fbLiixMREY9TKCUiIiJeURnte9l52Twy5xEAHun1CA1jGlZghc5R+55I5Vl9cDXg2s57penVpBfvXfYeAGP+GMPHqz+u8DlFRMSzFEqJiIiIV1RG+96EpRPYkbKD+lH1GXX2qIot0Elq3xOpPJ6qlLLc0uUWHj/ncQDumnYXfyT/4ZHzioiIZyiUEhEREa+wQqlgN8bElNS+dyTjCM/+/iwAz/d9nsiQyAqu0DklVUr5sn1PoZRUF0czjjp2y+uc2Nlj532277Nc0/4acu25XDXlKrYf3+6xc4uISMUolBIRERGv8HT73n9/+y+p2al0SezCrV1urfgCnaRKKZHKsebgGgBa1GpBbFisx84bYAvg4ys/plv9bhzJOMKVX15Jvj3fY+cXERH3KZQSERERr/BEKGUFQpuPbGbi8okAjO0/lsCAQA+s0Dl+WymVneK7RYh4gKdb9wqLDInkxxt+JCokir8O/cXag2s9fg0REXGdT0OpMQvG0OPdHkSPiSbhlQSu/PJKNh/ZXO7zvl7/Ne0mtCPsuTA6TezEjC0zvLBaERERqQhPzpR6ZO4j5NnzuLTNpVzQ4gLPLNBJGnQuUjkcoVRi10o5f4PoBpzT5BwAft/1e6VcQ0REXOPTUOq3Xb8xrMcwFg9dzJxb5pBrz6X/Z/05mXOy1Ocs3L2QG769gaGnDWXV3au4su2VXPnllaw7tM6LKxcRERFXeapSat6Oefy4+UcCbYG8cuErnlugk/y1fS81K9V3ixDxACuU6lKv4jvvlaZPkz4ALEheUGnXEBER5/k0lJp580xu63obHRI60KVeFz664iOSU5NZsX9Fqc8Zv2Q8F7W6iFG9RtE+vj3P9n2W0+ufzoSlE7y4chEREXFVbq75tmIzpQwemv0QAPd0v4d2ddt5aHXOy8gw30ZE+Fn7niqlpArLzstm45GNQOW071l6N+0NmJVShmFU2nVERMQ5fjVTKjXb/Atf7fDapR6zaPci+rXoV+S+AS0HsGjPokpdm4iIiFSMJ9r3ko8cZtWBVcSGxvL0uU97bnEuUPueiOdtOLyBPHsetcJq0TimcaVdp0eDHoQGhnI44zB/H/270q5TGQzDYMW+FWTl+fAHjoiIh/lNKGU37Dww8wF6Ne5Fx4SOpR534MQBEiMTi9yXGJXIgRMHSjw+OzubtLQ0xy09Pd2j6xYRERHneKJ9b+eRQwA80fsJ4iPjPbQy15TUvqdKKZGKKTzk3GazVdp1QoNCOaPRGUDVmyv17cZv6f5udx6Z84ivlyIi4jF+E0oN+2kY6w6t48trv/ToeceMGUNsbKzjlpSU5NHzi4iIiHOsUCo42PXnWqFUXk4gzeOac98Z93luYS7y10qpzLxMsvN8mI6JVEBl7rx3qqo6V2r5vuUAzN8537cLERHxIL8IpYbPGM70LdOZN2QejWIalXlsvah6HDx5sMh9B08cpF5UvRKPHz16NKmpqY7bhg0bPLZuERERcV5FKqXS8w+b7+SG82K/FwkLCvPcwlzkb4POY0JjsGFWllijEESqmtUHVwPeCaUKz5WqSnak7ABg45GNCqBFpNrwaShlGAbDZwxn6qap/HrrrzSv1bzc55zV+Cx+2fFLkfvmbJ/DWY3OKvH40NBQYmJiHLfo6GiPrF1ERERcU5FQ6v2/zA1Ngu0xXJd0nQdX5Zr8/IKB7YUrpXzZvhdgCyAmNAZQC59UTYZhsObAGsA7odRZjc4i0BbIrtRdJKcmV/r1PGXHcTOUyrPnseGw/tAuItWDT0OpYTOG8dnaz5h89WSiQ6M5cOIAB04cIDM303HMrVNvZfTc0Y6P7z/jfmZuncnYhWPZdGQT/5n/H5bvW87wnsN98SmIiIiIk9wNpVYfWM33274wn0tMpc6bKU9mwUsUv2nfA82VkqptV+ouUrNTCQ4I9sqOmtGh0ZxW/zQAFuyqOi18249vd7xvtTuKiFR1Qb68+MTlEwE47+Pzitz/4RUfclvX2wBITk0mwFaQnZ3d+GwmXz2ZJ+c9yeO/Pk7r2q35fvD3ZQ5Hl8plGLBlC7RoAUE+/YoSERF/ZlUYuRpKPTz7YQgy06CcLN/+j6ZwKBUW5h+DzsEMpXal7nI6lDqeeZwXFrzABS0u4KJWF1Xo2luPbeXt5W9zbdK1jgHSUrMZhsEbS98gKiSKO067o9zjrYClQ0IHQgLdKKV0Q58mfVi+bzkLkhdwU+ebvHLNikjPTudo5lHHxxUNpbLzshnzxxiuS7qODgkdKrg6EanOrr7a+WO/+8718/v0lZ3xtFHuMfNvm1/svus6XMd1HXxXui9F/fADXHUVPPYYjBnj69WIiIi/cqdSal/6Pn7Z8QsBwQnYMYOt/HwIDKyUJZYrI8N8GxoKAQFVt1Lqg1Uf8OqiV3l10atc0fYKXhvwmlNjFAo7mXOSMX+M4ZWFr5CTn8M7K99h0dBFJMVrU5mabtrf07h/5v2AGVANPX1omcd7c8i5pXfT3oxbPK7KzJWy5klZrBlc7np/1fs889szLNm7hJ9v+rlC5xKR6i02tuB9w4CpU837unc371uxAlJSXAuvCvOLQedStW3caL5dtsy36xAREf/mTii1K2UXAA1r13bc58sAyKqUiogw3/rDoHNwPZRavHex4/0fNv9A+zfb85/5/ykyQqE0hmHw7YZvaf9me55f8Dw5+TnUDq9NWnYal06+lMMnD7vzKUg1kZufy6g5oxwf3/PTPfy287cyn+MIpRK7VuLKijqnyTmAOTS8KnzNWvOkokKiAFhzYA2GUf4f+EuzaM8ix3lERMry4YcFt8REuP562LHDrIr67jvYvh0GD4a6dd07v0IpqbCTJ823u3f7dh0iIuLfrFAqONj55+xOM//n0qRuguO+zPJzk0pTeOc98I9B5+B6KLV071IA3hz4Jn2b9yU7P5tnfnuGpLeS+H7T96X+srvx8Eb6f9afa7++lt1pu2ka25Spg6ayefhmWtRqwY6UHVz91dXaGawGm7R8En8f/Zv4iHiuancVefY8rv7qarYe21rqc3xRKVU3oq6jqu+P5D+8dl13WZVSFzS/gJDAEFKzU9mVusvt81k/A/af2M/xzOMeWaOIVH8ffAAPP1y0Yj0wEEaONB9zh0IpqbDCoVQF/mAjIiLVnDuVUrtT/wml4ho6wix/DKV8XSkVG2rW1jsTSh08cZDk1GRs2Lil8y3MvWUuX1/3NY1jGrMzZSdXTbmKgZMH8vfRvx3PSc9OZ9TsUXSe1Jm52+cSGhjK0+c+zYZhG7iy3ZXUjajLtBumERMawx/Jf/Cv6f+qUBWHVE3HM4/zzG/PAPDs+c/y2dWf0aNBD45lHuPSyZeWGH4czzzuCFe61Ovi1fX2adIHgAXJ/j/s3KqUalOnDR3izRlQ7s6VSslKKfL9vf7w+gqvT0Rqhrw82LSp+P2bNoHd7t45FUpJhVmhVGYmHDvm27WIiIj/ciuU+qdSqlFMI0cQ5E+hVFVs31u2z+y3bx/fnujQaGw2G9cmXcvGYRt5/JzHCQkMYebWmXR8qyOj547mkzWf0HZCW15d9Cp59jwub3s5G4Zt4D/n/YeI4AjHeZPik/jq2q8ItAXyyZpPeOnPlyrjUxU/9vyC5zmaeZSk+CSGnj6UiOAIfhj8A41iGrH56Gau/+Z6cvNzizxn7cG1ADSNber4OvaW3k17A1SJuVJWpVTzuOaOijJ3Q6nl+5YX+Xj9IYVSIuKc22+HoUNh3Dj44w/zNnYs3Hmn+Zg7FEpJhVlDX0EtfCIiUjp3Qqk9aXsAaBzT2BEE+cNMqarcvme17fRs2LPI/ZEhkTx/wfOsu3cdF7e6mFx7Li/++SJDvh/C/hP7aVW7FT/d+BM/DP6BFrValHjuAa0GMP6i8QCM/mU0UzdOdfpzyLPn8caSN2j6elNe+fMVp58n/mHbsW28sfQNAMb2H0tQgLmfUv3o+vw4+EcigiOYu30u98+8v0gVnS9a9yy9m5ih1KoDq0jPTvf69V3hCKVqNadLollR5m4oZf0MsKhSSkSc9eqr8MgjZhDVp495GzcORo2CV9z8X7dCKakwq1IKFEqJiEjpKlIp1Ti2sSMA8qdKKWtNOTnul617gluhVIOeJT7euk5rfrrxJ34c/CMtarUgIjiC5/uaYdXA1gPLPf+wnsMY1mMYADdPvZlV+1eV+5zfd/3O6W+fzoiZI0hOTebD1R+W+xzxL4/98hg5+Tn0b9mfi1pdVOSx0+qfxudXf44NGxOXT2TC0gmOx6xd5HwRSjWObUyzuGbYDbtj8Lc/MgzD0b7niUop62dAp4ROAKw7tK7CaxSRmiEgwAyl9u41d9xLSTHff+QR93dGViglFaZQSkREnJH7T9eOOzOl/L19DwpCN19wNpQyDMPRvndqpVRhNpuNy9pext/D/+bwqMM83vtxQoNCSz3+VK9f9Dr9W/YnIzeDy764jH3p+0o8bl/6Pm7+7mbO/ehc/jr0FzGhMQBsObaFnHwf/oOKS/5M/pNvNnxDgC2AVy98tcRjrmx3JS/2exGAB2Y9wMytMwHfVkoB9GlqzpXy5xa+wxmHOZlrvuBuGtfUMXtrV+oul4eUG4bBkr1LALi9q9lro0opEXFFXh7MnQtffAE2m3nfvn1w4oR751MoJRWmUEpERJzhaqVUbn4uB04cAPynfc9qWT+1Ugp8uy5nQ6ntx7dzLPMYIYEhdErsVO55AwMCi8yNclZQQBBfXfsV7eu2Z2/6Xi7/4nIycgv6/XPyc3h14au0ndCWz/8yK2ju7nY320ZsIyY0hjx7XpFBzOK/7IadkbNHAjD0tKFlfl2NOnsUt3e9Hbth5/qvr2f1gdWOeUa+CqWsFj5/HnZuVUk1jG5IWFAYcWFxNItrBhTM5HLW3vS9HDhxgEBbIDd1vgmAQycPcSTjiEfXLCLV065d0KkTXHEFDBsGhw+b97/0krkrnzsUSkmFKZQSERFnuBpK7Uvfh4FBSGAI8ZHxftW+F/FPTmPtCAhVI5Sy2nZOq3caIYEulKy5ITYslmk3TKNOeB1W7F/BkO+HYDfszNk2hy6TujBqzihO5JzgjIZnsOyuZUy6dBJ1I+o6dhbT8OWqYcq6KSzdu5SokCj+e/5/yzzWZrMx6dJJ9G7Sm/ScdPp+3Jdcey6xobE0jW3qpRUXZVVKLdmzhOw8Hw+HK0XheVIWd1v4lu01KyU7JnQkITLBEW7p+01EnHH//dC9Oxw/XvAHOoCrroJffnHvnAqlpMIUSomISHkMoyCUKhzklMWaJ9UwuiEBtgC/bN+z2fxj2LmroVRZrXue1LJ2S6YOmkpwQDDfbPiGrpO60v+z/mw6son4iHg+uPwDFg5dSLcG3RzPcYRSainye5m5mTz2y2MAPNbrMepF1Sv3OSGBIXw36Dta1GrB8Syz9axLvS7YrB4QL2tduzUJkQlk52c7Wlv9TeF5UpauiV2Bgplczjr1Z4C+30TEFQsWwJNPFv8DY7Nm5mwpdyiUkgorvPtecrLv1iEiIv4rL6/gfWcrpRw778U2BvCL9r1TQykoCKX8oVIqMy+zzFlMS/d5N5QC6N20N+9e9i4Afx36iwBbACN6juDv+/7m9tNuJ8BW9OVohwT9klxVjF8ynuTUZBrFNOLBsx50+nl1I+oy/YbpxIbGAgUBiy/YbDa/nyvlqJSKq3il1Kk/A1SZKCKusNshP7/4/Xv2QHS0e+d0O5Taemwrs7bOIjPXfHVWeGtXqVkKV0rt3evb3YdERMQ/FR4C7mwoZQ05bxxTNJTyp0opKBh27stKKWtAOEBqVmqJx+Tm57Jy/0rAu6EUwJCuQ3jj4jcY3HEwq+5exfiLxzuCtFPpl+Sq4dDJQ7yw4AUAxlwwxuXZY+3j2zP9xulc1e4qhvUcVhlLdJq/z5UqqX3PGna+/tB6pzcFsBt2R/tejwY9AIXAIuKa/v3h9dcLPrbZzAHnTz8NA8vfoLdEQa4+4WjGUQZ9M4hfd/yKzWZjy31baFGrBUN/HEqtsFqMHTDWvZVIlVU4lMrNhYMHoX59361HRET8j1uhVFrBznuAX82U8rdKqcCAQGJCY0jLTiMlK4X4yPhix6w/vJ6svCxiQ2NpVbuV19c4vOdwhvccXu5x1i/JW49tJTsv26Vd/8R7np73NOk56XRv0J0bO93o1jnOaXIO5zQ5x8Mrc51VKfVn8p/k2/MJDHBzX/NKUlL7XtPYpsSGxpKancrGwxsdIVVZNh/ZTHpOOuFB4Y7vM7XviYgrxo6FAQMgKcl83XPjjbBlC9Sta+7G5w6XK6UenPUgQQFBJD+YXOQvIoM6DGLmtpnurUKqrJycgpaMyEjzreZKiYjIqXJzC94PcvJPYlYoVVUqpXwZSkH5c6WsWTI9GvYo1jLnT+pH1ScuLI58I5/NRzf7ejlSgvWH1vPOyncAGNt/rF9/PTmjU0InYkJjSM9JZ83BNb5eThH59nySU835GIUrpWw2m6OFz9k1WzOzujXoRlCA+YO4fXx7bNg4knGEQycPeXDlIlIdNWoEa9bA44/Dgw/CaafBiy/CqlWQkODeOV3+P8jsbbN5qd9Ljr9aWlrXac2ulF3urUKqrMJVUu3amW8VSomIyKkK77zn7DzjqjZTypfte+B8KNWzgXdb91xls9nUwufnRs0Zhd2wc1W7qxxVRlVZYECgo2LL3+ZK7U3fS649l+CAYBpGNyzymKtzpUr6GRARHOEIu/T9JiLOCAqCm2+Gl1+Gt96CO+8s+rrI5fO5+oSTuSdL7Bk/lnlM5dU1kBVKBQVBixawYoVCKRERKa5wKOUsa6aUP7XvWZt7+Fv7HrgQSnl5npQ7OsR34M/df6qlyA/N3jabn7f+THBAMC/1e8nXy/GY3k16M2PLDBYkL+CBMx/w9XIcrNa9JrFNirUVuh1KnfIzoEN8B7Yf3876w+s5v/n5FVuwiFQ7P/7o/LGXX+76+V0OpXo36c0naz7h2b7PAmDDht2w8/KfL3N+M/0Qq2msUCoyEhqbf8hWKCUiIsW4Gkpl52Vz8ORBwD/b9yIK/X3OHwadQ9mh1Mmck46Ap0qEUhq+7Lc+XvMxAHd3u5vWdVr7eDWeY1V8Ldi1AMMwsDlb0lnJth/fDkCLWi2KPVY4lCpvzdl52Y7wqkfDHkUe6xDfgWl/T1OllIiU6Mori35ss8Gp+9xZP35K2pmvPC6377184cu8s/IdLv78YnLyc3hk7iN0fKsjv+/6vVr9tUScY/3FWKGUiIiUxQqlgoOdO35f+j4AwoLCqBtRF/D/9j1/rpRauX8ldsNOo5hG1I/2/91I1L7nvzYd2QRAvxb9fLwSz+reoDthQWEczjjsV7PMHDvvFRpybmlftz1BAUEczzrumMFXmjUH15Brz6VOeJ1i51IILCJlsdsLbrNnQ9eu8PPPkJJi3n7+GU4/HWa6OWLc5VCqY0JH/h7+N+c0Pocr2l7ByZyTXN3+albdvYqWtVu6twqpsqxKqYgIhVIiIlI6VyulCu+8Z/313x/a9/x60HloHFByKOUYct6gR7HH/JH1S/K249vIyvPxP6w4GIbB5iNmYNO2blsfr8azQgJDOLPRmYB/zZVyhFK1iodSoUGhJMUnAbDmQNnDzgu37p1aUdUxoSNghlLGqeUPIiKFPPAAjB9v7sAXE2PeBgyAceNgxAj3zunWVhmxYbE80ecJvrruK2bcNIPn+j5XJf7qJp6n9j0REXGGy6HUKfOkwL/a96raoPOl+6rOPCmAxMhEaofXxm7YHZU54nt70/dyMvckgbbAEtvJqrreTXoDsCB5gY9XUsCaKVVSpRQ4P1fK2nmvpJ8B7eq2I8AWwLHMY462aRGRkmzbBnFxxe+PjYWdO907p8uh1MytM/kj+Q/Hx28ufZOuk7py47c3cjzzuHurkCqrpFBq/37Iy/PdmkRExP+4Gko5dt77Z54UqH2vPI5QKjul2GNVacg5aAc+f2VVSbWo1YKQQBd2LagirLlSVaVSCqBrYlcAVh9cXeZ5yvoZEBYURstaZseLvt9EpCw9esDIkXCwUH598CCMGgU93XyJ4XIoNWrOKNKy0wD46+BfjJw9koGtB7IjZQcjZ490bxVSZRUOpRITzVkhdjvs2+fbdYmIiH/JzTXfutq+VziUUvte2WLDYoHilVKHTh5iZ8pObNjoVr+bD1bmHiuUWndonY9XIhZr1lJ1a92znNnoTAJtgSSnJrMrZZevl0NWXpZjvl5FKqVSs1IdFYeltfBqrpSIOOODD8wilCZNoFUr89akCezdC++/7945Xd59b8fxHY7e5W83fstlbS7jhQteYOX+lQz8fKB7q5Aqq3AoFRAADRuaZXu7d5tfnCIiIlCxmVIWte+VrbT2vWV7zbaddnXbOYKrqkC/JPsfq1KqXZ12Pl5J5YgKiaJbg24s3buUBckLaBrX1KfrsYKxyOBIx4YPp+pSrwtg7tKXmpVa4vf48n3LAWgW14z4yPgSz9MhvgPfb/pelVIiUqZWrWDtWpgzBzb9013fvj3061ewA5+rXK6UCgkMISPX3HJt7va59G/ZH4Da4bUdFVRScxTefQ80V0pERErm7kypxrFq33NWqaHUP7NkTt0G3t852vcUSvmN6l4pBYXmSu3y/Vypwq17pw4nt9QOr+2oKF17cG2JxzjTvqvvNxFxls0G/fubg81HjIALL3Q/kAI3KqXOaXIOI2eNpFfjXizdu5Qp104B4O+jfxf5a6bUDIUrpUChlIiIlMwKpYKDnTu+rJlSvqqUys8v+DwiIgrut9r3/LVSyvELaYOqMU/KYlVK7Ti+g4zcDCKCI8p5hlQ2RyhVp/qGUn2a9mHsorH8nuz7uVLWkPPyhsp3rdeV3Wm7WXNwDb2b9i72uGPIeRk/AwpXJhqGUWoIJiJy8iT89hskJxe8LrK4swOfy6HUhIET+PdP/+abjd8w8ZKJNIxpCMDPW37molYXub4CqdKsUMp6ca5QSkRESuJKpVRWXhaHMw4DRdv3fD1TqvB1q0qllGEYVW7IuSUhMoG6EXU5knGEjYc30q1B1ZmHVR1l5mY62smqc6VUr8a9ANh0ZBOHTh4iITLBZ2vZfnw7UPo8KUvXel2Z9ve0UudKOfMzoG2dtgTaAknJSmFf+j7H73giIoWtWgUDB5odUydPQu3acOSImQckJLgXSrncvtcktgnTb5zOmnvWMPT0oY77X7voNf538f9cX4FUaadWSllzpBRKiYhIYa6EUlaVVHhQOLXDazvu93WlVOHrWkEU+M+gcyuUysjNICff/AffkbKDo5lHCQkMoXNiZx+uzj1qKfIfW45twcAgLiyO+IiS5xJVB3Ui6tAxoSNAkR3H3WU37PyZ/CeZua7/4HK07zkRSkHJw873pu1lb/peAmwBnF7/9FLPERoUSqvarQB9v4lI6R58EC67DI4fN1+XLV4Mu3ZBt27w6qvundPlUAog357Ptxu+5bnfn+O5359j6sap5Nvz3VuBVGmlte8lJ/tmPSIi4p/cCaUaxzYu0kLi65lSVigVGmpu7mHxl0HnMaExjvdTs1KBggqJrvW6EhoU6pN1VYQjlNLwZZ+zhpy3rdO22rd2WXOlJi2fRJ49z+3zGIbBPdPv4ZwPz+HJX590+fmFZ0qVxQql1h1aR25+bpHHrNa9DvEdiAyJLPM8jhY+fb+JSClWr4aHHjJfBwUGmq99GjeGl1+Gxx9375wuh1Jbj22l/ZvtufX7W/lu43d8t/E7bp56Mx3e6sC2Y9vcW4VUWZopJSIiznAllLKGnJ86q7Jw+55heHJ1zilpyDn4T/teUEAQ0SHRQEELn7XzXmnbwPs7q2JFlRu+VxOGnFvu7X4v4UHhzNk+h4dnP+z2eV5f/DrvrnwXgBlbZ7j8fGumVHmVUs3imhETGkN2frbjv5PFlfZdVSaKSHmCgwv+MJeQUFCMEhvrfgbgcig14ucRtKzdkt0P7mbl3StZefdKkh9Ipnmt5oyY6UYDoVRppe2+d/iw71+ci4iI/8j954/3ToVSaf/svFdoyDkUDYN8UZVUWijlL4POofhcqaX7quY8KUvh4cviW1bY0a5OOx+vpPJ1SuzEJ1d9AsD4JeOZtHySy+eY/vd0Hpr9kONja0aVs1KzUjmedRwov1IqwBbgaM89tYVPoZSIeNJpp8Ey8+9dnHsuPPUUfP45PPAAdOzo3jldDqV+2/UbL/d7uciMhzoRdXjxghf5bedv7q1CqqxTK6Vq1y54sb5nj2/WJCIi/set9r0yQilf/OHD3yuloCCUSs1OJc+ex4p9K4AqHEr980vyzpSdnMg54ePV1GyO9r0aUCkFcG3StTx3/nMADJ8xnLnb5zr93LUH13LDtzdgYHDX6Xe5NaPKat2rG1GXqJCoco/vmtgVgDUH1jjusxt2lu9bDjgZSv0TAm84vAHDF+WoIuL3XngB6tc333/+eahVC+691yxKeecd987pcigVGhhKek56sftP5JwgJNCJV5pSrZy6+57NphY+EREpzqX2PatSKrZoKBUcbP5/Bnwz7Ly8Sil/CqVSslJYf2g9mXmZxITG0KZOG98uzE11IuqQGJkIwMbDG328mprLMIyC9r06NSOUAni89+Pc3Plm8o18rvv6OkcwV5aDJw5y2ReXcSLnBH2b9+XNgW/Sp0kfABbsWuD0tZ1t3bM4hp0fXO24b8vRLaRmpxIeFO4IeMvSpk4bggKCSMtOc/xxQETEYhhmy95ZZ5kfJyTAzJmQlgYrVkCXLu6d1+VQ6tI2l/Kvaf9iyZ4lGIaBYRgs3rOYe366h8vbXu7eKqTKOrVSChRKiYhIcVYoFRxc/rGlzZSy2Xy7A5/Vsl5apZS/te9ZbTs9GvQgwObW3jZ+QS18vnfw5EHSstMIsAU4dmirCWw2G+9e9i5nNz6blKwULv3iUo5mHC31+Ky8LK6cciXJqcm0rt2ar6/7muDAYHo3NQen/578u9PXtiqlWtRq4dTxhXfgs6qcrJ8Bp9c/neDA8n/4hgSG0Lp2a0DfbyJSnGFAq1ae/z3f5Vco/7v4f7Ss3ZKz3j+LsOfDCHs+jF4f9KJV7VaMv2i8Z1cnfk+hlIiIOMMT7Xvg2x34rCDMqg62+GP7XkpWimPXrao65NyiHfh8b9ORTYA5ULsq7uJYEWFBYUwdNJVmcc3Yemwr1359LTn5OcWOMwyDO364g8V7FlMrrBbTb5zuGHdi7ea3+sBq0rLTnLquq5VSHRI6EGgL5EjGEfal7wMoEkw7SzvwiUhpAgKgdWs4Wno27955XX1CXFgcPwz+gc3DN/PNdd/wzXXfsHn4ZqYOmkpsWKxnVyd+T6GUiIg4w9lQKiM3g6OZ5qudU9v3wLeVUlVt0LkrA479mYYv+55jnlQNat0rLCEygWk3TCM6JJr5O+fz75/+XWzm0nO/P8cX674gKCCIb67/pkjLbMOYhrSo1QK7YWfh7oVOXXN7ynag/CHnlrCgMNrHtwcKhp27s9GBvt9EpCwvvgijRsG6dZ47p9u13K3rtOaytpdxWdvLalQZrxSlUEpERJzhbChlVUlFBkcSG1r8j11WVZI/hVL+WCm1N30v6w6ZrxirfCil9j2fq4nzpE7VMaEjX177JQG2AN5f9T7jFo1zPPbV+q94av5TALw18C36Nu9b7Pl9mro2V8rVSimALonmQJfVB1aTk5/jCKcUSolIRX3yifk659ZbYelSc35UeLi50VnhmzuCnDlo5KyRTp9w3IBx5R8k1YJhFMzXUCglIiJlyc0135YXSlnzpBrHNsZmTTUvxB/a96pCKDVvxzzyjXwaRDegYUxD3y6qgqxfkpNTk0nPTic6NNrHK6p5rFCqXd12Pl6Jbw1sPZBx/cfxwKwHGDVnFG3qtKFeVD2GfD8EgAfPfJC7ut1V4nN7N+nNR6s/cmqulGEY7EzZCThfKQXmXKnP//qcNQfXsPbgWnLyc6gdXtvpuVRQfAe+kn4Oi0jNc/vtcNFF8Prrnj+3U6HUqgOrnDqZDf3QqkkK/5W68HwNhVIiInIqVyulSponBWrfK48VSu1K3QVU/SopgFrhtagfVZ/9J/az4fAGzmh0hq+XVOM42vfq1txKKcuIM0aw8chG3l7xNjd8ewNRIVFk5WVxSetLeOXCV0p9nlUptXTvUrLysggLCiv12IMnD5KZl4kNG01imzi9tsLDzgu377oSLLWu3ZrggGBO5JwgOTWZpnFNnX6uiFRfVsfykCGeP7dTodS8IfM8f2Wp8qzWPSg5lEpJgRMnICrKq8sSERE/5GwotTut5J33LGrfK5sVSlmq+pBzS4eEDuw/sZ/1h9c7HUptP76dfp/048ZON/Jc3+cqeYXVV3ZetmMnuJrcvmex2Wy8cfEbbD22lV92/MLJ3JN0TOjI5GsmExgQWOrzWtZqSb2oehw4cYCle5c6QqqSWK17jWIaERLoxO4Q/7Da96y1ges/A4IDg2lbty3rDq1j/eH1CqVExKGyCiednimVb89n7cG1ZOYWfxWYmZvJ2oNrsRt2jy5O/JsVSoWFQWCh/wfHxEDsP2NAVC0lIiJQEEoFl7MruaN9rwpWSuXnQ16ed9d0qlNDqepQKQXu7cA3duFYdqTsYNyicZzMOVn+E6RE245vw27YiQ6Jpl5UPV8vxy8EBwbz9XVf06NBD1rVbsW0G6YRExpT5nNsNptjF77y5kpZIaArrXsA8ZHxNIxuiIHBtM3TAPd+BmjHSxEpyQUXwOmnl31zh1OVUgCfrv2UCUsnsOTOJcUeCw4M5o4f7uCBMx/g5s43u7cSqXJKGnJuadwYUlPNUKp9e++uS0RE/I/T7Xvp/7TvlbDzHvh2ppQ1R7G0SikwW/iCnH515XmnhlLdG3T3zUI8zNXhy2nZaXyy9hMAMvMy+WnLT1zf4fpKW191tunIJsBs3dN8oQK1wmux5M4l2A17mRVShfVp2oevN3zN78m/8wRPlHqcVSnlyiwoS9d6XdmbvpdcuznIz51qSQ07F5GSDBhQOV1QTr9sen/V+zx89sMl/tANCgjikV6PMGHpBIVSNUh5odS6daqUEhERk9Pte6n+375XuGUdCiqlwAzLSvr/orcU3rGwbZ22xUKqqsrVHfg+WfMJJ3JOOD7+av1XCqXc5Jgnpda9Ymw2G4E25wIpwFEptXD3QvLseQQFlPyrmKNSyoWd9yxdErvw05afAGga25TEqESXz6EdL0WkJKNGQUKC58/rdPve5iObObPRmaU+3qNBDzYe2eiRRUnVUNLOexYNOxcRkcJcnSlVldr3goIK2th9Pey8cAhVXVr3AJLikwBzEH5qVmqZxxqGwYSlEwAYetpQAH7a8lORkEqcZ+28p1Cq4jomdCQuLI4TOSdYfWB1qcdVJJSyhp2D+z8DrEqpDYc3aDyLiACVN08KXAilTuaeJC07rdTH03PSycjN8MiipGoor1IKFEqJiIjJmVDqRM4JUrJSAP9s3ystlAL/GXYeG1ZQKVWdQqm4sDgaRjcEzF+Uy/LLjl/YfHQz0SHRjBswjla1W5GVl8X0v6d7Y6nVjhVKtavbzscrqfoCAwLp1bgXUPZcqe3HtwOuz5QCz4RSLWu3JCQwhIzcDHal7HLrHCJSvVi771UGp0Op1rVbs3D3wlIf/yP5D1rXbu2RRUnVYIVSp7YxQEEolZzsvfWIiIj/yjXHm5QZSu1JM+dJRYdElzo02B/a90oKpawWPl+HUkEBQY4Wvuqy857F2ZaiN5e9CcCtXW4lJjSG65PMtr0p66dU7gKrIcMwCtr36qpSyhOsXfd+T/69xMfz7HmONmZ3KqVa1m5JVIg59MXdUCooIMgRQqqFT0QAduyA+PjKObfTodSNnW7kyV+fZO3BtcUeW3NgDU/Ne4obO93o0cWJf1OllIiIOMuZSinHznulVEmBf7bvQUFY5uv2PYAX+73Ig2c+WK0qpcC5HcF2pezix80/AjCsxzAAxyypn7f8XGbVvxR3JOMIx7OOY8OmPz57SOEd+IwSSg92p+4m38gnNDCU+tH1XT5/gC2ASZdM4rFej3FOk3PcXqf1/bbu0Dq3zyEi1UfTppXXwuf0oPMHz3yQn7f+TLd3utGvRT/a1THT801HNzF3+1x6Ne7Fg2c+6NLFf9/1O68sfIUV+1aw/8R+pg6aypXtrizzOW8ufZMJyyawM2UnTWKb8ETvJ7i1y60uXVc8w9lQyjAqtwdVRET8n1OhVDnzpEDte864p/s9vl5CpXD8kny49F+SJy2fhN2wc0HzC2gfb27/2zmxM23rtGXz0c1M2zyNmzrf5JX1VgdW616T2CaEB5fwhS8u69agG+FB4RzNPMrGIxsd89Is1jyppnFNCbA5XT9QhCe+xrUDn4h4i9M/6YIDg5l982ye7/s8+9P3887Kd3h7xdvsT9/P832fZ/YtswkODHbp4idzTtIlsQtvDnzTqeMnLpvI6F9G859z/8P6f6/nmfOeYdiMYUzbPM2l64pnlBVKNfpn06SMDDh+3HtrEhER/2SFUsFlvFSw2vecCaX8rVLKat/zh0qp6srRvldKpVRWXhbvrXoPKKiSAnOHNKta6qsNX1XyKqsXte55XkhgiGPzqJLmSu047v6Qc08q7/tNRMRTnK6UAjOYeqTXIzzS6xGPXPzi1hdzceuLnT7+07Wfcne3uxnUcRAALWq1YNneZbz050tc1vYyj6xJnFfW7nvh4VC3Lhw5YlZL1a7t3bWJiIh/caV9r1FMo1KP8eVMKev/e/5eKVVdWRUl+0/s53jmcWqF1yry+Ffrv+JIxhEaxzQu9rrw+g7X8+zvzzJz60xSs1KLDISX0m06sgnQznue1qdpH+btnMfvyb9zd/e7izxmVUq1qNXCF0tzsCqlNh7ZSL49n8CAQJ+uR0SqL5dCKV/Lzs8mLCisyH3hweEs3buU3PzcEiu1srOzyS70Z8v09PRKX2dNUValFJgtfFYo1aWL99YlIiL+x6X2PT+fKVXSBh8KpSpfTGgMjWMaszttN+sPry82L8cacH5P93sICij6ErdDfAfa123PxiMb+XHzj9zS5RaXrp2Zm8moOaPYm7633GPrhtfl1f6vVovgy2rfUyjlWdZcqd93/Y5hGNgKzbmwQilfV0q1qNWCsKAwsvKy2JGyg1a1W/l0PSLiPxYsgLffhm3b4JtvoGFD+PRTaN4cznFjlF2VCqUGtBzAe6ve48p2V3J6/dNZsX8F7618j1x7LkcyjpQ4DHDMmDE888wzPlht9VfW7ntghlKrVmnYuYiIOBdKudK+528zpdS+5x0dEjqYodShoqHU0r1LWbp3KSGBIdx5+p3Fnme18D3z2zN8teErl0OpVxa+4gi9nNEophFPn/e0S9fwR1YoZe3EJp5xZqMzCQoIYk/aHnal7qJZXDPHY472vVq+DaUCAwJpV7cdqw+sZv2h9QqlRASAb7+FW26Bm24yf9e3XvekpsILL8CMGa6f073peT7yf33+j4tbXcyZ759J8LPBXPHlFQzpMgSg1EGAo0ePJjU11XHbsGGDN5dcrTlTKQUKpURExLVKKX9t36sqg86rs9KGL1uB0aAOg0iITCjxudclXQfArK2zSMlKcfqa+9L38dKfLwHwyNmP8Palb5d6e+CMBwB4e8Xb5ObnuvKp+Z3c/Fy2H98OaKaUp0WGRNKtfjeg+Fwpf6mUAg07F5HinnsOJk2Cd98tOie0Vy9YudK9c1apSqnw4HA+uOID3r70bQ6ePEj9qPq8s+IdokOiiY+ML/E5oaGhhFp/vgTS0rQVsKcolBIREWfl/vP7eWmhVFp2GmnZ5v+j/bF9Lz+/IFhTpZTvdEzoCBT9JfnwycNMWTcFKDrg/FQdEjrQIb4D6w+v54dNPzCk6xCnrvl/v/4fGbkZnNXoLF7s92KRVqtT5eTn8MW6L8xdpTdNdQxYr4q2H99Onj2PyOBIGkY39PVyqp0+TfuwZO8Sft/1u6NyLyM3gwMnDgC+r5QChVIiUtzmzdCnT/H7Y2MhJcW9c7pdKZWTn8PmI5vJs+e5ewq3BQcG0yimEYEBgXy5/ksubXOp21umivvKC6WaNDHfKpQSEanZ7HbI++flQmmhlNW6FxcWR1RIVKnn8lX7XuHrqVLKdxy/JBfaEez9Ve+TnZ9N9wbd6dmwZ5nPd3UXvtUHVvPh6g8BGDdgXJmBFJg7q/2r278AXGr380dW616bOm3K/bzFddZcqQXJBZVSO1N2Aub8tFphtUp6mldpBz4ROVW9erB1a/H7//gDWri5P4PLSU5GbgZDfxhKxPMRdHirA8mpyQDcN+M+XvzjRZfOdSLnBKsPrGb1gdWA2UO9+sBqxzlHzx3NrVNvdRz/99G/+WztZ2w5uoWle5cy+JvBrDu0jhcueMHVT0M8QJVSIiLijNxCXUzBxfckAZzbeQ98175X+HoKpXynfXx7AA6ePMjRjKPk2/OZuHwiYFZJlReeWC18s7fN5njm8TKPNQyDh2Y/hIHBoA6DOLPRmU6t8e5udxNoC+T3Xb+z9uBap57jjzYf+WfIuVr3KsU5Tc7Bho3NRzdz8MRBoNA8qbjmfhEEWiHwpiObyLfn+3g1IuIP7roL7r8fliwBmw327YPPP4eHH4Z773XvnC6HUqPnjmbNwTXMv21+kZ3w+rXox5T1U1w61/J9yznt7dM47e3TABg5eySnvX0aT817CjC3/LUCKoB8ez5jF42ly6QuXPjphWTlZbHwjoVFhgOK91hbY5cXSu3ZY/6VXEREaiar7Q1Kr5Ry7LxXxpBz8F37nnW9kBAIKOHVk9r3vCMqJMrxum/94fVM/3s6yanJ1Amvw6AOg8p9fvv49nRK6ESePY/vN31f5rE/bfmJX3f8SmhgKC/2c/4Prw1jGnJ1+6sBeHNp1a2W2nRkE6Cd9ypLrfBajnbUP5L/AArNk/KD1j0w1xEeFE52fjbbjm/z9XJExA889hjceCNccAGcOGG28t15J9x9N9x3n3vndHmm1Pebv2fKtVM4s9GZRRL8Dgkd2HbMtR9W5zU7D+Npo9THP7ryoyIft49vz6q7V7l0Dak85e2+16CBmZ7m5MDhw5CY6L21iYiI/3AqlEp1LZTydkVSWUPOQZVS3tQhvgM7U3ay/tB6vtv0HQB3nn4n4cGl/Mc5xaAOg/jr0F98teErbj/t9hKPyc3PZdScUQDcf8b9Lv8BdFiPYXy94Ws+++szXrrwJeLC4lx6vj+w2vcUSlWePk378Nehv/h91+9ck3RNkUopfxBgC6BTYieW7l3Kyv0raVOnja+XJCI+ZrPBE0/AqFFmG9+JE5CUBFGlT14ol8uVUodPHi5xV5OTOSf9osxUvKe89r3gYKhf33xfLXwiIjWXFUoFBEBgYMnHWDOlymvf81WllFUdXNofYqxKKYVSlc9qKfp247fM3T4XGzbu6X6P08+/roPZwjd3+1yOZhwt8Zh3VrzDpiObqBtRl8d7P+7yGvs07UPHhI5k5Gbw0eqPXH6+P7BCqXZ12/l4JdXXqXOlrEqpFrXcHMxSCXo2MOe0Ld271McrERF/EhJihlE9e1YskAI3QqnuDbrz098/OT62YQZR7618j7ManVWx1UiVUl4oBZorJSIiBaFUaVVSUKh9r4yd96CgIikvr2B4ujc4Wyml9r3KZw1f/mXHLwBc1vYylyqZ2tRpQ9d6Xcmz5zF109Rij6dkpfCf3/4DwDPnPUNsWKzLa7TZbI6dAN9a9hZ2o2rNMTiWeYwjGUcAVB1TiXo3NUOp1QdWk5qVWtC+5yeVUoBj8wCFUiIC5h/fXnkFBg6E7t3h9NOL3tzhcvveCxe8wMWfX8yGwxvIs+cxfsl4NhzewMLdC/nttt/cW4VUSc6GUkuWKJQSEanJrEHnToVSTrbvgfnCqKJ/nXOW2vf8h1UpZbHCH1dcn3Q9qw+s5qv1X3Hn6XcWeeyFBS9wJOMI7eq2c+yk546bO9/Mo3MfZcuxLczZNocBrQa4fS5vs4acN4ppRGRIGS/0pEIaRDegZa2WbDu+jYW7Fxa07/nJTCmAHg17ALBy/0ry7HkEBbj866OIVCNDh8Ls2XDttWaVlCea5VyulDqnyTmsvns1efY8OiV0Yva22SREJrBo6CK6NehW8RVJlZCXV/CXb1VKiYhIWcqrlDIMo2CmlJOVUuDdFr7yQikNOvee9vHtHZX6beq0oV+Lfi6fw2rh+3XHrxw+edhx/47jOxi/ZDwAr174aoV+AY8KieL2rubMqgnLJrh9Hl/QPCnv6dO0DwA/bP6B1OxUAL/axKlNnTbEhMaQmZfJ+kPrfb0cEb80Zgz06AHR0ZCQAFdeCZs3Fz0mKwuGDYM6dcw/qF1zDRw8WPSY5GS45BJzVEBCgjm36dSq8PnzzYqk0FBo1Qo++qj4et58E5o1M18znXEGLD2l0NGZtZRm+nT4/nuYOBH+8x94+umiN3e4HEoBtKzdkncvf5eldy1lw7ANfHb1Z3RK7OTeCqRKsmZrgHOhVHJy6ceIiEj1Vl4olZqdyslcs/y2vJlSAQEF5/GnUEqVUt4TERxBy9otAbNKKsDm+svZVrVbcXr908k38ou08I3+ZTQ5+Tlc0PwCBrYeWOG1/rvHvwH46e+fHFUwVYFVKaVQqvJZc6W+XPclAImRiUQElzK8zgcCbAH0aGBWS6mFT6Rkv/1mhjyLF8OcOWaFeP/+BZ1FAA8+CNOmwddfm8fv2wdXX13weH6+GUjl5MDChfDxx2bg9NRTBcfs2GEec/75sHo1PPCAufPdrFkFx0yZAiNHmgHRypXQpQsMGACHDjm/lrI0bGiGb57k8v/FZ2yZwayts4rdP2vrLH7e8rNHFiX+z/oGs9kK/jpcElVKiYiIFUoFB5f8uFUlVTu8tlO/jPli2LmzlVIKpbzjtQGvcf8Z93PX6Xe5fY7rk64H4Kv1XwGwaPcipqyfgg0bY/uP9cgGPm3qtKF/y/4YGExcPrHC5/OWTUc3AdC2rkKpymZVSllVUv7UumfRXCmRss2cCbfdBh06mCHQRx+ZRRkrVpiPp6bC++/DuHHQty906wYffmiGT4sXm8fMng0bNsBnn0HXrnDxxfDss2bVk/U6atIkaN4cxo6F9u1h+HCzje611wrWMm4c3HUX3H67OYh80iSz8uqDD5xfS1nGjoVHH4Vduzz0j4cbodRjcx8j38gvdr+BwWO/POaRRYn/KzxPqqzXbAqlRESkvEopa+e98uZJWaxgyJsBkAad+5dL21zK6xe9TnhwKf9BnGC18M3bOY+DJw4ycvZIAG7vejtd6nXxyDqhYObV+6veJzPXy9tGukmVUt7TolYL6kfVd3zsT0POLY5Qap9CKRFnpJoZM7Vrm29XrDCrp/oV6jZv1w6aNIFFi8yPFy2CTp0gMbHgmAEDIC0N1q8vOKbfKR3rAwYUnCMnx7xW4WMCAsyPrWOcWUtZunc3X3+1aGFWTNWuXfTmDpcb5bcc20JSfFKx+9vVbcfWY1vdW4VUOc4MOYeCUGrfPrMfNkizEUVEapzyQilryHl5rXsWKwDyp0opte9VPS1qtaB7g+4s37ecW6bewuI9i4kIjuDZvs969DqXtL6EprFN2ZW6iy/Xfcntp93u0fN7Wp49z/Gavl3ddj5eTfVns9no07QPU9ZPAfwzlLLa99YfWs/JnJMafi81Rnp6OmlpaY6PQ0NDCS2rTQiw2822ul69oGNH874DB8zXQHFxRY9NTDQfs44pHEhZj1uPlXVMWpr5OuX4cbMNsKRjNm1yfi1lueEG2LsXXnjBfI5PBp3Hhsay/fj2YvdvPbaVyGD9gKopnA2lEhPNIMpuh/37K39dIiLif8oNpVKd23nP4ov2PWuWogadVy9WC9+c7XMAeLTXozSIbuDRawQGBDpmS01YNgHDMDx6fk/bmbKTXHsu4UHh5W48IJ5hzZUCMyz1Nw1jGtIgugH5Rj6rDqzy9XJEvCYpKYnY2FjHbcyYMeU+Z9gwWLcOvvzSCwv0gYULzVlUjz5qtiwOGVL05g6XQ6kr2l7BAzMfYNuxbY77th7bykOzH+Lytpe7twqpcpwNpQIDodE/f/hWC5+ISM1Ubvte+j/te07+AuzL9r2IUkZeqVKqarq+w/WO9xtEN+Chsx6qlOvccdodhAaGsnL/SpbsXVIp1/AUq3WvdZ3Wbg2RF9f1bloQSvnjTCnQXCmpmTZs2EBqaqrjNnr06DKPHz7c3J1u3ryC34EB6tUzXwulpBQ9/uBB8zHrmFN3wLM+Lu+YmBjztVHduubv3yUdU/gc5a2lLO3aef6Pgi7/n+blC18mMiSSdm+2o/n45jQf35z2b7anTngdXu3/qmdXJ37L+otxeaEUaK6UiEhNl5trvi2vUkrte+JtTeOaOgZNj7lgTKW1JdWNqMsNnW4AYMLSCZVyDU/ZfFTzpLytY0JHmsQ2ISworMQxKf6gZwOFUlLzREdHExMT47iV1rpnGGYgNXUq/PqrOYy8sG7dzM1efvml4L7Nm81h6GedZX581lnw119Fd8mbM8cMnJKSCo4pfA7rGOscISHmtQofY7ebH1vHOLOWsrz4Ijz0EMyfD0ePmq2DhW/ucHnCT2xYLAvvWMic7XNYc2AN4cHhdE7s7PgfutQMzlZKgUIpEZGaztmZUv7cvufs7ntq36t6vrr2KzYe2ch5zc6r1OsM6zGMj1Z/xFfrv2Js/7EkRiWW/yQf0JBz7wuwBTB/yHzSstOoF+VEqYIPqFJKpHTDhsHkyfDDD+bwb2s2U2ys+bohNhaGDoWRI81h4DExcN99Zgh05pnmsf37m+HTLbfAyy+b53jySfPc1muMe+6BCRPgkUfgjjvMAOyrr+CnnwrWMnKk2UbXvTv07Amvv27+7n777QVrKm8tZbnoIvPtBRcUvd8wzPlS+cX3xCuXW2OnbTYb/Vv2p3/L/u48XaoBK5QqrY2hMIVSIiI1W1mhlGEYBTOlqkD7niqlqp/EqESvBETdG3TnjIZnsGTvEt5b+R5P9Hmi0q/pDkelVF2FUt7kr217lm4NugGwI2UHRzKOUDeiro9XJOI/Jk403553XtH7P/zQnLsE8Npr5k5411xj/gFrwAB4662CYwMDzda/e+81A6LISDNc+u9/C45p3twMoB58EMaPN1sE33vPPJdl0CA4fBieesoMtrp2hZkziw4/L28tZZk3z8l/FBc4FUr9b8n/+Fe3fxEWFMb/lvyvzGNHnDHCIwsT/6ZKKRERcZYVSgUHF3/seNZxMvPMxKdhdEOnzqdKKamqhvcczpKpS5i0YhKPnvMoQQH+ty3xpiPmFk2qlJLC4sLiaFunLZuPbmbZ3mVc3PpiXy9JxG84s39FWBi8+aZ5K03TpjBjRtnnOe88WFXOfgPDh5u3iqylNOee6/pzyuPU/wlfW/waN3W6ibCgMF5b/Fqpx9mwKZSqIRRKiYiIs8qqlLKqpOpG1CU8uJTE5xSaKSVV1XVJ1zFy1kj2pO3hx80/cnX7q329pCJSs1I5eNKckKtKKTlVz4Y92Xx0M0v3LlUoJVJD/f572Y/3cWOqk1Oh1I77d5T4vtRcCqVERMRZZYZSLs6TAv9v37PmKoicKjQolLtOv4sX/niB1xe/zlXtrsLmR18sVute/aj6xITG+Hg14m96NuzJp2s/Zek+zZUSqalObVGEoq953Jkp5dLue7n5ubT8X0s2Ht7o+pWkWnEnlDp4UG0NIiI1UVmh1J60PYDzO++Bb9r3rF1ny2vfg4LdBkVKcm+PewkNDGVB8gJ+2vJT+U/wIseQc1VJSQkKDzs3nOlXEpFq5/jxordDh8yZVT16wOzZ7p3TpVAqODCYrDzVpUvBi3NnQqk6dQr+grx3b+WtSURE/JMV0pTVvudKpZQv2/dK2+DDWhOohU/K1iimEQ+c+QAAD89+mNx8/0kxHUPONU9KStAlsQvBAcEcyTjCrtRdvl6OiPhAbGzRW926cOGF8NJL5q6A7nAplAJzO9uX/nyJPHuee1eUasGV3fdsNrXwiYjUZE617zm58x7456Dzwp+bQikpz+hzRhMfEc/mo5t5Z8U7vl6Og0IpKUtoUChd6nUBzGopERFLYiJs3uzec13e8mPZvmX8sv0XZm+bTafETkQGFy2V+W7Qd+6tRKoUV9r3wAyltmxRKCUiUhM5075X1WdKBQSYn19OjlrVpXyxYbE8c94z/HvGv3l6/tPc1Pkm4sLifL0ste9JuXo26MnyfctZuncp13e43tfLEREvW7u26MeGAfv3w4svQteu7p3T5VAqLiyOa5Kuce9qUm24E0qBQikRkZrICqWCg4s/ZlVKuTJTyh933wNzXTk5qpQS59zV7S7eWPoGG49s5IUFL/DyhS/7dD359nz+Pvo3oEopKV3Phj15a/lbqpQSqaG6djU7oU4dK3fmmfDBB+6d0+VQ6sMrPnTvSlKtuBtKJSdXznpERMR/lVYpZRhGQaVUFW/fg4Jh56qUEmcEBQTxav9XuWTyJYxfMp57u99L81rNfbae9YfXk52fTWhgKM3imvlsHeLfrGHnK/avIM+eR1CAy79OikgVtmNH0Y8DAiA+vuhsTVc5PVPKbth56Y+X6PVBL3q824PH5j5GZq4XXw2KX1GllIiIOKu0UOpIxhHHBioNoxs6fT5/bN+DghdkqpQSZ13c6mIubHEhOfk5PPbLYz5dizXbamDrgQQGBPp0LeK/2tZtS3RINBm5GdqRXaQGatq06K1x44oFUuBCpdTzvz/Pf377D/1a9CM8KJzxS8Zz6OQhPrjCzRotqdJc2X0PoEkT861CKRGRmqe0UMqqkkqITCA0KNTp83m7UspuL6h+UiglnmSz2Xi1/6t0ndSVr9Z/xf1n3M/Zjc/2+jrSstP4eM3HAAzvOdzr15eqI8AWQPcG3Zm3cx5L9y6lU2InXy9JRCrZ//7n/LEjRrh+fqdDqU/WfsJbA9/i7u53AzB3+1wumXwJ713+HgE2lzfxkyrOld33QJVSIiI1WWmhlGPnPReGnIP3Z0oVvo7a98TTOid2ZuhpQ3lv1XuMnDWSRUMXYbPZvLqGT9Z8womcE7Sv257zm53v1WtL1dOzYU9HKDX09KG+Xo6IVLLXXnPuOJutkkOp5NRkBrYe6Pi4X4t+2LCxL32fS8NJpXpwt33v+HHzuc4+T0REqr7cXPNtsVAq9Z9QyoV5UuD99j1nQylVSom7nu37LF+s+4Ile5cwZf0UBncc7LVrG4bBm8veBGBYj2FeD8Sk6rHmSi3dp2HnIjXBqXOkPM3pEqc8ex5hQUWbBYMDg8nNz/X4osS/GYbroVRMjHkD2LOnctYlIiL+qbz2vUbRrv1xy9vte9Z1QkIgsIxRO6qUEnfVi6rHY+eYM6Uem/uYY9aaN/y641c2HdlEVEgUt3S5xWvXlarLCqX+OvgXGbkZXr9+bn4ubyx5gyHfD2H9ofVev76ImAyj+C587nC6UsowDG774TZCAwtmPmTlZXHPT/cQGVyQTHw36LuKr0r8Wna2OV8DXKt4ql0b0tIgJaVSliUiIn6q1FAq/Z9QysWKa1+175VVJQWqlJKKGXnWSCYtn8Su1F2MXzyeR8951CvXnbBsAgBDugwhJjTGK9eUqq1hdEPqR9Vn/4n9rD6w2qtz0ObtmMd9P9/H+sNmGPX52s8ZccYInj73aWLDYr22DpGa7JNP4JVXYMsW8+M2bWDUKLjFzb9rOF0pNaTrEBIiE4gNi3Xcbu58Mw2iGxS5T6o/q0oKXAulYv/58khN9ex6RETEv1mhVHBw0fv3p+8HoEF0A5fO56tKKYVSUpkigiMYc8EYAF744wUOnTxU6ddMTk3mx80/AmbrnogzbDYbPRr2AGDpXu+08O1J28PgbwbT95O+rD+8nroRdRnQcgD5Rj6vLX6NthPa8smaT7Abdq+sR6SmGjcO7r0XBg6Er74ybxddBPfc4/zsqVM5XSn14RUfuncFqXasnfdCQiDI6a8ghVIiIjVVaZVS+9L3AVA/ur5L5/PVTKnyQim170lF3dT5JsYvGc+K/Sv4z/z/8NYlb1Xq9SYtn4TdsNO3eV/ax7ev1GtJ9dKzQU9+3PxjpYdS2XnZvL74dZ79/VlO5p4kwBbAvd3v5b/n/5fa4bWZtXUWI2aO4O+jfzPk+yG8veJt3hz4Jl3rda3UdYnUVG+8ARMnwq23Ftx3+eXQoQP85z/w4IOun1Pb5onLXJ0nZVEoJSJSM5UWSu0/4V6lVOGKJE/MMiiPKqXEWwJsAYztPxaAd1a8w4bDGyrtWll5Wby78l0AhvcYXmnXkerJMey8EkOpWVtn0XlSZx775TFO5p6kV+NerPjXCiYMnEDt8NoADGg1gL/u/YuX+r1EZHAkC3cvpNs73Rj20zCOZR6rtLWJ1FT798PZJXTsnn22+Zg7FEqJy6xQKiLCtecplBIRqZlKCqUyczNJyUoBoH6Ue5VS4J0ASJVS4k3nNjuXK9tdSb6Rz6g5oyrtOl+v/5ojGUdoHNOYy9peVmnXkeqpe4PuAGw7vo2jGUc9eu6dKTu5aspVXPT5Rfx99G8SIxP55MpPWHD7ghIroEICQ3ik1yNsGr6JwR0HYzfsvLX8Ldq80YZ3V7xLSlYKqVmpZd5y8nM8+jlUd4Y3/iIkfqlVK7Nl71RTpkDr1u6d04XmKxGTKqVERMQVJYVSVpVUaGAocWFxLp3v1FCqvLCooqy2dVVKibe83O9lpv89nRlbZvDdxu+4uv3VHr+GNeD8nu73EBSgXwnENbXCa9GmThv+Pvo3y/ctZ0CrAR457++7fufizy8mIzeDQFsg959xP0+f97RTQ/gbxTTii2u+4O5udzN8xnDWH17Pv6b/i39N/1e5z40IjuDPO/6stm1/ry16jcd+eYzvrv+OS9pcUqFzvbPiHUb8PILvBn3HwNYDPbRCqSqeeQYGDYLff4devcz7/vwTfvml5LDKGaqUEpe5G0rF/PP/EoVSIiI1S26u+bZIKFVoyLnNZnPpfEFBEPDPKxhvDDu3rlFehbBCKfGU1nVaM/LMkQDcMvUWVh9Y7dHzL9u7jKV7lxISGMKdp9/p0XNLzdGjgWeHnW89tpWrplxFRm4G5zQ5hzX3rGHsgLEu7wp5XrPzWHX3Kl4f8Dq1wmo59ZyM3AxeXfiqO8v2e3vS9vDEr0+Qk5/D8J+Hk5Xn/v+kDp08xMOzHyY735z1JTXHunXm22uugSVLoG5d+P5781a3LixdCldd5d659WcRcZkqpURExBVlVUq5OuQcwGYzq5ZOnvRuKKX2PfGm5/o+x8oDK5m7fS6XfXEZS+9c6tb3S0neXPYmANd3uJ6EyASPnFNqnp4Ne/L5X5+zdF/FQ6mUrBQu++IyjmUeo0eDHsy+eTbhwe6XwQYHBnP/mfdz3xn3kZufW+axqw+s5sz3z+Sr9V8xtv9YEqMS3b6uP3ri1yfIzDP/R7YzZSdvLHmDUb3caw1+et7TpOekA/Drjl85fPIw8ZHxHlur+K/OnaFHD7jzThg8GD77zHPnVqWUuEyhVM02YwYMGAC7d/t6JSJSVVihVHBwwX2OnfdcnCdl8eYOfBp0Lr4QHBjM19d9Tds6bdmTtocrp1xJZm7FU9gjGUf4ct2XgAacS8UUHnZekRlDefY8rv/6ejYd2USjmEb8MPiHCgVShQXYAggNCi3zdkajMziz0Znk2nMdw/+rixX7VvDJmk8AGNFzBADPLXiOwycPu3yu9YfW887KdwBIiEwg38hn6qapnlus+LXffjN32HvoIahfH267DRYs8My5FUqJy6zZGgqlaqY33oDZs+Gjj3y9EhGpKkqslEp3b+c9ixUQ+VOllEIp8bS4sDim3zid2uG1Wbp3Kbf/cHuFBwy/v/J9svOz6d6guyNUEHFH13pdCQoI4tDJQySnJrt9nvt/vp852+cQGRzJtBumeawi0BXDegwDYNLySeTZ87x+/cpgGAYPzX4IgJs738xrF73GafVOIy07jWd+e8bl842aMwq7Yefq9lc72ou/Wu/mECGpcnr3hg8+MHfYe+MN2LEDzj0X2rSBl16CAwfcP7dCKXGZdt/zL4t2L2JP2h6vXW/PP5datMhrlxSRKq7M9j03K6WsAMifQim170llaFW7Fd9d/x3BAcFMWT/FrV8mLfn2fN5a/hZg/hLu6jw3kcLCgsLoktgFgGX7lrl1jglLJ/DW8rewYePzqz/32aDx65KuIz4inr3pe/lh0w8+WYOn/bD5B37b9RthQWG80PcFAmwBjO0/FjDDt42HNzp9rtnbZvPz1p8JDgjmpX4vcV2H6wCYt3Meh04eqpT1i3+KjITbbzcrp/7+G667Dt58E5o0gcsvd++cCqXEZWrf8x+rD6ym1we9GPTNIK9dc5/ZccPixWC3e+2yIlKFlRRKOdr33PyLuNr3pCY5t9m5TLp0EgDP/PYMX/z1hVvnmf73dJJTk6kTXodBHbz32kGqr8ItfK6auXUm98+8H4AX+73IFe2u8OjaXBEaFMq/upm79Fk7U1ZlOfk5PDLnEQAeOushGsc2BuD/2bvv+KbK7w/gn6SbbkYnlFVWmWXKBkVABGUJCC5AkaX+ZKj4Rdyi4gAVQRTFgQoioOylbJBZVpml0JYuKN27yf39cXh6051xs9rzfr36SmiTm6dtWno/Oec8/Rv3xyMtHoFG0uCV3a/odSyNVlNccTWz60yE1g5FE98m6BzUGVpJi/UX15vnk2A2LzQUeP11YP58wNMT2LLFuONwKMUMxqGU7TgYcxASJBy7dQwFmgKzP15uLnD3Ll1PTaV0nDHGqlJZpVR1at/jSilmTpPCJ2FO9zkAgIl/TcTRuKMGH0MMOH+247OKzexhNZuxO/BF3o7E2HVjoZW0mNhhIub2MG7wtpKe7/Q81Co19t7YiwvJF6y9HJMsO74MV+9ehb+7P17t+WqJj3084GM4qh2x+cpm7Lm+p8pjfX/6e5xPPg9fV1/M7zO/+P1jwsYA4Ba+mmr/fporFRAAzJ0LjBwJHDpk3LE4lGIGUyKUMnEcArvnTOIZADQg8mrKVbM/XkJCyX8fNfzvYcZYDaPRyL/zy5spVZ3a97hSipnbhwM+xCMtHkG+Jh/Dfx9u0ByfS3cuYdf1XVBBhamdp5pxlawmEZVSJ+JPQKPV6HWf29m3MfTXocjIz0Cfhn2wfOhym2glbeDdAMNbDgcgB7j26G7u3eI233f7vwtPF88SH29RtwWmdZ4GAJi9c3al37fM/EzM/5eCqDf7vonabrWLPyZa+Pbd3IfELBMGCjG7ER8PfPABzZHq1w+4dg344gt6/7ffAvfdZ9xxOZRiBjM1lCos5D/YlRKRFFF8/cJt87+ic+tWyX/zXCnGWFUKdIo4RSiVX5SPlNwUAKa371kilBIbfFQ1S5FDKWZuDmoHrB65Gu392yMpOwnDfhuGzPxMve779XGaJTWsxTA08mlkxlWymqRl3ZbwcPZAdmE2Lt6pekZRflE+Rq4diei0aDTxbYI/x/wJZwfnKu9nKWLg+U9nfkJ6nn22d7y3/z2k5qWijV8bTAqfVO5t3uz7JnxcfXAm6Qx+PPNjhcf66NBHSM5ORmjtUEzrMq3Exxr5NELX4K7cwldDPPQQ0LAhDTkfMQK4eBE4eJDmSxmaC5TGoRQzmLG773l4AOJFEG7hM12Rtgjnks4V/9sSZcYilFLf+83BoRRjrCrlhVLiFVUntRPquNUx6ri2OFOK2/eYJXg4e+Dvx/+Gv7s/ziadxYT1E6qsUMnMzyw+8RQn3YwpwUHtgM5BnQEAx29VPuxckiQ8v/l5HIw5CG8Xb2x+fDPq1qpriWXqrX+j/mhVtxWyC7Px05mfrL0cg127ew1fHaOZWJ8O/BQOaodyb1enVh3M700VUPP/mY+sgqwyt4lNj8WnR2gw+qIHF5UbHorZdNzCV/05OQHr1tGmVx99BLRoodyxHZU7FKspjN19T60GvLwokEpPp/5TZrzLdy4jXyOf+ViiUkoMOe/TB9i7Fzh/HsjIoO8rY4yVRzeUcnKiy+Kd9zwDjW7Z4PY9VpOFeIfgr3F/oe+qvth0ZRO6fdcNPq4+Fd7+bu5dZORnoHmd5hjQZIDlFspqhK5BXbH3xl68s/8drD63usLbZRdm42jcUTioHLD2sbVoVa+VBVepH5VKhZldZ2LG1hlYenwpZnadaROthfp6dferKNQWYnDoYAxsOrDS287sOhNfn/ga11OvY9GhRXi7f8mdPV//53XkFeWhT8M+eLRF+UPoR4eNxuyds7H/5n4kZCYYXf3MbN/ff5vv2BxKMYMZ274HUAufCKWYaSISIwBQpUGhttCi7XudOwPXrwMxMcDx48ADD5j9oRljdkqEUo6OcrVs8c57Rs6TAnjQOWPd6nfDj8N/xLg/x+Fkwkm97vNi1xehVnGjBFNW/8b98fHhj3Ej7QZupN2o8vZLBi+pMjCxpifbPYnXdr+GyymXsSd6j90Euftv7sf6i+uhVqnxyYOfVHl7F0cXfDTgIzz2x2NYdHgRpnSagmCvYABU9fbL2V8AAJ8N/KzCYC7EOwTd63fHkbgjWBe5Di90e0G5T4jVGFYNpfbf3I9FhxfhZPxJJGQlYMPYDcXD5Sqy+uxqfHz4Y1xNuQpvV288FPoQFj24CHVqGVf+zwxnaigFcCilBBFKPdz8YWy8tBFXU64ivygfLo4uZntMEUoFBQHdu1ModeQIh1KMsYqVu/Nepmk77wG22b7HlVLM0sa2GYtW9Vrp1cLv5eKFh5o9ZIFVsZpmUNNB2P3kbiRnJ1d529DaoegS3MUCqzKep4snnm7/NL46/hW+OvaVXYRSWkmL2TtnAwCe6/gcWvu11ut+o1qNQs8GPXEo9hD+98//sGr4KkiSVHysJ9s9iU5BnSo9xpjWY3Ak7gjWRq7lUIoZxaqhVHZBNtr7t8ekDpMwcu3IKm9/KOYQntr4FD4f9DmGNR+GW5m3MHXzVDy36TmsH8vD1SyFQynbIIacDwkdgn+j/0V6fjoup1xGO/92ZntM0b4XHEyh1Jo1PFeKMVa5ckOpLNN23gNss1KKQylmDe3825n1/37GqqJSqfBAk+r1CuWMrjPw1fGvsOnKJtxMu4mGPg2tvaRK/XruV5yIPwFPZ0+83e/tqu9wj0qlwmeDPkO377rhpzM/4cVuL+JG2g0ciDkAN0c3vH//+1UeY3TYaLy842UcjDmIWxm3iqutGNOXVet3H2r2EN67/z2MaDVCr9sfiTuCRj6N8GK3F9HYtzF6hfTC852ex7Fbx8y8UqaLQynrkyQJZxLPAADCA8OLXw0x97BzUSklQikAOHpU3u6dMcZKq6xSypTZE7Y4U4rb9xhjrHpoWbclHmj8ALSSFstPLLf2ciqVU5iDeXvmAQDm9ZoHfw9/g+7fNbgrxrcdDwkSXt7xMl7Z9QoAYHb32Wjg3aDK+9f3qo+eDXoCANZFrjNw9YzZ2e573et3R2x6LLZe3QpJkpCUlYR1F9dhSLMh1l5ajWLs7nuAHEplZCi3npooISsBt3Nuw0HlgNb1WqN1vXuhlBnnSkmSXCkVFAR06EAnYHfvAlevmu1hGWN2rrCQLnVDqfgs+mXC7XuMMcZs1cyuMwEA3576FnlFtvuL/fMjnyMuIw4h3iH4v/v+z6hjLHxgIVwdXbH/5n5EpUYhwCMAr/Z6Ve/7j2k9BgCwNpJ34WOGs6tQqmdIT6weuRpj142F83vOCPg0AN4u3lg6ZGmF98nPz0dGRkbxW2ZmpgVXXD1xpZT1iXlSLeu2hJuTm0VCqdRU+UQrKIhOMDvdazHnFj7GWEUqrZSyk/Y98WKMvqFUYSGg1Zp3TYwxxsxraPOhaODVACm5KVh7wTbDlispV/DhoQ8BULDk5lTFf1QVCPEOwcv3vVz873f7vwsPZw+97z86bDRUUOFw7GHEpscatQZWc9lVKBV5OxIvbX8JC/oswMkpJ7F9wnbcSLuBqZunVnifhQsXwtvbu/gtLCzMgiuufrRa+QSgVi3D78+hlDJEKNU+oD0AWKR9T7Tu1akjt6iIFj4OpRhjFRGhlJOT/L7imVJ21r5X1f97Ljr7THALH2OM2TdHtSOmdZ4GAPjq2FdWXk1Zd3PvYuivQ5FVkIXeIb0xrs04k473Wq/XEB4QjoFNB2Jih4kG3TfIMwi9G/YGwC18zHB2FUotPLgQPUN6Ym7PuWjn3w6DQgfh64e/xvcR3xe/6lravHnzkJ6eXvwWGRlp4VVXL+LVYoArpaxJhFId/DsAQHGlVFRqlNnKi3WHnAscSjHGqlK6UqpQU1i8Q5M9tO9ptXLApG+lFMAtfIwxVh082/FZODs443j8cZuaY1yoKcRjfzyGq3evIsQ7BH889gfUKtNO7b1cvHDq+VPY8cQOOKgdDL7/mDBu4WPGsatQKqcwp8wPm4OKfmAklD9p2cXFBV5eXsVvnp6eZl9ndSZa94Cq/zgvj5cXXXIoZZriUCqgAwAgwCMAvq6+0EpaXLpzySyPqTvkXBCh1PnzAHfGMsbKUzqUSspOAkCvQNetVdfo41qqfU83XKrq/z1HR0CloutcKcUYY/avnns9jG09FgCw9HjFI2MsSZIkzNw6E/9E/wMPZw9sfnyzwcPNzWFU2CiooMLRuKO4mXbT2sthdsSqoVRWQRYiEiOKT7CjU6MRkRiBmPQYAMC83fPw1Ianim8/rPkwrL+4HsuOL8P11Os4FHMIL25/EV2Du5r0aivTnwilatUC1EY8e7hSynRZBVm4dvcaALl9T6VSmb2FT3fIuRAUBISEUCXB8eNmeVjGmJ0rHUqJymZ/d3+TXtW1VPue7vGrCqVUKh52zhhj1Y0YeP77+d9xO/u2lVcDLD66GCtOrYAKKvw26je09W9r7SUBoBfJ+zbqC4Bb+JhhrBpKnYg/gfBvwhH+TTgAYNbOWQj/JhwL/l0AgGZOiIAKAJ7p8Aw+G/gZvjr+Fdp83QaP/fEYWtRpgfVj1ltl/TWRKUPOAQ6llHAu6RwkSAjyDIKfu1/x+8097Ly8SikAuO8+uuQWPsZYeUqHUvGZpu+8B1iuUkoc38kJcNCjm4FDKcYYq166BndFl6AuKNAUYMXJFVZdy5YrWzB752wAwCcDP8HQ5kOtup7SRAvfmgtrrLwSZk8crfng/Rr1g/Rm+W13ALBq+Koy73uh2wt4odsLZlwVq4yYKcWhlPWUbt0TLBVKBZU6j+zeHVi7lkMpxlj5CgvpsrhSSoEh54DlZkqJUErflnUx7Jzb9xhjrPqY0WUGnvnrGSzYuwDxmfF49/53UduttkXXcC7pHMb9OQ4SJDzX8bkSu+XZipGtRmLmtpk4Hn8c0anRaOzb2NpLYnbArmZKMevTbd8zBodSpiveec+/fYn3W6p9r3SllJgrdfQoIFWcMTPGaqiK2vcCPZQJpSxVKaVvKMWVUowxVv2MbzseT7V/ClpJi69PfI3mXzbHtye/hUarscjjJ2UlYdhvw5BVkIX+jfpj6ZClUIkhhjbE38Mf/Rr1AwD8EfmHdRfD7AaHUswg3L5nfRFJEQAqrpS6nnodOYU5UFpF7Xvh4VQZkJICXLum+MMyxuycudr3LDVTSlQIc6UUY4zVXE4OTvhx+I/49+l/0bpea6TkpmDK5im4b+V9+C/uP7M+dl5RHkasGYGb6TfRrHYzrBuzDk4OTmZ9TFMU78J3gXfhY/rhUIoZRKlQKj+f/2A3hkarwbmkcwDKhlJ+7n6o41YHEiTFd+ArLASSaMOsMu17zs5Ap050nVv4GGOliVDK6d7fz8XtewpVSlmqfU/fCmGulGKMseqrX6N+OP38aSwetBheLl44EX8C9628D5P/mozk7GTFH0+SJEz+ezKOxB2Bj6sPNo/fbPG2QUONbDUSDioHnEw4iai7UdZeDrMDHEoxg5gaSnl6yte5WspwV+9eRW5RLtyd3NHUt2mJj5lzB76kJGrNc3IC6tUr+3Eeds4Yq0iZ9j2FZ0px+x5jjDFLcnJwwkv3vYQrM6/g6fZPAwC+j/geLb5qga+OfYUibZFij/X+gffx67lf4ah2xLrH1qF5neaKHdtc6rnXw/2N7wfALXxMP1YddM7sj6mhlIMDBVOZmRRK+flVfR8mE/Ok2vm3g4O67DZQreu1xv6b+xUfdi5a9wIDAXU5UbaYK8WhFGOsNHO372k0VM3pZKZOBh50zhhjrDz+Hv5YNXwVpnSagplbZ+J04mm8sO0FLD66GMFewVUfoApaSYuDMQcBAEuHLMUDTR4w+ZiWMqb1GOy6vgtrL6zFa71es/ZymI3jUIoZxNTd9wBq4ROhFDNMRTvvCebagU8MOS/duieIUOrcOSArC/DwUPThGWN2TDeU0mg1xe0NSrXvAVSVZCuhFFdKMcZYzdKjQQ8cf+44vj31LV7f8zqiUqMQlapc29r/dfs/TOk0RbHjWcKIliMwdfNUnE48jWt3ryG0dqi1l8RsGIdSzCCm7r4HUCgVF8ehlDEq2nlPMFf7XkVDzoXgYKBBAyA2Fjh+HOjfX9GHZ4zZMd1QKjk7GVpJC7VKDT9300plRfgDUHCk2x6uJK6UYowxVhUHtQOmdp6KMa3HYN+NfYq18Pl7+KN3SG9FjmVJdWrVwdIhSxEeGF5m5AhjpXEoxQxiavsewDvwmULfSqnotGhkFWTBw1mZkiURSlVUKQXQXKnYWGrh41CKMSYUFtKls7Pcuufv7l9uC7IhVCoKgPLzzTtXiiulGGOM6au2W22MaDXC2suwCc93ft7aS2B2ggedM4NwKGU9iVmJSMpOglqlRlv/tuXepp57veLqg4u3Lyr22KJ9r6JKKYDnSjHGyqdbKaXUkHPBEsPOOZRijDHGGDMfDqWYQTiUsp4ziWcAAM3rNEctp4r7J80xV6qq9j1ADqWOHqWd+hhjDCgVSmXeC6VMnCcliKDInAGQmKXI7XuMMcYYY8rjUIoZhEMp66mqdU8oDqUUnCtV1aBzAAgPp5POO3eAKOVmOzLG7JwIpZyclNt5TxBVSZaolNJ3liJXSjHGGGOM6Y9DKWYQDqWsJyIpAkDFQ86F4mHnFq6UcnEBOnWi69zCxxgTym3fU7hSyhbb97hSijHGGGOsahxKMYOINgYOpSzP4EophUKprCwgI4OuV1YpBdCwc4BDKcaYzBIzpcxZlWTs7ntcKcUYY4wxVjUOpZhBRKWUvm0M5eFQynDZBdm4fOcyAD1CqXuVUjHpMcjMzzT5sUXrnqdn1Vuu87BzxlhpuqGUaN+rCZVSHEoxxhhjjFWNQylmEG7fs47zyechQYK/uz8CPAIqvW1tt9rFt4m8HWnyY+vTuieIUOrsWfm5whir2cobdG6PM6V40DljjDHGmPI4lGIG4VDKOs4k0c57VVVJCUq28IlQqqrWPQCoX5/etFrg+HGTH5oxVg0UFtKlo5MWiVmJAKp3+x5XSjHGGGOM6Y9DKWYQJUIpLy+65FBKf/rOkxKU3IFPtO/pUykFcAsfY6wkUSmVJ2VAI2mgggr+7v6KHJvb9xhjjDHG7BuHUswgSlZKieHZrGoilKpq5z1ByR34DGnfA3jYOWOsJBFKZRWlAgDqudeDk4OTIse2RPue2OCD2/cYY4wxxpTHoRTTW2EhUFRE17l9z3I0Wg3OJp0FYJ32PVEppU/7HiBXSh09CkiSyQ/PGLNzIpTKLEoBoNyQc8Cy7Xv6bvDBlVKMMcYYY/rjUIrpTXdwtRK77+XmyrNGWMWiUqOQXZgNN0c3NK/TXK/7iEqpuIw4pOeZlv4ZWinVsSMNNL59G7h+3aSHZoxVAyKUytDcAaDcPCnANtv3uFKKMcYYY0x/HEoxvYlQysGBQgdjiZlSAFdL6UO07rX1bwsHtYNe9/Fx9Sne3crUHfgMGXQO0AlZx450nVv4GGMilEorTAYABHkos/MeYJu773GlFGOMMcaY/jiUYnrTnSelUhl/HEdHuf2PQ6mqnUm8t/OefweD7qdEC59WCyTQDu56V0oBPOycMSYrDqUKKJSq7pVSHEoxxhhjjOmPQymmNyWGnAs8V0p/EUkRAPSfJyUosQPfnTvUYqlSAQEB+t+Ph50zxgTRpn23MBGA/c6U4vY9xhhjjDHlcSjF9MahlHUU77wXoN/Oe4ISO/CJIed+foCTAZtldepElxcv8rBzxmo6USmVkk9ll6K1WAnmrpSSJDnw4kopxhhjjDHlcSjF9Ca2xeZQynKSs5MRnxkPFVRo69fWoPsq0b5n6JBzoX59uszLA1JSjH54xlg1IEKpOwWUcivZvmfumVK6wZKhoRRXSjHGGGOMVY1DKaY3USllys57AodS+hHzpEJrh8LTxdOg+4bVCwMAxGfGIzU31ajHNzaUcnEB/P3pelycUQ/NGKsmRCh1O/deKGVH7Xu6YZeh7XtcKcUYY4wxVjUOpZjeuH3P8kTrnqHzpADA29UbDbwaADC+Wkq07+m7854uUS0VG2vUQ9c4kiShUFNo7WWwSmi0GmsvwS6JUKpIRf+JBHgYMKCuCuZu3xMVwo6O9KYPbt9jjDHGGNMfh1JMbxxKWd6ZpHs77xkRSgE6c6WMHHZubKUUADSgPIxDKT1oJS06f9sZrZa2MrqqjZnX5TuXUefjOmi/vD3239xv7eXYDUmSB53DoQB13OrAxdFFseObu31PHNeQCmFRKaXVAkVFyq+JMcYYY6w64VCK6Y1DKcszpVIKMH2ulAilTKmU4va9ql27ew2nEk4hKjUK7x9439rLYeVYeXol0vPTcTbpLPqu6osJ6ycgPjPe2suyeYW6xX8OBYrOkwIs176nb+seIAdlAFdLMcYYY4xVhUMppjcOpSwrtzAXl+5cAgC09zds5z3B1FBKtO9xpZR5Hb91vPj6F/99gai7UVZcDStNkiSsvbAWANCvUT+ooMKv535Fi69aYNGhRSjQFFh5hbarQPdL41Cg6M57gPnb94wJpVx0CsF42DljjDHGWOU4lGJ641DKsi7cvgCNpEHdWnWNPpHj9j37cOzWseLrhdpCvLbnNSuuhpV2PP44bqbfhLuTO7aO34oTU07gvvr3IasgC6/sfgXtl7fHrqhd1l6mTSpTKaXgkHPAcu17hoRSDg7y/CmulGKMMcYYqxyHUkxvYuAr775nGbqteyqVyqhjiB34krKTkJKTYtB98/OBO3foOrfvmdexeAql5vWaB7VKjXWR63Aw5qCVV8UEUSX1SItH4Obkho6BHXFo0iH88OgP8HP3w6U7lzDwl4EYvXY0YtJjrLxa21JcKaXSAmqN4qGULbbvATzsnDHGGGNMXxxKMb1V10qpJUuAoUOBzExrr6Sk4lDKv4PRx/Bw9kBD74YADG/hS0igSxcXoHZtwx9bVErFxdGwY1a+Ak0BTiecBgBMCp+EyeGTAQCzd86GVtJac2kMJVv3xrQeU/x+tUqNZzo8g8szL+Olbi/BQeWAPy/+iZZftcSHBz+ExE96AHIopXbQACrUiPY9QG7h4/Y9xhhjjLHKcSjF9FYdQymtFnjzTWDLFmD9euuuRZckSdh3cx8A44ecC8a28OkOOTemUEvcLz8fuH3b8PvXFOeSziFfkw9fV1809W2Kd/q/Aw9nDxy7dQy/n//d2sur8f679R9iM2Lh4eyBwaGDy3zcx9UHiwcvxunnT6NPwz7ILcrFvD3zsO3aNius1vYUV0o5UB+f0oPORUVSfj79PlcaV0oxxhhjjJkXh1J2JifHehU91TGUunJFXsMuGxoJcyj2EM4nn4eboxseavaQSccydti5KUPOAcDZGfD3p+vcwlcxMU+qa3BXqFQqBHgE4LWeNFNq3p55yC00UwkI08ua82sAAI+2eBSujq4V3q6tf1vsfXovnmz3JADwjKl75FCKSobM1b4HmCcAEm3rHEoxxhhjjJkHh1J2ZM4cCoQ+/NA6j18dQ6mjR+Xru3eb55V2Yyw9vhQAML7teNR2M6J3ToexoZQpQ84FHnZetePxtPNe1+Cuxe+b1X0WGng1QEx6DBYfXWyllTGtpMUfkX8AKNm6VxGVSoUhzYYAAPbH7Dfr2uyFCKW0akpnzNW+B5gnABKVUobOUuT2PcYYY4wx/XAoZUfq1KFLUcFiaUqGUl5edJmTU2p3Jgv77z/5elIScO6c9dYiJGQmYF3kOgDAjC4zTD6ese174nlmzJBzgUOpqulWSgluTm744IEPAAALDy5EUlaSVdZW0x2JPYJbmbfg5eKFgU0H6nWf3iG9AdBMuIz8DHMuzy7IlVJ0Ren2PUdHeac7c8yV4vY9xhhjjDHz4lDKjoiKFVHBYmmijUHJSikAyLDieZuolBKfky208H176lsUaYvQo0EPhAeGm3y8VnVbAQBu59zG7Wz9hzspUSnFO/BVLjM/E5G3IwEAXYK6lPjY+Lbj0TmoMzILMvHm3jetsbwaTww4r6p1T1ewVzCa+DaBVtLicOxhcy7PLhS/6OBQAB9XH72/joYQAZAthlJcKcUYY4wxVjkOpeyIqFixViglKqUMbWMoj5OT/Ee+tVr4srOBs2fp+rRpdGntUKpQU4jlJ5YDAGZ2manIMd2d3dHYpzEAw1r4uH3P/E4mnIQECSHeIfD38C/xMbVKjc8GfgaAgkpDK92YaQxt3dPVp2EfAMCBmwcUX5e90a2UUrp1TxD/l5izfc/Y3fe4UooxxhhjrHIcStkREQ5Uh/Y9wPpzpU6epBlSwcHAM8/Q+/bvt+5JxIZLG5CQlQB/d3+MChul2HHbB7QHQO1I+uL2PfMrr3VPV++GvTGy1UhoJS3m7JpjyaXVeIdiDiEhKwHeLt54sMmDBt1XtPDxXKmSoZTSQ84FERjZYqUUh1KMMcYYY5XjUMqOiFAqLU1upbOk6hZKida9++4DwsIofMnLAw4etM56AHnA+ZROU+Ds4KzYccVJ9Y6oHXrdXpK4fc8SioecB5UfSgHARwM+gpPaCduvbceOa/p9/5jpROve8JbD4eLoYtB9RaXUsVvHkFdUs1OJ4lBKXaj4PCnBFtv3eNA5Y4wxxph+OJSyI56eciBk6RY+SVJ2phQgh1LWmiklhpx36waoVMCD94ohrNXCdzbpLPbf3A8HlQOe7/S8osce1HQQAOBQ7CG9hi+np8vfbyUqpeLibGdnQ1tSVaUUAITWDsXMrtTKOWfXHGi0GousrSbTaDVYd5E2GzC0dQ8Amvo2RYBHAAo0BcXf45qqRPueR81p3+NKKcYYY4wx/XAoZUdUKusNO8/NpWAKqJ6VUoAcSu3caZ31LD1GVVIjW41EsJcJ5UnlaFq7KUJrh6JIW4R/ov+p8vaidc/X1/CTMV2BgYBaTcOOk5ONP051lJiViJj0GKhVanQK6lTpbd/o8wZqu9XG+eTz+P709xZaYc11MOYgErMS4ePqgwFNBhh8f5VKxXOl7inRvmemSilztu+JcJ4HnTPGGGOMmQeHUnbGWnOldNsFlRh0Dlg3lIqLo6+hgwPQ6V4eMODeuWdEhOUDlLS8NPxy7hcAwIwuM8zyGIObDgYAbL+2vcrbKtG6B9BA+4AAus4tfCUdv0Wte63qtoKHs0elt/V188WCPgsAAPP/nY/M/Eyzr68mE617I1qOMLqNludKEUvMlLJE+56h/+/xoHPGGGOMMf1wKGVnrLUDn5gn5eJCQY4SrBlKiSqpdu3kkw1/f6A9zQPHnj2WXc+qiFXIKcxBG782xRUWShscSqHUjqgdkETZWwWUGHIu8LDz8unTuqdrWpdpaFa7GZKzk7H46GIzrqxmK9IWFbfujW091ujjiJ/jw7GHUaQtUmRt9siSu+/Z0kwpbt9jjDHGGNMPh1J2xlrte0oPOQdsI5QSrXuCNVr4tJIWXx//GgAws8tMqFQqszxOv0b94OzgjBtpN3Al5Uqlt1WqUgrgUKoixUPO9QylnB2csaAvVUv9fPbnKoNFZpz9N/cjOTsZtd1q4/7G9xt9nDZ+beDj6oOsgixEJEYot0A7U1Bw73lqgfY9W5opxYPOGWOMMcb0Y9VQav/N/Rj22zAEfRoE1dsqbLy0sdLbP7PxGajeVpV5a/11a8ss2AZYq32vuoVSukPOdQ0cSJe7dskztMxtV9QuXL17FV4uXpjQboJZHiMiAlj0gTt6BNBJdlUtfCKUUqJSinfgK0uSJIMrpQDg0RaPwtXRFVfvXsWZpDPmWl6NJlr3RrYcCScHJ6OPo1ap0SukF4CaPVcqM/deKmPG9j2ulGKMMcYYs19WDaWyC7LR3r89lg5ZqtftlwxegoTZCcVvsS/HorZbbTwW9piZV2o7rN2+Vx1CqcJC4MQJul66UqpXL3qF+9Yt4NIly6znq+NfAQAmdphY5WwhY82cCbz9NuB2Yh4AYHtU5aGUCD25Uso8olKjkJqXChcHF7T1a6v3/TxdPDGk2RAAcnjClFOkLcKfF/8EYNyue6XxXCkgJZN2+3RyAtydFfwPRIclZkrxoHPGGGOMMfOwaij1ULOH8N7972FEqxF63d7b1RsBHgHFbyfiTyA1NxUTO0w080ptB7fvme7cOXr12scHaNas5Mfc3IDedB5pkRa+6NRobLmyBQAwvct0szxGXh5wnDrFcH5HN0AC9t3Yh9zCis/guH3PvESVVMfAjgZX44g5R2svrOUWPoXtvbEXd3LuoI5bHfRv3N/k4+nuwFdTv1cpWVkAgFqujmZ7DFtu3+NKKcYYY4yxytn1TKmVp1diQJMBaOjT0NpLsRjd9j1LnuNUp1BKzJPq1g1Ql/MToNvCZ27LTiyDBAkDmw5E8zrNzfIYJ07Iw4Zjb7igbvJo5Bbl4kBMxS1FSg465/a9skQo1SWoi8H3fbjZw3BzdENUahROJ55Wemk1mqg+G9VqFBzVpocoHQM7ws3RDSm5Kbh456LJx6vI5iubcSH5gtmOb4rULPrPw93NuF0M9cHte4wxxhhj9stuQ6n4zHhsu7oNz3Z8ttLb5efnIyMjo/gtM9O+t1IPCKDLwkLgzh3LPW5ODl0aui12ZWwhlCqPGHa+d6/OzlFmkFuYi5WnVwKgAefmcvBgyX97R84CUPFcKY0GSEyk60pWSt26Rcdmhu+8p8vd2R1Dmw8FwC18SirUFGL9xfUAlGndA2g4ffcG3QGYb67UoZhDGPbbMPRd1RdpeWlmeQxTpGfTfx6eZgylzNm+J/7v40HnjDHGGGPmYbeh1I8RP8LH1QfDWw6v9HYLFy6Et7d38VtYWJhlFmgmzs6Anx9dt2QLX3WqlBJDzkvPkxLatQPq1aPP+cgR863j9/O/427uXTT0blg8J8gcDh2iy/Hj6TLmSFcg16fCUCopicIjBwf5uWaKwEA6VlERHbumK9QUFlc4GRNKAXJowi18yvn3xr9IyU1BvVr10LdRX8WOa+65Ur+d/w0AkJKbgvf3v2+WxzBFeg4lRV61DEx1DGCu9j1Jko9p6AsyXCnFGGOMMaYfuwylJEnC9xHf48l2T8LZofJXX+fNm4f09PTit8jISAut0nyssQNfdQml7t4Frlyh610ryAPUarlaylwtfJIkFQ84n95lOhzUDmZ5HK0WOHyYrr/0EtC+PVBY4ADVuSdw8c5FxKTHlLmPeF4FBFCYZCoHBwqmAG7hA4DzyeeRV5QHH1cfhNYONeoYQ5oNQS2nWohOi8bJhJMKr7BmUrp1TxBzpfbf3K94gKjRarAucl3xv7849gWup15X9DFMlXFv9z3vWq5mewxzte/pBkrcvscYY4wxZh52GUrtu7kP1+5ew+SOk6u8rYuLC7y8vIrfPD09LbBC87LGsHNzhlJZWZZr6zpGXVNo1gyoU6fi25k7lPrv1n84lXAKro6umBxe9fPYWJcvUxDn5gaEhwPP3ut2dTv7IiABO67tKHMfJYecCzzsXKbbuqdSqYw6Ri2nWhjWfBgAYM35NYqtraYq0BQo3ron3Ff/PjiqHRGXEYeb6TcVPfb+m/uRlJ0EX1df3N/4fhRoCvDa7tcUfQxTZd0LpXw8zLPzHmC+9j3d43H7HmOMMcaYeVg1lMoqyEJEYgQiEiMA0E5kEYkRxdUb83bPw1Mbnipzv5WnV6JbcDe08WtjyeXaDDF8urqEUgCQkaHccSsj5klV1LoniFDq+HEKdZT21TGqkhrXZhzq1KokHTORmCfVrRttyT5hAp0s5cQ1A+I7Y3tU2RY+8bxSYsi5IIad20soJUlAdLR5NhMwZci5ruIWvkhu4TPVnut7kJqXCj93v+LKJqXUcqqFzkGdAVCIpCRR3TWy1Uh8PuhzqKDCH5F/4FDMIUUfxxTZeYUAgNpmDKXM1b4nQilHR3ozBFdKMcYYY4zpx6qh1In4Ewj/Jhzh34QDAGbtnIXwb8Kx4N8FAICErIQy7UXpeen4M/JPs1aX2Lrq0r7n7Cz/4W6pFr6qhpwLwcFAWBiFEv/8o+wakrOTi08mzTngHJDnSfXsSZe+vsDo0fc+eOpZ7L6+G4WawhL3Ec8rc1RK2Uv73ldfAU2aAD/9pPyxj8UbP+Rc10OhD8HdyR0x6THFQRczzupzqwEAo1uNrrSV9tw5IDXV8OOLuVJKDjsv0hbhz4t/AqCAsp1/u+L/F2ftnAWtpFXssUyRk1cEAPB1N1+Vsrna94zdeQ+Q/2/jSinGGGOs+tu/Hxg2jF7UV6mAjRtLflySgAULaKSJmxswYABw9WrJ29y9SwUEXl6Ajw8weTJ1FOk6exbo3Zv+zmjQAPj447Jr+eMPoGVLuk3btsDWrYavxdKsGkr1a9QP0ptSmbdVw1cBAFYNX4W9z+wtcR9vV2/k/C8Hz3V6zvILthHWaN8TOxApGUoB9EMHWCaU0mrl9r2qKqUA87Xwbby0EYXaQnQO6oxOQZ2UPXgppUMpQG7hw7kJyMjQ4L9b/5W4D7fvAX/9RZc7dyp73KyCLETeprl2plZKuTm54ZEWjwDgXfhMsfrs6uJQakK7CRXe7tgxmsn29NOGP0bxXCkFh53vu7EPt3Nuo45bHfRv1B8A8O7978LdyR3Hbh2zibbOrIIsFBVQi2pdM7bOm7t9z5hQSrTvWatS6pVXgCFDaIMJxhhjjJlXdjb9nbh0afkf//hj4IsvgOXLadMtd3dg0KCSfydMmABcuEDnnps3U9A1ZYr88YwMYOBAoGFD4ORJYNEi4K23gBUr5NscPgw8/jgFWqdPA8OH09v584atxdLscqZUTWfN9j1DdyCqiiWHnV+9SlUOrq60w15VRCi1c6eybVw7omiO0yPNH1HuoOVISgKuXaO0vnt3+f19+wKhoQAKPIDIx8rswicqpWpq+55WC5w4QdeV3hfhVMIpaCUtGng1QKBnoMnHEy18f0T+YTOVMfbkSOwRTP6bqote7fkqejToUeFtt26VKye1Bn6pezboCRVUuJJyBUlZymxBqdu65+TgBAAI8AjAa71optRre15DbqHCKY2BEjITAA1tRuLpZn+DzpWolLLGH3hFRcDnnwPbtlF1H2OMMcbM66GHgPfeA0aMKPsxSQIWLwbmzwcefZTOQ3/6ic65REXVxYvA9u3Ad99RR0+vXsCXXwK//y6fm61eDRQUAN9/D7RuDYwbB7z4IvDZZ/JjLVkCDB4MzJ0LtGoFvPsu0LEjdYHouxZr4FDKDlWX9j3AsqHUf/cKgjp1ovlKVenbl2534wYQFaXMGgo1hdh9fTcAYHDoYGUOWgFRJdWmDZWACioVpecAgFPPlgmlzFkpZQ/te9euyc/Hy5cNDyAqozvkXAmDQwfD09kTsRmx+C/uv6rvwIrdSLuB4WuGI1+Tj+Eth+ODBz6o9Pbi5yk7G7hu4AZ3vm6+aOvfFgBwIMb0Fr7SrXu6ZnWfhWDPYMSkx2DJf0tMfixTJGTJoZSLi3FD/fVhrplSokLYlEopa7TvxcbKFVKWfPGKMcYYq24yMzORkZFR/JZvxH/s0dFAYiK1yQne3hQ+HTlC/z5yhM7XOneWbzNgAO0KL85hjxwB+vShETjCoEF0viLGSxw5UvJxxG3E4+izFmvgUMoOibDg9m3L/cFbHUIpfYecCx4eQI97hRNKtfAdjTuKjPwM1K1V1yqte8LTTwMODhIQ2xMnz+QgOTu5+GPmGHQuQqn4eMvttGgsUSUFUKXETQU3TFM6lHJ1dMWjLR8FwC18hsjIz8Cw34YhOTsZHQI64OcRP0Otqvi/w6Ii+fcHAEREGP6YSs6V+jf6X6TkpqBerXro16hfiY/VcqqFhQ8sBAB8cOCDEj/blqZbKaX7B5TSuFKqJN0XUezhhQDGGGPMVoWFhcHb27v4beHChQYfIzGRLv39S77f31/+WGIi4OdX8uOOjkDt2iVvU94xdB+jotvofryqtVgDh1J2qHZt+VXYhATLPGZ1CqWqGnKuS+m5UqIq6cEmD1Z6EqyEykKpwEBg6NB7lQunJ2NXFH2COTlAWhq9W8lKKX9/+sWq0VjuOWus48dL/vviReWOrdTOe7rGhNXMFr4ibRHS8tIMvp9Gq8H4P8fjfPJ5BHgEYNPjm+Dh7FHpfc6dKzlo8swZgx9W0blSIoAc1WoUHNVlt4Wb0G4COgV2QmZBJt78902TH89Y8ZnxgJbKUs0ZSpl7ppQxbeu6g84tvTmmbijFlVKMMcaY8SIjI5Genl78Nm/ePGsvqVriUMoOqVRyFYulWvhsLZT6/nsa/CbWVZWcHNqtANC/UgqQQ6k9e5QZGCvmSZm7dS8nBzh1iq6XF0oBOgPPzzyFLZeopVA8n9zd5SH0SnBwkJ+ztv7KvQilRPCrVCiVnJ2Mm+k3oYJK0Sq5gU0HwsvFC7cyb+Fw7GHFjmurJEnC35f/RvMvm6Peonp4ZdcryMzP1Pv+c3fNxZarW+Dq6Iq/x/2N+l71q7zPwYMl/21MKCUqpc4knkF6nvEpfKGmEOsvrQdQtnVPUKvU+HTgpwCAFadW4ELyBaMfzxS67XuWqJRSuipJiUHnAM1/sCQOpRhjjDFleHp6wsvLq/jNRfc/eD0FBNBlUqmxoklJ8scCAoDkUsXtRUW0I5/ubco7hu5jVHQb3Y9XtRZr4FDKTll6Bz5z7b5nbCj16qvAt98Cb7yh3+1PnqQqncBAeei2Pjp1Anx9abeD0hU0hkrOTsbJhJMAKEgwp+PHgcJCCoIaNSr/NoMHA3X984GcetiySQ2tpC0x5Fyl8AgYe9iBr6hIDvMepa44xUKp47foCdSqXit4uSiX+Lk4umB4y+EAqn8L39WUq3j414fx6O+PIjotGkXaIiw6vAgtl7bEb+d+g1RFScq3J7/F50c/BwD8NPwndAnWr2JNVB0OvPdja0z7XqBnIEJrh0KChEOxhww/wD3/RP+Du7l34efuV1x9VZ6+jfpieMvh0EpazN011+jHM4WlQ6ncXGWrkpRo3wMs38LHoRRjjDFmOxo3psBnzx75fRkZNCtKbEbVvTt1q5w8Kd9GbK4juny6d6cd+QoL5dvs2gW0aEHnq+I2uo8jbiMeR5+1WAOHUnbK0jvw2dLue+npwJ07dH3JEtqqvSpiQNx99xkWtjg4AA88QNdNbeHbGbUTABAeEI4AD/NG0bqtexV9vo6OwLOTqPUn48gYnE44bZYh54I97MAXGUknop6eciil1A58Ss+T0iVa+NZFroNGa+NDu4yQXZCN1/e8jjbL2mDbtW1wdnDG671ex4axG9DUtyniM+Mxfv149PuxH84llb/d2D/R/2D61ukAgHf6vYPHWj+m9+OLn6dp0+gyNpZeuTKUEnOlRPA4utVoOKgdKr3txwM+hqPaEduubSv+/WNJ8ZnxxaGUPptLGEsEQFptyT/UTGVKKKUbwll62DmHUowxxphlZWXRi5bihcvoaLoeE0PnYv/3f7Q7399/01iIp56i8/nhw+n2rVpRwcBzz9G57aFDwMyZtMOeOO8fP57+vpg8GbhwAVizhs6FZ82S1/HSS7SL36efApcuAW+9RfNyZ86kj+uzFmvgUMpOWbpSypba96Kj5etaLf1gVtUeYeiQc12ihW+nied0Yp7UoKaDTDuQHiqbJ6XruWfvndRGPYjfDx41ayhlDzvwiWq4Tp1o10KAKqWUqL44Fn8vlApSPpR6sOmD8HbxRkJWgklVOLZGkiT8ceEPtFzaEgsPLkSBpgAPhT6E89PO4/0H3sfwlsNxfvp5vH//+3BzdMP+m/sR/k04Xtr2Uol5U1dSrmDU2lEo0hZhfNvxmN9nvt5riImh56yDA/0uaNyY3m+NuVIFmgJsuLQBQMWte7qa1WmGGV1mAADm7Jxj8cDS0oPOAWWrkkwJpVQquYXPkpVSksShFGOMMWZpJ04A4eH0BlBQFB4OLFhA/37lFeCFF2j8TJcuFGJt316ysnr1aqBlSyqIGDIE6NULWLFC/ri3N52PRkfTucrs2XT8KVPk2/ToAfz6K92vfXtg3Tpg40b5vEbftVgah1J2SoQGlpgppdHIr/TaQigl/uBu0QKoWxc4fx746KPK7yMqpQwZci6IUOroUSpvNIZW0hZXKph7npRWCxy+N1qoqlCqSROgRZc4AGqs+cW9RPue0uyhfU+EUl26AM2b0zasaWll+64NJUmSPORcz5YxQzg7OGNEqxEAqk8LX+TtSAz4eQDGrBuDuIw4NPZpjL/G/YUt47egWZ1mxbdzdXTF671fx6WZlzA6bDQ0kgZfHPsCzb9sjh9O/4CUnBQM/XUo0vLScF/9+7DykZVQGVAuKQLe8HD6/de+Pf3blLlSx28dR26h4VO591zfg9S8VAR4BKBXSC+97rOg7wL4uvriXPI5/BDxg8GPaQpLte/pjndQcti5KaEUUHLYuaXcvl1yKH9amv6zFxljjDFmnH796IWh0m+rVtHHVSrgnXdoh7u8PGD3bjrX0FW7NgVKmZl0bvz997QbvK527YADB+gYcXE00qa0xx4DLl+mvz/On6eAS5c+a7G0stv2MLtgyfY93T9ozRVKGRL2XL9Olx07AkOHAhMmAO++C4waBYSFlb19XBy9qdVA586Gr7FxYyA0FLh2Ddi7F3jkEcOPcTrhNG7n3Iansye6NzBvw25kJJ2IuLsDHTpUffvpU5zx0nEgdt8ARDkUAnCyWPtecnYytl/bjsfbPA4nBzP29+jpxAm67NKFTigbN6YQ9OJF04b/XU+9jru5d+Hs4Ix2/u2UWWwpY8LGYFXEKqyLXIclg5dU2dplDeeTz+Pvy39XOfspNiMWK0+vRJG2CK6OrpjXax7m9pgLN6eK04EQ7xD88dgf2H19N17Y9gIu3bmESX9PgqezJzILMhHiHYKNYzfC1dGwl4FKVx22b0+vOBkTSjXxbYIgzyDEZ8bjv1v/oV+jfgbdf82FNQD0a90TarvVxht93sCsnbMw/5/5GNt6LDxdPA1dusFyC3OpWs0CoZRKRT+veXm2FUpZo1Lq2jW6DAkBUlLo/+9bt6z/xyZjjDHGWEU4lLJTlmzfE6GU+MNfScZUSolQqmlT4PHHKVHesoV2kztwgNpsdIkqqbZtjQ/VHnyQ/tjfscO4UEq07j3Q5AE4O5jx7AzySXS3bjQ3qirPP+mHl19KgzajPrZvo/Yec1ZK6bbvvbb7NfwQ8QOiU6PxZj/rbV0P0KsJYofGLveKmVq1kkOp/v2NP/bxeCrBCg8IN9v3/4EmD8DX1RdJ2Uk4EHPA4MDD3E7Gn0TvH3ojt0j/1GB4y+H4fNDnaOTTSO/7DGgyAGemnsEX/32Bt/e9jcyCTHg4e2Dz45vh7+Fv8LpLh1Ii6DVm2LlKpULvkN5Yc2ENDtw07HuUX5SPjZc2AtCvdU/XjK4z8PWJr3Ht7jUsP7Ecc3uaf/D5ueR7s70sEEoBFBzl5SkbAIkNPkytlLJkKCUqiZs2pXVfvsyhFGOMMcZsG7fv2Snd9j0ldxsqj+48KaV3ZDMllGrShNazbBkNpj5yBPj667K31x1ybixR9vjTT8YFgTuidgCwrXlSgosL0G5gBACgIJ8SPXPOlEpIoF3uAOBI3BEAwPKTy1GgsfC+6aWcOUNDkuvUARo2pPe1akWXpu7AZ84h54KzgzNGthoJwPZa+G5l3MIjvz+C3KJcdA7qjOc6Plfp29ROU7HjiR3YMHaDQYGU4OzgjDk95uDyzMtY0GcBdj+5G2392xp8nMxMOajUrZQCqCKxqll25TF2rtSu67uQnp+OQI9A9AzR84f7HmcHZ7zW8zUAwLITyywyW2r5ieUAACfQ7hiWCKUA81RKGbvBhzXa93RDKUvPnmSMMcYYMwZXStkpUcmSk0OBjo+P+R5LvFqs9M57gOmhFEBhx0cfAdOnA/PmUSWTCBUA04acC0OG0DaZR47QjgV//KH/fdPz0nE4loY8WSKUOniQLvUNpQBgyrMOmL5R/rc5Qik/P9qBq7CQwtS6gTm4knIFAJCYlYj1F9djXJtxyj+wnnTnSYnw1Z5CKYAqaFaeXol1kevwxUNfwFFd8a94SQJOnwZaty45k0dp2QXZeOT3RxCfGY/W9Vpj95O74e3qbb4H1BHkGYS3+79t9P2PHqUZbY0ayb9zGzUCvLyo5fjSJertN4SYK3Uk9ggKNYV6t62KoPGxsMegVhn+etLjbR/H3F1zEZ0WjW3XtmFo86EGH0NfKTkp+O38bwAAtZaSGXsOpeypfU83lBJhGIdSjDHGGLNlXCllp9zcAF9fum7uPzjNtfMeIIdSmZl08lcVjQa4cYOui1AKAJ5/Hujdm9b6/PNy9VhRkTwnyJgh54JaDSxfTq2B69bRDgX62hO9BxpJgxZ1WqCxb2PjF6GHhATakUGtphBNX08P7ARVg6PF/w4MVH5tarUcdsXF0XwhrSR/07869pXyD2oA3VBKEDPKIiONP26hphCnEk4BMH8o1b9Rf9Rxq4PbObex78a+Sm/7yy+0c8drr5lvPVpJi6c2PoVTCadQt1ZdbHp8k8UCKSWUV3WoUpk27Ly1X2v4uvoiuzAbpxNP63WfvKI8/HX5LwCGt+4JtZxqYVL4JADm/1n7/vT3yCvKQ3hAOIoK6c8MJzOPjBNVSbYUSlm7fY8rpRhjjDFmDziUsmOW2oHPEqGUJFEwVZW4OAqanJ1Lzj1Sq4Fvv6VXpnfsoBNuADh3jk4svL1ptz5TtGsHvPQSXZ8xQ/+THzFPyty77gHySXTbtlTNoa9aTrXQaiBVc3n45pitqkF3B76IxAgAQHv/9nBUO+JQ7KHi91lDeaFUy5Z0mZBgWDWfrgu3LyC3KBfeLt4IrR1q2iKr4OTgpHcL329UyILVqynsNYc3/nkD6y+uh7ODMzaO3Wj2UFZpFbXCirlSxoRSapW6eOe8AzcP6HWfnVE7kZGfgWDPYJM2SpjWeRpUUGFH1A5cTblq9HEqo9Fq8PUJ6qOe3ukFaDRUdmipSiklAyClKqWs3b6nO8ePMcYYY8zWcChlxyy1A585QylXV/lkRZ+TfvEHd6NGZQeat2gBvHlvVvb//R+QnCy37nXtSsGVqd56i/7Qv34dWLiw6ttLkmTT86R0PfWEI9B+FRo8+p2yi9KhuwOfCKAGNR2EUa1GAQCWHltqtseuTGam3KKnG0p5e8s/Z8a28InWvS7BXYxquzKUqKT58+KfKNQUlnubnBzg33/p+u3bwOHDyq/j5zM/44ODHwAAvhv2ncFzkKytqIjadYGyP0+iUsqYYeeAPFdq3cV1tENdFUxt3ROa1m6KIc1oQN7Xx8sZwKeAbde24UbaDfi6+mJki7HF76+J7XuWrpTKzKT/9wCulGKMMcaY/eBQyo5Z6g9Oc4ZSgGFzpXR33ivPnDlUxXD3LvDii8oMOdfl6Ql88QVd//BD2tmoMpfuXEJMegxcHFzQt1FfZRZRCWPmSQmDWvYBRkxEQkvz7YKnuwOfCKU6BHTAzK4zAQCrz61Gam6q2R6/IqdPU7Ve/fpAQEDJj5k6V+qPSBpA1rOBZUKZfo36oW6tukjJTcHeG3vLvc2//5Y8Ud64Udk1HIw5iGc3PQsAeL3X63iy/ZPKPoAFnD1Lv/u8vGjuli7d9j1jNpoY0mwIHNWOOBp3FM2/bI7vT39fopVVV25hbnHr3tg2Y8u9jSFmdJkBAPgh4gdkF2SbfLzSRGvg5PDJcJTkQYTmDqVsuX3PUpVS4v/HOnXo/1XxIgCHUowxxhizZRxK2bHq0L4HGBdK6c6T0uXkBKxcSVVUa9bQ/CdAuVAKAEaMoMHnhYU0XL2yk1LRute3UV/UcjLDpHgd2dkUrgBAr16G3795HdozPC0vDXdz7yq4MpkIpWJiJJxNom3NOgR0QM8GPdHOvx1yi3LxQ8QPZnnsypTXuieYEkpdunMJu6/vhlqlxsQOE41foAEc1Y7FlWcVtfBt3UqXjRrR5caNyu3iGZ0ajRFrRqBAU4BRrUbh3fvfVebAFiaqDnv0KFuV2bo1ve/OHeN+/4bVC8POJ3aiVd1WuJ1zG5P/noweK3vgRPyJMrfdEbUDWQVZCPEOQbdgEwbj3TModBCa+jZFen46Vp9bbfLxdF1JuYIdUTugggrTukwrsTuhuWdK2XL7nqUqpXRb9wD5b4TERPO16DLGGGOMmYpDKTtmqfY9sfuePYRSANCxIzB7Nl0XgVpXBedLq1TAl1/Sq+D//CPP5inP9igKpSzRunfsGJ141K8PhIQYfv9aTrUQ7ElnMdfuXlN4dUS8cn/tRj6yC7Ph5uiG5nWaQ6VSYWYXqpb6+vjXFVaNmIsIpTp3LvsxU0Kpr49/DdzsCc/vr+LfjQ2rvoNCRAvf+kvry7TwSZIcSn34IT2Pr18Hzp83/XHT89Ix9LehuJNzBx0DO+LH4T9apGXRHCprhXVzk2fUGTNXCgD6N+6PM1PP4JMHP4GHswf+u/Ufun7bFc9veh53cu4U3063dU8ltoU0gVqlxvQu0wFQVZOkVBoJYNnxZQCoEqyJbxMU3nvqOTiUDfaUZo72PfF/n72075UOpfz96euu0QBJSZZZA2OMMcaYoezzbIEBsHz7Xi0zFfqIgdxKhVIAzX4KvTdTOjQUqFvX6OWVq0kT4I036PqsWUBaWtnb5BbmYv/N/QAsO+TcmNY9oWltOpuJuhulwIrKktv36ES4rX9bOKjpbHV82/HwdvFGVGoUdlzbYZbHr4g+lVKG7sCXmZ+JVRGrgOPTkX6zCSZOBP73P/12mTRVn4Z94Ofuh7u5d/FP9D8lPnbxIu1g6eICDBsGPPggvd/UFr4ibRHG/TkOkbcjEeQZhL/H/Q13ZzMl2RZQ1c+TKcPOBScHJ8zuMRtXZl7BE+2egAQJK06tQPMvm2PZ8WXIKsjC35f/BmD8rnvlmdhhItwc3XAu+RwOxhxU5JhZBVnFVY6iHVdUSpm7dQ8wb/uesf/3WXrQeelQysFBbkfmFj7GGGOM2SoOpewYt+9VzM0NWLUK8K2txdNPK1cJoGvOHNqdLSmJwobS9t3ch7yiPDTwaoBWdVuZZQ26TJknJYT6UpJnrkopEUql3nEBipzQwb9D8cfcnd3lLeuPm2/L+tzCXOQU5hT/OyVFfl6VVykVFkaX0dGGnfD+fPZnZOZnwjHu/uL3ffABMGGC+SsnKmvhE1VS/fvTyfbw4fRvU0Op2TtmY/u17XBzdMPf4/5GsFewaQe0opgYmnvm4FBxlaWpw851BXoG4ucRP2P/M/vRzr8dUvNSMX3rdLT4qgWyC7PR0LshugSVk5gaydfNF0+0ewKAcj9rq8+uRnp+OkJrh2Jg04EALBtK2WL7nrUrpQDegY8xxhhjto9DKTsm2vcSE2mnKHOxlVAqPZ0CBABorMfO8jc8VyPzJVecbDYSRVrlv0DOzsDX9zawWrZMrrYRxDypwaGDFWm7qYxGI+8UZsw8KSG09r1QKtU8oVTduvdOUCU1kBmE9gHtS3xctBVtu7pN8Wqt/KJ8fHjwQ9RdVBfh34QjqyALAHDi3hif0FDA17fs/fz86P2SBFy5ot9jSZKEpceXAqlNUJQWACcneq44OgK//w4MGEDziMxpbGsair3h0gYUaOThPlu20OUQ2oQNw4bRzpSnTgE3bxr3WH9d+gtfHKMdAH4Z+Qs6BXUyet22QFRJhYdX/HtPd9i5Uno37I2TU07iy4e+hI+rD+Iz6RWHMa3HKP47RAw8X39xffHjGKv4+Q5geufpxS2bIpQy9zwpwLZ337NWpRTAO/AxxhhjzPZxKGXH/PzolXyt1rzzImwllBLVLH5+tAteZQ7FHMKkvyehSCrExksbMWfnHNMXWo7+/YEnnqDAYurUksNkRShliXlSFy4AGRmAhwfQtq3xxykOpcxUKaVWy3OlkFEfHQI6lHn8waGDIUHCshPLFHvc7de2o+2ytpi3Zx5yCnNwJeUKPjn8CYDKW/cAmiFm6FypvTf2IvJ2JFziBhUfe9o0YMcOer4fOgR07w5cvWrKZ1W5XiG9EOARgNS8VOy5vgcA/YyJirqHH6bLevXkIPOvv4x7rE+PfAoAmN19Nka2GmnKsm2CPq2won3vyhX5d6QSHNWOmNl1Ji7PvIznOj6HrsFdiwMkJbUPaI9eIb1QpC3CipMrTDrWgZgDOJd8DrWcauGZDs8Uv9+e2/ckyb4GnRcWUoUfUDKU4h34GGOMMWbrOJSyYw4OQGAgXTdnC5+thVJVte5Fp0Zj+JrhKNAUoFMgVWws+W8Jlp9YbuJKy/fJJ4CPD1WaiMqpG2k3cDnlMhxUDnigyQNmeVxd4iT6vvuoGsdY5g6lAMA/8N6ZakYI2vqVTdDEwPOVp1eWaLMzxo20GxixZgQeWv0Qrt69Cn93f0zrPA0AsOjwItzKuFVcKVVRKAUYHkqJlqhG6U8CAPr2pffffz9w+DDtenftGn2/Dioz0qcMB7UDRrcaDQBYG0ktfLt3U1VlixYlf45MaeE7m3QWB2IOwEHlgJfve9m0RdsIfUIpf396kyRlhsSX5ufuhxXDVuC/Z/9DQx/zDMkXP2vfnPymRDWdoUSV1BNtn4Cvm1xuaI32PaVCKd3qJnto37t5k14UcXOT/y4AuFKKMcYYY7aPQyk7Z4k/OG1l9z19QqmM/AwM+21Y8e5f+57Zh/f6vwcAmLl1JnZf363Aikvy9wcWLqTr8+cDCQkoHtTdvUF3+Lj6KP6YpSkxTwqQB50nZycjIz/DxFWVz60O9WDWKewAT5eyJW+DQwejiW8TpOWl4bdzlWxtWIncwly8s+8dtFraChsvbSwOTC7PvIylQ5aiR4MeyCnMwfx/51dZKQUYFkrFpsfir0tUcpR1hUJREUoBNKPq6FF6vLt3gQceqHwHR1OI4dgbLm5AflF+mdY94dFH6XL/frlFVl9Lj1EgMbLVSLueIyVkZgJnz9L1qn6elBh2bk0jWo1AgEcAErMSseHiBqOOcSvjFtZfXA8AmNG1ZEWXPc+U0g237KF9T7TuNWlC1Z0Ch1KMMcYYs3UcStk5MVfKnH9wmnv3PaVCqSJtEcatG4cLty+U2P3r9d6v44l2T0AjafDYH4/h8p3Lyi3+nilTaCByRgbtxrc96t48qabm33UPkCs7TJknBQBeLl7wc/cDYL4d+CQv6jHxLSi/z9BB7VBczfTVccO2rJckCX9f/hutv26NN/e+ibyiPPRr1A9npp7BZ4M+g7erN1QqFT4b+BkAYNX+nYiPp7bC8PCKj2tIKPXNyW+gkTS4z3MMbsU6w8EB6NGj5G38/YG9e4ERI+jEffx4GoJuwKeql54hPRHoEYj0/HTsuLYL27bR+0XrntCkCdCuHVVaiOBKH2l5afjl3C8A5B3X7N3Ro9QS3aiR/Pu1IkoOO7cGZwdnPN/peQDGDzxfcXIFirRF6B3SG+3825X4mD1XSonjODgYPxPLku175c2TAjiUYowxxpjt41DKzlniD057ad+bs3MOtl3bVmb3L5VKhe+GfYceDXogLS8NQ38bipQcA8tBqqBWA8uX0+XvvwNbV7UBJGBQqPnnSd26Ra0bajXQrZvpx2vqS2c15mrhy3SlZMcpu+KSt0nhk+Dq6IqIxAgcjj2s13GvplzFw78+jEd/fxTRadEI9gzG76N+xz9P/YPWfq1L3LZb/W54vM3jQDxttxcWJlX6/Bah1OXLlW8qkF+UXzyfp2vhbABAx47lz0CrVQv44w9gNt0M//sfMFPhXEetUuOxsMcAAN9sPobERJo7Vl54aUwL36qIVcgpzEEbvzboHdLb5PXaAkOqDs0x7NzSpnSaAke1Iw7GHMSZRMM+kQJNAVacoud7eXOvCgvp0h5nSokKYWOrpADrVEpVFErFxSkfejPGGGOMKYFDKTsn/uCs6TOllp9YjiX/LQEA/DTipzK7f7k4umDD2A1o5NMI1+5ew6i1o0yaoVKe8HDgzTfpesH2d+G661u09+uo6GOUR1RJtW9f9QB4fZh7rlSSAw1xKrzrX+FtarvVxvg24wHI82oqkl2Qjf/t+R/aLGuDbde2wUnthNd6voZLMy9hbJuxFe5atvCBhXBI6A4A8G8eW+ljNGxIJ6eFhfLzsDzrItfhds5t1Peqj6yrFHjptu6V5uBAM8m+/ppCxa+/lk8ulSJa+PbsoGRgwAC5gkPXiBF0uX27fEJeGa2kLf7ezOwy0+w7TFqKPvOkBN32Pa3WbEsyqyDPoOLh9FX9rJW2/uJ6JGYlItAjECNajSjz8erQvmdKhbAtVUplZ1MlL2OMMcaYreFQys5Zsn3PmqFUURFw4wZdL/1H9+7ruzFzK5WYvNf/PYwOG13uMfzc/bDp8U3wdPbEvpv7MH3LdINaw/SxYAHwwLTNALTIO/wsxo5RK7pFeXmUmicliFAqKlX59r3cwlzcUh0FAKTfrjxBE/Np1kWuQ2JWYpmPS5KEPy78gZZLW+KDgx+gQFOAgU0H4ty0c1g4YCE8nD0qPX5Dn4YIyaKT8XOO36NQU1jhbdVqoGVLul5ZC59ogXq+0/M4sJ9+vVYWSgnTpslVbof1KwzTW/cG3RHsGYz8SzRwv3TrntC+PYVvubnArl1VH3dX1C5cu3sNXi5emNBugoIrtp6iImrfA/T7eWrenIKH7OzKw0pbJwae/3L2F6Tmpup9v6+Oyc93Z4eyyZMIpYxtfzOEudr3lKiUsmQoFRpa8v3u7rQRB8AtfIwxxhizTRxK2bma0r4XF0cnjM7OJee8XL5zGY/98Rg0kgZPtHsCr/d+vdLHauPXBr+P/h1qlRorT6/EZ0c+U+AzKOlu+wXAY2Ph6KTBhg3Agw8aPjzaEErNkxLMWSl14fYFaD1vAgBuJztU2tbSMbAjejTogUJtIb49+W2Jj0XejsSDPz+IMevGIC4jDg29G2LD2A3YPmE7WtRtoddaJAlIi6bPNdl7M745+U2lt69qrtTJ+JM4GncUTmonPBL0PK5epYHD+n5fxNwppUMptUqNh4OeAeIo9XroofJvp1IZ1sInAriJHSZWGQDai7Nn6fedtzfQunXVt3d0BNreG41mzy18vUJ6oa1fW+QW5WJVxCq97hORGIFDsYfgqHbElE5Tyr2NJSullG7fUzKUMnf7niRVXCkF8FwpxhhjjNk2DqXsXHVq38vIqLgFRlQhNG5MVSsAkJKTgqG/DUVaXhp6NOiBb4d9q1cL0ZBmQ4oHXc/dNRebLm8y9VMolpSVhNOJp4HW67BuUwZ8fCg06tlTrvRSUmqqfDKsdKWUOUKpiMQIoNYdqJ3oLK2qkyQxp2b5yeUo1BQiIz8Dc3bOQfvl7bEneg9cHFzwZt83cXHGRQxvOdygFrLoaCD1rhqOThrA/yze2vsW0vLSKrx9VaGUaH0a03oMLp6sB4Cqj0SVQlXMFUoBQEDS0wDUUAeeQW2/is/aRSi1aVPls7OiU6Ox5QpNRJ/eZbpyC7UyEfB2706tlfqw92HnAM3dE4Pqlx5fCq1UdS+i2HVxVKtRCPQMLPc21aF9z5RQylLtewkJtF4HB6p2LI1DKcYYY4zZMg6l7JyoGkpPl8MjJUmSPF/G3LvvSRKQlVX+bUrPkyrQFGDU2lG4dvcaGvk0woaxG+Dq6Kr3Y77Y7UU83+l5SJDw+J+PGzzgtyI7o3YCoCqfRwf54uBBoEEDGpDdvTtw+rQiDwONBvjuO6rm0GgorKtfX5lji1DqVuYt5BTqMVzIABGJEYAK8KpHw03i4iq//eiw0fB390d8Zjxe2PYCWnzVAp8e+RRF2iI80uIRRM6IxFv93oKbk+FnjseP02WHDmqEBTZDSm4K3t//foW3ryyUSslJwa/nfgVAQdq+ffR+fVr3hO403grnzik/++Xqf/Q91YZuxo6oHRXerlcvoHZtquwTAU15lp1YBgkSBjYdiOZ1miu7WCsyZJ6UUB2GnQPAhLYT4O3ijajUKOy4VvFzBABSc1Ox+txqAJXvulgddt+zh/Y9USUVElJ+qySHUowxxhizZY7WXgAzjZcX7aaVlUV/cDZX+PywoIBCD8B8lVJubtQGU1QEnLsZg7nHxiE1r+Rck9t//x+A53EkezVaLX0P2QXZiM2IhaezJzY9vgl+7n4GPaZKpcKXD32Ja3evYU/0HvRZ1QdBnlXs/66H5OxkAMDgpoMBUGh05AgwZAi1BvXpA6xbBwwyclM+SaJB1K+8Apw/T+9r3Bj48UeTl16stltt+Lr6IjUvFddTr6ONXxvFjn0mic7cA4M0SIsHYiufLw5nB2c81/E5vHfgveL2utDaoVgyeAmGNBti0lpEKNW1iwpDH/wEQ34dgi+OfYFpXaahiW/Zafq6oZQkUbubsPL0SuRr8tExsCPuq38fnttP7+/TR//1BAbS9zI6GvjvP2r7VEJREbB9+73FNtuKtRcuYHjL4eXe1tERGDaMnk8bN5YfquUW5mLl6ZUA5FlE1YUxoZQYdm7PlVIA4O7sjokdJmLxf4vxxIYnKv2dml2QjdyiXLTzb4eeDSr+YtX09j1RKWXu9r3KWveAkjvwMcYYY4zZGg6lqoHgYKrEiY9XPpTSrb4yVyilUlG1VEoK8M3h33Ak8UjZG92icqo015NIu3MJAOCodsTvo383OjRxcnDCH4/9gZ7f98TFOxeRka9MeYoKquLdrAD6/uzfD4waBezZAwwdSlVOTz9t2HFPnwbmzqVjAICvLzB/PjBjRvm7qZkitHYojscfx7W71xQLpbSStrgiLbSxCy6eqDqUAoCpnafiq+NfoUBTgPm952NW91lwcTT9ExahVOfOwODQwXiwyYPYdX0XXtv9GtY+trbM7UNDKbTJyqKTuwYN6P0arQbLTiwDQFVSKSkqXLhAHzMklAKohS86mlr4lAql/vuP2jy9fIqQUf8o/r58BrmFuRVWlw0fTqHUhg3AZ5+VDN8A4Pfzv+Nu7l009G5ocjBoS2Ji6Pvq4AB07ar//dq1o8vYWODuXao0s1czus7AshPLcDf3Lu7m3q3y9rO7z660Zbamt+9ZulKqqlCKK6UYY4wxZos4lKoGgoIolDLHH5wilHJyMu8OSiKU2nb+MFAX+HTgp+gU2Kn441PXh+MSgHdHTULvgY8CoOAk2CvYpMf1dfPF6edP49itY3rNUdFHgEdAmWHb3t7A1q3ApEnA6tXAM89QxVS7dlSB06oV7e5WXvAXG0vh088/U4WOszPwwgvA//5HwZQ5NK3dtDiUUkp0ajQyCzLh4uCCsKZe2AT9XrkP9grGpRmX4OzgDF83ZT5hjQY4eZKud+lClXOfDvwUHb7pgD8i/8ChmEPoGVKyAsTZmYKpS5eoWkqEUluvbsWNtBuo7VYbj7d5HNvujShr3RqoW9ewdfXoQc+PylrnDLWFRj/h4YcccNi3AW6m38S2a9tKBKe6Bg6kE/GbN6klTVQCAbTj4ZfHvgRAs6Qc1HoOXrID4mseHm5YAO/tLVe4nT0L9OtnluVZRGjtUFyaeQk3025WeVsvFy90COhQ6W0K721oaY/te6Jt3R4GnVcVSonWbg6lGGOMMWaLOJSqBsz5Kqi5h5wLYq7UndQCeAV7YUaXGSWqYZLvhRePdm+Dto2UfWwXRxf0bthb2YOWw9kZ+OknOkH46CNg82Z60xUSQgFVWBhdXr8OLF4sv9I+bhzwwQd0EmxOob7KDzuPSIwAQDsgNtRQmKFPpRQA+Hv4K7YOgIKl7Gx6Xou2vLb+bTGpwyR8d/o7zNo5C0cmH4FaVXLsXqtWcig1cCC9Tww4nxw+GW5Obth/r3XPkHlSgmgbO3qUgjN9h21XZutWuhwyRIX6/mOw6PAirL2wtsJQqlYtai/duJHedEOpo3FHcTrxNFwdXTE5fLLpi7MhxrTuCe3bUygVEWHfoRQANPJphEY+jRQ5ljUqpUTLuak/O/Y06JwrpRhjjDFmz3jQeTVgzh34LB1KIc8bw1sOLxFIpaVRWwxg/jDG3NRq4MMPKXRYsgSYOpXCi3q0WRtiYoAdO4DPPwemTKHb5uVRG9ixY8Bvv1nmayCGnUelRil2TBFKdQjoUFxlZK0ZJydO0GXHjiVPXt+9/124O7nj2K1jWHN+TZn7lR52fiXlCnZE7YAKKkzrPA0AioecG9q6BwBt2tCMuMxMFLcAmuLWLap2UqmAwYNpZ0AA2HRlU6VD7MUufBs3lny/CODGtRmHOrXqFL8/IQF4801gwQJg2TK637FjFDqKYMLWHTxIl8aGUoD9DztXmvjem7PKVnDV2edCiRBI/J9jygYftta+l5wsV68xxhhjjNkKrpSqBsQOfOZ4FVS0MJg7lPLylgCogHxvjAl7pMTHoqPp0s+PTtirg27d6E1XSgqFHeItMpJO6l56CXjkkbKzfcxJhFKKVkolRQC4F0rdyxz1rZRSmpgn1aVLyfcHeATgtV6v4Y1/38Bre15DWL2wEtVSHkE+ABrgxJksnEuKxpL/lgAAHm7+MBr7NkZqqhxMGFMp5eAA3HcfsHs3zZUS84qMJaqkunWjVsI6Uic09mmM6LRobL26FaPDRpd7v6FDKUA9c4Z+/ho3BpKykrD2As3a0h1wvn49BagpKRWvo25dGuQeGEhf83feoePbiowM2vUQMC6Uqi7DzpVmjUopgEIgY/7PiowE/viD3kQo7OVl/JpEpVRRkXKVj6Wlp8s/e03K7s8AgH7+nJwokEpIoIpcxhhjjDFbwaFUNWCJ9j1TXi3WR75DMgB/uBb548GmJSc8X79OlxX9wV1d1KkD9OpFb9YmQqmY9BjkF+UrMlhcDDnvENAB9e+dMN6+TSeQulUOllBRKAUAs7rPwjcnv0FMegw6fNOh5AfjOwI4iZNnc9BuuZwYzegyAwC1gEkSbTgQEGDc2nr0kEOpqVONO4Ygt+7RpUqlwpjWY/DRoY+w5sKaCkOpOnWo0mvvXuCvv4D/+z/gu1PfoVBbiG7B3dApqBMyMykw/eEHuk+HDhSoJSTIb4mJdCJ85w69nTsH7NxJLW4DBpj2uSnp6FFAqwUaNZJDfkOISqnISPp8LVEZZA8sGUo5OMjBiyFzpS5ckIOoyEj5/U5O1KL7/PPGr0n391p+vnn+HxVVUn5+gKdn+bdRq+l5ffMm/Z3AoRRjjDHGbIkNvVbNjFUd2vfiC2hHveYeXeDsUPIMpqaEUrbEz90PHs4e0Epa3Ei7YfLxUnJSEJtBZVHt/Nuhdm25ssHSc04KCuSKlvJCqVpOtbDs4WVo6N0Q/u7+Jd78GqbSjXL8UA+t4O/uj1GtRmFgUxowZUrrntCjB10ePmz8MQA6Cd61i64//LD8ftHCt+XKFmQVZFV4f9HCt2EDUKQtKt5hcGbXmTh8mEKoH36gCr5582iXP9G6999/1Iqal0fB49mz1JY6aBAdc8cO0z43pZkyTwqgMMvLi55bly4ptiy7Z8lQCpBDoKpCqagoajcNC6OW2bffpkDKyYl+VlatApKSaOafKf/vlA6lzKGq1j1B/J1grZZpxhhjjLGKcKVUNSBe2Y+Pp1f7lWyLsUQopdFqcD33NIC+CHEp26/EoZTlqVQqhNYORURiBK7dvVZmN0FDnUmiKqkmvk3g5UL9MA0aAFeuUAtfVSdUSjp3jk6WfX0rfk4NbT4UQ5sPLfdjjVZQxcGf90eid6n5+CKUMqZ1T+jWjYKeqCg6MfY3csb7gQP08xsQUHJYeXhAOJr6NkVUahS2XNmCsW3Glnv/4cOpQurgQeCnw9twK/MW6roE4sKacXj6Q/pd07Ah7QpZ+usgqNXUOlS3LtC2Lc3p2bGD3hYtMu7zMgcxnN7YUEqlomqpAwco8GzbVrGl2TVLh1JubjSPrbIZTv/8Q+2pIrhydqaKqMceozZpHx/l1uPoSD8DWq355krpG0rxDnyMMcYYs1VcKVUNBAbSSZFok1GSJUKpAzEHkK2mv5S9UL/Mx/X9o5spq6kvfcGVmCulO+RcECdJlp4rJVr3Onc2bk5X6WHnQmYmcOoUXTcllPLxAVq3putHjhh/HNG699BDJYNqlUqFsa0piFobubbC+zdsCISH0wn1olUXgTvN4PTDMXz4gSO0WuCpp2jmVEWBVHkefJC+5ufOmaey0xg3bshhothR0Rg87LwsMVTbkqEUUHGl1N69ciDVvTvthpqcDGzaRM9nJQMpwdzDzg2tlOJQijHGGGO2hkOpasDJieZJAMqf6FkilFp7YS3gkg4AyMwoOwmWK6WsQ8lh58WhlH+H4vdZawe+yuZJ6aOiUOrwYRpm3KiR/LkZS4kWvi1b6FK3dU8QLXxbr25FZn5mhccQLXyXNowAvjmNhCv14esLrFkD/Pijzq6ZeqpTh8JAQG4ttLbvvqM5YAMGmBZ8i2o0fUKpK1coLFyyxPjHswe21L63bx/9LOTm0tf+33+BJ580/Dls7JpspX2PQynGGGOM2Rpu36smgoKo1efWrZKtOqbSZ/e9nMIcfHfqO4xqNQrBXsEGHb9IW4Q/L/4JuPYDQDsJlfh4EbVKARxKWVpxKJVqnkopEdxYulLqxAm6VDqUUqJ1T+jRA1ixwvhQ6to1Cj4cHcsfKN7Ovx2a12mOKylXMHXLVDT2aVzucZIC/QG8ANxtBoCOtWqVfIJrjIEDKRjcsQN4+mnjj6OEwkJg5Uq6bspAa0CulIqIoJCroiq8PXuA0aOBtDR6zjzzjPmDEWsRoZSlBr+LSqnSVUkHDtCw/5wcmmu2fr28M565icfhSinGGGOMsfJxKFVNBAcDp08r/wenPrvvvbLrFSw9vhS/nvsVRyYfgcqAnqj9N/cjOTsZHl5aZKFsKBUXR8GUs7Nxu2Ix4ylVKZVflI+LdyjBsXb7XmamvNW70qGUmEukRCglZhudOEEVFoaeQG/bRpe9e5cfeIgWvnf3v4tfz/1a8YEkAAE9gdthmD4vBl++3dzkmXWDBgHvv0+VUkrPwDPU33/TDoEBAcCjj5p2rNataQe4O3do58Hyfl99/TXw4otUUQdQ1c7atcBzz5n22LbKGjOlgJKVUgcPUmVUTg4Fohs3Wna3T3NWSuXny78/OZRijDHGmL3iUKqaMNcOfFW17128fRHLTywHAPx36z+subAG49qM0/v4ay/QTJv+LTpiE8qGUqJ1r3Fj65681kQilLqRdgNF2iI4qo37dRF5OxJF2iL4uvqivpc8M8wa7XubN1MgEBpqfLWPCKViYoCsLMDDg054jx2j9ysRSjVtCtSrRzvXnTpF828MIVr3hgyp+Dazu8+GJEnIyM+o9FhFHX5FI/cwzBkw0agZXKXddx9tXX/nDn1uop3PGpbTry5MmmR6NY+bG9CiBe3iFhFRMpQqKqKh8UuX0r8nTKDbLlhAlWccSimjdPve4cMUSGVnU5WfpQMpwLyVUjduUFWeu7vcwl8R3d33KqvkY4wxxhizNKuGUvtv7seiw4twMv4kErISsGHsBgxvObzS++QX5eOdfe/gl3O/IDErEYEegVjQdwEmhU+yzKJtlDgBMlelVEWh1Cu7X4FG0sDH1QdpeWl4bfdrGN5yOFwdq/7Lv7h1D8Cj7ftVGkpx657lBXkGwdXRFXlFeYhJj0ETX+O+Cbqte7pVdNZo3/v9d7ocp39uWkadOnJgdOkShSpHj1IrWHAwBaimUqmohe+vv+jE2pBQ6vZtmpcDVB5Kebt649373zVtoUZwcgIeeIACgp07rRdKXbsG7N5NX2ulQqEOHSiUOnNG/tqnpgJjxtBjAcAHHwCvvUYVWm+9Rd/fK1eA5s2VWYMtsValVF4ebRIweDAFx/ffTz9L4uOWZM5B57qte1WFTOJvhPx82gWzTh3l18MYY4wxZgyr1p5kF2SjvX97LB2yVO/7jFk3Bnui92DlIytxeeZl/DbqN7SoY9p29dWBuUrzKwul9lzfg81XNsNR7Yh/n/4XwZ7BuJl+E0uO6je9d++NvbiTcwd1a9XFgDDqpcrIoFdxBRFK8c57lqdWqRXZga+8eVKA3L6XkiLPLjOntDRg+3a6PnasaccKC6NL0cKn27qnVAWCscPOv/uOwoAuXeR12hqxy92OHdZbw7ff0uXgwTScXgmld+C7coUqw3bvphbo9euBefPoORIYSI8N0ND46shaodS+fdQmmpkJ9O9Pu+tV1oJuTuZs3zNkZ1pXV6BuXbrOLXyMMcYYsyVWDaUeavYQ3rv/PYxoNUKv22+/th37buzD1glbMaDJADTyaYTuDbqjZ0hPM6/U9lk6lNJoNZi9czYAYFrnaegQ0AELH1gIAPjg4AdIzk6u8tiidW9Uq1Go40tFexqN/JiA/Ec3V0pZhxJzpSKSIgCUDaV8fOTnlSVOkjZupJPk1q2BNm1MO1bpuVJKDjkXdEMp3aC2MhqN3JI2fbpya1HaoEF0efgwBdGWlp8PfP89XTd1wLku3WHnu3cD3bpRMNWgAXDoEDCi1H91EyfS5Y8/ynOmqhNrhVI//kiBVN++1g2kAPO27xkSSgE8V4oxxhhjtsmupvT8fflvdA7qjI8PfYzgz4LR/MvmmLNzDnILy9n/+Z78/HxkZGQUv2VmVrz9uT0TpfmWmin145kfcSbpDLxdvLGg7wIAwIR2E9ApsBMy8jPw1t63Kj1uoaawuHVvTOsxcHenIcFAyRY+bt+zLlMrpSRJwplEKhspHUqpVJYddr5mDV2aWiUFlAyl8vOpfQ8A+vQx/dhCp07U6paYSLNj9LFlC826ql1bmc/TXJo0obleRUVyq6ElbdhAM62Cg4GHH1buuGLn0ytXqAoqLY0qpY4dK39X1GHDAF9fCgn27FFuHbaisJAuLT1TCqAh/5s3V75zrCVYqn1PHxxKMcYYY8wW2VUodT31Og7GHMT55PPYMHYDFg9ejHWR6zB9a8UlAQsXLoS3t3fxW5it9rOYSPyxeeeOsm0Coq1K95XmrIIszP9nPgDgjT5voG4t6glQq9T4dOCnAIAVJ1cg8nZkhcf9J/of3M29Cz93P/Rp2AcqFeDlRR/jUMp2mFopdTP9JtLz0+Hs4IyWdVuW+biYK3X5stFL1MudO7TbG6B8KHX8OJ1w+vvT8GqluLkBHTvSdX1b+MQg7cmTrTM/xxCiWmrnTuOPYWx10Tff0OWzzwKOCk5W9PenN0mitT3xBIVuAQHl397FBRg/nq6vWqXcOmyFpSulRMjdqxewdSttQmBtolLK2u17AIdSTDkaDT+PGGOMKceuQimtpIVKpcLqkavRNbgrhjQbgs8GfYYfI36ssFpq3rx5SE9PL36LjKw4KLFntWvLf/wqWS1VXqXUokOLkJCVgCa+TTCz68wSt+/bqC+GtxwOjaTB3F1zKzyubuue2NVNbF0vQqm0NBrICigzPJoZztRQSsyTal2vNZwdyp6ZiiHXr7xC7U3msn49/REdHq7MQGkRSl27Joddffoov6OVIXOlrlyhgEelAqZNU3Yd5mDqXKlx4yjsOXvWsPtdugTs3Uu7eT77rHGPXZnBg+l78MEHwE8/Vb3bm2jh27CBfudVJyKUMnVnQ33NmkVfxx07bCOQAsxXKaXVGj5zUXcHPsZM8fHHFAKvX2/tlTDGGKsO7CqUCvQMRLBnMLxdvYvf16puK0iQEJdR/l9ZLi4u8PLyKn7z9PS01HItSqWS/+A0Zyh1K+MWFh1eBAD4aMBHcHF0KXOfjwd8DEe1I7Ze3Yrd13eX+XiBpgAbLm0AQK17QulQKjqaLv38bOcEo6YRodT11OvQSlqD7y9CqfYB7cv9+Btv0E5sWVl0Mm/oUG99KbHrnq7gYMDTk4KuH36g9yk5T0owJJRatowuhwyxjxC3f3+qUoqKkis+9HXqFLVj3rkDPPaYYXOpVqygy6FD5coaJa1cCSQlyQPNq9KxI804y8uTW0yrC0tXSnl6AsOHW3eGVGnmGnR+6xYd09ERCAnR7z5cKcWUsoH+hKt2v7MYY4xZh12FUj0b9ER8ZjyyCrKK33cl5QrUKjXqe5nh7MLOiLlSSv7BWTqU+t8//0NuUS56NuiJUa1GlXufZnWaYUaXGQCA2TtnQ6Mt2WOz5/oepOalwt/dH71Dehe/v3QoxTvvWV8D7wZwUjshX5OPWxmGP7GKd97z71Dux2vVAv7+m7ZsF8HUkSMmLLgciYnyIPIxYyq/rb5UKrlaSszDUnKelCBCqbNnaXBzRbKz5XBsxgzl12EOnp5Az3t7VBhaLfXJJ/L1K1eAKVP0GwafmyvvdKfkgHNdDg5AvXr6316lAp55hq5XtxY+S4dStshcg85FkNuwof4tqCKE5VCKmSIvjzZzAKjqVN+NOBhjjLGKWDWUyirIQkRiRPGJa3RqNCISIxCTHgMAmLd7Hp7a8FTx7ce3HY86tepg4l8TEXk7Evtv7sfcXXMxqcMkuDnZ+AAVC1D6VdCtW4GUFLru6wucSjiFn878BAD4bNBnUFVSBrCg7wL4uvribNJZrIpYVeJjayOpdW902Gg4qB2K3186lOKd96zPUe2Ixr5UdmNMC19xKFVqyLmuWrVoh6z+/Sl4GTRIHhyuhHXrqNXlvvuARo2UO64IpQBqn23dWrljC0FBtGatloZlV+TXX+nnpkkTeVaTPTBmrtTNm8Ba+hWCr7+mE/I1a+RKscqsW0ctwSEhtvV1mjCBwqyjR6m9sLrgUMp87XuGzpMCuFKKKeP0aXkTg+Rk88+EZIwxVv1ZNZQ6EX8C4d+EI/ybcADArJ2zEP5NOBb8S7u5JWQlFAdUAODh7IFdT+5CWl4aOq/ojAnrJ2BY82H44qEvrLJ+W6Nk+96hQ8Do0fQK2FNPAUFBEmbvnA0JEsa3HY+uwV0rvX9tt9p4o88bAID5/84vrm4r0BRgw0Wq+x7buuTE6YoqpTiUsi5j50ql5qbiZvpNABW37wkimOrXTw6mKgthDCFa95TejU43lOrTh2YUmUNVLXySJA84nzbNfOswBzFX6p9/5JOcqnz+ObVNDhhAn+9HH9H7X34ZOHGi8vuKAedTpsi7fdqCgABquwSqV7UUh1Lma98zJZRKSTHPboCsZvjvv5L/FpXIjDHGmLGsevrSr1E/SG9KZd5WDV8FAFg1fBX2PrO3xH1a1m2JXU/uQs7/chD7ciw+HfQpV0ndo1T73rlzNG8lN5dOlL77Dth0ZRP23tgLFwcXfHD/B3odZ0bXGQitHYrErER8fOhjAMCuqF1Iz09HoEcgeob0LHF7DqVsU1NfOusxNJQ6m0QTqBv5NIKPq0+Vt3d3py3c+/ShGUEDB9LOdqaIjaWAVaWi2UNKKh1KmYsIpSoaBH/kCHDmDJ38TppkvnWYQ3g4tbplZurXtpmaSr+PAGDuvX0UXn6Z5ggVFND3ODW1/PueP09fQ0dH2/w6iRa+n382fldBW8OhlPnb9wwJpXx95ZBMydmTrGYRlcy+vnS5d6/VlsIYY6yasKPX1FlVlCjNv36dqlTS0mjeyx9/AFAXFu+kN6v7LDT0aajXsZwdnPHRACpj+OTwJ4jLiCtu3Xss7DGoVSWffhxK2abiSqlUw0IpfVr3SnN3B7ZsAXr3pufBgw9WXf1SGdHm1bu3/POhFN1QyhxDzgURSh05Qm18pYkqqccfpzZCe6JW0/cY0G+u1LJlND+rXTv5fioVzdNq3Bi4cYPCnfJmnIgqqUceAQIDlVi9soYOBerUobBA7OhozyRJrn6ryaGULVVK6W6IwjvwMWOJSqmZ9zZf3reP50oxxhgzDYdS1Yip7XuJiVSdkpAAtG1L7VS1agHLTyzHlZQr8HP3w2u9XjPomCNajkDvkN7ILcrFnJ1zsPHSRgAld90TvLzoMiMDKCqi2TEAh1LWZmz7XkRSBACgvX/lrXuleXjQPLNeveRg6tQpgw5RTOwMpHTrHkDPy969Kbxtb9inaJC2bSmsy8gAIiNLfiwp6V5wDPsZcF6avnOl8vOBL+51as+ZU3JnOx8f+jo4O9Pg/M8+K3nf7GyqQAKAqVMVWbbinJ1pthQgD623ZxqNfKLq5GTdtViTLVVKATxXipkmKYnCf5WKQikXF/qb8ZrhIycZY4yxYhxKVSO67XuGvmqVlkY7n0VFUcXBjh1Ump2am4q39r0FAHin3zvwcvEy6LgqlQqfDaIzxDUX1iAjPwPBnsHo3qB7mdvqVkrFxVEw5eIif17MOnRDKcmAJ5YxlVKCCKZ69qTn5oABhgdTUVHU/qdW03w0pTk4APv3AwcPmnc+kaMj0K0bXS89V+q776gapVs3oFMn863BnETF08mTwJ07Fd/ul1/ohKh+fWDcuLIf79QJWLyYrr/6asl2xzVr5EHwDzyg2NIVJ1r4Nm6suA3RXojWPYArpQBlQ6m7d+n3ImD4iza8Ax8zhaiSCgsD/Pzk/5u4hY8xxpgpOJSqRkR4k5sr/8Gqj9xcamk5cwbw96eKBdHe8v6B93E39y7C6oVhcsfJRq2rc1BnPNHuieJ/l9e6B5QMpUTrXuPG9jW4uTpq5NMIapUaOYU5SMxK1Os+BZoCXEi+AMC4UAoAPD2BbduofS01lSpqYmKqvp8gWvfuv5/+eLZn5Q07LyoCli+n6/ZaJQXQ75p27ShIr6htTasFPvmErv/f/1VceTN1KgVWGg1Vx92+Te8XrXvPP2/bv086dKCvRUGBPKDfXnEoRczRvieqpAIDqYrSEFwpxUwhQikRRonWdR52zhhjzBQ2/Oc5M5SbmzxTRt8WvqIiOnk7cIDa57ZvB0KpMAaFmkIsO0H7rH/y4CdwVDsavbYP7v8Aro701/nYNuX3UumGUuKPbm7dsz5nB2c09KY5Yvq28B2MOYhCbSF8XH2K72sMEUyFh1MVzciRFKLqQ5zUl1dVY2/KC6U2baKKwrp1lR/ibmmiha+iuVJbtgCXLtHvqOeeq/g4KhWwYgXQogWddD/xBFVgHTtGQZaoRLJVKpW8RnvfhU83lOL2PWUrpYxt3QM4lGKmG/K3RQAAQFhJREFUEUPO77uPLvv1o0ueK8UYY8wUHEpVM4bswKfVAs8+Sye3rq502aGD/PHzyeeRU5gDH1cfDAodZNK6Gng3wPYJ2/HziJ9xX/37yr1NeZVSHErZBtHCF5Uapdftvz7+NQBgXOtxUOkO/zGClxewYQMNgT55Epg+veo/fi9eBM6epda3ESNMenibIE4Arl6Vq3/EgPPJk+VqDHulO1eqvO/tokV0OXWqPHuuIp6eNF/KzY2O98gj9P6RI+2jYm7CBHreHjtWdoaYPRGhlJNTyflfNY05K6U4lGKWpNHIO+KKSqn77qOf8bg4+e82xhhjzFAcSlUz+v7BKUm0pfqPP9I8nLVry25rfzye/vroEtSl3HY7Q/Vt1LdEG19pHErZLkOGncdlxBUPtJ/RVZm+soYNqfJJraYKkmXLKr+9GHA+aJD97UhXHl9foHVrun7kCFUN7dlDJ/u2OrjbED17UoiUkACcP1/yY//9R5WcTk7ASy/pd7y2bYGvKRctrhq1l6+Tnx/w8MN03Z6rpX79lS49Pa27Dmuz1Uop3n2PGeriRSAzk1pGxf9HtWoBXbvSdW7hY4wxZiwOpaoZfXbgi4qiqgGxQ9X33wPDhpW93bFbxwBQKGUJ3L5nu5r60tmPPqHUNye+gUbSoG/Dvmjj10axNQwYAHz0EV1/6aWSg6x1SZJ5d92zFt0WPhHKDR0KNGpktSUpxtVVbgMp3cInZklNmGDYpgfPPANMnEjXW7SQZ5/YA9HC9/PP1GJtb1asoGHzAPD669Zdi7WZY9C5EqFUfDxVSzOmLzFPqkuXkpt76LbwMcYYY8bgUKqaqax9Lz2dqqNataLdnRwcgCVLgKeeKv9YIpTqGtzVPIstRYRSRUX0ihxg3B/dTHn6VkrlF+VjxakVAICZXWcqvo7ZsyloKiqiHfXKC1/PnqVKIhcX4NFHFV+C1YhQatcuuYLGngecl1beXKmoKGD9ero+Z47hx1y6lEKtNWvsq4Xs4YeBevWAxMSK52wZKimJfu/rznoyh99+k6vSXnuNfmZrMltr3wsMpJ+FoiK5FbgqGzcC771nnwEpU07pIeeCCPx5Bz7GGGPG4lCqmimvfU/s0tWsGZ2gFRbSCeDZs8CLL5Z/nKyCLFy4TbunWSqU8vCQTxxzcuiycWOLPDSrgm4oJVUy0OnPi38iOTsZQZ5BeLSF8omQSgWsXAm0aUMn7KNHlz3JFlVSQ4ZUPX/InohQ6tQpICODNiR48EHrrklJIpQ6cED++f/sM6rmeOghuV3EEG5uFIq0b6/cOi3ByYkqwwBlWvi2bqWfmREjqNXm7FnTj1meTZuAJ5+kasXp04EPPjDP49gTpdv3cnPl/9+NCaWcnGiXXUC/uVJpacD48cAbb8i7WLKaSQw5Lx1K9ehBc/BiYoAbNyy+LMYYY9UAh1LVTOlQatcuGl4+bRq9KtqyJZ2gbN8OhIVVfJxTCaeglbSo71UfgZ6BZl83QPOCdEMEf3/Dt7tm5tHEl/oo0/PTkZKbUuHtvjr2FQBgaqepcHIwz5Zb7u40+NzHh+Yr6c4Zqq6tewCFynXqyP+ePp1+ZqqLFi2ABg2oomT/ftpt8Ycf6GNz51p3bdYgWvj+/tv4+T+FhcArr1Dl1Z07FOqeOQN07gwsXKhs5cs//9AukBoNBVNffmlf1WnmonT7XnQ0XXp5lfx9YAhDhp3//LO84+kbbwApFf/6Z9VYVhZwgV6nLBNKubvT7xSAW/gYY4wZpxqd0jBAbt+7fp3mRA0cSH9I1K5NJwlnz1LVQVWO36Ih55aqkhJECx/A86RsiZuTG+p71QdQcQvfqYRTOBJ3BE5qJzzX6Tmzric0FFi9mk56ly+nuWgAcOIEPfdr1aJ5S9WJSiVXS7m5yaFFdaFSlWzh+/prOhnu1EmeWVKTtG8PhIdTJWBYGPD++3IFmT5u3KDNK8TOhS+8QO979FEKq15/nQbMX7pk+lqPHqVdDvPzgeHD6eexOgWmplC6fU+3dc/Y0M+QDVGWL6frLi5AaioFU6zmOXGCqlYbNCh/th/PlWKMMWYK/rOxmhF/bKamAps3U0n1Sy/RVvIzZ1Lpvj6OxVt2yLnAoZTtqmqu1NJjSwEAo8NGI8AjwOzrGTIEePttuj5tGm1VLaqkhg2rnlV2gwfT5cSJtCNfdSNCqc2bga+o6A5z59bcipuffgI6dqQdr+bPp2q5lSupGqkyGzdSoHX0KFUU/vkn8MUXQEgIVRn+9BP9rj12jG63eLHxQ6/FCx3Z2dRO+vvv9P8OI0q27129CrzzDl0PDTX+OPruwHfwIBAZSb9L//iD3vfNN0BEhPGPrQRJoq/FqlW0g3AlHeVMIaJ17777yv84z5VijDFmCg6lqhk/P6qKAqhS5Px5OuEQ79OXpYecCxxK2a5QXzoLirobVeZjKTkp+PU87QFvjgHnFfnf/6hCo6CAdpT87Td6/7hxFluCRT3/PLVJff65tVdiHg88QBU2165Ru3GjRsCoUdZelfW0aUNh6+rV9LWIjweefZaqqLZsKXsynp9PL0KMGEGzgLp1A06fpp8NQaWi9rrz56mSNi8PePll4P775dYwfV25QkFUWhpV8W3YIIcwjOhWShkbnkgShZHh4VSx4uNj2gB5fSulxAypxx+noH/MGAovX3zRskFQfj7tOrpoEVXi+fsDzZtTOP/MM8C2bZZbS01V0ZBzoWdP2jwnOhqIjbXcuhhjjFUPHEpVM2o1DQo+epSGzrZoYfgxkrOTcSPtBlRQoVNgJ+UXWQkOpWxX09o0VfdaatlKqR8ifkBeUR46BHRA9/rdLbYmtZqqPpo3p1f94+Np1oqoKKpuHByA/v0BZ2drr8Q8fH1pELcwaxZX3ajVNGj60iUa/O7rSy3ZQ4dSkHScOq1x7RoFQ198Qf+eM4f+L2jUqPzj1q9PswWXL6dKmH37gLZtgRUr9AscYmKAAQOA5GSaW7hlS/WsTjSVCOm0WuNmeKWkUDD77LNUjdavH1WnVRQO6KM+dWJXGkrduSNXR4ndFBctotbhAwfkqlRzkCRq4X3lFQo7vLzo8pVXgL/+osDa2RkIuFeQu3Gj+dbC6PtR0ZBzwdOTWq0BbuFjjDFmOA6lqqGwMNP+YBXzpFrWbQlvV+8qbq0s3VDKmJ2FmPlU1L6n0Wrw9fGvAQAzu8yEysK9Vt7edFLi4UH/Hj5crk5g9ke08Pn6ApMmWXcttsTFhSqaoqLo5NzFhVplunalKpaOHWlnxjp1qP1x0aKq27VVKqq+O3sW6N2bQo/nn6eT/VatqFVn0CCqkHnuOarOeecdYMkSCqRiY2nzjJ07qXqHlaX7u8jQFr5duygo3LCBvpcffwzs2UNzfUyhT6XUqlVUgdq5sxw2hIQA8+bR9blz6fliDps20QsLixZRhVRBAVCvHv1uX7QIOHSIdiAVO1Nu3swtfOYUG0u73To60u+ZinALH2OstFdeoQ0zzPX/Bas+avhr0Kw81mrdA7hSypZVFEptu7YN0WnR8HX1xeNtH7fG0tCqFbB+PfDJJ/JJE7NPzz1HLYpTpnDlTXl8fYGPPgJmzKCh0z//TCflAAVLv/4qV8Loq0kTOpFcsoQGoCcn01tVGjak4KRePYM/jRpDt50xP58qSqqSl0e/xxYvpn+3bEnf1/BwZdZUVSil1VLFHEAhpa45c2iQ/Y0bwIcfAu++q8yadIldN/v3B55+mqqkyhvs3q8f/Y5ISKBAtpNlC7trDNG6164dbSJSkb59KTTkSqnq59ln6W+s0FD6faT71rQpt22z8sXE0O8ElYoqu/lvOlYZDqVYGcfjqVLK0kPOATmUcnEBAgMt/vCsEk19qXTtTs4dpOWlwcfVBwCw9DgNOJ8UPgm1nCr5i9XMHnyQ3ph9Cw4G9u+39ipsX0gIDXmeNYsqaMLCgFdfNb7dUa2mSqxnnqG5MOnp8ltGRsl/p6fTH5fz5xsegNU0ajVVORUW6lcpdf48tWueO0f/nj6d/qivLAwwlAil0tPp1evSJwr//kuDxL28ys7nc3MDPv2UWgoXLaK5Tkq+gJSaCmzdSteXLKFKsYq4uNBctA0bKJjlUMo8qmrdE3r1kmcC3rolP8+Yfdu+nWbaAdQuLlrGBbWafge0bEkvEE6ZYtpGDKz6WLuWLvv04d8HrGocSrESJEmyiUqpxo15S3Fb4+niCX93fyRlJyHqbhQ6BXXC1ZSr2H5tO1RQYVrnadZeImM1Tvv2NAhdKb6+1XNnR2tydaVQKj+/8tutWEFDxPPzadOS778HHn5Y+fV4eVG7c1YWhQfNm5f8+PLldPnkk3JbtK4RI2hTgj17qKVzwwbl1rZuHbXrtW1beSAlDBtGj79pE/Dmm8qtg8lEpVRFO+8J3t5UzXfyJFVLjR9v/rWZ240bVBFaU3eALSykFysAqmJ+6CGab6j7lpFBQeS1axQO//QT7ewaEmLdtTPrq+6bDzFl8Wk/KyE6LRopuSlwdnBGO/92Fn980QbSrJnFH5rpoXQL37ITywAADzV7qHgQOmOMMZlobamsUurAARoonp8PDBlCc77MEUgJ4lXruLiS709MlAeHl27dE1QqqmJycKDb7tql3Lp++YUun3hCv9sPGULrOXmSNrpgyiospK8toN+sUjFXqjq08H3zDb1A+s471l6J9Xz9NQVPdetSRe6IEdRa/OOPFFampdHP3T//0G1btwaSkmhX5Kwsa6+eWdOVK9RW7eAAjB5t7dUwe8ChVDX09+W/8ceFP4y6r6iS6hDQAS6Olm8SHzWKBqjW5D8CbJluKJVdkI3vT38PgAacM8YYK0sMO68olMrOpjY4SaIZSps3A/7+5l1TRTvwff897RLYo0fllUqtWwMz7/3af+klCi9MFRNDrbsqFfC4nuMJ/f3lHTu3bDF9Dayks2fpeevrq9+LhdUllNJqKYQBqE01JcW667GGO3eAt96i6++/X/5mFioVjdro3x+YNo1ab/38gDNnqNJSq7Xkipkt+f13unzwQQo1GasKh1LVzNoLa/Ho749izLoxuJJyxeD7F7fuBVm+dQ+g//Q+/pi2GGe2R4RSUalRWH1uNdLz09HUtykGhQ6y8soYY8w2iVCqova911+nXRXr16cKJEu0CpU37FyjkQecT51a9THeeotONi5eBJYuNX1NotWjTx/DdhgcOpQuxcB/phzRute1q34jFXr3pufv5cs0gN5e7dwJXL9O17Oz6eeyplmwgCqh2rcHJk/W7z4hIVQ96exMl/Pnm3GBzGZJkvz7XN8XGBjjUKoaOX7rOJ7e+HTxv42plhKhVJdgyw85Z7ZPDDu/evdq8YDz6V2mQ63iXyWMMVaeytr39u0DvviCrn/3XckdaM2pvFBqxw7g5k2gdm392i18fICFC+n6m2/qt2NjZcRsNH1b94Rhw+hy1y4gN9e0NbCS9B1yLvj6UogBKL9hRXIycPu2ssesyNdf06WoFvziC9oYoKY4d47aFwHaBdTBQf/7du8uD0ZfuJB2iGU1y9mz1Pbp4gIMH27t1TB7wWeS1URseiwe+f0R5BXlIdCDtq1bG7nWoGMUaYtwKuEUAOsMOWe2T1RKHYk9grNJZ+Hm6IaJHSZaeVWMMWa7KqqUysqitj2AhggPsmDBaXmhlDgJffpp2mVPHxMnAh070rDj//3P+PWcPUsnws7Ohs8fadeOKqtyc2nnQKYcfYec6zJHC19qKrWMhoWZf3bYjRty1d3atfSY6enAV1+Z93FthSQB//d/1Ho3ahTQr5/hx3jiCaoABYBnnwUOH1ZyhczWida9hx+mjTUY0weHUtVAVkEWHvn9ESRmJaKNXxscmXwEjmpHnE06i0t3Lul9nAvJF5BblAsvFy80r9O86juwGkeEUhpJAwB4ot0T8HXjrboYY6wiFc2UevVVIDqaWl4++cSyayodSsXGyifiFQ04L4+Dg1zptXIlcOKEcesRVVIPP1z+7JrKqFRyC9+mTcY9vjnl59POZHv20MyuN98EnnkGGDBA/trZort3aVgxIM/t0ocIMfbuVW4tP/9MM47u3AGmT6fgxFy++YaOP2AA0LKlHLZ+/nnNGN791180uNzFheZpGevdd2kwekEBVcvcvKnYEpkNkyQ5lOJd95ghOJSyc1pJiyc3PImIxAjUq1UPmx7fhIY+DTGw6UAAhrXwFbfuBXXhdixWLl83X9R2q1387xldZlhxNYwxZvvKa98Tu1UBFOZY+tXk0rvvffcdVUb07w+0aGHYsXr2pMoISaKh54YGBlot8OuvdH3CBMPuK+jOlTJnYKGPkyeB8eOpjSkoiKrOmjWjkGPyZNrI5ccfKaR66SXbre46Rn8SolkzoE4d/e/XuzddXrxoeksnQN/P5cvlf//1F1UwmUN+Pv0sABR+AcCYMUBoKA07F9WE1nT1Kn2NxS6ZSsrPB2bPputz5tDug8ZSq4GffqIZsbdvU5ttZqYiy2Q27L//qNrQw8O8O8iy6oeTBzv3+p7XsfHSRjg7OGPjuI1o5NMIADAmbAwAw1r4ioecc+seq4SoluoV0gvtA9pbeTWMMWbbSrfvZWYCkybR9alTKaywNLH7XmJiyRNxQ6qkdH34IVCrFrXpGBoY7N9P4Zi3t/EnMfffT48fF0c7f1lLfDwwcCAN+T16lIZ9SxIFUy1bUovmlCm0m9nIkXSfiROp/dHWiNY9fedJCXXqyLOYlJgrtX8/BVzu7sCsWfS+F14wz3ypdeuoGis4WJ5V5ugot6J98on155a98w5w8CDtdqf0WhYvpgHvgYHAa6+ZfjwPD+Dvv2mXzHPnKLzWaEw/LrNdYsD5o4/S72TG9MWhlB1bFbEKHx36CADw/SPfo0eDHsUfe7Tlo3BSO+F88nlE3o7U63jH4uVKKcYqMqDxAKhVaszrNc/aS2GMMZtXulJq7lxqZWnUSN523tL8/Kj1TqulQCo+HqhXj9ptjBEcLJ/EvvqqYSfLonVv9Gg5wDOUq6sc7llrFz5JorDx7l2qDlm3Djh+nKqFsrMpWNm+naptXn+dqqWaNKHnwssvW2fNlTF0yLkuJVv4RJXU+PE0OLttWwqkXnrJ9GOXJqoXn3+ewijhiSeozTYxkVowreXOHeCPew0QiYnKVm4lJADvvUfXP/qIAiUlNGhA1W0uLhRQiYCPVT8ajfyiBLfuMUNxKGWnDtw8gCmbpgAA5veejwntSta8+7j6YFAoTU1de6Hqly2zC7JxPvk8AK6UYpV7p/87SJydiCHNhlh7KYwxZvN0Z0rt2iWfSH7/PeDpaZ01OThQNQRAVTsAtZY5Oxt/zNmzqQLr5k2av6OP/HwKbwDjW/cEUdlirblSS5fSDoaurtSOOGoU0LkzhX0qVdnbe3gAq1bRx77/3rbmYUmS3L5nyJBzQalh58nJwJ9/0vVp0+j5uXIltYb99puyX7OICKr0c3SkjQd0OTnJoetHH9GcJGv48Uf6mRG/Uz76SLlqqf/9j2Zmde1q+s9iad26yWHexx/T58Gqn337KCz19aWKUcYMwaGUHYq6G4URa0agUFuI0WGj8Xb/t8u9XXEL34W1kKoYsnA68TS0khZBnkEI9gpWfM2s+nBQO6Ceez1rL4MxxuyCOIG8fZuCHwCYMYPmN1mTmCuVkEDBSOkTcUPVqkUnyQBVtCQkVH2frVuBtDQKs0SQYSzR+nfsGJCUZNqxDHXpElXAATQculUr/e7Xu7c8w+e556gSxhZcu0YVXy4utLuhofr0ocvz5037nH74ASgspKAkPJze16ULzTsCqP01Lc344+tatowuR40CAgLKfnziRApyY2NpVpKlabVyoP3pp1RpqVS11IkT9LUGgCVLKPRT2vjxwPz5dP2556hykFUvYsD5qFGmvcDBaiYOpexMel46hv02DCm5Kegc1Bk/Dv+xwqHkj7R4BM4Ozrh45yIu3L5Q6XF5nhRjjDGmPNG+t3gxndA2aUIzmKwtWOf1p4EDaV2mevxxqorIypJPQCvzyy/y/Uw9EQ4MpMokANiyxbRjGaKggNq78vJoZtQMA/f/ePddoHVrCtKmTbP+oHZAbt3r2NG4k8t69ehzAoADB4xbg24IM3VqyY+99RYNYI+PlwMqU6Slyc9FMeC8NFdXOXhcuBAoKjL9cQ3x77805NzTE3jqKfnny9RqKbFBAUDPY2Mq4/T19tvA4MEUNH7wgfkeh1leQYFc9fr449ZdC7NPHErZkSJtEcasG4OLdy4i2DMYf437C7WcKp4i5+3qjcGhgwFU3cJXHEoFcSjFGGOMKUVUSuXk0OUPPyg3r8UUuqFU6ZN+Y6lUFL4B9HmePl3xbdPS5PlPSrUL6e7CZynvvEM77tWuTS1K5bXqVcbVlSpvHB3ppE4MCrYmMeTclIBCVL4ZO1dq1y4gOpoG4I8dW/Jjbm7UxgfQ5a5dRi8TAH39c3IoSBO7B5ZnyhSgbl0aBi6qQixFVHI9+ST9/njqKWWqpX7/ndoWa9Uyf1iuVstzq377jb6OrHrYtQtITaUqQ1OrXlnNxKGUHZm1YxZ2Ru1ELada+PvxvxHkGVTlffRt4ROhVJdgHnLOGGOMKUVUSgHAiy/KrU3WJnbgCwqSwxwl3HcfvVIuSTTAu6I/Pf78k15db93auBax8oi5Ujt3yoPlzenwYaqaASgYCKr6z7JydewILFhA12fMAG7dUmZ9xjJ25z1dpoZSYsD500+Xv4tX797AzJl0/bnnqDrPGJIkDzifPr3yUFF3B8D336dqLkuIjwc2bqTrIkB2cqI5UIDx1VI5OcArr9D1118vGVSbS6dOVC2l0cjtvsz+iTB9zBiaWciYoTiUsiN9G/aFu5M7fhnxCzoGdtTrPsNaDIOLgwsup1zGueRz5d7mdvZtRKdFAwA6B3VWbL2MMcZYTeflRZehobbVsjJqFIVBn35acqcxJXz4IVUA7dsnn0yXJtqlnnjC8OqiioSHUzCUnW36kO2qZGZS1YpWS1Uro0ebdrx582heUloazR4zpI1PkpRr+8vNpaHfgGmVUv360cnp2bNyW4++4uLkIebPP1/x7RYuBBo2pOH6xu7q9u+/wOXLVH30xBNV337GDMDHh+aIiSHs5vb99xTi9OxJuw8KplRLSRKFcHFx9DUUYZsliDBt1SrrB7AAfS0++wxo355C8tatgbAw+a1VK/ktLIxeXEhPt/aqbUdODu2wCPCue8x4HErZkVFhoxD9UjRGtNJ/z2YvF6/iXdIqauE7EX8CANCiTgv4uPqYvE7GGGOMkWeeAV54gbZDd3e39mpkTZsCZ86Y5yQiJESe9TNnDu0YpisuTg6NlJw/olLJVV/m3s3u//6P2o8aNgS++ML04zk6UhuZqyvt4qdPyBATQ3OO6tYFBgxQZie206dpXpK/P30fjeXnB7z6Kl2fOpWCE32tXEkhTJ8+FAJUxMMDWLGCrn/5JXDwoOHrFFVSTz0lB8iV8fKSZzC99575Z4BpNPLnWLrN1tnZ+GqpxYtpFzwHB/p6u7kpsly99OpF39uCAgrFrSk/n773s2dTgBoZSW8XL8pvly7Jbxcv0nOtTRtg2zbrrt1WbNlClYoNG5p3Jhmr3jiUsjPG7Ho2pnXlLXw85Jwxxhgzj6AgCi303ZGtunj1VRo+fv06ncTp+u03Opnv3ZtOZJSkO1fKXIHBxo3y/KiffqK5R0po2VJuB5wzB4iKKnsbSaK2wTFjaDj9J5/QTnn//EOzuTQa09Yghpx362Z6Bdubb1L1SUoKtdjp8/0oKgK+/ZauT5tW9e0HDgQmTaLrkycbFszcuiVX8unzWMKLL1Igdvas+eeXbdtGGyTUrl1+NZ4x1VI7d8qh8aefAg88oNhy9SbCtG++sd6uk8nJwP33U9WmgwP9LP37b9m3vXvltz/+oEA/Lg4YMoRedLh71zrrtxWidW/cOOWqXlnNw6FUDTC0+VC4Orri6t2riEiMKPPxY/EcSjHGGGNMOR4ecrviu+/SCaCwejVd6tMuZagHHqBqo5s3gfPnlT9+YiIFLABVKSk9I+zFF6n1LTub5imJkKmwEPj1VwqLevakk2ONhj7fzz+nqpkNG0zfjU6JIeeCszPw8890uXkzDb+vyubNFBbVqweM0LMx4NNPKQC9coV2eNPXt9/KFVlt2uh/v9q15V0WzV0tJWZrTZwob5qgy9BqqatXaXC8VkvHfPFFZderrwcfpPlSOTny5giWdOEC/SwdPkyh8vbtVC3Vr1/Zt7595bfRo6nC9OWXKYD58Udq96uoTbm6S08Htm6l67zrHjMFh1I1gIezBx5u9jCAsi18kiTJQ86DeMg5Y4wxxpTx1FM0xDsjg6pmAAqKzpyhQc2mzmEqT61acuWH0lUskkTVOHfuUAXQO+8oe3yAdihbtQrw9AQOHaKQZeFCqoaZMAE4fpyG50+eTJU6u3dTK+GPP9L9Fy827SRfiSHnutq2pVASoLa3Gzcqv70IYSZNKrlJQGV8fOT7LVpEoWdVFWOFhXJb3PTp+j2OrlmzqOXt2DH6HpjDjRvyCX9ls7X0rZbKyAAefZTmlnXvTjv6WauyRaWSw7SvvrLsjKbt2+nzv3GDqp6OHqX2V325u9MMqkOHqLoxMZEC1HHjgNu3zbZsm7RxI7VAtmyp3IYVrGbiUKqGKG7hiyzZwncj7Qbu5NyBk9oJ7QPaW2t5jDHGGKtm1Go5IFmxAjh3Tq6SGjKEKk7MQezCp/RcqW++oZDAxYU+D31DE0M1bCh/3d59l4Z4x8fTnKd33qFZUt99V3Lo9bhx8m5ms2YB69cb9piSRG2WN29SYNBZwX1vZs+m6q6sLGp3qmjXuuvXaZ4WAEyZYthjPPIIVWpotVSB17Ilfb8q2oVx40YgIYG+pvpWZOny85PX+M475tmJ79tv6fsyYADQrFnFt9OnWkqjoVDz4kXaZW/9evM9f/X16KNUZZSeDixdapnH/PJL4OGHaaOCPn0ohG3Z0rhjde9OM9hee43a/9asoRloa9aYf9aYrfj9d7p8/HFu3WOm4VCqhni42cNwc3TD9dTrOJVwqvj9okqqfUB7uDqWUxfMGGOMMWak3r2pIkqrpbDk11/p/RMmmO8xH6bicBw9qkzlQm4uVeCIHco+/JBOps1p4kRg5Ei6Hh5Os6tu3gTeeIMCkfLMnUuzkSSJvr5iPlRVUlPpsUQr13PP6Tf0W18ODlTJ5e5OA+6XLCn/dqJyadAgmpdlqO++AxYsoLDz2jUaDN6oEVWapaWVvK0YcP7ccxTqGGPuXLrvwYMUUJk6z0tXQQENIAfKDjgvT1XVUm+8QZWDrq4UyAUEKLdWY6nVtOskQC2o2dnme6yiImq5fPFF+l00aRKwaxdQp45px3V1pefX0aMUEt+5QwHxyJElW5aro9u36WsI8K57TAFSDRMbGysBkGJjY629FIt7bO1jEt6C9MrOV4rfN3vHbAlvQZq+eboVV8YYY4yx6ioqSpKcnSWJ4hJJ8vKSpJwc8z5meDg91qpVxh+joECSli+XpKAgee0PPSRJGo1y66xMYaEkXbsmSVqtYfd5+GFaa926knT1auW3P3xYkkJC6PbOzpL0xReGPZ4hli+nx3FxkaQLF0p+LC9PkurVo49v2GDa42RmStLnn0tSgwby983TU5LmzpWkW7fosQFJcnCQJFNPB376SZLUajreuHH0nFHC2rV0zIAA/Y/57bfyfXR/vn79Vf46rF6tzPqUUlgoSU2a0NoWLzbPY6SmStLAgfQYKpUkffyxeZ7j+fmS9OabkuToSI8VGChJe/cq/zilXbggSS+/LElvvGH689kQy5bR59mxo+Ue05JqcmZgDVwpVYOU18LHO+8xxhhjzJyaNKHBwMKoUebfgl53Fz5DabW0o1RYGFWpxMcDISE0qHvTJqrwsARHR5p5Y0hbjKMjtdR06kRVGw89VP7uZlot8PHHVMkWE0OPc/gw8MIL5mvDmTIFGDyYZtA89RTNdRI2bKDKi+Bg+XtnLA8PmrMVFSUPos7MpGq3xo3lCrRHHgHq1zftsZ58ktq1xNd99OiKWwYNIWZkPfsszV/Tx1NPUeunbrXUyZPy7oSvvAKMH2/62pTk6Eg7dQL0/cnPV/b4589Tm93OnTRvbsMGqnAzx3Pc2Rl46y3gxAlqCUxIoN393nlH2So6gCLG/fupVbl1a6o0e/dden6PH0+zzsxNd9c9xkxm7VTM0mpy6pldkC25v+8u4S1Ix+KOSYWaQqnW+7UkvAXpQvKFqg/AGGOMMWaE9HRJ8venV9b//df8j3fsmFwhk5+v3320WknavFmS2reXK0vq1ZOkJUuokseeJCRIUsOG9Dl0716yciYpSZIGDZI/x3Hj6PtjCXFxkuTrS4/71lvy+/v2Lfs+pWg0krRpkyT16iV/zoAk7dql3GNs3kwVYIAkPfigJGVlGX+sS5foOGq1JN28adh9V6yQq6WuX5ek+vXp30OGSFJRkfFrMqe8PEkKDqZ1rlihzDE1Gqq8Et+T+vUl6dQpZY6tj6wsSXrmGfm5dv/99DNpqqIiSfrzT0nq1k0+tkolSSNGyD9D4q1HD6q4Kyw0/XFLi42lxwUMf47ai5qcGVgDh1I1zLh14yS8BWnOjjnSmcQzEt6C5PmBp1SksdH/qRhjjDFWLVy6ROGAJWg0dGKub/iwf3/J0MLLS5LefZdawexVZKQk+fjQ5zNqFH1N/v2X2ooASXJ1pZYvc7XrVeS33+T2uePHaZ3i33Fx5n3sQ4ckaexYSZoxQ/k2zH/+kSR3d/pcevWSpLQ0447z8st0jGHDDL9vfr4cRorwr0UL49diKZ9/Tmtt0sT0ECU+Xm7XA6idNTFRkWUa7McfJalWLVqHn5/xQWhODrW/hobKn5eLiyQ9/7wkXbki3+7UKUl6+mlJcnKSbxcSIkmLFlEboykKCqjd9/33JalLFzp2z56mHdOW1fTMwNJUklRT9gcgcXFxaNCgAWJjY1Hf1JpdO7Th4gaMXDsSId4hmN97PqZsnoL+jfrjn6f/sfbSGGOMMcYU8+yzNCw6LIzamvLyaGh5eW9iyLKrK7Wwvfqq6UOQbcH+/cCDD9Lg7N69aRt7rRZo1QpYuxZo08Y66xo7lh6/ZUvaBW3FCmD4cGqvsmdHjlDLZHo6tVDu2GHY8yg3l1oYU1OBLVtol0pDffutvDOgtze1cjVvbvhxLCk7mwa137kD/PKL8RshbNxIP/cpKdQi/Omn1IJrzZ3hLl0Cxoyh3UdVKtpN8623qHWxMkVFtFviX3/RroFicLqvLw1tnzmTdo8sT2IisGwZvYnNHtzdaWfK9u2pbTU4mN7q1Su/JVmjAc6cAf75B/j3X/pdkpVV8jY//UQtrNVRTc8MLM2qodT+m/ux6PAinIw/iYSsBGwYuwHDWw6v8PZ7b+xF/x/7l3l/wuwEBHjot41ETX+C5Rbmwu8TP2QVZKG9f3ucSTqDV3u+ig8HfGjtpTHGGGOMKWbLFv3nEzk6ApMn0y5lwcHmXZel/fZbyVlCkyYBX3xBJ6nWkpJCgVhiovy+7dtp5z17d/o0MHAgBSxt2tAOZfrudvfTT8DTT1OIGhVFOxcaqqCAHvf6dZqpNniw4cewhvffB+bPpxlJZ88aNrstO5vm1n37Lf07PBxYvZrCV1uQm0vrE7O+evemnUjFqWh2Nn3OERH0/Dl9mkIs3RlbISG0A+jkyTQ3TR95efQ4n39O87XK4+QEBAXJIVVQEO30uW8fhaO6atcG+vWjWVkPPEChcnVV0zMDS6siozWv7IJstPdvj0kdJmHk2pF63+/yzMvwcpH3qvVzr2BvXFaGm5MbHmnxCH499yvOJJ0BwEPOGWOMMVb9DBlCQ6jv3KGqidJvrq7y9bp1qaqkOnr8ceDuXWDpUqrSeOIJa6+IqodWrgQefpj+3bgxVXRVB+HhVFXywAMUBPTpA+zeTaFCVcSA8ylTjAukABq4ffgwVWs1bWrcMaxhxgwavn/hAvD331Q5p4/jx6my6upVqkSaO5eGfjs7m3W5BnFzo+9tv370vT1wAOjQgZ7zERHAlStUwViapydV3D33HPDYY/oPvRdcXSmEnjgR2LOHKsni4ujt1i0gKYk2HLh5k95K8/Ki5+/99wP9+wPt2lluowdWs9hM+57qbZXelVKpr6bCx9XHqMfh1BP469JfGL5mePG/Y/4vBg28G1hvQYwxxhhjrMaZPp1ajL74gtomq5OoKAqmbt6kQGrOHKBBA6qOqV8f8PMreYJ/5gwFFY6OQGys/tVV1cn//gd88AHQuTO1HVbWdldQQDv2vfUWtbrVr0+VZv3LNtXYlGvXqH311KmS7w8IoEBTvHXoQDuXmjMEKiykakURUom3OnXo69ixY9VthtUVZwaWZZdPsw7LOyBfk482fm3wVt+30DOkp7WXZFcGhQ6Cp7MnMgsyEeARgPpe/IPGGGOMMcYsa+lSmo1jK21WSmralCpiBgygSpgXXyz5cScnapcSIZWoVBkxomYGUgDwf/8HLF4MnDhBVVPe3jRLSfctKYku796V7zd2LIWbvr7WWrn+QkOpku2774CMDDmAssb33MmJgtIGXJugmKVLKSxNTKT5XV9+CXTlpqQq2VUoFegRiOUPL0fnoM7I1+Tju1Pfod+P/fDfs/+hY2DHcu+Tn5+PfJ2G3MzMTAut1na5Orri0ZaP4pezv6BrcFeorDn9jzHGGGOM1UgqFQ2ir64aNKBgavFiCqZE61RCAlWp3LhBb7qmTrXCQm1EvXrU3rZ4MfDaa1XfvnZtuu0TT1h3mLmhXFyoXZFVL2vW0Nyv5cuBbt3ouTloEHD5MlVGsorZVSjVom4LtKjbovjfPRr0QFRqFD4/+jl+HvFzufdZuHAh3n77bUst0W7M7z0fSVlJmNtjrrWXwhhjjDHGWLXk50ctabp026bi4qhdLzaWBpzbevuZuc2bR0O+c3Loa+fvT5el3/z9KZTiGUfMVnz2Gc3/mjiR/r18OW248f33+oWsNZldhVLl6RrUFQdjD1b48Xnz5mHWrFnF/7516xbCqvNLMnpqUbcFdj6509rLYIwxxhhjrEbhtqmK+fnRYHjGbEFmZiYyMjKK/+3i4gIXF5cytysoAE6epFBVUKupfffIEUus1L7ZfbYckRSBQI/ACj/u4uICLy+v4jdPT08Lro4xxhhjjDHGGGP2JiwsDN7e3sVvCxcuLPd2d+4AGg1V8Ony96eqSFY5q1ZKZRVk4drda8X/jk6NRkRiBGq71UaIdwjm7Z6HW5m38NOInwAAi48uRmOfxmjt1xp5RXn47tR3+Cf6H+x8git+GGOMMcYYY4wxpozIyEgEBwcX/7u8KilmOquGUifiT6D/j3Lj9Kyd1Gb3dPunsWr4KiRkJSAmPab44wWaAszeORu3Mm+hllMttPNvh91P7kb/xjW8+ZoxxhhjjDHGGGOK8fT0hJeXV5W3q1sXcHCg3SF1JSXV3N00DaGSJEmy9iIsKS4uDg0aNEBsbCzq169v7eUwxhhjjDHGGGPMRhiTGXTrBnTtCnz5Jf1bqwVCQoCZM3nQeVXsftA5Y4wxxhhjjDHGmLXMmgU8/TTQuTOFU4sXA9nZ8m58rGIcSjHGGGOMMcYYY4wZaexY4PZtYMECGm7eoQOwfXvZ4eesLA6lGGOMMcYYY4wxxkwwcya9McOorb0AxhhjjDHGGGOMMVbzcCjFGGOMMcYYY4wxxiyOQynGGGOMMcYYY4wxZnEcSjHGGGOMMcYYY4wxi+NQijHGGGOMMcYYY4xZHIdSjDHGGGOMMcYYY8ziOJRijDHGGGOMMcYYYxbHoRRjjDHGGGOMMcYYszgOpRhjjDHGGGOMMcaYxXEoxRhjjDHGGGOMMcYsjkMpxhhjjDHGGGOMMWZxHEoxxhhjjDHGGGOMMYvjUIoxxhhjjDHGGGOMWZyjtRdgaVqtFgCQkJBg5ZUwxhhjjDHGGGPMloisQGQHzLxqXCiVlJQEAOjatauVV8IYY4wxxhhjjDFblJSUhJCQEGsvo9pTSZIkWXsRllRUVITTp0/D398farX9dS9mZmYiLCwMkZGR8PT0tPZymA3g5wSzBfw8ZKXxc4KVxs8JVho/J5gt4OchK02r1SIpKQnh4eFwdKxxdTwWV+NCKXuXkZEBb29vpKenw8vLy9rLYTaAnxPMFvDzkJXGzwlWGj8nWGn8nGC2gJ+HjFmX/ZUKMcYYY4wxxhhjjDG7x6EUY4wxxhhjjDHGGLM4DqXsjIuLC9588024uLhYeynMRvBzgtkCfh6y0vg5wUrj5wQrjZ8TzBbw85Ax6+KZUowxxhhjjDHGGGPM4rhSijHGGGOMMcYYY4xZHIdSjDHGGGOMMcYYY8ziOJRijDHGGGOMMcYYYxbHoVQFFi5ciC5dusDT0xN+fn4YPnw4Ll++XOI2eXl5mDFjBurUqQMPDw+MGjUKSUlJxR8/c+YMHn/8cTRo0ABubm5o1aoVlixZUuIYBw8eRM+ePVGnTh24ubmhZcuW+Pzzz6tcnyRJWLBgAQIDA+Hm5oYBAwbg6tWrJW7z/vvvo0ePHqhVqxZ8fHz0/tzPnj2L3r17w9XVFQ0aNMDHH39c4uMXLlzAqFGj0KhRI6hUKixevFjvY9uzmvqcyMvLwzPPPIO2bdvC0dERw4cPL3ObvXv3QqVSlXlLTEzU6zGY/uz9eXjjxg1MnjwZjRs3hpubG5o2bYo333wTBQUFVR5779696NixI1xcXBAa+v/t3XlM0+cfB/A3MCgWh4oil4DoBi7qgGk0dAcyD9g8mMuMN7rMA8VMzBSjsqh/bF7zCKjBZRHMNDqvybZ4bAreuEysAyriMcUxC26ZqHiUo5/fH/vRUKGl4FZs+34l/ef7PP08z1M/+cR8aJ6+hKysLKPxkydPYuTIkfD394eTkxMOHDjQbEx74Kg5odVqMWHCBISGhsLZ2RnJycmN5mRlZTWqS+7u7s3u2dbZek4AwKhRoxAUFAR3d3f4+flh8uTJuH37drOxWSdMc9S8YK14fthDDtbT6XSIiIiAk5MTLl682Gxs1iYi09iUMuHEiRNISkrCuXPn8NNPP6GmpgbDhg3Dw4cPDXPmzZuH77//Hnv27MGJEydw+/ZtvP/++4bx/Px8dO3aFdu3b4dGo8GSJUuwaNEibNy40TDHw8MDc+bMwcmTJ1FcXIzU1FSkpqbiyy+/NLu/1atXIy0tDRkZGfj555/h4eGB2NhYPHnyxDCnuroaY8aMwaxZsyw+9/379zFs2DAEBwcjPz8fa9aswbJly4z28+jRI/To0QMrV66Er6+vxbFtnaPmRF1dHdq1a4ePP/4YQ4YMMTu3pKQEWq3W8OratavF65BlbD0PL1++DL1ejy1btkCj0WD9+vXIyMjA4sWLzca9ceMGhg8fjpiYGFy8eBHJycmYNm0ajhw5Ypjz8OFDhIeHY9OmTS36TG2do+aETqeDt7c3UlNTER4ebnKep6enUV0qLS01G9ce2HpOAEBMTAx2796NkpIS7Nu3D9evX8cHH3xgNi7rhHmOmhesFc8Pe8jBeikpKfD397fo3KxNRM0QssidO3cEgJw4cUJERCorK8XV1VX27NljmFNcXCwAJC8vz2Sc2bNnS0xMjNm1Ro8eLZMmTTI5rtfrxdfXV9asWWN4VllZKQqFQnbu3NlofmZmpnTo0MHsmvU2b94snTp1Ep1OZ3i2cOFCCQsLa3J+cHCwrF+/3qLY9sZRcqKhKVOmSHx8fKPnubm5AkDu3r3b4pj0bGw5D+utXr1aQkJCzK6dkpIivXv3Nno2duxYiY2NbXI+APn222/NxrRXjpITDUVHR8vcuXMbPW9trbM39pAT2dnZ4uTkJNXV1SbnsE60jKPkRUOsFc8XW83BgwcPSq9evUSj0QgAUavVZtdmbSIyj9+UstC9e/cAAF5eXgD+6dLX1NQYfXOkV69eCAoKQl5entk49TGaolarcfbsWURHR5ucc+PGDZSXlxut3aFDBwwcONDs2pbIy8vDW2+9BTc3N8Oz2NhYlJSU4O7du88U2944Sk60REREBPz8/DB06FCcOXPGaus6MnvIw+bWBv6pTU9/Uy82Ntaq+W0rHCUnLFVVVYXg4GAEBgYiPj4eGo3mX4lrS2w9J/7++2/s2LEDKpUKrq6uJmOzTrSMo+SFpVgrrM8Wc7CiogLTp0/H119/DaVS2fwhwdpE1Bw2pSyg1+uRnJyM119/HX369AEAlJeXw83NrdG9PD4+Pibv0Tl79iy++eYbzJgxo9FYt27doFAo0L9/fyQlJWHatGkm91Mf38fHx+K1LVVeXt5k3IbrkmPlhCX8/PyQkZGBffv2Yd++fQgMDMSgQYNw4cKF/3xtR2YPeXjt2jWkp6dj5syZJuPWx24q7v379/H48WOz73UkjpQTlggLC8PWrVuRnZ2N7du3Q6/XQ6VSoays7Jlj2wpbzomFCxfCw8MDnTt3xq1bt5CdnW32rKwTlnOkvLAEa4X12WIOigimTp2KxMRE9O/f3+KzsjYRmcemlAWSkpJQVFSEXbt2tTpGUVER4uPjsXTpUgwbNqzR+KlTp3D+/HlkZGRgw4YN2LlzJwBgx44daN++veF16tSpVu/hab179zbEfeedd/61uI6AOWEsLCwMM2fORL9+/aBSqbB161aoVCqLLpWk1rP1PPzjjz8QFxeHMWPGYPr06YbnDeMmJia2+myOiDlhLCoqCgkJCYiIiEB0dDT2798Pb29vbNmypcV7s1W2nBMLFiyAWq3Gjz/+CBcXFyQkJEBEALBOPCvmhTHWCuuzxRxMT0/HgwcPsGjRIpNzWJuIWu6Ftt7A827OnDn44YcfcPLkSXTr1s3w3NfXF9XV1aisrDTq5ldUVDS6/PvSpUsYPHgwZsyYgdTU1CbXCQkJAQD07dsXFRUVWLZsGcaPH49Ro0Zh4MCBhnkBAQHQarWGtfz8/IzWjoiIsPhsBw8eRE1NDQCgXbt2hnM1/IWL+rj1Y+R4OdFaAwYMwOnTp58pBplm63l4+/ZtxMTEQKVSNbp4tOGv2Hh6ehrO1VRt8vT0fOZctReOlhOt4erqisjISFy7dq3VMWyJredEly5d0KVLF4SGhuKVV15BYGAgzp07h6ioKNaJZ+BoedEajlYrrM1WczAnJwd5eXlQKBRG6/Tv3x8TJ07Etm3bWJuIWqON77R6bun1eklKShJ/f3+5cuVKo/H6i/j27t1reHb58uVGF/EVFRVJ165dZcGCBRavvXz5cgkODja7N19fX/niiy8Mz+7du/evXnTe8MLIRYsW8aJzcdycaMjURedNGTJkiIwePbrFa5B59pCHZWVl8vLLL8u4ceOktrbWorVTUlKkT58+Rs/Gjx/PS0LFcXOiIVOXFz+ttrZWwsLCZN68eS1ew5bYQ048rbS0VABIbm6uyTmsE+Y5al40xFrRtmw9B0tLS6WwsNDwOnLkiACQvXv3yu+//24yNmsTkXlsSpkwa9Ys6dChgxw/fly0Wq3h9ejRI8OcxMRECQoKkpycHDl//rxERUVJVFSUYbywsFC8vb1l0qRJRjHu3LljmLNx40b57rvv5MqVK3LlyhX56quv5MUXX5QlS5aY3d/KlSulY8eOkp2dLQUFBRIfHy8hISHy+PFjw5zS0lJRq9WyfPlyad++vajValGr1fLgwQOTcSsrK8XHx0cmT54sRUVFsmvXLlEqlbJlyxbDHJ1OZ4jl5+cn8+fPF7VaLVevXm3RZ2xrHDUnREQ0Go2o1WoZOXKkDBo0yPC+euvXr5cDBw7I1atXpbCwUObOnSvOzs5y9OhRSz9espCt52FZWZm89NJLMnjwYCkrKzNa35zffvtNlEqlLFiwQIqLi2XTpk3i4uIihw8fNsx58OCBITcByLp160StVktpaWmLPmNb46g5ISKGf+9+/frJhAkTRK1Wi0ajMYwvX75cjhw5ItevX5f8/HwZN26cuLu7G82xR7aeE+fOnZP09HRRq9Vy8+ZNOXbsmKhUKunZs6c8efLEZFzWCfMcNS9EWCueF7aeg0+7ceOGRb++x9pEZB6bUiYAaPKVmZlpmPP48WOZPXu2dOrUSZRKpYwePdroP9FLly5tMkbDLn1aWpr07t1blEqleHp6SmRkpGzevFnq6urM7k+v18unn34qPj4+olAoZPDgwVJSUmI0Z8qUKU2u39xfk3799Vd54403RKFQSEBAgKxcudJovL4AP/2Kjo42G9fWOXJOBAcHN/m+eqtWrZKePXuKu7u7eHl5yaBBgyQnJ6f5D5VazNbzMDMz0+QZmpObmysRERHi5uYmPXr0MDpz/XhTcadMmdJsbFvmyDnR3J6Tk5MlKChI3NzcxMfHR9599125cOFC8x+qjbP1nCgoKJCYmBjx8vIShUIh3bt3l8TERCkrK2v27KwTpjlyXrBWPB9sPQefZmlTSoS1icgcJ5H/3wxIRERERERERERkJfz1PSIiIiIiIiIisjo2pYiIiIiIiIiIyOrYlCIiIiIiIiIiIqtjU4qIiIiIiIiIiKyOTSkiIiIiIiIiIrI6NqWIiIiIiIiIiMjq2JQiIiIiIiIiIiKrY1OKiIiIiIiIiIisjk0pIiIiIiIiIiKyOjaliIiIyOZNnToVTk5OcHJygqurK3x8fDB06FBs3boVer3e4jhZWVno2LHjf7dRIiIiIjJgU4qIiIjsQlxcHLRaLW7evIlDhw4hJiYGc+fOxYgRI1BbW9vW2yMiIiKip7ApRURERHZBoVDA19cXAQEBeO2117B48WJkZ2fj0KFDyMrKAgCsW7cOffv2hYeHBwIDAzF79mxUVVUBAI4fP44PP/wQ9+7dM3zratmyZQAAnU6H+fPnIyAgAB4eHhg4cCCOHz/eNgclIiIishNsShEREZHdevvttxEeHo79+/cDAJydnZGWlgaNRoNt27YhJycHKSkpAACVSoUNGzbA09MTWq0WWq0W8+fPBwDMmTMHeXl52LVrFwoKCjBmzBjExcXh6tWrbXY2IiIiIlvnJCLS1psgIiIiehZTp05FZWUlDhw40Ghs3LhxKCgowKVLlxqN7d27F4mJifjrr78A/HOnVHJyMiorKw1zbt26hR49euDWrVvw9/c3PB8yZAgGDBiAzz///F8/DxEREZEjeKGtN0BERET0XxIRODk5AQCOHj2KFStW4PLly7h//z5qa2vx5MkTPHr0CEqlssn3FxYWoq6uDqGhoUbPdTodOnfu/J/vn4iIiMhesSlFREREdq24uBghISG4efMmRowYgVmzZuGzzz6Dl5cXTp8+jY8++gjV1dUmm1JVVVVwcXFBfn4+XFxcjMbat29vjSMQERER2SU2pYiIiMhu5eTkoLCwEPPmzUN+fj70ej3Wrl0LZ+d/rtXcvXu30Xw3NzfU1dUZPYuMjERdXR3u3LmDN99802p7JyIiIrJ3bEoRERGRXdDpdCgvL0ddXR0qKipw+PBhrFixAiNGjEBCQgKKiopQU1OD9PR0jBw5EmfOnEFGRoZRjO7du6OqqgrHjh1DeHg4lEolQkNDMXHiRCQkJGDt2rWIjIzEn3/+iWPHjuHVV1/F8OHD2+jERERERLaNv75HREREduHw4cPw8/ND9+7dERcXh9zcXKSlpSE7OxsuLi4IDw/HunXrsGrVKvTp0wc7duzAihUrjGKoVCokJiZi7Nix8Pb2xurVqwEAmZmZSEhIwCeffIKwsDC89957+OWXXxAUFNQWRyUiIiKyC/z1PSIiIiIiIiIisjp+U4qIiIiIiIiIiKyOTSkiIiIiIiIiIrI6NqWIiIiIiIiIiMjq2JQiIiIiIiIiIiKrY1OKiIiIiIiIiIisjk0pIiIiIiIiIiKyOjaliIiIiIiIiIjI6tiUIiIiIiIiIiIiq2NTioiIiIiIiIiIrI5NKSIiIiIiIiIisjo2pYiIiIiIiIiIyOrYlCIiIiIiIiIiIqv7H/yd1ci8SExmAAAAAElFTkSuQmCC\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"# Create a dual-axis plot\\n\",\n \"fig, ax1 = plt.subplots(figsize=(12, 6))\\n\",\n \"\\n\",\n \"# Plotting price close on the first axis\\n\",\n \"ax1.set_xlabel(\\\"Date\\\")\\n\",\n \"ax1.set_ylabel(\\\"Price Close\\\", color=\\\"green\\\")\\n\",\n \"ax1.plot(ohlcv_df[\\\"price_close\\\"], color=\\\"green\\\")\\n\",\n \"ax1.tick_params(axis=\\\"y\\\", labelcolor=\\\"green\\\")\\n\",\n \"\\n\",\n \"# Instantiate a second axes that shares the same x-axis\\n\",\n \"ax2 = ax1.twinx()\\n\",\n \"ax2.set_ylabel(\\\"Volume Traded\\\", color=\\\"blue\\\")\\n\",\n \"ax2.plot(ohlcv_df[\\\"volume_traded\\\"], color=\\\"blue\\\")\\n\",\n \"ax2.tick_params(axis=\\\"y\\\", labelcolor=\\\"blue\\\")\\n\",\n \"\\n\",\n \"# Title and layout\\n\",\n \"plt.title(\\\"Price Close and Trading Volume Over Time\\\")\\n\",\n \"fig.tight_layout()\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"id\": \"1GzZfwEqQM0L\"\n },\n \"outputs\": [],\n \"source\": [\n \"# Calculate 7-day and 30-day moving averages\\n\",\n \"ohlcv_df[\\\"7_day_MA\\\"] = ohlcv_df[\\\"price_close\\\"].rolling(window=7).mean()\\n\",\n \"ohlcv_df[\\\"30_day_MA\\\"] = ohlcv_df[\\\"price_close\\\"].rolling(window=30).mean()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\",\n \"height\": 607\n },\n \"id\": \"Yn02gj5pQcoH\",\n \"outputId\": \"5ef86567-6dea-40dd-dd86-65eb5c8f89ad\"\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"# Plotting moving averages\\n\",\n \"plt.figure(figsize=(12, 6))\\n\",\n \"plt.plot(ohlcv_df[\\\"price_close\\\"], label=\\\"Closing Price\\\", color=\\\"skyblue\\\")\\n\",\n \"plt.plot(ohlcv_df[\\\"7_day_MA\\\"], label=\\\"7-Day Moving Average\\\", color=\\\"green\\\")\\n\",\n \"plt.plot(ohlcv_df[\\\"30_day_MA\\\"], label=\\\"30-Day Moving Average\\\", color=\\\"red\\\")\\n\",\n \"\\n\",\n \"plt.title(\\\"Closing Price and Moving Averages\\\")\\n\",\n \"plt.xlabel(\\\"Date\\\")\\n\",\n \"plt.ylabel(\\\"Price\\\")\\n\",\n \"plt.legend()\\n\",\n \"plt.grid(True)\\n\",\n \"plt.xticks(rotation=45)\\n\",\n \"plt.tight_layout()\\n\",\n \"plt.show()\"\n ]\n }\n ],\n \"metadata\": {\n \"colab\": {\n \"include_colab_link\": true,\n \"provenance\": []\n },\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"name\": \"python\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 0\n}\n" }, { "path": "pyairbyte_notebooks/PyAirbyte_Document_Creation_RAG_with_Langchain_Demo.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"\\\"Open\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"y4_-FI2aLFYP\"\n },\n \"source\": [\n \"# PyAirbyte Demo\\n\",\n \"\\n\",\n \"This demo uses the PyAirbyte libary to read records from Github, converts those records to documents, which can then be passed to LangChain for RAG.\\n\",\n \"\\n\",\n \"#### Prerequisites:\\n\",\n \"\\n\",\n \"- A Github personal access token. For details on configuring authetication credentials, refer to the Github source connector [documentation](https://docs.airbyte.com/integrations/sources/github).\\n\",\n \"\\n\",\n \"- OpenAI API Key. You can create one by signing up on https://openai.com/ and\\n\",\n \"going to the \\\"Keys\\\" tab on left sidebar.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"LZ5mUBw_g2kT\"\n },\n \"source\": [\n \"## Install PyAirbyte and other dependencies\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"id\": \"kZjfRtSGoQHJ\"\n },\n \"outputs\": [],\n \"source\": [\n \"# Add virtual environment support for running in Google Colab\\n\",\n \"!apt-get install -qq python3.10-venv\\n\",\n \"\\n\",\n \"# Install PyAirbyte & Langchain modules\\n\",\n \"%pip install --quiet airbyte langchain langchain_openai langchainhub chromadb\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"3MtTFRoYg6bP\"\n },\n \"source\": [\n \"## Load the Source Data using PyAirbyte\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"id\": \"TEzG3kXznVtz\"\n },\n \"outputs\": [],\n \"source\": [\n \"import airbyte as ab\\n\",\n \"\\n\",\n \"# Configure and read from the source\\n\",\n \"read_result = ab.get_source(\\n\",\n \" \\\"source-github\\\",\\n\",\n \" config={\\n\",\n \" \\\"repositories\\\": [\\\"airbytehq/pyAirbyte\\\"],\\n\",\n \" \\\"credentials\\\": {\\n\",\n \" \\\"personal_access_token\\\": ab.get_secret(\\\"GITHUB_PERSONAL_ACCESS_TOKEN\\\")\\n\",\n \"\\n\",\n \" }\\n\",\n \" },\\n\",\n \" streams=[\\\"issues\\\"],\\n\",\n \").read()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"nM4mWwmkhLS_\"\n },\n \"source\": [\n \"## Read a single record from stream to examine the fields\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"id\": \"kj1u-znnuYKu\"\n },\n \"outputs\": [],\n \"source\": [\n \"first_record = next((record for record in read_result[\\\"issues\\\"]))\\n\",\n \"\\n\",\n \"# Print the fields list, followed by the first full record.\\n\",\n \"display(list(first_record.keys()))\\n\",\n \"display(first_record)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"ZiGGcJdUjnsf\"\n },\n \"source\": [\n \"## Use PyAirbyte `to_documents()` method on a dataset\\n\",\n \"\\n\",\n \"This demo uses a new `to_documents()` method, which accepts record property names which point to specific aspects of the document.\\n\",\n \"\\n\",\n \"When we set `render_metadata=True`, then metadata properties are also published to the markdown file. This option is helpful for small-ish documents, when passing the entire document to the LLM. It will be less helpful on long documents which are planned to be split into smaller chuns.\\n\",\n \"\\n\",\n \"Note: We use `rich` to print the documents as markdown, although that's not strictly necessary.\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"id\": \"TmA_bF6rjoXy\"\n },\n \"outputs\": [],\n \"source\": [\n \"import textwrap\\n\",\n \"from rich.console import Console\\n\",\n \"\\n\",\n \"# convert incoming stream data into documents\\n\",\n \"docs = list(read_result[\\\"issues\\\"].to_documents(\\n\",\n \" title_property=\\\"title\\\",\\n\",\n \" content_properties=[\\\"body\\\"],\\n\",\n \" metadata_properties=[\\\"state\\\", \\\"url\\\", \\\"number\\\"],\\n\",\n \" render_metadata=True,\\n\",\n \"))\\n\",\n \"\\n\",\n \"# print a doc comprising github issue\\n\",\n \"console = Console()\\n\",\n \"console.print(str(docs[10]))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"0mkPWz8NkTbH\"\n },\n \"source\": [\n \"## Use Langchain to build a RAG pipeline.\\n\",\n \"\\n\",\n \"Here, we just show a generic method of splitting docs. This and the following steps are copied from a generic LangChain tutorial.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"id\": \"s1mFwmOGkNqe\"\n },\n \"outputs\": [],\n \"source\": [\n \"# Split the docs so they can be stored in vector database downstream\\n\",\n \"from langchain.text_splitter import RecursiveCharacterTextSplitter\\n\",\n \"\\n\",\n \"splitter = RecursiveCharacterTextSplitter(chunk_size=512, chunk_overlap=30)\\n\",\n \"\\n\",\n \"chunked_docs = splitter.split_documents(docs)\\n\",\n \"\\n\",\n \"print(f\\\"Created {len(chunked_docs)} document chunks.\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"SdRovb3c1eoH\"\n },\n \"source\": [\n \"Now we can publish the chunks to a vector store. Ensure you have added your `OPENAI_API_KEY` to the secrects tab on left.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"id\": \"Lj7rXrF417tX\"\n },\n \"outputs\": [],\n \"source\": [\n \"from langchain_community.vectorstores import Chroma\\n\",\n \"from langchain_openai import OpenAIEmbeddings\\n\",\n \"import os\\n\",\n \"\\n\",\n \"os.environ['OPENAI_API_KEY'] = ab.get_secret(\\\"OPENAI_API_KEY\\\")\\n\",\n \"\\n\",\n \"# store into vector db\\n\",\n \"vectorstore = Chroma.from_documents(documents=chunked_docs, embedding=OpenAIEmbeddings())\\n\",\n \"print(\\\"Chunks successfully stored in vectorstore.\\\")\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"MSP8TgBf38VN\"\n },\n \"source\": [\n \"Set up a RAG application using LangChain.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"id\": \"kaamV9Q93v52\"\n },\n \"outputs\": [],\n \"source\": [\n \"from langchain_openai import ChatOpenAI\\n\",\n \"from langchain import hub\\n\",\n \"from langchain_core.output_parsers import StrOutputParser\\n\",\n \"from langchain_core.runnables import RunnablePassthrough\\n\",\n \"\\n\",\n \"retriever = vectorstore.as_retriever()\\n\",\n \"prompt = hub.pull(\\\"rlm/rag-prompt\\\")\\n\",\n \"llm = ChatOpenAI(model_name=\\\"gpt-3.5-turbo\\\", temperature=0)\\n\",\n \"\\n\",\n \"\\n\",\n \"def format_docs(docs):\\n\",\n \" return \\\"\\\\n\\\\n\\\".join(doc.page_content for doc in docs)\\n\",\n \"\\n\",\n \"rag_chain = (\\n\",\n \" {\\\"context\\\": retriever | format_docs, \\\"question\\\": RunnablePassthrough()}\\n\",\n \" | prompt\\n\",\n \" | llm\\n\",\n \" | StrOutputParser()\\n\",\n \")\\n\",\n \"print(\\\"Langchain RAG pipeline set up successfully.\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"3eDeZ6-74DH7\"\n },\n \"source\": [\n \"Ask a question.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"id\": \"_haRAZyJ4Gjf\"\n },\n \"outputs\": [],\n \"source\": [\n \"console.print(rag_chain.invoke(\\\"Show me all documentation related issues, along with issue number, each on a new line.\\\"))\"\n ]\n }\n ],\n \"metadata\": {\n \"colab\": {\n \"provenance\": []\n },\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"name\": \"python\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 0\n}\n" }, { "path": "pyairbyte_notebooks/PyAirbyte_GA4_Demo.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"colab_type\": \"text\",\n \"id\": \"view-in-github\"\n },\n \"source\": [\n \"\\\"Open\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"En5baAhvYE_y\"\n },\n \"source\": [\n \"In this demo, we use PyAirbyte to extract data from Google Analytics 4, followed by a series of transformations and analyses to derive meaningful insights from this data.\\n\",\n \"\\n\",\n \"#### Prerequisites:\\n\",\n \"\\n\",\n \"- A Google Analytics account with access to a GA4 property. For details on configuring authetication credentials, refer to the [documentation](https://docs.airbyte.com/integrations/sources/google-analytics-data-api).\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"8awBDcLvRW2g\"\n },\n \"source\": [\n \"### Installing PyAirbyte\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"id\": \"xrhNw5kK5Lvx\"\n },\n \"outputs\": [],\n \"source\": [\n \"# Add virtual environment support in Google Colab\\n\",\n \"!apt-get install -qq python3.10-venv\\n\",\n \"\\n\",\n \"# Install PyAirbyte\\n\",\n \"%pip install airbyte\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"mYsTAS1wRgO_\"\n },\n \"source\": [\n \"### Load source data from Google Analytics 4 to local cache\\n\",\n \"\\n\",\n \"In this section, we establish a connection to GA4 via PyAirbyte. The source connector is configured with necessary parameters like the GA4 property ID, the service account JSON key, and the data period we are interested in. Check the [docs](https://docs.airbyte.com/integrations/sources/google-analytics-data-api) for more details on these parameters.\\n\",\n \"\\n\",\n \"After configuring the source connector, we perform a `check()` to ensure that the configuration is correct and the connection to the API is successful. Then, we list the available streams for this source and select the ones we are interested in syncing. In this case, we are only syncing the `pages` stream.\\n\",\n \"\\n\",\n \"Then, we proceed to read from the source into the internal DuckDB cache.\\n\",\n \"\\n\",\n \"Note: The credentials are retrieved securely using the `get_secret()` method. This will automatically locate a matching Google Colab secret or environment variable, ensuring they are not hard-coded into the notebook. Make sure to add your key to the Secrets section on the left.\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\",\n \"height\": 166\n },\n \"id\": \"9em82J2Q5WzN\",\n \"outputId\": \"f1b3fc28-cb4b-4f0f-d412-b702005c2e72\"\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
Installing 'source-google-analytics-data-api' into virtual environment \\n\",\n              \"'/content/.venv-source-google-analytics-data-api'.\\n\",\n              \"Running 'pip install airbyte-source-google-analytics-data-api'...\\n\",\n              \"\\n\",\n              \"
\\n\"\n ],\n \"text/plain\": [\n \"Installing \\u001b[32m'source-google-analytics-data-api'\\u001b[0m into virtual environment \\n\",\n \"\\u001b[32m'/content/.venv-source-google-analytics-data-api'\\u001b[0m.\\n\",\n \"Running \\u001b[32m'pip install airbyte-source-google-analytics-data-api'\\u001b[0m\\u001b[33m...\\u001b[0m\\n\",\n \"\\n\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n },\n {\n \"data\": {\n \"text/html\": [\n \"
Connector 'source-google-analytics-data-api' installed successfully!\\n\",\n              \"For more information, see the source-google-analytics-data-api documentation:\\n\",\n              \"https://docs.airbyte.com/integrations/sources/google-analytics-data-api#reference\\n\",\n              \"\\n\",\n              \"
\\n\"\n ],\n \"text/plain\": [\n \"Connector \\u001b[32m'source-google-analytics-data-api'\\u001b[0m installed successfully!\\n\",\n \"For more information, see the source-google-analytics-data-api documentation:\\n\",\n \"\\u001b[4;94mhttps://docs.airbyte.com/integrations/sources/google-analytics-data-api#reference\\u001b[0m\\n\",\n \"\\n\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n },\n {\n \"data\": {\n \"text/html\": [\n \"
Connection check succeeded for `source-google-analytics-data-api`.\\n\",\n              \"
\\n\"\n ],\n \"text/plain\": [\n \"Connection check succeeded for `source-google-analytics-data-api`.\\n\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"import airbyte as ab\\n\",\n \"\\n\",\n \"# Create and configure the source connector:\\n\",\n \"source = ab.get_source(\\n\",\n \" \\\"source-google-analytics-data-api\\\",\\n\",\n \" install_if_missing=True,\\n\",\n \" config={\\n\",\n \" \\\"property_ids\\\": [ab.get_secret(\\\"GA4_PROPERTY_ID\\\")],\\n\",\n \" \\\"date_ranges_start_date\\\": \\\"2023-10-01\\\",\\n\",\n \" \\\"credentials\\\": {\\n\",\n \" \\\"credentials_json\\\": ab.get_secret(\\\"GA4_JSON_KEY\\\"),\\n\",\n \" \\\"auth_type\\\": \\\"Service\\\",\\n\",\n \" },\\n\",\n \" },\\n\",\n \")\\n\",\n \"\\n\",\n \"# Verify the config and creds by running `check`:\\n\",\n \"source.check()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\"\n },\n \"id\": \"Y5H9BNlgOT6J\",\n \"outputId\": \"3557acb8-6d15-4d97-9b69-5595201c0a85\"\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"['daily_active_users',\\n\",\n \" 'weekly_active_users',\\n\",\n \" 'four_weekly_active_users',\\n\",\n \" 'devices',\\n\",\n \" 'locations',\\n\",\n \" 'pages',\\n\",\n \" 'traffic_sources',\\n\",\n \" 'website_overview',\\n\",\n \" 'user_acquisition_first_user_medium_report',\\n\",\n \" 'user_acquisition_first_user_source_report',\\n\",\n \" 'user_acquisition_first_user_source_medium_report',\\n\",\n \" 'user_acquisition_first_user_source_platform_report',\\n\",\n \" 'user_acquisition_first_user_campaign_report',\\n\",\n \" 'user_acquisition_first_user_google_ads_ad_network_type_report',\\n\",\n \" 'user_acquisition_first_user_google_ads_ad_group_name_report',\\n\",\n \" 'traffic_acquisition_session_source_medium_report',\\n\",\n \" 'traffic_acquisition_session_medium_report',\\n\",\n \" 'traffic_acquisition_session_source_report',\\n\",\n \" 'traffic_acquisition_session_campaign_report',\\n\",\n \" 'traffic_acquisition_session_default_channel_grouping_report',\\n\",\n \" 'traffic_acquisition_session_source_platform_report',\\n\",\n \" 'events_report',\\n\",\n \" 'weekly_events_report',\\n\",\n \" 'conversions_report',\\n\",\n \" 'pages_title_and_screen_class_report',\\n\",\n \" 'pages_path_report',\\n\",\n \" 'pages_title_and_screen_name_report',\\n\",\n \" 'content_group_report',\\n\",\n \" 'ecommerce_purchases_item_name_report',\\n\",\n \" 'ecommerce_purchases_item_id_report',\\n\",\n \" 'ecommerce_purchases_item_category_report_combined',\\n\",\n \" 'ecommerce_purchases_item_category_report',\\n\",\n \" 'ecommerce_purchases_item_category_2_report',\\n\",\n \" 'ecommerce_purchases_item_category_3_report',\\n\",\n \" 'ecommerce_purchases_item_category_4_report',\\n\",\n \" 'ecommerce_purchases_item_category_5_report',\\n\",\n \" 'ecommerce_purchases_item_brand_report',\\n\",\n \" 'publisher_ads_ad_unit_report',\\n\",\n \" 'publisher_ads_page_path_report',\\n\",\n \" 'publisher_ads_ad_format_report',\\n\",\n \" 'publisher_ads_ad_source_report',\\n\",\n \" 'demographic_country_report',\\n\",\n \" 'demographic_region_report',\\n\",\n \" 'demographic_city_report',\\n\",\n \" 'demographic_language_report',\\n\",\n \" 'demographic_age_report',\\n\",\n \" 'demographic_gender_report',\\n\",\n \" 'demographic_interest_report',\\n\",\n \" 'tech_browser_report',\\n\",\n \" 'tech_device_category_report',\\n\",\n \" 'tech_device_model_report',\\n\",\n \" 'tech_screen_resolution_report',\\n\",\n \" 'tech_app_version_report',\\n\",\n \" 'tech_platform_report',\\n\",\n \" 'tech_platform_device_category_report',\\n\",\n \" 'tech_operating_system_report',\\n\",\n \" 'tech_os_with_version_report']\"\n ]\n },\n \"execution_count\": 3,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# List the available streams available for GA4 source\\n\",\n \"source.get_available_streams()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\",\n \"height\": 303,\n \"referenced_widgets\": [\n \"733f09d5b4584b0382560faab0629b0c\",\n \"3330876bb7d547b28191270cb53c3e57\",\n \"42b10528846241e0aac16458a1b8655f\",\n \"d7c54b0aaa4442a19254501027caf793\",\n \"24a7bb9210cc4c00b19f12715f36ef8a\",\n \"3031c476bc464ed08e90b5e91ee6393b\"\n ]\n },\n \"id\": \"4btpDgjkulF0\",\n \"outputId\": \"bbedf001-9441-432d-9f69-7d9b5ae121fc\"\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/markdown\": [\n \"## Read Progress\\n\",\n \"\\n\",\n \"Started reading at 21:14:13.\\n\",\n \"\\n\",\n \"Read **901,527** records over **8min 0s** (1,878.2 records / second).\\n\",\n \"\\n\",\n \"Wrote **901,527** records over 91 batches.\\n\",\n \"\\n\",\n \"Finished reading at 21:22:14.\\n\",\n \"\\n\",\n \"Started finalizing streams at 21:22:14.\\n\",\n \"\\n\",\n \"Finalized **91** batches over 6 seconds.\\n\",\n \"\\n\",\n \"Completed 1 out of 1 streams:\\n\",\n \"\\n\",\n \" - pages\\n\",\n \"\\n\",\n \"\\n\",\n \"Completed writing at 21:22:21. Total time elapsed: 8min 7s\\n\",\n \"\\n\",\n \"\\n\",\n \"------------------------------------------------\\n\"\n ],\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n },\n {\n \"data\": {\n \"text/html\": [\n \"
Completed `source-google-analytics-data-api` read operation at 21:22:21.\\n\",\n              \"
\\n\"\n ],\n \"text/plain\": [\n \"Completed `source-google-analytics-data-api` read operation at \\u001b[1;92m21:22:21\\u001b[0m.\\n\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"# Select the streams we are interested in loading to cache\\n\",\n \"source.select_streams([\\\"pages\\\"])\\n\",\n \"\\n\",\n \"# Read into DuckDB local default cache\\n\",\n \"cache = ab.get_default_cache()\\n\",\n \"result = source.read(cache=cache)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"Y98iL4XxtINW\"\n },\n \"source\": [\n \"### Read data from the cache\\n\",\n \"\\n\",\n \"Read from the already-written DuckDB cache into a pandas Dataframe. After the data is in the cache, you can read it without re-configuring or re-creating the source object. You can also select a stream to read from.\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"id\": \"yjzVJH6BtOpW\"\n },\n \"outputs\": [],\n \"source\": [\n \"# Read from the cache into a pandas Dataframe:\\n\",\n \"pages_df = cache[\\\"pages\\\"].to_pandas()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"nUXbpYeuvKEf\"\n },\n \"source\": [\n \"### Transform and analyze the data\\n\",\n \"\\n\",\n \"Let's take our pages data and run some analysis with `pandas` and `matplotlib`.\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"id\": \"8HHsoj07vvHV\"\n },\n \"outputs\": [],\n \"source\": [\n \"import pandas as pd\\n\",\n \"import matplotlib.pyplot as plt\\n\",\n \"\\n\",\n \"# Convert 'date' to datetime format\\n\",\n \"pages_df[\\\"date\\\"] = pd.to_datetime(pages_df[\\\"date\\\"], format=\\\"%Y%m%d\\\")\\n\",\n \"\\n\",\n \"# Convert 'screenpageviews' to integers\\n\",\n \"pages_df[\\\"screenpageviews\\\"] = pd.to_numeric(\\n\",\n \" pages_df[\\\"screenpageviews\\\"], errors=\\\"coerce\\\"\\n\",\n \")\\n\",\n \"\\n\",\n \"# Convert 'bouncerate' to floats\\n\",\n \"pages_df[\\\"bouncerate\\\"] = pd.to_numeric(pages_df[\\\"bouncerate\\\"], errors=\\\"coerce\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"DUfKikLt6bCF\"\n },\n \"source\": [\n \"#### Trend Analysis Over Time\\n\",\n \"\\n\",\n \"Let's aggregate data by date and observe trends in total pageviews and average bounce rate over time.\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\",\n \"height\": 564\n },\n \"id\": \"8sMWEbrFxum0\",\n \"outputId\": \"be947d1e-0656-41ea-9601-9ff15ecc0e89\"\n },\n \"outputs\": [],\n \"source\": [\n \"# Aggregate data by date\\n\",\n \"trends = pages_df.groupby(\\\"date\\\").agg(\\n\",\n \" total_pageviews=(\\\"screenpageviews\\\", \\\"sum\\\"),\\n\",\n \" average_bounce_rate=(\\\"bouncerate\\\", \\\"mean\\\"),\\n\",\n \")\\n\",\n \"\\n\",\n \"# Plotting the trends\\n\",\n \"fig, ax1 = plt.subplots(figsize=(12, 6))\\n\",\n \"\\n\",\n \"ax1.set_xlabel(\\\"Date\\\")\\n\",\n \"ax1.set_ylabel(\\\"Total Pageviews\\\", color=\\\"tab:blue\\\")\\n\",\n \"ax1.plot(trends.index, trends[\\\"total_pageviews\\\"], color=\\\"tab:blue\\\")\\n\",\n \"ax1.tick_params(axis=\\\"y\\\", labelcolor=\\\"tab:blue\\\")\\n\",\n \"\\n\",\n \"ax2 = ax1.twinx()\\n\",\n \"ax2.set_ylabel(\\\"Average Bounce Rate\\\", color=\\\"tab:green\\\")\\n\",\n \"ax2.plot(trends.index, trends[\\\"average_bounce_rate\\\"], color=\\\"tab:green\\\")\\n\",\n \"ax2.tick_params(axis=\\\"y\\\", labelcolor=\\\"tab:green\\\")\\n\",\n \"\\n\",\n \"plt.title(\\\"Trends in Total Pageviews and Average Bounce Rate Over Time\\\")\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"xLEqImdc8kf9\"\n },\n \"source\": [\n \"#### Segmentation of Data by Date or Page Type\\n\",\n \"\\n\",\n \"Compare metrics on weekdays versus weekends or across different types of pages.\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\",\n \"height\": 1000\n },\n \"id\": \"dekYkkauAL6K\",\n \"outputId\": \"40ddf88c-d2b7-4d85-dda4-31b29589b5d7\"\n },\n \"outputs\": [],\n \"source\": [\n \"import seaborn as sns\\n\",\n \"\\n\",\n \"# Label rows as 'Weekday' or 'Weekend'\\n\",\n \"pages_df[\\\"day_type\\\"] = pages_df[\\\"day_of_week\\\"].apply(\\n\",\n \" lambda x: \\\"Weekend\\\" if x in [\\\"Saturday\\\", \\\"Sunday\\\"] else \\\"Weekday\\\"\\n\",\n \")\\n\",\n \"\\n\",\n \"# Aggregating data for weekday vs weekend\\n\",\n \"day_type_agg = (\\n\",\n \" pages_df.groupby(\\\"day_type\\\")\\n\",\n \" .agg(\\n\",\n \" average_pageviews=(\\\"screenpageviews\\\", \\\"mean\\\"),\\n\",\n \" average_bounce_rate=(\\\"bouncerate\\\", \\\"mean\\\"),\\n\",\n \" )\\n\",\n \" .reset_index()\\n\",\n \")\\n\",\n \"\\n\",\n \"# Plotting Average Pageviews\\n\",\n \"plt.figure(figsize=(12, 6))\\n\",\n \"sns.barplot(x=\\\"day_type\\\", y=\\\"average_pageviews\\\", data=day_type_agg, palette=\\\"coolwarm\\\")\\n\",\n \"plt.title(\\\"Average Pageviews: Weekdays vs Weekends\\\")\\n\",\n \"plt.ylabel(\\\"Average Pageviews\\\")\\n\",\n \"plt.show()\\n\",\n \"\\n\",\n \"# Plotting Average Bounce Rate\\n\",\n \"plt.figure(figsize=(12, 6))\\n\",\n \"sns.barplot(x=\\\"day_type\\\", y=\\\"average_bounce_rate\\\", data=day_type_agg, palette=\\\"viridis\\\")\\n\",\n \"plt.title(\\\"Average Bounce Rate: Weekdays vs Weekends\\\")\\n\",\n \"plt.ylabel(\\\"Average Bounce Rate\\\")\\n\",\n \"plt.show()\"\n ]\n }\n ],\n \"metadata\": {\n \"colab\": {\n \"include_colab_link\": true,\n \"provenance\": []\n },\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"name\": \"python3\"\n },\n 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demo, we use PyAirbyte to extract data from Github, followed by a series of transformations and analyses to derive meaningful insights from this data. In particular, we demonstrate PyAirbyte capabilities for extracting data incrementally.\\n\",\n \"\\n\",\n \"#### Prerequisites:\\n\",\n \"\\n\",\n \"- A Github personal access token. For details on configuring authetication credentials, refer to the Github source connector [documentation](https://docs.airbyte.com/integrations/sources/github).\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"id\": \"bM4Te8XEWECV\"\n },\n \"outputs\": [],\n \"source\": [\n \"# Add virtual environment support in Google Colab\\n\",\n \"!apt-get install -qq python3.10-venv\\n\",\n \"\\n\",\n \"# Install airbyte\\n\",\n \"%pip install --quiet airbyte\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"qTmx2CsKDgXy\"\n },\n \"source\": [\n \"### Load source data from Github to local cache\\n\",\n \"\\n\",\n \"In this section, we establish a connection to Github via PyAirbyte. The source connector is configured with necessary parameters like the credentials repository we are interested in. Check the [docs](https://docs.airbyte.com/integrations/sources/github) for more details on these parameters.\\n\",\n \"\\n\",\n \"After configuring the source connector, we perform a `check()` to ensure that the configuration is correct and the connection to the API is successful. Then, we list the available streams for this source and select the ones we are interested in syncing.\\n\",\n \"\\n\",\n \"Then, we proceed to read from the source into the internal DuckDB cache.\\n\",\n \"\\n\",\n \"Note: The credentials are retrieved securely using the `get_secret()` method. This will automatically locate a matching Google Colab secret or environment variable, ensuring they are not hard-coded into the notebook. Make sure to add your key to the Secrets section on the left.\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\",\n \"height\": 149\n },\n \"id\": \"JjFChgoNXIfE\",\n \"outputId\": \"1097be03-026f-464c-da35-33d17fc4385c\"\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
Installing 'source-github' into virtual environment '/content/.venv-source-github'.\\n\",\n              \"Running 'pip install airbyte-source-github'...\\n\",\n              \"\\n\",\n              \"
\\n\"\n ],\n \"text/plain\": [\n \"Installing \\u001b[32m'source-github'\\u001b[0m into virtual environment \\u001b[32m'/content/.venv-source-github'\\u001b[0m.\\n\",\n \"Running \\u001b[32m'pip install airbyte-source-github'\\u001b[0m\\u001b[33m...\\u001b[0m\\n\",\n \"\\n\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n },\n {\n \"data\": {\n \"text/html\": [\n \"
Connector 'source-github' installed successfully!\\n\",\n              \"For more information, see the source-github documentation:\\n\",\n              \"https://docs.airbyte.com/integrations/sources/github#reference\\n\",\n              \"\\n\",\n              \"
\\n\"\n ],\n \"text/plain\": [\n \"Connector \\u001b[32m'source-github'\\u001b[0m installed successfully!\\n\",\n \"For more information, see the source-github documentation:\\n\",\n \"\\u001b[4;94mhttps://docs.airbyte.com/integrations/sources/github#reference\\u001b[0m\\n\",\n \"\\n\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n },\n {\n \"data\": {\n \"text/html\": [\n \"
Connection check succeeded for `source-github`.\\n\",\n              \"
\\n\"\n ],\n \"text/plain\": [\n \"Connection check succeeded for `source-github`.\\n\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"import airbyte as ab\\n\",\n \"\\n\",\n \"# Create and configure the source:\\n\",\n \"source = ab.get_source(\\n\",\n \" \\\"source-github\\\",\\n\",\n \" install_if_missing=True,\\n\",\n \" config={\\n\",\n \" \\\"repositories\\\": [\\\"airbytehq/quickstarts\\\"],\\n\",\n \" \\\"credentials\\\": {\\n\",\n \" \\\"personal_access_token\\\": ab.get_secret(\\\"GITHUB_PERSONAL_ACCESS_TOKEN\\\"),\\n\",\n \" },\\n\",\n \" },\\n\",\n \")\\n\",\n \"\\n\",\n \"# Verify the config and creds by running `check`:\\n\",\n \"source.check()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\"\n },\n \"id\": \"gyn_ymCiEVD6\",\n \"outputId\": \"e5ca7ea5-5082-4ddf-9234-74051853a5a3\"\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"['issue_timeline_events',\\n\",\n \" 'assignees',\\n\",\n \" 'branches',\\n\",\n \" 'collaborators',\\n\",\n \" 'comments',\\n\",\n \" 'commit_comment_reactions',\\n\",\n \" 'commit_comments',\\n\",\n \" 'commits',\\n\",\n \" 'contributor_activity',\\n\",\n \" 'deployments',\\n\",\n \" 'events',\\n\",\n \" 'issue_comment_reactions',\\n\",\n \" 'issue_events',\\n\",\n \" 'issue_labels',\\n\",\n \" 'issue_milestones',\\n\",\n \" 'issue_reactions',\\n\",\n \" 'issues',\\n\",\n \" 'organizations',\\n\",\n \" 'project_cards',\\n\",\n \" 'project_columns',\\n\",\n \" 'projects',\\n\",\n \" 'pull_request_comment_reactions',\\n\",\n \" 'pull_request_commits',\\n\",\n \" 'pull_request_stats',\\n\",\n \" 'projects_v2',\\n\",\n \" 'pull_requests',\\n\",\n \" 'releases',\\n\",\n \" 'repositories',\\n\",\n \" 'review_comments',\\n\",\n \" 'reviews',\\n\",\n \" 'stargazers',\\n\",\n \" 'tags',\\n\",\n \" 'teams',\\n\",\n \" 'team_members',\\n\",\n \" 'users',\\n\",\n \" 'workflows',\\n\",\n \" 'workflow_runs',\\n\",\n \" 'workflow_jobs',\\n\",\n \" 'team_memberships']\"\n ]\n },\n \"execution_count\": 3,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# List the available streams available for the Github source\\n\",\n \"source.get_available_streams()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\",\n \"height\": 355\n },\n \"id\": \"w94J7TIFiBt1\",\n \"outputId\": \"65633720-0948-472b-cf81-f587b772abb5\"\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/markdown\": [\n \"## Read Progress\\n\",\n \"\\n\",\n \"Started reading at 22:16:25.\\n\",\n \"\\n\",\n \"Read **314** records over **6 seconds** (52.3 records / second).\\n\",\n \"\\n\",\n \"Wrote **314** records over 4 batches.\\n\",\n \"\\n\",\n \"Finished reading at 22:16:32.\\n\",\n \"\\n\",\n \"Started finalizing streams at 22:16:32.\\n\",\n \"\\n\",\n \"Finalized **4** batches over 2 seconds.\\n\",\n \"\\n\",\n \"Completed 4 out of 4 streams:\\n\",\n \"\\n\",\n \" - reviews\\n\",\n \" - stargazers\\n\",\n \" - pull_requests\\n\",\n \" - issues\\n\",\n \"\\n\",\n \"\\n\",\n \"Completed writing at 22:16:34. Total time elapsed: 8 seconds\\n\",\n \"\\n\",\n \"\\n\",\n \"------------------------------------------------\\n\"\n ],\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n },\n {\n \"data\": {\n \"text/html\": [\n \"
Completed `source-github` read operation at 22:16:34.\\n\",\n              \"
\\n\"\n ],\n \"text/plain\": [\n \"Completed `source-github` read operation at \\u001b[1;92m22:16:34\\u001b[0m.\\n\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"# Select the streams we are interested in loading to cache\\n\",\n \"source.set_streams([\\\"pull_requests\\\", \\\"issues\\\", \\\"reviews\\\", \\\"stargazers\\\"])\\n\",\n \"\\n\",\n \"# Read into DuckDB local default cache\\n\",\n \"cache = ab.get_default_cache()\\n\",\n \"result = source.read(cache=cache)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"7iFNbPbPFB3q\"\n },\n \"source\": [\n \"### Read again to sync changes\\n\",\n \"\\n\",\n \"The PyAirbyte Github source connector has the ability to read data incrementally by default, meaning that after the first successful data sync, only updates and new records will be synced in subsequent reads.\\n\",\n \"\\n\",\n \"For more information on sync modes for this source, you can refer to the [docs](https://docs.airbyte.com/integrations/sources/github).\\n\",\n \"\\n\",\n \"Let's read again, and see how no records will be loaded to cache.\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\",\n \"height\": 332\n },\n \"id\": \"UOm1jvefGMrR\",\n \"outputId\": \"a4fc014c-0d48-4186-c21b-1ba6e890f9ae\"\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/markdown\": [\n \"## Read Progress\\n\",\n \"\\n\",\n \"Started reading at 22:17:30.\\n\",\n \"\\n\",\n \"Read **0** records over **3 seconds** (0.0 records / second).\\n\",\n \"\\n\",\n \"Finished reading at 22:17:33.\\n\",\n \"\\n\",\n \"Started finalizing streams at 22:17:33.\\n\",\n \"\\n\",\n \"Finalized **0** batches over 0 seconds.\\n\",\n \"\\n\",\n \"Completed 4 out of 4 streams:\\n\",\n \"\\n\",\n \" - reviews\\n\",\n \" - stargazers\\n\",\n \" - pull_requests\\n\",\n \" - issues\\n\",\n \"\\n\",\n \"\\n\",\n \"Completed writing at 22:17:33. Total time elapsed: 3 seconds\\n\",\n \"\\n\",\n \"\\n\",\n \"------------------------------------------------\\n\"\n ],\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n },\n {\n \"data\": {\n \"text/html\": [\n \"
Completed `source-github` read operation at 22:17:33.\\n\",\n              \"
\\n\"\n ],\n \"text/plain\": [\n \"Completed `source-github` read operation at \\u001b[1;92m22:17:33\\u001b[0m.\\n\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"result = source.read(cache=cache)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"EZfsqKfJGg_Z\"\n },\n \"source\": [\n \"### Read data from the cache\\n\",\n \"\\n\",\n \"Read from the already-written DuckDB cache into a pandas Dataframe. After the data is in the cache, you can read it without re-configuring or re-creating the source object. You can also select a specific stream to read from.\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"id\": \"E5-ADp0dGnGR\"\n },\n \"outputs\": [],\n \"source\": [\n \"# Read from the cache into a pandas Dataframe:\\n\",\n \"reviews = cache[\\\"reviews\\\"].to_pandas()\\n\",\n \"stargazers = cache[\\\"stargazers\\\"].to_pandas()\\n\",\n \"pull_requests = cache[\\\"pull_requests\\\"].to_pandas()\\n\",\n \"issues = cache[\\\"issues\\\"].to_pandas()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"EDs_pEAaGP5X\"\n },\n \"source\": [\n \"### Transform and analyze the data\\n\",\n \"\\n\",\n \"Let's take our Github data and run some analysis with `pandas` and `matplotlib`.\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\",\n \"height\": 216\n },\n \"id\": \"DkUfVobeGbMG\",\n \"outputId\": \"5c0c1712-66e9-4a58-dc04-8c9709359a36\"\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"\\n\",\n \"
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1airbytehq/quickstarts1689921852PRR_kwDOKQqr2s5kuik8{'avatar_url': https://avatars.githubuserconte...COMMENTEDhttps://github.com/airbytehq/quickstarts/pull/...https://github.com/airbytehq/quickstarts/pull/59{'html': {'href': https://github.com/airbytehq...2023-10-20 12:23:232023-10-20 12:23:222023-10-20 12:23:2329ca06ae449b07f17c1be6bb104264b6d62dafccCONTRIBUTOR
2airbytehq/quickstarts1689945893PRR_kwDOKQqr2s5kuocl{'avatar_url': https://avatars.githubuserconte...COMMENTEDhttps://github.com/airbytehq/quickstarts/pull/...https://github.com/airbytehq/quickstarts/pull/59{'html': {'href': https://github.com/airbytehq...2023-10-20 12:38:032023-10-20 12:38:032023-10-20 12:38:0329ca06ae449b07f17c1be6bb104264b6d62dafccCONTRIBUTOR
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\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"import pandas as pd\\n\",\n \"import matplotlib.pyplot as plt\\n\",\n \"\\n\",\n \"# Assuming 'pull_requests' dataframe is available and has a 'created_at' column in datetime format\\n\",\n \"pull_requests[\\\"created_at\\\"] = pd.to_datetime(pull_requests[\\\"created_at\\\"])\\n\",\n \"pr_trend = pull_requests.resample(\\\"M\\\", on=\\\"created_at\\\").size()\\n\",\n \"\\n\",\n \"plt.figure(figsize=(12, 6))\\n\",\n \"pr_trend.plot(kind=\\\"line\\\", color=\\\"blue\\\", marker=\\\"o\\\")\\n\",\n \"plt.title(\\\"Monthly Pull Requests Trend\\\")\\n\",\n \"plt.xlabel(\\\"Month\\\")\\n\",\n \"plt.ylabel(\\\"Number of Pull Requests\\\")\\n\",\n \"plt.grid(True)\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"OK5jXo0nJcnW\"\n },\n \"source\": [\n \"#### Open vs Closed Issues\\n\",\n \"\\n\",\n \"Compare the number of open versus closed issues.\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\",\n \"height\": 564\n },\n \"id\": \"WFtnoUE5Jhus\",\n \"outputId\": \"4ea9b4a2-1b6d-4f69-8eee-4d7df78a0109\"\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"# Assuming 'issues' dataframe has a 'state' column\\n\",\n \"issue_status_count = issues[\\\"state\\\"].value_counts()\\n\",\n \"\\n\",\n \"plt.figure(figsize=(8, 6))\\n\",\n \"issue_status_count.plot(kind=\\\"bar\\\", color=[\\\"blue\\\", \\\"green\\\"])\\n\",\n \"plt.title(\\\"Open vs Closed Issues\\\")\\n\",\n \"plt.xlabel(\\\"State\\\")\\n\",\n \"plt.ylabel(\\\"Number of Issues\\\")\\n\",\n \"plt.xticks(rotation=0)\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"ETo6-qu4JsI_\"\n },\n \"source\": [\n \"#### Star Growth Over Time\\n\",\n \"\\n\",\n \"Plot the cumulative count of stars over time.\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\",\n \"height\": 580\n },\n \"id\": \"NC7PthQMJxpd\",\n \"outputId\": \"654b3bc8-9eaa-469a-f2d1-bdf139e5e04b\"\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"# Assuming 'stargazers' dataframe has a 'starred_at' column in datetime format\\n\",\n \"stargazers[\\\"starred_at\\\"] = pd.to_datetime(stargazers[\\\"starred_at\\\"])\\n\",\n \"star_growth = stargazers.resample(\\\"M\\\", on=\\\"starred_at\\\").size().cumsum()\\n\",\n \"\\n\",\n \"plt.figure(figsize=(12, 6))\\n\",\n \"star_growth.plot(kind=\\\"line\\\", color=\\\"gold\\\", marker=\\\"*\\\")\\n\",\n \"plt.title(\\\"Cumulative Star Growth Over Time\\\")\\n\",\n \"plt.xlabel(\\\"Month\\\")\\n\",\n \"plt.ylabel(\\\"Cumulative Stars\\\")\\n\",\n \"plt.grid(True)\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"lwacZs7vKiTd\"\n },\n \"source\": [\n \"#### Issue Resolution Followed by Pull Requests\\n\",\n \"\\n\",\n \"This analysis checks if there's a trend in issue resolutions followed by pull requests, which might indicate active development in response to issues.\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"id\": \"STHueLOoKoZ9\"\n },\n \"outputs\": [],\n \"source\": [\n \"# Convert created_at and closet_at to datetime\\n\",\n \"pull_requests[\\\"created_at\\\"] = pd.to_datetime(pull_requests[\\\"created_at\\\"])\\n\",\n \"issues[\\\"closed_at\\\"] = issues[\\\"closed_at\\\"].dt.tz_localize(None)\\n\",\n \"\\n\",\n \"# Resample both dataframes to get monthly counts\\n\",\n \"monthly_closed_issues = issues.resample(\\\"M\\\", on=\\\"closed_at\\\").size()\\n\",\n \"monthly_created_prs = pull_requests.resample(\\\"M\\\", on=\\\"created_at\\\").size()\\n\",\n \"\\n\",\n \"# Combine into a single dataframe\\n\",\n \"combined_issues_prs_df = pd.concat([monthly_closed_issues, monthly_created_prs], axis=1)\\n\",\n \"combined_issues_prs_df.columns = [\\\"Monthly Closed Issues\\\", \\\"Monthly Created PRs\\\"]\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\",\n \"height\": 580\n },\n \"id\": \"-nRXduEbKr4_\",\n \"outputId\": \"5a0fe25b-0f1b-4400-d943-88da874f969d\"\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"combined_issues_prs_df.plot(kind=\\\"line\\\", figsize=(12, 6))\\n\",\n \"plt.title(\\\"Monthly Closed Issues vs Monthly Created Pull Requests\\\")\\n\",\n \"plt.xlabel(\\\"Month\\\")\\n\",\n \"plt.ylabel(\\\"Count\\\")\\n\",\n \"plt.grid(True)\\n\",\n \"plt.show()\"\n ]\n }\n ],\n \"metadata\": {\n \"colab\": {\n \"include_colab_link\": true,\n \"provenance\": []\n },\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"name\": \"python\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 0\n}\n" }, { "path": "pyairbyte_notebooks/PyAirbyte_Postgres_Custom_Cache_Demo.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"R8XtHKK4PujA\"\n },\n \"source\": [\n \"# PyAirbyte Custom Postgres Cache Demo\\n\",\n \"\\n\",\n \"Below is a pre-release demo of PyAirbyte, showcasing how to use Postgres as a\\n\",\n \"Cache.\\n\",\n \"\\n\",\n \"This notebook is designed to be run in a Google Colab only. It installs packages on the system and requires sudo access. If you want to run it in a local Jupyter notebook, please proceed with caution.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"Lyxh2NLuQJUf\"\n },\n \"source\": [\n \"## Install PyAirbyte\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"id\": \"9DEgu1WpQNt-\"\n },\n \"outputs\": [],\n \"source\": [\n \"# Add virtual environment support for running in Google Colab\\n\",\n \"!apt-get install -qq python3.10-venv\\n\",\n \"\\n\",\n \"# Install PyAirbyte\\n\",\n \"%pip install --quiet airbyte\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"QdCDkVEgfPod\"\n },\n \"source\": [\n \"## Install and setup Postgres (optional)\\n\",\n \"\\n\",\n \"If you are _not_ running this notebook on Google Colab, or you prefer to use an existing database, please skip the following setup and proceed to the next section.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"id\": \"xmJAMk2bfdss\"\n },\n \"outputs\": [],\n \"source\": [\n \"# Install postgresql server\\n\",\n \"!sudo apt-get -y -qq update\\n\",\n \"!sudo apt-get -y -qq install postgresql\\n\",\n \"!sudo service postgresql start\\n\",\n \"\\n\",\n \"# Setup a password `postgres` for username `postgres`\\n\",\n \"!sudo -u postgres psql -U postgres -c \\\"ALTER USER postgres PASSWORD 'postgres';\\\"\\n\",\n \"\\n\",\n \"# Setup a database with name `pyairbyte_demo` to be used\\n\",\n \"!sudo -u postgres psql -U postgres -c 'DROP DATABASE IF EXISTS pyairbyte_demo;'\\n\",\n \"!sudo -u postgres psql -U postgres -c 'CREATE DATABASE pyairbyte_demo;'\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"cXJ_cRQV7nIb\"\n },\n \"source\": [\n \"## Locating your Data Source\\n\",\n \"\\n\",\n \"To see what data sources are available, you can check [our docs](https://docs.airbyte.com/using-airbyte/airbyte-lib/getting-started) or run the following:\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"id\": \"tfjct5EQ7nIb\"\n },\n \"outputs\": [],\n \"source\": [\n \"# Import PyAirbyte\\n\",\n \"import airbyte as ab\\n\",\n \"\\n\",\n \"# Show all available connectors\\n\",\n \"ab.get_available_connectors()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"JWWeEbTVEDFz\"\n },\n \"source\": [\n \"## Load the Source Data using PyAirbyte\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"1PhfWpS8QVzE\"\n },\n \"source\": [\n \"Create and install a source connector:\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\",\n \"height\": 133\n },\n \"id\": \"5BI9hIeUvxXE\",\n \"outputId\": \"1fbe2871-ffe4-403b-c4a5-cb15cd4ee260\"\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
Installing 'source-faker' into virtual environment '/content/.venv-source-faker'.\\n\",\n              \"Running 'pip install airbyte-source-faker'...\\n\",\n              \"\\n\",\n              \"
\\n\"\n ],\n \"text/plain\": [\n \"Installing \\u001b[32m'source-faker'\\u001b[0m into virtual environment \\u001b[32m'/content/.venv-source-faker'\\u001b[0m.\\n\",\n \"Running \\u001b[32m'pip install airbyte-source-faker'\\u001b[0m\\u001b[33m...\\u001b[0m\\n\",\n \"\\n\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n },\n {\n \"data\": {\n \"text/html\": [\n \"
Connector 'source-faker' installed successfully!\\n\",\n              \"For more information, see the source-faker documentation:\\n\",\n              \"https://docs.airbyte.com/integrations/sources/faker#reference\\n\",\n              \"\\n\",\n              \"
\\n\"\n ],\n \"text/plain\": [\n \"Connector \\u001b[32m'source-faker'\\u001b[0m installed successfully!\\n\",\n \"For more information, see the source-faker documentation:\\n\",\n \"\\u001b[4;94mhttps://docs.airbyte.com/integrations/sources/faker#reference\\u001b[0m\\n\",\n \"\\n\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"import airbyte as ab\\n\",\n \"\\n\",\n \"# Create and install the source:\\n\",\n \"source: ab.Source = ab.get_source(\\\"source-faker\\\")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\",\n \"height\": 34\n },\n \"id\": \"Ww7NA9cQQjQI\",\n \"outputId\": \"894457f1-2691-456d-bda6-438c027e4376\"\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
Connection check succeeded for `source-faker`.\\n\",\n              \"
\\n\"\n ],\n \"text/plain\": [\n \"Connection check succeeded for `source-faker`.\\n\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"# Configure the source\\n\",\n \"source.set_config(\\n\",\n \" config={\\n\",\n \" \\\"count\\\": 50_000, # Adjust this to get a larger or smaller dataset\\n\",\n \" \\\"seed\\\": 123,\\n\",\n \" },\\n\",\n \")\\n\",\n \"# Verify the config and creds by running `check`:\\n\",\n \"source.check()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"EwSqhsrmgcQ4\"\n },\n \"source\": [\n \"## Define a Postgres Cache\\n\",\n \"\\n\",\n \"Define a PyAirbyte Cache from the existing PosgreSQL database.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"id\": \"jJeZN6phg-1v\"\n },\n \"outputs\": [],\n \"source\": [\n \"from airbyte.caches import PostgresCacheConfig, PostgresCache\\n\",\n \"\\n\",\n \"#Define a Postgres Cache and pass the necessary configuration\\n\",\n \"pg_cache = PostgresCache(\\n\",\n \" PostgresCacheConfig(\\n\",\n \" host=\\\"localhost\\\",\\n\",\n \" port=5432,\\n\",\n \" username=\\\"postgres\\\",\\n\",\n \" password=\\\"postgres\\\",\\n\",\n \" database=\\\"pyairbyte_demo\\\"\\n\",\n \" )\\n\",\n \")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"Que5DAqtEJu0\"\n },\n \"source\": [\n \"## Read Data into the PostgreSQL Cache\\n\",\n \"\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\",\n \"height\": 338\n },\n \"id\": \"qQVRO69c2DoA\",\n \"outputId\": \"7bc4bb21-c302-45cd-fb23-93e8deaca239\"\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/markdown\": [\n \"## Read Progress\\n\",\n \"\\n\",\n \"Started reading at 22:27:43.\\n\",\n \"\\n\",\n \"Read **100,100** records over **59 seconds** (1,696.6 records / second).\\n\",\n \"\\n\",\n \"Wrote **100,100** records over 13 batches.\\n\",\n \"\\n\",\n \"Finished reading at 22:28:42.\\n\",\n \"\\n\",\n \"Started finalizing streams at 22:28:42.\\n\",\n \"\\n\",\n \"Finalized **13** batches over 8 seconds.\\n\",\n \"\\n\",\n \"Completed 3 out of 3 streams:\\n\",\n \"\\n\",\n \" - users\\n\",\n \" - products\\n\",\n \" - purchases\\n\",\n \"\\n\",\n \"\\n\",\n \"Completed writing at 22:28:51. Total time elapsed: 1min 8s\\n\",\n \"\\n\",\n \"\\n\",\n \"------------------------------------------------\\n\"\n ],\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n },\n {\n \"data\": {\n \"text/html\": [\n \"
Completed `source-faker` read operation at 22:28:51.\\n\",\n              \"
\\n\"\n ],\n \"text/plain\": [\n \"Completed `source-faker` read operation at \\u001b[1;92m22:28:51\\u001b[0m.\\n\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"# Select all of the source's streams and read data into the previously defined Postgres cache:\\n\",\n \"source.select_all_streams()\\n\",\n \"read_result: ab.ReadResult = source.read(cache=pg_cache)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"wSru-wGpHZrh\"\n },\n \"source\": [\n \"## Working in SQL\\n\",\n \"\\n\",\n \"Since data is cached in the Postgres database, we can query the data with SQL.\\n\",\n \"\\n\",\n \"We can do this in multiple ways. One way is to use the [JupySQL Extension](https://jupysql.ploomber.io/en/latest/user-guide/template.html), which we'll use below.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"id\": \"xdotIOg70nuL\"\n },\n \"outputs\": [],\n \"source\": [\n \"# Install JupySQL to enable SQL cell magics\\n\",\n \"%pip install --quiet jupysql\\n\",\n \"# Load JupySQL extension\\n\",\n \"%load_ext sql\\n\",\n \"# Configure max row limit (optional)\\n\",\n \"%config SqlMagic.displaylimit = 200\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"id\": \"2tA6L1dHZ2w0\"\n },\n \"outputs\": [],\n \"source\": [\n \"# Get the SQLAlchemy 'engine' object for the cache\\n\",\n \"engine = read_result.cache.get_sql_engine()\\n\",\n \"# Pass the engine to JupySQL\\n\",\n \"%sql engine\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\",\n \"height\": 35\n },\n \"id\": \"0eaYnErPaFsH\",\n \"outputId\": \"6012ea51-cf61-404a-e731-dcc68b325c1c\"\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"['airbyte_raw.users', 'airbyte_raw.purchases']\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"# Get table objects for the 'users' and 'purchases' streams\\n\",\n \"users_table = read_result.cache[\\\"users\\\"].to_sql_table()\\n\",\n \"purchases_table = read_result.cache[\\\"purchases\\\"].to_sql_table()\\n\",\n \"display([users_table.fullname, purchases_table.fullname])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\",\n \"height\": 289\n },\n \"id\": \"VjeTOtKHHiA5\",\n \"outputId\": \"e00b7f64-33ae-424c-93da-6bd0b1e8d757\"\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"Running query in 'postgresql+psycopg2://postgres:***@localhost:5432/pyairbyte_demo'\"\n ],\n \"text/plain\": [\n \"Running query in 'postgresql+psycopg2://postgres:***@localhost:5432/pyairbyte_demo'\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n },\n {\n \"data\": {\n \"text/html\": [\n \"10 rows affected.\"\n ],\n \"text/plain\": [\n \"10 rows affected.\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n },\n {\n \"data\": {\n \"text/html\": [\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
idnameproduct_idpurchased_at
40Kelvin80None
44Jen1None
24Nestor20None
19Marquitta93None
43Tari26None
28Porter10None
1Murray41None
9Travis70None
5Osvaldo89None
46Rodger35None
\"\n ],\n \"text/plain\": [\n \"+----+-----------+------------+--------------+\\n\",\n \"| id | name | product_id | purchased_at |\\n\",\n \"+----+-----------+------------+--------------+\\n\",\n \"| 40 | Kelvin | 80 | None |\\n\",\n \"| 44 | Jen | 1 | None |\\n\",\n \"| 24 | Nestor | 20 | None |\\n\",\n \"| 19 | Marquitta | 93 | None |\\n\",\n \"| 43 | Tari | 26 | None |\\n\",\n \"| 28 | Porter | 10 | None |\\n\",\n \"| 1 | Murray | 41 | None |\\n\",\n \"| 9 | Travis | 70 | None |\\n\",\n \"| 5 | Osvaldo | 89 | None |\\n\",\n \"| 46 | Rodger | 35 | None |\\n\",\n \"+----+-----------+------------+--------------+\"\n ]\n },\n \"execution_count\": 11,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"%%sql\\n\",\n \"# Show most recent purchases by purchase date:\\n\",\n \"SELECT users.id, users.name, purchases.product_id, purchases.purchased_at\\n\",\n \"FROM {{ users_table.fullname }} AS users\\n\",\n \"JOIN {{ purchases_table.fullname }} AS purchases\\n\",\n \"ON users.id = purchases.user_id\\n\",\n \"ORDER BY purchases.purchased_at DESC\\n\",\n \"LIMIT 10\"\n ]\n }\n ],\n \"metadata\": {\n \"colab\": {\n \"provenance\": []\n },\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.9.6\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 0\n}\n" }, { "path": "pyairbyte_notebooks/PyAirbyte_Shopify_Demo.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"En5baAhvYE_y\"\n },\n \"source\": [\n \"In this pre-release demo, we use PyAirbyte to extract product-related data from Shopify, followed by a series of transformations and analyses to derive meaningful insights from this data.\\n\",\n \"\\n\",\n \"### Prerequisites:\\n\",\n \"- An active [Shopify store](https://www.shopify.com/).\\n\",\n \"\\n\",\n \"For more information about setting up your Shopify store to work with PyAirbyte, check the [docs](https://docs.airbyte.com/integrations/sources/shopify).\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"8awBDcLvRW2g\"\n },\n \"source\": [\n \"## Install PyAirbyte\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"id\": \"xrhNw5kK5Lvx\"\n },\n \"outputs\": [],\n \"source\": [\n \"# Add virtual environment support for running in Google Colab\\n\",\n \"!apt-get install -qq python3.10-venv\\n\",\n \"\\n\",\n \"# Install PyAirbyte\\n\",\n \"%pip install --quiet airbyte\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"mYsTAS1wRgO_\"\n },\n \"source\": [\n \"## Configure the source PyAirbyte connector\\n\",\n \"\\n\",\n \"In this section, we configure the Shopify source connector to access product data via PyAirbyte. The connector is configured with necessary parameters like the store name and credentials. [Check the docs](https://docs.airbyte.com/integrations/sources/shopify) for more details on this configuration.\\n\",\n \"\\n\",\n \"After configuring the source connector, we perform a `check()` to ensure that the configuration is correct and the connection to Shopify is successful.\\n\",\n \"\\n\",\n \"*Note*: Some config details are retrieved securely using Colab's `userdata`, ensuring that sensitive credentials are not hard-coded into the notebook. Make sure to add your information to the Secrets section on the left.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\",\n \"height\": 149\n },\n \"id\": \"9em82J2Q5WzN\",\n \"outputId\": \"9c275156-e51d-4b7f-a8f3-9bd0e98aee42\"\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
Installing 'source-shopify' into virtual environment '/content/.venv-source-shopify'.\\n\",\n              \"Running 'pip install airbyte-source-shopify'...\\n\",\n              \"\\n\",\n              \"
\\n\"\n ],\n \"text/plain\": [\n \"Installing \\u001b[32m'source-shopify'\\u001b[0m into virtual environment \\u001b[32m'/content/.venv-source-shopify'\\u001b[0m.\\n\",\n \"Running \\u001b[32m'pip install airbyte-source-shopify'\\u001b[0m\\u001b[33m...\\u001b[0m\\n\",\n \"\\n\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n },\n {\n \"data\": {\n \"text/html\": [\n \"
Connector 'source-shopify' installed successfully!\\n\",\n              \"For more information, see the source-shopify documentation:\\n\",\n              \"https://docs.airbyte.com/integrations/sources/shopify#reference\\n\",\n              \"\\n\",\n              \"
\\n\"\n ],\n \"text/plain\": [\n \"Connector \\u001b[32m'source-shopify'\\u001b[0m installed successfully!\\n\",\n \"For more information, see the source-shopify documentation:\\n\",\n \"\\u001b[4;94mhttps://docs.airbyte.com/integrations/sources/shopify#reference\\u001b[0m\\n\",\n \"\\n\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n },\n {\n \"data\": {\n \"text/html\": [\n \"
Connection check succeeded for `source-shopify`.\\n\",\n              \"
\\n\"\n ],\n \"text/plain\": [\n \"Connection check succeeded for `source-shopify`.\\n\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"import airbyte as ab\\n\",\n \"from google.colab import userdata\\n\",\n \"\\n\",\n \"# Create and configure the source connector:\\n\",\n \"source = ab.get_source(\\n\",\n \" \\\"source-shopify\\\",\\n\",\n \" install_if_missing=True,\\n\",\n \" config={\\n\",\n \" \\\"shop\\\": userdata.get(\\\"SHOP\\\"),\\n\",\n \" \\\"credentials\\\": {\\n\",\n \" \\\"auth_method\\\": \\\"api_password\\\",\\n\",\n \" \\\"api_password\\\": userdata.get(\\\"SHOP_API_PASSWORD\\\")\\n\",\n \" }\\n\",\n \" }\\n\",\n \")\\n\",\n \"\\n\",\n \"# Verify the config and creds by running `check()`:\\n\",\n \"source.check()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"-4OCBJd2tL4W\"\n },\n \"source\": [\n \"## Load source data from Shopify to local DuckDB cache\\n\",\n \"\\n\",\n \"Now, we list the available streams for the source Shopify. This is optional but can be useful.\\n\",\n \"\\n\",\n \"Then, we select the streams we want to sync to the local cache, and proceed to `read()` to get the records.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"id\": \"R4udSNyObVAv\"\n },\n \"outputs\": [],\n \"source\": [\n \"# List the available streams available for the Shopify source\\n\",\n \"source.get_available_streams()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\",\n \"height\": 355\n },\n \"id\": \"AlqnXX6ibZqX\",\n \"outputId\": \"ee680a1b-20ea-4cb6-e59d-ac95827d1151\"\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/markdown\": [\n \"## Read Progress\\n\",\n \"\\n\",\n \"Started reading at 22:20:53.\\n\",\n \"\\n\",\n \"Read **52** records over **16 seconds** (3.2 records / second).\\n\",\n \"\\n\",\n \"Wrote **52** records over 4 batches.\\n\",\n \"\\n\",\n \"Finished reading at 22:21:09.\\n\",\n \"\\n\",\n \"Started finalizing streams at 22:21:09.\\n\",\n \"\\n\",\n \"Finalized **4** batches over 1 seconds.\\n\",\n \"\\n\",\n \"Completed 4 out of 4 streams:\\n\",\n \"\\n\",\n \" - product_variants\\n\",\n \" - products\\n\",\n \" - customers\\n\",\n \" - collections\\n\",\n \"\\n\",\n \"\\n\",\n \"Completed writing at 22:21:10. Total time elapsed: 17 seconds\\n\",\n \"\\n\",\n \"\\n\",\n \"------------------------------------------------\\n\"\n ],\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n },\n {\n \"data\": {\n \"text/html\": [\n \"
Completed `source-shopify` read operation at 22:21:10.\\n\",\n              \"
\\n\"\n ],\n \"text/plain\": [\n \"Completed `source-shopify` read operation at \\u001b[1;92m22:21:10\\u001b[0m.\\n\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"# Select the streams we are interested in loading to cache\\n\",\n \"source.select_streams([\\\"products\\\", \\\"product_variants\\\", \\\"collections\\\", \\\"customers\\\"])\\n\",\n \"\\n\",\n \"# Read into DuckDB local default cache\\n\",\n \"cache = ab.get_default_cache()\\n\",\n \"result = source.read(cache=cache)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"ZKBR6MilSJta\"\n },\n \"source\": [\n \"## Read data from the cache\\n\",\n \"\\n\",\n \"Now we can read from the already-written DuckDB cache into a pandas Dataframe. After the data is in the cache, we can read it without re-configuring or re-creating the source object. We can also select a stream to read from.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {\n \"id\": \"lFlveLjfGYof\"\n },\n \"outputs\": [],\n \"source\": [\n \"# Read from the cache into a pandas Dataframe:\\n\",\n \"products = cache[\\\"products\\\"].to_pandas()\\n\",\n \"product_variants = cache[\\\"product_variants\\\"].to_pandas()\\n\",\n \"collections = cache[\\\"collections\\\"].to_pandas()\\n\",\n \"customers = cache[\\\"customers\\\"].to_pandas()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"p7vX1_w4SYaF\"\n },\n \"source\": [\n \"## Data analysis\\n\",\n \"\\n\",\n \"Let's analyze the data!\\n\",\n \"\\n\",\n \"Please note that since we used a Shopify sample store for this demo, the data is limited. If you have access to an actual store, you can select more streams and do a more extensive analysis here.\\n\",\n \"\\n\",\n \"### 1. Product Category and Variant Analysis\\n\",\n \"Analyze product types, variants, and their inventory levels.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"metadata\": {\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\",\n \"height\": 1000\n },\n \"id\": \"B8orH6pnmcy7\",\n \"outputId\": \"13552705-0b38-4992-b58b-9b25cf963bdd\"\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\",\n 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\",\n 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\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"import pandas as pd\\n\",\n \"import matplotlib.pyplot as plt\\n\",\n \"\\n\",\n \"# Product Type Analysis\\n\",\n \"product_type_counts = products['product_type'].value_counts()\\n\",\n \"product_type_counts.plot(kind='bar')\\n\",\n \"plt.title('Product Type Distribution')\\n\",\n \"plt.xlabel('Product Type')\\n\",\n \"plt.ylabel('Count')\\n\",\n \"plt.show()\\n\",\n \"\\n\",\n \"# Variant Price Range Analysis\\n\",\n \"product_variants['price'] = pd.to_numeric(product_variants['price'], errors='coerce')\\n\",\n \"product_variants['price'].hist(bins=20)\\n\",\n \"plt.title('Product Variant Price Distribution')\\n\",\n \"plt.xlabel('Price')\\n\",\n \"plt.ylabel('Number of Variants')\\n\",\n \"plt.show()\\n\",\n \"\\n\",\n \"# Inventory Level Analysis\\n\",\n \"product_variants['inventory_quantity'] = pd.to_numeric(product_variants['inventory_quantity'], errors='coerce')\\n\",\n \"product_variants['inventory_quantity'].hist(bins=20)\\n\",\n \"plt.title('Inventory Level Distribution')\\n\",\n \"plt.xlabel('Inventory Quantity')\\n\",\n \"plt.ylabel('Number of Variants')\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"DXhO3hHNnY_1\"\n },\n \"source\": [\n \"### 2. Customer Segmentation\\n\",\n \"Segment customers based on available attributes.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"metadata\": {\n \"id\": \"5Mh2MiCLohWL\"\n },\n \"outputs\": [],\n \"source\": [\n \"import json\\n\",\n \"\\n\",\n \"# Parse JSON\\n\",\n \"def parse_json(x):\\n\",\n \" try:\\n\",\n \" return json.loads(x.replace(\\\"'\\\", \\\"\\\\\\\"\\\"))\\n\",\n \" except:\\n\",\n \" return {}\\n\",\n \"\\n\",\n \"# Apply the function to unpack JSON content\\n\",\n \"customers['email_marketing_consent'] = customers['email_marketing_consent'].apply(parse_json)\\n\",\n \"\\n\",\n \"# Extract relevant fields\\n\",\n \"customers['consent_opt_in_level'] = customers['email_marketing_consent'].apply(lambda x: x.get('opt_in_level', None))\\n\",\n \"customers['consent_state'] = customers['email_marketing_consent'].apply(lambda x: x.get('state', None))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"metadata\": {\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\",\n \"height\": 1000\n },\n \"id\": \"k5wYK_yWpZxY\",\n \"outputId\": \"ace4b70d-12b9-466e-b89a-bb1133c06524\"\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\",\n 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\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"# Analyze the distribution of opt-in levels\\n\",\n \"opt_in_level_counts = customers['consent_opt_in_level'].value_counts()\\n\",\n \"\\n\",\n \"# Analyze the distribution of consent states\\n\",\n \"consent_state_counts = customers['consent_state'].value_counts()\\n\",\n \"\\n\",\n \"# Opt-in level distribution\\n\",\n \"opt_in_level_counts.plot(kind='bar')\\n\",\n \"plt.title('Distribution of Consent Opt-In Levels')\\n\",\n \"plt.xlabel('Opt-In Level')\\n\",\n \"plt.ylabel('Number of Customers')\\n\",\n \"plt.show()\\n\",\n \"\\n\",\n \"# Consent state distribution\\n\",\n \"consent_state_counts.plot(kind='bar', color='orange')\\n\",\n \"plt.title('Distribution of Consent States')\\n\",\n \"plt.xlabel('Consent State')\\n\",\n \"plt.ylabel('Number of Customers')\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"3qb7zkCOnwoH\"\n },\n \"source\": [\n \"### 3. Collection Popularity and Strategy\\n\",\n \"Analyze collections based on the number of products.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"metadata\": {\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\",\n \"height\": 538\n },\n \"id\": \"V-yF_f2Anzlk\",\n \"outputId\": \"593d2568-20c7-4b04-8212-8c931f5ff623\"\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"collections['products_count'] = pd.to_numeric(collections['products_count'], errors='coerce')\\n\",\n \"collections.groupby('title')['products_count'].sum().plot(kind='bar')\\n\",\n \"plt.title('Product Count in Each Collection')\\n\",\n \"plt.xlabel('Collection')\\n\",\n \"plt.ylabel('Total Products')\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"46eXv1UmoE07\"\n },\n \"source\": [\n \"### 4. Inventory Analysis\\n\",\n \"Examine inventory levels across product variants.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 10,\n \"metadata\": {\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\",\n \"height\": 572\n },\n \"id\": \"gGEYdVa7oMIg\",\n \"outputId\": \"63f9e5e6-340f-4749-dd10-bfb7ad0e1839\"\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"# Product Variants Inventory Analysis\\n\",\n \"product_variants.groupby('product_id')['inventory_quantity'].sum().plot(kind='bar')\\n\",\n \"plt.title('Total Inventory Quantity by Product')\\n\",\n \"plt.xlabel('Product ID')\\n\",\n \"plt.ylabel('Total Inventory Quantity')\\n\",\n \"plt.show()\"\n ]\n }\n ],\n \"metadata\": {\n \"colab\": {\n \"provenance\": []\n },\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"name\": \"python\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 0\n}\n" }, { "path": "pyairbyte_notebooks/PyAirbyte_Snowflake_Cortex_Github.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"n3fUVL_19YGb\"\n },\n \"source\": [\n \"# Storing vector data into Snowflake using PyAirbyte, Snowflake Cortex\\n\",\n \"\\n\",\n \"In this notebook, we'll illustrate how to load data from airbyte-source into Snowflake using PyAirbyte, and afterwards convert the stream data into vector. In this, we've used source-github and stream 'issues', but you can replace the source according to your requirements.\\n\",\n \"\\n\",\n \"## Prerequisites\\n\",\n \"\\n\",\n \"1. **GitHub Access Token**:\\n\",\n \" - Follow the instructions in the [Github Connector Docs](https://docs.airbyte.com/integrations/sources/github) to set up your github and get api_token.\\n\",\n \"\\n\",\n \"2. **Snowflake**:\\n\",\n \" - To set up snowflake, follow these [instructions](https://docs.airbyte.com/integrations/destinations/snowflake#login-and-password).\\n\",\n \"\\n\",\n \"\\n\",\n \"## Install PyAirbyte and other dependencies\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"id\": \"OhMxXpazUzBX\"\n },\n \"outputs\": [],\n \"source\": [\n \"# Add virtual environment support for running in Google Colab\\n\",\n \"!apt-get install -qq python3.10-venv\\n\",\n \"\\n\",\n \"# First, we need to install the necessary libraries.\\n\",\n \"!pip3 install airbyte snowflake-connector-python\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"KqfoQD2dAJtd\"\n },\n \"source\": [\n \"# Setup Source Github\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"id\": \"QwV29bK4VbL4\"\n },\n \"outputs\": [],\n \"source\": [\n \"import airbyte as ab\\n\",\n \"\\n\",\n \"source = ab.get_source(\\n\",\n \" \\\"source-github\\\",\\n\",\n \" config={\\n\",\n \" \\\"repositories\\\": [\\\"airbytehq/quickstarts\\\"],\\n\",\n \" \\\"credentials\\\": {\\n\",\n \" \\\"personal_access_token\\\": ab.get_secret(\\\"GITHUB_API_KEY\\\"),\\n\",\n \" },\\n\",\n \" },\\n\",\n \")\\n\",\n \"source.check()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"9avgcWxs9Ura\"\n },\n \"source\": [\n \"Reads the data from the selected issues stream, extracting the GitHub issues data for further processing.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"id\": \"s8VFZEK8XbA0\"\n },\n \"outputs\": [],\n \"source\": [\n \"source.get_available_streams()\\n\",\n \"source.select_streams([\\\"issues\\\"]) # we are only interested in issues stream\\n\",\n \"read_result = source.read()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"id\": \"MzeTFZ58YVEo\"\n },\n \"outputs\": [],\n \"source\": [\n \"issues = [doc for doc in read_result[\\\"issues\\\"].to_documents()] # Will be useful for vector_embedding\\n\",\n \"issue_df = read_result['issues'].to_pandas() # Converting data to pandas frame\\n\",\n \"print(str(issues[5]))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"6fivYEfdBafd\"\n },\n \"source\": [\n \"# Loading data into Snowflake\\n\",\n \"It uses the snowflake.connector module to connect to Snowflake with the provided credentials fetched from secrets, Make sure to add your key to the Secrets section on the left.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"id\": \"2EM-oHR3cXXc\"\n },\n \"outputs\": [],\n \"source\": [\n \"from snowflake import connector\\n\",\n \"conn = connector.connect(\\n\",\n \" account=ab.get_secret(\\\"SNOWFLAKE_HOST\\\"),\\n\",\n \" role=ab.get_secret(\\\"SNOWFLAKE_ROLE\\\"),\\n\",\n \" warehouse=ab.get_secret(\\\"SNOWFLAKE_WAREHOUSE\\\"),\\n\",\n \" database=ab.get_secret(\\\"SNOWFLAKE_DATABASE\\\"),\\n\",\n \" schema=ab.get_secret(\\\"SNOWFLAKE_SCHEMA\\\"),\\n\",\n \" user=ab.get_secret(\\\"SNOWFLAKE_USERNAME\\\"),\\n\",\n \" password=ab.get_secret(\\\"SNOWFLAKE_PASSWORD\\\"),\\n\",\n \" )\\n\",\n \"cur = conn.cursor()\\n\",\n \"\\n\",\n \"print(ab.get_secret(\\\"SNOWFLAKE_SCHEMA\\\"))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"85uLgQ7JBgoI\"\n },\n \"source\": [\n \"A function to create a Snowflake table based on the schema of a Pandas DataFrame and then uses this function to create a github_issue table in Snowflake from the issue_df DataFrame.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"id\": \"X96O3Is-i3v4\"\n },\n \"outputs\": [],\n \"source\": [\n \"import pandas as pd\\n\",\n \"\\n\",\n \"def create_table_from_dataframe(conn, df, table_name):\\n\",\n \" cursor = conn.cursor()\\n\",\n \" database = ab.get_secret('SNOWFLAKE_DATABASE')\\n\",\n \" print(database)\\n\",\n \" cursor.execute(f'USE DATABASE {database}')\\n\",\n \" schema_name = ab.get_secret('SNOWFLAKE_SCHEMA')\\n\",\n \" cursor.execute(f'USE SCHEMA {schema_name}')\\n\",\n \" columns = []\\n\",\n \" for column, dtype in zip(df.columns, df.dtypes):\\n\",\n \" if pd.api.types.is_integer_dtype(dtype):\\n\",\n \" snowflake_type = 'INTEGER'\\n\",\n \" elif pd.api.types.is_float_dtype(dtype):\\n\",\n \" snowflake_type = 'FLOAT'\\n\",\n \" elif pd.api.types.is_bool_dtype(dtype):\\n\",\n \" snowflake_type = 'BOOLEAN'\\n\",\n \" elif pd.api.types.is_datetime64_any_dtype(dtype):\\n\",\n \" snowflake_type = 'TIMESTAMP'\\n\",\n \" else:\\n\",\n \" snowflake_type = 'STRING'\\n\",\n \"\\n\",\n \" columns.append(f'\\\"{column}\\\" {snowflake_type}')\\n\",\n \"\\n\",\n \" create_table_sql = f'CREATE TABLE {table_name} ({\\\", \\\".join(columns)});'\\n\",\n \" cursor.execute(create_table_sql)\\n\",\n \"\\n\",\n \"# Example usage:\\n\",\n \"create_table_from_dataframe(conn, issue_df, 'github_issue') # Keep table name according to your requirments\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"RePkO21hCSZx\"\n },\n \"source\": [\n \"upload_dataframe_to_snowflake that uses Snowflake's pandas integration (write_pandas) to upload a Pandas DataFrame (issue_df) into a Snowflake table ('GITHUB_ISSUE').\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"id\": \"Ku61-vxBt7Xh\"\n },\n \"outputs\": [],\n \"source\": [\n \"from snowflake.connector.pandas_tools import write_pandas\\n\",\n \"def upload_dataframe_to_snowflake(conn, df, table_name):\\n\",\n \" success, nchunks, nrows, _ = write_pandas(conn, df, table_name)\\n\",\n \" if success:\\n\",\n \" print(f\\\"Successfully inserted {nrows} rows into {table_name}.\\\")\\n\",\n \" else:\\n\",\n \" print(\\\"Failed to insert data.\\\")\\n\",\n \"\\n\",\n \"upload_dataframe_to_snowflake(conn, issue_df, 'GITHUB_ISSUE') # Remember to use table name in uppercase\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"ppxeChRQCmm8\"\n },\n \"source\": [\n \"# Vector Embedding the Data\\n\",\n \"Now we utilize the RecursiveCharacterTextSplitter from langchain.text_splitter to segment documents (issues) into smaller chunks based on specified parameters (chunk_size and chunk_overlap).\\n\",\n \"\\n\",\n \"Then we organize the chunked documents into a Pandas DataFrame (df) with columns for page content (PAGE_CONTENT), metadata (META), and type (TYPE), ensuring all data is represented as strings for consistency and analysis.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"id\": \"5ti5uUpsu2oU\"\n },\n \"outputs\": [],\n \"source\": [\n \"from langchain.text_splitter import RecursiveCharacterTextSplitter\\n\",\n \"import pandas as pd\\n\",\n \"\\n\",\n \"splitter = RecursiveCharacterTextSplitter(chunk_size=512, chunk_overlap=50)\\n\",\n \"chunked_docs = splitter.split_documents(issues)\\n\",\n \"print(f\\\"Created {len(chunked_docs)} document chunks.\\\")\\n\",\n \"\\n\",\n \"\\n\",\n \"for doc in chunked_docs:\\n\",\n \" for md in doc.metadata:\\n\",\n \" doc.metadata[md] = str(doc.metadata[md])\\n\",\n \"\\n\",\n \"df = pd.DataFrame(chunked_docs, columns=['PAGE_CONTENT','META','TYPE']) # please use uppercase\\n\",\n \"# Convert all columns to string\\n\",\n \"df = df.astype(str)\\n\",\n \"print(df.head(3))\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"wfSflgGwDkjN\"\n },\n \"source\": [\n \"Now we establish a new table for storing vector embedded data. First, we create a data and store chunked data, and then we vector embed the data.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"id\": \"qFK12XSiNej_\"\n },\n \"outputs\": [],\n \"source\": [\n \"create_table_from_dataframe(conn, df, 'vector_github_issues')\\n\",\n \"upload_dataframe_to_snowflake(conn, df, 'VECTOR_GITHUB_ISSUES') #use uppercase\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"1Wjq35UMECIp\"\n },\n \"source\": [\n \"Now, using Snowflake Cortex, we will turn the page content column into embedding and store them in the embedding column. Different embedding models are available [here](https://docs.snowflake.com/en/sql-reference/functions/embed_text_1024-snowflake-cortex).\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"id\": \"vHHZ5fn7Rcs2\"\n },\n \"outputs\": [],\n \"source\": [\n \"\\n\",\n \"cur = conn.cursor()\\n\",\n \"\\n\",\n \"# Step 1: Add the new column to store the embeddings\\n\",\n \"\\n\",\n \"# We are using vector dimension 1024\\n\",\n \"alter_table_query = \\\"\\\"\\\"\\n\",\n \"ALTER TABLE VECTOR_GITHUB_ISSUES\\n\",\n \"ADD COLUMN embedding VECTOR(FLOAT, 1024);\\n\",\n \"\\\"\\\"\\\"\\n\",\n \"cur.execute(alter_table_query)\\n\",\n \"\\n\",\n \"# Step 2: Update the new column with embeddings from Cortex\\n\",\n \"# Note: Using a subquery to avoid issues with updating the same table in place\\n\",\n \"update_query = \\\"\\\"\\\"\\n\",\n \"UPDATE VECTOR_GITHUB_ISSUES\\n\",\n \"SET embedding = (\\n\",\n \" SELECT SNOWFLAKE.CORTEX.EMBED_TEXT_1024('nv-embed-qa-4', page_content)\\n\",\n \");\\n\",\n \"\\\"\\\"\\\"\\n\",\n \"cur.execute(update_query)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"1uyFarH2Fjvf\"\n },\n \"source\": [\n \"This approach demonstrates how to seamlessly integrate data retrieval from an Airbyte source, such as GitHub issues, and efficiently store it in Snowflake for further analysis. By utilizing PyAirbyte for data extraction and Snowflake's capabilities for data warehousing\"\n ]\n }\n ],\n \"metadata\": {\n \"colab\": {\n \"provenance\": []\n },\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"name\": \"python\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 0\n}\n" }, { "path": "pyairbyte_notebooks/PyAirbyte_Snowflake_Custom_Cache_Demo.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"R8XtHKK4PujA\"\n },\n \"source\": [\n \"# PyAirbyte Custom Snowflake Cache Demo\\n\",\n \"\\n\",\n \"In this demo, we use PyAirbyte to ingest cryptocurrency data from [CoinAPI.io](https://www.coinapi.io/) into Snowflake.\\n\",\n \"\\n\",\n \"### Prerequisites\\n\",\n \"- CoinAPI [API key](https://www.coinapi.io/get-free-api-key?product_id=market-data-api).\\n\",\n \"- A Snowflake account with a database configured to work with PyAirbyte. Find specific details around config in our [documentation](https://docs.airbyte.com/integrations/destinations/snowflake).\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"Lyxh2NLuQJUf\"\n },\n \"source\": [\n \"## Install PyAirbyte\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"id\": \"9DEgu1WpQNt-\"\n },\n \"outputs\": [],\n \"source\": [\n \"# Add virtual environment support for running in Google Colab\\n\",\n \"!apt-get install -qq python3.10-venv\\n\",\n \"\\n\",\n \"# Install PyAirbyte\\n\",\n \"%pip install --quiet airbyte\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"EwSqhsrmgcQ4\"\n },\n \"source\": [\n \"## Define a Snowflake Cache\\n\",\n \"\\n\",\n \"Define a PyAirbyte Cache for Snowflake.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"id\": \"jJeZN6phg-1v\"\n },\n \"outputs\": [],\n \"source\": [\n \"from airbyte.caches import SnowflakeCache\\n\",\n \"from google.colab import userdata\\n\",\n \"\\n\",\n \"# Define a Snowflake Cache and pass the necessary configuration\\n\",\n \"sf_cache = SnowflakeCache(\\n\",\n \" account=userdata.get(\\\"SNOWFLAKE_ACCOUNT\\\"),\\n\",\n \" username=userdata.get(\\\"SNOWFLAKE_USERNAME\\\"),\\n\",\n \" password=userdata.get(\\\"SNOWFLAKE_PASSWORD\\\"),\\n\",\n \" warehouse=\\\"AIRBYTE_DEVELOP_WAREHOUSE\\\",\\n\",\n \" database=\\\"AIRBYTE_DEVELOP\\\",\\n\",\n \" role=\\\"AIRBYTE_DEVELOPER\\\",\\n\",\n \" schema_name=\\\"PYAIRBYTE_DEMO\\\"\\n\",\n \")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"JWWeEbTVEDFz\"\n },\n \"source\": [\n \"## Load the Source Data using PyAirbyte\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"1PhfWpS8QVzE\"\n },\n \"source\": [\n \"In this section, we establish a connection to CoinAPI.io to access cryptocurrency data via PyAirbyte. The connector is configured with necessary parameters like the API key, environment setting, symbol ID for the specific cryptocurrency index (in this case, COINBASE_SPOT_INDEX_USD), and the data period we are interested in. Check [the docs](https://docs.airbyte.com/integrations/sources/coin-api) for more details.\\n\",\n \"\\n\",\n \"We select all available streams for the source, which you can consult using the `get_available_streams()` method, or the docs. Then, we proceed to read from the source into Snowflake.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"id\": \"5BI9hIeUvxXE\"\n },\n \"outputs\": [],\n \"source\": [\n \"import airbyte as ab\\n\",\n \"\\n\",\n \"# Configure and read from the source\\n\",\n \"read_result = ab.get_source(\\n\",\n \" \\\"source-coin-api\\\",\\n\",\n \" config={\\n\",\n \" \\\"api_key\\\": userdata.get(\\\"API_KEY\\\"),\\n\",\n \" \\\"environment\\\": \\\"production\\\",\\n\",\n \" \\\"symbol_id\\\": \\\"COINBASE_SPOT_INDEX_USD\\\",\\n\",\n \" \\\"period\\\": \\\"1DAY\\\",\\n\",\n \" \\\"start_date\\\": \\\"2023-01-01T00:00:00\\\"\\n\",\n \" },\\n\",\n \" streams=[\\\"ohlcv_historical_data\\\", \\\"trades_historical_data\\\", \\\"quotes_historical_data\\\"],\\n\",\n \").read(cache=sf_cache)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"uPcaLfSCYrdo\"\n },\n \"source\": [\n \"## Read data from Snowflake\\n\",\n \"\\n\",\n \"Read from the already-written Snowflake table into a pandas Dataframe. After the data is in the cache, you can read it without re-configuring or re-creating the source object.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"metadata\": {\n \"id\": \"z-nOAXR6Y-ui\"\n },\n \"outputs\": [],\n \"source\": [\n \"# Read from the cache into a pandas Dataframe:\\n\",\n \"ohlcv_df = read_result[\\\"ohlcv_historical_data\\\"].to_pandas()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"b6cMu9G2ZVxK\"\n },\n \"source\": [\n \"## Run data transformations\\n\",\n \"\\n\",\n \"- Convert `time_period_start` to datetime for easy handling of dates.\\n\",\n \"- Convert numeric columns to numeric types for calculations.\\n\",\n \"- Calculate `daily_movement` to analyze daily price changes in the market.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"metadata\": {\n \"id\": \"hmRZZ5PsZYVW\"\n },\n \"outputs\": [],\n \"source\": [\n \"import pandas as pd\\n\",\n \"\\n\",\n \"# Convert 'time_period_start' to datetime format and necessary columns to numeric\\n\",\n \"ohlcv_df['time_period_start'] = pd.to_datetime(ohlcv_df['time_period_start'])\\n\",\n \"numeric_columns = ['price_open', 'price_high', 'price_low', 'price_close', 'volume_traded', 'trades_count']\\n\",\n \"ohlcv_df[numeric_columns] = ohlcv_df[numeric_columns].apply(pd.to_numeric, errors='coerce')\\n\",\n \"\\n\",\n \"# Calculate daily price movement\\n\",\n \"ohlcv_df['daily_movement'] = ohlcv_df['price_close'] - ohlcv_df['price_open']\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"knjH8Cx_zyCa\"\n },\n \"source\": [\n \"## Write Dataframe to Snowflake\\n\",\n \"\\n\",\n \"Get a SQL engine from the Snowflake cache\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"metadata\": {\n \"id\": \"dsn6JSZzfBBw\"\n },\n \"outputs\": [],\n \"source\": [\n \"engine = sf_cache.get_sql_engine()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"nhTzao0y0ObW\"\n },\n \"source\": [\n \"Now, we can write our transformed Dataframe back to Snowflake in a new table called `daily_movement`.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 14,\n \"metadata\": {\n \"id\": \"gy1TzBf-f6If\"\n },\n \"outputs\": [],\n \"source\": [\n \"from snowflake.connector.pandas_tools import pd_writer\\n\",\n \"\\n\",\n \"ohlcv_df.to_sql('daily_movement', engine, index=False, method=pd_writer, if_exists='replace')\"\n ]\n }\n ],\n \"metadata\": {\n \"colab\": {\n \"provenance\": []\n },\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.9.6\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 0\n}\n" }, { "path": "pyairbyte_notebooks/PyAirbyte_as_an_Orchestrator_Demo.ipynb", "content": "{\n \"nbformat\": 4,\n \"nbformat_minor\": 0,\n \"metadata\": {\n \"colab\": {\n \"provenance\": []\n },\n \"kernelspec\": {\n \"name\": \"python3\",\n \"display_name\": \"Python 3\"\n },\n \"language_info\": {\n \"name\": \"python\"\n }\n },\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"source\": [\n \"# Using PyAirbyte as a data orchestrator for hosted Airbyte connections\\n\",\n \"This demo showcases how to automate and monitor Airbyte Cloud sync jobs using PyAirbyte. It includes setting up job executions, handling dependencies, sending real-time status updates, and visually representing job details and outcomes on a timeline.\"\n ],\n \"metadata\": {\n \"id\": \"zSQIhJC1wTt4\"\n }\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"id\": \"Anj2W5pm0n8E\"\n },\n \"outputs\": [],\n \"source\": [\n \"# Add virtual environment support for running in Google Colab\\n\",\n \"!apt-get install -qq python3.10-venv\\n\",\n \"\\n\",\n \"# Install PyAirbyte\\n\",\n \"%pip install --quiet airbyte\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"source\": [\n \"## Job execution and notifications using PyAirbyte\\n\",\n \"Here, we initialize an Airbyte Cloud workspace with PyAirbyte to manage data sync jobs. We show how Slack can be used for real-time notifications on job statuses.\\n\",\n \"\\n\",\n \"The code below does the following:\\n\",\n \"\\n\",\n \"- Workspace initialization: Sets up the workspace with necessary credentials.\\n\",\n \"- Slack notifications: Configures a webhook for sending job statuses to a Slack channel.\\n\",\n \"- Job data tracking: Collects and stores execution details like start time, duration, and status for visualization.\\n\",\n \"- Run syncs: Executes jobs sequentially based on dependency success and sends detailed outcomes to Slack (or simply print them).\"\n ],\n \"metadata\": {\n \"id\": \"QtUXa_7tuXks\"\n }\n },\n {\n \"cell_type\": \"code\",\n \"source\": [\n \"import requests\\n\",\n \"from airbyte import cloud\\n\",\n \"from datetime import datetime\\n\",\n \"from google.colab import userdata\\n\",\n \"\\n\",\n \"# Initialize the Airbyte Cloud workspace\\n\",\n \"workspace = cloud.CloudWorkspace(\\n\",\n \" workspace_id=userdata.get(\\\"AIRBYTE_WORKSPACE_ID\\\"),\\n\",\n \" api_key=userdata.get(\\\"AIRBYTE_API_KEY\\\"),\\n\",\n \")\\n\",\n \"\\n\",\n \"# Slack webhook URL - replace with your actual Slack webhook URL\\n\",\n \"slack_webhook_url = \\\"https://hooks.slack.com/services/XXX/YYY/ZZZ\\\"\\n\",\n \"\\n\",\n \"# Initialize job data for visualization\\n\",\n \"job_data = []\\n\",\n \"\\n\",\n \"def send_slack_message(message, webhook_url):\\n\",\n \" \\\"\\\"\\\"Sends a message to a Slack channel specified by the webhook URL.\\\"\\\"\\\"\\n\",\n \" payload = {'text': message}\\n\",\n \" response = requests.post(webhook_url, json=payload)\\n\",\n \" return response.status_code\\n\",\n \"\\n\",\n \"def trigger_sync_and_check(connection_id):\\n\",\n \" \\\"\\\"\\\"Triggers a sync job for a specified connection ID and checks the job status.\\\"\\\"\\\"\\n\",\n \" start_time = datetime.now()\\n\",\n \" connection = workspace.get_connection(connection_id=connection_id)\\n\",\n \" sync_result = connection.run_sync()\\n\",\n \" end_time = datetime.now()\\n\",\n \" duration = (end_time - start_time).total_seconds()\\n\",\n \" status = str(sync_result.get_job_status())\\n\",\n \"\\n\",\n \" # Store job data for visualization\\n\",\n \" job_data.append({\\n\",\n \" 'connection_id': connection_id,\\n\",\n \" 'start_time': start_time,\\n\",\n \" 'end_time': end_time,\\n\",\n \" 'duration': duration,\\n\",\n \" 'status': status\\n\",\n \" })\\n\",\n \"\\n\",\n \" return status, sync_result, start_time, duration\\n\",\n \"\\n\",\n \"def format_sync_details(sync_result, start_time, duration):\\n\",\n \" \\\"\\\"\\\"Format sync details for Slack message, including start time and duration.\\\"\\\"\\\"\\n\",\n \" details = (\\n\",\n \" f\\\"Job ID: {sync_result.job_id}\\\\n\\\"\\n\",\n \" f\\\"Start Time: {start_time.strftime('%Y-%m-%d %H:%M:%S')}\\\\n\\\"\\n\",\n \" f\\\"Duration: {duration:.2f} seconds\\\\n\\\"\\n\",\n \" f\\\"Records Synced: {sync_result.records_synced}\\\\n\\\"\\n\",\n \" f\\\"Bytes Synced: {sync_result.bytes_synced}\\\\n\\\"\\n\",\n \" f\\\"Job URL: {sync_result.job_url}\\\"\\n\",\n \" )\\n\",\n \" return details\\n\",\n \"\\n\",\n \"def run_syncs():\\n\",\n \" \\\"\\\"\\\"Handle sequential sync jobs where the second job depends on the success of the first.\\\"\\\"\\\"\\n\",\n \" # Connection IDs for the two jobs. Replace with your connection IDs\\n\",\n \" first_connection_id = \\\"df2898ce-b21f-43c0-8c82-bd8c6dd24931\\\"\\n\",\n \" second_connection_id = \\\"86075252-9004-41fc-b5e3-f61f20388ff5\\\"\\n\",\n \"\\n\",\n \" first_status, first_sync_result, first_start, first_duration = trigger_sync_and_check(first_connection_id)\\n\",\n \" if \\\"SUCCEEDED\\\" in first_status:\\n\",\n \" message = f\\\"First sync succeeded (Connection ID: {first_connection_id}).\\\\nDetails:\\\\n{format_sync_details(first_sync_result, first_start, first_duration)}\\\\nProceeding to second sync.\\\"\\n\",\n \" send_slack_message(message, slack_webhook_url)\\n\",\n \" print(message)\\n\",\n \"\\n\",\n \" second_status, second_sync_result, second_start, second_duration = trigger_sync_and_check(second_connection_id)\\n\",\n \" if \\\"SUCCEEDED\\\" in second_status:\\n\",\n \" message = f\\\"Second sync succeeded (Connection ID: {second_connection_id}).\\\\nDetails:\\\\n{format_sync_details(second_sync_result, second_start, second_duration)}\\\"\\n\",\n \" else:\\n\",\n \" message = f\\\"Second sync failed (Connection ID: {second_connection_id}). Status: {second_status}\\\\nDetails:\\\\n{format_sync_details(second_sync_result, second_start, second_duration)}\\\"\\n\",\n \" else:\\n\",\n \" message = f\\\"First sync failed (Connection ID: {first_connection_id}). Status: {first_status}\\\\nDetails:\\\\n{format_sync_details(first_sync_result, first_start, first_duration)}\\\\nSecond sync not initiated.\\\"\\n\",\n \"\\n\",\n \" send_slack_message(message, slack_webhook_url)\\n\",\n \" print(message)\"\n ],\n \"metadata\": {\n \"id\": \"geGeKxan8jc1\"\n },\n \"execution_count\": null,\n \"outputs\": []\n },\n {\n \"cell_type\": \"code\",\n \"source\": [\n \"run_syncs()\"\n ],\n \"metadata\": {\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\"\n },\n \"id\": \"bOODO9mw850l\",\n \"outputId\": \"b431b0db-ad1e-4e9a-eb96-823910d66b54\"\n },\n \"execution_count\": null,\n \"outputs\": [\n {\n \"output_type\": \"stream\",\n \"name\": \"stdout\",\n \"text\": [\n \"First sync succeeded (Connection ID: df2898ce-b21f-43c0-8c82-bd8c6dd24931).\\n\",\n \"Details:\\n\",\n \"Job ID: 10575770\\n\",\n \"Start Time: 2024-04-16 20:08:57\\n\",\n \"Duration: 155.78 seconds\\n\",\n \"Records Synced: 2100\\n\",\n \"Bytes Synced: 766685\\n\",\n \"Job URL: https://api.airbyte.com/v1/workspaces/ab2f3ba2-94e6-45b9-9d26-58b3f1d52295/connections/df2898ce-b21f-43c0-8c82-bd8c6dd24931/job-history/10575770\\n\",\n \"Proceeding to second sync.\\n\",\n \"Second sync succeeded (Connection ID: 86075252-9004-41fc-b5e3-f61f20388ff5).\\n\",\n \"Details:\\n\",\n \"Job ID: 10575853\\n\",\n \"Start Time: 2024-04-16 20:11:32\\n\",\n \"Duration: 123.54 seconds\\n\",\n \"Records Synced: 1\\n\",\n \"Bytes Synced: 214665\\n\",\n \"Job URL: https://api.airbyte.com/v1/workspaces/ab2f3ba2-94e6-45b9-9d26-58b3f1d52295/connections/86075252-9004-41fc-b5e3-f61f20388ff5/job-history/10575853\\n\"\n ]\n }\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"source\": [\n \"## Job execution timeline visualization\\n\",\n \"With the data collected from the syncs we previously ran using PyAirbyte, we can create a visualization that provides a clear and intuitive representation of a series of job executions over time.\\n\",\n \"\\n\",\n \"Each job is displayed as a rectangle along a timeline, where the width of each rectangle corresponds to the job's duration. The colors indicate the status of each job—green for successful completions and yellow for failures. This graph is particularly useful for quickly assessing the overall health and efficiency of a workflow. It allows viewers to see at a glance:\\n\",\n \"\\n\",\n \"- Job Durations: How long each job took to complete.\\n\",\n \"- Job Start Times: When each job began relative to the start of the timeline.\\n\",\n \"- Job Outcomes: The success or failure of each job.\"\n ],\n \"metadata\": {\n \"id\": \"rPhUDRYOtPcr\"\n }\n },\n {\n \"cell_type\": \"code\",\n \"source\": [\n \"import matplotlib.pyplot as plt\\n\",\n \"import matplotlib.patches as patches\\n\",\n \"from datetime import datetime, timedelta\\n\",\n \"\\n\",\n \"def get_color(status):\\n\",\n \" \\\"\\\"\\\"Returns color based on the job status.\\\"\\\"\\\"\\n\",\n \" if \\\"SUCCEEDED\\\" in status:\\n\",\n \" return 'green' # Green for success\\n\",\n \" elif \\\"FAILED\\\" in status:\\n\",\n \" return 'yellow' # Yellow for failure\\n\",\n \" else:\\n\",\n \" return 'gray' # Gray for any other status\\n\",\n \"\\n\",\n \"def draw_job_graph(job_data):\\n\",\n \" \\\"\\\"\\\"Draws a graph of job executions where rectangles represent the duration and color indicates status.\\\"\\\"\\\"\\n\",\n \" fig, ax = plt.subplots(figsize=(14, 3))\\n\",\n \"\\n\",\n \" # Start position and time tracking\\n\",\n \" start_time = min(job['start_time'] for job in job_data) # Find the earliest start time\\n\",\n \" end_time = max(job['start_time'] + timedelta(seconds=job['duration']) for job in job_data) # Find the latest end time\\n\",\n \" total_duration = (end_time - start_time).total_seconds()\\n\",\n \"\\n\",\n \" for job in job_data:\\n\",\n \" start_offset = (job['start_time'] - start_time).total_seconds()\\n\",\n \" width = job['duration'] # Duration in seconds\\n\",\n \" color = get_color(job['status']) # Get color based on job status\\n\",\n \" rect = patches.Rectangle((start_offset, 0), width, 1, linewidth=1, edgecolor='black', facecolor=color)\\n\",\n \" ax.add_patch(rect)\\n\",\n \"\\n\",\n \" # Adding text inside the rectangle\\n\",\n \" ax.text(start_offset + width / 2, 0.5, f\\\"{job['connection_id']} ({job['duration']}s)\\\",\\n\",\n \" verticalalignment='center', horizontalalignment='center', color='black', fontweight='bold')\\n\",\n \"\\n\",\n \" # Set the limits and labels of the x-axis\\n\",\n \" ax.set_xlim(0, total_duration)\\n\",\n \" ax.set_ylim(-0.5, 1.5)\\n\",\n \" ax.set_xlabel(\\\"Time (seconds from start)\\\")\\n\",\n \" ax.set_title('Job Execution Timeline')\\n\",\n \" plt.xticks(range(0, int(total_duration) + 1, 20)) # Set x-ticks to appear every 20 seconds\\n\",\n \" plt.yticks([])\\n\",\n \" plt.grid(True, which='both', axis='x', linestyle='--', linewidth=0.5)\\n\",\n \" plt.show()\"\n ],\n \"metadata\": {\n \"id\": \"AsOSYXuMi1mS\"\n },\n \"execution_count\": null,\n \"outputs\": []\n },\n {\n \"cell_type\": \"markdown\",\n \"source\": [\n \"## Visualize sync results\"\n ],\n \"metadata\": {\n \"id\": \"eWejfDkBv07x\"\n }\n },\n {\n \"cell_type\": \"code\",\n \"source\": [\n \"draw_job_graph(job_data)\"\n ],\n \"metadata\": {\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\",\n \"height\": 333\n },\n \"id\": \"dGIhuTDxjAPF\",\n \"outputId\": \"cf09410f-3b6b-4037-d152-2e6b5a7f0499\"\n },\n \"execution_count\": null,\n \"outputs\": [\n {\n \"output_type\": \"display_data\",\n \"data\": {\n \"text/plain\": [\n \"
\"\n ],\n \"image/png\": 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},\n \"metadata\": {}\n }\n ]\n }\n ]\n}" }, { "path": "pyairbyte_notebooks/RAG_using_github_pyairbyte_chroma.ipynb", "content": "{\n \"nbformat\": 4,\n \"nbformat_minor\": 0,\n \"metadata\": {\n \"colab\": {\n \"provenance\": []\n },\n \"kernelspec\": {\n \"name\": \"python3\",\n \"display_name\": \"Python 3\"\n },\n \"language_info\": {\n \"name\": \"python\"\n }\n },\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"source\": [\n \"# **End-to-End RAG Tutorial Using Github, PyAirbyte, Chroma, and LangChain**\\n\",\n \"This notebook illustrates the complete setup of a Retrieval-Augmented Generation (RAG) pipeline.
\\n\",\n \"We extract data from a GitHub repository using PyAirbyte, store the data in a Chroma vector store, and use LangChain to perform RAG on the stored data.
\\n\",\n \"## **Prerequisites**\\n\",\n \"**1) OpenAI API Key**:\\n\",\n \" - **Create an OpenAI Account**: Sign up for an account on [OpenAI](https://www.openai.com/).\\n\",\n \" - **Generate an API Key**: Go to the API section and generate a new API key. For detailed instructions, refer to the [OpenAI documentation](https://beta.openai.com/docs/quickstart).\\n\",\n \"\\n\",\n \"**2) Github Personal Access Token**:\\n\",\n \" - **Create a Github Account**: Sign up for an account on [Github](https://www.github.com/).\\n\",\n \" - **Generate an API Key**: Cick on your profile icon->Settings->Developer Settings and generate a new API key. For detailed instructions, refer to the [Github documentation](https://docs.github.com/en/authentication/keeping-your-account-and-data-secure/managing-your-personal-access-tokens).\\n\"\n ],\n \"metadata\": {\n \"id\": \"lv1fce-k_Ul5\"\n }\n },\n {\n \"cell_type\": \"markdown\",\n \"source\": [\n \"# **Installing Dependencies**\\n\",\n \"First Thing First !
\\n\",\n \"Lets get the dependencies installed before anything else!!\"\n ],\n \"metadata\": {\n \"id\": \"mbc7jQsW-kGs\"\n }\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": true,\n \"id\": \"5FFGW2iOhOqT\"\n },\n \"outputs\": [],\n \"source\": [\n \"# Add virtual environment support for running in Google Colab\\n\",\n \"!apt-get install -qq python3.10-venv\\n\",\n \"\\n\",\n \"# First, we need to install the necessary libraries.\\n\",\n \"!pip3 install airbyte langchain langchain-openai chromadb python-dotenv langchainhub langchain-chroma\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"source\": [\n \"## **Source Setup : Github with PyAirbyte**\\n\",\n \"The code you see below configures an Airbyte source to pull out data from a github repository.\\n\",\n \"\\n\",\n \"You can also customize the configuration according to your own needs.\\n\",\n \"See [this](https://docs.airbyte.com/integrations/sources/github#reference)\\n\",\n \"\\n\",\n \"Note that we here only fetch data from the Commits Stream
\\n\",\n \"To know about all the available streams go [here](https://docs.airbyte.com/integrations/sources/github#supported-streams)\\n\"\n ],\n \"metadata\": {\n \"id\": \"aBfzg42A_mhd\"\n }\n },\n {\n \"cell_type\": \"code\",\n \"source\": [\n \"import airbyte as ab\\n\",\n \"\\n\",\n \"source = ab.get_source(\\n\",\n \" \\\"source-github\\\",\\n\",\n \" config={\\n\",\n \" \\\"credentials\\\": {\\n\",\n \" \\\"personal_access_token\\\": \\\"your_personal_access_token\\\"\\n\",\n \" },\\n\",\n \" \\\"repositories\\\": [\\\"your_github_username/your_repository_ID\\\"]\\n\",\n \" }\\n\",\n \")\\n\",\n \"source.check()\\n\",\n \"\\n\",\n \"source.get_available_streams()\\n\",\n \"source.select_streams([\\\"commits\\\"])\\n\",\n \"cache = ab.get_default_cache()\\n\",\n \"result = source.read(cache=cache)\\n\",\n \"\\n\",\n \"commits_details = [doc for doc in result[\\\"commits\\\"].to_documents()]\\n\",\n \"\\n\",\n \"print(str(commits_details[0]))\"\n ],\n \"metadata\": {\n \"collapsed\": true,\n \"id\": \"vMiRDXs1ii3S\"\n },\n \"execution_count\": null,\n \"outputs\": []\n },\n {\n \"cell_type\": \"markdown\",\n \"source\": [\n \"# **Split Documents into Chunks**\\n\",\n \"Large documents are split into smaller chunks to make them easier to handle. This also helps in improving the efficiency of the retrieval process, as smaller chunks can be more relevant to specific queries.\\n\",\n \"
\\n\",\n \"Here we set each chunk size to 512 characters and adjacent chunks will overlap by 50 characters to ensure continuity of context\\n\",\n \"
\\n\",\n \"Then the loop converts all metadata to string format to ensure consistent processing later in the pipeline.\"\n ],\n \"metadata\": {\n \"id\": \"f4yYrQ9lLVRJ\"\n }\n },\n {\n \"cell_type\": \"code\",\n \"source\": [\n \"from langchain.text_splitter import RecursiveCharacterTextSplitter\\n\",\n \"\\n\",\n \"splitter = RecursiveCharacterTextSplitter(chunk_size=512, chunk_overlap=50)\\n\",\n \"chunked_docs = splitter.split_documents(commits_details)\\n\",\n \"\\n\",\n \"for doc in chunked_docs:\\n\",\n \" for md in doc.metadata:\\n\",\n \" doc.metadata[md] = str(doc.metadata[md])\"\n ],\n \"metadata\": {\n \"collapsed\": true,\n \"id\": \"_shmtR8Zl6d5\"\n },\n \"execution_count\": null,\n \"outputs\": []\n },\n {\n \"cell_type\": \"code\",\n \"source\": [\n \"from langchain_openai import OpenAIEmbeddings\\n\",\n \"import os\\n\",\n \"\\n\",\n \"os.environ['OPENAI_API_KEY'] = ab.get_secret(\\\"YOUR_OPENAI_API_KEY\\\")\\n\",\n \"embeddings = OpenAIEmbeddings()\"\n ],\n \"metadata\": {\n \"collapsed\": true,\n \"id\": \"CNgHPzULmB0j\"\n },\n \"execution_count\": null,\n \"outputs\": []\n },\n {\n \"cell_type\": \"markdown\",\n \"source\": [\n \"# **Setting up Chroma**\\n\",\n \"Create and configure a Chroma vector store to store the document embeddings.
\\n\",\n \"First we initialize Chroma Client\\n\",\n \"
\\n\",\n \"Then we create Chroma Vector Store from Documents\\n\",\n \"
\\n\",\n \"Finally we use embedding function when accessing the collection\\n\",\n \"
\\n\",\n \"Since currently there is a waitlist for Chroma,We initialize the Chroma client in persistent mode (local file)\"\n ],\n \"metadata\": {\n \"id\": \"TwHTBa43M7fM\"\n }\n },\n {\n \"cell_type\": \"code\",\n \"source\": [\n \"import chromadb\\n\",\n \"from langchain_chroma import Chroma\\n\",\n \"from chromadb.utils import embedding_functions\\n\",\n \"\\n\",\n \"persist_directory = 'chroma_db'\\n\",\n \"client = chromadb.PersistentClient(path=persist_directory)\\n\",\n \"collection_name = \\\"github_commits\\\"\\n\",\n \"\\n\",\n \"openai_lc_client = Chroma.from_documents(\\n\",\n \" documents=chunked_docs,\\n\",\n \" embedding=embeddings,\\n\",\n \" persist_directory=persist_directory,\\n\",\n \" collection_name=collection_name\\n\",\n \")\\n\",\n \"\\n\",\n \"openai_ef = embedding_functions.OpenAIEmbeddingFunction(\\n\",\n \" api_key=os.getenv(\\\"OPENAI_API_KEY\\\"),\\n\",\n \" model_name=\\\"text-embedding-ada-002\\\"\\n\",\n \")\\n\",\n \"collection = client.get_collection(name=collection_name, embedding_function=openai_ef)\\n\"\n ],\n \"metadata\": {\n \"collapsed\": true,\n \"id\": \"PzB1caQTmbuS\"\n },\n \"execution_count\": null,\n \"outputs\": []\n },\n {\n \"cell_type\": \"markdown\",\n \"source\": [\n \"# **Querying Chroma and RAG Pipeline**\\n\",\n \"Finally we use LangChain to retrieve documents from Chroma and generate responses using an OpenAI chat model.\"\n ],\n \"metadata\": {\n \"id\": \"MoCApKIWNpBd\"\n }\n },\n {\n \"cell_type\": \"code\",\n \"source\": [\n \"from langchain_openai import ChatOpenAI\\n\",\n \"from langchain import hub\\n\",\n \"from langchain_core.output_parsers import StrOutputParser\\n\",\n \"from langchain_core.runnables import RunnablePassthrough\\n\",\n \"\\n\",\n \"# Initialize the LLM\\n\",\n \"llm = ChatOpenAI(model_name=\\\"gpt-3.5-turbo\\\", temperature=0)\\n\",\n \"\\n\",\n \"# Set up the retriever from the Chroma vector store\\n\",\n \"retriever = openai_lc_client.as_retriever()\\n\",\n \"\\n\",\n \"# Set up the prompt\\n\",\n \"prompt = hub.pull(\\\"rlm/rag-prompt\\\")\\n\",\n \"\\n\",\n \"# Function to format documents\\n\",\n \"def format_docs(docs):\\n\",\n \" return \\\"\\\\n\\\\n\\\".join(doc.page_content for doc in docs)\\n\",\n \"\\n\",\n \"# Create the RAG chain\\n\",\n \"rag_chain = (\\n\",\n \" {\\\"context\\\": retriever | format_docs, \\\"question\\\": RunnablePassthrough()}\\n\",\n \" | prompt\\n\",\n \" | llm\\n\",\n \" | StrOutputParser()\\n\",\n \")\\n\",\n \"\\n\",\n \"print(\\\"Langchain RAG pipeline set up successfully.\\\")\\n\",\n \"\\n\",\n \"# Example query\\n\",\n \"response = rag_chain.invoke(\\\"Which are the commit messages of latest commits?\\\")\\n\",\n \"print(response)\\n\"\n ],\n \"metadata\": {\n \"id\": \"uDPPklkxod8-\"\n },\n \"execution_count\": null,\n \"outputs\": []\n }\n ]\n}" }, { "path": "pyairbyte_notebooks/README.md", "content": "# PyAirbyte Notebooks Quickstart\n\nThis quickstart will help you get started quickly with PyAirbyte.\n\n## Quickstart Quicklinks\n\nTo jump right in, click on any of the below links to open a new Colab notebook from the provided quickstart template.\n\n1. [Basic Features Demo](https://colab.research.google.com/github/airbytehq/quickstarts/blob/master/pyairbyte_notebooks/PyAirbyte_Basic_Features_Demo.ipynb) - Walks through the basic functionality of PyAirbyte and how to use it in a Notebook environment.\n2. [CoinAPI Demo](https://colab.research.google.com/github/airbytehq/quickstarts/blob/master/pyairbyte_notebooks/PyAirbyte_CoinAPI_Demo.ipynb) - Shows how to provide credentials securely and perform basic graphing.\n3. [GitHub Demo](https://colab.research.google.com/github/airbytehq/quickstarts/blob/master/pyairbyte_notebooks/PyAirbyte_Github_Incremental_Demo.ipynb) - Demonstrates how to get data from GitHub, how to analyze GitHub metrics and how to refresh your cache data incrementally.\n4. [GA4 Demo](https://colab.research.google.com/github/airbytehq/quickstarts/blob/master/pyairbyte_notebooks/PyAirbyte_GA4_Demo.ipynb) - A Google Analytics demo showing how to analyze page views and other GA metrics.\n\n## How to use these Quickstarts\n\nThere are three ways to use the quickstart resources here.\n\n### Google Colab\n\nGoogle Colab (\"Colab\" for short) is a hosted version of Jupyter. Because it is hosted by google, most people can access Colab using their existing Google account. To use these notebooks in Colab, click on the \"Open in Colab\" badge at the top of the file.\n\nNote:\n\n- Colab doesn't come with virtual environment (\"venv\") support by default. For this reason, our demo workbooks start by installing venv support as a prerequisite, using `apt-get`.\n\n### Self-Hosted Jupyter\n\nIf you have a self-hosted Jupyter instance, you can load any of the notebooks in this directory.\n\n### VS Code Notebooks\n\nYou can run these notebooks natively in VS Code if you have the Python extension installed. You can also use GitHub Codespaces to open a new VS Code devcontainer in your web browser.\n\n## Securely Managed Secrets\n\nYou can pass secrets to PyAirbyte by using the `get_secret()` function. This call will retrieve a named secret from any of the following locations:\n\n1. Google Colab Secrets\n2. Environment Variables\n3. Masked User Input (via [getpass](https://docs.python.org/3/library/getpass.html))\n\nIf you are using Google Colab, we suggest using the Colab secrets feature. For other environments, you can set your secret values in environment variables.\n\nNote: The `get_secret()` implementation in PyAirbyte is provided for your convenience as a secure runtime-agnostic default secrets interface. You are always free to use any secrets management platform you are most familiar with.\n\n**Warning:** Please do not enter your secrets directly into notebook cells. Doing so can cause the secret to be leaked into logs and/or in the \"previous versions\" look-back of the notebook. Instead, simply call `get_secret()` without pre-initializing the value. If the value is not already initialized, you will be prompted for secret values interactively and all values will be masked during input to avoid accidental leakage. This is performed using the Python standard library [`getpass`](https://docs.python.org/3/library/getpass.html).\n" }, { "path": "pyairbyte_notebooks/rag_using_gdrive_pyairbyte_pinecone.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"id\": \"8f104c9d-933a-45fd-9d2c-57e6d56a56b1\",\n \"metadata\": {},\n \"source\": [\n \"# End-to-End RAG Tutorial Using GoogleDrive, PyAirbyte, Pinecone, and LangChain\\n\",\n \"\\n\",\n \"This notebook demonstrates an end-to-end Retrieval-Augmented Generation (RAG) pipeline. We will extract data from Google Drive using PyAirbyte, store it in a Pinecone vector store, and then use LangChain to perform RAG on the stored data. This workflow showcases how to integrate these tools to build a scalable RAG system.\\n\",\n \"\\n\",\n \"## Prerequisites\\n\",\n \"\\n\",\n \"1. **GoogleDrive**:\\n\",\n \" - Follow the instructions in the [GoogleDrive Source Connector Documentation](https://docs.airbyte.com/integrations/sources/google-drive) to set up your google-drive and obtain the service account json\\n\",\n \"\\n\",\n \"2. **Pinecone Account**:\\n\",\n \" - **Create a Pinecone Account**: Sign up for an account on the [Pinecone website](https://www.pinecone.io/).\\n\",\n \" - **Obtain Pinecone API Key**: Generate a new API key from your Pinecone project settings. For detailed instructions, refer to the [Pinecone documentation](https://docs.pinecone.io/docs/quickstart).\\n\",\n \"\\n\",\n \"3. **OpenAI API Key**:\\n\",\n \" - **Create an OpenAI Account**: Sign up for an account on [OpenAI](https://www.openai.com/).\\n\",\n \" - **Generate an API Key**: Go to the API section and generate a new API key. For detailed instructions, refer to the [OpenAI documentation](https://beta.openai.com/docs/quickstart).\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"8e1d3a0b-d446-43b0-b9dd-691fd837e5eb\",\n \"metadata\": {},\n \"source\": [\n \"## Install PyAirbyte and other dependencies\"\n ]\n },\n {\n \"cell_type\": \"raw\",\n \"id\": \"ffebf71e-2b9a-4a1a-b448-9812b49f120c\",\n \"metadata\": {},\n \"source\": [\n \"!pip3 install airbyte openai langchain pinecone-client langchain-openai langchain-pinecone langchainhub \"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"7b51a295-8c8e-483d-a931-24ec2590dffa\",\n \"metadata\": {},\n \"source\": [\n \"## Setup Source GoogleDrive with PyAirbyte\\n\",\n \"\\n\",\n \"The provided code configures an Airbyte source to extract data from an GoogleDrive Folder contains CSV file named NFLX.csv\\n\",\n \"\\n\",\n \"To configure according to your requirements, you can refer to [this references](https://docs.airbyte.com/integrations/sources/google-drive#reference).\\n\",\n \"\\n\",\n \"Note: The credentials are retrieved securely using the get_secret() method. This will automatically locate a matching Google Colab secret or environment variable, ensuring they are not hard-coded into the notebook. Make sure to add your key to the Secrets section on the left.\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 14,\n \"id\": \"6ddd2f96-8680-4a19-a02b-32fd0b703912\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdin\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Enter the value for secret 'service_json': ········\\n\"\n ]\n },\n {\n \"data\": {\n \"text/html\": [\n \"
Connection check succeeded for `source-google-drive`.\\n\",\n       \"
\\n\"\n ],\n \"text/plain\": [\n \"Connection check succeeded for `source-google-drive`.\\n\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"import airbyte as ab\\n\",\n \"\\n\",\n \"service_json = ab.get_secret('service_json')\\n\",\n \"\\n\",\n \"source = ab.get_source(\\n\",\n \" \\\"source-google-drive\\\",\\n\",\n \" install_if_missing=True,\\n\",\n \" config={\\n\",\n \" \\\"folder_url\\\": \\\"https://drive.google.com/drive/folders/1txtyBv_mfXYjn0R_-oxV3Vg5QOi-6XaI\\\",\\n\",\n \" \\\"credentials\\\": {\\n\",\n \" \\\"auth_type\\\": \\\"Service\\\",\\n\",\n \" \\\"service_account_info\\\": f\\\"\\\"\\\"{service_json}\\\"\\\"\\\",\\n\",\n \" },\\n\",\n \" \\\"streams\\\": [{\\n\",\n \" \\\"name\\\": \\\"NFLX\\\",\\n\",\n \" \\\"globs\\\": [\\\"**/*.csv\\\"],\\n\",\n \" \\\"format\\\": {\\n\",\n \" \\\"filetype\\\": \\\"csv\\\"\\n\",\n \" },\\n\",\n \" \\\"validation_policy\\\": \\\"Emit Record\\\",\\n\",\n \" \\\"days_to_sync_if_history_is_full\\\": 3\\n\",\n \" }]\\n\",\n \" \\n\",\n \" },\\n\",\n \" \\n\",\n \")\\n\",\n \"\\n\",\n \"# Verify the config and creds by running `check`:\\n\",\n \"source.check()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"b12930b8-ff69-46e9-9daf-89b5ed49b68c\",\n \"metadata\": {},\n \"source\": [\n \"This is a basic process of fetching data from a Google Drive CSV source using Airbyte and converting it into a list of document objects, making it suitable for further processing or analysis.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 16,\n \"id\": \"a8abe757-b263-4574-b220-da715c4d827e\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/markdown\": [\n \"## Read Progress\\n\",\n \"\\n\",\n \"Started reading at 11:20:50.\\n\",\n \"\\n\",\n \"Read **0** records over **2 seconds** (0.0 records / second).\\n\",\n \"\\n\",\n \"Finished reading at 11:20:53.\\n\",\n \"\\n\",\n \"Started finalizing streams at 11:20:53.\\n\",\n \"\\n\",\n \"Finalized **0** batches over 0 seconds.\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"------------------------------------------------\\n\"\n ],\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n },\n {\n \"data\": {\n \"text/html\": [\n \"
Completed `source-google-drive` read operation at 16:50:53.\\n\",\n       \"
\\n\"\n ],\n \"text/plain\": [\n \"Completed `source-google-drive` read operation at \\u001b[1;92m16:50:53\\u001b[0m.\\n\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n },\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"```yaml\\n\",\n \"_ab_source_file_last_modified: '2024-06-04T04:00:24.000000Z'\\n\",\n \"_ab_source_file_url: NFLX.csv\\n\",\n \"_airbyte_extracted_at: 2024-06-07 11:20:25.946000\\n\",\n \"_airbyte_meta: {}\\n\",\n \"_airbyte_raw_id: 01HZS6VC3573GHEKRHMW1KA41T\\n\",\n \"adj_close: '254.259995'\\n\",\n \"close: '254.259995'\\n\",\n \"date: '2018-02-05'\\n\",\n \"high: '267.899994'\\n\",\n \"low: '250.029999'\\n\",\n \"open: '262.000000'\\n\",\n \"volume: '11896100'\\n\",\n \"```\\n\",\n \"\\n\"\n ]\n }\n ],\n \"source\": [\n \"# This code reads data from a Google Drive CSV source and converts it into a list of document objects.\\n\",\n \"\\n\",\n \"source.select_all_streams() # Select all streams from the Google Drive source\\n\",\n \"read_result = source.read() # Read the data from the selected streams\\n\",\n \"documents_list = []\\n\",\n \"\\n\",\n \"# Convert the read data into document objects and add them to the list\\n\",\n \"for key, value in read_result.items():\\n\",\n \" docs = value.to_documents()\\n\",\n \" for doc in docs:\\n\",\n \" documents_list.append(doc)\\n\",\n \"\\n\",\n \"# Print the Single row of the csv \\n\",\n \"print(str(documents_list[0]))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"a04c48f5-1801-47b9-9244-1cf1b560c291\",\n \"metadata\": {},\n \"source\": [\n \"## Use Langchain to build a RAG pipeline.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 17,\n \"id\": \"ebef2365-3570-43ad-b05d-a9e3ef86aef1\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"RecursiveCharacterTextSplitter from the langchain library to split documents into smaller chunks of 512 characters with a 50-character overlap.\\n\",\n \"It then converts all metadata values in each chunk to strings and prints the total number of created document chunks.\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 18,\n \"id\": \"81cada6b-c1bb-4cbf-9a40-30f2547ed15c\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Created 1009 document chunks.\\n\"\n ]\n }\n ],\n \"source\": [\n \"from langchain.text_splitter import RecursiveCharacterTextSplitter\\n\",\n \"from langchain.vectorstores.utils import filter_complex_metadata\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"splitter = RecursiveCharacterTextSplitter(chunk_size=512, chunk_overlap=50)\\n\",\n \"chunked_docs = splitter.split_documents(documents_list)\\n\",\n \"chunked_docs = filter_complex_metadata(chunked_docs)\\n\",\n \"print(f\\\"Created {len(chunked_docs)} document chunks.\\\")\\n\",\n \"\\n\",\n \"for doc in chunked_docs:\\n\",\n \" for md in doc.metadata:\\n\",\n \" doc.metadata[md] = str(doc.metadata[md])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 32,\n \"id\": \"50b756b3-745b-49ff-a57a-d1d6371243fe\",\n \"metadata\": {\n \"scrolled\": true\n },\n \"outputs\": [],\n \"source\": [\n \"from langchain_community.embeddings import HuggingFaceEmbeddings\\n\",\n \"## Create Embeddings using HuggingFace sentence-transformers/all-mpnet-base-v2 model\\n\",\n \"embeddings=HuggingFaceEmbeddings()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"33c36b1f-f03b-4f78-bbe1-9e9035af5a1d\",\n \"metadata\": {},\n \"source\": [\n \"## Setting up Pinecone\\n\",\n \"Pinecone is a managed vector database service designed for fast similarity search and real-time recommendation systems, offering scalability, efficiency, and ease of integration.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 43,\n \"id\": \"a61ce285-9d58-43e0-808b-786803364d8f\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from pinecone import Pinecone, ServerlessSpec\\n\",\n \"import os\\n\",\n \"os.environ['PINECONE_API_KEY'] = ab.get_secret(\\\"PINECONE_API_KEY\\\")\\n\",\n \"index_name = \\\"gdriveairbyteindex\\\"\\n\",\n \"\\n\",\n \"pc = Pinecone()\\n\",\n \"\\n\",\n \"# Create pinecone index if not exists otherwise skip this step\\n\",\n \"if not (pc.list_indexes()[0]['name'] == index_name):\\n\",\n \" pc.create_index(\\n\",\n \" name=index_name,\\n\",\n \" dimension=768, \\n\",\n \" metric=\\\"cosine\\\", \\n\",\n \" spec=ServerlessSpec(\\n\",\n \" cloud=\\\"aws\\\",\\n\",\n \" region=\\\"us-east-1\\\"\\n\",\n \" ) \\n\",\n \" )\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 44,\n \"id\": \"92f6e244-302b-4b6e-b490-3678412ba589\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"{'dimension': 768,\\n\",\n \" 'index_fullness': 0.0,\\n\",\n \" 'namespaces': {'': {'vector_count': 1009}},\\n\",\n \" 'total_vector_count': 1009}\"\n ]\n },\n \"execution_count\": 44,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"index = pc.Index(index_name)\\n\",\n \"index.describe_index_stats()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"c2034d80-b359-4240-a695-c959619fae4a\",\n \"metadata\": {},\n \"source\": [\n \"## PineconeVectorStore\\n\",\n \"PineconeVectorStore to store and index high-dimensional vectors extracted from documents, leveraging embeddings provided by Hugging Face\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 28,\n \"id\": \"874ddc8a-035e-436c-8a14-a7ed24fc4adb\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from langchain_pinecone import PineconeVectorStore\\n\",\n \"\\n\",\n \"pinecone = PineconeVectorStore.from_documents(\\n\",\n \" chunked_docs, embedding=embeddings, index_name=index_name\\n\",\n \")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"87e9990b-4982-44b6-9163-b12a3109e741\",\n \"metadata\": {},\n \"source\": [\n \"## RAG\\n\",\n \"Retrieval Augumented Generation provides the Large Language Model (LLM) the context and ask the Large Language Model (LLM) to use the context to generate the response.\\n\",\n \"\\n\",\n \"This RAG implementation uses the vector databases to the store the text doc embeddings (generated from the data from your knowledge base) and based on the given query, this code retreives the relevant information from the pinecone vector database and add that text context to your prompt. This will be used by the llm to generate the response\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 46,\n \"id\": \"b6d3490a-40e9-48d5-a8f1-5e6571f7e036\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Langchain RAG pipeline set up successfully.\\n\"\n ]\n }\n ],\n \"source\": [\n \"from langchain_openai import ChatOpenAI\\n\",\n \"from langchain import hub\\n\",\n \"from langchain_core.output_parsers import StrOutputParser\\n\",\n \"from langchain_core.runnables import RunnablePassthrough\\n\",\n \"import os\\n\",\n \"\\n\",\n \"os.environ['OPENAI_API_KEY'] = ab.get_secret(\\\"OPENAI_API_KEY\\\")\\n\",\n \"\\n\",\n \"retriever = pinecone.as_retriever()\\n\",\n \"prompt = hub.pull(\\\"rlm/rag-prompt\\\")\\n\",\n \"\\n\",\n \"llm = ChatOpenAI(model_name=\\\"gpt-3.5-turbo\\\", temperature=0)\\n\",\n \"\\n\",\n \"\\n\",\n \"def format_docs(docs):\\n\",\n \" return \\\"\\\\n\\\\n\\\".join(doc.page_content for doc in docs)\\n\",\n \"\\n\",\n \"rag_chain = (\\n\",\n \" {\\\"context\\\": retriever | format_docs, \\\"question\\\": RunnablePassthrough()}\\n\",\n \" | prompt\\n\",\n \" | llm\\n\",\n \" | StrOutputParser()\\n\",\n \")\\n\",\n \"print(\\\"Langchain RAG pipeline set up successfully.\\\")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 47,\n \"id\": \"f059fe04-bf3a-4bd8-b674-1f421f3bb5f4\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"The source data is about stock market information for Netflix (NFLX) on different dates, including details like opening price, closing price, high, low, adjusted close, and volume traded. The data includes specific dates ranging from 2018-10-31 to 2019-10-18. The information is extracted from a CSV file named NFLX.csv.\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(rag_chain.invoke(\\\"What is the source data about?\\\"))\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python (gdriveenv)\",\n \"language\": \"python\",\n \"name\": \".venv-source-google-drive\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.9.19\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 5\n}\n" }, { "path": "pyairbyte_notebooks/rag_using_github_pyairbyte_weaviate.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"source\": [\n \"# End-to-End RAG Tutorial Using Github, PyAirbyte, Weaviate\\n\",\n \"\\n\",\n \"In this notebook, we'll illustrate how to load data from github into Weaviate using PyAirbyte, then afterward retrieving In this, we've used source-github and stream 'issues' of it, but you can replace the source according to your requirements.\\n\",\n \"\\n\",\n \"## Prerequisites\\n\",\n \"\\n\",\n \"1. **Github**:\\n\",\n \" - Follow the instructions in the [Github Connector Docs](https://docs.airbyte.com/integrations/sources/github) to set up your github and get api_token.\\n\",\n \"\\n\",\n \"2. **Weaviate Account**:\\n\",\n \" - **Create a Weaviate Account**: Sign up for an account on the [Weaviate website](https://weaviate.io/).\\n\",\n \" - **Create a Cluster**: Follow this [instruction](https://weaviate.io/developers/weaviate/quickstart#step-1-create-a-weaviate-database) to create database and obatain weaviate API_KEY and URL.\\n\",\n \"\\n\",\n \"3. **OpenAI API Key**:\\n\",\n \" - **Create an OpenAI Account**: Sign up for an acco\\n\",\n \" unt on [OpenAI](https://www.openai.com/).\\n\",\n \" - **Generate an API Key**: Go to the API section and generate a new API key. For detailed instructions, refer to the [OpenAI documentation](https://beta.openai.com/docs/quickstart).\\n\",\n \"\\n\",\n \"\\n\",\n \"## Install PyAirbyte and other dependencies\"\n ],\n \"metadata\": {\n \"id\": \"5pBIRIujjIw4\"\n }\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"id\": \"4dv5cOvKi7xH\"\n },\n \"outputs\": [],\n \"source\": [\n \"# Add virtual environment support for running in Google Colab\\n\",\n \"!apt-get install -qq python3.10-venv\\n\",\n \"\\n\",\n \"# First, we need to install the necessary libraries.\\n\",\n \"!pip3 install airbyte weaviate-client python-dotenv\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"source\": [\n \"#Setup Source Github\\n\",\n \"\\n\",\n \"Note: The credentials are retrieved securely using the get_secret() method. This will automatically locate a matching Google Colab secret or environment variable, ensuring they are not hard-coded into the notebook. Make sure to add your key to the Secrets section on the left.\"\n ],\n \"metadata\": {\n \"id\": \"p-S0HQbWlqiM\"\n }\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"id\": \"wo8v-XcGi7xI\"\n },\n \"outputs\": [],\n \"source\": [\n \"import airbyte as ab\\n\",\n \"\\n\",\n \"source = ab.get_source(\\n\",\n \" \\\"source-github\\\",\\n\",\n \" config={\\n\",\n \" \\\"repositories\\\": ab.get_secret('GITHUB_REPOSITORY'),\\n\",\n \" \\\"credentials\\\": {\\n\",\n \" \\\"personal_access_token\\\": ab.get_secret('GITHUB_ACCESS_TOKEN'),\\n\",\n \" },\\n\",\n \" },\\n\",\n \")\\n\",\n \"source.check()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"source\": [\n \"Reads the data from the selected issues stream, extracting the GitHub issues data for further processing.\"\n ],\n \"metadata\": {\n \"id\": \"mScNlHAMlzpR\"\n }\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"id\": \"FV9E_J09i7xJ\"\n },\n \"outputs\": [],\n \"source\": [\n \"# In this notebook we are focused on only issues stream\\n\",\n \"# checkout all stream here : https://docs.airbyte.com/integrations/sources/gitlab#supported-streams\\n\",\n \"\\n\",\n \"print(source.get_available_streams())\\n\",\n \"source.select_streams([\\\"issues\\\"])\\n\",\n \"cache = ab.get_default_cache()\\n\",\n \"result = source.read(cache=cache,force_full_refresh=True)\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"id\": \"X45nwGPAi7xJ\"\n },\n \"outputs\": [],\n \"source\": [\n \"issues_details = result['issues'].to_pandas() #coverting data from issues stream to pandas dataframe\\n\",\n \"\\n\",\n \"print(issues_details.columns)\\n\",\n \"print(issues_details[10])\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"source\": [\n \"#Setting up Weaviate\\n\",\n \"Connect to the weaviate instance, Enter your weaviate cluster url and API_KEY\"\n ],\n \"metadata\": {\n \"id\": \"s7Z8OTeOmFS_\"\n }\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"id\": \"UyzpsIKPi7xK\"\n },\n \"outputs\": [],\n \"source\": [\n \"import weaviate\\n\",\n \"\\n\",\n \"client = weaviate.Client(\\n\",\n \" url = ab.get_secret('WCD_URL'), # Replace with your Weaviate endpoint\\n\",\n \" auth_client_secret=weaviate.auth.AuthApiKey(api_key=ab.get_secret('WCD_API_KEY')), # Replace with your Weaviate instance API key\\n\",\n \" additional_headers = {\\n\",\n \" \\\"X-OpenAI-Api-Key\\\": ab.get_secret('OPENAI_API_KEY') # Replace with your Openai API key\\n\",\n \" }\\n\",\n \")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"source\": [\n \"Weaviate stores data in collections. Each data object in a collection has a set of properties and a vector representation.\"\n ],\n \"metadata\": {\n \"id\": \"RcUwvnBHnLO0\"\n }\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"id\": \"CQm5WxEzi7xK\"\n },\n \"outputs\": [],\n \"source\": [\n \"collection_name = \\\"issues\\\" # name of collection\\n\",\n \"class_obj = {\\n\",\n \" \\\"class\\\": collection_name,\\n\",\n \" \\\"vectorizer\\\": \\\"text2vec-openai\\\", # If set to \\\"none\\\" you must always provide vectors yourself. Could be any other \\\"text2vec-*\\\" also.\\n\",\n \" \\\"moduleConfig\\\": {\\n\",\n \" \\\"text2vec-openai\\\": {},\\n\",\n \" \\\"generative-openai\\\": {} # Ensure the `generative-openai` module is used for generative queries\\n\",\n \" }\\n\",\n \"}\\n\",\n \"\\n\",\n \"client.schema.create_class(class_obj)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"id\": \"vMxfH8r8i7xL\"\n },\n \"outputs\": [],\n \"source\": [\n \"\\n\",\n \"# Batch imports are an efficient way to add multiple data objects and cross-references.\\n\",\n \"client.batch.configure(batch_size=100) # Configure batch\\n\",\n \"\\n\",\n \"#The following example adds objects to the collection.\\n\",\n \"with client.batch as batch: # Initialize a batch process\\n\",\n \" for i,d in enumerate(issues_details): # Batch import data\\n\",\n \" properties = {\\n\",\n \" \\\"issue_details\\\": d, # You can also change property name here and also add multiple property\\n\",\n \" }\\n\",\n \" batch.add_data_object(\\n\",\n \" data_object=properties,\\n\",\n \" class_name=collection_name\\n\",\n \" )\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"source\": [\n \"Weaviate has integrated generative search capabilities, so that the retrieval and generation steps are combined into a single query. This means that you can use Weaviate's search capabilities to retrieve the data you need, and then in the same query, prompt the LLM with the same data.\\n\",\n \"\\n\",\n \"This makes it easier, faster and more efficient to implement generative search workflows in your application.\\n\",\n \"\\n\",\n \"You can checkout more ways of query [here](https://weaviate.io/developers/weaviate/starter-guides/generative#data-retrieval).\"\n ],\n \"metadata\": {\n \"id\": \"ihPrmv3DqRL6\"\n }\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"id\": \"4YnIQV_pi7xL\",\n \"outputId\": \"287b69a9-2789-40b7-b1e5-c03754068638\"\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"/*\\n\",\n \"Summary of Pagination Handling in Github Connector issues in airbytehq/quickstarts repository:\\n\",\n \"\\n\",\n \"- Pagination handling in the Github Connector issues involves retrieving a limited number of issues at a time from the Github API and then using pagination to fetch the next set of issues.\\n\",\n \"- This ensures that large datasets of issues can be efficiently retrieved without overwhelming the API or the system.\\n\",\n \"- The Github Connector in the airbytehq/quickstarts repository likely implements pagination logic to handle the retrieval of issues in a systematic and efficient manner.\\n\",\n \"- Pagination parameters such as page number and page size are typically used to control the retrieval of issues in batches.\\n\",\n \"- Proper pagination handling is crucial for managing large volumes of data and ensuring smooth and efficient data retrieval from the Github API.\\n\",\n \"*/\\n\"\n ]\n }\n ],\n \"source\": [\n \"response = (\\n\",\n \" client.query\\n\",\n \" .get(class_name=collection_name, properties=[\\\"issue_details\\\"])\\n\",\n \" .with_near_text({\\\"concepts\\\": [\\\"title\\\",\\\"comments\\\"]})\\n\",\n \" .with_generate(single_prompt=\\\"Use {issue_details}, Give me summary of Pagination Handling in Github COnnector issues in airbytehq/quickstarts repository\\\") # do not forget to add USE {property_names} in prompt\\n\",\n \" .with_limit(1)\\n\",\n \" .do()\\n\",\n \")\\n\",\n \"\\n\",\n \"print(response[\\\"data\\\"][\\\"Get\\\"][\\\"Issues\\\"][0][\\\"_additional\\\"][\\\"generate\\\"][\\\"singleResult\\\"])\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.10.11\"\n },\n \"colab\": {\n \"provenance\": []\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 0\n}" }, { "path": "pyairbyte_notebooks/rag_using_gitlab_pyairbyte_qdrant.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"3s3Jow7_BjQi\"\n },\n \"source\": [\n \"# End-to-End RAG Tutorial Using Gitlab, PyAirbyte, Qdrant, and LangChain\\n\",\n \"\\n\",\n \"This notebook demonstrates an end-to-end Retrieval-Augmented Generation (RAG) pipeline. We will extract data from an gitlab using PyAirbyte, store it in a qdrantvector store, and then use LangChain to perform RAG on the stored data. This workflow showcases how to integrate these tools to build a scalable RAG system.\\n\",\n \"\\n\",\n \"## Prerequisites\\n\",\n \"\\n\",\n \"1. **Gitlab Account**:\\n\",\n \" - Follow the instructions in the [Gitlab Docs](https://docs.gitlab.com/ee/user/profile/personal_access_tokens.html#create-a-personal-access-token) to set up your gitlab account and obtain the necessary access token.\\n\",\n \"\\n\",\n \"2. **Qdrant Account**:\\n\",\n \" - **Create a Qdrant Account**: Sign up for an account on the Qdrant [website](https://qdrant.tech/)\\n\",\n \" - **Create Cluster**: Open the Qdrant dashboard and establish a new cluster. After building a new cluster, you will see an option for creating API_key; copy the URL and API_key from there.\\n\",\n \"\\n\",\n \"3. **OpenAI API Key**:\\n\",\n \" - **Create an OpenAI Account**: Sign up for an acco\\n\",\n \" unt on [OpenAI](https://www.openai.com/).\\n\",\n \" - **Generate an API Key**: Go to the API section and generate a new API key. For detailed instructions, refer to the [OpenAI documentation](https://beta.openai.com/docs/quickstart).\\n\",\n \"\\n\",\n \"\\n\",\n \"## Install PyAirbyte and other dependencies\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"id\": \"XclQfDX9MQsw\"\n },\n \"outputs\": [],\n \"source\": [\n \"# Add virtual environment support for running in Google Colab\\n\",\n \"!apt-get install -qq python3.10-venv\\n\",\n \"\\n\",\n \"# First, we need to install the necessary libraries.\\n\",\n \"!pip3 install airbyte langchain langchain-openai qdrant-client python-dotenv langchainhub\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"Hzira-9BQq0h\"\n },\n \"source\": [\n \"## Setup Source Gitlab with PyAirbyte\\n\",\n \"\\n\",\n \"The provided code configures an Airbyte source to extract data from a gitlab.\\n\",\n \"\\n\",\n \"To configure according to your requirements, you can refer to [this references](https://docs.airbyte.com/integrations/sources/gitlab#reference).\\n\",\n \"\\n\",\n \"Note: The credentials are retrieved securely using the get_secret() method. This will automatically locate a matching Google Colab secret or environment variable, ensuring they are not hard-coded into the notebook. Make sure to add your key to the Secrets section on the left.\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"id\": \"-2pgyG5aMGq0\"\n },\n \"outputs\": [],\n \"source\": [\n \"import airbyte as ab\\n\",\n \"\\n\",\n \"source = ab.get_source(\\n\",\n \" \\\"source-gitlab\\\",\\n\",\n \" config={\\n\",\n \" \\\"credentials\\\":{\\n\",\n \" \\\"auth_type\\\":\\\"access_token\\\",\\n\",\n \" \\\"access_token\\\": ab.get_secret(\\\"GITLAB_ACCESS_TOKEN\\\"),\\n\",\n \" },\\n\",\n \" \\\"projects\\\" :ab.get_secret(\\\"GITLAB_PROJECT\\\")\\n\",\n \" }\\n\",\n \")\\n\",\n \"source.check()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"id\": \"_Phdo7l_MGq2\"\n },\n \"outputs\": [],\n \"source\": [\n \"# In this notebook we are focused on only issues stream\\n\",\n \"# checkout all stream here : https://docs.airbyte.com/integrations/sources/gitlab#supported-streams\\n\",\n \"\\n\",\n \"source.get_available_streams()\\n\",\n \"source.select_streams([\\\"issues\\\"])\\n\",\n \"cache = ab.get_default_cache()\\n\",\n \"result = source.read(cache=cache)\\n\",\n \"\\n\",\n \"issues_details = [doc for doc in result[\\\"issues\\\"].to_documents()] # Fetching data for issues stream only\\n\",\n \"\\n\",\n \"print(str(issues_details[10]))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"jJ5Na_O2Sn1U\"\n },\n \"source\": [\n \"# Use Langchain to build a RAG pipeline.\\n\",\n \"\\n\",\n \"The code uses RecursiveCharacterTextSplitter to break documents into smaller chunks. Metadata within these chunks is converted to strings. This facilitates efficient processing of large texts, enhancing analysis capabilities.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"id\": \"wravAgJhMGq3\"\n },\n \"outputs\": [],\n \"source\": [\n \"from langchain.text_splitter import RecursiveCharacterTextSplitter\\n\",\n \"splitter = RecursiveCharacterTextSplitter(chunk_size=512, chunk_overlap=50)\\n\",\n \"chunked_docs = splitter.split_documents(issues_details)\\n\",\n \"print(f\\\"Created {len(chunked_docs)} document chunks.\\\")\\n\",\n \"\\n\",\n \"for doc in chunked_docs:\\n\",\n \" for md in doc.metadata:\\n\",\n \" doc.metadata[md] = str(doc.metadata[md])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {\n \"id\": \"tcXR48fsMGq4\"\n },\n \"outputs\": [],\n \"source\": [\n \"from langchain_openai import OpenAIEmbeddings\\n\",\n \"import os\\n\",\n \"\\n\",\n \"## Embedding Technique Of OPENAI\\n\",\n \"os.environ['OPENAI_API_KEY'] = ab.get_secret(\\\"OPENAI_API_KEY\\\")\\n\",\n \"embeddings=OpenAIEmbeddings()\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"Mh_lGwiJUkLg\"\n },\n \"source\": [\n \"## Setting up Qdrant\\n\",\n \"\\n\",\n \"Qdrant is leading open source vector database and similarity search engine designed to handle high-dimensional vectors for performance and massive-scale AI applications.\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"id\": \"uZXgQF-xMGq4\"\n },\n \"outputs\": [],\n \"source\": [\n \"from qdrant_client import QdrantClient, models\\n\",\n \"\\n\",\n \"client = QdrantClient(\\n\",\n \" location=ab.get_secret(\\\"QDRANT_URL\\\"), # As obtain above\\n\",\n \" api_key=ab.get_secret(\\\"QDRANT_API_KEY\\\"),\\n\",\n \")\\n\",\n \"\\n\",\n \"collection_name = \\\"gitlab_issue\\\" # Give collection a name\\n\",\n \"client.create_collection(\\n\",\n \" collection_name=collection_name,\\n\",\n \" vectors_config=models.VectorParams(\\n\",\n \" size=1536, # vector dimensions\\n\",\n \" distance=models.Distance.COSINE,\\n\",\n \" ),\\n\",\n \")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"id\": \"nU9KjhMHMGq4\"\n },\n \"outputs\": [],\n \"source\": [\n \"from langchain.vectorstores.qdrant import Qdrant\\n\",\n \"\\n\",\n \"qdrant = Qdrant(\\n\",\n \" client=client,\\n\",\n \" collection_name=collection_name,\\n\",\n \" embeddings=embeddings,\\n\",\n \")\\n\",\n \"\\n\",\n \"qdrant.add_documents(chunked_docs, batch_size=20)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"8zwaoOteU7oi\"\n },\n \"source\": [\n \"Now setting up a pipeline for RAG using LangChain, incorporating document retrieval from Pinecone, prompt configuration, and a chat model from OpenAI for response generation.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 10,\n \"metadata\": {\n \"id\": \"y2e-raMYMGq4\",\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\"\n },\n \"outputId\": \"418f679a-dafe-4f21-d443-54e73e8c1fde\"\n },\n \"outputs\": [\n {\n \"output_type\": \"stream\",\n \"name\": \"stdout\",\n \"text\": [\n \"Langchain RAG pipeline set up successfully.\\n\"\n ]\n }\n ],\n \"source\": [\n \"from langchain_openai import ChatOpenAI\\n\",\n \"from langchain import hub\\n\",\n \"from langchain_core.output_parsers import StrOutputParser\\n\",\n \"from langchain_core.runnables import RunnablePassthrough\\n\",\n \"\\n\",\n \"retriever = qdrant.as_retriever()\\n\",\n \"prompt = hub.pull(\\\"rlm/rag-prompt\\\")\\n\",\n \"llm = ChatOpenAI(model_name=\\\"gpt-3.5-turbo\\\", temperature=0)\\n\",\n \"\\n\",\n \"\\n\",\n \"def format_docs(docs):\\n\",\n \" return \\\"\\\\n\\\\n\\\".join(doc.page_content for doc in docs)\\n\",\n \"\\n\",\n \"rag_chain = (\\n\",\n \" {\\\"context\\\": retriever | format_docs, \\\"question\\\": RunnablePassthrough()}\\n\",\n \" | prompt\\n\",\n \" | llm\\n\",\n \" | StrOutputParser()\\n\",\n \")\\n\",\n \"print(\\\"Langchain RAG pipeline set up successfully.\\\")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 13,\n \"metadata\": {\n \"id\": \"Ihbo8bllMGq4\",\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\"\n },\n \"outputId\": \"feec51cc-2cfd-4634-e075-baa571b52b5f\"\n },\n \"outputs\": [\n {\n \"output_type\": \"stream\",\n \"name\": \"stdout\",\n \"text\": [\n \"The programming languages mentioned in the context are Java and JavaScript.\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(rag_chain.invoke(\\\"Which programing languages are mentioned in issues most?\\\"))\"\n ]\n }\n ],\n \"metadata\": {\n \"colab\": {\n \"provenance\": [],\n \"toc_visible\": true\n },\n \"kernelspec\": {\n \"display_name\": \"myenv\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.10.11\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 0\n}" }, { "path": "pyairbyte_notebooks/rag_using_jira_pyairbyte_pinecone.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"id\": \"82f45520-6a6d-49b2-9dfd-ea33231e7b39\",\n \"metadata\": {},\n \"source\": [\n \"# End-to-End RAG Tutorial Using Jira, PyAirbyte, Pinecone, and LangChain\\n\",\n \"\\n\",\n \"This notebook demonstrates an end-to-end Retrieval-Augmented Generation (RAG) pipeline. We will extract data from Jira using PyAirbyte, store it in a Pinecone vector store, and then use LangChain to perform RAG on the stored data. This workflow showcases how to integrate these tools to build a scalable RAG system.\\n\",\n \"\\n\",\n \"## Prerequisites\\n\",\n \"\\n\",\n \"1. **Jira**:\\n\",\n \" - Follow the instructions in the [Jira Source Connector Documentation](https://docs.airbyte.com/integrations/sources/jira) to set up your jira airbyte source\\n\",\n \"\\n\",\n \"2. **Pinecone Account**:\\n\",\n \" - **Create a Pinecone Account**: Sign up for an account on the [Pinecone website](https://www.pinecone.io/).\\n\",\n \" - **Obtain Pinecone API Key**: Generate a new API key from your Pinecone project settings. For detailed instructions, refer to the [Pinecone documentation](https://docs.pinecone.io/docs/quickstart).\\n\",\n \"\\n\",\n \"3. **OpenAI API Key**:\\n\",\n \" - **Create an OpenAI Account**: Sign up for an account on [OpenAI](https://www.openai.com/).\\n\",\n \" - **Generate an API Key**: Go to the API section and generate a new API key. For detailed instructions, refer to the [OpenAI documentation](https://beta.openai.com/docs/quickstart).\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"318e598e-10b5-4fd3-891f-e316cbc4a341\",\n \"metadata\": {},\n \"source\": [\n \"## Install PyAirbyte and other dependencies\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"3954735e-6c45-4d15-b2a2-4fc705c935b7\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"!pip3 install airbyte openai langchain pinecone-client langchain-openai langchain-pinecone langchainhub \"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"b5f0ec02-f31c-44ff-ac7e-49af6f608c9e\",\n \"metadata\": {},\n \"source\": [\n \"# Setup Source Jira with PyAirbyte\\n\",\n \"\\n\",\n \"The provided code configures an Airbyte source to extract issues data from jira data\\n\",\n \"\\n\",\n \"To configure according to your requirements, you can refer to [this references](https://docs.airbyte.com/integrations/sources/jira#reference).\\n\",\n \"\\n\",\n \"Note: The credentials are retrieved securely using the get_secret() method. This will automatically locate a matching Google Colab secret or environment variable, ensuring they are not hard-coded into the notebook. Make sure to add your key to the Secrets section on the left.\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"936c79e8-2eff-4d4a-ae51-74d8f14c7005\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"import airbyte as ab\\n\",\n \"import json\\n\",\n \"\\n\",\n \"projects = json.loads(ab.get_secret('projects_list'))\\n\",\n \"\\n\",\n \"source = ab.get_source(\\n\",\n \" \\\"source-jira\\\",\\n\",\n \" install_if_missing=True,\\n\",\n \" config={\\n\",\n \" \\\"api_token\\\": ab.get_secret('jira_api_token'),\\n\",\n \" \\\"domain\\\": ab.get_secret('jira_domain') ,\\n\",\n \" \\\"email\\\": ab.get_secret('jira_email_id'),\\n\",\n \" \\\"start_date\\\": \\\"2021-01-01T00:00:00Z\\\", # optional field, can be ignored \\n\",\n \" \\\"projects\\\": projects\\n\",\n \" },\\n\",\n \"\\n\",\n \")\\n\",\n \"\\n\",\n \"# Verify the config and creds by running `check`:\\n\",\n \"source.check()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"89205323-0676-4869-bcf6-fbbb31333a5a\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"source.select_streams(['issues']) # Select only issues stream\\n\",\n \"read_result: ab.ReadResult = source.read()\\n\",\n \"documents_list = []\\n\",\n \"\\n\",\n \"for key, value in read_result.items():\\n\",\n \" docs = value.to_documents()\\n\",\n \" for doc in docs:\\n\",\n \" documents_list.append(doc)\\n\",\n \"\\n\",\n \"print(str(documents_list))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"6c76d164-8894-41b6-aee6-a3de0a70aa26\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# store and display the issues stream in data frame\\n\",\n \"issues_df = read_result[\\\"issues\\\"].to_pandas()\\n\",\n \"display(issues_df)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"770f07e7-9dd6-4bd3-b180-fc322eb1e209\",\n \"metadata\": {},\n \"source\": [\n \"## Use Langchain to build a RAG pipeline.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"8194e7ba-4aef-4fdf-afef-93a5dee0e36c\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from langchain.text_splitter import RecursiveCharacterTextSplitter\\n\",\n \"from langchain.vectorstores.utils import filter_complex_metadata\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"splitter = RecursiveCharacterTextSplitter(chunk_size=512, chunk_overlap=50)\\n\",\n \"chunked_docs = splitter.split_documents(documents_list)\\n\",\n \"chunked_docs = filter_complex_metadata(chunked_docs)\\n\",\n \"print(f\\\"Created {len(chunked_docs)} document chunks.\\\")\\n\",\n \"\\n\",\n \"for doc in chunked_docs:\\n\",\n \" for md in doc.metadata:\\n\",\n \" doc.metadata[md] = str(doc.metadata[md])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"01eeea04-f774-4a6b-840a-999aed9f80ec\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from langchain_openai import OpenAIEmbeddings\\n\",\n \"import os\\n\",\n \"\\n\",\n \"os.environ['OPENAI_API_KEY'] = ab.get_secret(\\\"OPENAI_API_KEY\\\")\\n\",\n \"\\n\",\n \"embeddings=OpenAIEmbeddings()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"6ac9b188-2972-42b4-a9bb-7d752b0eeaf3\",\n \"metadata\": {},\n \"source\": [\n \"## Setting up Pinecone\\n\",\n \"\\n\",\n \"Pinecone is a managed vector database service designed for storing, indexing, and querying high-dimensional vector data efficiently.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"a1f11593-3ea2-4762-ad23-e21c87d44ece\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from pinecone import Pinecone, ServerlessSpec\\n\",\n \"os.environ['PINECONE_API_KEY'] = ab.get_secret(\\\"PINECONE_API_KEY\\\")\\n\",\n \"\\n\",\n \"index_name = \\\"airbytejiraindex\\\"\\n\",\n \"\\n\",\n \"pc = Pinecone()\\n\",\n \"\\n\",\n \"# Create pinecone index if not exists otherwise skip this step\\n\",\n \"if not (pc.list_indexes()[0]['name'] == index_name):\\n\",\n \" pc.create_index(\\n\",\n \" name=index_name,\\n\",\n \" dimension=1536,\\n\",\n \" metric=\\\"cosine\\\",\\n\",\n \" spec=ServerlessSpec(\\n\",\n \" cloud=\\\"aws\\\",\\n\",\n \" region=\\\"us-east-1\\\"\\n\",\n \" )\\n\",\n \" )\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"de7efdc6-4363-4c3e-bd6e-86b955f2e505\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"index = pc.Index(index_name)\\n\",\n \"\\n\",\n \"index.describe_index_stats()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"98bdb85f-be90-47b0-a778-9232c81427c1\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from langchain_pinecone import PineconeVectorStore\\n\",\n \"\\n\",\n \"pinecone = PineconeVectorStore.from_documents(\\n\",\n \" chunked_docs, embedding=embeddings, index_name=index_name\\n\",\n \")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"6f5d31fa-6f88-4755-9405-6f7c84495607\",\n \"metadata\": {},\n \"source\": [\n \"## RAG\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"beb7f799-3558-49f4-8c20-2a45ac61c034\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from langchain_openai import ChatOpenAI\\n\",\n \"from langchain import hub\\n\",\n \"from langchain_core.output_parsers import StrOutputParser\\n\",\n \"from langchain_core.runnables import RunnablePassthrough\\n\",\n \"\\n\",\n \"retriever = pinecone.as_retriever()\\n\",\n \"prompt = hub.pull(\\\"rlm/rag-prompt\\\")\\n\",\n \"\\n\",\n \"os.environ['OPENAI_API_KEY'] = ab.get_secret(\\\"OPENAI_API_KEY\\\")\\n\",\n \"\\n\",\n \"llm = ChatOpenAI(model_name=\\\"gpt-3.5-turbo\\\")\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"def format_docs(docs):\\n\",\n \" return \\\"\\\\n\\\\n\\\".join(doc.page_content for doc in docs)\\n\",\n \"\\n\",\n \"rag_chain = (\\n\",\n \" {\\\"context\\\": retriever | format_docs, \\\"question\\\": RunnablePassthrough()}\\n\",\n \" | prompt\\n\",\n \" | llm\\n\",\n \" | StrOutputParser()\\n\",\n \")\\n\",\n \"print(\\\"Langchain RAG pipeline set up successfully.\\\")\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"ae3dd498-74a3-4868-8529-bdad7d949d46\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"print(rag_chain.invoke(\\\"Summarize the issue of key IT-20\\\"))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"f62b7761-c463-430d-8150-3e816897cad9\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"print(rag_chain.invoke(\\\"What is the source data about?\\\"))\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3 (ipykernel)\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.9.19\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 5\n}\n" }, { "path": "pyairbyte_notebooks/rag_using_s3_pyairbyte_pinecone.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"3s3Jow7_BjQi\"\n },\n \"source\": [\n \"# End-to-End RAG Tutorial Using S3, PyAirbyte, Pinecone, and LangChain\\n\",\n \"\\n\",\n \"This notebook demonstrates an end-to-end Retrieval-Augmented Generation (RAG) pipeline. We will extract data from an S3 bucket using PyAirbyte, store it in a Pinecone vector store, and then use LangChain to perform RAG on the stored data. This workflow showcases how to integrate these tools to build a scalable RAG system.\\n\",\n \"\\n\",\n \"## Prerequisites\\n\",\n \"\\n\",\n \"1. **AWS S3 Bucket**:\\n\",\n \" - Follow the instructions in the [AWS S3 Source Connector Documentation](https://docs.airbyte.com/integrations/sources/s3) to set up your S3 bucket and obtain the necessary access keys.\\n\",\n \"\\n\",\n \"2. **Pinecone Account**:\\n\",\n \" - **Create a Pinecone Account**: Sign up for an account on the [Pinecone website](https://www.pinecone.io/).\\n\",\n \" - **Obtain Pinecone API Key**: Generate a new API key from your Pinecone project settings. For detailed instructions, refer to the [Pinecone documentation](https://docs.pinecone.io/docs/quickstart).\\n\",\n \"\\n\",\n \"3. **OpenAI API Key**:\\n\",\n \" - **Create an OpenAI Account**: Sign up for an account on [OpenAI](https://platform.openai.com/docs/overview).\\n\",\n \" - **Generate an API Key**: Go to the API section and generate a new API key. For detailed instructions, refer to the [OpenAI documentation](https://platform.openai.com/api-keys).\\n\",\n \"\\n\",\n \"\\n\",\n \"## Install PyAirbyte and other dependencies\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"id\": \"ij3THvimBjQk\"\n },\n \"outputs\": [],\n \"source\": [\n \"# Add virtual environment support in Google Colab\\n\",\n \"!apt-get install -qq python3.10-venv\\n\",\n \"\\n\",\n \"# First, we need to install the necessary libraries.\\n\",\n \"!pip3 install airbyte openai langchain pinecone-client langchain-openai langchain-pinecone python-dotenv langchainhub\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"8jDsEZdiBjQl\"\n },\n \"source\": [\n \"## Setup Source S3 with PyAirbyte\\n\",\n \"\\n\",\n \"The provided code configures an Airbyte source to extract data from an Amazon S3 bucket containing CSV files.\\n\",\n \"\\n\",\n \"To configure according to your requirements, you can refer to [this references](https://docs.airbyte.com/integrations/sources/s3#reference).\\n\",\n \"\\n\",\n \"Note: The credentials are retrieved securely using the get_secret() method. This will automatically locate a matching Google Colab secret or environment variable, ensuring they are not hard-coded into the notebook. Make sure to add your key to the Secrets section on the left.\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"id\": \"U7DxyLVUBjQl\"\n },\n \"outputs\": [],\n \"source\": [\n \"import airbyte as ab\\n\",\n \"\\n\",\n \"source = ab.get_source(\\n\",\n \" \\\"source-s3\\\",\\n\",\n \" config={\\n\",\n \" \\\"streams\\\": [\\n\",\n \" {\\n\",\n \" \\\"name\\\": \\\"\\\",\\n\",\n \" \\\"format\\\": {\\n\",\n \" \\\"filetype\\\": \\\"csv\\\",\\n\",\n \" \\\"ignore_errors_on_fields_mismatch\\\": True,\\n\",\n \" },\\n\",\n \" \\\"globs\\\": [\\\"**\\\"],\\n\",\n \" \\\"legacy_prefix\\\": \\\"\\\",\\n\",\n \" \\\"validation_policy\\\": \\\"Emit Record\\\",\\n\",\n \" }\\n\",\n \" ],\\n\",\n \" \\\"bucket\\\": ab.get_secret(\\\"S3_BUCKET_NAME\\\"),\\n\",\n \" \\\"aws_access_key_id\\\": ab.get_secret(\\\"AWS_ACCESS_KEY\\\"),\\n\",\n \" \\\"aws_secret_access_key\\\": ab.get_secret(\\\"AWS_SECRET_KEY\\\"),\\n\",\n \" \\\"region_name\\\": ab.get_secret(\\\"AWS_REGION\\\")\\n\",\n \" }\\n\",\n \")\\n\",\n \"source.check()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"rtSCjGcQBjQl\"\n },\n \"source\": [\n \"This is a basic process of fetching data from an S3 bucket using Airbyte and converting it into a format suitable for further processing or analysis.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"id\": \"mlg7K8GUBjQm\"\n },\n \"outputs\": [],\n \"source\": [\n \"source.select_all_streams() # Select all streams\\n\",\n \"read_result = source.read() # Read the data\\n\",\n \"documents_list = [doc for value in read_result.values() for doc in value.to_documents()]\\n\",\n \"\\n\",\n \"print(str(documents_list[10]))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"L6KM7KPMBjQm\"\n },\n \"source\": [\n \"# Use Langchain to build a RAG pipeline.\\n\",\n \"\\n\",\n \"The code uses RecursiveCharacterTextSplitter to break documents into smaller chunks. Metadata within these chunks is converted to strings. This facilitates efficient processing of large texts, enhancing analysis capabilities.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"id\": \"LXvSJoUSBjQm\"\n },\n \"outputs\": [],\n \"source\": [\n \"\\n\",\n \"from langchain.text_splitter import RecursiveCharacterTextSplitter\\n\",\n \"\\n\",\n \"splitter = RecursiveCharacterTextSplitter(chunk_size=512, chunk_overlap=50)\\n\",\n \"chunked_docs = splitter.split_documents(documents_list)\\n\",\n \"print(f\\\"Created {len(chunked_docs)} document chunks.\\\")\\n\",\n \"\\n\",\n \"for doc in chunked_docs:\\n\",\n \" for md in doc.metadata:\\n\",\n \" doc.metadata[md] = str(doc.metadata[md])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"id\": \"kCF7gZTMBjQm\"\n },\n \"outputs\": [],\n \"source\": [\n \"from langchain_openai import OpenAIEmbeddings\\n\",\n \"import os\\n\",\n \"\\n\",\n \"os.environ['OPENAI_API_KEY'] = ab.get_secret(\\\"OPENAI_API_KEY\\\")\\n\",\n \"## Embedding Technique Of OPENAI\\n\",\n \"embeddings=OpenAIEmbeddings()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"5TKCIAhYBjQm\"\n },\n \"source\": [\n \"## Setting up Pinecone\\n\",\n \"\\n\",\n \"Pinecone is a managed vector database service designed for storing, indexing, and querying high-dimensional vector data efficiently.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"id\": \"iXb5YPnhBjQn\"\n },\n \"outputs\": [],\n \"source\": [\n \"from pinecone import Pinecone, ServerlessSpec\\n\",\n \"from pinecone import Pinecone\\n\",\n \"\\n\",\n \"os.environ['PINECONE_API_KEY'] = ab.get_secret(\\\"PINECONE_API_KEY\\\")\\n\",\n \"pc = Pinecone()\\n\",\n \"index_name = \\\"s3-quickstarts-index\\\" # Replace with your index name\\n\",\n \"\\n\",\n \"\\n\",\n \"# Uncomment this if you have not created a Pinecone index yet\\n\",\n \"\\n\",\n \"# spec = ServerlessSpec(cloud=\\\"aws\\\", region=\\\"us-east-1\\\") // Replace with your cloud and region\\n\",\n \"# pc.create_index(\\n\",\n \"# \\\"quickstarts\\\",\\n\",\n \"# dimension=1536, // Replace with your model dimensions\\n\",\n \"# metric='cosine', // Replace with your model metric\\n\",\n \"# spec=spec\\n\",\n \"# )\\n\",\n \"\\n\",\n \"index = pc.Index(index_name)\\n\",\n \"\\n\",\n \"index.describe_index_stats()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"6kRv6s7zBjQn\"\n },\n \"source\": [\n \"PineconeVectorStore is a class provided by the LangChain library specifically designed for interacting with Pinecone vector stores.\\n\",\n \"from_documents method of PineconeVectorStore is used to create or update vectors in a Pinecone vector store based on the provided documents and their corresponding embeddings.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"id\": \"3hToKOPsBjQn\"\n },\n \"outputs\": [],\n \"source\": [\n \"from langchain_pinecone import PineconeVectorStore\\n\",\n \"\\n\",\n \"pinecone = PineconeVectorStore.from_documents(\\n\",\n \" chunked_docs, embeddings, index_name=index_name\\n\",\n \")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"vj0hSWo2BjQn\"\n },\n \"source\": [\n \"Now setting up a pipeline for RAG using LangChain, incorporating document retrieval from Pinecone, prompt configuration, and a chat model from OpenAI for response generation.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"id\": \"N0gE_LbmBjQn\"\n },\n \"outputs\": [],\n \"source\": [\n \"from langchain_openai import ChatOpenAI\\n\",\n \"from langchain import hub\\n\",\n \"from langchain_core.output_parsers import StrOutputParser\\n\",\n \"from langchain_core.runnables import RunnablePassthrough\\n\",\n \"\\n\",\n \"retriever = pinecone.as_retriever()\\n\",\n \"prompt = hub.pull(\\\"rlm/rag-prompt\\\")\\n\",\n \"llm = ChatOpenAI(model_name=\\\"gpt-3.5-turbo\\\", temperature=0)\\n\",\n \"\\n\",\n \"\\n\",\n \"def format_docs(docs):\\n\",\n \" return \\\"\\\\n\\\\n\\\".join(doc.page_content for doc in docs)\\n\",\n \"\\n\",\n \"rag_chain = (\\n\",\n \" {\\\"context\\\": retriever | format_docs, \\\"question\\\": RunnablePassthrough()}\\n\",\n \" | prompt\\n\",\n \" | llm\\n\",\n \" | StrOutputParser()\\n\",\n \")\\n\",\n \"print(\\\"Langchain RAG pipeline set up successfully.\\\")\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"id\": \"axbwi9j8BjQn\"\n },\n \"outputs\": [],\n \"source\": [\n \"print(rag_chain.invoke(\\\"What are some best documentaries to watch?\\\"))\\n\"\n ]\n }\n ],\n \"metadata\": {\n \"colab\": {\n \"provenance\": []\n },\n \"kernelspec\": {\n \"display_name\": \"myenv\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.10.11\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 0\n}\n" }, { "path": "pyairbyte_notebooks/rag_using_shopify_pyairbyte_langchain.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"3s3Jow7_BjQi\"\n },\n \"source\": [\n \"# End-to-End RAG Tutorial Using Shopify, PyAirbyte, Pinecone, and LangChain\\n\",\n \"\\n\",\n \"This notebook demonstrates an end-to-end Retrieval-Augmented Generation (RAG) pipeline. We will extract data from an Shopify bucket using PyAirbyte, store it in a Pinecone vector store, and then use LangChain to perform RAG on the stored data. This workflow showcases how to integrate these tools to build a scalable RAG system.\\n\",\n \"\\n\",\n \"## Prerequisites\\n\",\n \"\\n\",\n \"1. **Shopify**:\\n\",\n \" - Follow the instructions in the [Shopify Connector Docs](https://docs.airbyte.com/integrations/sources/shopify#setup-guide) to set up your Shopify and obtain the necessary access keys.\\n\",\n \"\\n\",\n \"2. **Pinecone Account**:\\n\",\n \" - **Create a Pinecone Account**: Sign up for an account on the [Pinecone website](https://www.pinecone.io/).\\n\",\n \" - **Obtain Pinecone API Key**: Generate a new API key from your Pinecone project settings. For detailed instructions, refer to the [Pinecone documentation](https://docs.pinecone.io/docs/quickstart).\\n\",\n \"\\n\",\n \"3. **OpenAI API Key**:\\n\",\n \" - **Create an OpenAI Account**: Sign up for an acco\\n\",\n \" unt on [OpenAI](https://www.openai.com/).\\n\",\n \" - **Generate an API Key**: Go to the API section and generate a new API key. For detailed instructions, refer to the [OpenAI documentation](https://beta.openai.com/docs/quickstart).\\n\",\n \"\\n\",\n \"\\n\",\n \"## Install PyAirbyte and other dependencies\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"id\": \"XclQfDX9MQsw\"\n },\n \"outputs\": [],\n \"source\": [\n \"# Add virtual environment support for running in Google Colab\\n\",\n \"!apt-get install -qq python3.10-venv\\n\",\n \"\\n\",\n \"# First, we need to install the necessary libraries.\\n\",\n \"!pip3 install airbyte openai langchain pinecone-client langchain-openai langchain-pinecone python-dotenv langchainhub\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"Hzira-9BQq0h\"\n },\n \"source\": [\n \"## Setup Source Shopify with PyAirbyte\\n\",\n \"\\n\",\n \"The provided code configures an Airbyte source to extract data from a Shopify store.\\n\",\n \"\\n\",\n \"To configure according to your requirements, you can refer to [this references](https://docs.airbyte.com/integrations/sources/shopify#reference).\\n\",\n \"\\n\",\n \"Note: The credentials are retrieved securely using the get_secret() method. This will automatically locate a matching Google Colab secret or environment variable, ensuring they are not hard-coded into the notebook. Make sure to add your key to the Secrets section on the left.\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\"\n },\n \"id\": \"-2pgyG5aMGq0\",\n \"outputId\": \"f20c47bf-1dcb-42bd-ddbb-a7b1ab939afe\"\n },\n \"outputs\": [],\n \"source\": [\n \"import airbyte as ab\\n\",\n \"\\n\",\n \"source = ab.get_source(\\n\",\n \" \\\"source-shopify\\\",\\n\",\n \" config={\\n\",\n \"\\n\",\n \" \\\"credentials\\\":{\\n\",\n \" # There are two methods available for authentication 'api_password' and 'oauth2.0',\\n\",\n \" # Choose one of them (https://docs.airbyte.com/integrations/sources/shopify#airbyte-open-source)\\n\",\n \" \\\"auth_method\\\": \\\"api_password\\\",\\n\",\n \" \\\"api_password\\\": ab.get_secret(\\\"API_PASSWORD\\\")\\n\",\n \" },\\n\",\n \" \\\"shop\\\":ab.get_secret(\\\"STORE_NAME\\\"),\\n\",\n \" \\\"start_date\\\": ab.get_secret(\\\"START_DATE\\\"),\\n\",\n \" \\\"bulk_window_in_days\\\": 30, # change this according to your requirement (Defines what would be a date range per single BULK Job)\\n\",\n \" \\\"fetch_transactions_user_id\\\": False\\n\",\n \" }\\n\",\n \")\\n\",\n \"source.check()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"GO0QI-qNRNyz\"\n },\n \"source\": [\n \"This is a basic process of fetching data from an using Airbyte and converting it into a format suitable for further processing or analysis.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"id\": \"_Phdo7l_MGq2\"\n },\n \"outputs\": [],\n \"source\": [\n \"# List the available streams available for the Shopify source\\n\",\n \"# Available Stream for shopify here(https://docs.airbyte.com/integrations/sources/shopify#supported-streams)\\n\",\n \"source.get_available_streams()\\n\",\n \"# Select the streams we are interested in loading to cache\\n\",\n \"source.select_streams([\\\"products\\\", \\\"product_variants\\\", \\\"collections\\\", \\\"customers\\\"])\\n\",\n \"cache = ab.get_default_cache()\\n\",\n \"result = source.read(cache=cache)\\n\",\n \"\\n\",\n \"product_details = [doc for doc in result[\\\"products\\\"].to_documents()] # Fetching data for products stream only\\n\",\n \"\\n\",\n \"print(str(product_details[10]))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"jJ5Na_O2Sn1U\"\n },\n \"source\": [\n \"# Use Langchain to build a RAG pipeline.\\n\",\n \"\\n\",\n \"The code uses RecursiveCharacterTextSplitter to break documents into smaller chunks. Metadata within these chunks is converted to strings. This facilitates efficient processing of large texts, enhancing analysis capabilities.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"id\": \"wravAgJhMGq3\"\n },\n \"outputs\": [],\n \"source\": [\n \"from langchain.text_splitter import RecursiveCharacterTextSplitter\\n\",\n \"splitter = RecursiveCharacterTextSplitter(chunk_size=512, chunk_overlap=50)\\n\",\n \"chunked_docs = splitter.split_documents(product_details)\\n\",\n \"print(f\\\"Created {len(chunked_docs)} document chunks.\\\")\\n\",\n \"\\n\",\n \"for doc in chunked_docs:\\n\",\n \" for md in doc.metadata:\\n\",\n \" doc.metadata[md] = str(doc.metadata[md])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"id\": \"tcXR48fsMGq4\"\n },\n \"outputs\": [],\n \"source\": [\n \"from langchain_openai import OpenAIEmbeddings\\n\",\n \"\\n\",\n \"## Embedding Technique Of OPENAI\\n\",\n \"os.environ['OPENAI_API_KEY'] = ab.get_secret(\\\"OPENAI_API_KEY\\\")\\n\",\n \"embeddings=OpenAIEmbeddings()\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"Mh_lGwiJUkLg\"\n },\n \"source\": [\n \"## Setting up Pinecone\\n\",\n \"\\n\",\n \"Pinecone is a managed vector database service designed for storing, indexing, and querying high-dimensional vector data efficiently.\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"id\": \"uZXgQF-xMGq4\"\n },\n \"outputs\": [],\n \"source\": [\n \"from pinecone import Pinecone, ServerlessSpec\\n\",\n \"from pinecone import Pinecone\\n\",\n \"import os\\n\",\n \"\\n\",\n \"os.environ['PINECONE_API_KEY'] = ab.get_secret(\\\"PINECONE_API_KEY\\\")\\n\",\n \"pc = Pinecone()\\n\",\n \"index_name = \\\"shopifyproductsindex\\\" # Replace with your index name\\n\",\n \"\\n\",\n \"\\n\",\n \"# Uncomment this if you have not created a Pinecone index yet\\n\",\n \"\\n\",\n \"# spec = ServerlessSpec(cloud=\\\"aws\\\", region=\\\"us-east-1\\\") #Replace with your cloud and region\\n\",\n \"# pc.create_index(\\n\",\n \"# name=index_name,\\n\",\n \"# dimension=1536,\\n\",\n \"# metric='cosine',\\n\",\n \"# spec=spec\\n\",\n \"# )\\n\",\n \"\\n\",\n \"index = pc.Index(index_name)\\n\",\n \"\\n\",\n \"index.describe_index_stats()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"evdVEEsTUwhE\"\n },\n \"source\": [\n \"PineconeVectorStore is a class provided by the LangChain library specifically designed for interacting with Pinecone vector stores.\\n\",\n \"from_documents method of PineconeVectorStore is used to create or update vectors in a Pinecone vector store based on the provided documents and their corresponding embeddings.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"id\": \"nU9KjhMHMGq4\"\n },\n \"outputs\": [],\n \"source\": [\n \"from langchain_pinecone import PineconeVectorStore\\n\",\n \"\\n\",\n \"pinecone = PineconeVectorStore.from_documents(\\n\",\n \" chunked_docs, embeddings, index_name=index_name\\n\",\n \")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"8zwaoOteU7oi\"\n },\n \"source\": [\n \"Now setting up a pipeline for RAG using LangChain, incorporating document retrieval from Pinecone, prompt configuration, and a chat model from OpenAI for response generation.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"id\": \"y2e-raMYMGq4\"\n },\n \"outputs\": [],\n \"source\": [\n \"from langchain_openai import ChatOpenAI\\n\",\n \"from langchain import hub\\n\",\n \"from langchain_core.output_parsers import StrOutputParser\\n\",\n \"from langchain_core.runnables import RunnablePassthrough\\n\",\n \"\\n\",\n \"retriever = pinecone.as_retriever()\\n\",\n \"prompt = hub.pull(\\\"rlm/rag-prompt\\\")\\n\",\n \"llm = ChatOpenAI(model_name=\\\"gpt-3.5-turbo\\\", temperature=0)\\n\",\n \"\\n\",\n \"\\n\",\n \"def format_docs(docs):\\n\",\n \" return \\\"\\\\n\\\\n\\\".join(doc.page_content for doc in docs)\\n\",\n \"\\n\",\n \"rag_chain = (\\n\",\n \" {\\\"context\\\": retriever | format_docs, \\\"question\\\": RunnablePassthrough()}\\n\",\n \" | prompt\\n\",\n \" | llm\\n\",\n \" | StrOutputParser()\\n\",\n \")\\n\",\n \"print(\\\"Langchain RAG pipeline set up successfully.\\\")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"id\": \"Ihbo8bllMGq4\"\n },\n \"outputs\": [],\n \"source\": [\n \"print(rag_chain.invoke(\\\"What type of products do we sell?\\\"))\"\n ]\n }\n ],\n \"metadata\": {\n \"colab\": {\n \"provenance\": []\n },\n \"kernelspec\": {\n \"display_name\": \"myenv\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.10.11\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 0\n}\n" }, { "path": "pyairbyte_notebooks/rag_with_fb_marketing_milvus_lite.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"This notebook demonstrates simple RAG (Retrieval-Augmented Generation) pipeline with Facebook Marketing, Milvus Lite and PyAirbyte.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Prerequisites\\n\",\n \"- [PyAirbyte](https://airbyte.com/product/pyairbyte)\\n\",\n \" \\n\",\n \" PyAirbyte is an open-source that packages Airbyte connectors and makes them available in Python. We will connect to \\n\",\n \" `source-facebook-marketing`, and retrive its streams\\n\",\n \"- [Milvus Lite](https://milvus.io/docs/milvus_lite.md)\\n\",\n \" \\n\",\n \" Milvus Lite is the lightweight version of [Milvus](https://github.com/milvus-io/milvus) that enables vector emdeddings and similarity search\\n\",\n \" into the Python application.\\n\",\n \"- OpenAI API Key\\n\",\n \" \\n\",\n \" Go to the [API Keys page](https://platform.openai.com/api-keys) to create the new secret key.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 1. Install and set dependencies\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"%pip install airbyte pymilvus openai milvus-model\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"import json\\n\",\n \"import os\\n\",\n \"import airbyte as ab\\n\",\n \"from openai import OpenAI\\n\",\n \"from pymilvus import MilvusClient\\n\",\n \"\\n\",\n \"\\n\",\n \"# in production, you might want to avoid putting the key here.\\n\",\n \"os.environ[\\\"OPENAI_API_KEY\\\"] = \\\"sk-proj-****\\\"\\n\",\n \"openai_client = OpenAI()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 2. Set the source\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Connect `source-facebook-marketing` to fetch streams\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"source = ab.get_source(\\n\",\n \" \\\"source-facebook-marketing\\\",\\n\",\n \" config={\\n\",\n \" \\\"start_date\\\": \\\"2024-06-01T00:00:00Z\\\",\\n\",\n \" \\\"account_id\\\": \\\"account\\\",\\n\",\n \" \\\"access_token\\\": \\\"token\\\"\\n\",\n \" },\\n\",\n \" install_if_missing=True,\\n\",\n \")\\n\",\n \"source.check()\\n\",\n \"source.select_all_streams()\\n\",\n \"result = source.read()\\n\",\n \"\\n\",\n \"for name, records in result.streams.items():\\n\",\n \" print(f\\\"Streams of {name} has {len(records)} records.\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 3. Milvus Lite & Text Embedding\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"{\\\"type\\\": \\\"DEBUG\\\", \\\"message\\\": \\\"Created new connection using: 5a7d992d451b41db831d254213b64892\\\", \\\"data\\\": {}}\\n\"\n ]\n }\n ],\n \"source\": [\n \"milvus_client = MilvusClient(\\\"./milvus_source_fake.db\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"This will create the `milvus_source_fake.db` if this is the first initialization. There are some [limitations](https://milvus.io/docs/milvus_lite.md#Limits), but this quick setup for local development should be enought to test the pipeline.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Let's focused to get the `products` data. We will keep it simple by just getting the relevant fields:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"data = []\\n\",\n \"for record in result.streams[\\\"products\\\"]:\\n\",\n \" make = record[\\\"make\\\"]\\n\",\n \" model = record[\\\"model\\\"]\\n\",\n \" year = record[\\\"year\\\"]\\n\",\n \"\\n\",\n \" text = f\\\"{make} {model} {year}\\\"\\n\",\n \" data.append(text)\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"['Mazda MX-5 2023',\\n\",\n \" 'Mercedes-Benz C-Class 2023',\\n\",\n \" 'Honda Accord Crosstour 2023',\\n\",\n \" 'GMC Jimmy 2023',\\n\",\n \" 'Infiniti FX 2023']\"\n ]\n },\n \"execution_count\": 9,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"data[:5]\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from pymilvus import model\\n\",\n \"\\n\",\n \"openai_ef = model.dense.OpenAIEmbeddingFunction(\\n\",\n \" model_name='text-embedding-3-large', # Specify the model name\\n\",\n \" api_key=os.environ[\\\"OPENAI_API_KEY\\\"], # Provide your OpenAI API key\\n\",\n \" dimensions=512 # Set the embedding dimensionality\\n\",\n \")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 11,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"embedded_data = openai_ef.encode_documents(data)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 12,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"{\\\"type\\\": \\\"DEBUG\\\", \\\"message\\\": \\\"Successfully created collection: products\\\", \\\"data\\\": {}}\\n\",\n \"{\\\"type\\\": \\\"DEBUG\\\", \\\"message\\\": \\\"Successfully created an index on collection: products\\\", \\\"data\\\": {}}\\n\"\n ]\n }\n ],\n \"source\": [\n \"milvus_client.create_collection(\\\"products\\\", dimension=512)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 13,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"embedded_docs = []\\n\",\n \"for _id, embedded_text in enumerate(embedded_data):\\n\",\n \" embedded_docs.append({\\\"id\\\": _id+1, \\\"vector\\\": embedded_text, \\\"text\\\": data[_id]})\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 14,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"{'insert_count': 50,\\n\",\n \" 'ids': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50],\\n\",\n \" 'cost': 0}\"\n ]\n },\n \"execution_count\": 14,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"milvus_client.insert(collection_name=\\\"products\\\", data=embedded_docs)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 4. Inspect the search results\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 15,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"question = \\\"Give list of products from Suzuki\\\"\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 16,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"search_res = milvus_client.search(\\n\",\n \" collection_name=\\\"products\\\",\\n\",\n \" data=[\\n\",\n \" openai_ef.encode_documents([question])[0]\\n\",\n \" ], # Use the `emb_text` function to convert the question to an embedding vector\\n\",\n \" limit=3, # Return top 3 results\\n\",\n \" search_params={\\\"metric_type\\\": \\\"COSINE\\\", \\\"params\\\": {}}, # Inner product distance\\n\",\n \" output_fields=[\\\"text\\\"], # Return the text field\\n\",\n \")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 17,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[\\n\",\n \" [\\n\",\n \" \\\"Suzuki SJ 410 2023\\\",\\n\",\n \" 0.5219288468360901\\n\",\n \" ],\\n\",\n \" [\\n\",\n \" \\\"Isuzu VehiCROSS 2023\\\",\\n\",\n \" 0.38782158493995667\\n\",\n \" ],\\n\",\n \" [\\n\",\n \" \\\"Jaguar S-Type 2023\\\",\\n\",\n \" 0.35628464818000793\\n\",\n \" ]\\n\",\n \"]\\n\"\n ]\n }\n ],\n \"source\": [\n \"retrieved_lines_with_distances = [\\n\",\n \" (res[\\\"entity\\\"][\\\"text\\\"], res[\\\"distance\\\"]) for res in search_res[0]\\n\",\n \"]\\n\",\n \"print(json.dumps(retrieved_lines_with_distances, indent=4))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 5. Use OpenAI ChatGPT to get the RAG response\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Let's define the system and user prompts for the Language Model. This prompt is assembled with the retrieved documents from Milvus.\\n\",\n \"\\n\",\n \"We also use OpenAI ChatGPT to generate a response based on the prompts.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 18,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"context = \\\"\\\\n\\\".join(\\n\",\n \" [line_with_distance[0] for line_with_distance in retrieved_lines_with_distances]\\n\",\n \")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 19,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"SYSTEM_PROMPT = \\\"\\\"\\\"\\n\",\n \"Human: You are an AI assistant. You are able to find answers to the questions from the contextual passage snippets provided.\\n\",\n \"\\\"\\\"\\\"\\n\",\n \"USER_PROMPT = f\\\"\\\"\\\"\\n\",\n \"Use the following pieces of information enclosed in tags to provide an answer to the question enclosed in tags.\\n\",\n \"\\n\",\n \"{context}\\n\",\n \"\\n\",\n \"\\n\",\n \"{question}\\n\",\n \"\\n\",\n \"\\\"\\\"\\\"\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 20,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"\\n\",\n \"Suzuki SJ 410 2023\\n\",\n \"\\n\"\n ]\n }\n ],\n \"source\": [\n \"response = openai_client.chat.completions.create(\\n\",\n \" model=\\\"gpt-3.5-turbo\\\",\\n\",\n \" messages=[\\n\",\n \" {\\\"role\\\": \\\"system\\\", \\\"content\\\": SYSTEM_PROMPT},\\n\",\n \" {\\\"role\\\": \\\"user\\\", \\\"content\\\": USER_PROMPT},\\n\",\n \" ],\\n\",\n \")\\n\",\n \"print(response.choices[0].message.content)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Summary\\n\",\n \"\\n\",\n \"This shows how easy to build RAG pipeline in Python for quick local development which helps us to speed our development iterations. All within the comport of Python environment.\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"pyairbyte-hackathon\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.11.0\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n" }, { "path": "pyairbyte_notebooks/rag_with_pyairbyte_and_milvus_lite.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"This notebook demonstrates simple RAG (Retrieval-Augmented Generation) pipeline with Milvus Lite and PyAirbyte.\\n\",\n \"The focus is to showcase how to set these components for a fully local development in Python.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Prerequisites\\n\",\n \"- [PyAirbyte](https://airbyte.com/product/pyairbyte)\\n\",\n \" \\n\",\n \" PyAirbyte is an open-source that packages Airbyte connectors and makes them available in Python. In this tutorial, we will just use the \\n\",\n \" `source-faker`, but it's easy to set it up for other sources.\\n\",\n \"- [Milvus Lite](https://milvus.io/docs/milvus_lite.md)\\n\",\n \" \\n\",\n \" Milvus Lite is the lightweight version of [Milvus](https://github.com/milvus-io/milvus) that enables vector emdeddings and similarity search\\n\",\n \" into the Python application.\\n\",\n \"- OpenAI API Key\\n\",\n \" \\n\",\n \" Go to the [API Keys page](https://platform.openai.com/api-keys) to create the new secret key.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 1. Install and set dependencies\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"%pip install airbyte pymilvus openai milvus-model\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"import json\\n\",\n \"import os\\n\",\n \"import airbyte as ab\\n\",\n \"from openai import OpenAI\\n\",\n \"from pymilvus import MilvusClient\\n\",\n \"\\n\",\n \"\\n\",\n \"# in production, you might want to avoid putting the key here.\\n\",\n \"os.environ[\\\"OPENAI_API_KEY\\\"] = \\\"sk-proj-****\\\"\\n\",\n \"openai_client = OpenAI()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 2. Set the source\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"For simplicity, we will just use `source-faker` to generate some data.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/markdown\": [\n \"## Read Progress\\n\",\n \"\\n\",\n \"Started reading at 08:19:11.\\n\",\n \"\\n\",\n \"Read **150** records over **1 seconds** (150.0 records / second).\\n\",\n \"\\n\",\n \"Wrote **150** records over 3 batches.\\n\",\n \"\\n\",\n \"Finished reading at 08:19:13.\\n\",\n \"\\n\",\n \"Started finalizing streams at 08:19:13.\\n\",\n \"\\n\",\n \"Finalized **3** batches over 1 seconds.\\n\",\n \"\\n\",\n \"Completed 3 out of 3 streams:\\n\",\n \"\\n\",\n \" - products\\n\",\n \" - users\\n\",\n \" - purchases\\n\",\n \"\\n\",\n \"\\n\",\n \"Completed writing at 08:19:14. Total time elapsed: 3 seconds\\n\",\n \"\\n\",\n \"\\n\",\n \"------------------------------------------------\\n\"\n ],\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n },\n {\n \"data\": {\n \"text/html\": [\n \"
Completed `source-faker` read operation at 15:19:14.\\n\",\n       \"
\\n\"\n ],\n \"text/plain\": [\n \"Completed `source-faker` read operation at \\u001b[1;92m15:19:14\\u001b[0m.\\n\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n },\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Streams of products has 50 records.\\n\",\n \"Streams of users has 50 records.\\n\",\n \"Streams of purchases has 50 records.\\n\"\n ]\n }\n ],\n \"source\": [\n \"source = ab.get_source(\\n\",\n \" \\\"source-faker\\\",\\n\",\n \" config={\\\"count\\\": 5_0},\\n\",\n \" install_if_missing=True,\\n\",\n \")\\n\",\n \"source.check()\\n\",\n \"source.select_all_streams()\\n\",\n \"result = source.read()\\n\",\n \"\\n\",\n \"for name, records in result.streams.items():\\n\",\n \" print(f\\\"Streams of {name} has {len(records)} records.\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Here we can see there are streams of `products`, `users`, and `purchases`. All of them has 50 records.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 3. Milvus Lite & Text Embedding\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"{\\\"type\\\": \\\"DEBUG\\\", \\\"message\\\": \\\"Created new connection using: 5a7d992d451b41db831d254213b64892\\\", \\\"data\\\": {}}\\n\"\n ]\n }\n ],\n \"source\": [\n \"milvus_client = MilvusClient(\\\"./milvus_source_fake.db\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"This will create the `milvus_source_fake.db` if this is the first initialization. There are some [limitations](https://milvus.io/docs/milvus_lite.md#Limits), but this quick setup for local development should be enought to test the pipeline.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Let's focused to get the `products` data. We will keep it simple by just getting the relevant fields:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"data = []\\n\",\n \"for record in result.streams[\\\"products\\\"]:\\n\",\n \" make = record[\\\"make\\\"]\\n\",\n \" model = record[\\\"model\\\"]\\n\",\n \" year = record[\\\"year\\\"]\\n\",\n \"\\n\",\n \" text = f\\\"{make} {model} {year}\\\"\\n\",\n \" data.append(text)\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"['Mazda MX-5 2023',\\n\",\n \" 'Mercedes-Benz C-Class 2023',\\n\",\n \" 'Honda Accord Crosstour 2023',\\n\",\n \" 'GMC Jimmy 2023',\\n\",\n \" 'Infiniti FX 2023']\"\n ]\n },\n \"execution_count\": 9,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"data[:5]\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from pymilvus import model\\n\",\n \"\\n\",\n \"openai_ef = model.dense.OpenAIEmbeddingFunction(\\n\",\n \" model_name='text-embedding-3-large', # Specify the model name\\n\",\n \" api_key=os.environ[\\\"OPENAI_API_KEY\\\"], # Provide your OpenAI API key\\n\",\n \" dimensions=512 # Set the embedding dimensionality\\n\",\n \")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 11,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"embedded_data = openai_ef.encode_documents(data)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 12,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"{\\\"type\\\": \\\"DEBUG\\\", \\\"message\\\": \\\"Successfully created collection: products\\\", \\\"data\\\": {}}\\n\",\n \"{\\\"type\\\": \\\"DEBUG\\\", \\\"message\\\": \\\"Successfully created an index on collection: products\\\", \\\"data\\\": {}}\\n\"\n ]\n }\n ],\n \"source\": [\n \"milvus_client.create_collection(\\\"products\\\", dimension=512)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 13,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"embedded_docs = []\\n\",\n \"for _id, embedded_text in enumerate(embedded_data):\\n\",\n \" embedded_docs.append({\\\"id\\\": _id+1, \\\"vector\\\": embedded_text, \\\"text\\\": data[_id]})\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 14,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"{'insert_count': 50,\\n\",\n \" 'ids': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50],\\n\",\n \" 'cost': 0}\"\n ]\n },\n \"execution_count\": 14,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"milvus_client.insert(collection_name=\\\"products\\\", data=embedded_docs)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 4. Inspect the search results\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 15,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"question = \\\"Give list of products from Suzuki\\\"\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 16,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"search_res = milvus_client.search(\\n\",\n \" collection_name=\\\"products\\\",\\n\",\n \" data=[\\n\",\n \" openai_ef.encode_documents([question])[0]\\n\",\n \" ], # Use the `emb_text` function to convert the question to an embedding vector\\n\",\n \" limit=3, # Return top 3 results\\n\",\n \" search_params={\\\"metric_type\\\": \\\"COSINE\\\", \\\"params\\\": {}}, # Inner product distance\\n\",\n \" output_fields=[\\\"text\\\"], # Return the text field\\n\",\n \")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 17,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[\\n\",\n \" [\\n\",\n \" \\\"Suzuki SJ 410 2023\\\",\\n\",\n \" 0.5219288468360901\\n\",\n \" ],\\n\",\n \" [\\n\",\n \" \\\"Isuzu VehiCROSS 2023\\\",\\n\",\n \" 0.38782158493995667\\n\",\n \" ],\\n\",\n \" [\\n\",\n \" \\\"Jaguar S-Type 2023\\\",\\n\",\n \" 0.35628464818000793\\n\",\n \" ]\\n\",\n \"]\\n\"\n ]\n }\n ],\n \"source\": [\n \"retrieved_lines_with_distances = [\\n\",\n \" (res[\\\"entity\\\"][\\\"text\\\"], res[\\\"distance\\\"]) for res in search_res[0]\\n\",\n \"]\\n\",\n \"print(json.dumps(retrieved_lines_with_distances, indent=4))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 5. Use OpenAI ChatGPT to get the RAG response\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Let's define the system and user prompts for the Language Model. This prompt is assembled with the retrieved documents from Milvus.\\n\",\n \"\\n\",\n \"We also use OpenAI ChatGPT to generate a response based on the prompts.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 18,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"context = \\\"\\\\n\\\".join(\\n\",\n \" [line_with_distance[0] for line_with_distance in retrieved_lines_with_distances]\\n\",\n \")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 19,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"SYSTEM_PROMPT = \\\"\\\"\\\"\\n\",\n \"Human: You are an AI assistant. You are able to find answers to the questions from the contextual passage snippets provided.\\n\",\n \"\\\"\\\"\\\"\\n\",\n \"USER_PROMPT = f\\\"\\\"\\\"\\n\",\n \"Use the following pieces of information enclosed in tags to provide an answer to the question enclosed in tags.\\n\",\n \"\\n\",\n \"{context}\\n\",\n \"\\n\",\n \"\\n\",\n \"{question}\\n\",\n \"\\n\",\n \"\\\"\\\"\\\"\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 20,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"\\n\",\n \"Suzuki SJ 410 2023\\n\",\n \"\\n\"\n ]\n }\n ],\n \"source\": [\n \"response = openai_client.chat.completions.create(\\n\",\n \" model=\\\"gpt-3.5-turbo\\\",\\n\",\n \" messages=[\\n\",\n \" {\\\"role\\\": \\\"system\\\", \\\"content\\\": SYSTEM_PROMPT},\\n\",\n \" {\\\"role\\\": \\\"user\\\", \\\"content\\\": USER_PROMPT},\\n\",\n \" ],\\n\",\n \")\\n\",\n \"print(response.choices[0].message.content)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Summary\\n\",\n \"\\n\",\n \"This shows how easy to build RAG pipeline in Python for quick local development which helps us to speed our development iterations. All within the comport of Python environment.\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"pyairbyte-hackathon\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.11.0\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n" }, { "path": "pyairbyte_notebooks/sentiment_analysis_airbyte_gsheets_snowflakecortex.ipynb", "content": "{\n \"nbformat\": 4,\n \"nbformat_minor\": 0,\n \"metadata\": {\n \"colab\": {\n \"provenance\": []\n },\n \"kernelspec\": {\n \"name\": \"python3\",\n \"display_name\": \"Python 3\"\n },\n \"language_info\": {\n \"name\": \"python\"\n }\n },\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"source\": [\n \"# Sentiment analysis using Airbyte Cloud, Google sheets, and Snowflake Cortex\\n\",\n \"\\n\"\n ],\n \"metadata\": {\n \"id\": \"SK1X63WQ0xNk\"\n }\n },\n {\n \"cell_type\": \"markdown\",\n \"source\": [\n \"## Setup Airbyte Source as Google Sheets\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"* Login to your airbyte cloud account and Select Google sheets as source\\n\",\n \"* Provide the service account json and Spreadsheet link as per the airbyte documentation mentioned [here](https://docs.airbyte.com/integrations/sources/google-sheets#set-up-the-google-sheets-source-connector-in-airbyte)\\n\",\n \"\\n\"\n ],\n \"metadata\": {\n \"id\": \"WMVZ-XWi1iVy\"\n }\n },\n {\n \"cell_type\": \"markdown\",\n \"source\": [\n \"### Setup Source - Google sheets Airbyte Cloud Screenshot![Screenshot 2024-06-26 at 12.04.11 AM.png](data:image/png;base64,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)\"\n ],\n \"metadata\": {\n \"id\": \"NSkQlOaP0_3M\"\n }\n },\n {\n \"cell_type\": \"markdown\",\n \"source\": [\n \"## Setup Airbyte Destination as Snowflake Cortex\\n\",\n \"\\n\",\n \"\\n\",\n \"* Login to your airbyte cloud account and Select Snowflake Cortex as destination\\n\",\n \"* Provide the required credentials per the airbyte documentation mentioned [here](https://docs.airbyte.com/integrations/destinations/snowflake-cortex#prerequisites)\\n\"\n ],\n \"metadata\": {\n \"id\": \"eZp_Tar02PuC\"\n }\n },\n {\n \"cell_type\": \"markdown\",\n \"source\": [\n \"## Setup Destination - Snowflake Cortex Airbyte Cloud Screenshot\\n\",\n \"![Screenshot 2024-06-26 at 12.04.26 AM.png](data:image/png;base64,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ZaYkP2FGolb+/TT/0m7Z4eIBtdX/T5Fxr35iYwYOU00oGwvGoxu+vj9MmhQG7so6OcH42fJiBFTZf/+w377M2XKIE2a3CsvvNhC0qZN47fv3ns6mJB9/fp3yqjRMQFwZ4WDB4/IdRWaiI594SIFZNGid527fes3VH7SzPjestVD0rdvS3l50HgZO3amb3/gSs2aVUSD3fYSX1Bfz/3VrIUyfPgU66GGmDC23U4/9cGAIUM7+AX/7f0JBfU3bdop9et1F31gQh+ImDp1oGTJkslubj4XL/pd+rwwTtat3eJXrpbNmtWWbs8/HquNX8U4Nnr0GGtC7gUK5JYlS96LN0DvPMTdd7WTtWs3S63a1eRtaxb+wCXaeljjhRfflo+mfSenT5/x250vX055vvvj0qjRPX7l9obtVbtONRkypIM0bNBLVq3aYO+WTZs/NWH8Tp2Gy/SP5vrKA1cmTuwrNe660a9Y+/XiS2/LtKmx+6UPpAx6uY3cdltFvzbnzp2XWg90NA9oaJj/u7ljggb6GzboLQsWrDAPm/zw4xui9zwLAggggAACCCCAAAIIIIBAeAsQ1A/v8fNq7wnqe3XkuW4EEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBPwFCOr7e7CVCAGC+heQdFbw8uViZkB/4IFb5K23e0maNBEXKiRyzRnU79uvpbRvN9SEjCtVKi3/Wv+nIWk7tK8z0r/0UougR5469Vvp2mWk2aeh//Lli0u6dFFmtv5t2/aa8sefeEBeffVZv/ZDh06S4cOmmtnyV6ycJBER/tdg989upEF9Dew7Fw3P31WjrSmyH1r46KPv5Msv5svhI//I8mXrzT49dvXqlcy6huK7dGniO0x8Qf23xn0i/fu/56ubP38uqVLlWjPDuz3bv84A/9nnQ8ys676K1oodPL/l1goSOKP+li27rBntu5uZ4StWvEamTbNC+lmvcDY3gfiH6naTf/45acp1RvcSJQrKr9ZM9fa4XHVVHhMgj+utBX4H/G9D35pQ8bomog9CtGhRV/r1bxWsWkhlz3V4XWbO/MG01VnoS5UqJCdPnrbC7huMmY7DmLHd5KGH7oh1fNtL7+njx0/JvHnLzAMEZaw3Rpw+fVZmzxlpgvpvv/Wp6NsYdKZ9e1Hj9NY9p0tX6w0BaupcOnYcJjOmf2+K9N686aZrzfXrGyXsL7FffPFpeaZ1PWczE9LXsL6G9tVJvZyLzurfssUgUzRl6gC5446Ye8xZh3UEEEAAAQQQQAABBBBAAIHwEyCoH35jRo/F9zcOp4U1B4VZ7L9/2J9aeOHvrdZfAq16F7ZFzp8/7zueXa5/U8qfP2fMAfmNAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACrhUgqO/aoXFvx+wvhJxfGukXRhqg1Z9cubK6t/NWz5L6C95evd6QDz/4ylxz5cqlpf+AZ2KFkxMCsYPwGvJPkyaNtGlT34ScdRZ9XfSBgGfbvmZC0br9/fdjpXSZIrrqWzQs3ezJfhIdfU6efvpB6dW7uWTIkM7s1zGbMmWOCaxrQc9ezaRdu0d9bfVBgPvve85sf/3NCDM7vW+ntfLcc8Nk5oyYcLWWv/xyW3myWS1nFfMWgAED3hMNqutbAey+a6UjVlC/bJmGpv7kKQN8QX2/A1gbcQX1V678Sx58sKvobOw33lhWXh3cTkpboXF7cYbt69W7U0aP8X8rgB08Dwzqb926x8ykv3v3AalQoYR8NP1lyRoQ0t+395DUqtVZdu3aL7fffr0MHNRadOZ3XfR+nzv3F2n9zKty5sxZadiwpgwb3tHuVoKfe/YclMqVnjD1Jk3uL3feWTnBNompMHjwBBk18iPz0MiIkV2kXr3qvocvTpw4JX1fekcmT54tUVGRMtV6MKFqVf83QdheGuZPa92DQ15rLw/Wvd13PwX24ZtvFkuLpwea4s1bPjMPhwTW0e2RI6bJa69NNKH/YcM6ij4IkDFjelP1wIHD0rnzCPl+7q/mIYC534+RYsWu8juM3l/6tgkdowUL3zFvoNAK+saAO25vLdu375VHHr1LRo7s7NeODQQQQAABBBBAAAEEEEAAgfAVSOq/4ySXxMGDR80bDPXNe/q3Hf3/qWN+xLeeXOfmuO4T0L/FBS52kb3P/tR6un7hx7lNUD/QkW0EEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBMJJgKB+OI2WS/oa7EsjLwf1T506I23bDJY5c5b4Rqho0Sul5j1VTLD7llvKm+Cxb2eQFTuor7saNLxbhg/vFKvWvn1/y623tBANWut+rWcvx46dkBsqP2FmfNcg+8efDDZfDtv77c8+vcfJ+PFfmi+IZ80aJhWvj5ntXMf0hspPigbHn3/+cXmuYyO7ifmS8PqKTWX//sNSq3Y1+WrWArn33pvl/fEv+OroSoMGvWThgpXy8MPVzUztzp2XGtTXQP306XPll1/WyFtv9ZScOWM/DKLXpdenIfqf5r/lPH3QGfV37NhnQvr6Wa5ccZk+42XzkIFfQ2ujcaM+5gGJsmWLyqefDZHMmTMGVjEmrVq9Ysq//PJ1qWQ9sJGYxfmAxPc/vOH38EFi2gers3jR7/LIIz3MLn0YQx/KCFz0gYfatTubWepz5MgiS5dN8LtH7aC+tntzXHfrIYnbAw/ht52YoL6Onb65QO81DdJroD5w0Rn/te8rlv9pHubQhzqci+6vcWcb0bdDPPbYvTJkaAezW98GoW+FyJ07u8z7aZxkz57F2Yx1BBBAAAEEEEAAAQQQQACBMBYgqB/Gg+fhruvfPwIXu8jeZ39qPV2/8OPcJqgf6Mg2AggggAACCCCAAAIIIIAAAggggAACCCCAAALhJEBQP5xGyyV9DfalkZeD+vawzJz5g7z55seybu0Wu8h8XnFFRrnrrhulTdv6ZtZ2v53/bTiD+hMn9ZMaNW4IVs2EmDWI3ax5bRk0qI2vzscf/ygd2g81M6gvXPSeFCqUz7fPuaIPFdx+WyvZuXO/tGhRV/r1b+Xb3aPHWJk44WszY/1nnw/xla9Zs0nuqdle8ubLKTNmvGLNXP6MCauvWTPNzIyuFY8fPynXlm0kZ60AeLBg96UG9X2diWdl0cJV8uijPc1DCGvXTZcsWTL5atvBc3tGfZ0dv3697ibwrQH8GTNfCRru3mvNpq8z3us9rw8/3HxzOd8xA1durvKUmdH9hReeltZt6gXuDrqts8c/8URfs2/lqskmaB604kUUdu0yUqZO/dbMRv/jj2/4xijwEOvXb7VC721N8Xvv95H77qvqq2J76b37xx8XxtlXIWAlMUH97t3HyKSJ34g9BgGH8G3OmP69dOw4TPTc6/+cYcbTt9NamTdvmTzW+AVzr3/19Qgzq77e03pvjxvXQ+o8eJuzOusIIIAAAggggAACCCCAAAJhLkBQP8wH0KPd178lBS52kb3P/tR6un7hx7lNUD/QkW0EEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBMJJgKB+OI2WS/oa7EsjgvoXBmfFij/lm68XyQ8//GaFnDf7dujrzuvVqy6vD+soUVGRvnJdcQb1/1j7UdCZ3bVep07DZfpHc81M/VOnDdQis9jlhYsUkEWL3rWLg37q7P+ff/6TlCpVWH6wgtz2ov19vOlLZib+1VYIP2vWK8yusWNnysuDxkvDhjVl2PCOUvXmp0zA3Rlc//bbn6V5s/4mFL569VS/kLweJDmD+qdPn5H167fJmDEzzMz2er6Fi96RIkWu1FWz2MFzDYmPHt3VGofusnXLbild5v/s3Qec1NTax/FnC0vvbekgCAhSLYj62rBhwYaCV2x0UQEb6gURBLGCgAqIFBUFC2JBFEGsKFdAmg1RQekdpMO2N0/GzGRmMsvO7szuzOaXj2uSk3byPbOF5J+TuubDB0499OuG+vBFv74jzfbS0HjRoimeHTr8/84+T8v7738lN954sTw7sp/DGsFFCxeukE43DDQXLF7yitSoUTl4pTBLrAcGclIPfVOCvqnh9tuvlOGP9/YeyfJq06apzHrvaW95qImcBPXPbNtN9M0I99x7o9x/f5dQu5K1azfJ/53teYBk6Q+vSrVqlYLW1YdS9OGUU4w3F6Qay/UtDxcbb7CY+srgoHUpQAABBBBAAAEEEEAAAQQQiG8Bgvrx3X5urb09hG8ZENS3JBgjgAACCCCAAAIIIIAAAggggAACCCCAAAIIIOAeAYL67mnriJ0pQf2cU2rP9R8aofg33pgr69ZtNjfUHr/HjXvQ7BHc2pMV1Ncw/8ZNH1nFQeMH7h8r06d/Kmed3ULefnuEd7n2MK49jV9yyRkyZeoj3nKniTGj35Snn55mPgygDwVYw7FjaXJy0xvN3vFfeulhueLKs81FHTs+JNqLv1X23/+Ok1dfmSN9+3aSBx+6xVzn4YfHyWuvzpH/+7+W8uZbj1u79I4jFdQ/dOiIaCh8yZJf5LfVfxumm2Tnzn/MHse8BzMmQgX1ixcvaga/NQyug75RQN8sEGrQ8P8TI14xF4d6S4G17Z69B2T/voNy6qknyQcfPmsVZztes2a9nH+e580Icz5+Tlq2bJjt+jlZWLfOVeabDYYM7Sk9eoQ+N92XPiSgDwu0b99WJk0e5N29FdQ/Xu/31gY5CerXq3u16GdMh+ws9ab1hg3bzPX0YZRzzmllTtv/t3v3PvPNEHv27DeLSxsPlXz55XhJTa1oX41pBBBAAAEEEEAAAQQQQACBQiBAUL8QNKILT4GgvgsbnVNGAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQMBBgKC+AwpF2QsQ1M/ex2mp9vo+dOgkM+Cuy6e9PlQuuOBU76p5Depf1r6/rFz5u3TteqUMG+7rGd17ANvEzHcWSL9+o8wHBdZvmC36cIA19Oj+uHxsvA2gc+eLjZ7/+5mh/SZNOktWZqZYvex/Nn+x3HrrUGnR4kT5+JPR5qZWL+56bK1D4BCJoP7LL38gTz/1mmhY3z5o+L5R4zrSuFFdefPNeeaiUEF9+3Y6rdt+OHukNGlSL3CROT9s2GSZMH6W47JQhbVqVZX/fT8l1GK/crvLmLH3SceOF/gtD3dGbU5scJ252cuTBspll52Z7S7uvWe0vPXWfGl7ZjPj7QFPeteNdFDfXi/vQXIwMXr0vXL9De0c1+zff5S88/YCc5k+/DJhwkOO61GIAAIIIIAAAggggAACCCAQ3wIE9eO7/dxae4L6bm15zhsBBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAX8Bgvr+HszlQICgfg6QHFZJT8+QVi27iPYGftdd18vD/73Nu1Zeg/o3d3lUPv98qVzQ7jSZNm2Id79OE88++7o8N2qGVKhQRn78aYbfKhp81gC09kz+w7LXZP787+W2Wx+T009vKu+9/7S5roaumxrh/fT0dFm5arrs2aO9m/cyl32/eKrUrFnFb586Yw+kvzF9mJx3XuugdbTg7LN6mG8euP/+LnLPvTd615kyZbY8MmiCOa9B+P/cdImcespJUr9BTalSpbz5sIE+qKAPLOiQXVD/1tsulz59Oprr7tr1j9SpkyqfzB1jvmHA3Nj2P3uP+l99/ZJtSejJIslJUqdutdArBCy55OK+8tNPfwb1ah+wWtCsvuVg0+YdZjvaH/qof8I1cuTIMRk0qKvc0ccT2g/a+N+C6659UP73v5/k8ivOlokTH/auFumgvu74hHrXiD6wog9y3HrbFd5jZTdR1Whb7S0/cFAvbeuMjExzUWJigsx672k57bQmgasyjwACCCCAAAIIIIAAAgggEOcCBPXjvAFdWn2C+i5teE4bAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAIEAAYL6ASDMHl+AoL7PaPmy38xQdHkj9N7Y6NX9eMMttwyRBZ8tMYPqGli3hrwG9a1gdY0alWXxkles3TqOrV7zTz65vnw6b6zfOvoQQYvm/5HMzCxZsOBFeW3ax+ZbAB588Bbp26+Td93OnQbKN9+skBfHDZAdO/bKkEcnykkn1ZPPFrzgXcc+kdeg/uWX3yMrlq8xHxiYPmOY2RO+ff86rYFzDZ7rECqoX79+Tfn6G0/g/rtvVxlvDhhohr3PP/8U41yHmm8ZMHfw7/9mzfpC7r7rWXNOXdU30sOLL86UEY9PlaJFU8y6OT3oEHhMDeK3bnWz+QCEPnQwcNDt3lWshx06Xt9Oxoy511vuNKEPXOzdu1969Lxahgzp4V3F+jydeVZzeeedJ7zloSY++WSRdO823Fy87q/3JSWlSNCqZ53ZQ/76a7N06nSRjHrO80BF0Eo5KNBw/hXG52HVqj+kVetGUrVKBZk7d5GceGItmTf/ecdj52C3rIIAAggggAACCCCAAAIIIBCjAgT1Y7RhqFa2AgT1s+VhIQIIIIAAAggggAACCCCAAAIIIIAAAggggAACrhEgqO+apo7ciRLU91nedeczoiH7ihXLmj3QFymS7FsYMKW9iTdv9h85cOCwXH9DOxk92heizmtQX4PK3bp6gtIavtcQvtOgoexzz+ktO3fulb59O8mDD90StNo1Vw+QxYt/NsPfr78+V/7+a4vM/XSsNGvm2+fEl96ToUMnmeexY/se+fLLZdKvf2cZMODmoP1pQV6C+vomAu0lXsfPjuwnN954seMxxo55S5566jVzWaigfmDw/KUJs+Sxxyab2zh56EMIp7S+2Qzzj5/woHTocI7jsbVw376DUsah9/eQG/y7YMuWneYbCfRNBeee21remP6Y+YaA7LZ7eeL7RrD+ZXMVfaCi8Ul1vas/OniiTJr0gVSrVkm+/GqClCpV3LvMPrFkyS9y9VUPmEUz3hwu55zTyrs4L0H9P/6c5fggxaCBE2Tq1NlmoP6LL8eHPMdjx9JEf8bogwtOg/XZS0pKNN+EoG+G0M/0wYOHs/0MOu2LMgQQQAABBBBAAAEEEEAAgdgXIKgf+21EDYMFCOoHm1CCAAIIIIAAAggggAACCCCAAAIIIIAAAggggIAbBQjqu7HV83jOBPV9gNo7vvaSr8Pwx3vL7bdfaU4H/k/NnnnmdRkz+k1zUWDoO69BfQ03n9Gmq2zbttsMQmuwvlix4KBzz55PyJyPFoo+UPCZEfBu0KBmYFVl/Lh3ZfjwKVK9emXZvHmHVKlSXpYtn+YXrP799w1y3rm9pWzZUuYbBfQhhDkfPyctWzYM2p8W5CWor9vrsfSYQx/rKd27X6VFfoM+eHBVhwfMHtt1QU6D+rruHb2fkg8//No8v5cnDZT27dtqsXe4/bbHZN6876Vy5XLy/gfPSN261b3LrAkN22sdK1UqL48M7iqXXuq/D2u9UOMpU2bLI4MmmIv1YYDRY+4JGVTXXv779R1pvvVAw/UasrcPP/30p1x+2T3mgw2BD4RY6+nDIhddeKesX79N6tRJlW8WviwafLeGcIP633//s1x7zQBz80/mjpHmzRtYu/KOV6xYIx2uvM986KFnr2vk0Ue7e5fZJ/TtDNOmfWI+tDDx5f9KcnKSd/GGDdvkgvP7iD7U0KPHVTJkaE9zmRXeTzY+13Pnjjbf7uDdiAkEEEAAAQQQQAABBBBAAIG4FiCoH9fN59rKE9R3bdNz4ggggAACCCCAAAIIIIAAAggggAACCCCAAAII+AkQ1PfjYCYnAgT1fUrpaely1lk9ZOPG7WbhNdecJ3fe1VHq168pKSlFzID6Lz+vk7Fj35Kvv15urnP66U1l5rtP+gWj8xrU1x3//PNaMyytIWwNiuuDA9qrug5aNnr0DDOEn5CQIC+OGyBXXeXcO/yff240e3g3NzT+Fyrs3eb0273nXbVqBfONArpvpyGvQX2rl/jy5UvLc6PvMcxbSIkSxUT9V676Q+67d7RovTMzs8zDhxPU19D3lVfcK6tX/232Pj/n49F+DzBo3fUtA7/99rfUqlVVhg3vZTwUcbKU/rf3fO2Z/sEBL5jLtUf9hd++bL5hwckhVJl+T/Xo/rh88skic5XatatK125XydlnN5caNarIrl3/mPXTHum/XbjSXOfEE2vJh7NHOvbiP2PGPLn/vjHmevfce6Pceef13l7uNez+34fHyeefLzUftPjgw2fNhzvsdQs3qK/t0LRpZ/Nzpm8F0AcqSpcuIZWMN01oeN4a9A0NDw543pzVsH6nGy6URo3rmA9JaDvoWxFefPEdsx379jPe+PCg/xsfutw0WL744gdJTa0oX339kvdtARkZmXLpJX3ll1/WmQ+LqIv9wQPr+IwRQAABBBBAAAEEEEAAAQTiT4Cgfvy1GTUW822BgQ7G5R9zsEL81lgLfddbs4xp+7wY10kyvfuz1tNrYKmpFTw75P8IIIAAAggggAACCCCAAAIIIIAAAggggAACCCAQswIE9WO2aWK3YtYNIftNI71hpGFZ/apYsUzsVt6oWaRv8Gpv7r17PSmLFv3oPW8NCZcrV9oMWHsLjYlrrz1fRo7qZ4b47eWRCOrr/r76apnccvMQszf1xMQEM+StDwysX79V0owwtQ4DB90uffp0NKdD/e//zu4pa9duMhcH9v5vbfPQQy/KtNc+Nme7dLlUnnr6bmtR0DivQf2//tos/7nxEfn7763mvvWNABpU1zoeOXJM9Fwfevg2GfH4VHN5OEF93UD3f+ml/WX/voNmSF/D+qVKFTf3pf/TNwt0uPJ+0Z7zddDjnXBCDdm2fY+5jZbpgwPattojfm4GvcH62NCX5eWXPzju5vrmgpcmPiw1a1YJue6zz74uz42aYS7Xz4CG/w8fPmqegx5Ly95863Fp06Zp0D7CDerrDoYMMeo+8X2/fekDKW3bNvMrGznyDRk1crq3rEKFMuZDDxuMz6j1oMUZZ5wsr7/xmPfhAl3Z+h7RaX3zwWWXnamT3mH5st/kSqPHfv35NHhwN+nV+1rvMiYQQAABBBBAAAEEEEAAAQTiVyDS13GiJbFr1z7zoXG9JpSYmGg+lK4dGmifBp6xc+cG0aoP+y1YAb0+EThYRdYya6zr6bTvyz5PUD/QkXkEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBOJJgKB+PLVWjNTV6aaRm4P62izp6Rny3HMzZN6n/5M1v28we3q3mkt7Xj/55BPMXu67d7/KKvYbWyFkvXG7cdNHfsvsMw/cP1amT/9Uzjq7hbz99gj7Iu+09jiuYWgNLtuHOnWryR1GePnmWy6zFztODxs2WSaMn2XeYF7143TzoYPAFefN+15uv+0xs/i114ZIuwtPC1zFO5/XoL7uSHuV73v3s7LQ6FFeva2hXr3q8syzfc2g/GXt+5vF4Qb1daPP5i+W24zz0c93+/ZtzTC4/Q0B+lDAs8+8LnM+/s6vfYsWTTHb4/HH7zDD8Fa9cjtesXyNTJz4XtBxtGf6k4ze5++++wa5/Iqzc7T7SZM+MMPz1hsfrI00nH/ffTeZ9bbK7OPcBPV1e/3MTJjwruzYsdfc3aBBXeWOPtfZd21O65sBJr38ofmAhH2hvgGib98bzM+o3X7v3v3mWx70M6CfM/28OQ3WwyPFixeVBQteFP3MMyCAAAIIIIAAAggggAACCMS3AEH9+G4/t9Zery8FDlaRtcwa63o67fuyzxPUD3RkHgEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQACBeBIgqB9PrRUjdXW6aeT2oL69abTn+jVr1sumTTukYcNaUqdONbPnNPs6+TGtvb+vX79Njh1Lk1q1qhgh8mpmT/D5cexoHuPo0WPy009r5R8jvN2ocV3jrQGVo3m4oH3v33/I6Nl/ixlG117q69atbj7QELRiHgv0YQRtw82bd0qVKuWNNqwqyclJYe9V33Khb1TYsGGbFCuWYn4eq1aN7qvR9cGADKP+tWqnhvzM6c8R/XxuNr5PEo3eBhsbDyGULVsq7PNjAwQQQAABBBBAAAEEEEAAgcItQFC/cLdvYT07ve4ROFhF1jJrrOvptO/LPk9QP9CReQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEE4kmAoH48tVaM1NXpphFB/RhpHKqBAAIIIIAAAggggAACCCCAAAIIFCIBgvqFqDFddCr2EL512gT1LQnGCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAgi4R4CgvnvaOmJnSlA/YpTsCAEEEEAAAQQQQAABBBBAAAEEEEAgGwGC+tngsChmBQjqx2zTUDEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBPJVgKB+vnIXjoMR1C8c7chZIIAAAggggAACCCCAAAIIIIAAArEuQFA/1luI+jkJENR3UqEMAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAH3CRDUd1+b5/mMCernmZAdIIAAAggggAACCCCAAAIIIIAAAgjkQICgfg6QWCXmBAjqx1yTUCEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBApEgKB+gbDH90EJ6sd3+1F7BBBAAAEEEEAAAQQQQAABBBBAIF4ECOrHS0tRT7sAQX27BtMIIIAAAggggAACCCCAAAIIIIAAAggggAACCLhXgKC+e9s+12dOUD/XdGyIAAIIIIAAAggggAACCCCAAAIIIBCGAEH9MLBYNWYECOrHTFNQEQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEClSAoH6B8sfnwQnqx2e7UWsEEEAAAQQQQAABBBBAAAEEEEAg3gQI6sdbi1FfFSCoz+cAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEVICgPp+DsAUI6odNxgYIIIAAAggggAACCCCAAAIIIIAAArkQIKifCzQ2KXABgvoF3gRUAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQACBmBAgqB8TzRBflSCoH1/tRW0RQAABBBBAAAEEEEAAAQQQQACBeBUgqB+vLefuehPUd3f7c/bxK/DR7IUya9YXsnv3PmnWrL707ddZKlcuF78nRM0RQAABBBBAAAEEEEAAAQQQQAABBBBAoMAFCOoXeBPEXwUI6sdfm1FjBBBAAAEEEEAAAQQQQAABBBBAIB4FCOrHY6tRZ4L6hfMzkJGRKdu27ZKNG7fLpk075NChI1K9emWpWbOK1KhRWUqUKFY4T9wlZ/XkE6/K88+/7Xe2FSuWlU/njZVq1Sr5lTODAAIIIIAAAggggAACCCCAAAIIIIAAAgjkVICgfk6lWM8rQFDfS8EEAggggAACCCCAAAIIIIAAAggggEAUBQjqRxGXXUdNgKB+1GjzfceZmVny5RdLZdq0T+TzL36Q9LT0kHVo0KCm3HTTpXL9DRdK+fKlQ67HgtgT2LBhm7Q9o5s4fe926nSRjHquf+xVmhohgAACCCCAAAIIIIAAAggggAACCCCAQFwIENSPi2aKrUoS1I+t9qA2CCCAAAIIIIAAAggggAACCCCAQGEVIKhfWFu2cJ+XU9g3K8tzztYya6ylvuutWca0fV4kMzPTGx621tPweGpqhcKNWMBnp9ZTpsyWlybMMnvPD6c6KSlF5Morz5ZHBneXypXLhbMp6xaQwGfzF8uttw51PHrz5g3kk7ljHJdRiAACCCCAAAIIIIAAAggggAACCCCAAAIIHE+AoP7xhFgeJGDdELLfNNIbRvrqX/2qWLFM0DaxVBAvN3hjyYy6IIAAAggggAACCCCAAAIIIIAAAgUhEC/XcXbt2idJSYnmV2JioiQkJPz7Jd7pgvDjmAUjoNdPAweryFpmjXU93/VWgvqBbgUxv3//Ienfb5TMnbsoT4evUrWCTJz4sJx2WpM87YeNoy+wbt1mOfusHo4Huuaa8+SFFx9wXEYhAggggAACCCCAAAIIIIAAAggggAACCCBwPAGC+scTYnmQgO/Gke8mUjwG9Rs2rGfcKA06PQoQQAABBBBAAAEEEEAAAQQQQAABBGJAQIPNa9asM2vSqFG9GKhR6CoQ1A9t48Yl9hC+df4E9S2J2B7/9tvf0q3rcNHgdiSG5CLJMnhwN+nWrUMkdsc+oijw8MPj5LVX5/gdoVSp4jL307FSr151v3JmEEAAAQQQQAABBBBAAAEEEEAAAQQQQACBnAoQ1M+pFOt5BeI9qP/nn+slPT1DTjihlhQxbpQwIIAAAggggAACCCCAAAIIIIAAAgjEnkBaWrqsXbtBkpOTpH792rFXQVuNCOrbMJg0e8gPZCCoHygSe/P79h2UC9vdKZs27Yh45cZPeFA6dDgn4vtlh5ET0Pse0177WN577yvZtesfad68gTzwQBepU7da5A7CnhBAAAEEEEAAAQQQQAABBBBAAAEEEEDAdQIE9V3X5Hk/4XgP6m/YsFUOHTos1atXkdKlS+YdhD0ggAACCCCAAAIIIIAAAggggAACCERcYP/+g7J583YpUaK41KqVGvH9R3KHBPUjqRn/+6JH/fhsw169npCPZi/MtvLFixeVpk1PkNq1U6VEyWKyccM2480fG4yfVdmH+8uUKSnzP3tBatasku3+WYgAAggggAACCCCAAAIIIIAAAggggAACCCBQuAQI6heu9syXs4n3oP6uXXtl5849UrZsKUlNrZwvZhwEAQQQQAABBBBAAAEEEEAAAQQQQCA8ga1bd8g//xyQSpXKS8WK5cLbOJ/XJqifz+AxfjiC+jHeQA7Ve/PNeXLfvWMclniKSpcuIXfccZ3ccuvlUr58ab/1tL3nz18so5+bIStX/u63zD5z2mlN5N1ZT0lSUqK9mGkEEEAAAQQQQAABBBBAAAEEEEAAAQQQQACBQixAUL8QN260Ti3eg/rHjqXJunUbTZ4TTqglRYokR4uK/SKAAAIIIIAAAggggAACCCCAAAII5EIgLS1d1q7dYG5Zr15NSUkpkou95N8mBPXzzzoejkRQPx5ayVdHvV7cskUX88EgX6lvqnLlcvLG9GFmT/q+0uAp3c9ddz0rcz4K3Sv/8y/cL9dee37wxjFSog9Hbdq0Q3bs2GP2/l+3bvWoP1iwfv024+f9JklLS5MaNapIkyb1YkQjb9UoCMu81ZitEUAAAQQQQAABBBBAAAEEEEAAAQQQQCAaAgT1o6FayPcZ70F9bZ4tW3bIvn0H6FW/kH9WOT0EEEAAAQQQQAABBBBAAAEEEIhPAas3/TJlSkm1arH/RkSC+vH5OYtWrQnqR0s2OvudM+db6dljhOPOk41OXj79dKw0blzHcXlgYWZmlnTuPFC+XbgycJE5f955rc3Qv33hI4MmyJQps+1F3unHhvWSbt06eOdDTdzR+yn58MOvHRe/OG6AXH31uY7LtPDnn9fKq6/MkdmzvzGumR/0W08fkmrQoKZx/nXlPzddIm3bNvNbHmrm4YfHyWuvzglaXKduNfnuu0lm+TffrJARj0+VVav+8K5XrFiK/Ln2PXM+P1127twrLZrf5K2HfaJHj6tkyNCe9qKQ05G07NjxIVn03Y9Bx9K3O/zy69uSmJgQtEwLxo97V4YPn+K4bOSofsbn82LHZdr2TZt0Ev0MBw76+dHPEQMCCCCAAAIIIIAAAggggAACCCCAAAIIhC9AUD98M9dvURiC+vZe9atUqWi8rriM69sVAAQQQAABBBBAAAEEEEAAAQQQQCAWBPbs2Sfbt+8yqxIPvelrRQnqx8InJ3bqQFA/dtoiJzXpevswI4z/P8dVe99xrTzySDfHZaEK16xZLxddeJekp2cErZKUlChLl74qVapW8C774YfV0uHK+7zz9olzz20t02cMsxcFTWdkZEqzk290fCNAiRLFZOWqN0THgcP2bbvlrrufDflQQeD6CQkJRsj7InlkcDezA5zA5fb54wX19aGCO/s8HRQKtwf189Mlr0H9aFiOHfuWPPXka3ZW7/T8z14I+eaBW28dKp/NX+xd1z7R8fp2MmbMvfYi7/SXXy6Tm/7ziHfePvHcc/fIDZ0utBcxjQACCCCAAAIIIIAAAggggAACCCCAAAI5FCCon0MoVvMJFIagvp7NP//sl61bd5onRljf175MIYAAAggggAACCCCAAAIIIIAAAgUlYA/pp6ZWMsKgpQuqKmEdl6B+WFyFfmWC+vHTxHv27JeWLbtIelp6UKW1N/lVRsi9dJmSQcuOV9Cv3yiZ+c4Cx9UGG0H3Xr2v9Vt2RpuusmHDNr8yndE6/PLrW1K8eNGgZVbB4sU/yzVXO/d2Hqon9GXGwwHduj8uGjAPd6hcuZw8/UxfufjiNiE3zS6oP336Y3LuuXc4mtuD+rrz/HLJS1A/WpYrV/4ul7Xv72g8YkQfufW2y4OWaW/4Jzft7PjQhq5cs2YV+X7x1KDttOCZZ16X0c/NcFz2w7LXJDW1ouMyChFAAAEEEEAAAQQQQAABBBBAAAEEEEAgewGC+tn7sNRBoLAE9fXUdu3aKzt37jHPsmzZUlKxYnkpYrzOmAEBBBBAAAEEEEAAAQQQQAABBBBAIP8E0oyQ7K5de7zhwkqVyhvXacrlXwXyeCSC+nkELGSbE9SPnwadM+db6dljhGOFzzmnlcx4c7jjsuMVzvloofTs+YTjak695D/5xKvy/PNvO67/yquD5aKLQofiw93211/XyeWX3StHjx5zPF5OCvXBgc+/GC+1a1d1XD27oP75558ir0z9yHG7wKB+uOdm32k42+Y2qB9NSw3dN292o+jDJIHDNdecJy+8+EBgsWh9Lmx3V1C5vUCD+hrYDxw63TBQFi5cEVgsDRvWli++HB9UTgECCCCAAAIIIIAAAggggAACCCCAAAII5EyAoH7OnFjLJlCYgvp6Wvae9XVeA/slS5aQYsWKSnJyshhv9GVAAAEEEEAAAQQQQAABBBBAAAEEEIigQFaWSHp6uhw5clQOHjzkDejrIeKpJ32LhKC+JcFYBQjqx8/nYPLkD2XwIy85VnjI0J7So8dVjsuOV7h//yGzZ/P09IygVRs1qmOE3Mf5la/+9S9p1+5OvzJr5uZbLpMnn3ReputoMFsD2oFDuXKlZeXK1yXZ1jHNsWNp0t7opV2PF2ooXbqEtG7dWNav3yrr1m0OtZqcd15reWP6MMfloYL62klOgnHBXevhNAQG9fPLJTdB/fyw7N37SZn94TdBVKF6xp86dbYMGjghaH17wZgx90rH69vZiyQjI1NOanyD8fv4sF+5znTvfpUMfaxnUDkFCCCAAAIIIIAAAggggAACCCCAAAIIIJAzAYL6OXNiLZtAYQvq66npRXXtXX/fvgO2M2USAQQQQAABBBBAAAEEEEAAAQQQQCC/BMqU0bcdlpOUlCL5dciIHYegfsQoC8WOCOrHTzOOeHyqvPjiTMcKT58xTLT3+9wObU6/XTZu3B60uXYU88uvbwWVt7ugj6xe/XdQeahQtq64ZctOOfWUW4O20YKbbrpUnn7mbr9lTz35mowdG3xsXalZs/ry+Ig+0qLFiUYHNknmdtu27ZYXjJ7+p0yZ7bcfa0Z7ddfe3QOHUEH9wPVOPLGWtDnjZKNn/lTZa/Qc/9dfm+XlSQP9VssPl9wE9fPDcvr0T+WB+8f6eVgzPyx7zXiwraI1a45DBfvtK3XufLGMHNXPXiQ//7xWLr7I/7NirTBt2hC5oN1p1ixjBBBAAAEEEEAAAQQQQAABBBBAAAEEEAhTgKB+mGCs7ukRyhPW901nZmaavWHvLt0AAEAASURBVK5ozysVK5aJWyYN7O/ff1AOHTpihPePGT27Bfd4FLcnR8URQAABBBBAAAEEEEAAAQQQQACBGBLQIGhKSoqUKFFMSpcuGZcBfYuToL4lwVgFCOrHz+egX9+RMnPm544VnvvpWDO87rgwB4WXGT3Xr1z5u+Oaf659z3ija4rfMg3Qa/jbadAe+LUn/sDhjTfmyoAHng8sNudnznxS2p7ZzLssPS1dWrW6WXbv3uctsyaaNKkn78x8QrQXfqdhyKMT5eWXPwhaVKlSOVmy9JWgn9/HC+rrdvogRNOmJwTtM7Ag2i56vHCD+vllqQ966AMfTsOECQ/JlR3+z29Rq5ZdZPv2PX5lgTN16laT776b5Ff8ytSPZODA8X5lOqNvQNCHSvT3NAMCCCCAAAIIIIAAAggggAACCCCAAAII5E6AoH7u3Fy9VWHsUd/VDcrJI4AAAggggAACCCCAAAIIIIAAAgjkSYCgfp74Ct3GBPXjp0k73TBQFi5c4VhhDaBXr17ZcVlOCrvcNFi++OIHx1W/WzRZ6tRJ9Vu2fv02aXtGV78ya2bQoK5yR5/rrFnvuFvX4TJ37iLvvDWhPa0vWfqqJCYmWEXy2fzFcuutQ73z9olF/5ti9Gpf1V7kN62f6RbNbzLeSvuPX7nOBD4QoGXZBfU1pD/z3SdFe9PPyRBtF61DuEH9/LT8v7N7ytq1m4KounXrII8N6+UtX7dus5x9Vg/vfHYTgb3x33XnM/Lee18GbaIPemj7MiCAAAIIIIAAAggggAACCCCAAAIIIIBA7gUI6ufezrVbEtR3bdNz4ggggAACCCCAAAIIIIAAAggggAACDgIE9R1QXFxEUD9+Gj+WgvqqduWV98myH1YHAToFptOMHvKbNuksBw8eDlq/Z69r5NFHu/uV9737WXn33S/8ynSmXr3qsvDbl4PKAwt69nxC5ny0MLBY+vXvLAMG3OxXnl1Q36lufhs7zETTRQ8XblA/Py0HDZwgU6fODlJp1qy+6FsfrGHGjHly/31jrFlznGz0iJ+ZkSGZmVl+5S+OGyBXX32ut+yMNl1lw4Zt3nlr4qGHb5W7777BmmWMAAIIIIAAAggggAACCCCAAAIIIIAAArkQIKifCzS3b0JQ3+2fAM4fAQQQQAABBBBAAAEEEEAAAQQQQMAuQFDfrsE0Qf34+Qz06zvS6DH8c8cKawhaw9C5HS5r319WrvzdcfM/174nxYqlBC2bPPlDGfzIS0HlGrj+6cfpUrpMSe8yfROAPmjgNHz8yWhp0eJEv0WXX3aPrFixxq/Mmhn+eG9rMuT4qy+Xy/z53wctv/TStjJ5yiC/8uyC+q+++qhceNHpfusfbyaaLnrscIP6+Wmp5rfd+lgQUVJSovy6+m0pWbK4uax//1HyztsL/Nbr2PEC2bp1d9BbI7rc3F6eeuouc91t23ZL61b+D1pYO3H6HFnLGCOAAAIIIIAAAggggAACCCCAAAIIIIBAzgQI6ufMibVsAgT1bRhMIoAAAggggAACCCCAAAIIIIAAAgi4XoCgvus/An4ABPX9OGJ6ZsTjU+XFF2c61nH6jGFy7rmtHZflpLDN6bfLxo3bg1YtW7aU/PLrW0HlWrB9+x459ZRbJCMjM2j5xIkPy+VXnO0tHzp0kkx86T3vvDURqod8DWNrKDvSQ5s2TWXWe0/77Ta7oP4q44GDihXL+q1/vJlouuixww3q56flgQOHjTcndJL09IwgprfeflzOPrulWd72jK6yfr1/r/hjn7/faPNd8vjwqX7bNmhQU7762vNAiL4lQd+WEDiUL19aVv04QxITEwIXMY8AAggggAACCCCAAAIIIIAAAggggAACYQgQ1A8Di1U9AgT1+SQggAACCCCAAAIIIIAAAggggAACCCDgEyCo77NgSoSgfvx8CkL11K5nMGRoT+nR46pcncz+/Yfk5KadHcPVjRrVkc+/GBdyv507DZRvvlkRtLxTp4tk1HP9veXnntNL/vhjo3femrjn3hvl/vu7WLPmWIP/9epe5fgAgN+KuZhp2LC2fPHleL8tswvqrzZ6gbe/GcBvw2xmouFiHS6coH5+W2odr71mgHz//c9Wdb1jbWdt7y1bdhoPeNzqLdeJhIQEWb7idePhj91y8UV3+y3TmRUr35DKlcvJkCEvy8sT3w9afmWH/5MJEx4KKqcAAQQQQAABBBBAAAEEEEAAAQQQQAABBMITIKgfnhdrGwIE9fkYIIAAAggggAACCCCAAAIIIIAAAggg4BMgqO+zYIqgfjx9Bj7++Dvp0f1xxyqfc04rmfHmcMdlxysM1Uu5bqe99Gtv/aGGt96aL/feMzpocZWqFWTZstfMAPbff2+VM9t2C1pHC7SndO0x3T6kpaVL3Tq5e+jAvh+n6Tp1q8l3303yWxSNoH40XKxKhxPUz29LrePo52bIM8+8blXXO7Y+S++//5Xc2cf/rQZNmtST+Z+9YN7Padmii/nWAO+GxsRLLz0sV1x5tlxxxb2yfNlv9kXm9LMj+8mNN14cVE4BAggggAACCCCAAAIIIIAAAggggAACCIQnQFA/PC/WNgQI6vMxQAABBBBAAAEEEEAAAQQQQAABBBBAwCdAUN9nwRRB/Xj6DOzdu180xKzh68AhJaWIrFr1Rq56f+/Xb5TMfGdB4C7N+cGDu0mv3tc6LtPC/fsOSvPmN8mxY2lB68z9dKw0a1Zfpk6dLYMGTghafvLJ9eXTeWODyrVAe/jfs2e/47ILLzrdsTwnhVWrVJCnn/HvsT0aQf1oueg5hhPU1/Xz01KPp0F6DdQHDqVLl5Bffn1b/vvfcTLttY/9Ft/R5zoZNKirWXb3Xc/KrFlf+C2/7fYr5JFHukmjRtdLusPnf/GSV6RGjcp+2zCDAAIIIIAAAggggAACCCCAAAIIIIAAAuELENQP38z1WxDUd/1HAAAEEEAAAQQQQAABBBBAAAEEEEAAAZsAQX0bBpNmRyeBDFlZnhK9tqqDNbamna656qqZmZneda11MjOzJDW1grkf/pd3gW5dh8vcuYscd9T7jmvNMLPjwhCFa9asl4suvEvS0zOC1khKSpSlS18V7R0/u6F7t+HyySfBdRow4Gbp17+z3NzlUfn886VBu9Bgtga0nYZ27e6U1b/+FbSoffu2MmnyoKDyvBREI6iv9YmGi+433KB+flpq/TIyMs2HA/YZD3EEDtprvvamr587+6Bvg9C3QuigD43owyP2oXHjOjLiiTvl2msG2IvN6fr1a8rX37wUVE4BAggggAACCCCAAAIIIIAAAggggAACCIQvQFA/fDPXb2HdENIbRda03jDSi8X6VbFiGdcbAYAAAggggAACCCCAAAIIIIAAAggg4B4BgvruaeucnKleMw0crCJrmTXW9axrrJ6xfZ6gfqBjNOY1EK8BcKchuUiyfGr0Yq+h5pwM+hBF584D5duFKx1XP++81vLG9GGOy+yFH81eKL16PWEvMqdPOaWxvP3OE9K0SSc5cuSY3/KEhARZvGSqVK/u3Av6LbcMkQWfLfHbRmeqGg8NLFs+Lag8LwXRCupHw0XPM9ygfn5aWu3Qs8cImTPnW2vWO77vvptk1Kjp3gd6dEGxYilGT/tvSdGiKeZ627ftllatbvZuoxP6edEHUcaPe9evXGe6dr1Shg3vHVROAQIIIIAAAggggAACCCCAAAIIIIAAAgiEL0BQP3wz12/hu3Hku2lEUN/1HwsAEEAAAQQQQAABBBBAAAEEEEAAAdcKENR3bdM7nrg9hG+tQFDfkoi9cVpaurRs0UX27t3vWLnKlcuZ4fqmTU9wXG4VHjuWJnfd9azM+WihVRQ0fv6F++Xaa88PKg8s0BB+i+b/kQMHDvstSkxMkOeeuyeod3RdqU2bpjLrvaf91rfPvD7tE3nwwRfsRd7pRf+bIrVrV/XOO038/dcW2bNnv5QpW1LKli0lZcuUFH2QwWmIVlA/Gi5a/3CD+vlpafm+/vpceXDA89asd5ySUkT0s2cfzj23tUyf4f9AiNNbADSs7/Tz6pVXB8tFF7Wx75JpBBBAAAEEEEAAAQQQQAABBBBAAAEEEMilAEH9XMK5eTOC+m5ufc4dAQQQQAABBBBAAAEEEEAAAQQQQCBQgKB+oIi7552CrwT1Y/sz8c7bC6R//1EhK1m6dAm5447r5JZbL5fy5Uv7raftPX/+Yhn93AxZufJ3v2X2mdNOayLvznpKkpIS7cUhp/v1HSkzZ34etLxUqeJBAX5d6ckn75Sbb7ksaH2rQB9EaGE8kJBuPJgQOLRs1VDeemuE6L6dhv37D8n5590hW7bs9FtcokQx6dnrGnnggS5+5dEK6utBIu2i+ww3qJ+fllo/Hdav3yZtz+jqmTnO/wcP7ia9el/rt9bQoZNk4kvv+ZU5zejDFz///GbIz4LTNpQhgAACCCCAAAIIIIAAAggggAACCCCAQGgBgvqhbVgSQoCgfggYihFAAAEEEEAAAQQQQAABBBBAAAEEXClAUN+VzR7ypAnqh6SJ6QV97nhKPvjg62zrWLx4UdGe9WvXTpUSJYvJxg3bZM2aDbJ5845stytj9D4//7MXpGbNKtmuZ1/4+edL5eYuj9qLQk4nJyfJ8hWvS4UKZUKuowt69hghc+Z867hO2zObybgXB0iVqhX8lq9bt9kI4o+VRd/96FduzejDB2eccbI1a46jGdSPhku4QX09yfyytMOedWYP+euvzfYix+kFC16UxifV9Vv25ZfL5Kb/POJX5jRzvDczOG1DGQIIIIAAAggggAACCCCAAAIIIIAAAgiEFiCoH9qGJSEECOqHgKEYAQQQQAABBBBAAAEEEEAAAQQQQMCVAgT1XdnsIU+aoH5ImphesH/fQbnwwrtk48btEa/n+AkPSocO54S13/T0DGnVsovs3r3vuNtdcMGpMu31ocddTx8oaHfBnbLPONdQQ7161c3gfUrRIvLHHxtl8fc/S5pDL/y6fcuWDeWjOaMkISHBb3fRDOpHwyU3Qf38srTDZudqracPWixfPs2a9Y6PHDkmTU7qJEePHvOWOU0MGHCz9Ovf2WkRZQgggAACCCCAAAIIIIAAAggggAACCCCQCwGC+rlAc/smBPXd/gng/BFAAAEEEEAAAQQQQAABBBBAAAEE7AIE9e0aTBPUj9/PgAbTu94+TP78c2NETiK5SLIMHtxNunXrkKv95SSYrTse+/z9ct115+foGLM//EZ6934yR+tmt1JKShH5dN5YadiwdtBq2dV79eq3pbTxhoG8DNnt377fnLrkJqivx8kPS/v5zJ27SLp1HW4vCprueH07GTPm3qByLejcaaB8880Kx2VW4UcfjZJWrRtZs4wRQAABBBBAAAEEEEAAAQQQQAABBBBAII8CBPXzCOjGzQnqu7HVOWcEEEAAAQQQQAABBBBAAAEEEEAAgVACBPVDybiznKB+fLf7gQOH5Z7+o+Tjj7/L04loz+YTJz4sp53WJNf7Wbz4Z7nm6gHZbl+sWIqs+nG6lCxZPNv17AsnT/5QHntssqSH6Cnfvq7TtB7rJePczj//FKfFkl2QPhJB/Ui75DaorycfbUs7sL71oWnTzpKRkWkv9pt+/oX75dprnR/aGD/uXRk+fIrf+vaZsmVLyY8/zZCkpER7MdMIIIAAAggggAACCCCAAAIIIIAAAgggkAcBgvp5wHPrpgT13drynDcCCCCAAAIIIIAAAggggAACCCCAgJMAQX0nFfeWEdQvHG0/depseWnCe7Jhw7awTkh7mr/yyrPlkcHdpXLlcmFtG7iyfpbanH67bNq0I3CRd/7KDv8nEyY85J3P6cTSpb9Kr55PyNatu3K6ibles2b15Zln+4mOQw3RDupH2iUvQX01iKZloPFVHe43jxdYrvMJCQmyfMXrIT93v/yyTi668C6nTc2yy68423y4JOQKLEAAAQQQQAABBBBAAAEEEEAAAQQQQACBsAUI6odNxgYE9fkMIIAAAggggAACCCCAAAIIIIAAAggg4BMgqO+zYEqEoH7h+RRkZmbJV18tk2mvfSwLPl+abQ/0DRrUlJtuulSuv+FCKV++dMQQHh8+VcaNmxlyf5OnDJJLL20bcnl2C44ePWa+OWDGjHny3berHD+7ur322t+2bTPp3PliufyKs8xAeHb7jXZQX48dSZe8BvW1PtGy1H3bh5Ej35BRI6fbi7zTTZrUk/mfveCdD5zQn02tWnaRHTv2Bi4y5596+m7p0uVSx2UUIoAAAggggAACCCCAAAIIIIAAAggggEDuBAjq587N1VsR1Hd183PyCCCAAAIIIIAAAggggAACCCCAAAIBAgT1A0BcPktQv3B+ADIyMmXbtl1m7/YbN26XQ4eOSPXqlaVmzSpSo0ZlKVGiWFyf+O7d+8y3B2zZslP0KzkpSSpULCtVqpSX5s0bSNGiKXF9fvlZeSzzU5tjIYAAAggggAACCCCAAAIIIIAAAgggENsCBPVju31isnYE9WOyWagUAggggAACCCCAAAIIIIAAAggggEABCRDULyD4GD0sQf0YbRiqhQACCCCAAAIIIIAAAggggAACCCCAAAIIIIBAPgsQ1M9n8MJwOIL6haEVOQcEEEAAAQQQQAABBBBAAAEEEEAAgUgJENSPlGTh2A9B/cLRjpwFAggggAACCCCAAAIIIIAAAggggAACCCCAAAJ5FSCon1dBF25PUN+Fjc4pI4AAAggggAACCCCAAAIIIIAAAgiEFCCoH5LGlQsI6ruy2TlpBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQSCBAjqB5FQcDwBgvrHE2I5AggggAACCCCAAAIIIIAAAggggICbBAjqu6m1j3+uBPWPb8QaCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAgi4QYCgvhtaOcLnaN1oysoSyczMFJ3XcUaG56tixTIRPiK7QwABBBBAAAEEEEAAAQQQQAABBBBAIHYFnIL6iYmJkpDgqXOCNRG7p0DNIihgXT+171KvpepgLbPGVpnOe74861jT1vVX+3qZmVmSmlpBixgQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAghgUI6sdw48Rq1aybSIFBfb1BpGH9cuVKSWLiv3chY/UkqBcCCCCAAAIIIIAAAggggAACCCCAAAIRENBrYnv3HpCkpETzmpgnoJ9gTBPUjwBvXO7Cun5qrzxBfbsG0wgggAACCCCAAAIIIIAAAggggAACCCCAAAIIuEOAoL472jmiZ2ndaLKC+rrzjIwMo1d9T1C/dOniUqRIckSPyc4QQAABBBBAAAEEEEAAAQQQQAABBBCIRYG0tHTZv/+wN6iflJRkVpOgfiy2Vv7Uybp+aj8aQX27BtMIIIAAAggggAACCCCAAAIIIIAAAggggAACCLhDgKC+O9o5omdp3WiyB/X1Fczam75+FS2aLCVLFo/oMdkZAggggAACCCCAAAIIIIAAAggggAACsShw8OBhOXo03Qzqe3rVTzSrSVA/Flsrf+pkXT+1H42gvl2DaQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEE3CFAUN8d7RzRs7RuNOnNJZ3WkL6OraC+zleqVDaix2RnCCCAAAIIIIAAAggggAACCCCAAAIIxKLAzp3/iIbyNaSvXwkJCea8jo3/zEGnGdwjYF0/tZ8xQX27BtMIIIAAAggggAACCCCAAAIIIIAAAggggAACCLhDgKC+O9o5omdp3WgKDOpnZmpYP8MM7JcrV1KSk5Mjelx2hgACCCCAAAIIIIAAAggggAACCCCAQCwJpKeny969B/8N6ScZAX0N5xPUj6U2Koi6WNdP7ccmqG/XYBoBBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAXcIENR3RztH9CytG01WUF/n9Ut71Nfe9HVcpEiilClTKqLHZWcIIIAAAggggAACCCCAAAIIIIAAAgjEksC+fQckLS3TDOpbvep7etL3BPatjvTpUT+WWi36dbGun9qPRFDfrsE0AggggAACCCCAAAIIIIAAAggggAACCCCAAALuECCo7452jvhZWuF8e1jfCulrz/o6Xa5cKaNX/aSIH5sdIoAAAggggAACCCCAAAIIIIAAAgggUNAC6ekZRm/6B4xe9BPNnvSTknScaPao7wnri3e6oOvK8fNXgKB+/npzNAQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEYlWAoH6stkyM18u62aRBfQ3l62AF9a2e9fWGZMWKZWL8TKgeAggggAACCCCAAAIIIIAAAggggAAC4Qvs2rXPfMuk1ZO+FdTXPXkC+5590pt++LbxvoV17dR+HvSob9dgGgEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABdwgQ1HdHO0f8LK2bTVaP+hrS1zLtTT8jI8Mca1mpUiWkePGUiB+fHSKAAAIIIIAAAggggAACCCCAAAIIIFBQAocPH5MDBw7ZetNPMnvV11C+r1d9T+0I6hdUKxXcca1rp/YaENS3azCNAAIIIIAAAggggAACCCCAAAIIIIAAAggggIA7BAjqu6OdI36W1s0mK6iv856gfua/YX0dZ5qh/YoVy0pyclLE68AOEUAAAQQQQAABBBBAAAEEEEAAAQQQyG+B9PQM2bXrH0lK0nB+ojFONEP6voB+gmg43/jPHAjq53cLFfzxrGun9poQ1LdrMI0AAggggAACCCCAAAIIIIAAAggggAACCCCAgDsECOq7o52jcpbWDSe9yWT1qO8J63t61c/I8PSyr+MqVcqbNyyjUhF2igACCCCAAAIIIIAAAggggAACCCCAQD4I6Nskt2/fY4bzNYCvIX1PYN8K51s96nsqQ0g/HxolBg9hXTe1V42gvl2DaQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEE3CFAUN8d7RyVs7RuOOlNJp22vjS0rzctNaDvmdae9T1hfb15yYAAAggggAACCCCAAAIIIIAAAggggEC8Cej1LSukr73n05t+vLVg/tXXum5qPyJBfbsG0wgggAACCCCAAAIIIIAAAggggAACCCCAAAIIuEOAoL472jkqZ2ndcPKME4J61deQvt7A1OUZGRlmeL9y5XKSnJwUlfqwUwQQQAABBBBAAAEEEEAAAQQQQAABBKIhkJ6eITt27DXfGKk96Fu96XsC+/696RtdWpjLtR70qB+N1oj9fVrXTe01Jahv12AaAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAF3CBDUd0c7R+0srZtOeqNJp60vq1d97Vnf6lXfmi5btpSUK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)\\n\",\n 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CECCCCAAAIIIIAAAggggAACCCCAAAIIeEuAoH5s/UFQPza3tF4rFYL68+cvlZYtekfsx0KFCkrDhmfJ4wM7y1FHlY/YPpYGzW7oKYsWfW9XfeedoXLmWbVi2QzrIIAAAggggAACCCCAAAIIIIAAAgjkkQBB/TyC9+huCep7tGNch0VQ34XBWwQQQAABBBBAAAEEEEAAAQQQQAABBBBAIAoBgvpRIAVpQlA/CAqzwgukU1DfkahSpYJMmzZQqlWv5MyK2ytB/bhRsiEEEEAAAQQQQAABBBBAAAEEEEAgTwQI6ucJu2d3SlDfs13jOzCC+j4K3iCAAAIIIIAAAggggAACCCCAAAIIIIAAAlEJENSPiilLI4L6WUiYEUkg1YL61apVlMFD7vSd9oEDGbJ69Tr5cdlq+fDDRfLPP9vssiOPPELeNhXvNbQfz8nrQf1Nm7bIihVr7Clv3LjFvpYvX0bKlStj39eqVd2+8gMBBBBAAAEEEEAAAQQQQAABBBBIVwGC+una88HPm6B+cBcvzSWo76Xe4FgQQAABBBBAAAEEEEAAAQQQQAABBBBAIBkECOrH1ksE9WNzS+u1Ui2of9JJNWT2xyOD9umqVWulZYve8uefm+zyPn1ulU6drw/aNtaZXg3qOwF9J5wf7vxOPLG6ENgPJ8QyBBBAAAEEEEAAAQQQQAABBBBIZQGC+qncu9k/N4L62TfL7TUI6ue2OPtDILjAwYMZsn//PjlwYL/5d0D0c7C/ocHXZm48BfLlyyf58uWXAgUKmH8FpWDBQvZzPPfBthBAAAEEEEAAAQQQQAABBBBAILkFCOrH1n8E9WNzS+u10imorx09Zcosua/7M7bPL7vsHHn5lT4h+1+r7y/55idZu3aDFChYwFbfr1PnRClevGjIdbIT1F+9er2p9P+r/PnXZqlcubyccMLRUqPGUZI/f76Q249lgVbQX758jV3VqZ6vFfSdKvoa4td/ThttSFjfcvEDAQQQQAABBBBAAAEEEEAAAQTSUICgfhp2ephTDhYyPXgwcwVnmfOqc53rreadeX/oc0ZGPsnIOBRazWyXYeYdlIoVjwhzBCyKJEBQP5IQyxFIrICG8/ft+8+G9BO7J7aeEwEN6xcqVNiG9nOyHdZFAAEEEEAAAQQQQAABBBBAAIHUECCoH1s/EtSPzS2t13JuHLlvGmmVkwMHtPLJQSlbtqTnfebPX2or5euBhquor8t//vkPaXhhJ30rRxxRSr7/YbJ97/6xZ89eebj38/LGG3Pkv//2uhdJyVLF5ab2V8iDPW8KGqiPJqi/Zs166dVzlMybt8Rv2/rhlFNqyuAh3eT004/PsiyWGe6QfjTh++y2j+WYWAcBBBBAAAEEEEAAAQQQQAABBBDwsgBBfS/3Tu4fmzuE7+ydoL4j4Y1Xgvre6AeOIv0EMjIOmHsouwnoJ1nXa2C/cOGi5h5XgSQ7cg4XAQQQQAABBBBAAAEEEEAAAQTiKUBQPzZNgvqxuaX1WukW1P/ll7Vy4QV32D4//PCSNqivQ4A6kz6g0PHWx2TWrP85s4K+3nrrNfLogMztuBtECupv3rxVrr76PvltzZ/u1fzea0X9QU90k7ZtL/ebn90PsYbu3eudf/7pvsr72d0/7RFAAAEEEEAAAQQQQAABBBBAAIFkFCCon4y9lrhjJqifONt4bZmgfrwk2Q4C0QtoBf09e3ZFvwItPSdQpEgxW2HfcwfGASGAAAIIIIAAAggggAACCCCAQK4IENSPjZmgfmxuab1WugX1p06dLd3vHW77/NJL68kr4x7x6/8eD4yQiRM/tPOqVa8krVpdKldcUd9ccN4r7723QCaZZRs3brHLH+p9s3Tt2sxv/XBB/d27/5NmzXrK0iUr7TpXXd1AbrihsZx7zimy6td18tFHi2XEs9Ps8NjFihWRz+Y8L1WqVPDbfnY+vPnmHNs8VCV9HYlApwYNTrev7h9OWL98+TJBl7vb8h4BBBBAAAEEEEAAAQQQQAABBBBIJQGC+qnUmzk/F4L6OTdM9BYI6idamO0j4C+gVfT37t3jP5NPSSlw2GFFbHX9pDx4DhoBBBBAAAEEEEAAAQQQQAABBHIkQFA/Nj6C+rG5pfVa6RTU/+mn36R1qz7y11+bbZ/36XOrdOp8va//33jjM7mz21P2c9GihWX2xyOlRo3KvuX6ZtGi76V5s142TK+V+Ge+/ZScdVYtX5twQf1evUbJ+Fffs22vv76RPDviPnFX89cFL7/8jvR5+AXb5rLLzpGXX+lj32f3hxO01/WaNm0YdPVwQX1dQZfrQwlU1Q/Kx0wEEEAAAQQQQAABBBBAAAEEEEhRAYL6KdqxMZ4WQf0Y4XJxNYL6uYjNrtJegJB+6n0FCOunXp9yRggggAACCCCAAAIIIIAAAghEI0BQPxqlrG0I6mc1YU4EgVQL6uvpPjeqh++sMzIy5Pff/pLlK9bI3LlLZPu2nXZZxYplbcjeXbFeQ/xz535jl/d/9Hbp2PFa33bcbzRIr4F6ndrfdKUMGtTFtzhUUH/fvv1y2qmtZZvZf8mSxeSHHyZLwUIFfeu539St00HWrdsoWlV/5c8zsoT53W1DvXeC+qGq6Ydazz3f2QZV9d0qvEcAAQQQQAABBBBAAAEEEEAAgVQXIKif6j2cvfMjqJ89r7xoTVA/L9TZZzoK7Nv3nxl9eFc6nnrKn3ORIsWkUKHCKX+enCACCCCAAAIIIIAAAggggAACCBwSIKh/yCI77wjqZ0eLtlYgFYP6kbq2WvVKMnXq41K16pG+phkZB031/OtkvwnU67Ro8cty9NGHlvsamjeff75UbmzZ287SivvzF4z1LQ4V1P/yyx/lumsfsO0aNTpLXpv4qG+dwDddOg+WmTPn2dlffjVOKlcuH9gk4ud4VMPftGmLPVeC+hG5aYAAAggggAACCCCAAAIIIIAAAikkQFA/hTozDqdCUD8OiAneRLIF9ffu3Sc//rhKli1bZQq6/GIKthSVk0+uKaeccqwcd9zRMRVuSTBxSm1+z5698tpr79pzOvXU46RevVNT5vzenjlHNmz8RypWLCdXXXVBXM8rI+OA7Ny5La7bZGPeEihevJTkz1/AWwfF0SCAAAIIIIAAAggggAACCCCAQMIECOrHRktQPza3tF4r3YL6deueLKPH9JIKFQ736/cNG/6VM05va+cVLVpYfv7l9ZA3RDb8/Y+ccUY7X9tfVr3h21aooP7bb8+Tzp0G+9oVClFNXxvoKAAHDmTYtpMmD5ALLzzTt160b958c45t2rRpw5CraMV8nWrVqm5fg/2IZjvB1mMeAggggAACCCCAAAIIIIAAAgggkKwCBPWTtecSc9wE9RPjGs+tJlNQf/XqddKl8+Pyyy+/ByW49LL68uST3e1oq0EbMNMnoJb//rtNDjuskH3Iwbcgwpt//tkq9eq2sa1uueU66fVQxwhrJM/iG27oLt99u1LOrnOyTJ586H5EPM5g9+4dsn//vpg2tXTp96Yo0EJZv/4vKVz4MFMkqaqcf/655sGUmlm2t2zZCjPy8fws890zdP2rrrrMzgrVvmrVo8y9j+PlmGOqS4ECh8LnO3bskPHjp7g3F/Z9mzYtpHTpUubBmszjKl++nDRvfp1dZ8uWrTJp0vSw65csWVLatWtp2wTbhi4YN26i7Nq1O+x2brjhGjnyyAq2TbD2xYsXN/e9ypl7V7XNgxqZ7cJuMMjCggULSdGiJYIsYRYCCCCAAAIIIIAAAggggAACCKSiAEH92HqVoH5sbmm9VioG9SdM6OfXp/ff/6z8bcL1OjVufLZMeK2/33L98PPPf0jDCzvZ+YFV8gMbq1n1ateai9IH7KI1v800Q4IWtO9DBfXHv/qe9Oo1KnBTET8PffpuufHGSyO2C2wQTcA+Xm0C981nBBBAAAEEEEAAAQQQQAABBBBAIJkFCOonc+/F/9gJ6sffNN5bTJag/oL5S6Rbt0GyY8cuWyTm+OOryXHm319/bpQlS34yxVsyrzcfe+zRZkTWgVK2bJl4U6XU9u65e7C8997nJrxc1ox6+2rU50ZQP2oqX0MN6GtQP7vT/v37zX2R/rJ48ZdZVs2XL59cdNGF8uCD90iRIkV8y2fOfF+eeupZ3+dgb+rVO9u0ecwuitS+bNkjZNiwQWZE5Wq2/d9/b5BmzdoH22zQeVOnvmJGPa5kRkHOPC4N/48dm3l8v/++Vtq0Cf+gR6VKFWXatHF228G2oQuuvbaV/PPPv7ZNqB+jRw+Xk06qZReHa58/fz7zEER96dPnQftQRKjthZqvQX0N7DMhgAACCCCAAAIIIIAAAggggEDqCxDUj62PCerH5pbWa6VaUP+kk2rI7I9H+vXpiuVr5JJLuplK9Qft/GnTBsp5DWr7tfnnn21y6imt7DwN3WuV/IIFD1VZcTf+44+/5Zx6t9hZJUsWkxU/HaqYEiqo/+478+WOOwbZdY4++kgZOvQe9yZDvj/22CpS4cgjQi4PtWD+/KWyceMWc0H2dClXLvgNnUhB/U2btpgqN0ulfPky0qDB6aF2xXwEEEAAAQQQQAABBBBAAAEEEEAgpQQI6qdUd+b4ZAjq55gw4RtIhqC+hvAvuuh2Wbf2b1Odu4QMG97DXLs9NJLqzp275dVxM02g+DXr1bv3bdLh5msTbpfMOyCon7X3ElVRP5Zq+vq389FHB8vHH88xI0QUFQ3X6z8N5X/77ffy7rsfyb59+8y9h3Pk8cf7igbMdXLC7BqOv/32DnZe4I9y5Y6Q2rVPtbPd7Tt1yrxvow8I/P77HzJv3kL59dc1tiL+Cy8MlypVKsuePf/JggWL/Tb5/vuz5Isvvpazzjpdrr66id+y886rZ4/Z2U+ooH7v3vebgk5ZA+567ueeW9fvWN3b0AVO8L516+Zy/PHH+u3f+VCnzplSqlRJ+9Fp36JFUzn22GPsvN9++0O+/36ZrFjxs+zdu1fOOaeODBrU19znKuhsIqpXqupHxUQjBBBAAAEEEEAAAQQQQAABBFJCgKB+bN1IUD82t7ReKx2C+trBd9/9tMyY/ont61NPrSkffPiMrVrkdL46HFvzenORdq+dNe/z0VKzZhVnsd/rp598aYYq7WfnnXBCNfn0s0OV8kMF9b/+eoVcc/V9dp1atarJJ58eWsdv43H64AT1TzyxuhnetXrQrUYK6q9YsUaWm4ccwm0j6IaZiQACCCCAAAIIIIAAAggggAACCCSxAEH9JO68BBw6Qf0EoMZ5k8kQ1F+xfLUJAN9pz7zjbTeYKuI3B1Vo2eIB+eab5XLmmSfK1GlP+tps3bJd9pkRXrXIjAb9A6fdu/fIzp177OyyZUv7rn3v2rVH9J8pXu6r0H/gQIasWvWHlClTUipUCF0kxtnnYYcVMgHh4nbbev38l19+N9XJj5LixYsGHkbQz/qQwtq1G2TTpn/t/ipXLi8FCgQvkuMcr4a2jziitN2ezvv225+kevWjpFKlcuY8d5vq7v+JBvX/97/v7bacivpFixaOeFzxqKivfxf++muz+bfJd05aId49aZvNm7faWYULF5KSJTMN3W30vXM++v7ww0uZ88mvb31TNPtyGiciqH/wYIYZBSLzPJz9RPM6atSLMnnyDDnssMNkxIghvmrwzrrLl/8kd931oLkns0f69u0pF1/c0C4KFoh31gn2Gq79rl27pUuX7ub7vlo6d75VNAgfbHr66ZHy5pvv2pB+jx53B2vie4DAHbJ3V9SfPfstv5EBgm0k1LE6wfshQx71hfqDre/MC9d+7dr15lzvlS1btkqjRuebhyV6O6tF/VqihP4N8f8eRr0yDRFAAAEEEEAAAQQQQAABBBBAIGkECOrH1lUE9WNzS+u19CJv5j/5/1f9nGGG2c2Q/fsPmov3mRU6vIykofSWLTIvNgarqK/HrjcCzm9wu6kkss+eysjnHpCmTRva986P228baIbKXWA/duvWXHo91MFZ5PeqlfG1Qr5OXbo0k94PH7qpEiqov9/cRDnj9LZm+NJt9kL7N0smhKx0/913v5gL8iWlatUj/fabnQ/RVMOPFNR3lhPUz448bRFAAAEEEEAAAQQQQAABBBBAINkFCOonew/G9/j12mng5Mxyljmv2i7zWquuo9dZD33OyMhnRvzMsMsPtcuwo4BWrBg6LK1tmcILJENQXwPlbdv0sifyYM9bpGPH64Oe1BYTyN+5Y5dosv6ooyr42kQKYD83cooMH55Zjf+bJVN9ofAhQ16RsWNeNxXNTRXz72bI889Pk+dHTTNB98xQf60Ta5iA9DnSrVurLAFxZ5+NGteVZ57pIb16PiMffrjQ3Ds4YKt0n332SdK8+aVyzbUNfcfpfqO/C3pcEya8a66LHwp6lzcPB3S46Rq5/Y5m7ub2vXO8pc1DBIsXT5B77nlSPv3kf6by+n65//6b5I5OzeXR/i/YbWZZ2cxo1+4qeaRvp2CLfPNyGtT/8MMFMtyMfKAPOziThvA73na93GYewtCHKZzp0kvukNWr10m1apXk40/GOrP9Xm9s2UO+/vpH++DEInPO7gro2dmXbtTps7PrnGxC8oN9+1m/fqOMem6KfVijU+cWog80RDvt2/efCdOb72Q2ph07dsg119xo+61Pnx5y6aWNg67thPm1qv6gQf1sm1Bh9qAbMDMjtX/ppQkybtxEOe+8c+SJJzL3EbitVArq67npQxBdu95vRywYPXp4lockAs8/8HORIsXM9zj670jg+nxGAAEEEEAAAQQQQAABBBBAAIHkECCoH1s/EdSPzS2t13JuHLlvGqViUF87uV+/seamxFu2vzUEr1XztRqQM82Z8420ad3HftSqNa+/MVjq1DnJWWxfNbzerWtmJSNd96NZz5qhSI/2tQkV1NcGjz76kox+4Q3btnbt42Ta9EFSooR/1aHlprLS5ZffI/vNjYejjipvjnGMqcJymG/72XkTqaq+VszXKVjFfaeavi4PfKBB5zEhgAACCCCAAAIIIIAAAggggAACqSpAUD9Veza283KH8J0tENR3JLzxmgxBfXc4XKvRT5w4SDSwHu0UKoDtrB9NUL9zl5Yy9KlX7Sp6bdspaqMzbupwjTz88O3O5uyrs8+GjerIAVOI5vPPv7GV+rUa/v79+20brSI/cmQvufSy+n7r6ofHHx8r416Z6ZtfokQxU5n9UOC7izmee7u38y3XN76gvhk14LqmjeXVcW/7ljtB/ScGvSSTJn3ge9hAGxQtWsS2a9XqclOAp6NvnWBv3H1xyy3XRWzv3sbs2YvN/YHH7QM2Ol9HJdi2bYfv86mn6nX/J31he3e/vP3Os2b02mPcm5MNG/6RBufdZB/gadPmSunXv7NveXb3pSs6fRYY1H+wxzB5443MEYd1H7qvaKc9e3aawHfmSMTRrjNr1qcyYMAQW01/1qw37agHwdbduXOXGZXgb+NVwDzMkHmfJVLwPnA7kdq//PJr8sorr0n9+vVk8OD+gavbz6kW1NeT6tLlPvn++2XSqlUz8z7870QgSqFCh5n7UsFHgAhsy2cEEEAAAQQQQAABBBBAAAEEEEheAYL6sfUdQf3Y3NJ6rXQK6v/773Y595xbZPv2zJsBfft2NFV7mvr1/6hRM+Txx16x8zREf8kl9aRJk3NltxnS94P3F8qnn35lb2Do0LvPP99Trrq6gd/64YL6Gr7v0OFR+eyzr+06J5pqRddcc77UO+cUW03ryy9+NDc1ptuhbrWBVurXiv2xTk5VfV0/O1Xx3SH97KwX63GyHgIIIIAAAggggAACCCCAAAIIIOAlAYL6XuqNvD8Wgvp53weRjiAZgvp6Dr16PSMzps+2p1PaBNHbmurvl192npxQq7oNwIc7z1ABbGcddyA8WEV9baeh+hYtLpX2ppp9zZpV5Msvl8nIEZNFq/3rNGBAV7mxVRP7Xn84+9T1zP/koYdukyZXNDDFZ4rJvHlfy0ATxP/zz022OvuUqUNM1e6avnVfHPu6CUVnXme//oaL5eabr7MFY378cZWMHfuGGbF2rm0bGBp3gvrOhnTkgSZXnG+L2uTPn9+MRFvKWST33D3YjJD7uRx5ZFmZvyDzAQTfwjBvYg3qL126Qtq1fchUl98r+vCCPjhwwgnVZevWHTJ9+iwZ/MTLdq/33ttWunS90b5fu/ZvadTwVvs+2IMJOtqAjhCg0/QZT8npp9ey72PZl67o9FlgUH/Ao6Nl/Ph37LaHDr0/5CgItkHAj507t5n7FwcC5ob/qMF4DcifeOLxMmbMs+EbByx1gvfVqlWVnj3vDVia+bFGjepSvHgx+8FpX6vW8ea75b8vfRCga9f7zOgHq6Vz51uldevmmRsI+BmPoP7w4U9I4cJZiy7VrFnD/I5kFmwKdazXXtvKjDrxrw3Un3qqf/EoPdTixYtLjRrVfEfttB8y5FE599y6vvnuN845XXBBffPQzCPuRRHf589fwOzz0O9axBVogAACCCCAAAIIIIAAAggggAACSSlAUD+2biOoH5tbWq+VTkF97ehnn5lqbhCMt32u1W4WLnpR9KaIe+r7yBh58cVDlX7cy5z3Ax7rJLfccrXz0fcaLqivjXbv/s/cDHlIvvl6hW+dYG+uuKK+PDeqh1/F/2DtIs3Lbug+u+0j7Z/lCCCAAAIIIIAAAggggAACCCCAQLIJENRPth5L7PES1E+sbzy2nixB/d2795iq9SPl7Zlz/E67bNky0qDBGXJ5k/OkUaO6pvp4fr/l+iFUANtpGE1Qv27dU2TCa4NEi9A4k4bWr7qym2zc+K8ce+zR8sGHo5xFvn3qDA2eawDdPX3xxQ8muN7LVpN3V4M/cOCAnF67uQ20n3aaVph/yq+iulbyb3rdPbJy5W/22vyXX032PajgDup3u7OV3H13G/cu/d7ndlC/Q4c+smD+EtGq+ZMmD84yEq4+mKAPKOhoBV9/M9W3vNWNPeSrr36UGsdUkVmzMkP5zom0ad1T1LFatUry8Sdjndmm4E9s+wr1Pdm2badMnfKhHHFEaTNSQSO//vDtNMSbHTu22Ir/IRYHnT106Eh566135corLwsZtg+6opnphNlDLdf5I0c+KbVrn2qbuNs/+OA9dt5+MwLEH3+sk8WLv5Dff19rRj4obQovDZMqVSrb5YE/nFD71Vc3kR497g5cbD87+3E/EKDbbtMmfLX6F14YLiefnPkARrBt6Mad4H3QHZuZZ55ZW555ZrBvsdM+XFBf/bUfTjnlJHPuT/vWjeaNPpxTokSZaJrSBgEEEEAAAQQQQAABBBBAAAEEkliAoH5snUdQPza3tF4r3YL6GpSvX7+jbPj7H9vvnTpfL336ZFa0cb4IajJm9JtmONR3zcXcv53Z9rVWrWqm8soN0qz5RX7znQ+RgvrabsuW7TLs6ckybdrHZljcnc6q9gZMjRqVTSWetnK1qbQfr8kdvtdtapX8cuXK2H/6WSvvO/82btyis7JVgd+uwA8EEEAAAQQQQAABBBBAAAEEEEAgRQQI6qdIR8bpNAjqxwkygZtJlqC+Q7B48XcydepH8vHsRTbM7szX1+OOO1r69e8iGqp3T6EC2E6baIL6L73cXy644CxnFd/rqOemyLBhr9kA/9JvZ9gK+brQ2aeGdr/97tB834rmzU3te8vChd/aSvBaEV6nZctWyXXXZoadX3ypn1x44dl2vvvHBx/Ml7vufMLOmjV7tKkWfpR97w7qz/54jFSvHjxYrY1zM6ivfwfOPutGez2/v+mf1m2usMfr/vHdtyutmc57550RUsuMqKvTFBOQ72Me0NDpvfefk+OPz6yMrtfkz6vfzj7ocNddreVO80+nnOzL6bPAivp2wzH+2L7932yvOWrUizJ58gxb7V3D5NmZnDB7yZIlzO9B1u+rbuvmm9uYhxuOtpt12ofaR4UK5eXppwea9lVDNTHLR8qbb74rOQnqX3hhAylYsECWfXTs2N48IJD5/XaO1R321xWc4H3t2qeY+zZls2yjevVq5uGNzO+Hu324oP5LL02QceMmWsOhQx/Pss1IM0qWPDxSE5YjgAACCCCAAAIIIIAAAggggECSCxDUj60DCerH5pbWa+lF38x/mReAM99nyIEDGbJ//0EpW7Zk2vpkZBy0Qf11azdIAXOBtUqVCnaI3XiB7Nu331Rz+UvWr99khuwtaW7CVA06NGo89hcY1g+1zfLly9ghiDXIz4QAAggggAACCCCAAAIIIIAAAgikowBB/XTs9dDnrNdLAydnlrPMedV2mddXdR297nroc0ZGPhPIzbDLD7XLsCHdihWP0FlMMQokW1DfOc3//tsrX325TObM/Uo+eH++/P33ZruocOHD5LWJA2343WkbKYAdTVB/7rxXpHLl8s4mfa+zZy+WLp0fs5+nTnvSVO8+0b539lmlypHy2ZyXfO3dbx57bIy8Ou5tWz1eQ/46GsDkSR/II488Z5t9Pn+cVKxYzr2Kff/rqrVy2WWd7Punht5ngsqN7Ht3UP+nle/4Vf8P3EhuBvVX/7pWLr0083j1fE455djAwzH3U/bLnDlf2fnDhveQq666wL7funWH1D+3nehIAu5RAtxOn3w6Vo4+upJtn5N9OX2W10H9adPekBEjxtjQ+ZtvTsxiFW5GqDB7qHWc9rr81lvb2WYbNmwyD0t8YN8//PAD5rsWvPCSbWB+xCOoP3v2W+b3oIizyaCvzrGGCuqHC967N+gE+8O179Wrn8yfv1iaNLlEHnroPvfqUb0nqB8VE40QQAABBBBAAAEEEEAAAQQQSGoBgvqxdR9B/djc0not58aR+6bRwYME9VP1S6GBfZ20Wo9TPV/D+TrVqpVZad9+4AcCCCCAAAIIIIAAAggggAACCCCQpgIE9dO040OctjuE7zQhqO9IeOM1WYP6bj39nk2a9L4MGviSaID/hBOqy7vvZVZh13aRAtiRgvr58+eTH5fPtEF69371vbsC/pAnu0vTpo1tE2efdeqcIpMmZ1a/D1z35ZffMsf8op3tPAgw4NHRMn78O2ZfBWT5irdEK/IHTnqOp5x8vZ3dqVNzue/+m+x7rwb1Z320ULp2HRh4GiE/P/BAB7n9jma+5d26DZKPPlwgxx57tHzw4Sg73xmNQB+M0AcknCkn+3L6LJ5B/R07tvgeMHKOMdLrypW/mNB8N9vsjTdek/Llsz6soQsXL/5S3n77A/MASUXp1u122z5UmN0uDPIjVHunqn/FihXM79ZLUqhQoSBrZ85KtaD+3r17pWXLm819oM1mROke5iGTzN/pkAABC/R3tkQJijkFsPARAQQQQAABBBBAAAEEEEAAgZQTIKgfW5cS1I/NLa3XIqif1t3PySOAAAIIIIAAAggggAACCCCAAAIIBAgQ1A8ASfOPBPW9/wVIhaC+o9yr1zMyY/psG27/6uspUqpUcbvIF8A++ySZPGWI09z3OmzYazLquSn28zdLpkrJkpnruYPvn372olStWtG3jvPmQxMgv9MEyXWaPuMpXyV/Z5+VKpWTeZ+Ps8sDf/Tv94K89tq7UrRoYVmydLp9EGDixPekX9/nbdNQFfXdVePdDwe4j9dLFfVXrvxNrryiqz0nfajg3HNPC6Tw+1ypUnmpUOHQSBnuUQs+/Oh5OeKI0nLuOW3tyMaPPtpVWrVu4ls/J/ty+iyeQf2dO7eZkT8O+I4v2jc33dRJfv11jZx22skyfPhgE5Qv6LeqjkDQpUt3Wb58pbRv30puuy3zYY1QwXu/lV0fQrXftm27Davv2LHD7KejtGp16MEJ1+r2bSoF9XWk6D59Bsi8eQuldOlSMm3aq1KsWNHAUw77OX/+AlK8eKmwbViIAAIIIIAAAggggAACCCCAAALJL0BQP7Y+JKgfm1tar0VQP627n5NHAAEEEEAAAQQQQAABBBBAAAEEEAgQIKgfAJLmHwnqe/8LkAxB/aFPvSqffvqFFCxY0ATtB5vgbJGgsO7AvYbjNSSvU4cOfWTB/CU2uL/4fxOzhJ6d5do2VFB/9JhHpHHjutrEb3rmmYkycsRkG7LXsL2G7nVyQt/6fsnSaabCdjF96ze1btVTvvzyBxvu15C/Tj/88Is0ve4e+/6ll/vLBRecZd+7f7gfDpg16wWpcUwVu9irQX0NP59xenPZtWuPBFbLd59XqPf79u034f52snXLdrnnnrZSrvzh8nDvEbYfFy2aIKXLlPStmpN9OX0Wz6D+nj07Zd++vb7ji/bNH3+sk86d75WtW7fJhReeJ23atDQjRRwnOrrD+vX/x95ZwFlVtGH8pZZm6W6kFGmQ+CREkZAUkO4SA0EMVEJAQClFQbq7U5QW6e6QkmbpZYEFdoHvfec6555bG4fd9S73md+PPTF5/jP37jLnmWeu0E8/jaGtW3fwmE5OkyaNogwZ0quiPQnvPdUbVvoZM+bS2LGTeeFKMpo7d4o6uivnRRDqi3v+4cNHac2aDUqknzRpUvr55x8ob9487h45zHsJEvhRokS2xT5hJkQkCIAACIAACIAACIAACIAACIAACIBArCYAob617oNQ3xo3n84Fob5Pdz8eHgRAAARAAARAAARAAARAAARAAARAAARAwIkAhPpOQHz8EkJ97x8AsUGo/9tvf1HXj79XMHv17sQO4rVcwN6794Dq1ulK585doZdeyk6rfh9tpBkyZAqNG7tAXf865ht6880yRtypU+eVMP7hQ5uY2pNQv3CRfOyuPYQF+fGMvNeu3aIa7BQvAnLnOrXoWxK3aVOXvvq6vZFPTjZt2kPt2vZR95o1q0l9v31fnYtTetEijejRo8fkrs7Hj0Oofr1udOLEP2rhgewcECdOHJU3MkL97t2H0vJlG9WiB1lgIALwiIRbtwLptdLNVNK2betSz68cn8tTGc2b9aQdOw5RgYK5WPQ9xGWxxZ49R2nJ4vWUv0BOqvVORQfxvZTZp89omjXzNyrA8WnTpqLNvPCiatWyNGr01y5VWq1L95k7of6NG3fYJT0RL8Rwv0jEpRH/3ggJeUQPHz7wFB3m/WPHTtDHH3/B+R+qdOLw7ufnRyIql+9WOR86dAAVK2bfoUAL72VRS+rUqdyWnydPTvrhh/4qTqcvUCAfjR8/0iG9jMHGjduo+ho3fpc++KCDQ7y+8BahvvBJmNC2UEa3TR979uxOJUsWU5d16jShW7duc38mUYt/nj59SkFB93RSFtknohEjBlKhQi8b9yJzkihREl5E4r4dkSkHaUEABEAABEAABEAABEAABEAABEAABLybAIT61voHQn1r3Hw6F4T6Pt39eHgQAAEQAAEQAAEQAAEQAAEQAAEQAAEQAAEnAhDqOwHx8UsI9b1/AMQGoX5w8EMlDg8OfqRE6SJsr1O3EuXKlZVu3bxDR46cpmHDptHFiwEKeOfODenTHq0M+GYH+lSpUlCj996mKlVeU+714ojPqmd2LrcJdT0J9UUMX6dOZWrTtg7ly5eDdmw/RCNHzqK9e4+pegYO+pgaNqxq1KlF31pE34PbU6t2JXYlT0IbeHeAQYMm0vXrt5Xwe978oUqArjPLogJZXCChfv0q1LpNHY7PpZ5z4oRFtGLFJhXXu09natHiHXUuPyIj1J8yeSl99914lVcWEVR9qywlT5FMif+NAt2cmIX6b1crT02b1nCTynYrZ87MlDlzOnWxdct+at++L7vLh1K5ckVY4N+BHeJzqP7cvfso9egxjC5x/+XIkYl+/+NXJaA2F7xv33Fq1LCH+ZYS6YtY3zlYrUv3mbNQf+nSDfTlFz+p3RJmzRqsFhs41+np+tmzp3TvXqCn6HDvHz16nKZMmcWLHHaR7BYgQQTm5cuXoY4dWxtO+rogLbzX1+6O+fK9RBMn/qKidHp3Qn1JsGLF7/T99z+y8DwBzZo1kTJmtDn3m8v1FqG+uU3O54MH91XM5L4W6pvTyM4E6dKlpddeK0m1alWjrFmzmKMjdZ4smT+P67iRyoPEIAACIAACIAACIAACIAACIAACIAACsY8AhPrW+gxCfWvcfDoXhPo+3f14eBAAARAAARAAARAAARAAARAAARAAARAAAScCEOo7AfHxSwj1vX8AxAahvlAUofaHHwwkcbEPKzRpWp16s+u+OIrrIC71nTr2Vy72+p4+iii7cqVShjDek1BfhP/Dhk5V2aRsKVMHd475hui75MtKWP3nn7tVcnHkf/LkiToXEb84wr/1lt3hX5fZv/84mjZ1mb5UDvQPHtic1eXm++83ou6ftjTi5SQyQv1//rnMzvUfslu7bScByf/hR02oa1ebW75cuwtmob67ePO9L75sy+L8+sYt2Rmh2yc/GIJzf/9k6jwo6L5KkzhxQvpl1FdUoUIJI4/55M0qHdSOCXJP8m7bPoMF5PZ+Nqe1UpfRZzwmZs/+3iiuQ4dvaeOGXepa+AinyITg4Hs8XkIik8UlrSxwCAgIYMf4RCwoT+MSjxveQSB+/AS8oCOZdzQGrQABEAABEAABEAABEAABEAABEAABEIhWAhDqW8MLob41bj6dC0J9n+5+PDwIgAAIgAAIgAAIgAAIgAAIgAAIgAAIgIATAQj1nYD4+CWE+t4/AGKLUF9I3rhxR7nAi2hf3Nd1SJo0MRUsmIvq1qtC77Fbvrsgbvy9e42iv/7aSzfZhT9jxrRKEP5pj5YkjukDv5ugsnkS6h8/sYwmT15Cv/w8m+7fD1ZpEydORM2a1aAen7WmePEcHbTNou9Jk/qpusUJXwv8pf5u3Vsox3x37ZXPjtQ1Y8ZKEnG8DunSpaJWrWpTJ941wDlERqgveQ8dOklffD6CTp26wJsKPKNKlUrS+Al9nYt1uH4eob4UJAJ6ea6TJ88b5SZM6EelWBzf99suylHfiHA6+Zl3MJBdDCQ0blKd+vf/wCmF42Vk6zL3mVmov2bNdvqMHf+TJUtC06d/R7lyZ3WsKJwrEemLWB/hxScgIn0R6yOAAAiAAAiAAAiAAAiAAAiAAAiAAAi8+AQg1LfWxxDqW+Pm07kg1Pfp7sfDgwAIgAAIgAAIgAAIgAAIgAAIgAAIgAAIOBGAUN8JiI9fQqjv/QMgNgn1zTTFhV3E3mnSpKTs2TOSuNNHNIgrf/r0qcNN7k74/vTpMzp79iIL7p+wqDwzJUrk57Ycd6Lvx49DVJtF8J01awYXcb+7gkTYf/HiNbVIIX36VJQlS3rOF89dUsv3ZOGBCPBl8YAnh3rLhXvIGBBwk65cuU7+KZJRdubovNDBQzZLt6OiruDgh+Tnl8Ay+6hw1bf08MgUYwTgph9jqFERCIAACIAACIAACIAACIAACIAACHgFAQj1rXUDhPrWuPl0Lgj1fbr78fAgAAIgAAIgAAIgAAIgAAIgAAIgAAIgAAJOBCDUdwLi45cQ6nv/AIitQv2YIOtOqB/Ret0J9SOaF+lePAJPnz7hnRjuvngPhicyCCRNmoLixo3aRTRG4TgBARAAARAAARAAARAAARAAARAAARDwOgIQ6lvrEgj1rXHz6VwQ6vt09+PhQQAEQAAEQAAEQAAEQAAEQAAEQAAEQAAEnAhAqO8ExMcvIdT3/gEAob7nPoJQ3zMbxESeQEjII3r48EHkMyKH1xNIlCgJ70aR0OvbiQaCAAiAAAiAAAiAAAiAAAiAAAiAAAhEHQEI9a2xhFDfGjefzgWhvk93Px4eBEAABEAABEAABEAABEAABEAABEAABEDAiQCE+k5AfPwSQn3vHwAQ6nvuIwj1PbNBjDUCjx4F0+PHD61lRi6vJODnl4gSJkzslW1Do0AABEAABEAABEAABEAABEAABEAABKKPAIT61thCqG+Nm0/nglDfp7sfDw8CIAACIAACIAACIAACIAACIAACIAACIOBEAEJ9JyA+fgmhvvcPAAj1PffRqF/m0IwZK1SCLVunU9y4cTwndopp17YPHT16mooUyU9jxvZyisWlLxOAWP/F6X2I9F+cvsSTgAAIgAAIgAAIgAAIgAAIgAAIgEBkCUCoH1litvQQ6lvj5tO5INT36e7Hw4MACIAACIAACIAACIAACIAACIAACIAACDgRgFDfCYiPX0Ko7/0DAEJ97+8jtPDFIxAS8ogePnzw4j2YDz1RokRJKEGChD70xHhUEAABEAABEAABEAABEAABEAABEAABMwEI9c00In4OoX7EWSHlvwQg1MdQAAEQAAEQAAEQAAEQAAEQAAEQAAEQAAEQAAE7AQj17SxwRgShvvePAgj1vb+P0MIXk8DTp09I3PVDQ0NezAd8QZ8qfvwElDBhYt5hI94L+oR4LBAAARAAARAAARAAARAAARAAARAAgYgQgFA/IpRc00Co78oEd8IhAKF+OIAQDQIgAAIgAAIgAAIgAAIgAAIgAAIgAAIg4FMEINT3qe4O92Eh1A8X0X+eAEL9/7wL0AAfJyBCfXHYh2DfuweCCPTFQV+OCCAAAiAAAiAAAiAAAiAAAiAAAiAAAiAAob61MQChvjVuPp0LQn2f7n48PAiAAAiAAAiAAAiAAAiAAAiAAAiAAAiAgBMBCPWdgPj4JYT63j8AINT3/j5CC32DwLNnT5VY/8mTUHry5AnvSPLU7a4kvkHjv33KOHHiUJw4cSlevHj8L74S58s1AgiAAAiAAAiAAAiAAAiAAAiAAAiAAAhoAhDqaxKRO0KoHzleSM0EINTHMAABEAABEAABEAABEAABEAABEAABEAABEAABOwEI9e0scGabP3Xm8OyZ7Y4W8euj3NXzrXzG5/brp0/j0NOndtGqLd1TvveMMmZM7VwFriNBAEL9SMBCUhAAARAAARAAARAAARAAARAAARAAARAAARAAARBgAhDqWxsGEOpb4+bTufSLI/NLI3E5efJEnE+eUZo0yX2aDx4eBEAABEAABEAABEAABEAABEAABEAABEDAtwhAqO9b/R3e05pF+DothPqahHccIdT3jn5AK0AABEAABEAABEAABEAABEAABEAABEAABEAABGIPAQj1rfUVhPrWuPl0rhdZqP/4cQgFBd2nBw8e0uPHj3nhwROf7ms8PAiAAAiAAAiAAAiAAAiAAAiAAAiAAAhEF4H48eORn58fO7AkouTJk/J5guiqKtrLhVA/2hHHqgog1Pf+7oJQ3/v7CC0EARAAARAAARAAARAAARAAARAAARAAARAAARDwLgIQ6lvrDwj1rXHz6VwvolBfBPo3b96hu3fv+XTf4uFBAARAAARAAARAAARAAARAAARAAARA4L8ikCJFMt6pMWWsFOxDqP9fjRrvrBdCfe/sF3OrINQ308A5CIAACIAACIAACIAACIAACIAACIAACIAACIAACIRPAEL98Bm5SwGhvjsquBcmgRdNqB8YGERXr94wntnfPxklTZqEEiVKSPHjx6c4cYwonIAACIAACIAACIAACIAACIAACIAACIAACEQBgWfPiHcyDKWHDx/R/fsPKDDQbp6QMWNa8vdPHgW1xFwREOrHHOvYUBOE+t7fSxDqe38foYUgAAIgAAIgAAIgAAIgAAIgAAIgAAIgAAIgAALeRQBCfWv9AaG+NW4+netFEuqLi/6NG7dVf4pAP02aVJQgQXyf7l88PAiAAAiAAAiAAAiAAAiAAAiAAAiAAAjENIGQkFDe7fC2IdhPmzaVcteP6XZYrQ9CfavkXsx8EOp7f79CqO/9fYQWggAIgAAIgAAIgAAIgAAIgAAIgAAIgAAIgAAIeBcBCPWt9QeE+ta4+XSuF0Wob3bST58+DaVKlcKn+xUPDwIgAAIgAAIgAAIgAAIgAAIgAAIgAAL/NYHbt+/StWs3VTNik7M+hPr/9cjxrvoh1Peu/nDXGgj13VHBPRAAARAAARAAARAAARAAARAAARAAARAAARAAARDwTABCfc9swoqBUD8sOohzS+BFEOo/fhxCZ89eVM8Hkb7bbsZNEAABEAABEAABEAABEAABEAABEAABEPhPCJjF+rlyZSU/vwT/STsiUymE+pGh9eKnhVDf+/sYQn3v7yO0EARAAARAAARAAARAAARAAARAAARAAARAAARAwLsIQKhvrT8g1LfGzadzvQhC/StXrtPdu/fI3z8ZZcyYzqf7Ew8PAiAAAiAAAiAAAiAAAiAAAiAAAiAAAt5G4OrV6xQYeI9SpEhGmTJ5/9wNhPreNoL+2/ZAqP/f8o9I7RDqR4QS0oAACIAACIAACIAACIAACIAACIAACIAACIAACICAnQCE+nYWkTmDUD8ytJBWEYjtQn2zm37u3NkoQYL46FkQAAEQAAEQAAEQAAEQAAEQAAEQAAEQAAEvIhASEkpnzlxQLYoNrvoQ6nvR4PGCpkCo7wWdEE4TINQPBxCiQQAEQAAEQAAEQAAEQAAEQAAEQAAEQAAEQAAEQMCJAIT6TkAieAmhfgRBIZmdQGwX6t+8eYdu3LgNN317l+IMBEAABEAABEAABEAABEAABEAABEAABLyOgHbVT5s2FaVJk9Lr2mduEIT6Zho4h1Df+8cAhPre30doIQiAAAiAAAiAAAiAAAiAAAiAAAiAAAiAAAiAgHcRgFDfWn9AqG+Nm0/niu1C/QsXrtKDB8GUOXN6Sp48qU/3JR4eBEAABEAABEAABEAABEAABEAABEAABLyVQFDQfbp8+RolSZKYsmXL6K3NVO2CUN+ruyfGGwehfowjj3SFEOpHGhkygAAIgAAIgAAIgAAIgAAIgAAIgAAIgAAIgAAI+DgBCPWtDQAI9a1x8+lcsV2of/r0eQoNfUK5c2ejBAni+3Rf4uFBAARAAARAAARAAARAAARAAARAAARAwFsJhISE0pkzFyh+/HiUJ092b22maheE+l7dPTHeOAj1Yxx5pCuEUD/SyJABBEAABEAABEAABEAABEAABEAABEAABEAABEDAxwlAqG9tAECob42bT+eK7UL9EyfOqv7Lly8XxYnj012JhwcBEAABEAABEAABEAABEAABEAABEAABryXw7BnR33/b5nHy58/lte2UhkGo79XdE+ONg1A/xpFHukII9SONDBlAAARAAARAAARAAARAAARAAARAAARAAARAAAR8nACE+tYGAIT61rj5dK4XRajv7S94fXqQ4eFBAARAAARAAARAAARAAARAAARAAARAgAlowwVvn8eBUB/D1UwAQn0zDe88h1DfO/sFrQIBEAABEAABEAABEAABEAABEAABEAABEAABEPBeAhDqW+sbCPWtcfPpXBDq+3T34+FBAARAAARAAARAAARAAARAAARAAARAIMYIQKgfY6hRURQSgFA/CmFGU1EQ6kcTWBQLAiAAAiAAAiAAAiAAAiAAAiAAAiAAAiAAAiDwwhKAUN9a10Kob42bT+eCUN+nux8PDwIgAAIgAAIgAAIgAAIgAAIgAAIgAAIxRgBC/RhDjYqikACE+lEIM5qKglA/msCiWBAAARAAARAAARAAARAAARAAARAAARAAARAAgReWAIT61roWQn1r3Hw6F4T6Pt39eHgQAAEQAAEQAAEQAAEQAAEQAAEQAPH0/GoAAEAASURBVAEQiDECEOrHGGpUFIUEINSPQpjRVBSE+tEEFsWCAAiAAAiAAAiAAAiAAAiAAAiAAAiAAAiAAAi8sAQg1LfWtRDqW+Pm07kg1Pfp7sfDgwAIgAAIgAAIgAAIgAAIgAAIgAAIgECMEYBQP8ZQo6IoJAChfhTCjKaiINSPJrAoFgRAAARAAARAAARAAARAAARAAARAAARAAARA4IUlAKG+ta6FUN8aN5/OBaG+T3c/Hh4EQAAEQAAEQAAEQAAEQAAEQAAEQAAEYowAhPoxhhoVRSEBCPWjEGY0FQWhfjSBRbEgAAIgAAIgAAIgAAIgAAIgAAIgAAIgAAIgAAIvLAEI9a11LYT61rj5dC4I9X26+/HwIAACIAACIAACIAACIAACIAACIAACIBBjBCDUjzHUqCgKCUCoH4Uwo6koCPWjCSyKBQEQAAEQAAEQAAEQAAEQAAEQAAEQAAEQAAEQeGEJQKhvrWsh1LfGzadzQajv092PhwcBEAABEAABEAABEAABEAABEAABEACBGCMAoX6MoUZFUUgAQv0ohBlNRUGoH01gUSwIgIDPErh69RodOfI3FSiQh7JkyeSzHPDgIAACIAACIAACIAACIAACIAACIPAiE4BQ31rvQqhvjZtP54JQ36e7Hw8PAiAAAiAAAiAAAiAAAiAAAiAAAiAAAjFGAEL9GEONiqKQAIT6UQgzmoqCUD+awKJYEPBA4PHjEBXj55fAQwrcju0Eli79nW7evE3Nm79Lfn5+sf1x0H4QAAEvJxAQcJ1CQkIpa1YsDPLyrkLzQAAEQAAEQAAEQAAEXjACEOpb61AI9a1x8+lcEOr7dPfj4UEABEAABEAABEAABEAABEAABEAABEAgxghAqB9jqFFRFBKAUD8KYUZTUbFVqH/8+D+0YcNOunQxgIIfPqJcObNQnpey0RtvlKaECSGMjabhEqPFiqB92rTlqs6MGdLQO7Uqhlv/wQN/085dh1W6SpVK0ksvZQ83T0wmOHjwJLVr21tVOXFSPypcOG9MVh+tdV2//pRmznxAx4+HqnoKFIhPzZoloXTp4lqu9++/Q2nLlse0Y8djypQpHpUv70dlyvhRsmRxIl3munWP6PBh2yIJT5nLlvWj0qWf7/tD3PQXL17F5RSjEiUKG1U9fvyYjh49SXHixKEiRV427uMk+ggcOHCU5O8Q4S3cY3N4nmcJCQmhq1ev05UrAXT//gPKkCEdZc6cgVKm9I8WJM/T1mhpkA8UumfPQdq5cx/VqvUWi/Uz+8AT4xFBAARAAARAAARAAARAwDsIQKhvrR8g1LfGzadzQajv092PhwcBEAABEAABEAABEAABEAABEAABEACBGCMAoX6MoUZFUUgAQv0ohBlNRcU2of6FC1ep55c/sXD3kFsiGTOmpa5dm9G7Dd6M9cJMtw8YjTcPHTqpHHlTp/annDm9Q+hXp3ZXFjefVosvduycSUmTJg6TQNs2vemvv/ZS3LhxaOOfk1ncnTbM9DEdOeqXOfTjjzNUtZ980pw++LBxTDchWuoTgX737oF0794zh/JFUD98uL8S7DtEhHPB2mr64otAGjXqvktKEeyvWJGGZCFAZELDhrfot98ehpmlV6/k9OWXycNME17kypVr6eLFK9SmTWN207fvmnD3bhAvZFjEYzMuderUIrxi/pN4WWQggnYRcr8IYezY6fT06VPq2LE5xYsX77kf6d69+zzG7/NCkWT8L8lzlxeZAqw+y61bd2j58tX04EGwS3WygKFcuVIu95/3htW2Pm+9vpxfFmPMmLGQUqdOSXXqVPNlFHh2EAABEAABEAABEAABEIhRAhDqW8MNob41bj6dC0J9n+5+PDwIgAAIgAAIgAAIgAAIgAAIgAAIgAAIxBgBCPVjDDUqikICEOpHIcxoKio2CfX37TtOHTt8S3fuBBk00qRJSblyZaZz567Q9eu3jftdPmhM3bo1N65xEj6BsmWa040bd6h2nUo0bFiP8DPEQIpJk5bQoIETVE1Dh33KAsTKHmu9ffsulS3Tgp48ecKu64Vp+oyBHtP+VxEBATepT+/Rqvpv+3VhQXSa/6opUVaviN/fe+8WC6KJxelJqHZt22KKZcuCafLkByyQJpozJzXVqJEownW+//4d3k3hAWXPHo8+/jgZlSqVgC5despO9cE0f34wpUkTl1avThspsX7p0tfpyJEQXgCQnLm7d/kvVcqPihe3i+sj3OB/E16/fpMWLFih3MqdxbLeLtQXQbsIrEWo37lzy8g+ulemj2rB+I4de2nv3kO8W0JR3i2hSIw+s5VnuXHjFi1btpoePXrEn5WXeAFWdkqSJBFdu3aD9uw5RMHBwfTKK/mpQoUyUfosVtoapQ3w0cJkbMoYrVevOmXMmN5HKeCxQQAEQAAEQAAEQAAEQCBmCUCob403hPrWuPl0Lgj1w+/+c+eu0q5dR1XC8uULW3aw2bPnOB04cJLOn79KsuVt7txZeNvabDxBWzBcFx13rbx79z6tXbuTznP7Ll++QWnTpeTysvKkVG7Knz+Huyxh3gsNfUIbN+yhkycv0PkLAeSXID5ly56BChXKwy8FCoWZVyKfPJFJ5o0qXdGi+VRb1EU4P7ZvP8zuLNcodark9EYVR+cLc5nSjgIFIvZcly9fp61bbY5UNWuWp8SJE6pW3LsXTL//vi2cFnmOrlGjnJoEdJdi1aptvOVmsNvncJde7h0+fJq30T3nNjp58iRqrGXJko4n7SO2fae5vEI8DgoUzOm27LBu7t/3N506fVElcdePuo6E7KRTq/brDkVFdX85FI4LEAABEAABEAABEAABEAABEACBWE8AQv1Y34U++QAQ6nt/t8cWob7M59ao3oVE6CzhvffepsZNqvN8bh7DOf/PP3fTt33HkLjuSxg+vAfPwVVS5/gRPgFvFOpfv3aLXn+9tZo/r1S5FI0f38fjg8yZvYp69Rql4gcN6koNGr7lMS0iooZAaChR0aIBdPbsE+rZMzl9842jG/2AAUE0aFAQL6aJR/v3Z6D4ETDBv3TpCeXLF0Dp08eljRvTUY4cjk7oPXvepZEj79FHHyWjwYNTRPhBMma8QkFBz/h9UEby93cv1I9wYR4Sbtiwhd9ZnKLXXivOgv9XHVJBqO+AI0YuolowHtuE+hs3bqVjx05SsWKF+D1lCQfmQUH3eNHLCn7f+ljt/pAwoZ9D/PNcRDX352mLL+W9cyeQZs9ewgucslDNmm/60qPjWUEABEAABEAABEAABEDgPyMAob419BDqW+Pm07kg1A+/+7t1G0Hz5q5VCTt0rEt9+3YIP5MpxerVO2jE8Fl08OAp0137adq0KemTbo2pefPqlIDF8eEFcVsaOmQmzZ27hrd6dL/NadWqr9GXPVtFSLD/9OkzGj9uMb8gWEpXrtxwW70I5D/u2pjdfiq4jZebjx49pty56qn4fv07Ubt2tT2mNUd07DiIVq7YTEWK5KXfVv1ojnIoU7b4Xb9hNKVIkdQhjbuLP/7YTm3b9FdRu/dMNRZXnD17mf5XPnL9Zy5/x87JlDWrq4vDBV7YULZMO5LPU3zuw/37Z1AqXngQXhj43WTe+nZBeMnY2aQANWtejV0kKjlsNeuc0Vxe6dKv0OIlPzgnCfNa2l+e+Zz754pK564fdR3+/sno6LG5DuWZx0BU9JdD4bgAARAAARAAARAAARAAARAAARCI9QQg1I/1XeiTDwChvvd3e2wR6vfuPYpmz1qlgNas+TqN+PFzQ6BvpnzpYgDVr9+dbt0KZLFvDlr5m024bU6jz2Wu+CKnFwfprFkzsHg3mY5yOIaEhFJg4D11L1WqFOwQbhP53rx5RznQ582bg+LGjeOQR18Ech0hbPKSMGECSp7cNjcrhh1///0PZc6UjvxThj8PKmVJW8XEJmnSROyKnJnb4Che1vU5H4ODH/LChQAKZUW18IjvRiktZYsRjQj1JZQrX9Rw1Jf5ZD82HXEO4lovBjI3btxmQXVqdhBP57FNMg8v/4RR6tQ2UxW5PnDgBD9LFmP+2bkOfd26dS/asnmfavv2HTM89lPzZj3ZTfgQs/ajbdunG7x1OXKMKEfHPk+unk2e+cCBvylJ4kRs8pLLXKw6l/ndU6cucDvjUZYs6SlZsiQuaczlynjz9E5D99uDB8HMJ12YzvtRNcZcGhuBGwsXBlPLlreVEP/w4QxucxQqZBPyT5uWit591+a27zbhvzdnznxAHTveodatk/D8f0qXpKdPh1LhwteUm/6ePa7vG1wy8I3AwKc8Rq/ymIhDV69mcpckSu7NnLmIRJD/7rs1+XOR1qHMqBDqy/iRcuTznDx5MjZGCp+nNOLevftslPSAx2RS/g5xHZfirC7fS9On2963tG7dSLU9bty4/HmyGTk5PIzThXx/hIQ85s9JfLffF/K3wMOHD/m7Ni4lSmQvT+eLHz+B8VmQZ7x1644ykJJnlO/n8IKNy11VfqpU/kaeiAjGI8JU0oSGhtD27ft4IcZJNugqQCVLFlbNEj7CyTnIDgUihn/48BGlTJkiQhyljOd5Fuc2zJixULWhZcuGbvt97dq/2HzsDFWtWpHy5MnpnD3c6+dpq3CRsSzsUqRI7nbcyPcg0TPFzh1jaaAeQ57Gqow9EbBLOqnneRYkSFnyOZLPk5+fn+pXT+0yw/PEST9f4sSeP8e2vEFqpxj5PCTm3z9hBfkMS/saNarFJmapw0qKOBAAARAAARAAARAAARAAgSggAKG+NYgQ6lvj5tO55D/ltn88VWCcP1UTWqGhz/g/wRGbZP+vIEb3C97g4EdUpHAz5ZQuz5iOXet375mmJqoj8szDh83iFwIzjaTp06fiyaKsPJHnxw4tV/jFxBXeTvWZiheh+oKFgz06tksiEZq3bNGXzpy5pPKIKDwvu/JnyZqObt4MpNOnLvLE0H0VJy8NRv7cQ4m71Q03P8QBvkuXH2jtmp1GrGyznCNHRnrME3dnz1x2EO/LQoXevdu7fWljFmm7E3gbFTidRFSoL9lEqP7LqM+cSnC99CTUlxcvrVp965qB78jOBPJyJWnSxGonAXeJZs3qzy8UXCeGfhwxm4YMmWFkGfBdZ3bwqGVcezrRond5MZY3X3Z7MjXxdk9tdS2TyzrIGJkw8Wv10kjfMx91efreuvWjI7wLgeT588+91LRJL52d3PWjriM8ob4U8rz9ZTQEJyAAAiAAAiAAAiAAAiAAAiAAAi8Egeiex4kqSDdvBvHcTxwWa8VVgikRWNn+0b/H8AVXUdUWlPPfE5A5U+egb+k4fZR0eo5VhFmSTl8/fRqH5wGfqmt7uqdqbjBjRtf5Juc6ce2ZQGwQ6svcaZnXmrP47IESSC9YMCxMsd2I4dNp9GibScaq30fz7qWmuUNGcezYGfr++8m0dct+Y0zJ91SFCiXYwKWtS3px6m/frq+C+McfY+jY8bM0etQcFtufU/dEzF717XI0YMCHLmL1d9/tTgdZ3C2LC74b+DF98cWPSnQuzyJ15s2bnXr36cwO3K+qspx/SFsHD56k2qrjRNxds2YF1dY0aVyFzJJOxNuDBk+k5cv+VDvUyj0R3BcsmJu+6dWBXdALyC0Vatb4wHgWfU8ff/mlJ71drby+VLxG/TKHBb0r1GIIHZGOxfqtW9Wmjp0a6FvG8YcfJrPZzUK1KGE7C+g/+WQIrV+3QwlSe/RoRZ06NzTSujtZsmQ9fdZjuIoaOOhjatiwqksycd7/3/9aqe+E6tX/x3P7XzqkiSxHc5+vWPkLreP2TpwgIuz77PBfnCZN7meUL4sDBgwYRyf/Pq/ElBIh7xekHZ9/0dZhPtpc7rTp31HZskWMcuRE+m3goIm0Yrm93+R+gQI5qVv3lvTGG6Xl0iFYHWPyuRo7dgG/w7jO7zneo2zZMjqUG5GLDz64Q1OmPOBxnZzffbh/H9avXxB/3oI8Cu+d69m06RHt2xfCn8eE7ATuukjk3r1nJO74yZPH5bZHrM0HD4Yw6+v08svxeQfoiIn7ndsV3rWIbqdMmas+1506tVBHc57nEepfv36Tdu8+QP/8c8FcJI+tDFSuXCl+95bG4b5cyO/M7dv30IkTp5VYXCeQBQQVK5altGntvzt//XWqjnY4ZsiQlhc+1XS45+7iyJETtGnTduabT5XtnEY7ffv7p6CmTW2GVZJG5xPH9yJFXqY1azbxjgcBxveyCKvLlCnuUUQuz7hly07+Tj9lfPbk+7Fo0UIspC/C43u64tCxY3OX7+bIMBX2u3btd34sdS3O5eJgbg779h3iRT1HySbEtsWkSZOKv6NKc5+5H7PP8yzmuvW5LCw6dOi4+nv81VcL6tsOx23bdrNx1hE1hoR/RMPztPXMmXM8lg/yO9lbRnWy8Cx37uz83fqaw4KGlSvX8nvgS/zZLcl9+oqR3nwi407GkSyekPw6yOKTjRu38YKyK2phi9yX37m5c+fgMVrGoR6dx9NRRP5Sx759h7lPg41ksujklVfyU+nSxYx75hPpgy1bdqldNuRcgozPwoVf4TxF+ftinirP3fiUneW3bt3Fv5vPGGNb8of1mZf4DRu2qsUk8rkpVsz93xWSDgEEQAAEQAAEQAAEQAAEQCBqCECob40jhPrWuPl0Lv2iyPzS6NkzCPX1oFi0aAN99OFQ5ZL+lCchRFQ/fXpfeqNKKZ3E49Es0i9QMCd91bM15yvpMLkpwvsff5xDC+avU+VIuVOm9FYTT84FixD/jcpdlMORTJJ37vwudehQh9KbhOMy2bJo4QYaOnQGXbp0XU2YzJ49gMqWc53MkL5/772v+aXKAVXV22+XoR6fNeeJyFwOVW/evJ8GDZzKk11/q/vt29ehb/t1dEgjFzEh1Jd6fh3zBdWu7dnZX9J4EupLnKfQsGFPfll0kCpVKk4zWZAfmVC+XAeeYL6sXhTJ5FPRovnY6WpEuEWEJXqXzCLS3737GE2f9hstWfKnmtxNk8ZflZ0tm6u7jy5PV9yqdU0aOLCLvgz32K7tAPr9921GuucV6ktB0dVfRiNxAgIgAAIgAAIgAAIgAAIgAAIgEGsIQKgfa7oKDTURkDk056Bv6Th9lHRybruGUN+ZW3Rdxwah/po126nL+wMUgo8+bkof87+wQlDQfTZruaiSZM+eiXfvTGEkl3nI9xp9ZojMRbwuY05cayWIO/x8XgggDvE6mMXV/VmM3+/bMSq9LEgyG4XUql2J55Y/dTBq0SLqaix2f8jC6I0bdqn5a3M+MT+ZNn0gi/fy6irVUYxqGjboYbRV0onY7+HDxype3PgXLx7hIrCWueaWLb6mvXuPGeWJ+Y3OJ8/83cCPqG7dN1S8tFFE5mZRqXbtHT6iB735ZhmjnO++G09TJi81rsU1XhYd6CCC727dW+hLdTSE+uwgX7feGzR1yjIjPiJCfTGIKfNaM27fIyr/v2L8DsB1/nna1GXUv/84Ve6Ysb2oShW7YNMKR3Ofy3gbOXKW0WazUH/u3D+oD+/2oPszR45MdPv2XcMQSMbfgoXDjDFoLtdZqC/907LFVywGPa7qElFp4sQJjZ2BRcw6bnxvtaDEaAyfWB1jixauVQtHpCx3ixvMdXg6L1bsGgtJQ2nt2rQspvVzm2zbtsc8hm7wjg7x+dmeXyS/fv0jqlXrJouL/VjY7eha77YBfHPFiof8PucWVauWiObNS82fDdn94AkvlInHYlY//kx6yhnx+5cvX6WlS/9QLvetWjVyyWhVqH/t2g3+nK9SgnMR+WbJkkktVBLRvnzWRSxcr14N5e6tK5XvtCVLfufdA64p5++sWTOpdp0/L2ZV99hZPBk7btfmd2C2hRAi7hXx9d9/n1ZFFChg+y6SdCVK2Jzjddnujlpwb1WoLyLrS5eu8GcnUO1EIK74Fy5c5rEfrL5PRQyfNWtml6rXr9+sFiJIhCxAkHwizBbXcxHq7917yK1QP7JMhfXZsxeUAFo3QjMSgXvq1PYFU2ZRvyygEFdz6QdZrCBBFkkIJ+dg9Vmcy4nMtYwraVutWlWZb8R3mrDa1oMHjyrhurRRdoPIli2zWkQifS3jL0OGdPz+sqraPUXSnDp1Vi3eEI4NGrwjtxyCvHOeOnWuKkPi9YIV+azJ+Ne7SEg98rvz3LlL/Jl5xAs/cvAuApUcygrr4vffN3D/n1dJZIzJYgtZ6CHjSIIshJBFGM5B71gg953HpyxOOX78tFuhvrCQRQoyluX3QKZM6fl3QWL+TFxSC+/kM1+/fg3eXcb+t42uW4T969b9RTlzZuPvddvveB2HIwiAAAiAAAiAAAiAAAiAQNQTgFDfGlMI9a1x8+lc+sWRvFyyn0OorweFuIuLy7gIw2VL2U2b9qlzER+HFY4cOUM1anSjUH458vrrRdkF/RvektPz1n8jf5rLjizTVJEighcxvHPQzvMykTl2HLsAsbDeU7hy5QbVq/u52hI4bdqUtGfPVLXYwJx+yuQV9PXXv6pbH37YkN2LWjksIjCnlcnSD9h5f9WqbSqNOP+XKVPInCTGhPri5L5+w2h2nHF1WNENikmh/p49x6l2rU9V1cOGd6VPu/+kzv/cNJadq7LqJrk9amG9O3d65wwrV26hrh8PUy9zZOHF/PmDXPpLl6fzJk+ehPbsnaZ2CdD3PB1lzLxWuo3xQkbSRYVQP7r6y9Nz4D4IgAAIgAAIgAAIgAAIgAAIgID3EoBQ33v7Bi3zTEDmTJ2DvqXj9FHS6TlWPuNz+zUc9Z0pRt11bBDqjx0znwXwNsfnX8d84yAcjwyJGzfuUKOGPXje9yqLJ1NSr96d2HikpBp369ftpH79xlBg4D3Kzbu6zps7RDnAS/lmcbWYwIije/sO7yqB/MGDf1OPT4ep3V8l7ZKlP7HDbh45VUGLqCVfypQp6PsfurF7fiEW24XS+PELSZ5Ngjiljx3XW53LDxF7S1tlYYE4nQ8a3JUFswVZzPhMuet36zZECeTLlStCU6d9Z+ST+I8+HEirV9sMRbp/2pJFvG+wkNSfn2MP72w6hXeCvah2pt2+YwaL/xIZecuWaa6MbmrXqcQ73fYw7uuTCdxe2YlAQv133+RdUesqp/ejR0/zsyxSLvAS1/fb96lZM7sLtxbqS5yE9u3rU/Uar7PgOB2LcOMaInZbrPuf3bsP5d0BNqpFDlu2TlP9Z04piy9kYUJKXrywjV3748ePr6KtcjT3uRRUmftHdgzIz872YviTIYNtfr1SpXZ06WIA77KbSS22kAUeIgj944+t1O2TH1R/ffBhY95FoLlqj7lcs1BfhP6yGGX9+p1q3vrzL9qweLYiP6c/u6IfoqHcb0eOnFb9NWPmIIdFHVbH2Gpu4wcfDFTtatTobbV4Q11E4keGDFd4HD6jw4czUK5c7tXuZ88+YbfrAH7HE4cCAiIuBnbXDNav8mf2Or+zCeFdM1LyDsBJ3CVzuTdq1H36/PNAdm1PwOLvZ3TypG1hjiRMmTIuL1pJxKZQ/ixcj+OSN6I3xCn8jz82Kqf6hg1ruWQT8fDMmYvUmBfH/YgE+TzPm7dUCdgLFszLizTKqPySV8S8q1f/qQTEIhYXZ30dzIsGxMFeC/Ilftu2PWwsddhFXCzliQO9CIM7d26pi4rQ8XmF+lKJfBeJe78sDtDh+PFT7BC+RS1MEGFyypT+OooXfRziz8ZeHldJuP+q8w4L9nyHD5+gv/7abqQ1O5ZbZSqF7dixV4n/xQ29RAnH3TAk/uTJM7xo5S9uU1JuUzWHNp0+/Y8Snct3U+PGdVQaySPB6rPYclv7KYsPVq1az7yTq/bIQqCIBKttlfE/Z85S9f1YuXJ5/t3xklGdvEOdO3epEtaLuFxE5hLku1aE+GLw1aRJPfU71MjEJ+K2L4L2VKlSqmfQcbL7gSyYyJ49K+8+U0XfVt/HS5faFrBUq1aZv7OyG3GeTnSf+vn5UZ06bzvsRCFC/YULV6qsbds2UeNUl7NnzwHauXO/6ud69ao79Lf+vOi05vEp9/RCCHHPf/vtymoxjtyXz6gsPpHnk36Tz4T5d7ikuXIlQC1SkIUQ7hYMSRoEEAABEAABEAABEAABEACBqCMAob41lhDqW+Pm07n0iyPzSyM46tuGREDALSpZoqWa+JgytTfduX2PJ6OHq4mKAwdmUHLeDthTaNToK+VULyL5jX+O4UmW5J6SGvebNP5GLQR45ZXctHrNz8Z9Odmx4wjVr/e5uvcZu95/0q2JQ7y7C3Fir1Pb9jJi0uReDsJ+cfApVlS2Wg5ml4SiNHee/UWIu7LknuSpXOl9dkC4xi9qXNsoE1G5c9VT2d0JvD2VqxcgFCmSl35b9aNDMnOZXbo0oFmz/lALJipWFNf7fi5CdZ05JoX6X345Sjnev/pqHvr9j5FUtkxbnlwLoI8+aqQWP+g2uTtqYX1EhPqSX57/sx4jVVEjf+7BTkOVHYrV5ckihtu3g9Tiie9/+IiaN6/mkM7dxbBhM0l2gUidOgWF8ORhEG+B7K4fdR3u2hyT/eXuGXAPBEAABEAABEAABEAABEAABEDAuwlAqO/d/YPWuSdgFuHrFBDqaxLecYwNQn1xShfHdAmb/prC7rIRc9F2Jjx69FwaMXy6uu1O8C9CcBGES/j66w7Uuo3NEMYsrq5QoQQby/R1mFudM+d36vXNLypff3bcb9zYPp+oRdQSOX/BUN5NtIBKJz/k8/H6/1qzePkmpWMn/60sQNfh11/n8XzjNBbjJaVFi39UQnAdJ8eNG3dTh/Z95ZSmzxjIxjA21+s9e45S4/dsc+Ft29alnl+1V2n0jx07DlHzZj3V5Q9DuisRv44LS6gv4vOiRRoqV35x/p83fyiL5u3CThFS1qv7CTtyn2OX32S0a/dsg5FZqP/hR02oa9dmusoIH8198O23XahpsxpG3suXr1Olim0VT7kv8TpY5Wiur2zZIjR5Sj+H55Xyz5y+yO8NOquqvvq6vVq4oOuV48qVf7GoOg6L1F8ydj0wl2sW6pv7TcadjD9zkAUbtWt9pIxoZIcDGb86WB1jsjhg+fKN7Kh9k5rwmJUdGiITQlnr7u9/WWW5di0TG964F7nfv/+M3aSvqHSBgZl5EUVkanFM+913QbwLbhAVL56AF57IQg/HeE9XX355l37++Z6Kzps3Pn9e/Pi9U1z+zD1m4etjfo8lOxMkpsmTU1l21z927CR/Lre6iIN1m6wI9SWvvDcQJ/w8eXKp8aTLk6MWW4vjtojVddi5cx8vZjjICzpepvLl7QJ+iRexr7iBi8O3iPJ1+K+F+uKanz17Ft0c4yhCfRHsOy9GmDRpthJwiwO7OJw7hzVrNilHdrnvLIS2wlTKCU+oP336fPUuUBZqmF32Ja8EnT8qn8VWcuR+itO8CMzlaBbGR6SU5+EuuzncunXbEOKb69u6dRcdOHBU7eBQunQxI0o+U/LZKlWqqNolwYjgEy1oL1OmBO+MYTdH0zsFVKv2BovxbaJ/nU+eOSQkxGHRh47zdAwIuKGiMmRw/dtjwYIV6vPkvCuB5iTi/kyZMrgULa734n4vwTw+AwOD+F3mIrUTRqNGtRwWe+hCli9frdz2y5YtwX9T2J9b4gMD73L+xeqzHdkFN7p8HEEABEAABEAABEAABEAABCJOAEL9iLMyp4RQ30wD5xEiAKG+Z0y/jl5IAwZM4smO5Dy5MoO39Q2hIoWbqon8IUM/pqZN33ab+RoL/Ev8K/AfPPgDatHSPuHuNsO/N8Wtvn27AepKHOPz589hJNdicHmBs337RBd3fCOh08mbVT7kCaCzVPOd/9E4duHXYdmyTfR+5+/VpSwkyJvXcaJHp3M+mvM5O8abRdruBN7OZenriAr1pcz06VKxE8pglbX/gM7Utq2rq4tEmoX6u3ZPcdjmWdfrfGzYsCc7OR1kNxtZBNDfOdrttby8KVqkuXKp6tOnPXXsVE/tjCA7JIjz0I6dk10mns0FhSV6N6fT5/J5feed7rR/399u26nLK1myIOXKnZnmz1vHL1Hy0B+rbeJ+XY7zUVw9SpeSF2q31AKDJUv+VLsxuOtHXUd4Qv3o7i/nZ8A1CIAACIAACIAACIAACIAACICA9xOAUN/7+wgtdCUAob4rE2+7ExuE+j17/kQL5q9RwrNDhxc6uNZGhmenjv2UY3nhIvlYpDjcbdbq1bqwuPM8vcNu5iNGfKbSmMXVvft0phYt3nHIe+tWIJUr20LtttmkaXV25v/AiNci6lSpUtDOXbOM+/rEvAhh67bplI7ncCW833kAOzNv551nX6efRrruUCsi6+LFGilR6DffdKRWrWurfJMmLaFBAycoVnv3zWUXX1fH8UuXrimxrsRJu3QIS6gvbu5163RVSWWhQsWKJXU247hq1Wb6+CPb/PPqNWNZIGkT3ZqF+mvWjmORZmYjT0RPZKFA+XKt6ObNOyzYLESzZtvqkfwTJiyi7wdPUkXNnTeERdwFjWKtcjT3+YDvPqL33nN9nyFu/aVLNVV1FSiQk4YN/4zy5bO/lzAaYToxl2sW6ut+E2H/vv3z1Y4HpmzqtG+fX9mRfaXLog6rY8y5/Mhei5O+OOqL1vvu3cweRfMigk+R4rLaJUUc9cVZ30qYPTuYd2O4zYsD4rLLelp+BxRxxf+lS09Y7B2q2lipUkLVZt2GHTse807QN9XOAD17JqdvvoncggVdzuHDx9nFfQcL6nNS1aoV9W3jaFWobxTg5uTq1WskouSECRPy+57GRoqjR//mhQzbeBwl5p0ZqroVjRuJ/z35L4X6SZMmoZYtGzo3SV3fuHGLd0hezu+MMrCjeTV1TwTfM2cu5B0nUlGjRrbvPufMFy9e4YUoq9VtsxDaOZ3ztSemkk4L7d056j94EMzu7/N4N+v0vACqunOx6vry5QASR/ds2TLzu6q31L3ofBZ3jQjhncyXLFnFu6fcopdfzsff5WXdJXN7LzrbKgtLZIFJ7tw5eAFUJaN+zSxVKn9eBFfXuC+/E6ZMmcui+1D+ndyAFwrZf9etXbuJdzc4S1mzZuIdeF5n1/nERr6oPlmxYg2/D7zMpm6l1S4VUr4W26dNm5p34HH/HvbSpSu0bJnr+JR2S/sLFcrPO8673xlej+08eXLwd00lh0eSRQgTJtj+1mjXrokS/DskwAUIgAAIgAAIgAAIgAAIgECUEoBQ3xpOCPWtcfPpXBDqe+7+Km904YnPc0poL4J7CSISX77sL97a9xV2AfrBbeZJk5az+9AYtYXt/gMzlUO524QRvCnbWBYt0own8AN5ErcOfduvYwRzek7Wof139NtvW3kSKxetWWtzSvKc2h7z8OFjKvRKY+V606NHc+rWvYkRGRNC/XbtavO2x0Np0aINvFWiHwvQf6aXXspqtEGfxJRQf+XKLdSxw0Alxt+9ZxpP6qfmybML7H7UWTVl3ryBVP5/rtuH6naGJXrXaZyPEyYspT69x7FrTzw6cHCmWkii0+jyRKjft28HJeqXuOXLh1HxEgV0MpejjAUZE/ISZdv2SdTg3S+jRKgfnf3l8hC4AQIgAAIgAAIgAAIgAAIgAAIg4PUEINT3+i5CA90QgFDfDRQvuxUbhPriLC/O6BIWLR7BYri8lihqIXrDhlVp4KCP3ZbxSdfvlRN69uyZaN368SqNWVw9deoAKle+qEteXXadOpVp6LBPjXgtoi5R4mWaM9d1Tnz2rFXUu/colV7qk3oliPD/+vXb6lwc1N2F7dsPsrj4ATVuUp3697fNwev2Z82agTZsnOgum8d7+hlq16lEw4b1cEhnbudfm6ewGNXVWdjsMC8MhIUEs1D/xN/L1TyqQ+ERvNCLGmQe9q/NU9kRPLXKKU7+hw+fUq716zdMcCjNKkdzn5sF9Q6F88UXn4/g+fZ1xm1ZBFKq5CtU+rVX2cm8qMuiEk/l6n4zjzuj0H9PPPWB1THmXL6Va3HUF2f9c+cyUtq07u3tb9x4yjtCXFVO+uKobyVs2PCI6te/pcT+S5emZnFxQivFeMyzdOlDNpe6xQ70CWjbtnQe04UVIe7Y4pJtFpSb0z+vUP/OnUDe/eC6ciQXwbSUd+vWHbWThJ9fAmrXzrZoROoM5U6ZN2+5cteWa2mTuNVnzpyJ38O4fnYlzX8p1DcL16Ut5iBGSRMmzKQECeIbz3jmzDk2nNrIJlq5WIhdwZzcOBfn9GnT5qtrT0L9yDCVgsIS6uvdDSSdCODdBekXGSfJkyfjnZzfVUmi6lnc1ed8T/r4t9/WKWG5MK9R481IfR9HVVsDAq4rF3oZv0FB93gn8EAez/dUc3PmzKZc/s1tl0UZEi8O82nS2L73dVvcjZ2bN2/z9/JK/hw84eeLq1z1s2TJxML9zLzQx9pCHFkYcOnSVX7XfIvbe1e1W+p5+PCRamq5cqXUrg9ycerUWZIdHfLly01VqrxufhTjPDj4oVpoIDfM43PLll108OBRqlChDO/Ont9Ibz7Ri0LM48gcP27cDF44+EQtYEiWLKk5CucgAAIgAAIgAAIgAAIgAAJRTABCfWtAIdS3xs2nc0Go7777Dx8+TW9Xtb3oEEG+CPMlrF69g9q07qfcfLZum8gTg67b/X3WYyRvy/cHFSuen1asGO6+gkjclW1bSxRvqXLMmNmPKlcuEYnc7pOWL9eBtxS9TB980IC++rqN+0Qe7rZo3oddm3ZTzZrladz4r4xUMSXUv3v3PlV54wOS7YCLFMlLy1iELqJ1c4gpob6MBRkTIsYXUb4OMnZkDDVsVIV+/LG7vu1y1MJ6d+70Lon/vXHq1EWqWKGTulq85AcqXdo2NuWGLk+E+kuXDVVjOCLtaPze1+yUs5/efKs0O6b0oTKvtY0yoX509de/OHAAARAAARAAARAAARAAARAAARCIRQQg1I9FnYWmGgQg1DdQeO1JbBDqT5++gvp9O0Yx9ORuHh7gwDtBVLKkzTilW7fm1OUDu/u0Oe+QIVNo3NgF6taRo4vZjTYBO1Pv5t1c+6p7nkTbnkTuWkRdstQrNHv29+aq1LlZfL123XgWNGcic1tdMri5UaFCCZo46VsV807ND+nEiX+obNkiJG2NTPD0DFJG/35jWfS6nA124tGx40vUHL9z2TLHXeiV+up2584N6dMerdR5VAn1Dx48Se/W76bK/PrrDtS6TR0WiF+hN6t0UPc++LAxffJJc3UuP56HY0T6XOoQ4e1PP85kd+/fWLh5X24ZIWnSxLyLbAPq1KmhMiaSCE/l6n4rV64ITZ3mvt82bdpD7dr2UeVPntKfHZyLqXMrY0xljIIfIsAXIf6uXelZnOze4f7o0VDeBeGaEvKLoD+yYe/eEBbu3lCO92PGpGTxqd05O7JleUofHPyMRexXuT+f8buTTCykjuMpqcf7589f5EU+69gcKAU1aVLPJZ1Vof7t24H8TmkzXbt2w6FMEeCKe7uIgp2F+pJQPo8iLJd4OddB8hUv/qqLCPi/FOoXLJiXd0Eup5vochSnehEnt2rVSO0SsHfvIfVsxYq9SmXKFHdJr2+MHTuNFyA8cxBCS5xVpmEJ9fftO8Q7eu/VVYd7fP992/fj8z5LuBWZEminedmJoG7d6mrcmKLDPX3etoq4fuvW3UrkriuThVcpU/rzThtJ6fz5S7zjiqtQf9eu/bR79wEqVqwQ97ftHe/q1Rvp9OlzyjE/b97cujjjKIsAtm/foxYlyNjWIV26NCSielm8EtEg9R88eIweP7Z/juLHj692dJCyr1+/qcosUuRlVeS+fYdV3eb2uqtr7NjpaoGMWaivHfpr1nxTLa5xl0/ujR8/Qy1E6NChGb/jtX/3ikBfhPoS2rRpzKZtUbuoSRWMHyAAAiAAAiAAAiAAAiAAAgYBCPUNFJE6gVA/UriQWAhAqO9+HPTtM44nCZayO0F62r5jkjFpL1sQFi3SnN0GgsjZUV6X1KrVt7R2zU4XIbuOj+zx4MFTVL2abUvedetHU4ECOSJbhEv6fHkb0P37wTTgu8480eF+20KXTP/e0AsRtBhcp4spob7Ut2XzAd6q92s1fj/p1oQ++8z+8kLiY0Kof+vWXZ5Ua0GhPCaGDe/KW1ZWlapVGDtmEW8PPZG3qkysXO8TJ3Y/kaSF9ZER6j948JDyvmRzKhk7tidvYf0/Xa2LUF8WjEh/JUzoR3v3TXNw39eZzp69TK//r6NiOX16X3qjSqkoFepLPdHRX7r9OIIACIAACIAACIAACIAACIAACMQeAhDqx56+QkvtBCDUt7Pw1rPYINQ/efI81ajeRSFs0rQ6zx1+ECbOvXuP0dy5f/C8NLGwszYVLGgT8ZV5rTm74d6hsBz1u33yAxvIbOK5bbsjvSdxtbkRnkTuVkXUpUs1ZTHpXSXG/oQXFoQVkrPAMXeerCrJRx8Oot9/38IixHT056bJYWVzifP0DJJw5syV1LfPryqPJ0f9s2cuUtWqtt1SfxjSnerVe0OljyqhvhRW9a1OdPbsJSpatADNXzCURo+eSyOGT1f1rF4zlp2Ts6hz/cMqx4j0ua5DjvLuQ3Y4kLG3c8ch2rnzsBH94UdNqGvXZuraU7kffzSYVq3arHZU0Ds5GAX8ezJn9irq1cu2+4L0rfSxBKtj7N9in+tQrdpNNrF5RDNmpOL+Tuy2rCVLHlKzZrfYITohP2Mat2k83Tx5MpQdqW/w5/Yp7xqRgrp3T+Yp6XPfL1HiGu8SHUqbNqWjEiUSRLq8Gzdu0fz5y92K5qUwK0J9cd2eM2eJcu1Onz4ti+vz8U4S6ShFimRKnCvvlSZNmu2xTqlXhMTixH/16jUWNv/DCytuyW02MSrKz2nf0Tg6hfoimp47dym7mafgnQvsixiOHDnBvLeTuJ3Xrm1/R6Qa+O8P+WyJo755MYJ2U8+TJyd/51Q0JzfO7927T9On2xZdmYXQz8M0LKG+bpMIwCtXLm+0w9NJihQ2Z3edz8qzeCrb3f3Nm3fSoUPHeOwk589qdbXgwV26sO49T1svXrzCv1vXqHdp8qzyL1261CzQT6Zc/c+ePc+/uza4FeoHBgaxwdsiYyeCkJAQ5UYvbvmtWr3nYkZmfgb5jFy5EsD/rvFuBqfVgo84/MeBjLfMmcNfOKQXCcgitVdfLcA7t2SmVKlS8vtL24IhzdXsqK8X7bhbdKDb5slRX5dXsWJZjzsz6LyyuKFFiwa6SHU0j/vOnVsa7+cdEuECBEAABEAABEAABEAABEAgyghAqG8NJYT61rj5dC4I9V27X7YSFAf7Gzfu0IcfNqSeX7V2SPTFF7/QjOmreLIlM23Zats62JygRvVP6MCBk9SyVU0aNMj28sUcH9nzdWt3UcuWfVW2AwdnsmtLysgW4ZDeLPQeM+ZLqlXb/baFDplMF98PnkYjR85lZ6SMJLsK6BCTQn2pUy+miBcvLi1ZMoSKlyigmxIjQv1Jk5ZTr2/GKBH8gQMzKHkK+/aLsgtCqZKtlNPJyJ978IuGykbbzCdWhPqSP0/uejyx/dhloYUuTy+ikL4uzmM5iHch6NOnPbsf2SeQdTtkQYEsLJDdIbZsnagmFKPSUV/XE9X9pcvFEQRAAARAAARAAARAAARAAARAIPYQgFA/9vQVWmonAKG+nYW3nsUGob6w047jYqoxfcZANgGxz2c6s+3cqT+tW7dDzT1u3zGDhYA2QV3Hjv1ow/qdvNNoflqwcJhzNnUtCwJkYcA771SgET9+ru55ElebC/Akcrcqou7Qvi9t3Libxc12t3xzfZ7OJ4xfSN9/bxPo7903l4WN9nlXnefChav05MlTFmwmpdSp/fVt8vQMkuDw4VNUr+4nKq2490u7nIMsEJCFAhJWrx5DuXLbFg9EpVB/1C9zeBdWm1vwxo0T2a2+v9pBoHDhvLRw0QjnJpFVjhHpc5fKTDf+/vscNX7vc+Wyny5dKn4XYFtM4KnciRMX0+BBtvnl/QfmU+LEiUyl2U6/7TuGBfErWNxqL09irI4xlwos3BgwIIjf4wSxO3ciXsyR2m0JzZvfpsWLg6lnz+T0zTc2cbLbhE43L19+okT6588/oU8/TcYLdFI4pYj45cqVD1ko/4yd+ROyIU9cl4zyPSiO+qz7pUuXMjL/yDvqi3P7xIkzlct127aN+fvH0YDIilBfhPWrV//JfZ6G+7mmi+hWC5jNInaXh3O6sX//Edq2bTclSJCA2rdvasQ+j1D/5MmzJG7tIr52J5w/fvwkbdiw1aNQP3HixNS6dSOjLeYT2Ulg4cKV3D8ZqE6daioqKOgefxYWKsF048Z1zMmNc3FnX7lyrbo2C/Wfh2lYQn0tkE6dOiUbZblvk9E408nzPIupmDBP9+w5yIuH9ilxed261ZRYP8wMHiKfp61//bWdf4+coMKFX6by5Uu51HDs2En+nbfVrVBfEi9evEotNqlfvwbJuF+37i9ehBf2TgzOlYjb/Nq1f5EsOMiRIyvVqFHFOYnL9Zw5S3nR3B2qVu0NXgiWzSVeyjt58oyDo/7Dh49o8uQ5apcAZyG9LuCffy7wwqX16tI8PqUsKbNQoQL0+uuv6eQOx0uXrtCyZaspd+4c9PbblRziAgJu0KJFK9XfP23b2nYRckiACxAAARAAARAAARAAARAAgSglAKG+NZwQ6lvj5tO5INR37X6zMN6dg/2OHUeofj3bC46ly4bydsMFHQqpV/dznjA6whNZb9HwEbaJf4cEkbzYvHk/vdfoa5Vr564p7Mxhc5mJZDFGcnGAz5mzrnJ9+Omn7tSgYfgTOUZmPtHC7vz5c9D6DaONqJgW6kt91d7uyg4S53lyKTOtXvMzO2jYJv9jwlFfL8ioWbM8jRv/lcFBnzRq9JVykq9QoRjNnjNA33Y4amF9ZBz1ZbI8Z47a6kWUs5O/Lk8L9aUyWUwgiwpy585Cm/4a6zARLgyLF2updoiQBSmyMEVCdAj1o7q/VEPxAwRAAARAAARAAARAAARAAARAIFYRgFA/VnUXGvsvAQj1vX8oxBahvriNi+u4hDRpUrJwcxhlYdd75zBl8lL67jubQUyNGq/TTyO/MJKMHjWHRoywibzHjO3FImBHEdyK5X9St25DVPqvv+5ArdvYxJaexNVGwXziSeRuVUT988hZbPgyS83ZLlo8ggWwjgLBQN61duDACep+lTdfM+LFyb1Z0y9V01q2rEW9encyN5PnXPexILaXumd2vZcb5cu1pGvXblGlSiVp/IS+Ko3+ERoqu+U2IpmnLFwkH82bN4TEYViHx49DeN6/mxLNywKA3XvmGHOpUSnUl0UGb1Rur6qVxRSy+4GEXr06svlPbXVu/mGVY0T6fN++42pByIXzV6nvt++zaNhRSN6GOW9m3ilTJqedu2YpHp7K3b37KDVpbHtv0qZNXfrqa9sz6mc5d+4K1XrnIxIn5TffLEO/jvlGRz2XUF8MbYLu3qN06d2L7I1KPJwcOxZKpUpdY7fzOLRvX3oWv9rHhGQRkX2xYtd43DyjXbvSs7A2voeSHG/fufOUn/MGSfnt2iXlz4K/Y4JIXnXufIfd1R/Q8OH+vLjDdfHK3LnB1LbtbRYQ+7EwPm0kS7cnX7Lkd+XeXbVqJf5MOu7ubEWof+DAUdq6dZdHQbIIn0UA7SzUv3z5Ki84uMoO4AUpUSLHBQOPHz/mBQWzlelQu3ZNlTO/PIG8Oxk7dpp6mPbtm7GQP2J9JRnu3Amk2bOXKGFys2b1uWxe8WAKIkgWYbInR31JWq1aZX5Xld2Uy3a6Zs0mOnXqLC+welmJoXWCSZPm8Lh6xLtzv8lGSln0beP4++/refeNC+raLIS2ylQKErG7iN6LFn2FypYtadSlT6ZMmccmUQ9515Za/Hsqlb5tHGU3A1l4IO0VN3QdrD6Lzh/W8ejRv+nPP7ephSMi0peFBM8TrLZVj4E333yd8ubN7dIEWZAiiyg8udDr55AxLUJ9ca2XhRuygMMcxExO4u7cuctGXK+ao9S5jAkZGxkypCMR/YcX5LMin5mWLRsaLvo6j/yNO23afOXSb3bUl/iZMxepXTRKlSrK78GL6CzqKJ+1xYt/U2NBbpjHZ2DgXd49YLES2suCD+3cby5AFqDIQpQyZUrw92shcxSPedvOBOaFLQ4JcAECIAACIAACIAACIAACIBClBCDUt4YTQn1r3Hw6F4T6rt3fufNgWr5MnAxy0dp1v7gkEGavlW7Dk4TXqXmL6uzu86FDmk6dBtGK5ZvpjSqleOK0r0OclQsRoleu9L7KuvK3ETyBls9KMQ55Cr/alLdaDWT3l7b0fpd3HeLCu/j4o6H8EmkDb1lclObO+85ILi84cueyObb369+JJ59dXyoYiU0nHTsOopUrNvMkZV76bdWPphhSL03CKvPw4dM8idmdZPGBuS+iW6h/8uQFqlSxs2rrhInfsItNWYd2y8Xs2aupx6c/qcniXbunUsaMrlviamF9ZIT60m/SfxJkfMk400GXZxbqm8eP9Jf0mw4L5q/jLYuHqwnrPXun8cSr7WVBdAj1pc6o7C/9DDiCAAiAAAiAAAiAAAiAAAiAAAjEHgIQ6seevkJL7QQg1Lez8Naz2CLUF37duw/lueeNCqU/i59ff724cnYXk41z/1yhBQvWsFP0ARUvIn4R84uoX4fr12+zgLIHXboYoO5/w+JuEaXLOF3PTvv9+41lAeA95QQvQnQRWEvwJK7W5coxqoX6AQE3qWGDT1n0e4PnRtPSkKHdWXRYUAlyL126Rl/1/IkFvAeUWH7V76NZ4GoTqopTfpf3B6jnkXZ98klzqlvvDZsL+5b91L//OBb4XVGO7bLbgDZvkbTvdx7ATr7bWRiYmEb+/CXvTJqV0mdIYwh2x41dQEOGTJGkLHCsohYyFCiQi44cOU0TJywyRPO9+3SmFi3eUenkR1QK9aW89xp9Rnv3HpNTFWTBwJatUx36WsdZ5RiRPpcdD8SxX8Lb1crzrqydFWcRv69ds81Y9NGg4VvsOt9VpfNUrvTb+9xvsuNDnDhx6PPPW9M7tSqqHYK3bTtIw4ZOVZwTJ06odpSQXSF0sLoY5Mzpi9SoUQ+6dy+Y+vXvwudv6yIjddSO+S+9FJ93603D49Um1g8IeMpuzzfYbTqU6tVLzA7ojsLlr766yy7rj3jBSQqqXDmhUad8J9WqdZO2b3/Mn9fEbKSTit8TGNFhnngqc/XqR9yGmyyOjkNjxqRkJ+1ELIS1ueZLXJMmt1hg/Yx+/TUlC3JtO3CEWZGHSO1W//LL+ahiRcf3HlaE+iLsnj9/ufoMNmv2rsNOC1euBLBj/DoKCQlxEeovX76aLl68ogTRIow2h337DjPbPZQ1aybmXNUcRXPnLqVbt+7QW29VpJdeyukQF9aFfIdOnDiL2xKqFhWIE7h8LuV6y5adSmgv52EJ9WUHAtk1wN/fvuuCFmf7+flxXA3+TrYv2Ni37xA/x17+DkvMfVuDXeKTGU08ePAo17vLuDYLoa0ylcLEif2PPzby5zK1qjN+fMeFKUeOnKBNm7arNtWtW93hWe7ff8DfkWsU39deK+4gIrf6LMYDejiR9ooAXtpZu/bblD699UUougqrbdV9Im2QfjYHHSf3PAn15R3q1Knz1LPIWJKFDrIoxDnIorKpU+crcb18BuWzqIPsGrF69Ua1gKN06WJUokRhHeXxqBd8FC1aiBdn2HeSkbJkF4nTp8+pvM5C/cuXA1R/SzpZ2JEjRza1g83Nm3d4wcde/t59wP/uq78/zONTCtMu/VmyZFKO+bKTkAQR+O/ff5hkZ4fkyZOpz4TsRmEO27btUWlEwC9CfgQQAAEQAAEQAAEQAAEQAIHoJQChvjW+EOpb4+bTuWTyyfaP/j3K9VPl1h0a+ownhu0TSt4IKqqLH5i0AABAAElEQVRf8AbdvU8FCti3pzRP8puf/8GDh8bl2X+WqElEfaNP73E0YcJSypEzE79omKBvWz7Ki5WXC76n8v808lNq0OANy2XpjG+9+SEdPXqWGjeuSuLKHpmgneSlHdIeHf4Lob7UPXLkXPp+8DTVDC1cj26h/qCBU+iXX+arOmWCUNxenIN5jHz9TRvq0qWBcxLSwvrICPXNOyysWfsLT9LlMsrV5ZmF+hLZoMGXtG3rIV7U4Oj+X7vWp+yecpwnZCvRL6M+M8qJLqG+VBBV/WU0FicgAAIgAAIgAAIgAAIgAAIgAAKxhkBUz+NE14PfvBnEIpo4LNCKy2JD+Rfn339knEdX3SjX+wjI3Klz0Ld0nD5KOttcq+SReVb79dOncVig9FTF29M9VaKljBmtOUFLOQjETrCufeStXESA16f3r+zm/keYTRSR/rhxvSlfvhwu6c6evaSE3rdv31Vx4hotY03KlpCencXnzR/KO7OmV9fyw5O42kjAJ1Et1JeyRUjdmF3WdVtFrCdu9bLgQIePP25KH/E/cwgOfkTNm31JBw+eNG5LXpmDliDzsSIcr12nkrrWP5Yu3cDmKcP0pTpOnzGQxX52MaMI/adNXWakkXcA5rnc999vRN0/bWnEy0lUC/VnzfyNRfH23WorVixJEyb2dajTfGGFY0T63HlRhNSZI0cmtbhCdhiQkDlzOm7btyyYtjmFh1Wu9FvLFl+zyPK4yiu/P8UNXVz0JYjwWXaCkMUl5mBVqD+BF1d8P3iSKkr6WPraSrh1y+Z+f+JEKLeR2GHfJirdtesxvysjyp8/PgtP07JI1a62v3DhCb9LClDViUh/xYo0RtVduwbyO6L76lpE/6zR9hh+/jklO+/bRP5hlSmf8T597tKwYfdUWcmTx2HRrR8dOhTK/cWN5CALBrp2tYu91c1I/hAB8fTpC9RnrHnzdx1ya6G+3BSRbVihQYN3DCf8RYt+o4CA60qsnzlzRv4OSK6ub9y4yc7sWZVTvbOj/pUr15jpav5ee6IEzSLKl/F68eJlNZ7ke08c7LNmzezQjN27D/DOB/vV32vivJ4xY3peDFXGIY2nCy2ql/gECRLwMyZVTvvx4yegN94ozy7mGzwK9fPly0PyPLJIQITcUrc4hj94EKzaIq752bI5tlXqWbfuL941+oycuuQrXboo9+8Jft5gB8dySWuFqeQTZ/Xp0xeqY+LEiRTbypXL8btg+98h2nVfPr8i6Jfnkc/whQuX1MIFSSuLDsw7kkjZVp9F8roLQUH31C4HT/hDKO/hnAXd5jypUvmrnQnM98I6t9JWac/ChSsVC1lcIWNPdl64ejVAud+LQF92XfAk1Jf2iMheC+PFpV7c6t0FvWuCxMnOBpkyZVDu9rK4xbZgJLlauGDe1cBdOXJP2vTHHxvU35vCKUuWjOp3nnzGZJGMOPNfunRF7fYguz6Yg7jby8IO89+5Ei/f6++885Yah/K3rbNQX+6tWLFWlRs3bhxuf0a1+EN2CpDf47KopX796g4LV3S9srBHFqN42mlCp8MRBEAABEAABEAABEAABEAgaghAqG+NI4T61rj5dC794sj80siXhfozZ/5On3/2c6TGxLhxPanmO/8z8qxevYPatO6nrsWRX5z5IxJEkL9mzU6V9PXXi/LkiH1i7O2qHys3cnFuFwf3iAYRZ1+6fJ0nBVPwRKJ98rtv3/E0ftwSnuDxp337Z6gXzxEpU3YRKF2qtUo6YkQ3avTem0Y2cULIlauucrdv06YWDfjO5jhvJPBwohcNlC33KjtG2bZ+1kkjIv6Xydn69T6n3buP8YRhKlq3fjRPxB6ltm36q2J27Z6iXiboMj0dGzbsSVu3HOSXBMVp5ixbXndp5TmFgThBRTTkz5+D1m+wv3jR+bSwPjJC/V7fjGEHnuXqWffsna4c+53Lcxbqyw4RslOETGbu3DVFja0jR85Q1bc+UlkXL/mBSpd+RRdD0SnUj6r+MhqLExAAARAAARAAARAAARAAARAAgVhDAEL9WNNVaKiJgLM4SaJkLtV2tJ2Y0+j5Vgj1bYxi4mdsEuprHuI6PnXacp6P3G8I4EQUKeL6Zs1qUMtWtd2ag+j8R4+eVgJlcaTXQfKLQ/+XPdsZomodF5a4WqeJDqG+lC1t/eGHKQ7PKsK9vHlzUE9ua/n/FdNNcDiKa+/gQZPYcXuTEiZKpIh5CxbMrZ6xZElHQaHOvGjhWhrK7u16McAXX7Sh9h3sgmP5jP7y82x2R1/JotpAnU05ybdi7p06NzTu6ZOoFurfuRPECyNaGIsrRoz4TLnP6/rcHSPLMSJ9LvWI6HP06LkkiwfMPGQ3hvLli1Lfb7sYOzNI+vDKlWcbNHACizQ3sRjYJvaXfPnz56Ru3VtQlSqvyaVDsCrUv8g7SzRt8gWLZO/RwEEfs3C0gkO5kbm4fv0p9et3l0XMwczE9t2eIEEc3lkhMfXunYLHh12kL+XK74Hq1W+ys/RjFs/7U5s2dhf7Ro1u8bi1mz2F1Y7Zs1Oz4DaRShJWmbqMWbMesGv+fSXQl3YmThyHF6L48YKYxNS8ub0NOr2Voxa7i2u4iLR1MAv19T1Px9atGxnCahHbb9y4lQXK/6gFa5JHvq9efbWgcuqeNm2++my3a9fUoTgREotz/tWr14z7Ig6XNonTuIiO3QUR6h88eMwQo7dubTPCcpfW+d6hQ8f4XdwJJdCXxQDiCC5O5LITxOzZSzwK9cX1XBzOxaH80qWrxve6LGgoU6Y4u/u7f08oIvTNm3fSiROneSGCbcGF1Fus2KvKLX3KlHluhfpWmcrz3rkTqBzPr1+/qR7/9dfLUKFC9h0u5ObevYd4jB1TCw1UIv4huwK8+moB1S5nkb6ksfosunzno945wPm+u2sRszdqVNtdlNt7VtsqYn1x+L92zf5+ULiIU70sfJDFHGEJ9UU0v2rVetUmcdOXRSuewrFjJ1U/yOdOB1mwJotbKlR4TfWHvh/eUXan2LBhs3LB12lTppSdQMrzbhH/qL52dtTX6eSZJb/8E2F/unRpeJHSS2qxztix09Vn2lmoL3llUYjsCnHy5FljbMv9TJnSq0UB5u8WuS9BFoRMmTJXfbbfe6+O+p6wxeAnCIAACIAACIAACIAACIBAdBGAUN8aWQj1rXHz6Vz6xZFMANrPfddRv17dz3li9YgSri9c9H2YY6NuHZsD+VtvvUZTpvY20srkc+HCzUjc+Vu0rEGDB39gxIV1MmrUAuWwLi8b9h+YwRN+dkcSHSeTMJv+GstODenDKkrFyfa0xYu1UFsdi5u7uLrrsG/vCZ607q4ufx3zBU8ER2wCW5zrxRFdJgoPHpql3I90mXIsV649yRbNBQrmZPeMUeYot+eyOKHQK42Vk4OI/kX8bw4REepLeqnzLRad378fzFu+lqN32e2/XdsBqqioFuqbHe0//7wFlStvd2Myt13Of/ttK40bu1jd/v2PkTyJmcchSWSF+teu3aaKFTqxc8Z9nvh3XQyhy3MW6ofyy5ZSvLhA8vfo0ZxfiDShL774hWZMX+W2r6JTqC8AoqK/HEDiAgRAAARAAARAAARAAARAAARAIFYQgFA/VnQTGulEQOZMnYO+peP0UdLpOVYI9Z2pRd91bBTqaxoyl3zp0jV6xHO5OXJmVmJQHReRozjVX7gQoMw8srELvz+Lq701BLKA+9Lla0rgnCdPNnbkDcNm3PQQ4nZ/4cJV5awtOwzI3HREggj1RcyaMWMat2I/2YHg4sVr7NwrDtip1CIJd+LTiNQVk2mscgyvjSJcvcrmNAHXbinjm4wZ7QLt8PK6ixfB5fnzV5Vzc6ZMabkfnq88d3XIPTGGefjwESVNmthTkkjdf/ToGZ09axNM58oVj52f44SZ//79Z1x32GnCLMBNZETKDA5+RmfOhPKCl/gs2I3a+uW9jLjqi6BWXK2jKsgYu3nTtpuGOM7Hjx+xz7I4ygcF3VfpU6b0dzAvCqttd+/e4/7zU//CSucuTsav5BXH9LDCkSMnaNOm7bzzcT61eEDSiphZnPXFNdzfP7nb7x/nMiXPnTt3lUu9LECQhQwRCVaZStkipJa+Fld2d/WJaZWItGVXANldICLu7VKu1WeRvDEdrLZV2N24cVv9zvb3j/iYvHz5Ki1d+odyyK9bt1q4jyt/U967d5/ffT7g75kk4e5kEV6BUo4s1PD3TxHh/vRUpvx+HT9+hjIIa9++mdsxJHmFcWBgkBLry8IV2Y3AU5AFK+vXb+YFXf/jHYUc36d6yoP7IAACIAACIAACIAACIAACz0cAQn1r/CDUt8bNp3PpF0cQ6rN4+NxVKle2nRoP3//wEbuPhD1JosXz8fnFwN6905Q7vR5MvXuNpYkTl6mJiUWLv3dwK9dpzEcRXouzvLwYEKH5+Alfm6OVe3uF1zupSe2KFcXxvZ/HSQ+dURzzxTlfgojmRTxvDlLf0aNn1UuIDRt/dXDFMafT58eOneWtRD9RjvkNWAj/08hPdZRx7N7tR5o7d41qmywoyJ07ixHn7mTe3LXUrdsIFeXs0C83IyrUl7Tm3RBk94D163fLbYpqoX7XrsNpwfx1PJGWVrnTi/uTp2BetNG+fR36tl9Hh6RaWB8RR3154dCh/Xe8zeR29TJq9ZqfeaIqu9vynIX6kuj773mRxU9zVbvXrR9FpUq2VgsbBg3qwg5dNR3KiW6hvlT2vP3l0GBcgAAIgAAIgAAIgAAIgAAIgAAIxAoCEOrHim5CI50ImEX4OgpCfU3CO46xWajvHQTRChAAARBwJCBO9vv2Hab69WvwDr3pHCNxZRBwJ9Q3InECAk4ExE1fXPUrVSrHu8PkdYr1rkv5+1d2wfC0G8SVKwG0ZMnvlC1bZjaGe+u5Gy/1zZu3TC2wa9KkXoQX5Dx3xSgABEAABEAABEAABEAABHycAIT61gYAhPrWuPl0Lgj17d0/fNgs3qJ0phJBHzg408HR3p7Kfnbp0nV6rXQb5ZLVr38natfOvq2iOLu/UbmLEt6nSJGURfvfeHReF5F+06a9SFzu48WLS/PmD+KtMAvZK/r3bNKk5dTrmzHqShzwf/ypm0cnkEWLNlDXj4cpp/oKFYrR7Dk2d3lzoYcPn6aaNbqpSQ8R8U+f3lc55ZjT6PP9+/6m1q2/5e2C76gFCRv/HEOpU6fQ0cbx0KHTVO3tj9W1OBXNmz+Q8uTJasSbT1av3kEdOwxkN4VQVdbuPVNdnicyQn0pu1Wrb2ntmp3maqJUqC/uTUV4twQ5dn6/PvXqZVvY4VCh00WPT3/iLVFXU9q0KWkPL+iIHz+ekSKiQn1xdfqgyxDeAnW/yiu7I8guCc5Bl+dOqC/jtWyZtsphqGy5V2nb1kPKZWjvvunsnOHoYBETQn1p+/P0l/Oz4xoEQAAEQAAEQAAEQAAEQAAEQMD7CUCo7/19hBa6EoBQ35WJt92BUN/begTtAQEQiO0E5L3N3btB7BieKEwH7Nj+nM/bfgj1n5fgi59fdm+JGzceHT9+kv78c5tysm/atJ7aPcGbn37Nmk106tRZ5WxfoUIZ9e5ct/fatRtsELdZufOXK1eKihR5WUdZPp469Q+tWfOn2plCdqhAAIH/s3cXYFZUfRzH/wu7pJR0SEoZlIqKKAgYlCCCgICEYNAgIR0qIS0i3Y2ghAqYKK0S8kqIEoI0SEps8Z5z1rnM3r1bw7LcZb7zPOydOGfic8b3eZ97f/MfBBBAAAEEEEAAAQQSR4CgvjNngvrO3Fzdi6D+jeEvV66l/HXwmDz33GMybXqfGxtimHupTnfZtOk3KVHiXlm5akyklps375QmjfuZquU6gF+9enlp+MqzqvpAHvVa35Ry8MBRWbNmq0yZsky99u+S6Tt0aFtp3KRqpP1YC3qsdEX1lSs3mlV582aXFq/VkvLlS5jX8p45c1590fOXTJ++Qtav+9W0KVz4Hlm+YoTohwV8Tfaq++lUm1deeU5q1igvefPlMAH6/fuOyMJF38iypT+YZf2qzylTekqlyo/42p1ZN2TwTBk7dpGZz5o1owr415CHHykupUsXVQH3a7Jly25jNn3656Y6v65IP2NGP6lcJeo+4xvU1w8SVK7UWr0+9bzn/BKyov7ixd+ZByD0znVF+/vvL+g5TnQzG9bvkHr1epjNM2f2kyrPlPU0tYL16dKlkSWfDvWs168U1eH848fPqHtki6xetcn46wYNGz4rHwxr77OahLU/X0F93bd5s4GiH5CwpiavVpMhQ9pYi57PxArq38x4eU6WGQQQQAABBBBAAAEEEEAAgSQjQFA/yQwVJ2oTIKhvw/DTWYL6fjownBYCCCBwhwsQ1L/DBzgBLm/58tVy5Mhxsyf9e+izz1aUAgUivy07AQ6T4Lu4cOGS6HO/ePGSKbKWOfPd5sGdo0ePy5UrV83x8uXLI5UrPxmlCJuTk9m1a695OKhs2dLq989kTnZBHwQQQAABBBBAAAEEEHAgQFDfAZrqQlDfmZurexHUjxj+n3/eJbVrdTULEyf2kBo1y8fpvpgzZ5V07zbWtP1+zXhVWSDylyu7dx+QV5sMkKNHT8W4v9SpU0qXLo1NlfaYGuoA98ABk2Xy5GUxNTPbSpUqIhMn9ZA8ebLF2HapCuF36jhKgoNDYmynK8LrBxgeeqhYjO30OQ7oP1m9RWC5eduA1Vh/AaW32ac0aVLJoEGtpd7Lle2rPfPxDerrjvpBhpav3XiDQEIG9RvU7yVr124346zHOy6TvuZHHm5qQvf6vtL3lzVZwXprOaZP/bBF7z4tpFGj56NtZu0vuqD+999vkcaN+nr6f/PtR+r1mgU8y9ZMYgX19fGcjpd1rnwigAACCCCAAAIIIIAAAggkHQGC+klnrDjTGwIE9W9Y+OscQX1/HRnOCwEEELizBX7/fZ9s3LhF/WZUUMqVe/jOvliuLt4C+v9Dfvrpl3L16jX1tvKMqvL8A5IzZ8y/2cb7ILeww6VLl+Xnn7fJvn0HPcXEkidPLpkyZZDChQtIqVJR3w5/C0+HXSOAAAIIIIAAAggggMAtECCo7wyVoL4zN1f3IqgfMfw6bK9D97qy+a875sb56f9z5y5KqZKNzRcUbdrUlZ69mke5ny5duiILF34t06aukIMHj0banjlzBqlarZy83fkVyZb97kjbYlrYvm2vTJr0mXzx5QZTld5qGxgUKMWL5ZN27V6W6qoyflynI0dOybRpy2X+vK881f2tvrlzZ5VmzWuYgHiGDHdZq2P9XLduuwqlL5Uff9gqoaFhkdrfdVdq84aBdu1fVpUjckXaZl9wEtTX/Tt1GiWLFn5jdpVQQf1jx05L2UeamYcNur/zqrRvX99+qjHODxw4VSZO+NTcV9t/neN5w4EVrPfV+e6700uOHJklb94c5sGRauo+0W80iGmy9hddUF//9/6EfnPEX8flkUfuk6XLhvncXWIG9fUJOBkvnyfOSgQQQAABBBBAAAEEEEAAAb8WIKjv18PDyUUjQFA/Ghg/Wk1Q348Gg1NBAAEEEEAAgTtKQP9/4atXr5rfwtOlu0sCAgLuqOvjYhBAAAEEEEAAAQQQcLMAQX1no09Q35mbq3sR1E/c4b9w4V85dOi4BF8LkYKFckvGjOlu6gR0AF4HyI8ePS3ZsmWSe+7JLoGByW9qnydPnpXDh09IkAr9582b/abPUT/McPDAMTl+4oykUPvMrsLnhQrlkVSpYg6d39RF0BkBBBBAAAEEEEAAAQQQQAABBPxOgKC+3w0JJxQHAYL6cUC6zU0I6t/mAeDwCCCAAAIIIIAAAggggAACCCCAAAIIIJDkBAjqOxsygvrO3Fzdi6C+q4efi0cAAQQQQAABBBBAAAEEEEAAAQQSTYCgfqJRc6AEFCCon4CYt2hXBPVvESy7RQABBBBAAAEEEEAAAQQQQAABBBBAAIE7VoCgvrOhJajvzM3VvQjqu3r4uXgEEEAAAQQQQAABBBBAAAEEEEAg0QQI6icaNQdKQAGC+gmIeYt2RVD/FsGyWwQQQAABBBBAAAEEEEAAAQQQQAABBBC4YwUI6jsbWoL6ztxc3YugvquHn4tHAAEEEEAAAQQQQAABBBBAAAEEEk2AoH6iUXOgBBQgqJ+AmLdoVwT1bxEsu0UAAQQQQAABBBBAAAEEEEAAAQQQQACBO1aAoL6zoSWo78zN1b0I6rt6+Ll4BBBAAAEEEEAAAQQQQAABBBBAINEECOonGjUHSkABgvoJiHmLdkVQ/xbBslsEEEAAAQQQQAABBBBAAAEEEEAAAQQQuGMFCOo7G1qC+s7cXN2LoL6rh5+LRwABBBBAAAEEEEAAAQQQQAABBBJNgKB+olFzoAQUIKifgJi3aFcE9W8RLLtFAAEEEEAAAQQQQAABBBBAAAEEEEAAgTtWgKC+s6ElqO/MzdW9COq7evi5eAQQQAABBBBAAAEEEEAAAQQQQCDRBAjqJxo1B0pAAYL6CYh5i3ZFUP8WwbJbBBBAAAEEEEAAAQQQQAABBBBAAAEEELhjBQjqOxtagvrO3Fzdi6C+q4efi0cAAQQQQAABBBBAAAEEEEAAAQQSTYCgfqJRc6AEFCCon4CYt2hXBPVvESy7RQABBBBAAAEEEEAAAQQQQAABBBBAAIE7VoCgvrOhJajvzM3VvQjqu3r4uXgEEEAAAQQQQAABBBBAAAEEEEAg0QQI6icaNQdKQAGC+gmIeYt2RVD/FsGyWwQQQAABBBBAAAEEEEAAAQQQQAABBBC4YwUI6jsbWoL6ztxc3YugvquHn4tHAAEEEEAAAQQQQAABBBBAAAEEEk2AoH6iUXOgBBQgqJ+AmLdoVwT1bxEsu0UAAQQQQAABBBBAAAEEEEAAAQQQQACBO1aAoL6zoSWo78zN1b0I6rt6+Ll4BBBAAAEEEEAAAQQQQAABBBBAINEECOonGjUHSkABgvoJiHmLdkVQ/xbBslsEEEAAAQQQQAABBBBAAAEEEEAAAQQQuGMFCOo7G1qC+s7cXN2LoL6rh5+LRwABBBBAAAEEEEAAAQQQQAABBBJNgKB+olFzoAQUIKifgJi3aFcE9W8RLLtFAAEEEEAAAQQQQAABBBBAAAEEEEAAgTtWgKC+s6ElqO/MzdW9COq7evi5eAQQQAABBBBAAAEEEEAAAQQQQCDRBAjqJxo1B0pAAYL6CYh5i3ZFUP8WwbJbBBBAAAEEEEAAAQQQQAABBBBAAAEEELhjBQjqOxtagvrO3Fzdi6C+q4efi0cAAQQQQAABBBBAAAEEEEAAAQQSTYCgfqJRc6AEFCCon4CYt2hXBPVvESy7RQABBBBAAAEEEEAAAQQQQAABBBBAAIE7VoCgvrOhJajvzM3Vve6UoH6RIgUkIMDVQ8nFI4AAAggggAACCCCAAAIIIIAAAn4rcP26yN69B8z5FS1awG/PU5/YmTMXJTAwQJInT6a+b9L/Av77J555v74ATi5BBQjqJyjnLdkZQf1bwspOEUAAAQQQQAABBBBAAAEEEEAAAQQQQOAOFiCo72xwCeo7c3N1r6Qe1N+375CEhoZJwYL3SFBQoKvHkotHAAEEEEAAAQQQQAABBBBAAAEE/FUgJCRU9u8/rALwyaVQobz+eprmvAjq+/XwJPrJEdRPdPJ4H5CgfrzJ6IAAAggggAACCCCAAAIIIIAAAggggAACLhcgqO/sBiCo78zN1b2SelD/8OHjcvnyFcmVK5ukS5fW1WPJxSOAAAIIIIAAAggggAACCCCAAAL+KnDx4r9y9OhJSZMmtdxzTw5/PU1zXgT1/Xp4Ev3kCOonOnm8D0hQP95kdEAAAQQQQAABBBBAAAEEEEAAAQQQQAABlwsQ1Hd2AxDUd+bm6l5JPah/5sw5OX36rGTIcJfkyJHV1WPJxSOAAAIIIIAAAggggAACCCCAAAL+KnD8+Ck5f/6SZMmSSTJnzuivp2nOi6C+Xw9Pop8cQf1EJ4/3AQnqx5uMDggggAACCCCAAAIIIIAAAggggAACCCDgcgGC+s5uAIL6ztxc3SupB/WDg0PkwIG/zRgWLHiPBAUFuno8uXgEEEAAAQQQQAABBBBAAAEEEEDA3wRCQkJl//7D5rQKFMgjKVIE+dspRjofgvqROFy/QFDf/28Bgvr+P0acIQIIIIAAAggggAACCCCAAAIIIIAAAgj4lwBBfWfjQVDfmZureyX1oL4evGPHTsmFC5eoqu/qO5mLRwABBBBAAAEEEEAAAQQQQAABfxWwqumnT3+X5Mzp/29EJKjvr3fS7Tkvgvq3xz0+RyWoHx8t2iKAAAIIIIAAAggggAACCCCAAAIIIIAAAiIE9Z3dBQT1nbm5utedENS3V9XPli2zZMqU3tVjysUjgAACCCCAAAIIIIAAAggggAAC/iJw9uwFOXnyjDmdpFBNX58oQX1/uXv84zwI6vvHOMR0FgT1Y9JhGwIIIIAAAggggAACCCCAAAIIIIAAAgggEFWAoH5Uk7isIagfFyXaRBK4E4L6+oLOn78ox4+fNtdGWD/SELOAAAIIIIAAAggggAACCCCAAAII3BYBe0g/R44s6m2I6W7LecT3oAT14yt2Z7cnqO//40tQ3//HiDNEAAEEEEAAAQQQQAABBBBAAAEEEEAAAf8SIKjvbDwI6jtzc3WvOyWorwfxzJlzcvr0WTOeGTLcJZkzZ5KgoEBXjy8XjwACCCCAAAIIIIAAAggggAACCCS2QEhIqPqe5qwqrHDJHDpLlkzqe5qMiX0ajo9HUN8x3R3ZkaC+/w8rQX3/HyPOEAEEEEAAAQQQQAABBBBAAAEEEEAAAQT8S4CgvrPxIKjvzM3Vve6koL4eSHtlfb2sA/tp06aRVKlSSmBgoAQE6LVMCCCAAAIIIIAAAggggAACCCCAAAIJJXD9ukhoaKhcvXpN/v33siegr/eflCrpWx4E9S0JPrUAQX3/vw8I6vv/GHGGCCCAAAIIIIAAAggggAACCCCAAAIIIOBfAgT1nY0HQX1nbq7udacF9fVgBgeHmOr6Fy5EVG1z9QBz8QgggAACCCCAAAIIIIAAAggggMBtEEifXr/tMKOkSBF0G45+c4ckqH9zfndab4L6/j+iBPX9f4w4QwQQQAABBBBAAAEEEEAAAQQQQAABBBDwLwGC+s7Gg6C+MzdX97oTg/rWgOrA/sWL/8rly1dVeD9YVXYLszbxiQACCCCAAAIIIIAAAggggAACCCCQgAKBgclVKD+FpEmTStKlS5skA/oWB0F9S4JPLUBQ3//vA4L6/j9GSfUM9W8MekqKD50lVXPOGwEEEEAAAQQQQAABBBBAAAEEEEAgcQQI6jtzJqjvzM3Vve7koL6rB5aLRwABBBBAAAEEEEAAAQQQQAABBBBwJEBQ3xHbHduJoL7/D21SDerv2XNQvv/+Jzny9wm5cvWaFMifWwrde49UqlRWUqZM4RP+wIEj8u23m6NsC0yeXLJkzSQ5c2aRBx8sHGOw/OrVYJkz53OzD9320UcfjLI/veLSpcuycOFqz8MqtWtXkixZMvpsqwPts2atMNtyZM8sNWpW8NnOvnLHr3vlp59/M6sqVnxY7r03r33zbZ/fseMPea1FX3MeU6cNlBIlCt/2c0qoE1B1jWTjhjBV4Oi6FC2aTPLmSxavXV+4cF32/Rku+/dflyuXr0uBgsmkUKFkkiNnQLz2Y2/822/hsvf3cDl79rpkyhQgRYokkwcejN952ffHPAIIIIAAAggggAACCCCAAAIIIIBAzAIE9WP2iW4rQf3oZFgfrQBB/Whp2IAAAggggAACCCCAAAIIIIAAAggg4EIBgvouHPQYLpmgfgw4frIpqQX1Dx8+Lj3eGSObN//Pp2COHFmkQ4dG8lLdKhIQEDn4vHLlOmnfbojPftbKDBnTSe3aT0u7tg1Fz3tP//xzXh4t28isbtGitvTo2dK7iXo7bai0bDlA1q/bZra1bFlHur/TIko7+4paL3SQXbv2mYcMNv80V9KmTW3fHGW+RfO+snbtVkmWLEDW/DDdPGQQpdFtXDHuowUyevQccwYdOzaWNm0b3MazSbhD//FHuMyeGSKnTl03O61bL1AqVwmM8wF++1+4TJkcLNeuRe3yfNVAeaFWoLpvo26Lbo3+73f8xyHypzov7+newsnkrdZB6m098dih905YRgABBBBAAAEEEEAAAQQQQAABBBDwKcB3Lj5ZYl1JUD9WIhp4CxDU9xZhGQEEEEAAAQQQQAABBBBAAAEEEEDAzQIE9d08+lGvnaB+VBN/W5OUgvrbtu2R11sNkHPnLnoYM2fOKAUK5JK//jqmwtNnPetbt2kgnTo19izrGXtQP1u2uz1h+Ev/XpFTJ/+J1DZ3nuwyfnwvKV68YKT1cQnqd+82Sj799FvT74VaFWX48LejPDQQaadqYdq0pTJ40BSzeviIt6VWrae9m3iWz569II8/1kTCwsLkscdKyOw5gzzb/GXmxIkz0q/vx+Z0BgxsLdnVmwKS8qSD9Us/C5Ef1oSZy0iVSuTKFZH4BPW3bQuTKZNC1FsWRJ6uFCj33Z9MUqv97D8QLl9+EWaq61eomFwaNAyKE1W4yuaPGB4s+/eFS7ZsAWaf+fIHyF8Hr8v334XKyZPXpaCq1P92lxTqgY447ZJGCCCAAAIIIIAAAggggAACCCCAAAJxFCCoH0cor2YE9b1AWIxdgKB+7Ea0QAABBBBAAAEEEEAAAQQQQAABBBBwjwBBffeMdVyulKB+XJRub5ukEtS/cOFfqVa1tegAuJ7q139OBZqryv33F/KE4H/44RcZ0H+C6Kr7eho5sovUfKGimdd/7EH9iZP6SqVKZT3bjh49JVu37JJVqzfI6lXrzfqsKsz/5ZfjJKOtsn5sQf0xY+bKR2Pnm/5PlC8tU6b0k8DA2Cuu6wcFnnyymQrfh0vFpx+RyZP7ec7Ne2bB/JXSp884s3rw4A4qLP6MdxOWE1BAB+L79bkmp09flyxZAqRJ0yD54fsw2bo1LF5B/Q9HB8vu3eHycv0gFapPHukMjx69Lu+/e02Sq9XDR6aSFCkibfa5sGF9mMyeFSKZMgVIl24p5O67b1TO/+ef6zL8g2A5e/a6vKrO9/FykY/nc4esRAABBBBAAAEEEEAAAQQQQAABBBCIswBB/ThTRWpIUD8SBwtxESCoHxcl2iCAAAIIIIAAAggggAACCCCAAAIIuEWAoL5bRjpu10lQP25Ot7NVUgnq9+07TubPW2moqld/UkaN7uYJ6Nv9jvx9QurU6Sw6UF+kSD75QgXtrSmmoL7VRt+znTsPl89X/GBW6cr2usK9NcUU1P/kk6+kZ48PTVP9AMHceUM8Vfut/jF9NmvWR9av22aC/Zs2z5EMGe7y2bxxox6yefP/JGXKFLJx02xJly5tlHb6rQOHDh1Xx08l+fPnUgFw30Hty5eviv6XLFmACnpnMPvRy7/++rvql1ty5swSZd/6oYn9+w+r80snuXNnU6HyqBXgQ0JC5fz5S6avvo6gIN8PK1y5clU9WHFCncMVdaysMVbeP336nNnfXXelkVSpIpLsuv/+/Ufk3nvvMR5RTjYBVoSEiHRod1Uqqmr3tV4MUscRmTwxJF5B/eBgkbc7XVUPYqgq+CNTSuo0N0L11ikOU8F6XR3/rTYppESJ2EvgD+h/TY4fuy5NmwepNytEHd9Nm8Jk5vQQyZEzQPr1VyfNhAACCCCAAAIIIIAAAggggAACCCCQYAIE9Z1REtR35ubqXgT1XT38XDwCCCCAAAIIIIAAAggggAACCCCAgJcAQX0vEJcvEtT3/xsgKQT1r10LlscebSyXLl2WYsULyOLFI2IMZY8aOVs+/nihwV+56mMV4s4bMb9ynbRvN8TMe1fUt4+UDpm/+mov+eXnnSZgvnHTjdB8dEH9tWu3SquWA1QQO0zuuSeHLPpkuKq+ntG+21jnly79Trp2GWnaDRrcXurVezZKH115v3z5phIefl2qVi0vH459J1Kb3bv3y5Ah02TD+u2e9TokX736U/JOjxaSOXPkc/rgg+kyedISyaDeGrBJhf47dhwm3327WbRBly5N5Y0363n2M2vWCpk2banohyGsKW3a1PJKo2qqX+NIgX39doOWr/U3zWbNfl8ef7yk1cV8nlcPEgwaPNU8EBEcrJLw/03FiuWXTp1fjfS2A73p6tVgefCBOqbVwIFt5KGH75N+6uGN7dv3SmhoqBmnMmWKy6hRXUW/CcE+6ftn4sTFcuzYKWndur4ZH/v22OZ1uH7//nApXPhGeD6+QX11itL17avKVb3pYbTvivlWxf0OnVJIsWI3juXr/M6duy49ul9T1y3ywfBU6sGFqK2uXhXp1iXimIOHplRvhoj6cEDUXqxBAAEEEEAAAQQQQAABBBBAAAEEEIiLAEH9uChFbUNQP6oJa2IRIKgfCxCbEUAAAQQQQAABBBBAAAEEEEAAAQRcJUBQ31XDHevFEtSPlei2N0gKQf2vv94krd96z1i1a/+KtFf/YpouXtQV3/82TfLmzSmZMqU383GpqG/td9Gi1dKr51izOHRoR6nzUhUz7yuov2vXPmnYoLupTK+r0i9cNMxUsbf2FddPXcn+sUcbyZUr1+SJ8qVlxox3o3SdNXO5vPvuJLN+wsQ+Urnyo542hw4dk3p1u5i3CeiVOkSvHxzQIXc96TD+Z5+NihRU9wT1VdX72i9Wkpkzlpu2+o89qK8fINAPEugpefJkUrBgHjlw4KgJyet1Vao8JuMn9NazZoopqK/P59UmPWXbtj2mbUBAgKROndL46RW6+v+kyX3lqaceMtv1H3tQX4//kiXfyJEjJ825hIWFe9oVLpxX5s0fqkLp6TzrPlVtu3cfbZZ9PdzgaRiPmfgG9fWuJ6kq/Nu2hsnrbwZJ6dKRK+BfunRder5zTb1NQWTYiFTqumI+mV9+CZOpk0PUgyvJpEPHiLcL+OoxZnSw7NkdLq+1CpKHH45lp752wDoEEEAAAQQQQAABBBBAAAEEEEAAAZ8CBPV9ssS6kqB+rEQ08BYgqO8twjICCCCAAAIIIIAAAggggAACCCCAgJsFCOq7efSjXjtB/agm/rYmKQT1J074RIYPn2nodBhch8KdTPEJ6v/vf39InRc7mcO0bPWSCno3N/PeQf2mzWpJ3bpvi650nzp1KpkzZ5CUKFnEyemZPp07D5cVy9eYAPr6DbOiVMCv/3JX2bp1twmib1QV8AN1sltNZ89ekJfrdZGDB4+aIP7gIR3koYeKm8r7urp+p07DzBsJypUrKTNnvW/66D9WUN9a0bJlHala7UnJnTurJEuWzDzkoCvoV6z4mmlSTW179722kj59WrO/ceMWypTJS8w2e+X86IL6OlSvH7r47rufRAf0uynXmjUrqOvMoCr6/0+GD5shO3fui7CcO1hKlChs9m0P6idLFiAlSxaV3n3ekPvvL6gq5Z9W1fU/lh9/3GLa9ujZUlq0qG3m9Z+vVm+QNm0GmeWXX35O3h/UzrPN6YyToP7eveEy4eNgNSai3kIQJA88mEy9hSBAjVm4zJkVIidPXpcXagXK81UjxjSmc/vmm1BZ8kmoPPpYcmnWXJXVj2aaMT1ENm8Kk5fqBar/bmLfbzS7YTUCCCCAAAIIIIAAAggggAACCCCAgJcAQX0vkDguEtSPIxTNbggQ1L9hwRwCCCCAAAIIIIAAAggggAACCCCAAAIE9bkH7AIE9e0a/jmfFIL6uoK8riSvpx/XzpCcObM4woxPUP/atWApWaKuqkgfLrVqPS3DR7xtjmkP6uv1OlT+55+HzLamzV6Q3r1fd3RuVid7wH3AgNYq0F3N2iRHj56SihVaiP7vSq/X261p/PhFMnLELBOg//Sz0ZIvX05rk/lcs+YXadWyv5mfrR4meOyxEmbeHtRv266hdOjQyKy3/5k/b6X07TvOrFq2fIzcd18hz2btM3fuF+Z4OjxvVbK3X4c9wL9lyy5pUL+b6d+seS3p1auVZ196Rj9o8ELNduatAvYq/fagfubMGeXrbyZKunRpPX0PHDgizz7zhlmuoYL/o0Z19WzT57hixRo5fvyMevPB8+bNAp6NDmecBPX1of7557p6sCFEDuwPVw8qiHoYQtQ9pt9+ECAtVdV7XSE/LtPyZaGy8stQeebZQPW2h+gD+J8uCZWvvwpVD18EmocA4rJv2iCAAAIIIIAAAggggAACCCCAAAIIxC5AUD92I18tCOr7UmFdjAIE9WPkYSMCCCCAAAIIIIAAAggggAACCCCAgMsECOq7bMBjuVyC+rEA+cHmpBDU79FjjCz+5GtTgf1/vy2RlClTOJKLT1BfH6BM6fpy8eK/8uyzj8u4j3uZY9qD+t4nERQUKJ8sHqGqvN8Isnu3iW05TKW2nyjXVM6cOSePPPKAzJs/xNNlypRPZeiQaWZ54aJhUqZMcc+2t958T775ZpPoivdjPuzuWW/N6LB6mdIvy+XLV83DBPqhAj3Zg/pffzNJ8ufPZXXxfK5etV7ath1slnUIXofrs2TJ6Nnuaya6oP60aUtl8KApKqAeINu2fyJp0qSK0r1/v/Em/J81292yQb1VQE/2oP5zzz8hH33UI0q/mjXayp49B6VAwTzy1VcTomxPyBVOgvpXLl+Xzz8Pk/XrQuXatYiQfpAqhq/ndWj/wRLJpFbtIMmVSy3EMi1ZHCrffB0q1WsESo2a0Qf1P18RKl98HipVngmUl+pG3y6Ww7EZAQQQQAABBBBAAAEEEEAAAQQQQMBLgKC+F0gcFwnqxxGKZjcECOrfsGAOAQQQQAABBBBAAAEEEEAAAQQQQAABgvrcA3YBgvp2Df+cTwpBfV0pXleM19Onn42SBx8s7AgzPkH9Y8dOy1NPNjPHsVev9w7qB6iE9Ztv1hMdog8JCTVB96XLxqgK6akdnaPuZL1BQIfZ166bKdlUYF1PL9buKL/99qfcc08O+e77KWad9afc403k1KmzZlFXovc1bdq0Qy5duiwNGlZVx2hjmtiD+r/vXWEC9N59g4NDpEaNdqoK/N9mk34gQVfkf+ih++TxciWldOli5iEKe7/ogvodOwyVL75YK3nz5pRvv5ts7+KZt1fwX7tuhuTIkSVSUL916/rSqXMTT3trxtq3bq/73copvkF99RIEGTc2WL2BIVxyqiB+gwZBki9/MtFB/aNHr8tXq0Ll55/DJEOGAOnZO4V6M0LMYX1dTV9X1a9QMbkaT7WTaKYF80PkhzVhppq+rqrPhAACCCCAAAIIIIAAAggggAACCCCQMAIE9Z05EtR35ubqXgT1XT38XDwCCCCAAAIIIIAAAggggAACCCCAgJcAQX0vEJcvEtT3/xsgKQT1Z8/+XAYOiKiQ/t777aR+/eccwcYnqP/ddz/JG68PNMfp1KmxtG7TwMx7B/WtbValeN3ohVoVZcSILqa9kz87dvwhL9XpZLrq6vXNmteSv/46JlUqtzLr2rRtIB07Nvbs+vy5i/Lwww09y7HNPPXUQzJ12gDTLC5Bfd3w9OlzZgy+/nqThIaGRjpErlxZpU/fN8T+gEB0Qf0a1dvK778flHIq4D9z1vuR9mMt/PjjFnmtRT+zOH3Gu1K+fOlIQX3v67f6WUH97Nkzy7r1M63Vt+QzvkH9nzaHyfRpIeqhgwDp9k5KSe31HIcO8s+eFSIbN4TJo48lV2MeffheX5AO3+sQfukyyeX1N6JvO2liiGzbGmbC/DrUz4QAAggggAACCCCAAAIIIIAAAgggkDACBPWdORLUd+bm6l4E9V09/Fw8AggggAACCCCAAAIIIIAAAggggICXAEF9LxCXLxLU9/8bICkE9f/445BUq9raYDZ8paoMHBhRDT463a1bd8vChatVlXeRpk1fkOLFC5qm8Qnqjx49R8Z9tMD0W7xkhJQsWdTM24P6lSs/KhMm9jHr9b2uw+Vr1241y0OHdpQ6L1Ux807+PPvMG3LgwBEpVaqYfLJ4uHz88UIZNXK22dVXX0+UAgVyR9pt2UdekbNnL5hQe0f1YEFMU7q70krBQnlMk7gG9a396YcC1q3fLtu27ZZ1a7fJvn2HzSb9ZoGZs96Txx8vaZajC+q3bzdE9DjEVFF/wfyV0qfPuIj9/Dhd9IMAV68Gy4MP1DHrkmJQf+6cEOUVJtWqB0rNF3xXtt+/P1yGDQ2WTJkCZNCQlOZao/vzx95wGTki2FTn79sv+rbvDrhmKvZ37pJCChdOFt3uWI8AAggggAACCCCAAAIIIIAAAgggEE8BgvrxBPuvOUF9Z26u7kVQ39XDz8UjgAACCCCAAAIIIIAAAggggAACCHgJENT3AnH5IkF9/78BkkJQXytaldhTpkwhs+cMktKli0WL++Yb78q3324W3XbT5jly111pTNu4BvV/+XmnNGvWR65dC5Y8ebLL92umeo5lD+q3aFFbevRs6dmmq87r8zxz5pyqmJ5Kli4d7QnEexrFcUY/JKAfFtDTGnX8N9Q16Ur0JUoUliWfjoqyl1Yt+6t2v4i9Wn6URj5WxDeo772LZcu+ly5vjzCra9R4SkaN7mbmowvqT536mQwZPFWSJQuQ7b9+Ypy89zmg/wSZM+dzyZo1k2zYGPFwQlIP6o8bGyy//RYurzYNksfL+a5sf/HidenW5ZokV5tHf5hKAn3n+Q1XSIhI545X1dsNRPqooH6uXOqpFK/p2NHr6i0I18x+Ro5OJUHRF9736skiAggggAACCCCAAAIIIIAAAggggEBsAgT1YxPyvZ2gvm8X1sYgQFA/Bhw2IYAAAggggAACCCCAAAIIIIAAAgi4ToCgvuuGPMYLJqgfI49fbEwqQX17yD5z5oyyRFW5z61C9N7TjOnL5P33J5vV1ao9KWM+7O5pYt/HxEl9pVKlsp5t1syePQel0Svd5cKFf82qjz7qIc89/4S1WWIK6utGP/64RVq+1l/0vV+sWH5ZvGSkeWDAs4M4zhw+fFwqPR3xEIAOwH/++Y+mZ58+r6uw9wtR9jL2w3nyofqXJk0q+fSzUVKo0D2R2uhK+IMGTTHrK1d51LM9LkH9L75YK/otBeFh4dK33xvqTQU3QuHBwSGiq/n/++8Vefa5cjJuXE9z3OiC+r/8sksaNogI8zdvXlt69rrxoIPu+Ndfx6RmjXZy5cpVqVLlMRk/obfZ380G9XX/ixcuSdZsd5v93eyfyRNDlEmY1K0XKJWrxJCo/+9A33wdKksWh8ojZZNLi9d8J+Y3bwqTGdND1H2TTDp0ShHrKU6epM5hS5g8+VRyeaVR1H3Onxui7scwKfNQcmn1etTtsR6ABggggAACCCCAAAIIIIAAAggggAAC0QoQ1I+WJsYNBPVj5GGjLwGC+r5UWIcAAggggAACCCCAAAIIIIAAAggg4FYBgvpuHXnf101Q37eLP61NKkF9bda583BZsXyN4cuQMZ08+WQZU0G+YMHc8tfBY7J48deyceOvZrsO8eswvw71W5M9qN+6dX0pUbKI2XTu7EXZtm23CaP/+edhE7LXGxo0rCrvvtvG6m4+Ywvq60aD3p8i06cvNe1faVRNBgxobebj+6f+y13NOVn9kqtS6+s3zIx0Tda2EyfOSL26b8uxY6clR44sMmx4ZylTprikSBEkR46clJ49xsiGDb+qau3JZeWqj6VAgdyma1yC+kOHTpcpk5eY9m93aSqvvlrTPBBw9uwFmTB+kUybFnGt+pi1a1cy7aIL6oepsP9bb70n33/3kwn8d+vWTGrUrCBZsmRUY7dDRgyfKTt37lOV9lOaNyeULFnU7O9mgvr79/0tL7/cRS5duiID322t5p8z+7yZP9EF9a9cEZk+LVi9jUGkabMgufvuiIcaTpy4Lv37XlPXLFK9RqBUqx5o5q1z2LkzXKZOCZErl6/Lyw2C5Omnb1Td/3RJqOzeHS4vvRQoxYons7qocb0u7797Td2vIi/pBwYq39jnt9+EyuJPQs0xevVJKblz33i4wrMDZhBAAAEEEEAAAQQQQAABBBBAAAEEHAsQ1HdGR1DfmZurexHUd/Xwc/EIIIAAAggggAACCCCAAAIIIIAAAl4CBPW9QFy+SFDf/2+ApBTUDw0NlX59x8uiRatjhNUh/UmqYn6RIvkitbMH9SNt8FrQYfauKkD+2msvem2RWCvq6w4hIaFS96W3Zdeufaa/d1X+KDuNZsW8uV9Kv34fe7ZWqPCwTJna37PsPaMD6Q1UtXodoNdTypQpJH36tHLq1FlP0/btX5F26p81xSWof+rkP2q/3eXQoWOmW2BgoOTJk81Uv7f+Gy9b9gGZNLmfpE2b2rSJLqivN165ck1ebdJLtm/fY9rqCv2pUqU0VfT1Cu0/YWIfqVjxYbNd/7mZoP6UKZ/K0CHTzL4ee6yEeQDAs2OHM9EF9TepqvgzVVV8Pb1QK1CqVrtRbV9Xv585I0SCg0U9iCDKMJmkTCVy5O/rasxU2l5NlVTY/qW6gZLsvzz+P/9cl149VOpfTTqk36Fj5Er7P6wJkwXzI46XPn2A5M0XIIf+uq7eCBGxvwYNg6RCxRuhf7Mj/iCAAAIIIIAAAggggAACCCCAAAII3LQAQX1nhAT1nbm5uhdBfVcPPxePAAIIIIAAAggggAACCCCAAAIIIOAlQFDfC8Tli1aI186gqz/rydpmfVrrIpavmyrRel7/Cw8PUP/CI/W5fj1crbuuqoffbfbHH2cCSSmob12hrsY+c9YK2bB+u+ee0GHv3LmzSSNVwf7Vpi+YSvJWe+szuqB+UFCgZMueWXLmzCKVni6rqsI/LVmz+b6v4lJRXx/vwP6/pVatjiZ8rsPyy5d/KPoBgvhM585dlMcfayL6AQU9jRrV1VSfj2kf+uGADz6YEckmWbIAKVw4n/To8Zo8Ub50pO5xCerrDsePn5bRo+eoNxr8oILmEcFwvT5Xrqymin77Do1UwP5GtfeYgvq6n762wYOmyOef/xhpf0WL5pdOnZuo6vCP6mae6WaC+n//fUJeadhdHfOSDBrcXmrUeMqzX6cz0QX1deB+2NBg9bCGSLsOQZI37w0TfaxDh8Ll8xWh6g0QN8L0KVOK3KPaPfFEcnns8cihev2/l6NHBsv+/eFSX1XaL/9k5O16n9u2hcmyz0JFV+23puzZA6TWi4FSunTU9lYbPhFAAAEEEEAAAQQQQAABBBBAAAEEnAsQ1HdmR1DfmZure1k/FOkvS2/Mh4t+fWto6HX1Ctp0rvbh4hFAAAEEEEAAAQQQQAABBBBAAAEE3CVAUN9d4x3b1ervTL0na5W1zfrU7azvWNWcmr+xTFDfWzHhlpNiUN+6eh0YP3LkpFy7Giz58udSVcpV4pnJCJxXQfgjR0+a/44KFbpHVayPXIndKZMOzB9T+z13/pIULJhHMmS4y+muTL8rV66q8PpxuXz5qnlQIkeOLDe1v+g6699srl695qn4H127hFivnilSDxGJqBcPxDidO3ddrl4RyZ4jQNRzJjFO11RRfR3oj2nSVfTPqQcFMmYKUG9SiGWHMe2IbQgggAACCCCAAAIIIIAAAggggAACsQoQ1I+VyGcDgvo+WVgZk4D1w5H9RyNdyYmgfkxqbEMAAQQQQAABBBBAAAEEEEAAAQQQuFMFCOrfqSPr7LrsIXxrDwT1LQn/+EzKQX3/EOQsEEAAAQQQQAABBBBAAAEEEEAAAQQQQMBtAgT1nY04QX1nbq7uRVDf1cPPxSOAAAIIIIAAAggggAACCCCAAAIIeAkQ1PcCcfkiQX3/vwEI6vv/GHGGCCCAAAIIIIAAAggggAACCCCAAAIIIOBfAgT1nY0HQX1nbq7uRVDf1cPPxSOAAAIIIIAAAggggAACCCCAAAIIeAkQpSL4UwAAQABJREFU1PcCcfkiQX3/vwEI6vv/GHGGCCCAAAIIIIAAAggggAACCCCAAAIIIOBfAgT1nY0HQX1nbq7uRVDf1cPPxSOAAAIIIIAAAggggAACCCCAAAIIeAkQ1PcCcfkiQX3/vwEI6vv/GHGGCCCAAAIIIIAAAggggAACCCCAAAIIIOBfAgT1nY0HQX1nbq7uRVDf1cPPxSOAAAIIIIAAAggggAACCCCAAAIIeAkQ1PcCcfkiQX3/vwEI6vv/GHGGCCCAAAIIIIAAAggggAACCCCAAAIIIOBfAgT1nY0HQX1nbq7uRVDf1cPPxSOAAAIIIIAAAggggAACCCCAAAIIeAkQ1PcCcfkiQX3/vwEI6vv/GHGGCCCAAAIIIIAAAggggAACCCCAAAIIIOBfAgT1nY0HQX1nbq7uRVDf1cPPxSOAAAIIIIAAAggggAACCCCAAAIIeAkQ1PcCcfkiQX3/vwEI6vv/GHGGCCCAAAIIIIAAAggggAACCCCAAAIIIOBfAgT1nY0HQX1nbq7uRVDf1cPPxSOAAAIIIIAAAggggAACCCCAAAIIeAkQ1PcCcfkiQX3/vwEI6vv/GHGGCCCAAAIIIIAAAggggAACCCCAAAIIIOBfAgT1nY0HQX1nbq7uRVDf1cPPxSOAAAIIIIAAAggggAACCCCAAAIIeAkQ1PcCcfkiQX3/vwEI6vv/GHGGCCCAAAIIIIAAAggggAACCCCAAAIIIOBfAgT1nY0HQX1nbq7uRVDf1cPPxSOAAAIIIIAAAggggAACCCCAAAIIeAkQ1PcCcfkiQX3/vwEI6vv/GHGGCCCAAAIIIIAAAggggAACCCCAAAIIIOBfAgT1nY0HQX1nbq7uRVDf1cPPxSOAAAIIIIAAAggggAACCCCAAAIIeAkQ1PcCcfkiQX3/vwEI6vv/GHGGCCCAAAIIIIAAAggggAACCCCAAAIIIOBfAgT1nY0HQX1nbq7uRVDf1cPPxSOAAAIIIIAAAggggAACCCCAAAIIeAkQ1PcCcfkiQX3/vwEI6vv/GHGGCCCAAAIIIIAAAggggAACCCCAAAIIIOBfAgT1nY0HQX1nbq7uRVDf1cPPxSOAAAIIIIAAAggggAACCCCAAAIIeAkQ1PcCcfkiQX3/vwEI6vv/GHGGCCCAAAIIIIAAAggggAACCCCAAAIIIOBfAgT1nY0HQX1nbq7uRVDf1cPPxSOAAAIIIIAAAggggAACCCCAAAIIeAkQ1PcCcfkiQX3/vwEI6vv/GHGGCCCAAAIIIIAAAggggAACCCCAAAIIIOBfAgT1nY0HQX1nbq7uRVDf1cPPxSOAAAIIIIAAAggggAACCCCAAAIIeAkQ1PcCcfkiQX3/vwEI6vv/GHGGCCCAAAIIIIAAAggggAACCCCAAAIIIOBfAgT1nY0HQX1nbq7uRVDf1cPPxSOAAAIIIIAAAggggAACCCCAAAIIeAkQ1PcCcfkiQX3/vwEI6vv/GHGGCCCAAAIIIIAAAggggAACCCCAAAIIIOBfAgT1nY0HQX1nbq7uRVDf1cPPxSOAAAIIIIAAAggggAACCCCAAAIIeAkQ1PcCcfkiQX3/vwEI6vv/GHGGCCCAAAIIIIAAAggggAACCCCAAAIIIOBfAgT1nY0HQX1nbq7uRVDf1cPPxSOAAAIIIIAAAggggAACCCCAAAIIeAkQ1PcCcfkiQX3/vwEI6vv/GHGGCCCAAAIIIIAAAggggAACCCCAAAIIIOBfAgT1nY0HQX1nbq7uRVDf1cPPxSOAAAIIIIAAAggggAACCCCAAAIIeAkQ1PcCcfkiQX3/vwEI6vv/GHGGCCCAAAIIIIAAAggggAACCCCAAAIIIOBfAgT1nY0HQX1nbq7uRVDf1cPPxSOAAAIIIIAAAggggAACCCCAAAIIeAkQ1PcCcfkiQX3/vwEI6vv/GHGGCCCAAAIIIIAAAggggAACCCCAAAIIIOBfAgT1nY0HQX1nbq7uRVDf1cPPxSOAAAIIIIAAAggggAACCCCAAAIIeAkQ1PcCcfkiQX3/vwEI6vv/GHGGCCCAAAIIIIAAAggggAACCCCAAAIIIOBfAgT1nY0HQX1nbq7uRVDf1cPPxSOAAAIIIIAAAggggAACCCCAAAIIeAkQ1PcCcfkiQX3/vwEI6vv/GHGGCCCAAAIIIIAAAggggAACCCCAAAIIIOBfAgT1nY0HQX1nbq7uRVDf1cPPxSOAAAIIIIAAAggggAACCCCAAAIIeAkQ1PcCcfkiQX3/vwEI6vv/GHGGCCCAAAIIIIAAAggggAACCCCAAAIIIOBfAgT1nY0HQX1nbq7uRVDf1cPPxSOAAAIIIIAAAggggAACCCCAAAIIeAkQ1PcCcfkiQX3/vwEI6vv/GHGGCCCAAAIIIIAAAggggAACCCCAAAIIIOBfAgT1nY0HQX1nbq7uRVDf1cPPxSOAAAIIIIAAAggggAACCCCAAAIIeAkQ1PcCcfkiQX3/vwEI6vv/GHGGCCCAAAIIIIAAAggggAACCCCAAAIIIOBfAgT1nY0HQX1nbq7uRVDf1cPPxSOAAAIIIIAAAggggAACCCCAAAIIeAkQ1PcCcfkiQX3/vwEI6vv/GHGGCCCAAAIIIIAAAggggAACCCCAAAIIIOBfAgT1nY0HQX1nbq7uRVDf1cPPxSOAAAIIIIAAAggggAACCCCAAAIIeAkQ1PcCcfkiQX3/vwEI6vv/GHGGCCCAAAIIIIAAAggggAACCCCAAAIIIOBfAgT1nY0HQX1nbq7uRVDf1cPPxSOAAAIIIIAAAggggAACCCCAAAIIeAkQ1PcCcfkiQX3/vwEI6vv/GHGGCCCAAAIIIIAAAggggAACCCCAAAIIIOBfAgT1nY0HQX1nbq7uldSD+itXbpR//71ixrBs2fslb97s0Y7n3r2HZMeOP832DBnSyjPPPBptW73hs8/WSFhYuGlToUIZyZo1Y4zt/Wnj4sXfmdNJmSJIar7wpD+dGueCAAIIIIAAAggggAACCCCAAAII+LUAQX2/Hp5EPzmC+olOHu8DEtSPNxkdEEAAAQQQQAABBBBAAAEEEEAAAQQQQMDlAgT1nd0ABPWdubm6V1IP6r/W4j1ZtWqjGcO2betJj57Noh3PDu1HiBVgDwoKlN17Fknq1Cl9tj9w4KiUf6KV2ZYsWYDs+N98yZQpnc+2/rgyd67q5rQyZLhLdu1e6I+nyDkhgAACCCCAAAIIIIAAAggggAACfilAUN8vh+W2nRRB/dtGH+cDE9SPMxUNEUDAocDmzf8zPcd+OM98WsuPPvqglFX/9NS+/Svm09cf3d7eV/fTU1z6fvjfMa3+up/Vv506pjWv1zMhgAACCCCAAAIIIIAAAggggAACcRUgqB9XqcjtCOpH9mApDgJJPag/e9aX8s4748yVlnmomKxYMSLaqy5TuomcOPGPZ/u8+e+KrpTva5ozZ5V07zbWbCpVuoh88cUoX838dh1Bff8YmtOnz8mePQfNyZw6dc586jczZMkS8XaGYsXym3X8QQABBBBAAAEEEEAAAQQQQAAB/xEgqO8/Y+EPZ0JQ3x9GIeZzIKgfsw9bEUDAuYAVsLeC+bHtSQfnvQP7jRv1kLj0T+i+sZ0r2xFAAAEEEEAAAQQQQAABBBBAwN0CBPWdjT9BfWduru6V1IP6hw6dkMcfa2HGMDAwuakenzZt6ihj+ueff0uFp96ItL5167rSq3fzSOushTatP5ClS38wix07NZSuXRtbm5LEJ0H92ztMVkDfCufHdDbFi+cXAvsxCbENAQQQQAABBBBAAAEEEEAAgcQVIKifuN7+fjSC+v4+QiJJLagfHBwiu3btk50798lvv/0padKklvvvLyQPPHCvFC6cVwICAvwf3eEZ6v+e/vzzsLlufe2XL18x137//feazxQpghzumW5OBTZs+NXcj979A5Mnl3vV/figui8zZEw6bxv2vo6bWdbheh2yt0/2CvrW+p9UO+8gvj10ryvi62r4VuV7q4q+7u+r75y5gz1t7X29++n+3se199XbmRBAAAEEEEAAAQQQQAABBBBAAIHoBAjqRycT83qC+jH7sNWHQFIP6utLKv9EKzlw4Ki5urnz3pWKFaNWyZ8x/XPp1Wt8JIESJe6VlavGRFpnLZRW1fdP/ld9f+myYfLII/dZm5LEJ0H92zdMuoL+7t0HzQlY1fN1BX2rir4O8et/VhvdkLC+4eIPAggggAACCCCAAAIIIIAAAn4hQFDfL4bBb06CoL7fDEW0J5KUgvoHDhyR1m+9r8Lqh3xez7PPlZNhwzqr8H4qn9uT8sp//70i3bqOlK++2ujzMu69N6+Mn9Bb8ufP5XM7K2+NQP9+42Xu3C9i3HnevDlV0aNWUqlS2RjbxWej/m/h7NkLoh/O0A+p+NtkBeSt87IH76113p9O+lj7cNpXB/X1QwD2wD5hfUuVTwQQQAABBBBAAAEEEEAAAQQQiEmAoH5MOtFvI6gfvQ1bohG4E4L6vXtNkOnTV5grbNOmrvTsFbVKfsvX3pOVKyN+AEiXPq1cvPCvJEsWIP/7bb5k9KoGs3//EXmy/Otmf7rtb6qNrtZvn8LCwmXfvr9Ftz18+KRkypROChXMLaXLFLU3izT/18Fj8teh42Zd0aL5JHv2u0XvZ+vWPbJp029SpEheee65xyL10QtnzpyXbVt/l8N/n5B06dKK7lukyD2SMmWKKG2tFb6C+nqsdTh865Y9kjdfDnnooWLi6+0D1j68P7dv32uu98yZC+rcM6lzyKcqwefzbuZZPn/+kvz66x9mOVOm9PLgg4U82+wz2vHIkVNmVeHC90jOnFnsmz3z2kp779p1wJjkzp1VBdwLSN682T1tYppx4hjT/nxts4f04xK+j297X8dkHQIIIIAAAggggAACCCCAAAIIJKwAQf2E9UzqeyOo7/8jmFSC+uvXbZO2bQfLpUuXTdV8/f1qYfXv+LFTsm3b7+q74jCDrQPrc+YOksyZM/o/fhzP8OjRU9K8eR/Zr74L1lPu3NnUd7sFJSw83FQU1yF+PenvvydP6ae+u05ahXPMySfRP/agfjH1fXvyZMnMlVy9GiwHDx713JeBgYEyclQXqVq1fIJcaccOQ+WLL9aq3xoyy7r1MxNknwm1E3toXlfB1yF9qxp+bMew99Vt4xOa12F7ewX/m+kblwcLYrsWtiOAAAIIIIAAAggggAACCCCAwJ0tQFDf2fgS1Hfm5uped0JQ/5uvf5KmTQeYcdRB+c8/HxlpTMPDr8sD9zcQHRzX4fj69Z+RDz9caNpMntJLqlUrF6n9nNkrpXv3j8w6vU23sU8//LBVBvSfIr///pd9tZkvVCiPdOnaSF544ako20aMmCsjR8wz60eN6iQFC+WWZuq8z569aNZVr/6ETJrc09NPfxHeW70FYMmS70W/Dtk+6Urt/fq3khdfrGhf7Zn3DurrNwoMGzZHzp2LOJZumDx5MqlZ80kZ8+HbUR5E8OxIzXz//RYZOGCK7N0btcqTDvu/9/5bot9O4D1t3PA/qVv3HbNav+VAv+3A19Sn9wSZNi3iQYuhH7STxo2fj9JsypRlMmTwTLly5Vqkbfphi6rVnpB3333DjG2kjf8t3Iyjr/1Ft85p6N7e78knS3kq70d3HNYjgAACCCCAAAIIIIAAAggggMCtFSCof2t9k9reCer7/4glhaC+DuFXrvy6HFHFWDJkuEtGje4mTz55482wOqg+c8YyGTVqjgHv1auVNGtey//x43iGb789XJYvW2Nad+vWXFq2qmMeVtArtM333/8snTsNM9//VqnymKmsbxrz55YL2IP623/9JFJxH/27xDr1gEmnjh/I5ctXzW8K69bPSpDvsP05qF/43hrGXYfzdVg+vlNCBe6dHN86d33Of/z5eXxPnfYIIIAAAggggAACCCCAAAIIIOAiAYL6zgaboL4zN1f3uhOC+voL4vuK15eQkFATON+5a6HcdVdqz7jqqu7VqnY0yw0aPCv1G1SRF2t3M8tNm1WXQYNae9rqmdZvDZVly34067yD46NHzTeBd3sHHXjX1d6tSYfHJ0zsITp4b5/sQf0+fV4THT4/duy0p4k9qK8fLmjV8n1Ztcr3a4CtTnXqPC1jP+piLXo+7UH9j9R2/SCD3qevSYf9PxzbxbxhwHv75s07pWGD3nLtWrD3Js/y3Xenl2XLh0tB9UYB+5QQQX19zm++MVhV1lnv2bX21W8TsIf2dSX+pcuGRXk7ws06eg4ah5nPPltjWkVXSX/duu1me/nypaLszQrr6wcwfG2P0oEVCCCAAAIIIIAAAggggAACCCBwywQI6t8y2iS5Y4L6/j9sSSGov2f3AVU0pZ3BbNnqJVUoJupbYfXG+i93VW9g3S1lyhSXhYuGmfbnVfGVkNAw8/3t3XdnMOu8/+hA9QX1Flk93XVXGkmVKoX5vlwXr9GTfuOp/h5bT2fOnJPTp89J4cL5fH4nbBp5/Tl+/LTof7lzZ5esWTN5bY198aEy9c35lShRWJZ8Ospnh5EjZsn48YvMd7+bf5rrCYzr7//1Pz1lyRL1LQOhoaGqQE3EdaZXb8hNkSLI5/6vXLmq3o57QnR7/TYDXSE+LpMufnP48HFT7T9fvpyeBwxi6quP8fffJ9V5XZB8+XIZ/5jaW9v099l/HTwiFy9dUdZZ4/RWBf3d/Z9/Hja/jeg3Fejxj88UU1Df2o+ufK+D9Xr66KMe8tzzkX/7sNrpB04OHDhi7it9r+iHUrwn3UZ/t6/3pwPtyZMn91TUT506pWfc7f30/w4fP37G3IPZst0tuXJljdM42PcR13l7RfybCbrbw/rxDdzb+8a3Mv7N9I2rEe0QQAABBBBAAAEEEEAAAQQQQODOECCo72wcCeo7c3N1rzshqK8HsF69HrJh/Q4zlnPmDpSnn37IM67jxi2WQe9PN8sTVYD++aqPywMPNJSL6ocLXQH/x7UTPW31TOlSjeXkybNm3eafpkuePNnM/J49f8lzz7WXUPVAQEBAgLz+xovy7LOPqh9Nisr27Xtl4YJvZMGCr0zbNGlSye49iyJVqrcH9XXQXP94UqdORXmifEnzpXtQUJB6fer9pn8vVUlfV8HXk65W37x5TdFV6f9VP0isW7tdBqvq8taPLLoKv/dDAVZQX//4oo9VrHh+ebVJNSlZqrCcOX1epk9fESn8PmNmX3nmmUfN8aw/+o0BtWt19fzAox8KqFGzvLreYqqKznbRwfRvv/nZNM+bN7sK64+QbNlu/EiTEEH9n3/eZc5BHyRHjszSX71FoLyqOq8fxFivxnvUyHnyyy+7zTm0bVtPevRsZuatPzfraO0ntk8raK/bRfeWg5iC+rqf3n7q1DlVSYuq+tqDCQEEEEAAAQQQQAABBBBAAIHbJUBQ/3bJ++dxCer757jYzyopBPXt4dnu77SQli3r2C/BM69D4f9euizqC2j1nXHE99K9e42VhQtXm++k166bod4smtnT3prRlfg/HrfALC5dNkbuv7+Q/PDDL9Lytf5m3erVE9T31QdMm717I94Uq0Ptzz5XTt57r60JS1v7sn+uWrVexoyeq4LgN962miNHFvUd+xPmYYO4hN11xfz773vRFLup+PQjMnlyP/shPPM6vP2PeohAT9nUNQYFRQTpP/hgukyetMSs/33viigPF/zysyo207C72T5+Qm/RFfntk37QYfCQqbJi+Q+eN9fqMH/x4gWld59WUqpUMXtzz/zu3ftl6NDpsl5VlLcmbVa27IMy8N02Ph9Y0P97Me6jBTJr1gr1Jt0LVjf1G0N2eUeNe3QB96NHT6m3645V33XvjFSgRp+bfqjj4Ucifjfw7FDN6HvqvfcmyR/qTbjaWE/mDbRVy0u37i1MmN2sjOVPXIL6//xzXh4t28jsSb/pQb/xwT6tXbtVPhg6TfT35Nakf5uoUOFh6fz2q1K0aH5rtXp77wSZPTvitw/Pyv9mmjSpIX37vRlptb4HR6v7e9++w5716dKlNW9laKUeerHuE2ujttT/LWTOnFHefOtl0eH/+ExWRfr4BuR9HaNxox5mnPQ2XZlfB/bjOt3MAwM30zeu50c7BBBAAAEEEEAAAQQQQAABBBBI+gIE9Z2NIUF9Z26u7nWnBPXtYfzWretKr943KhLpivA//rjNVAz632/zTRWX11sN8gTVt2ydZULg+kbYt+9veerJN8w94R3i11XdFyz4WraoYHjLlrXUF8yvRLp3dFX9h8o0MWFrveGrr8eqH0QKetrYg/r6C/PhIzpI/frPeLZbMyuWr5U33xxiFosWzSefLf0gSuWZrVv2qL69TCUhHY7fuGmaqZJk7cMK6uvlsmXvl7nzBop+eMA+NW82UL76arNZ1bFTQ+natbF9s5R/opWqfnPUrHvlledk2PD2kbbre6dB/d4mYK436AcJ5s5719MmIYL6AwdOlYkTPjX7HPdxN6ldu4Jn/3pGv5GggXLImzeHPPb4g9KmTV3P9oRw9OwslhkrqB9dNf1YupvN1j6oqh8XLdoggAACCCCAAAIIIIAAAgggcOsECOrfOtukuGeC+v4/akkhqG8POhcokFvmqtBuVlUZPC7Tzz//Jq80fMc07dP3DXn11ZpRuj333JuyX323XbhwXvly5cdmuz2o/64K4+uAtH4rrfcbYmu+UFGGD387SgD+6683Sds273ve1KoDz/a3nOrw+PjxvaO85TTKyakVVZ9vbcL+unr6wkUfSMmSRX0187nuZoL6utr8q016mbcUWDvXbxu4ejXiDbI6sP/+oHbqe+dK1mbzeejQMalXt4vocdOTfiBB/2+BFYjXD0tMmtxX7ruvkNlu/Rk8aIpMm7bUWjRhcf0GA2tq2uwF6d37dWvRfO7Y8Ye0aNFX9AMFetIB85Qpg0QHzvWkz3fmrPfNWxbMCvVHP7jRr+84z5t+daV//WCA9VaFvHlzyuIlI+JUyT8uQX2977KPRPwe4n0NkyYuVm8hnmGdmjmm9tVvMNCTfgvCgoXD1JsFcprlIYOnyrx5Kz3b9crUqSN+u2jY8HlVjKelaaf/eN+DGTOmU9d4yXNPPvhgYVn0ybBIb0fo3m2UfPrpt2Yf/Qe8JY0aVffsL7YZpwF3+4M49kB+dOvt52FvY6/gb19v32dc+uo2CfnAgf2YzCOAAAIIIIAAAggggAACCCCAwJ0jQFDf2VgS1Hfm5uped0pQf+fO/fLsMxGvDi5VuogK4Ue8PldXrS9e7GXzxfvDDxdXVd+Hm/GeN2+1dO3yoZkfM6az1K1X2czPnvWlqmwzzsy/9toLqjJORGjfrPjvj/XjnK6q7z117/6RzJm90qz+YFg79SXw854m9qC+/Rw9Df6badKkv3z3bUSl+s8/HymlVcV+X1OHDiNl8ScRXzh/8eUoVfmniKeZPag/c2Y/qfJMWc82a2atqsyvQ+56qlTpYZk9Z4C1SbaoBwFeqPm2WdavkV3zw3ifr5zVPxboBxv0DzT64YMtW2d7quonRFC/TesPZOnSH8x5tG9fX7q/86rnHGObSQjH2I5hbU+Iavj6ddN6TAjqW6p8IoAAAggggAACCCCAAAIIIHB7BAjq3x53fz2q9V2g/fxUXtdM1jbrU6/U8xHL+vPGcnh4gAqXhv+3zVofbgKnOXLELbAdcVT+egskhaC+PucePcao73O/NqefIcNd0lhVD3/+uSekaLH8plq+93VZy/p+erria3LkyEl55JEHZN78iCIv1nZd7V4H4fXUpUtTeePNembeHtTX393Wq/esqkL+ktxzTw7ZsWOvdHl7hOhAup6sKvxmQf3Ztm2PCrj3NN+r68rzPXq+po59v/z990lZMH+lTJ36mWnavv0roiuPxzYtXfqd+j5+pGmmHxSoU6eKentrBbNP74ro3vtyGtQPD78u7doOUsVqNppd6sruL75YSe6+O4N628AWEy4/sP9vU+Bm0+Y5nrC4Due/XK+L/PXXMdGV2/VDDhUqPCShoWGiq7u/r6rY6yC6ro6uA9TWNHPGclPhXi+/qcagUePqqkBQFjNuI0bMUhX915hxnjtvsBlHq1/37qPl0yXfmLD5rNnvycMP32/a7dq1z7wR4dSps6ai/vz5Q60uqmiOuh/+PmHC77NmDzLV8/VDBKtXb5BOHT8w/7vSpm0D6dgxcnEezw5sM3EJ6n/xxVrp2CHi+GM/Um8tfv4JswftV6NGO/OmAv3wRb/+b6riRfea5VWr1nnGvEGD542j7bBmf3q/+qGHdetn2jeZ+e3b90iTxhH3oH4Tg763dWV+/abhTz75SoYOmWbaderUWFq3aeDp/+7AieaNBnrFiBFd5IVaFT3bYpuxgvrxqaZvD9Tr/dvD9nrZCsx73y96W2x9rYr8vs7nZvrqYzMhgAACCCCAAAIIIIAAAggggAACBPWd3QME9Z25ubqX9cOR/Uej69fDTSWW0NDrqnpLuiTho6+jTOkmcvLkWfWFdnLZuWuh3HVXarGHxbt0aSydOjc016MD5o883MzM65C+Duvr6a03h8ry5T+a+dmz+0ulyo+Y+Zj+6GPr427d+rsM6D9ZDh8+YZq/06OptGv3sqerPaivK7/37NXcs82a0T8eFCxQ21Q2Sps2tXod8SJT4cjabv/UDwToBwP0NObDt6Vu3RtVf6ygvn6YQL9FIFOmqON46NAJefyxFqZ/vnw5ZMPGqWZe/xk9ar76oWKOWbb7eBrYZmrX6io//7zLrPloXFf1Y0dFM2+39662b+sufXpPUFWGVphVQz9oJ40b33i4YerU5dK3z0SzTVcOqt/gGfUDUTUpVjy/fRdR5hPKMcqOo1nx2WdrzBbr2n01s177W6xYfl+bzbq47CfazmxAAAEEEEAAAQQQQAABBBBAAIEEESConyCMd8xO9Hd/3pO1ytpmfep2ej5imaC+t9utWk4qQX1dYbx3749k+bI1kSh0BfXy5UvL81WfkKefLuvz++CRKug9fvwiUyxl/YbZpkq5tZOPxs5X32/PNdvW/DBdcubMYjbZg/pPPfWQTJnaP9IDAQsWrFLfzUZ8v6zD6DpMbU1Nm/aWDeu3SwZVwXzZ0tGSO092a5P5tALEOvT/7XeTI+03UkPbwuRJS2TUqNnmu29rta7SX7bsg/Lsc49L1arlTTDe2mZ9Og3qb9mySxWq6WZ206JF7UiV2vVKe9D5g2GdTYhfr//444UyauRsc03jJ/SWypUf1as909gP54kOdOvv3r//foqx0SH5UiXrmQB/48Y1TGDd00HN6O36rQhbt+6O8rDF4481Fl3ERR9nwsQ+9m6qoM4u+evgUfUwRwEVgI+o3q/fnKDfoKCnnr1aSvPmtSP10eF3/WDGAw/cax7KiLTRx0JMQX0dil+3bpv0eGeMqYCvH7JYt36W5/7TFfz1Gx+26wc71JsevN8SUbtWB9m5c5/ohz2Wr4gonGSdgg7+xxTUb9asj6xXx9ZV8+ephxT07wP2aejQ6TJl8hLRb0XYsnWhZ7s+p4Xq3tYPZNR+8Wn131Nye7cY56372lcw3ldH+z2kt/vqZ+3TO6ifkH19Vdy3HjrwPq6v62AdAggggAACCCCAAAIIIIAAAgi4U4CgvrNxJ6jvzM3VvawfjvSPSzfmk15QXw+ivcK8rg6vq8R/8MFsGTN6gRljXWVfV7K3pkpPt5bff//L/HDxy5aIii2lSjaSU6fOmS93d+1eqKropLSaez6PHDklOlC949c/ZPfugyaYr18Z7D3FFNTv0bOZtG0bUdnI3k8fW5+DNcVUTUiPl67ioyf9QIA+njVZQX39JfWBgzdetWtt158nT/wjpdXDDXq6557ssmlzRAUavWx/M0Cv3s2ldeu6erXPqXu3sTJnziqzrXfvFvJW65fMfEIE9bVrtaodZdeuA5GOravO6zckVKhQRqpVf0I9UJIh0vaEcoy00xgW4hKwT6g2MZwGmxBAAAEEEEAAAQQQQAABBBBAIAEECOonAOIdtIuI0H3kCyKoH9njdi8llaC+5bRp0w5ZuHC1fPP1RhPsttbrz8KF80r/Aa1VeP0B+2qxh7MHDmwjDV+p6tleo3pb9T33QXn88ZIya/b7nvX2oH7ffm9KE1XB3z7pyvHlHm9iivbo/en96knf8w+VaSAXL/4rvgLuus2lS5fl7NkLelZy585uguFmIZY/Bw4ckfnzVsqKFWtMON3ePKN6KKBz51cjXZve7jSoP23aUhk8aIoJ1G/dpgv7pLEfzszrtxToN13obZkypTfr3nh9oHz33U8mGK/fNOA9hYaGyrFjp83qLFkymkr8u3fvV2+obW/WrVo9XgoVuse7m+gHFfS1pE+f1gTLrQbVq7WRvXv/Mr9FfDy+tzzxRKkYH3zQ7mUfiXiLgS4KM2JkVylSJJ+1u3h/2oP6egwsp2vXgtVvJWc9+9O/VYwa1VWe+6+avmdDNDPaqUXzfrJx46/m95aduyLewmA1jymor+/Bhx9qIDp0P0D99/BKo2pWN8/njl/3yksvRRRgWrFirCruU8CzzemMVf3eO/huhd7tQfy4BO31eVh99bxVbT8h+3qfqz6OnnwdN2ILfxFAAAEEEEAAAQQQQAABBBBAAIEIAYL6zu4EgvrO3FzdS3/hGfEv6Qf1dRC6bZthZjx1WFyHxl+o+baqOrNHVU9JL7/umBfpB4MBA6bIpIkRXw7/uHaichCp8NQbpn/58qVk4aIbP2rolWFh4apyzDiZP3+1eXWsaWj7ky373ZJZVWnZvTsiVO4kqP/HH4elYoWIaji2Xcc6q6vp66r61nSzQf033xyiXsW71uzOXiXf2r/901593/7AQEIE9fVxLl++KoPenyGLF3+rfhy6bD+0mddVfN58s4507dZErAcbEsoxysGiWbFu3XbzgMeTT5byVBPybhpbUF9XTVq7drvohxD0/ceEAAIIIIAAAggggAACCCCAAAK3R4Cg/u1x99ej6u9OvSdrlbXN+tTtIr5r1X2oqO/tdquWk1pQ33LQQehfft4pa374RVZ+uU5OnDhjNqVMmULmzB0kpUoVs5qazxdrd5TffvtTyqkg98yZ75l1Ovj+7DMR32kPHdpR6rxUxdPHHtTX7XU/78mq5l6r1tMyfETE98v2hwLslea9+97Msn4j6s6df6rvQ7fKypXrZM9/36nrffbq1UqaNa/l2b3ToL4VBM+j3gbw/Zqpnv3FNmOZvFS3igwZ0jG25mb7gvkrpU+fcWZeV8bX1fa9p5On/lHFf/aa1Rs2zlbfA2cy858u+UYVzhntaa7fUlCuXEkp89B96g0Lj3geIPA0UDPdu42STz/91rOqRMki6g3C90vZRx80QX99D8V1sgf1Y+ozfca75s0PvtpcvRpsxnKPemBh//6/RT8AoR8++PffK6Z5YGCgenvw0khdrfHJnj2zqtIfUUjJanBA7ePZZyN+J8mRI4t5O4C1zfrUDwKsWfOLWRw1upvUqPGUtcnxZ3RBfasqvt6xDuvrKvV6nTXZA/zWOuvTV2Dee3/t1T59TVZfe1V8e9/oQvp6X776+joG6xBAAAEEEEAAAQQQQAABBBBAwL0CBPWdjT1BfWduru5l/XCkf1y6MZ80K+qfOXNeSpZoZK6jVKkiKmg/SO6/r76pOl+7dgUZ93HEa26tAf/xx23SsEFvszhoUGv15blIjx4fm+U+fV6TN9+qYzU1n3rbrJlfmHkdDteV3Ms9/qAUKZpPChbIJTqobw+tOwnq62so8eCNL2U/+WRwpHOIbiFrtkyq2tKNKj03G9Tv1Wu8zJj+uTlc166NpWOnhtEdWjp2HCmfLIr4UaB//1bS6vXapm1cg/p216EftJPGjZ/3eazg4BBZ++N2+emnnari0B71I9buSK9Jbt68prz3fsSX9wnl6PNEfKy0gvrFi+cXXcXI1xRbUH/PnoPmDQ0x7cPXflmHAAIIIIAAAggggAACCCCAAAIJK0BQP2E9k/re7CF861oI6lsS/vGZVIP6dj19n82b96WqAD9VdIC/aNH88vkXH9mbyMwZy+W99yZJ8uTJ1dtR54iufj5hwicyYvhMU41946Y5kjZtak8fe1BfV9rXFfe9JyuU/kKtijJiRBez+avVG6RNm0Fmfv78ofLwI/d7d0vwZf2WgW5dR5pK9cmSBYi+lrtVURw9OQ3qR/emgZhO/vy5i+pNrhHfhXfs2FjatG0QU3PPtncHTpRZs1Z4lmObWbxkhJQsWdTTbPmyNTJy5CwTcPesVDN6rHUAvU+f1yWDGm9r0iH1MaPnyty5X5o3H1jr9ae+B15/o6688UY91T+ZfZPPeXtQv0fPlqqifsQ9dPDgUfMWAN1Jn6s+Z1/T1KmfybiPFkQ5D32+6dOlVW8kPi7xDerb70Ffx/Re17VrM3PN3uvju2yF4L0D8N4V8O37jSmkr9tZ+/QVto+tr6+wfXQPE9jPSc/76uvdhmUEEEAAAQQQQAABBBBAAAEEEHC3AEF9Z+NPUN+Zm6t73QjnJ/2gvh7Iqs93kB07/jRfQOsK81aF/TFjOkvdepUjjbUOft9XvL5cuXJNqlUrJ8kDk3uqyH/77Tj1qtT8nvbaSbfVr1rV1XCWLR8uDz1UzLPdmhkyeKaMHbvILDoJ6uvjFCpYx/wYo3fy284FqmLOjS/grePE9nmzQf1x4xarKvbTzWFq1XpKPh7fPdpDVq/WSbZvj6gENGlSD6leo7xpu1W9yaCmeqOBnkqVLiJffDHKzHv/qV5d9d8W0X/o0LbSuElV7yY+l0+c+EeGD5ujfsBabbbrcdm9Z5GkS5fGPKyREI4+D+xjZVyq4ccW1Le2E9T3AcwqBBBAAAEEEEAAAQQQQAABBBJRgKB+ImIngUPp7+u8J2uVtc361O30fMQyFfW93W7V8p0Q1LdsevQYI4s/+dp8B/3LlgWSPn1aa5Po7yDLP/GqefPr4MEd1Pfdz0jtWh1UZfp9UqNmBRk1qqunrZ5xGtT/449DUq1qa7MvXVFeV5ZPjGnp0u+ka5eR5lDjJ/SWKlUeM/P2oP6e35dHCZ9v2PCrNH21V5R+7doOllWr1kuuXFnlhx8jvuuOy3U8WraR/PPPeXnxxUqi3ygQl2n27M9l4IAJpunceUMkZcqgGLsVLpxP0qRJFaXNjh1/yM8//yZbt+6WH1S1eP3Qhp50yHv2nEFRKvWHhISKfshBt/9p8/9UkZvfPPts266hdOjQyLMc3Yw9qL/9108iPezRq+dYWbQo4vv3D8e+I1WrRnz3b+1Lb9Nt9JQ/fy6pW/cZ0dX98+XLJTlzZpH3359sHjCJb1BfV+OvXq2N2e/bXZqqh0xKmPno/uTMmVWyZbs7us1xXu8rVG919hXWjy1or/tawXp7W73Ovmwdw/vTOh97W93X+0EC73562eprf0DAVzvWIYAAAggggAACCCCAAAIIIICAewUI6jsbe4L6ztxc3cv64Uj/uHRjPmlW1NcDOXTILFUpZKEZ0zx5ssnff580X15v2z5HvUo2Y5SxbtKkv3z37c8m3J0sWTI5f/6SZFeV8bdumx2p7f79R+TJ8q+bdTGFzmvX6qq+SN9l2jkJ6uuOLZq/K6tXbzL7mDS5p1RXlft9TbqizbVrIVKoUB5VkSZ5pCY3G9Tfs+cvqVwp4scYXZnp++8/Nm8MiHQQtbBr1wH15XxHCVU/CAQFBcqO/83z/HikX2tbrGg90a8x1lV4tmyd7anGY+3nnKpQpN8gEBYWblZ5B/X1wxT79x+Vs/9ckMfLPWh183zqHyrKlH5V9H709PU3H8l99xUw8wnhaHYUxz+xVdXXFfP15KvivlVNX29/8cWK+oMJAQQQQAABBBBAAAEEEEAAAQRukwBB/dsE76eHjQjdRz45gvqRPW73UlII6uuq999995OpLD5/wVCfIW3tOGrUHPl43AJD+uPaGSbsbPdt+Vp/E8CvWPFh6df/LXm64mtm85Sp/aVChYftTR0H9fX3uWVKvyz6+93mzWtLz14tI+1XL1y8+K/ot5rqKW/enKKr4Ec36eroY8bMNZsHDGyjKtbf57OpPXA/bHhnqV27kmk3fvwiGTlilplfsmSkCYLbd2Dfbg/4T5m8RIYOjQjob922UP0GcOOhB6u/rvauv5vWD0RYFfxbtewva1RI/r77CqmCPWOspp5PHY4/cuSkWdbhcB241wH7l+p0Muu8q+V7OsZz5syZc/LWm+/Jtm17TM+vvpogBQrmiXEvOuDeoH43Mz5Zs2aSDRsj/87hq3NMQf0TJ85Ilcqt5OrVYBW+zymrVo8397C1H+t+zJMnu9mWMmUKa5P51A9e6Acw4hvU1/dg6VL15PLlq5JQ1fIjnVg0C/9n777DpKbWOI6/wNKkS1WaIAjYQBQVbNhFwYpKVVAQ8NooihSlKCIqIk1RqjRBsIPXay+giAVQUJQqvSMdYYGb96wZMrOzu7PZ2dnM5JvnYSeTnHOSfM7oHzO/vHGG8Zctn5WqlXO/MzyfquG/G+yq9vrWbq9j6L+HHjr+ZOW0+oeG/LWfLhq+z2ix+0YS6s9oLPYjgAACCCCAAAIIIIAAAggggEBiChDUdzevBPXdufm61/FwfmIE9efNW2x9IR5c/f3MM0+V/308LOw8jxv3gTzRO6XSjd3gjjuvsn4QSflS3d6mgfBaNe80FWwqViwrc+aOThWO13B9+3YDAqFzt0H9Tz75Xtrc3d8cWkPyb78zyHrUcWX7VMyrfkF91VUPyF+rN0q+fHnl/Q8Gy1lnnRpok9Wgvg50o1UN/yerKr4ul19+rlWlJeWczAbrj4bo9QkGGurX5bbbLpdhw1Mej2w2WH8aXtZRli1ba942b36NDHruwUDFox1W+L51qz6BavzayBnU12usU7uV+UFIq+W/+eYz0uCi4Mo5Oka98+42PxSo1aJfpgTmJRqO5sQj/GNX1dfmmamK7wzpZ6ZfhKdFMwQQQAABBBBAAAEEEEAAAQQQyKQAQf1MgiV4c4L63p/geAjqf/jhN/LwQ4MM5hNPdpC77mqSCnbv3v2mQv5ff22UatUqyX8/ejlVmw/e/1K6dHnBFE1pf19TE+ovVaq49X31xMD3rnYntxX1tb9djVsD7O++N1QqVixnD2sK/rRo3l1+/PE3s/2zz0enqvQeaGytbN260zwJQMPX1113kfUd8uOp2uu+Lp2fs57K+o3ZpwFzvS5dvv76J7n3nj5mvU3bm6RXr/ZmXf+omQbT//hjtdnmDOprdfmWLR4329Vb3Z3L3DkLpE2bJ8wmrZyvFfR1GT5sqlUMaKpZHzGih1xrnbNz0ZsG9OYA/c76iy/GSHkrpK7h/Tq1bzffmd9++zXyzMCHnF3M+rRpH8nKFevk9DOqyk03XW76b9y4Td5+61NZuWqdNGt2ndSrd2ZQP+dNCLNmj7B+JzjFBPc/++x7Wbtmk/Tt18l6Im/RoD5trWuaY12bfl8+/4epqayDGltv0gvqa9vnn58gr70603QL/exq1Xu9OeCSS+rKuPGpfz/QG0m2bNkRNqivn2P9POuNDgsWzkh1s4f9GaxZq4pMn/58qptbfvrpN3n3nc+lRs1TpEnjy6SYdb32ot/VFypUQAoWTP3UArtNuNfMBvHDjeHc5gzqhwv+O9uGrudU39Dz4D0CCCCAAAIIIIAAAggggAACCCSuAEF9d3NLUN+dm697JVpQXyu7n3FGM+sL+gOBeX3wwTtEQ/PhllWrNlg/Ehz/Yl/bvPxKd+uL8ktTNW/erLf1o8ACs/2KK+vJ7bdfYX1xfrqssiq+fzNnofWjyEzzI4VdHd5tUF8PMPSlafLccynVbvRJALc1vULqX3iW+XHit99XWT8WTJc1azannMsV51mPve1n1u0/0Qjqb9myU266sWvgOGdYPyBodf+659aUuXMWyaxZc0T9dDmnbg2ZMWOg9cV3fvsUzGuvXq/IhPHHK8/UrFlZGjQ4WzZv3iFz5/5iKuKca4333XcplWCcQX0d4KEHX5C33vrCjKWPym3TtrFcfHFtOWpVOZpjncPMmZ/LCuvHDV3C3WCRVUczcCb+ZDZ0n9n2mTgVmiKAAAIIIIAAAggggAACCCCAgEsBgvou4RK0G0F9709sPAT1Dxw4KBec31IOHPjHBKdbtrxBbrq5oVSpUkF2WJXTlyxZIYOtAPi6dSnf+XbseLt07Zb6O23tf+EFLc33qlrFXgPuoeF1e8ayEtT/8YclcvfdvU3wXIPSj3e/R863qnhv2rRNpkyeLWPHvmMO88CDzeXhh1vah0zztUXzx60n0S42+xs0qC0dOtwu1atXkqPW4ymWLl0lY8e8Y31HvMjsP+ecmvLmjBcCY+3cuVvOr5dSfVyL1lx//SXW9+VXyVYrAP6aVTV/44at5km52sEZ1Nfv6e/v9LR5koHue+SRVnKzFcY3lebnLpSnnnrN+u57owlzz/t+ciAIriHv25t2NXNRuPAJ0r///XJZw3py7OhR+eSTefLkkyNNMF9D9VPfeFaHNsvIEdPkpZcmm3V9EoHOy8knlzZz9M7bn5l+WvxGbwjQGwN02WU9KbZBg7uMsz6ZYPToPlLVeoKuzuvixcutwkB9ZceOXeapBXpDhC5a7V+366I3EfTp09Fck1a9//ST76Rz5+fNvqa3Xy0DBz5s1tP7k1FQf/fufXLF5fcaY70p4HPr5gR10aVf31EyefIs85meOGmAXHhhSqEdfRpAt66DzQ0D2i5cRf0J49+TAQNSrkmf2nDN1fWlSNHCgSf2fmvNUbt2fY21fmZ69GxvChrpDRJ6k0i3boNlvfXfS2il//fe+8L6vA41v1VMnfqs6Oc3M4t9g4D2yWy43nkcZ9Derqbv3J/eelb6Rvtmg/TOk30IIIAAAggggAACCCCAAAIIIBDfAgT13c0fQX13br7ulWhBfZ3Me9o+JVrd3l7eenuQ9QVxcCUae5++Nqh/r/z11yazSX/c+OXXN6wqNMerr9htNUze1hp7j/XFdLhFA/XNml0jw4e/aXZnJaivA2ilf634n95Sy/qSeezYXlL5lJOCmkUjqK8DahD/phu7BR5jHHSQf9+cav1w8O57z1uPBg6u3KO79ccBvcFh/vwl4bpaP1x0saoFrZdhQ6eb/Vpxv1Wr6wJt/7Z+qLi9aQ/57bdVgW3hVi6ywvsjRz5m/SCRUmXJ2SYrjs5xIl13hu+1j1bJ1+pPdgUo/aHH/rd1699mWCrpR6pLOwQQQAABBBBAAAEEEEAAAQSyX4CgfvYbx9MRCOp7f7biIaivigsWLJUH/vOMqTCenmrzFo2sUHcHE24O1+6xR1+Ud6xK4vaiFe/POOP401bt7VkJ6usY+hSARx5+zhSn0ff58+czT5zVdV3Oq3eGjBr1hBQrVjhlQzp/NVT/4IPPWk9w/S2dViJ169aSkS/3CnyXajd+eeQ06ym4KSF4e5u+lixZXJ56+gETyNf3zqC+vtcbG1q1fFx++WWZvjWL8zo0+K9h9htvavjv3pSXVSvXye13PGqC9LolT548ZseRI0fMa9myJWXMmL6pQuD9+42SSZOOF64pXeZE2b1rb8BNg/vT33xeypUrZcbRP6Nfe8sq2jM+8L6M1Uef8LvL6qdLgQL5rCfVdjY3KOj70BsQdJuG1bU6v94IoIseZ8zYfuZmCLMhnT8ZBfW165gxb8ugZ8eZUTp1ukO6dL3LrOtn+q7WPc3vALpBb4IoVKigrF69wTwJuH792qKfw3BBfW3TpPEDgb7aP/TGD/0Mdn7kOXPjgu7Xz5rexLBnT8pvNFo4aMTInnLppefqbrO0b99PvvziB7OuN5HomJlZnEH3C6ybUyZPGZiZ7qatcwzdkJnAf1b66rHsGw3cnruOwYIAAggggAACCCCAAAIIIIAAAv4QIKjvbp4J6rtz83WvRAzqT3x9tvTokfJY4CJFTpDFS6ZZXwSnfJEebrJ79nxZXp8w2+yqU+c0mf3hkHDNzLZly9aaKu+LF68IfDmsOy67rK71hf798tnnP5qAvW7LalBf52b0a+/KBOt6/lq9UYcMLOXKlZQWLa+1Hpd8pyTlTQpst1eiFdTX8ZYu/UteHDxFPvroO/MjgH0M/UHjllsaWpWdWpgv/u3toa9acadvn9FW9ZyFsn79VrO7bNkTrUc0t5BWrRvJoEET0wzqa2P9QWLUqLdl0sQPZefOPYHh9aaKqlXLW08/uEwefqRZqkc72w2z4miPkdnX0LB+Wv31xoKa1qN57RB/Wu3YjgACCCCAAAIIIIAAAggggAACsRMgqB8763g4EkF9789SvAT1VVILeGgVcQ04azVwe9FwsxZlufmWK+XOO6+1N4d91UrjWu1eF61K/+F/U74LD22c1aC+jjfrg69k2PA3rKfKpjzVVLdpyPzqqy+0Kpy3k7xhvpvWNuGW5ORkE7b/4vP5snLleuu75pTQu4a4q1WvaD359hwTAE9rTA3r6w0KGvDWiu4NLqpjKvMnJeW2is2kVI4PDerreWh192cHjpPZs7821dl1mwb0a9Wqan2Hf6+cd97puinV8uuvy+T55yYEKv1rg6JFC8m5550hT1s3B2igPnTREPnIEW/I1Kkfmrm292u/GxpfKt2tJxPoXIcu7777ubw6aqYsX74msEtD6HoDRp8+nVLdEHDYerLwyy9Pl6lTPjQV9+1OxYsXkYssl7797hddj2SJJKivNw5cfdV95mYAvXHg089Gi96soIveBKHV7Z2fEX06gN5AMX/+r5aH/j6TJL8vfTfV6ahx98eGWNe91twQ0rDheTLaugHCuWhYf4T1GVy27LiN/jZRz7pRRK9Tb1JwLvrUg0et89HPyCSryn+VqhWcuyNad4blMxt4d/bVg2Wmmn40++oNBnruLAgggAACCCCAAAIIIIAAAggggEBaAgT105JJfztB/fR92BtGIBGD+mEuM+qb9u07IL/+usI80rVGjUoRf+nt5kR0jtat22JC7hpOr1GjckRVitwcK70++iSB1daTB7Zv3yXlrKB9pcrlAo8DTq+fc59eh/7QUqZMCWPn3JfRerL148P6DdtkgxX21x8DalqV6vXHikiXnHDUwL4u+gOcXT3frvpPQN/Q8AcBBBBAAAEEEEAAAQQQQAABzwkQ1PfclOToCRHUz1H+iA4eT0F95wVpVXANH2tV+EqVykX8fenKFevk2ms7mqEefbSN3NehqXPYqK/rfwObNm23/m2T8uXLhA2oZ/agWvndDqVXr145U4F//a5Vn4hrV7mP9Nj79x+UtWs3SXLyETnttMiPuXPnbqvfZilSuKCcUqV8RPOkNyGsX7/F+k54pzFzVtBP73y1/cYNW03IvPIpJ6dZnMYeQ4+zyaqkv9l6YoFW0Y/0OHb/aL7q03GXW5/nk6zz0HPJlStXxMPr7y07dli/O1g3gaR1o8bmzdutGwW2SrGiha3fJtK3OXDgoLkZI7OfEecJDxs2VYZb/+wlo8C9huy1vb7aS0Z97HZZ6atjZPZc7ePyigACCCCAAAIIIIAAAggggAAC/hYgqO9u/gnqu3PzdS/9kj3ln/z7qu+PmsrpycnHrB8JIqu64mtELh4BBBBAAAEEEEAAAQQQQAABBBBAIGEECOonzFRG5UII6keFMVsHidegvluU7t1fkrff+tSEuL/8alyOhrPdXgP9EIgHgdAAvJ6zVqk/31Gpfv6/wXxnQF/bOSva2+NEs68eQxfnzQT63nlcfc+CAAIIIIAAAggggAACCCCAAAIIpCVAUD8tmfS3E9RP34e9YQQI6odBYRMCCCCAAAIIIIAAAggggAACCCCAgG8FCOr7durDXjhB/bAsntroh6C+Vp//bclK+X3pKhk75m1TdLN9VnsAAEAASURBVKdZs+vkqacf8NRccDIIJKKAHbSP5No0jK+V9PXVXlq17BFUad/eHvqqfTRo71wi7Rtp9X7n2KwjgAACCCCAAAIIIIAAAggggIC/BQjqu5t/gvru3Hzdi6C+r6efi0cAAQQQQAABBBBAAAEEEEAAAQQQCBEgqB8C4vO3BPW9/wHwQ1C/T5+XZeqUDwOTUbnySfLGtOekdOkSgW2sIIBA9gpoYF8XraJvV9C3A/kalNfFfm/eOP5oX2c/e5e2Dw322/v01T6OVs6313W79rMr+z/077F1OwsCCCCAAAIIIIAAAggggAACCCAQqQBB/UilgtsR1A/24F0EAgT1I0CiCQIIIIAAAggggAACCCCAAAIIIICAbwQI6vtmqiO6UIL6ETHlaCM/BPW7PzbEhHRLlz5RzjmnphXsbS5FihTKUXcOjgACCCCAAAIIIIAAAggggAACCCCAAALxK0BQ393cEdR35+brXgT1fT39XDwCCCCAAAIIIIAAAggggAACCCCAQIgAQf0QEJ+/Jajv/Q+AH4L63p8FzhABBBBAAAEEEEAAAQQQQAABBBBAAAEE4kmAoL672SKo787N170I6vt6+rl4BBBAAAEEEEAAAQQQQAABBBBAAIEQAYL6ISA+f0tQ3/sfAIL63p8jzhABBBBAAAEEEEAAAQQQQAABBBBAAAEEvCVAUN/dfBDUd+fm614E9X09/Vw8AggggAACCCCAAAIIIIAAAggggECIAEH9EBCfvyWo7/0PAEF9788RZ4gAAggggAACCCCAAAIIIIAAAggggAAC3hIgqO9uPgjqu3PzdS+C+r6efi4eAQQQQAABBBBAAAEEEEAAAQQQQCBEgKB+CIjP3xLU9/4HgKC+9+eIM0QAAQQQQAABBBBAAAEEEEAAAQQQQAABbwkQ1Hc3HwT13bn5uhdBfV9PPxePAAIIIIAAAggggAACCCCAAAIIIBAiQFA/BMTnbwnqe/8DQFDf+3PEGSKAAAIIIIAAAggggAACCCCAAAIIIICAtwQI6rubD4L67tx83Yugvq+nn4tHAAEEEEAAAQQQQAABBBBAAAEEEAgRIKgfAuLztwT1vf8BIKjv/TniDBFAAAEEEEAAAQQQQAABBBBAAAEEEEDAWwIE9d3NB0F9d26+7kVQ39fTz8UjgAACCCCAAAIIIIAAAggggAACCIQIENQPAfH5W4L63v8AENT3/hxxhggggAACCCCAAAIIIIAAAggggAACCCDgLQGC+u7mg6C+Ozdf9yKo7+vp5+IRQAABBBBAAAEEEEAAAQQQQAABBEIECOqHgPj8LUF9738ACOp7f444QwQQQAABBBBAAAEEEEAAAQQQQAABBBDwlgBBfXfzQVDfnZuvexHU9/X0c/EIIIAAAggggAACCCCAAAIIIIAAAiECBPVDQHz+lqC+9z8ABPW9P0ecIQIIIIAAAggggAACCCCAAAIIIIAAAgh4S4Cgvrv5IKjvzs3XvQjq+3r6uXgEEEAAAQQQQAABBBBAAAEEEEAAgRABgvohID5/S1Df+x8AgvrenyPOEAEEEEAAAQQQQAABBBBAAAEEEEAAAQS8JUBQ3918ENR35+brXgT1fT39XDwCCCCAAAIIIIAAAggggAACCCCAQIgAQf0QEJ+/Jajv/Q8AQX3vzxFniAACCCCAAAIIIIAAAggggAACCCCAAALeEiCo724+COq7c/N1L4L6vp5+Lh4BBBBAAAEEEEAAAQQQQAABBBBAIESAoH4IiM/fEtT3/geAoL7354gzRAABBBBAAAEEEEAAAQQQQAABBBBAAAFvCRDUdzcfBPXdufm6F0F9X08/F48AAggggAACCCCAAAIIIIAAAgggECJAUD8ExOdvCep7/wNAUN/7c8QZIoAAAggggAACCCCAAAIIIIAAAggggIC3BAjqu5sPgvru3HzdK5GD+ocOHZY9e/bJ/v0H5dChQ5KcfMTXc83FI4AAAggggAACCCCAAAIIIIAAAtklkJSUR/LlyycnnFBAihQpZK3nza5DZfu4BPWznTiuDkBQ3/vTRVDf+3PEGSKAAAIIIIAAAggggAACCCCAAAIIIICAtwQI6rubD4L67tx83SsRg/oa0N++/W/ZvXuvr+eWi0cAAQQQQAABBBBAAAEEEEAAAQRySqBo0cJSsmTxuAzsE9TPqU+NN49LUN+b8+I8K4L6Tg3WEUAAAQQQQAABBBBAAAEEEEAAAQQQQACBjAUI6mdsFK4FQf1wKmxLVyDRgvq7du2RTZu2Ba65WLHCUqjQCVKgQH5JSkqSXLkCu1hBAAEEEEAAAQQQQAABBBBAAAEEEIiCwLFjYj3JMFkOHvxH9u3bL7t2HS+eUK5cKSlWrEgUjhK7IQjqx846Ho5EUN/7s0RQ3/tzxBkigAACCCCAAAIIIIAAAggggAACCCCAgLcECOq7mw+C+u7cfN0rkYL6WkV/27adZj41oF+yZAnJmzfJ1/PLxSOAAAIIIIAAAggggAACCCCAAAKxFjh8ONl62uHOQGC/VKkSprp+rM/D7fEI6ruVS8x+BPW9P68E9b0/R5whAggggAACCCCAAAIIIIAAAggggAACCHhLgKC+u/kgqO/Ozde9EiWo76ykX6ZMSSlRoqiv55WLRwABBBBAAAEEEEAAAQQQQAABBHJaYOfO3bJly3ZzGvFUWZ+gfk5/crx1fIL63pqPcGdDUD+cCtsQQAABBBBAAAEEEEAAAQQQQAABBBBAAIG0BQjqp22T3h6C+unpsC+sQCIE9Q8dOiyrVq0z10dIP+w0sxEBBBBAAAEEEEAAAQQQQAABBBDIEQFnWL9KlQqSL1/eHDmPzByUoH5mtBK/LUF9788xQX3vzxFniAACCCCAAAIIIIAAAggggAACCCCAAALeEiCo724+COq7c/N1r0QI6m/cuFV2794rxYoVlnLlSvt6Prl4BBBAAAEEEEAAAQQQQAABBBBAwGsCmzZtlV279krRooXlpJO8/90NQX2vfYJy9nwI6uesfyRHJ6gfiRJtEEAAAQQQQAABBBBAAAEEEEAAAQQQQACB4wIE9Y9bZGaNoH5mtGhrBOI9qO+spl+1akXJmzeJmUUAAQQQQAABBBBAAAEEEEAAAQQQ8JDA4cPJsnLlWnNG8VBVn6C+hz48HjgVgvoemIQMToGgfgZA7EYAAQQQQAABBBBAAAEEEEAAAQQQQAABBEIECOqHgET4lqB+hFA0Oy4Q70H97dv/lm3bdlJN//iUsoYAAggggAACCCCAAAIIIIAAAgh4TsCuql+qVAkpWbK4587PeUIE9Z0arBPU9/5ngKC+9+eIM0QAAQQQQAABBBBAAAEEEEAAAQQQQAABbwkQ1Hc3HwT13bn5ule8B/XXrt0k+/cfkJNPLiNFihTy9Vxy8QgggAACCCCAAAIIIIAAAggggIBXBfbs2ScbNmyRE04oKBUrlvPqaZrzIqjv6emJ+ckR1I85eaYPSFA/02R0QAABBBBAAAEEEEAAAQQQQAABBBBAAAGfCxDUd/cBIKjvzs3XveI9qL9ixRpJTj4iVatWlLx5k3w9l1w8AggggAACCCCAAAIIIIAAAggg4FWBw4eTZeXKtZKUlEdOPbWSV0/TnBdBfU9PT8xPjqB+zMkzfUCC+pkmowMCCCCAAAIIIIAAAggggAACCCCAAAII+FyAoL67DwBBfXduvu4V70H9P/5YZebvtNOqSK5cvp5KLh4BBBBAAAEEEEAAAQQQQAABBBDwrMCxYyJ//pnyPU6NGlU8e556YgT1PT09MT85gvoxJ8/0AQnqZ5qMDggggAACCCCAAAIIIIAAAggggAACCCDgcwGC+u4+AAT13bn5uleiBPW9/gOvrz9kXDwCCCCAAAIIIIAAAggggAACCCBgCdgFF7z+PQ5BfT6uTgGC+k4Nb64T1PfmvHBWOSMwbNhUmf/9r/K99c9eLrjgLDnf+vfQQy3sTWFfW7XsYfpF2l6PNdz6p8uD/46d0THCHpiNCCCAAAIIIIAAAggggAACCCCAAAIxFyCo746coL47N1/3Iqjv6+nn4hFAAAEEEEAAAQQQQAABBBBAAIGYCRDUjxk1B4qiAEH9KGJm01AE9bMJlmHjSkCD+Rqadwb0Qy9Aw/RpBem1nwb1Q5f0+tjBfmcfDflPnjLQuYl1BBBAAAEEEEAAAQQQQAABBBBAAAEPChDUdzcpBPXdufm6F0F9X08/F48AAggggAACCCCAAAIIIIAAAgjETICgfsyoOVAUBQjqRxEzm4aKh6D+wYOHZPLkWUbgrLOqi4aZ01tmzPhYdu3aK2VKnyg33tQwvaa+2Pftt4vkt99WpLrWpDx5pFr1SnLWmdWkWPEiqfb7aUP1ao0jutz0gvTOCvnOwTR4H+4zq+F++wYBZ3tdT6tPaDveI4AAAggggAACCCCAAAIIIIAAAgjkjABBfXfuBPXdufm6F0F9X08/F48AAggggAACCCCAAAIIIIAAAgjETICgfsyoOVAUBQjqRxEzm4aKh6D+jh275ILzWxqBe+65WXr0bJeuxtVX3SerV2+Q2rVryMy3Bqfb1g87+/Z5RaZMmZ3upVaqdJL06t1errji/HTbZWbnqlXrZefO3ZIvX14507oZwKuLM2CvgXqtgu8M1jv36zVEEqIP7bNsecqNJmkZhLZP74aAtMZgOwIIIIAAAggggAACCCCAAAIIIIBA7AQI6ruzJqjvzs3XvQjq+3r6uXgEEEAAAQQQQAABBBBAAAEEEEAgZgIE9WNGzYGiKEBQP4qY2TQUQf1sgvXQsM6gfs1aVSRP7tzm7PRJBXpDw5EjR8z7pKQkeXFIN2nU6OKonP0jDw+S2bO/kbJlS8qcua9HZcxoDxIakE8rUB9pO+f5tWrZw1TM122RBO+1ur72sZdIbgiw2/KKAAIIIIAAAggggAACCCCAAAIIIBBbAYL67rwJ6rtz83Uvgvq+nn4uHgEEEEAAAQQQQAABBBBAAAEEEIiZAEH9mFFzoCgKENSPImY2DUVQP5tgPTSsM6i/cNEMKVSoYODsDh06LHPmLJDOjzwn+/cflDx5cluh+olSqlTxQBu3K/EW1NdK+g9Z/9JanMH7tAL9zr7O4H0kQX3t67whIKPzcR6LdQQQQAABBBBAAAEEEEAAAQQQQACB2AoQ1HfnTVDfnZuvexHU9/X0c/EIIIAAAggggAACCCCAAAIIIIBAzAQI6seMmgNFUYCgfhQxs2koPwf1N2/eLhs2bDUV38uVKyW5c+dKV/no0WOyccMW2bxlhwmyly9f1gTb0+30785Vq9ZLUlIeqVixnNly5MhR2blzt1kvUuQEyZ8/X9hhDhw4KGvWbJLDh5Pl1FMrSMGCBcK2S29jekF9u59WvtdgvS4jRvSQa6+7yN4V9Lpv3wHRa1Ervf5ixQoH7dc32ubAgX/MeBpWz5MnT6CifsGC+YNuFLA76/8rNm3abv3bJmXKnCgnn1xacuVKfz7svll5dQbjM6pg7wzqZ9TWPidnn+wK99vH4hUBBBBAAAEEEEAAAQQQQAABBBBAIHYCBPXdWRPUd+fm614E9X09/Vw8AggggAACCCCAAAIIIIAAAgggEDMBgvoxo+ZAURQgqB9FzGwaym9B/YMHD8kzz4yWj/47NxCUV9ry5cvIfx5oJrfffk0q6V279lrh9Tdk2hv/Fe1vLxrub9XqBrmvQ9OwoXLt9+KLE+Wrr36S9es2m24lShSVvv06yRlnVJOrrmxvtr34YjdpcmNDe1jz+vffe2TgwLEy64OvRKve66LB9XPPrSV9+nSSmrWqmG2R/IkkqL9jxy654PyWZrg2bW+SXr1Szs0e/5tvfpbnBo2TpUtX25vMTQqXXXaedOl6l9SocUpge/9+o2TSpFmB986V1q0by5N9Ojo3yUcfzZWXhkyWFSvWBrYXKVJI2rW/Vdq3v03y5k0KbNcVvbni5ZHTpGTJ4tKx0x3WzQv5g/Zn5o0zSJ9R+D4zoX77HKpXa2yvCkH9AAUrCCCAAAIIIIAAAggggAACCCCAQNwLENR3N4UE9d25+boXQX1fTz8XjwACCCCAAAIIIIAAAggggAACCMRMgKB+zKg5UBQFCOpHETObhvJTUH/37n3Sonl3+eOP1UZTA94nn1wmKCCuAXUNqtvLLiswf/31/5EtVhV9XbSSfNmyJWXjxm12E7nnnpulR892gfe6kpycLG3bPCnz5v0S2J4vX95A6P7xHvfKs1YQX5fQoP4//xySu1r3kp9//t3s14r0hQsXFA3+65InT255aWh3uS6NqvemkeNPJEF9re5/fr0WptfdbW6U3r3vC4zw2qsz5fnnJwTe680GesOCVvvXpVSp4jJt+vNSufJJ5r1e19Sp/w3s1432kwCaN78uyOqTT+bJA/8ZIPq0Al2KFy8iu3fvDbw/66zq8uaM562nERwP63d/bIi8/fZnpr3e9NCy5Q1m3c0frfivYX1dMgrqO0P9kYTudczMBvWdNwM8+FALecj6x4IAAggggAACCCCAAAIIIIAAAggg4D0Bgvru5oSgvjs3X/ciqO/r6efiEUAAAQQQQAABBBBAAAEEEEAAgZgJENSPGTUHiqIAQf0oYmbTUH4K6mu4W0PeumhQ/q67mphq7Zs2bTPbv/12kZxwQgGZM/d10YruutihcK1m3/uJ++RGq/K9hslXrVwnTzwxUjTorcu3306U0mVONOv6p2ePYTJjxsfm/a23Xikafq9Ro4osWvSHDB82VebOXSj2fx/OoL4G1h98cKB8/L9vJX/+fDLgmQflyisvkEKFClrV7FfJo91eNDcaaDX5j/73ijmXwEHTWIkkqD979jfyyMODzAjDR/QI3ASg19m48YPmBoPatWtIn74dzdMAtMr/Rx/NMeejnZo1u06eevqBoDPQ8XRcvbFBTUOXhQuXSutWPU3ov+Hl9aRbt7tNZX69IUHtBj07znTp3LmV3P+fZoHuT/V/VSZO/MC8Hzy4m9x4U8PAvsyuOIP6F1xwlgnrhxvDGaBPr52zr7NPJKF757noOBndOOA8FusIIIAAAggggAACCCCAAAIIIIAAArEVIKjvzpugvjs3X/ciqO/r6efiEUAAAQQQQAABBBBAAAEEEEAAgZgJENSPGTUHiqKAHUR2DnkspXB2IKTsbGN/3ypyzNpv/bX+6L+jR3NZ/44G9Tl27Kipul2u3PFwtPM4rEcm4Kegvh0cL1assMz/4Q1THd9W0rD+p5/OM0Hxs88+zYTkdd/vv6+Un376TQrkzy9Nb7/abm5eP/98vnS4r79Zf2VUb7nqqgvNulaar1P7dvP5rFu3lkx9Y5Cpgm931sr+NzZ5UNav32I2OYP6WkX/zjseNdudgXm771arsv8113SUvXv3ywMPNpeHH25p70rzNb2gvobi58xZID0eH2oq4Gu1/jlzJ5oq+TqgnusPPyyWhQuWmhsbnDcj6P6bb3pYlixZIbVqVZX3PximmwKL7Z1WUL9NmydkrnVsrZqvRgUK5Av01ZVBg8bLmNFviT6J4Kefpwf26zlNn/aRnHhiMbn5lsst2zxB/TL7xlkpX0P4GqrXV3txBu51WySh+9A+GVXgDw3pR3IM+/x4RQABBBBAAAEEEEAAAQQQQAABBBCIvQBBfXfmBPXdufm6l/1DkfNHI/2B6MiRo9ajbY9JyZJFPO0TLz/wehqRk0MAAQQQQAABBBBAAAEEEEAAAQRiIBAv3+Ns375HkpJymVBqrly5RatQp/yTwHoMuDiERwT0+9PQxd5k77NftZ39fStB/VC17Hvvp6B+/36jZNKkWQaz++P3SOvWjQOBfDfC3323SO5q3ct01WrwHTrebtY12N/szsfM+pixfeWyy85LNfyro2bICy+kVJl3BvUnjH9PBgwYbf22UFzmfT85VT/d0O7evvLVVz/K1VdfKC+/0jtsG+dGZ1BfnwZQuPAJZvc//xySrVt3BprmzZskQ4Y8Ktded1FgW3orycnJck/bPqIOGqZf8ts7Qc3TC+rrf+vnndvM3AjQr9/90qLl9UF99c0vi/6U227rYrZ/8MFwqVmrSqo20dpQvVrjVENpWN9+YoK9M5IAfWhIP5I+zpsF9FgZBfvt8+EVAQQQQAABBBBAAAEEEEAAAQQQQCBnBAjqu3MnqO/Ozde97B+OCOqn/TH46ael1uN8l8maNZvM43GrVi0v1apVlHr1apnH9abdM/werZbz6afzZc1fm6yKQ1ulRIkiUqlSObmw/lnWuBXCd3Js3bx5h3zzzUKzpWzZE+WSS+o49ka2um7dFpk3b7FpXL58aalvHdu5OI/h3K7ruXPnsn5kKSblypWU006rZH6gDm1jv1+8eIX1OOO/JL/1I0eTGy+xN6f7usfy+d/H35s2F19c2xzH7jB79lyrKtI/9ttMvdard7pUrlwu0OeLL36S7dt3Bd5ntHL66VVE/7lZ1q7dbP0gssR0vfbaC63HTqf8kBRurD179sv//jfP7NJqahdfnP78Llzwpyxfsc6qiJVPGje5ODCkfX3h5jfQKGTlzz/XyC+/LDdbmza9ImQvbxFAAAEEEEAAAQQQQAABBBDImgBB/az50TtnBJwhfPsMCOrbEt549VNQf6X1PWATq5L9oUOHDX4xK7R+UYM6cu65teRSK0x/yiknpzkpy5evkfnfL5Y///xL1q3bLH/9tUFWr94QaN+l613SqdMd5v3E19+Xp556zax//c0EOemkUoF29oqzGr8zqN/5kedk1qyvTTO7Qr/dx37944/VsnbtJqlStYJ8/PEoe3Oar86gfpqNrB3jJzxlfZ96TtgmBw8esr5X/1mWWk8YWLlynXkagFrs23fAtE9KSpLfl74b1De9oP4qawx9MoAu5cqVkjPPrBbUV9/ojQBffvmj2T7kpcekceNLU7WJ1obQcH1a40YSug+tjq9jZdQvNKivNwlMnjIwrdNgOwIIIIAAAggggAACCCCAAAIIIIBADgsQ1Hc3AQT13bn5uhdB/bSn/2MrLD7kxamB4HJoy1KlissjnZtJq1aNRCv1ZLT8/fceeeH5KTJ9+ieyf//BVM21MlvDhnWl26OtpE6d01LttzdoALtVyyfN2xNOKCA//zxRihQtZO+O6LVrl6EybdrHpm2jRvVlzNjgqkXOY6Q3YMWKZaVd+5ukXbubwjZ7ZsB4GTlypuijmH/7fXrYNqEb//jjL7ni8vvN5okT+8qVV9ULNKl7TmvRmwjcLC+91EVuv+PKQNfGjbvIgp//CLzPaKVzl+bSrVurjJqF3a8B+MsbdjL7XhvdU264Ie2KTrM+mCMdOqT8gFHGuhFjwYJJYce0N952a3dz04UG+qe/OcDebP3ok3J94eY30Chk5ZWX35Knnx5ntq5bPyvdmzBCuvIWAQQQQAABBBBAAAEEEEAAgQwFCOpnSEQDDwoQ1PfgpIScUrwF9du0vUl69WofchXBb6+6sr0VpN8otWvXkJlvDQ7auXjxcunT52VTrT1oh/Wmbt1a8vSAB6V69UqBXRs2bBUNnC9YsDSwTVe0IEutWlVlyZIVZrszqP/MgDEyfvy75vtBDa/nyZMnqK+++d0KvN/Y5CGz3RnUb3zDA6JB/EiW/FbxkcVL3s6wqTOo36NnO6uifkHTR280GP3aW2Y9nJU98Nix78jIEdNkz5599ibzqjc6FC1SyNw0kNmg/sf/+1b+859ngsZL782jj7aR+zo0Ta+Jq33hQvUZDZRR6F77hwv+Z9QvXB8N62tonwUBBBBAAAEEEEAAAQQQQAABBBBAwFsCBPXdzQdBfXduvu5FUD/89L84eKoMHjwlsLNMmRJy6qkVpECBfLJq1Uaruv5GOXo05bHXtWtXt34seVY0NJ/WsmrVBusRwn2tSj3rTZMkK9hf3arKX75Cadm5Y7csW7bWPCJXd+qPE0Ne6iw33XRp2OFCQ/RPD+gobds2Cds23Eat6H9OnVaiFYR0CRfkdh5DqzAVKJgvMNShfw6bH4mOHDka2Na1a0vp0rVF4L29Eu2g/h139AxbBX/b1r9l27a/zQ9HNWpWtg8f9PrYY61Fq9nbix1k1+r25SuUsTen+XpX6+vl7jY3pLk/ox3nnXu3bNy4Tdq0bWw9+jkltB+uT/fuI2TypP8Gdn3x5SvmyQWBDY4VfbpArZp3yOHDydKrd1u5//7jP/TY1xdufh1DBK0S1A/i4A0CCCCAAAIIIIAAAggggECUBQjqRxmU4WIiQFA/JsxZOkg8BPW1Av6ZZ9wq+nnSiu9a+T2t5cCBg1L3nGamGnt6bbUi/bffLrIC+L/Ll1/8aH1v+rcZskSJovLR/16RE08sZsa46qoOst6qoK/B/JtvvsIcv5oV5NfvfQ8fPiLn1r3T9HMG9WfO+ER69BhqtutYp55aMdXpaqX49u36mu3OoP4DDwyU/3001xS3eWPaoFT9nBtySS45u/Zpzk1h151B/YWLZgQ96bZXz+Hy5pv/M/2GDX/c+r77+FNHdaPu0za66DU3bXq1OWblyiebJwUMGDBaXp/wvmQ2qK/V+G+4/j9m3K7d7raeWnu2WU/rz0knlZYyZU5Ma7er7eGC8RqK10C9LrquQX79N//fV+eBIgnRhx4joz6h7ams7xRnHQEEEEAAAQQQQAABBBBAAAEEEPCOAEF9d3NBUN+dm697EdRPPf3OkH7NWqdIzx5t5IorzwuqLq7B+5demiYzZ3xmBrjiynoyYcKTVmWh3KkG3L59l6kQr0Fy/TGkY8fbpL1VhV6rpduLBq3feftLGThwgmzZstMca/SYXiZEb7exX50het1Wo0Zl+fyLl+3dGb6OGfOe9HnytUC7cEFu5zE+/WyEVVmpSqC9rmhA/Ndfl8vLI9+STz753uzr3fse6XT/bUHtoh3UDxrc8WbIi2/ICy9Mlnz58sqq1cGPJ3Y0C1p1E2QPGiCTb7p0fsk8TaF69Yry5VdpP875ogbtgx45nd6NGF9/vUCaN0t5GsLHnwyXM86oGjgrN9dHUD/AxwoCCCCAAAIIIIAAAggggEA2CBDUzwZUhsx2AYL62U6c5QPEQ1BfL/Lii+62nha6XQoWtJ6SumCaCYaHu/i5cxZImzZPmF3NWzSS/v1TwuDh2trb9EYArYI/Zcpss+mpp/4jzZo3MlX3b7utS9A2u4++atj/isvbmU3OoL5WxNfK+LoMGvSI3HrbVWbd+ee558YHqtk7g/pjRr9l9RlvrnPBwjfDfmfuHCeS9fSC+mqqTyDQwjSVK59kblLQ0L29tLu3r3z11Y9SoUJZs08L5TiXR7u9KO+++3mmg/payOecOrebp+dmV7V853mGrocLxGtAP73q9eH6aPA+oyW037LlszLqYj0VuIe5QUAbEtbPkIsGCCCAAAIIIIAAAggggAACCCCAQMwFCOq7Iyeo787N170I6gdP/5IlK+X66ztLshWcv+SSOjJmbO/AY3SDW6a8GzZ0uvWjw0Tzpl//+6Rdu5tSNbvvvoEye9YcU0Ho1dd6BFV1D228fv1WE7xesWKdlC5dXL74cpSUKFEkqJkzRG/vePud56wvu8+w36b7etmlHWT58nWBNm6C+nZnvcGgbZv+oudUrFhhWfLbtKAbGgjq21Ii7733tdzfKaWC1IKFk63qSSWO7/x3Tef//HptzDt9fPPevQfkuuvqy9hxKWH80A7PDnxdhg9/03xWdMxcuXIFmhDUD1CwggACCCCAAAIIIIAAAggg4BEBgvoemQhOI1MCBPUzxZUjjeMlqD/wmTEyblxKkZEbbrhEBr/YzQqx5wky0ydytm7VwzzRVHc4q5cfOXLEKhbzvqxauU5qWoVVWrVqHNR3wYKlcsft3cy2J57sIHfd1cRUttcK97rMmPmC1KlT06zbfyZNmiX9+6UUFXEG9fVpqhpC14ItWpn/vfeHSrlypexu1lNn18stNz8i+/YdMNucQf358xdLyxaPm+0vDO5qPTn28kA/e0UtClpPqNUAd/36te3Nab6mF9TXTs8/P0Fee3Wm6W9fuz2YVr3X6veXXFJXxo3vb282r3qDw+UN77WK5+wIG9Tv0uUF+eD9L83TdBcsnGEK8TgHsMPoOh/Tpz+f6qm7P/30m7z7zudSo+Yp0qTxZVKs+PHv+rWwT6FCBcwNDc4xI1nXCvl6bHvRgP5D/1bRt7el92qft7aJNESf2T6h5+j8LKd3buxDAAEEEEAAAQQQQAABBBBAAAEEEIiNAEF9d84E9d25+boXQf3g6b/jjp4yd84iKVWquKl6HhqSD26d8k4rmmtlc61mrlXNncv33y+RW295zGx69NFW8kjn5s7dYdfNzQKNHrEeS3zEVN7v2+++oHbOoL4+qnf16g3WI4svk5EvpxwnqHHIm+++/dV6tG/KjySVTzlJ/lq90VTt1xsSnIvzGOEq6jvbzp+/xPpRJuXYWileK8bbC0F9W0Jkx47dcvZZLczjrXWudM5Cl+nTPxGtvK9Vnbp0bSEDn5kgRYsWksVLpoWtPGWH8W+99XIZPiLlRzh7THtfuBsx7Dahr1TUDxXhPQIIIIAAAggggAACCCCAQDQFCOpHU5OxYiVAUD9W0u6PEy9B/a1WGLylFa7WkLsu559/plzX6GKpW7eW7Nq1Vxb8/LvMmPGxrF+/xexvcmND0QC8c7nt1s7yyy/L/i0K86Q0aFDbhP3XrNkovXoOl3nzfjHNv/p6vJx8cmnrO8ldcuEFrcx3kldeeYE8/0IXKVKkkGgQ/5NPvpNuXQfLP/8cMn2cQX3d8N57X4hWm9f/BjSkf/XVF8pZZ58mK1estSr3f2gK3OiNBbo4g/rJyclyX/v+8s03P5vw+9NPPyBXWX210Mv+/QetthPldeuGA12effYRua1p6mr9ZqfjT0ZB/d2791lPBrjXOJYoUdR6Au0Y6/xOMCP06ztKJk+eZYqcTJw0QC688Gyzffv2v831z7GeYKCLVuH/fWnw01onjH9PBgwYbfb37NVOrrm6vhQpWth8Z6sbv5270Cre01e0oI3ORY+e7c0TcLWgyo8//ibdug2W9es2p6r0r7aPdx9qhfTzy9Spz5obL8xBIvzjrHCf2ZC+fYjq1Y7f6BFpiD6zfZxh/UhvCLDPj1cEEEAAAQQQQAABBBBAAAEEEEAAgewVIKjvzpegvjs3X/ciqH98+rds3iHnnnuX6CNrn332P9L6ruuP70xn7b///U7a3fu0afH5Fy+bL+Lt5o8/PlImTfxQTjqplPUjyVhJynv8kbt2m3Cvdr8yZU+Un36aGFSpxxmiH/Tcg9L9seHmh5kff3rd3GAQbjx7W8eOz1oVgL6xrrOmnFqtgrw5/dMsB/W1qlKN05qaH3cGv/iwNGt2jX0463HL42XkyJnmR5jffp8e2J7eyh9//GX9qHK/aTJxYl+58qp66TU3+4a8+Ia88MJkyZcvr6xaHfxjSlqd3QTZ0xor0u2NrnvY+iFtubRoca31o9hDqbo9+MAL8vbbX8hll9WV/k91EH36gS6zPxxiVbs6Laj9nj375YzT7zTuQ4d2kaa3Xxm03831EdQPIuQNAggggAACCCCAAAIIIIBAlAUI6kcZlOFiIkBQPybMWTpIvAT19SI1OH/vPX1k8eLl6V6zVsPv/cR9QU/Q1A5arf7ee56UgwdTwvUaRtdCHxs2bA2M17btzaKhcnu5v9PTVih/nnmbJ09uq9BKJVmzZpMJzWv43t4XGtTXDvoEAK1+H7pUqnSS9Onb0VyL7nMG9fX9gQMHrScD9JJFi/7Qt+b77TJlSsrWrTvM95m6TYPt4yc8HfTdt24Pt2QU1Nc+Y8a8LYOeHWe6d+p0h1UI5S6zrk8auKt1z4BZ6dIlrEr2BU0BHP0+WSv6f/XVj2GD+lokp0njBwJ9dcAHHmwuDz/c0oytfz788Bvp/Mhz5ncFfa83JOhvDHv27NO3Jow/YmRPufTSc817/dO+fT/58osfzHsdS8fMzOIMzC9bPiszXQNt3YTo3dwg4KzEH+kNAYGTZAUBBBBAAAEEEEAAAQQQQAABBBBAINsECOq7oyWo787N170I6h+f/nHjPpAneo8ylcsXLppiPdK36PGdLtb0y/g6tVvK9u27rKo6N0m//sGV8dMbct68xXLbrd1Nk7feHmRV+Tkz0NwZ1F+69E3r0bT3mEpBPXq2kQceuD3QLnRly5adUq9eG0m2qvsMHdZV5s5dFJWgvlYLql7tNlM1aMCATtKm7fFKPAT1g2fh2YGvy/Dhb1oVlMrJt9+NDd5pvTvnnNaiN4z06dNO7utwi5xvzdf69VutSkyp5/azT3+wHl/d14yxYMEk0Zs6nAtBfacG6wgggAACCCCAAAIIIIAAAl4QIKjvhVngHDIrQFA/s2Kxbx9PQX3V2bfvgLzw/OvywayvZNffe4LATj21olVAprFVef+GoO3ONxo8Hzp0ivVk2JRK8LpPA/hVqlQwge8bbrjE2dwKxh+RAU+PlpkzP7EC9P+Yfblz55JmzRvJo4+2kXPq3GG2hQvq646lS1fLd98ttP79InmT8sgZZ1azvpdsIuvWbTEhdm0TGtTXbX9b16Yhfw2y2zcW6PayZUvKfffdJq1aN4kopK99Ignq65MBrr7qPtEq/wUK5JNPPxttjqX99SkEWt1+1cp1+tYserPBU1a1//nzf5WRI6aFDeprw19/XWYVyxkiy5evNU8XaNjwPBk9pq8Zw/6j1zhi+BuybNkae5N5amq9emdI3373m4r6gR3Wit4c8ah1PnqjxSSryn+VqhWcu9NddxOWT2vAzIbo3YT7nX3cVv9P6/zZjgACCCCAAAIIIIAAAggggAACCCDgXoCgvjs7gvru3Hzdi6D+8el/tNsw6zGz/5Nz6taQWbNePL7D5dqmTdvl3LopVXsmT+kvl19+vGJORkNqyF+r1OujgJ8e0FHatm0S6OIM6v/113vW/vEy+rV3pVKlsjL327Fp/rgxbOh0GTRoorkB4aefJ0r37iOiEtTXCvFaKV6X/340VM4+u1rgXAnqByjMynff/ipNmz5u1uf/MEHKly8daLBs2VppeFlH895+MsNjjw63HiP9kVxySR2ZNn1AoK2u9Os3Rl579R3rscinyGefjQzap28I6qciYQMCCCCAAAIIIIAAAggggEAOCxDUz+EJ4PCuBAjqu2KLaad4C+rbOMnJySbsvmXLDutJoUlSrlwp88/en9Hr7t37ZMP6LZJsBfG1Sn7+/PnS7XLkyFErbL5GDh06LNWqVbQqvRdIt31GO3/8YYk0b55SbGbsuH5BFeOdffXmgPXrN8vevfvNzQRacT6nFr15YLkVpj/p5NJysvUvV65cEZ+K3mChT0TQecqbxpNzN2/ebt0osFWKFS0slSqfbG6gSOsA+tQBreifJ0+etJqE3e4M17utpm8P7Bwr0mr3zj6RHN8Z1L/ggrNEj8OCAAIIIIAAAggggAACCCCAAAIIIJDzAgT13c0BQX13br7uRVD/+PTffXc/+fST+XLDDRfJa6N7Ht/hcs0ZYP/s85elZs3KmRrpogbtzeN3H3roTun+eErgXwcIDeqvWbtZLr2kg6nmM2lyP7niivNSHUeD/xde0NZUZ+90/23Su/c90rnzkKgE9e0wecGC+WXpHzOsykPHf1iIh6D+uefWtB5V3CyVmXODXluDi852bnK1rk8fOL3WneYGjCFDOssdd14VGGf8+A+kd69R1g89JUVvpNBl9uy5cl/7Z0wFqN+tpyfoDzf2cu01D1mPyF4hHTreKk8+ea+9OfBqB/UjuT67kx5v+vRPzNt162dl6ocqewxeEUAAAQQQQAABBBBAAAEEEEhLgKB+WjJs97IAQX0vz07KucVrUN/rslrt/623P5PatU+Tc889PdXpvjxymgwZMtl8Z/njT9Os4H/+VG3YEH2BzAbl0zsDZ4g+0mr3zuNHGu6vXi3lKbwE9dObDfYhgAACCCCAAAIIIIAAAggggAACsRUgqO/Om6C+Ozdf9yKof3z6r2/0iCxatEzuuvsGGTjw/uM7XK599ukP1iOA+5rei36ZIqVKFc/USDfd2E1+/PF3adbsGhn8YkrFeh0gNKifZFXvaXZnL/nmm4Vy9dUXyITXn0x1HL0BQW9E0ApBc+eOlsqnnJTloP7evQdk2NBpMnLkTHO8Vq0bWRX7Hwg6djwE9YNOOI03+rSC7+aNS2Nv5jbrZ0I/G01vv9J6RHWXQOd773laPvroO7nzzqvlxSGPmO1aFevMM5pZj6c+KjNmDAzcLKCVn848o7m5OWPqG0/JZZfVDYxjr9hBfft9Zl8J6mdWjPYIIIAAAggggAACCCCAAAIZCRDUz0iI/V4UIKjvxVkJPieC+sEe0Xin30c2anS/rFq5TrQC/pNPdpSrr7nQVOFfZlWk//yz763vNqeIFiZpcFEdef31p6NxWMaIQCCaofdYB/X18iKpwh8BA00QQAABBBBAAAEEEEAAAQQQQAABBLIoQFDfHSBBfXduvu5FUP/49N9y82Myf/6SoKD08b2ZX5szZ6HceUcv03H+DxOkfPnSmRqk0XUPi1blb9O2sQwY0CnQN1xQ/8MPv5X27QaYR+lqoDz0WK1b97V+PPlBGjasK1OmPmXGirSi/plnniq1Tj8lcPzkw0fk999XyZ9/rhGt1K9L4yYXy8svd0/1KN94COrrI6FLlCgSuL5wK+r5/geDw+3K9LaxY9+XJ5941Txa+YcfJ5j++sOXBvI1mP/KqO5y442XBsa1b9h4+JFm8thjrc12e7713H9fOj3sY63toH4k12cfTB/fvGfPfvOWoL6twisCCCCAAAIIIIAAAggggEC0BAjqR0uScWIpQFA/ltrujkVQ351bRr1++uk36XBff9m1a69pqkVgilqhfa20by/Vq1eSESN6StVTK9ibeM1mgWHDpspw61+kFfAzOh0db/73v4pWx49kscP9mamOH+1zjuQ8aYMAAggggAACCCCAAAIIIIAAAgggkL4AQf30fdLaS1A/LRm2pylAUP84TYcOA2XWB3PkiivryaRJfY/vcLmmQfbLG6YE7Gd/OETq1DktUyOdd+7dsnHjNhPO1pC2vYQL6icnH5ELzm8rmzZtF2egW/usXbtZGtS/14Tqx094Uq655gIzVKRBffu44V7z5Mktt9zSUAYPfli0sn/oEg9B/UaN6suYsb1DTz3b3i9fvk4uu7SDGX+O9XSDKlVOloUL/5Qbru8suXPnkl9+fSPoxoEXB0+1fKdYj5euGbhZoHevUTJ+/Ady6aXnyBvTwlersoP6mbm+V15+S55+OuXJAQT1s+0jwMAIIIAAAggggAACCCCAgG8FCOr7durj+sIJ6nt/+gjqZ98crVmzUV4dNVNmzfpK9u8/aA6UlJQkVauWl/oNakvXrndbVfbzZ98JMDICCCCAAAIIIIAAAggggAACCCCAAAIIZIsAQX13rAT13bn5uhdB/ePT3+fJ12TMmPek8iknybffjjm+w+WaVho6vdadpvfQYV2ladMrIh5pj1VZ/cwzm4sG8Ae/+LA0a3ZNoG+4oL7utAPdZcqUkB+sCv52cP7Zga/L8OFvmgru874fF6h6H2lQv8FFZ8sZp1cJHF+sykklSxaTk8qVlEsvqyt6vLQWgvrhZeqd10Y2bNgqg557UFq1us6qOjVDBj4zQeqcc5rMnj0kqNPPPy2VJk26SlJSHlny23QpXLiguQFEbwR54ol7pWOnW4Pa228I6tsSvCKAAAIIIIAAAggggAACCHhFgKC+V2aC88iMAEH9zGjlTFuC+tnvrv8d/G1V0tcngurTRzWsz4IAAggggAACCCCAAAIIIIAAAggggAAC8StAUN/d3BHUd+fm614E9Y9P/8cffy9t2/Q3Gz79bITUquUIpx9vlmpNA/mffDLfbL/kkjpStuyJgTbXXvOQLF68QjJT1Vw7z5z5uTz80GAzznfzxkmlSmUDY6YV1Ndq+lpVX8P9r77aQxo3uVgOH04Wrcy/bdvfqSrzRxrUz4xF4CT/XRn60jR57rlJVrZfK8VPlRNPLBraJNX7jz76Tu69J6VK/My3npX69c9K1SZ0w5AX35AXXpgs+fLllVWr3w3dHfa9myB72IFcbOzaZahMm/ax3HjjpfLKqO7S7M5e8s03C+WRzs3l0UdbBY145MhROcu6aUM/Z6+/3kfOrl1dzqmT0ia9uXFzfVTUD6LnDQIIIIAAAggggAACCCCAQJQFCOpHGZThYiJAUD8mzFk6CEH9LPHRGQEEEEAAAQQQQAABBBBAAAEEEEAAAQR8KEBQ392kE9R35+brXgT1j0//oUOH5eyzW4pWs2991/Xy7LP/Ob4znbWRI2eKVo7XkPjCRZOlWLHCgdb2vvz588nX37wqFSqUCexLa0Xn5JabH7Oq4v8WtsJ6WkF9Ha99uwHy4YffilbBnzFjoLz33tdyf6dBprq+Vtl3Vr+PRVD/rbe+kIcefMFc6ugxveT66xukddmB7X37vCajR79n3n8/f3xEZvEW1H///a+lU8dBUrp0cdFr1CcvHDx4SN5973mpV+/0gIW90qHDQJn1wRxp3/4mOaduTTOnOpcLFk62m6R6JaifioQNCCCAAAIIIIAAAggggAACOSxAUD+HJ4DDuxIgqO+KLaadCOrHlJuDIYAAAggggAACCCCAAAIIIIAAAggggEACCBDUdzeJBPXdufm6F0H94Ol/8olXZezY900F+LffGSTnn39GcIOQd/qo36uvekDWrdtiQugaRncuGzduk0sv6SD79x+Uyy6rK1Om9jdjO9uErk+a+KE8/vhIs3nwiw9Ls2bXBDVJL6ivVdm1OrsuemPAY48Ol3nzFkuTGy+RUaMeDxonFkH9tWs3S4P698rRo8fk1lsvl+EjugWdQ+ibf/45JNdc/aAsX77OPEVg7rdjJXfuXKHNUr2Pt6D+zp175OyzmhuXpwd0lN69RkmRooWspy+8YT02Ok+q63vjjY+lW9ehUrPWKXKuFdSfMuUjadr0Chk6rGuqtvYGgvq2BK8IIIAAAggggAACCCCAAAJeESCo75WZ4DwyI0BQPzNaOdOWoH7OuHNUBBBAAAEEEEAAAQQQQAABBBBAAAEEEIhfAYL67uaOoL47N1/3IqgfPP379h2QKy6/3wTvi1rB6bFje5vq9MGtUt5pSL9Fiydkwc9/SJ48ueVNq4L9hReemarpuHEfyBO9R5ntN954qbw0tLNohf1wiwayH+8+QpKTj8hFF9eW6dMHpAr2pxfU1/nUGwNWrlwv9eufJd9996s5zMyZz0r9BmcFHTIWQX09YNs2/eXjj783x+7Y6VZ54ol7g87DfnPgwD/S5u7+MmfOQrNJ22n7SJZ4C+rrNV3f6BFZtGiZFClyguzZs18aNaovY6zPW7hlw4atUu+8NuazUKhQAdm794AMG95Nbrvt8nDNzTaC+mnSsAMBBBBAAAEEEEAAAQQQQCCHBAjq5xA8h82SAEH9LPHFpDNB/ZgwcxAEEEAAAQQQQAABBBBAAAEEEEAAAQQQSCABgvruJpOgvjs3X/ciqJ96+r//fom0btVHNLSvAfwbbrhYmre4RqpVqyAFCuSX1as2yJdf/ixjxrwnu3btNQMMGvSAtGrdKPVg1hY1bt9ugPz3v9+Z/ZUqlZV77r1JLr74bClfvozs3LFbFi9ZKa+/Plvmzllk2pQvX1reenuQVKxYNtWY6QX1tfHo196Vvn1HB/pVr15Rvvwq5UaBwEZrJVZBfQ2ZN7ruEdm27W9z+OuvbyBXX32BnFevllSyru+331fLjz/+Lm/N/FwWLvzTtNEnGcyY8Ywk5U1ynnKa61kJ6usNEX36tEtzbHtHsWKFpUKFMvbbLL8OenaiDBs2PTBOep8hbXR5w07y559rT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],\n \"metadata\": {\n \"id\": \"Zo_W8X5C5Rmy\"\n }\n },\n {\n \"cell_type\": \"markdown\",\n \"source\": [\n \"## Setting up the connector\\n\",\n \"\\n\",\n \"Setup the connection between Airbyte Google Sheets source and Snowflake Cortex destination\\n\",\n \"\\n\",\n \"\\n\"\n ],\n \"metadata\": {\n \"id\": \"0cJlfH2E586B\"\n }\n },\n {\n \"cell_type\": \"markdown\",\n \"source\": [\n \"## Install PyAirbyte and other dependencies\"\n ],\n \"metadata\": {\n \"id\": \"O2m0TjAWkcGs\"\n }\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"id\": \"5DC5-hZUkYSD\"\n },\n \"outputs\": [],\n \"source\": [\n \"!pip3 install airbyte openai langchain-openai snowflake-connector-python langchain_core\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"source\": [\n \"## Query the Records from Snowflake Cortex Airbyte Destination\"\n ],\n \"metadata\": {\n \"id\": \"lLUe1eM8D0R4\"\n }\n },\n {\n \"cell_type\": \"code\",\n \"source\": [\n \"from snowflake import connector\\n\",\n \"from google.colab import userdata\\n\",\n \"from typing import List\\n\",\n \"import pandas as pd\\n\",\n \"\\n\",\n \"def get_db_connection():\\n\",\n \" return connector.connect(\\n\",\n \" account=userdata.get(\\\"SNOWFLAKE_HOST\\\"),\\n\",\n \" role=userdata.get(\\\"SNOWFLAKE_ROLE\\\"),\\n\",\n \" warehouse=userdata.get(\\\"SNOWFLAKE_WAREHOUSE\\\"),\\n\",\n \" database=userdata.get(\\\"SNOWFLAKE_DATABASE\\\"),\\n\",\n \" schema=userdata.get(\\\"SNOWFLAKE_SCHEMA\\\"),\\n\",\n \" user=userdata.get(\\\"SNOWFLAKE_USERNAME\\\"),\\n\",\n \" password=userdata.get(\\\"SNOWFLAKE_PASSWORD\\\"),\\n\",\n \" )\\n\",\n \"\\n\",\n \"def fetch_table_data(table_name, columns):\\n\",\n \" conn = get_db_connection()\\n\",\n \" cursor = conn.cursor()\\n\",\n \"\\n\",\n \" # Construct the query\\n\",\n \" columns_str = \\\", \\\".join(columns)\\n\",\n \" query = f\\\"SELECT {columns_str} FROM {table_name} LIMIT 5;\\\"\\n\",\n \"\\n\",\n \" cursor.execute(query)\\n\",\n \" result = cursor.fetchall()\\n\",\n \"\\n\",\n \" # Get column names\\n\",\n \" col_names = [desc[0] for desc in cursor.description]\\n\",\n \"\\n\",\n \" cursor.close()\\n\",\n \" conn.close()\\n\",\n \"\\n\",\n \" # Convert the result to a pandas DataFrame\\n\",\n \" df = pd.DataFrame(result, columns=col_names)\\n\",\n \" return df;\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"data_frame = fetch_table_data(\\\"AMAZON_REVIEWS\\\", [\\\"DOCUMENT_ID\\\",\\\"DOCUMENT_CONTENT\\\"])\\n\",\n \"data_frame\\n\",\n \"\\n\"\n ],\n \"metadata\": {\n \"id\": \"dFY3Rf6cliaU\",\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\",\n \"height\": 206\n },\n \"outputId\": \"3c8d4ca9-e2b6-4f29-a9e1-2e245e10731d\"\n },\n \"execution_count\": 2,\n \"outputs\": [\n {\n \"output_type\": \"execute_result\",\n \"data\": {\n \"text/plain\": [\n \" DOCUMENT_ID \\\\\\n\",\n \"0 147706135155296294706750175152529247047 \\n\",\n \"1 310610605518759482714931752273988313361 \\n\",\n \"2 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I think its made a bit cheesier than the earlier versions; the paint looks not as clean as before\\\\\\\\nreviewTime: 2012-12-23\\\\\\\\nday_diff: 715\\\\\\\\nhelpful_yes: 0\\\\\\\\nhelpful_no: 0\\\\\\\\ntotal_vote: 0\\\\\\\\nscore_pos_neg_diff: 0\\\\\\\\nscore_average_rating: 0\\\\\\\\nwilson_lower_bound: 0\\\\\\\"\\\",\\n \\\"\\\\\\\"reviewerName: 2Cents!\\\\\\\\noverall: 5\\\\\\\\nreviewText: It's mini storage. It doesn't do anything else and it's not supposed to. I purchased it to add additional storage to my Microsoft Surface Pro tablet which only come in 64 and 128 GB. It does what it's supposed to and SanDisk has a long standing reputation that speaks for itself.\\\\\\\\nreviewTime: 2013-04-29\\\\\\\\nday_diff: 588\\\\\\\\nhelpful_yes: 0\\\\\\\\nhelpful_no: 0\\\\\\\\ntotal_vote: 0\\\\\\\\nscore_pos_neg_diff: 0\\\\\\\\nscore_average_rating: 0\\\\\\\\nwilson_lower_bound: 0\\\\\\\"\\\",\\n \\\"\\\\\\\"reviewerName: 1m2\\\\\\\\noverall: 5\\\\\\\\nreviewText: This think has worked out great.Had a diff. bran 64gb card and if went south after 3 months.This one has held up pretty well since I had my S3, now on my Note3.*** update 3/21/14I've had this for a few months and have had ZERO issue's since it was transferred from my S3 to my Note3 and into a note2. This card is reliable and solid!Cheers!\\\\\\\\nreviewTime: 2013-11-21\\\\\\\\nday_diff: 382\\\\\\\\nhelpful_yes: 0\\\\\\\\nhelpful_no: 0\\\\\\\\ntotal_vote: 0\\\\\\\\nscore_pos_neg_diff: 0\\\\\\\\nscore_average_rating: 0\\\\\\\\nwilson_lower_bound: 0\\\\\\\"\\\"\\n ],\\n \\\"semantic_type\\\": \\\"\\\",\\n \\\"description\\\": \\\"\\\"\\n }\\n }\\n ]\\n}\"\n }\n },\n \"metadata\": {},\n \"execution_count\": 2\n }\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"source\": [\n \"## Run Sentiment analysis from Snowflake Cortex Airbyte Destination Records\"\n ],\n \"metadata\": {\n \"id\": \"fqXE5ed4GqBD\"\n }\n },\n {\n \"cell_type\": \"code\",\n \"source\": [\n \"from snowflake import connector\\n\",\n \"from IPython.display import display, HTML\\n\",\n \"\\n\",\n \"def get_db_connection():\\n\",\n \" return connector.connect(\\n\",\n \" account=userdata.get(\\\"SNOWFLAKE_HOST\\\"),\\n\",\n \" role=userdata.get(\\\"SNOWFLAKE_ROLE\\\"),\\n\",\n \" warehouse=userdata.get(\\\"SNOWFLAKE_WAREHOUSE\\\"),\\n\",\n \" database=userdata.get(\\\"SNOWFLAKE_DATABASE\\\"),\\n\",\n \" schema=userdata.get(\\\"SNOWFLAKE_SCHEMA\\\"),\\n\",\n \" user=userdata.get(\\\"SNOWFLAKE_USERNAME\\\"),\\n\",\n \" password=userdata.get(\\\"SNOWFLAKE_PASSWORD\\\"),\\n\",\n \" )\\n\",\n \"\\n\",\n \"\\n\",\n \"def get_sentiment_analysis_from_snowflake(table_name):\\n\",\n \" conn = get_db_connection()\\n\",\n \" cur = conn.cursor()\\n\",\n \"\\n\",\n \"\\n\",\n \" query = f\\\"\\\"\\\"\\n\",\n \" SELECT\\n\",\n \" DOCUMENT_CONTENT,\\n\",\n \" CASE\\n\",\n \" WHEN SENTIMENT_SCORE > 0 THEN 'Positive'\\n\",\n \" WHEN SENTIMENT_SCORE < 0 THEN 'Negative'\\n\",\n \" ELSE 'Neutral'\\n\",\n \" END AS SENTIMENT\\n\",\n \" FROM (\\n\",\n \" SELECT\\n\",\n \" SNOWFLAKE.CORTEX.SENTIMENT(DOCUMENT_CONTENT) AS SENTIMENT_SCORE,\\n\",\n \" DOCUMENT_CONTENT\\n\",\n \" FROM {table_name}\\n\",\n \" LIMIT 5\\n\",\n \" );\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" cur.execute(query)\\n\",\n \" result = cur.fetchall()\\n\",\n \" col_names = [desc[0] for desc in cur.description]\\n\",\n \"\\n\",\n \" df = pd.DataFrame(result,columns=col_names)\\n\",\n \"\\n\",\n \" return df\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"response = get_sentiment_analysis_from_snowflake(\\\"amazon_reviews\\\")\\n\",\n \"\\n\",\n \"display(HTML(response.to_html(index=False)))\"\n ],\n \"metadata\": {\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\",\n \"height\": 345\n },\n \"id\": \"yGn9pM7cr_6h\",\n \"outputId\": \"d69ac85e-bcc6-457d-855e-3a12b57c91ee\"\n },\n \"execution_count\": 5,\n \"outputs\": [\n {\n \"output_type\": \"display_data\",\n \"data\": {\n \"text/plain\": [\n \"\"\n ],\n \"text/html\": [\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
DOCUMENT_CONTENTSENTIMENT
\\\"reviewerName: 0mie\\\\noverall: 5\\\\nreviewText: Purchased this for my device, it worked as advertised. You can never have too much phone memory, since I download a lot of stuff this was a no brainer for me.\\\\nreviewTime: 2013-10-25\\\\nday_diff: 409\\\\nhelpful_yes: 0\\\\nhelpful_no: 0\\\\ntotal_vote: 0\\\\nscore_pos_neg_diff: 0\\\\nscore_average_rating: 0\\\\nwilson_lower_bound: 0\\\"Negative
\\\"reviewerName: 1K3\\\\noverall: 4\\\\nreviewText: it works as expected. I should have sprung for the higher capacity. I think its made a bit cheesier than the earlier versions; the paint looks not as clean as before\\\\nreviewTime: 2012-12-23\\\\nday_diff: 715\\\\nhelpful_yes: 0\\\\nhelpful_no: 0\\\\ntotal_vote: 0\\\\nscore_pos_neg_diff: 0\\\\nscore_average_rating: 0\\\\nwilson_lower_bound: 0\\\"Negative
\\\"reviewerName: 1m2\\\\noverall: 5\\\\nreviewText: This think has worked out great.Had a diff. bran 64gb card and if went south after 3 months.This one has held up pretty well since I had my S3, now on my Note3.*** update 3/21/14I've had this for a few months and have had ZERO issue's since it was transferred from my S3 to my Note3 and into a note2. This card is reliable and solid!Cheers!\\\\nreviewTime: 2013-11-21\\\\nday_diff: 382\\\\nhelpful_yes: 0\\\\nhelpful_no: 0\\\\ntotal_vote: 0\\\\nscore_pos_neg_diff: 0\\\\nscore_average_rating: 0\\\\nwilson_lower_bound: 0\\\"Positive
\\\"reviewerName: 2&amp;1/2Men\\\\noverall: 5\\\\nreviewText: Bought it with Retail Packaging, arrived legit, in a orange envelope, english version not asian like the picture shows. arrived quickly, bought a 32 and 16 both retail packaging for my htc one sv and Lg Optimus, both cards in working order, probably best price you'll get for a nice sd card\\\\nreviewTime: 2013-07-13\\\\nday_diff: 513\\\\nhelpful_yes: 0\\\\nhelpful_no: 0\\\\ntotal_vote: 0\\\\nscore_pos_neg_diff: 0\\\\nscore_average_rating: 0\\\\nwilson_lower_bound: 0\\\"Negative
\\\"reviewerName: 2Cents!\\\\noverall: 5\\\\nreviewText: It's mini storage. It doesn't do anything else and it's not supposed to. I purchased it to add additional storage to my Microsoft Surface Pro tablet which only come in 64 and 128 GB. It does what it's supposed to and SanDisk has a long standing reputation that speaks for itself.\\\\nreviewTime: 2013-04-29\\\\nday_diff: 588\\\\nhelpful_yes: 0\\\\nhelpful_no: 0\\\\ntotal_vote: 0\\\\nscore_pos_neg_diff: 0\\\\nscore_average_rating: 0\\\\nwilson_lower_bound: 0\\\"Negative
\"\n ]\n },\n \"metadata\": {}\n }\n ]\n }\n ]\n}" }, { "path": "pyairbyte_notebooks/using_langchain_airbyte_package.ipynb", "content": "{\n \"nbformat\": 4,\n \"nbformat_minor\": 0,\n \"metadata\": {\n \"colab\": {\n \"provenance\": []\n },\n \"kernelspec\": {\n \"name\": \"python3\",\n \"display_name\": \"Python 3\"\n },\n \"language_info\": {\n \"name\": \"python\"\n }\n },\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"source\": [\n \"# **Illustrating the usage of *langchain_airbyte* package**\\n\",\n \"\\n\",\n \"The `langchain-airbyte` package integrates LangChain with Airbyte.
\\n\",\n \"\\n\",\n \"It has a very powerful function `AirbyteLoader` which can be used to load data as document into langchain from any Airbyte source!
\\n\",\n \"\\n\",\n \"This notebook demonstrates the usage of `langchain_airbyte` to load data from an Airbyte source (Github Repository) , store the data into a vector database, and perform a basic QnA on that data using FAISS and OpenAI embeddings.\\n\",\n \"\\n\"\n ],\n \"metadata\": {\n \"id\": \"5eHLkb0FkdXz\"\n }\n },\n {\n \"cell_type\": \"markdown\",\n \"source\": [\n \"# **Prerequisite**\\n\",\n \"**1) OpenAI API Key**:\\n\",\n \" - **Create an OpenAI Account**: Sign up for an account on [OpenAI](https://www.openai.com/).\\n\",\n \" - **Generate an API Key**: Go to the API section and generate a new API key. For detailed instructions, refer to the [OpenAI documentation](https://beta.openai.com/docs/quickstart).\\n\",\n \"\\n\",\n \"**2) Github Personal Access Token**:\\n\",\n \" - **Create a Github Account**: Sign up for an account on [Github](https://www.github.com/).\\n\",\n \" - **Generate an API Key**: Cick on your profile icon->Settings->Developer Settings and generate a new API key. For detailed instructions, refer to the [Github documentation](https://docs.github.com/en/authentication/keeping-your-account-and-data-secure/managing-your-personal-access-tokens).\\n\",\n \" \\n\",\n \"\\n\"\n ],\n \"metadata\": {\n \"id\": \"xwF4ZyZp2ji0\"\n }\n },\n {\n \"cell_type\": \"markdown\",\n \"source\": [\n \"## **Installing Dependencies**\\n\",\n \"Lets start by installing all the required dependencies!
\\n\",\n \"First of all we will create a virtual environment and then begin installing the dependencies.\\n\"\n ],\n \"metadata\": {\n \"id\": \"sC1_LEcYoU1x\"\n }\n },\n {\n \"cell_type\": \"code\",\n \"source\": [\n \"# Add virtual environment support for running in Google Colab\\n\",\n \"!apt-get install -qq python3.10-venv\\n\",\n \"\\n\",\n \"#Installing the necessary libraries\\n\",\n \"!pip3.10 install -qU langchain-airbyte faiss-cpu langchain-community langchain-openai\"\n ],\n \"metadata\": {\n \"id\": \"7lwuMXN3ocjE\"\n },\n \"execution_count\": null,\n \"outputs\": []\n },\n {\n \"cell_type\": \"markdown\",\n \"source\": [\n \"## **Load Data using AirbyteLoader**\\n\",\n \"Now we use `AirbyteLoader` to fetch data from the source `source-github`.
\\n\",\n \"You may use any other source you require, but fetch the data accordingly!
\\n\",\n \"Dont forget to add all the required fields!
\\n\",\n \"Refer the guide for your source [here](https://docs.airbyte.com/integrations/sources/)\\n\",\n \"\\n\",\n \"For more information regarding this package [refer](https://python.langchain.com/v0.2/docs/integrations/document_loaders/airbyte/)\\n\",\n \"\\n\",\n \"The last step of converting data to documents ensures that the raw data (GitHub commits) is converted into a standardized format that includes both the main content and any associated metadata.\"\n ],\n \"metadata\": {\n \"id\": \"MdmoHC1Fo7fn\"\n }\n },\n {\n \"cell_type\": \"code\",\n \"source\": [\n \"from langchain_airbyte import AirbyteLoader\\n\",\n \"from langchain.schema import Document\\n\",\n \"\\n\",\n \"# Configure the AirbyteLoader to load data from a GitHub repository\\n\",\n \"loader = AirbyteLoader(\\n\",\n \" source=\\\"source-github\\\",\\n\",\n \" stream=\\\"commits\\\",\\n\",\n \" config={\\n\",\n \" \\\"credentials\\\": {\\n\",\n \" \\\"personal_access_token\\\": \\\"your_personal_access_token\\\"\\n\",\n \" },\\n\",\n \" \\\"repositories\\\": [\\\"your_username/repository_name\\\"]\\n\",\n \" }\\n\",\n \")\\n\",\n \"\\n\",\n \"# Load documents from the specified GitHub source\\n\",\n \"docs = loader.load()\\n\",\n \"\\n\",\n \"# Convert incoming stream data into documents\\n\",\n \"docs = [Document(page_content=record.page_content, metadata=record.metadata) for record in docs]\\n\"\n ],\n \"metadata\": {\n \"id\": \"q-9PC6bzpQMp\"\n },\n \"execution_count\": null,\n \"outputs\": []\n },\n {\n \"cell_type\": \"markdown\",\n \"source\": [\n \"## **Split Documents into Chunks and Store these Chunks in Vector Store using FAISS**\\n\",\n \"Large documents are split into smaller chunks to make them easier to handle. This also helps in improving the efficiency of the retrieval process, as smaller chunks can be more relevant to specific queries.
\\n\",\n \"\\n\",\n \"The chunks of documents are transformed into vectors using an embedding model (OpenAI embeddings).
\\n\",\n \"These vectors are then stored in a FAISS vector store, which allows for efficient similarity search.
\\n\",\n \"The vector store indexes the vectors and enables fast retrieval of similar vectors based on a query.\"\n ],\n \"metadata\": {\n \"id\": \"61JWR7I_p7g5\"\n }\n },\n {\n \"cell_type\": \"code\",\n \"source\": [\n \"from langchain.text_splitter import RecursiveCharacterTextSplitter\\n\",\n \"\\n\",\n \"splitter = RecursiveCharacterTextSplitter(chunk_size=512, chunk_overlap=30)\\n\",\n \"chunked_docs = splitter.split_documents(docs)\\n\",\n \"\\n\",\n \"print(f\\\"Created {len(chunked_docs)} document chunks.\\\")\\n\",\n \"\\n\",\n \"# Store Chunks in Vector Store using FAISS\\n\",\n \"from langchain_openai import OpenAI, OpenAIEmbeddings\\n\",\n \"from langchain_community.vectorstores import FAISS\\n\",\n \"import os\\n\",\n \"\\n\",\n \"# Set the OpenAI API Key (make sure to set your own API key here)\\n\",\n \"os.environ['OPENAI_API_KEY'] = \\\"YOUR_OPENAI_API_KEY\\\"\\n\",\n \"\\n\",\n \"# Ensure filtered_docs is not empty\\n\",\n \"if not chunked_docs:\\n\",\n \" raise ValueError(\\\"No valid documents to store in the vector store.\\\")\\n\",\n \"\\n\",\n \"# Store document chunks in FAISS vector store\\n\",\n \"embeddings = OpenAIEmbeddings(openai_api_key=os.getenv(\\\"OPENAI_API_KEY\\\"))\\n\",\n \"vector_store = FAISS.from_texts([doc.page_content for doc in chunked_docs], embeddings)\\n\",\n \"\\n\",\n \"print(\\\"Chunks successfully stored in vectorstore.\\\")\"\n ],\n \"metadata\": {\n \"id\": \"vqI6yNtGqJC7\"\n },\n \"execution_count\": null,\n \"outputs\": []\n },\n {\n \"cell_type\": \"markdown\",\n \"source\": [\n \"## **Perform QnA on Stored Data**\\n\",\n \"Finally we perform the Question And Answer here.
\\n\",\n \"\\n\",\n \"When a query is made, the vector store retrieves relevant document chunks based on their vector similarity to the query.\\n\",\n \"The language model (OpenAI) then generates answers based on the retrieved chunks.\"\n ],\n \"metadata\": {\n \"id\": \"xUuF_RUvqVZM\"\n }\n },\n {\n \"cell_type\": \"code\",\n \"source\": [\n \"# Step 5: Perform QnA on Stored Data\\n\",\n \"from langchain.chains.question_answering import load_qa_chain\\n\",\n \"\\n\",\n \"# Initialize the LLM (OpenAI)\\n\",\n \"llm = OpenAI(openai_api_key=os.getenv(\\\"OPENAI_API_KEY\\\"))\\n\",\n \"\\n\",\n \"# Create a QnA chain\\n\",\n \"qa_chain = load_qa_chain(llm=llm, chain_type=\\\"stuff\\\")\\n\",\n \"\\n\",\n \"# Perform a QnA\\n\",\n \"query = \\\"What are the latest commits in the repository?\\\"\\n\",\n \"inputs = {\\\"question\\\": query, \\\"input_documents\\\": chunked_docs}\\n\",\n \"answer = qa_chain.invoke(inputs)\\n\",\n \"\\n\",\n \"print(\\\"QnA Result:\\\", answer)\\n\"\n ],\n \"metadata\": {\n \"id\": \"tHDAkPjYqXDb\"\n },\n \"execution_count\": null,\n \"outputs\": []\n }\n ]\n}" }, { "path": "satisfaction_analytics_zendesk_support/.gitignore", "content": "# Byte-compiled / optimized / DLL files\n__pycache__/\n*.py[cod]\n*$py.class\n\n# C extensions\n*.so\n\n# Distribution / packaging\n.Python\nbuild/\ndevelop-eggs/\ndist/\ndownloads/\neggs/\n.eggs/\nlib/\nlib64/\nparts/\nsdist/\nvar/\nwheels/\nshare/python-wheels/\n*.egg-info/\n.installed.cfg\n*.egg\nMANIFEST\n\n# PyInstaller\n# Usually these files are written by a python script from a template\n# before PyInstaller builds the exe, so as to inject date/other infos into it.\n*.manifest\n*.spec\n\n# Installer logs\npip-log.txt\npip-delete-this-directory.txt\n\n# Unit test / coverage reports\nhtmlcov/\n.tox/\n.nox/\n.coverage\n.coverage.*\n.cache\nnosetests.xml\ncoverage.xml\n*.cover\n*.py,cover\n.hypothesis/\n.pytest_cache/\ncover/\n\n# Translations\n*.mo\n*.pot\n\n# Django stuff:\n*.log\nlocal_settings.py\ndb.sqlite3\ndb.sqlite3-journal\n\n# Flask stuff:\ninstance/\n.webassets-cache\n\n# Scrapy stuff:\n.scrapy\n\n# Sphinx documentation\ndocs/_build/\n\n# PyBuilder\n.pybuilder/\ntarget/\n\n# Jupyter Notebook\n.ipynb_checkpoints\n\n# IPython\nprofile_default/\nipython_config.py\n\n# pyenv\n# For a library or package, you might want to ignore these files since the code is\n# intended to run in multiple environments; otherwise, check them in:\n# .python-version\n\n# pipenv\n# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.\n# However, in case of collaboration, if having platform-specific dependencies or dependencies\n# having no cross-platform support, pipenv may install dependencies that don't work, or not\n# install all needed dependencies.\n#Pipfile.lock\n\n# poetry\n# Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.\n# This is especially recommended for binary packages to ensure reproducibility, and is more\n# commonly ignored for libraries.\n# https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control\n#poetry.lock\n\n# pdm\n# Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control.\n#pdm.lock\n# pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it\n# in version control.\n# https://pdm.fming.dev/#use-with-ide\n.pdm.toml\n\n# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm\n__pypackages__/\n\n# Celery stuff\ncelerybeat-schedule\ncelerybeat.pid\n\n# SageMath parsed files\n*.sage.py\n\n# Environments\n.env\n.venv\nenv/\nvenv/\nENV/\nenv.bak/\nvenv.bak/\n\n# Spyder project settings\n.spyderproject\n.spyproject\n\n# Rope project settings\n.ropeproject\n\n# mkdocs documentation\n/site\n\n# mypy\n.mypy_cache/\n.dmypy.json\ndmypy.json\n\n# Pyre type checker\n.pyre/\n\n# pytype static type analyzer\n.pytype/\n\n# Cython debug symbols\ncython_debug/\n\n# PyCharm\n# JetBrains specific template is maintained in a separate JetBrains.gitignore that can\n# be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore\n# and can be added to the global gitignore or merged into this file. For a more nuclear\n# option (not recommended) you can uncomment the following to ignore the entire idea folder.\n#.idea/\n\n#Desktop Services Store\n.DS_Store" }, { "path": "satisfaction_analytics_zendesk_support/README.md", "content": "# Customer Satisfaction Analytics Stack With Zendesk Support, Airbyte, Dbt, Dagster and BigQuery\n\nWelcome to the \"Customer Satisfaction Analytics Stack\" repository! ✨ This is your go-to place to easily set up a data stack using Zendesk Support, Airbyte, Dbt, BigQuery, and Dagster. With this setup, you can pull Zendesk Support data, extract it using Airbyte, put it into BigQuery, and play around with it using dbt and Dagster.\n\nThis Quickstart is all about making things easy, getting you started quickly and showing you how smoothly all these tools can work together!\n\nBelow is a visual representation of how data flows through our integrated tools in this Quickstart. This comes from Dagster's global asset lineage view:\n\n![Global Asset Lineage](<./assets/Global_Asset_Lineage%20(3).svg>)\n\n## Table of Contents\n\n- [Prerequisites](#prerequisites)\n- [Setting an environment for your project](#1-setting-an-environment-for-your-project)\n- [Setting Up BigQuery to work with Airbyte and dbt](#2-setting-up-bigquery)\n- [Setting Up Airbyte Connectors with Terraform](#3-setting-up-airbyte-connectors-with-terraform)\n- [Setting Up the dbt Project](#4-setting-up-the-dbt-project)\n- [Orchestrating with Dagster](#5-orchestrating-with-dagster)\n- [Next Steps](#next-steps)\n\n## Prerequisites\n\nBefore you embark on this integration, ensure you have the following set up and ready:\n\n1. **Python 3.10 or later**: If not installed, download and install it from [Python's official website](https://www.python.org/downloads/).\n\n2. **Docker and Docker Compose (Docker Desktop)**: Install [Docker](https://docs.docker.com/get-docker/) following the official documentation for your specific OS.\n\n3. **Airbyte OSS version**: Deploy the open-source version of Airbyte. Follow the installation instructions from the [Airbyte Documentation](https://docs.airbyte.com/quickstart/deploy-airbyte/).\n\n4. **Terraform**: Terraform will help you provision and manage the Airbyte resources. If you haven't installed it, follow the [official Terraform installation guide](https://developer.hashicorp.com/terraform/tutorials/aws-get-started/install-cli).\n\n5. **Google Cloud account with BigQuery**: You will also need to add the necessary permissions to allow Airbyte and dbt to access the data in BigQuery. A step-by-step guide is provided [below](#2-setting-up-bigquery).\n\n## 1. Setting an environment for your project\n\nGet the project up and running on your local machine by following these steps:\n\n1. **Clone the repository (Clone only this quickstart)**:\n\n ```bash\n git clone --filter=blob:none --sparse https://github.com/airbytehq/quickstarts.git\n ```\n\n ```bash\n cd quickstarts\n ```\n\n ```bash\n git sparse-checkout add satisfaction_analytics_zendesk_support\n ```\n\n2. **Navigate to the directory**:\n\n ```bash\n cd satisfaction_analytics_zendesk_support\n ```\n\n3. **Set Up a Virtual Environment**:\n\n - For Mac:\n ```bash\n python3 -m venv venv\n source venv/bin/activate\n ```\n - For Windows:\n ```bash\n python -m venv venv\n .\\venv\\Scripts\\activate\n ```\n\n4. **Install Dependencies**:\n ```bash\n pip install -e \".[dev]\"\n ```\n\n## 2. Setting Up BigQuery\n\n#### 1. **Create a Google Cloud Project**\n\n- If you have a Google Cloud project, you can skip this step.\n- Go to the [Google Cloud Console](https://console.cloud.google.com/).\n- Click on the \"Select a project\" dropdown at the top right and select \"New Project\".\n- Give your project a name and follow the steps to create it.\n\n#### 2. **Create BigQuery Datasets**\n\n- In the Google Cloud Console, go to BigQuery.\n- Make two new datasets: `raw_data` for Airbyte and `transformed_data` for dbt.\n - If you pick different names, remember to change the names in the code too.\n\n**How to create a dataset:**\n\n- In the left sidebar, click on your project name.\n- Click “Create Dataset”.\n- Enter the dataset ID (either `raw_data` or `transformed_data`).\n- Click \"Create Dataset\".\n\n#### 3. **Create Service Accounts and Assign Roles**\n\n- Go to “IAM & Admin” > “Service accounts” in the Google Cloud Console.\n- Click “Create Service Account”.\n- Name your service account (like `airbyte-service-account`).\n- Assign the “BigQuery Data Editor” and “BigQuery Job User” roles to the service account.\n- Follow the same steps to make another service account for dbt (like `dbt-service-account`) and assign the roles.\n\n**How to create a service account and assign roles:**\n\n- While creating the service account, under the “Grant this service account access to project” section, click the “Role” dropdown.\n- Choose the “BigQuery Data Editor” and “BigQuery Job User” roles.\n- Finish the creation process.\n\n#### 4. **Generate JSON Keys for Service Accounts**\n\n- For both service accounts, make a JSON key to let the service accounts sign in.\n\n**How to generate JSON key:**\n\n- Find the service account in the “Service accounts” list.\n- Click on the service account name.\n- In the “Keys” section, click “Add Key” and pick JSON.\n- The key will download automatically. Keep it safe and don’t share it.\n- Do this for the other service account too.\n\n## 3. Setting Up Airbyte Connectors with Terraform\n\nAirbyte allows you to create connectors for sources and destinations, facilitating data synchronization between various platforms. In this project, we're harnessing the power of Terraform to automate the creation of these connectors and the connections between them. Here's how you can set this up:\n\n1. **Navigate to the Airbyte Configuration Directory**:\n\n Change to the relevant directory containing the Terraform configuration for Airbyte:\n\n ```bash\n cd infra/airbyte\n ```\n\n2. **Modify Configuration Files**:\n\n Within the `infra/airbyte` directory, you'll find three crucial Terraform files:\n\n - `provider.tf`: Defines the Airbyte provider.\n - `main.tf`: Contains the main configuration for creating Airbyte resources.\n - `variables.tf`: Holds various variables, including credentials.\n\n Adjust the configurations in these files to suit your project's needs. Specifically, provide credentials for your BigQuery connection. You can utilize the `variables.tf` file to manage these credentials.\n\n3. **Initialize Terraform**:\n\n This step prepares Terraform to create the resources defined in your configuration files.\n\n ```bash\n terraform init\n ```\n\n4. **Review the Plan**:\n\n Before applying any changes, review the plan to understand what Terraform will do.\n\n ```bash\n terraform plan\n ```\n\n5. **Apply Configuration**:\n\n After reviewing and confirming the plan, apply the Terraform configurations to create the necessary Airbyte resources.\n\n ```bash\n terraform apply\n ```\n\n6. **Verify in Airbyte UI**:\n\n Once Terraform completes its tasks, navigate to the [Airbyte UI](http://localhost:8000/). Here, you should see your source and destination connectors, as well as the connection between them, set up and ready to go.\n\n## 4. Setting Up the dbt Project\n\n[dbt (data build tool)](https://www.getdbt.com/) allows you to transform your data by writing, documenting, and executing SQL workflows. Setting up the dbt project requires specifying connection details for your data platform, in this case, BigQuery. Here’s a step-by-step guide to help you set this up:\n\n1. **Navigate to the dbt Project Directory**:\n\n Change to the directory containing the dbt configuration:\n\n ```bash\n cd dbt_project\n ```\n\n2. **Update Connection Details**:\n\n You'll find a `profiles.yml` file within the directory. This file contains configurations for dbt to connect with your data platform. Update this file with your BigQuery connection details.\n\n3. **Utilize Environment Variables (Optional but Recommended)**:\n\n To keep your credentials secure, you can leverage environment variables. An example is provided within the `profiles.yml` file.\n\n4. **Test the Connection**:\n\n Once you’ve updated the connection details, you can test the connection to your BigQuery instance using:\n\n ```bash\n dbt debug\n ```\n\n If everything is set up correctly, this command should report a successful connection to BigQuery.\n\n5. **Run the Models**:\n\n If you would like to run the dbt models manually at this point, you can do so by executing:\n\n ```bash\n dbt run\n ```\n\n You can verify the data has been transformed by going to BigQuery and checking the `transformed_data` dataset.\n\n## 5. Orchestrating with Dagster\n\n[Dagster](https://dagster.io/) is a modern data orchestrator designed to help you build, test, and monitor your data workflows. In this section, we'll walk you through setting up Dagster to oversee both the Airbyte and dbt workflows:\n\n1. **Navigate to the Orchestration Directory**:\n\n Switch to the directory containing the Dagster orchestration configurations:\n\n ```bash\n cd orchestration\n ```\n\n2. **Set Environment Variables**:\n\n Dagster requires certain environment variables to be set to interact with other tools like dbt and Airbyte. Set the following variables:\n\n ```bash\n export DAGSTER_DBT_PARSE_PROJECT_ON_LOAD=1\n export AIRBYTE_PASSWORD=password\n ```\n\n Note: The `AIRBYTE_PASSWORD` is set to `password` as a default for local Airbyte instances. If you've changed this during your Airbyte setup, ensure you use the appropriate password here.\n\n3. **Launch the Dagster UI**:\n\n With the environment variables in place, kick-start the Dagster UI:\n\n ```bash\n dagster dev\n ```\n\n4. **Access Dagster in Your Browser**:\n\n Open your browser and navigate to:\n\n ```\n http://127.0.0.1:3000\n ```\n\n Here, you should see assets for both Airbyte and dbt. To get an overview of how these assets interrelate, click on \"view global asset lineage\". This will give you a clear picture of the data lineage, visualizing how data flows between the tools.\n\n5. **Materialize Dagster Assets**:\n In the Dagster UI, click on \"Materialize all\". This should trigger the full pipeline. First the Airbyte sync to extract data from Faker and load it into BigQuery, and then dbt to transform the raw data, materializing the `staging` and `marts` models.\n\n## Next Steps\n\nCongratulations on deploying and running the Customer Satisfaction Analytics Quistart! 🎉 Here are some suggestions on what you can explore next to dive deeper and get more out of your project:\n\n### 1. **Explore the Data and Insights**\n - Dive into the datasets in BigQuery, run some queries, and explore the data you've collected and transformed. This is your chance to uncover insights and understand the data better!\n\n### 2. **Optimize Your dbt Models**\n - Review the transformations you’ve applied using dbt. Try optimizing the models or create new ones based on your evolving needs and insights you want to extract.\n\n### 3. **Automate and Monitor Your Pipelines**\n - Explore more advanced Dagster configurations and setups to automate your pipelines further and set up monitoring and alerting to be informed of any issues immediately." }, { "path": "satisfaction_analytics_zendesk_support/dbt_project/.gitignore", "content": "\ntarget/\ndbt_packages/\nlogs/\n\n.user.yml\n" }, { "path": "satisfaction_analytics_zendesk_support/dbt_project/README.md", "content": "Welcome to your new dbt project!\n\n### Using the starter project\n\nTry running the following commands:\n\n- dbt run\n- dbt test\n\n### Resources:\n\n- Learn more about dbt [in the docs](https://docs.getdbt.com/docs/introduction)\n- Check out [Discourse](https://discourse.getdbt.com/) for commonly asked questions and answers\n- Join the [chat](https://community.getdbt.com/) on Slack for live discussions and support\n- Find [dbt events](https://events.getdbt.com) near you\n- Check out [the blog](https://blog.getdbt.com/) for the latest news on dbt's development and best practices\n" }, { "path": "satisfaction_analytics_zendesk_support/dbt_project/analysis/.gitkeep", "content": "" }, { "path": "satisfaction_analytics_zendesk_support/dbt_project/dbt_project.yml", "content": "\n# Name your project! Project names should contain only lowercase characters\n# and underscores. A good package name should reflect your organization's\n# name or the intended use of these models\nname: 'dbt_project'\nversion: '1.0.0'\nconfig-version: 2\n\n# This setting configures which \"profile\" dbt uses for this project.\nprofile: 'dbt_project'\n\n# These configurations specify where dbt should look for different types of files.\n# The `model-paths` config, for example, states that models in this project can be\n# found in the \"models/\" directory. You probably won't need to change these!\nmodel-paths: [\"models\"]\nanalysis-paths: [\"analyses\"]\ntest-paths: [\"tests\"]\nseed-paths: [\"seeds\"]\nmacro-paths: [\"macros\"]\nsnapshot-paths: [\"snapshots\"]\n\nclean-targets: # directories to be removed by `dbt clean`\n - \"target\"\n - \"dbt_packages\"\n\n\n# Configuring models\n# Full documentation: https://docs.getdbt.com/docs/configuring-models\n\n# In this example config, we tell dbt to build all models in the example/\n# directory as views. These settings can be overridden in the individual model\n# files using the `{{ config(...) }}` macro.\nmodels:\n dbt_project:\n # Config indicated by + and applies to all files under models/example/\n staging:\n +materialized: view\n marts:\n +materialized: view\n" }, { "path": "satisfaction_analytics_zendesk_support/dbt_project/models/marts/analyze_satisfaction_score_over_time.sql", "content": "SELECT\n DATE_TRUNC(created_at, DAY) AS date,\n AVG(CAST(score AS FLOAT64)) AS avg_satisfaction_score\nFROM\n transformed_data.stg_satisfaction_ratings\nWHERE\n score IS NOT NULL\nGROUP BY\n date\nORDER BY\n date\n" }, { "path": "satisfaction_analytics_zendesk_support/dbt_project/models/marts/avarage_satisfaction_rating.sql", "content": "SELECT\n AVG(CAST(score AS FLOAT64)) AS avg_satisfaction_score\nFROM\n transformed_data.stg_satisfaction_ratings\nWHERE\n score IS NOT NULL\n\n" }, { "path": "satisfaction_analytics_zendesk_support/dbt_project/models/marts/feedback_analysis_for_low_score.sql", "content": "SELECT\n reason,\n COUNT(*) AS count\nFROM\n transformed_data.stg_satisfaction_ratings\nWHERE\n CAST(score AS INT64) <= 2\n AND reason IS NOT NULL\nGROUP BY\n reason\nORDER BY\n count DESC\n" }, { "path": "satisfaction_analytics_zendesk_support/dbt_project/models/marts/trend_analysis_by_score.sql", "content": "SELECT\n score,\n COUNT(*) AS count\nFROM\n transformed_data.stg_satisfaction_ratings\nWHERE\n score IS NOT NULL\nGROUP BY\n score\nORDER BY\n score\n" }, { "path": "satisfaction_analytics_zendesk_support/dbt_project/models/sources/zendesk_support_sources.yml", "content": "version: 2\n\nsources:\n - name: zendesk_support\n # Use your BigQuery project ID\n database: \"{{ env_var('BIGQUERY_PROJECT_ID', '') }}\"\n # Use your BigQuery dataset name\n schema: zendesk_airbyte_trial\n\n tables:\n - name: users\n description: \"Simulated user data from the Zendesk Support connector.\"\n columns:\n - name: id\n description: \"Unique identifier for the user.\"\n - name: active\n - name: alias\n - name: chat_only\n - name: created_at\n - name: custom_role_id\n - name: default_group_id\n - name: details\n - name: email\n - name: external_id\n - name: updated_at\n - name: iana_time_zone\n - name: last_login_at\n - name: locale\n - name: locale_id\n - name: moderator\n - name: name\n - name: notes\n - name: only_private_comments\n - name: organization_id\n - name: permanently_deleted\n - name: phone\n - name: photo\n - name: report_csv\n - name: restricted_agent\n - name: role\n - name: role_type\n - name: shared\n - name: shared_agent\n - name: shared_phone_number\n - name: signature\n - name: suspended\n - name: tags\n - name: ticket_restriction\n - name: time_zone\n - name: two_factor_auth_enabled\n - name: url\n - name: user_fields\n - name: verified\n\n - name: tickets\n description: \"Simulated tickets data from the Zendesk Support connector.\"\n columns:\n - name: id\n description: \"Unique identifier for the ticket.\"\n - name: updated_at\n - name: allow_attachments\n - name: allow_channelback\n - name: assignee_id\n - name: brand_id\n - name: collaborator_ids\n - name: created_at\n - name: custom_fields\n - name: custom_status_id\n - name: description\n - name: due_at\n - name: email_cc_ids\n - name: external_id\n - name: fields\n - name: follower_ids \n - name: followup_ids\n - name: forum_topic_id\n - name: from_messaging_channel\n - name: generated_timestamp\n - name: group_id\n - name: has_incidents\n - name: is_public\n - name: organization_id\n - name: priority\n - name: problem_id\n - name: raw_subject\n - name: recipient\n - name: requester_id\n - name: satisfaction_rating\n - name: sharing_agreement_ids\n - name: status\n - name: subject\n - name: submitter_id\n - name: tags\n - name: ticket_form_id\n - name: type\n - name: url\n - name: via\n\n\n - name: satisfaction_ratings\n description: \"Simulated satisfaction_ratings data from the Zendesk Support connector.\"\n columns:\n - name: id\n description: \"Unique identifier for the satisfaction_ratings.\"\n - name: updated_at\n - name: created_at\n - name: assignee_id\n - name: comment\n - name: group_id\n - name: reason\n - name: reason_id\n - name: requester_id\n - name: score\n - name: ticket_id\n - name: url\n\n - name: brands\n description: \"Simulated brands data from the Zendesk Support connector.\"\n columns:\n - name: id\n description: \"Unique identifier for the brands.\"\n - name: updated_at\n - name: created_at\n - name: url\n - name: active\n - name: brand_url\n - name: default\n - name: has_help_center\n - name: help_center_state\n - name: host_mapping\n - name: is_deleted\n - name: logo\n - name: name\n - name: signature_template\n - name: subdomain\n - name: ticket_form_ids\n\n - name: groups\n description: \"Simulated groups data from the Zendesk Support connector.\"\n columns:\n - name: id\n description: \"Unique identifier for the groups.\"\n - name: updated_at\n - name: created_at\n - name: url\n - name: deleted\n - name: description\n - name: default\n - name: is_public\n - name: name\n\n - name: organizations\n description: \"Simulated organizations data from the Zendesk Support connector.\"\n columns:\n - name: id\n description: \"Unique identifier for the organizations.\"\n - name: updated_at\n - name: created_at\n - name: url\n - name: details\n - name: domain_names\n - name: external_id\n - name: group_id\n - name: notes\n - name: organization_fields\n - name: deleted_at\n - name: shared_comments\n - name: name\n - name: shared_tickets\n - name: tags\n\n - name: tags\n description: \"Simulated tags data from the Zendesk Support connector.\"\n columns:\n - name: name\n - name: count\n\n - name: ticket_audits\n description: \"Simulated ticket_audits data from the Zendesk Support connector.\"\n columns:\n - name: id\n description: \"Unique identifier for the ticket_audits.\"\n - name: author_id\n - name: created_at\n - name: events\n - name: metadata\n - name: ticket_id\n - name: via\n\n - name: ticket_comments\n description: \"Simulated ticket_comments data from the Zendesk Support connector.\"\n columns:\n - name: id\n description: \"Unique identifier for the ticket_comments.\"\n - name: attachments\n - name: created_at\n - name: audit_id\n - name: author_id\n - name: body\n - name: event_type\n - name: html_body\n - name: metadata\n - name: plain_body\n - name: public\n - name: ticket_id\n - name: timestamp\n - name: type\n - name: uploads\n - name: via\n - name: via_reference_id\n\n - name: ticket_fields\n description: \"Simulated ticket_fields data from the Zendesk Support connector.\"\n columns:\n - name: id\n description: \"Unique identifier for the ticket_fields.\"\n - name: active\n - name: created_at\n - name: agent_description\n - name: collapsed_for_agents\n - name: custom_field_options\n - name: custom_statuses\n - name: description\n - name: editable_in_portal\n - name: key\n - name: position\n - name: raw_description\n - name: raw_title\n - name: raw_title_in_portal\n - name: regexp_for_validation\n - name: removable\n - name: required\n - name: required_in_portal\n - name: sub_type_id\n - name: system_field_options\n - name: tag\n - name: title\n - name: title_in_portal\n - name: type\n - name: updated_at\n - name: url\n - name: visible_in_portal\n \n - name: ticket_forms\n description: \"Simulated ticket_forms data from the Zendesk Support connector.\"\n columns:\n - name: id\n description: \"Unique identifier for the ticket_forms.\"\n - name: active\n - name: created_at\n - name: agent_conditions\n - name: default\n - name: display_name\n - name: end_user_conditions\n - name: end_user_visible\n - name: in_all_brands\n - name: name\n - name: position\n - name: raw_display_name\n - name: raw_name\n - name: restricted_brand_ids\n - name: ticket_field_ids\n - name: updated_at\n - name: url\n\n - name: ticket_metric_events\n description: \"Simulated ticket_metric_events data from the Zendesk Support connector.\"\n columns:\n - name: id\n description: \"Unique identifier for the ticket_metric_events.\"\n - name: instance_id\n - name: metric\n - name: time\n - name: type\n - name: ticket_id\n\n - name: ticket_metrics\n description: \"Simulated ticket_metrics data from the Zendesk Support connector.\"\n columns:\n - name: id\n description: \"Unique identifier for the ticket_metrics.\"\n - name: agent_wait_time_in_minutes\n - name: created_at\n - name: assigned_at\n - name: assignee_stations\n - name: assignee_updated_at\n - name: custom_status_updated_at\n - name: first_resolution_time_in_minutes\n - name: full_resolution_time_in_minutes\n - name: group_stations\n - name: initially_assigned_at\n - name: instance_id\n - name: latest_comment_added_at\n - name: metric\n - name: on_hold_time_in_minutes\n - name: updated_at\n - name: url\n - name: reopens\n - name: replies\n - name: reply_time_in_minutes\n - name: requester_updated_at\n - name: requester_wait_time_in_minutes\n - name: solved_at\n - name: status\n - name: status_updated_at\n - name: ticket_id\n - name: time\n - name: type\n \n \n \n \n \n \n \n\n" }, { "path": "satisfaction_analytics_zendesk_support/dbt_project/models/staging/stg_brands.sql", "content": "select\n *\nfrom {{ source('zendesk_support', 'brands') }}" }, { "path": "satisfaction_analytics_zendesk_support/dbt_project/models/staging/stg_groups.sql", "content": "select\n *\nfrom {{ source('zendesk_support', 'groups') }}" }, { "path": "satisfaction_analytics_zendesk_support/dbt_project/models/staging/stg_organizations.sql", "content": "select\n *\nfrom {{ source('zendesk_support', 'organizations') }}" }, { "path": "satisfaction_analytics_zendesk_support/dbt_project/models/staging/stg_satisfaction_ratings.sql", "content": "select\n *\nfrom {{ source('zendesk_support', 'satisfaction_ratings') }}\n" }, { "path": "satisfaction_analytics_zendesk_support/dbt_project/models/staging/stg_tags.sql", "content": "select\n *\nfrom {{ source('zendesk_support', 'tags') }}" }, { "path": "satisfaction_analytics_zendesk_support/dbt_project/models/staging/stg_ticket_audits.sql", "content": "select\n *\nfrom {{ source('zendesk_support', 'ticket_audits') }}" }, { "path": "satisfaction_analytics_zendesk_support/dbt_project/models/staging/stg_ticket_comments.sql", "content": "select\n *\nfrom {{ source('zendesk_support', 'ticket_comments') }}" }, { "path": "satisfaction_analytics_zendesk_support/dbt_project/models/staging/stg_ticket_fields.sql", "content": "select\n *\nfrom {{ source('zendesk_support', 'ticket_fields') }}" }, { "path": "satisfaction_analytics_zendesk_support/dbt_project/models/staging/stg_ticket_forms.sql", "content": "select\n *\nfrom {{ source('zendesk_support', 'ticket_forms') }}" }, { "path": "satisfaction_analytics_zendesk_support/dbt_project/models/staging/stg_ticket_metric_events.sql", "content": "select\n *\nfrom {{ source('zendesk_support', 'ticket_metric_events') }}" }, { "path": "satisfaction_analytics_zendesk_support/dbt_project/models/staging/stg_ticket_metrics.sql", "content": "select\n *\nfrom {{ source('zendesk_support', 'ticket_metrics') }}" }, { "path": "satisfaction_analytics_zendesk_support/dbt_project/models/staging/stg_tickets.sql", "content": "select\n*\nfrom {{ source('zendesk_support', 'tickets') }}\n" }, { "path": "satisfaction_analytics_zendesk_support/dbt_project/models/staging/stg_users.sql", "content": "select\n *\nfrom {{ source('zendesk_support', 'users') }}\n" }, { "path": "satisfaction_analytics_zendesk_support/dbt_project/profiles.yml", "content": "dbt_project:\n outputs:\n dev:\n dataset: transformed_data\n job_execution_timeout_seconds: 300\n job_retries: 1\n # Use an env variable to indicate your JSON key file path\n keyfile: \"{{ env_var('DBT_BIGQUERY_KEYFILE_PATH', '') }}\"\n location: US\n method: service-account\n priority: interactive\n # Indicate your BigQuery project ID\n project: \"{{ env_var('BIGQUERY_PROJECT_ID', '') }}\"\n threads: 1\n type: bigquery\n target: dev" }, { "path": "satisfaction_analytics_zendesk_support/dbt_project/tests/.gitkeep", "content": "" }, { "path": "satisfaction_analytics_zendesk_support/infra/.gitignore", "content": "# Local .terraform directories\n**/.terraform/*\n\n# .tfstate files\n*.tfstate\n*.tfstate.*\n\n# Crash log files\ncrash.log\ncrash.*.log\n\n# Exclude all .tfvars files, which are likely to contain sensitive data, such as\n# password, private keys, and other secrets. These should not be part of version \n# control as they are data points which are potentially sensitive and subject \n# to change depending on the environment.\n*.tfvars\n*.tfvars.json\n\n# Ignore override files as they are usually used to override resources locally and so\n# are not checked in\noverride.tf\noverride.tf.json\n*_override.tf\n*_override.tf.json\n\n# Include override files you do wish to add to version control using negated pattern\n# !example_override.tf\n\n# Include tfplan files to ignore the plan output of command: terraform plan -out=tfplan\n# example: *tfplan*\n\n# Ignore CLI configuration files\n.terraformrc\nterraform.rc" }, { "path": "satisfaction_analytics_zendesk_support/infra/airbyte/.terraform.lock.hcl", "content": "# This file is maintained automatically by \"terraform init\".\n# Manual edits may be lost in future updates.\n\nprovider \"registry.terraform.io/airbytehq/airbyte\" {\n version = \"0.3.3\"\n constraints = \"0.3.3\"\n hashes = [\n \"h1:0LmuAc5LvlMuOUPtNEaCAh9FHrV/C877bDJhm9Lz8MU=\",\n \"zh:0efa470b34d9b912b47efe4469c51713bfc3c2413e52c17e1e903f2a3cddb2f6\",\n \"zh:1bddd69fa2c2d4f3e239d60555446df9bc4ce0c0cabbe7e092fe1d44989ab004\",\n \"zh:2e20540403a0010007b53456663fb037b24e30f6c8943f65da1bcf7fa4dfc8a6\",\n \"zh:2f415369ad884e8b7115a5c5ff229d052f7af1fca27abbfc8ebef379ed11aec4\",\n \"zh:46fd9a906f4b6461112dcc5a5aa01a3fcd7a19a72d4ad0b2e37790da37701fe1\",\n \"zh:83503ebb77bb6d6941c42ba323cf22380d08a1506554a2dcc8ac54e74c0886a1\",\n \"zh:890df766e9b839623b1f0437355032a3c006226a6c200cd911e15ee1a9014e9f\",\n \"zh:8fd770eff726826d3a63b9e3733c5455b5cde004027b04ee3f75888eb8538c90\",\n \"zh:b0fc890ed4f9b077bf70ed121cc3550e7a07d16e7798ad517623274aa62ad7b0\",\n \"zh:c2a01612362da9b73cd5958f281e1aa7ff09af42182e463097d11ed78e778e72\",\n \"zh:c64b2bb1887a0367d64ba3393d4b3a16c418cf5b1792e2e7aae7c0b5413eb334\",\n \"zh:ce14ebbf0ed91913ec62655a511763dec62b5779de9a209bd6f1c336640cddc0\",\n \"zh:e0662ca837eee10f7733ea9a501d995281f56bd9b410ae13ad03eb106011db14\",\n \"zh:e103d480fc6066004bc98e9e04a141a1f55b918cc2912716beebcc6fc4c872fb\",\n \"zh:e2507049098f0f1b21cb56870f4a5ef624bcf6d3959e5612eada1f8117341648\",\n ]\n}\n" }, { "path": "satisfaction_analytics_zendesk_support/infra/airbyte/main.tf", "content": "// Airbyte Terraform provider documentation: https://registry.terraform.io/providers/airbytehq/airbyte/latest/docs\n\n// Sources\nresource \"airbyte_source_zendesk_support\" \"my_source_zendesksupport\" {\n configuration = {\n source_type = \"zendesk-support\"\n credentials = {\n source_zendesk_support_authentication_api_token = {\n credentials = \"api_token\"\n api_token = var.api_token\n email = var.email\n\n }\n\n }\n start_date = \"2020-10-15T00:00:00Z\"\n subdomain = \"self3836\"\n }\n name = \"Zendesk Support\"\n workspace_id = var.workspace_id\n}\n\n// Destinations\nresource \"airbyte_destination_bigquery\" \"bigquery\" {\n configuration = {\n dataset_id = var.dataset_id\n dataset_location = \"US\"\n destination_type = \"bigquery\"\n project_id = var.project_id\n credentials_json = var.credentials_json\n loading_method = {\n destination_bigquery_loading_method_standard_inserts = {\n method = \"Standard\"\n }\n }\n }\n name = \"BigQuery\"\n workspace_id = var.workspace_id\n}\n\n// Connections\nresource \"airbyte_connection\" \"zendesk_support_bigquery\" {\n name = \"Zendesk Support to BigQuery\"\n source_id = airbyte_source_zendesk_support.my_source_zendesksupport.source_id\n destination_id = airbyte_destination_bigquery.bigquery.destination_id\n configurations = {\n streams = [\n {\n name = \"users\"\n },\n {\n name = \"tickets\"\n },\n {\n name = \"satisfaction_ratings\"\n },\n {\n name = \"ticket_metrics\"\n },\n {\n name = \"ticket_metric_events\"\n },\n {\n name = \"ticket_comments\"\n },\n {\n name = \"ticket_audits\"\n }\n ]\n }\n}\n" }, { "path": "satisfaction_analytics_zendesk_support/infra/airbyte/provider.tf", "content": "// Airbyte Terraform provider documentation: https://registry.terraform.io/providers/airbytehq/airbyte/latest/docs\n\nterraform {\n required_providers {\n airbyte = {\n source = \"airbytehq/airbyte\"\n version = \"0.3.3\"\n }\n }\n}\n\nprovider \"airbyte\" {\n // If running locally (Airbyte OSS) with docker-compose using the airbyte-proxy, \n // include the actual password/username you've set up (or use the defaults below)\n username = \"airbyte\"\n password = \"password\"\n\n // if running locally (Airbyte OSS), include the server url to the airbyte-api-server\n server_url = \"http://localhost:8006/v1/\"\n}\n" }, { "path": "satisfaction_analytics_zendesk_support/infra/airbyte/variables.tf", "content": "variable \"api_token\" {\n type = string\n}\n\nvariable \"email\" {\n type = string\n}\n\nvariable \"workspace_id\" {\n type = string\n}\n\nvariable \"dataset_id\" {\n type = string\n}\n\nvariable \"project_id\" {\n type = string\n}\n\nvariable \"credentials_json\" {\n type = string\n}\n\n\n\n\n" }, { "path": "satisfaction_analytics_zendesk_support/orchestration/orchestration/__init__.py", "content": "" }, { "path": "satisfaction_analytics_zendesk_support/orchestration/orchestration/assets.py", "content": "import os\nfrom dagster import OpExecutionContext\nfrom dagster_dbt import DbtCliResource, dbt_assets\nfrom dagster_airbyte import AirbyteResource, load_assets_from_airbyte_instance\n\nfrom .constants import dbt_manifest_path\n\n\n@dbt_assets(manifest=dbt_manifest_path)\ndef dbt_project_dbt_assets(context: OpExecutionContext, dbt: DbtCliResource):\n yield from dbt.cli([\"build\"], context=context).stream()\n\n\nairbyte_instance = AirbyteResource(\n host=\"localhost\",\n port=\"8000\",\n # If using basic auth, include username and password:\n username=\"airbyte\",\n password=\"password\"\n)\n\nairbyte_assets = load_assets_from_airbyte_instance(airbyte_instance,)\n" }, { "path": "satisfaction_analytics_zendesk_support/orchestration/orchestration/constants.py", "content": "import os\nfrom pathlib import Path\n\nfrom dagster_dbt import DbtCliResource\n\ndbt_project_dir = Path(__file__).joinpath(\"..\", \"..\", \"..\", \"dbt_project\").resolve()\ndbt = DbtCliResource(project_dir=os.fspath(dbt_project_dir))\n\n# If DAGSTER_DBT_PARSE_PROJECT_ON_LOAD is set, a manifest will be created at runtime.\n# Otherwise, we expect a manifest to be present in the project's target directory.\nif os.getenv(\"DAGSTER_DBT_PARSE_PROJECT_ON_LOAD\"):\n dbt_parse_invocation = dbt.cli([\"parse\"], manifest={}).wait()\n dbt_manifest_path = dbt_parse_invocation.target_path.joinpath(\"manifest.json\")\nelse:\n dbt_manifest_path = dbt_project_dir.joinpath(\"target\", \"manifest.json\")" }, { "path": "satisfaction_analytics_zendesk_support/orchestration/orchestration/definitions.py", "content": "import os\n\nfrom dagster import Definitions\nfrom dagster_dbt import DbtCliResource\n\nfrom .assets import dbt_project_dbt_assets, airbyte_assets\nfrom .constants import dbt_project_dir\nfrom .schedules import schedules\n\ndefs = Definitions(\n assets=[dbt_project_dbt_assets, airbyte_assets],\n schedules=schedules,\n resources={\n \"dbt\": DbtCliResource(project_dir=os.fspath(dbt_project_dir)),\n },\n)\n" }, { "path": "satisfaction_analytics_zendesk_support/orchestration/orchestration/schedules.py", "content": "\"\"\"\nTo add a daily schedule that materializes your dbt assets, uncomment the following lines.\n\"\"\"\nfrom dagster_dbt import build_schedule_from_dbt_selection\n\nfrom .assets import dbt_project_dbt_assets\n\nschedules = [\n # build_schedule_from_dbt_selection(\n # [dbt_project_dbt_assets],\n # job_name=\"materialize_dbt_models\",\n # cron_schedule=\"0 0 * * *\",\n # dbt_select=\"fqn:*\",\n # ),\n]\n" }, { "path": "satisfaction_analytics_zendesk_support/orchestration/pyproject.toml", "content": "[build-system]\nrequires = [\"setuptools\"]\nbuild-backend = \"setuptools.build_meta\"\n\n[tool.dagster]\nmodule_name = \"orchestration.definitions\"\ncode_location_name = \"orchestration\"" }, { "path": "satisfaction_analytics_zendesk_support/orchestration/setup.py", "content": "from setuptools import find_packages, setup\n\nsetup(\n name=\"orchestration\",\n version=\"0.0.1\",\n packages=find_packages(),\n install_requires=[\n \"dagster\",\n \"dagster-cloud\",\n \"dagster-dbt\",\n \"dbt-core>=1.4.0\",\n \"dbt-bigquery\",\n ],\n extras_require={\n \"dev\": [\n \"dagster-webserver\",\n ]\n },\n)" }, { "path": "satisfaction_analytics_zendesk_support/orchestration/tmp3ks7pwhz/storage/ad21fadd-c131-4a7c-98a7-fa5ad3a929de/compute_logs/mdvhnoik.complete", "content": "" }, { "path": "satisfaction_analytics_zendesk_support/orchestration/tmp3ks7pwhz/storage/ad21fadd-c131-4a7c-98a7-fa5ad3a929de/compute_logs/mdvhnoik.err", "content": "" }, { "path": "satisfaction_analytics_zendesk_support/orchestration/tmp3ks7pwhz/storage/ad21fadd-c131-4a7c-98a7-fa5ad3a929de/compute_logs/mdvhnoik.out", "content": "" }, { "path": "satisfaction_analytics_zendesk_support/orchestration/tmp3ks7pwhz/storage/ad21fadd-c131-4a7c-98a7-fa5ad3a929de/compute_logs/uzgmeijp.complete", "content": "" }, { "path": "satisfaction_analytics_zendesk_support/orchestration/tmp3ks7pwhz/storage/ad21fadd-c131-4a7c-98a7-fa5ad3a929de/compute_logs/uzgmeijp.err", "content": "" }, { "path": "satisfaction_analytics_zendesk_support/orchestration/tmp3ks7pwhz/storage/ad21fadd-c131-4a7c-98a7-fa5ad3a929de/compute_logs/uzgmeijp.out", "content": "" }, { "path": "satisfaction_analytics_zendesk_support/orchestration/tmp3ks7pwhz/storage/f7507115-918d-443f-ab91-a065e84fa403/compute_logs/aeebjmfa.complete", "content": "" }, { "path": "satisfaction_analytics_zendesk_support/orchestration/tmp3ks7pwhz/storage/f7507115-918d-443f-ab91-a065e84fa403/compute_logs/aeebjmfa.err", "content": "" }, { "path": "satisfaction_analytics_zendesk_support/orchestration/tmp3ks7pwhz/storage/f7507115-918d-443f-ab91-a065e84fa403/compute_logs/aeebjmfa.out", "content": "" }, { "path": "satisfaction_analytics_zendesk_support/orchestration/tmp3ks7pwhz/storage/f7507115-918d-443f-ab91-a065e84fa403/compute_logs/zqbkkiww.complete", "content": "" }, { "path": "satisfaction_analytics_zendesk_support/orchestration/tmp3ks7pwhz/storage/f7507115-918d-443f-ab91-a065e84fa403/compute_logs/zqbkkiww.err", "content": "" }, { "path": "satisfaction_analytics_zendesk_support/orchestration/tmp3ks7pwhz/storage/f7507115-918d-443f-ab91-a065e84fa403/compute_logs/zqbkkiww.out", "content": "" }, { "path": "satisfaction_analytics_zendesk_support/orchestration/tmpb3ctnsbk/storage/0bc4e544-546d-44df-b79c-e75413c56ecb/compute_logs/xozgecli.complete", "content": "" }, { "path": "satisfaction_analytics_zendesk_support/orchestration/tmpb3ctnsbk/storage/0bc4e544-546d-44df-b79c-e75413c56ecb/compute_logs/xozgecli.err", "content": "" }, { "path": "satisfaction_analytics_zendesk_support/orchestration/tmpb3ctnsbk/storage/0bc4e544-546d-44df-b79c-e75413c56ecb/compute_logs/xozgecli.out", "content": "" }, { "path": "satisfaction_analytics_zendesk_support/orchestration/tmpb3ctnsbk/storage/0bc4e544-546d-44df-b79c-e75413c56ecb/compute_logs/yyxjctam.complete", "content": "" }, { "path": "satisfaction_analytics_zendesk_support/orchestration/tmpb3ctnsbk/storage/0bc4e544-546d-44df-b79c-e75413c56ecb/compute_logs/yyxjctam.err", "content": "" }, { "path": "satisfaction_analytics_zendesk_support/orchestration/tmpb3ctnsbk/storage/0bc4e544-546d-44df-b79c-e75413c56ecb/compute_logs/yyxjctam.out", "content": "" }, { "path": "satisfaction_analytics_zendesk_support/orchestration/tmpb3ctnsbk/storage/1eac78ed-12d1-4147-9c48-79b27dd586ed/compute_logs/iqvvuhde.complete", "content": "" }, { "path": "satisfaction_analytics_zendesk_support/orchestration/tmpb3ctnsbk/storage/1eac78ed-12d1-4147-9c48-79b27dd586ed/compute_logs/iqvvuhde.err", "content": "" }, { "path": "satisfaction_analytics_zendesk_support/orchestration/tmpb3ctnsbk/storage/1eac78ed-12d1-4147-9c48-79b27dd586ed/compute_logs/iqvvuhde.out", "content": "" }, { "path": "satisfaction_analytics_zendesk_support/orchestration/tmpb3ctnsbk/storage/1eac78ed-12d1-4147-9c48-79b27dd586ed/compute_logs/izklbfmq.complete", "content": "" }, { "path": "satisfaction_analytics_zendesk_support/orchestration/tmpb3ctnsbk/storage/1eac78ed-12d1-4147-9c48-79b27dd586ed/compute_logs/izklbfmq.err", "content": "" }, { "path": "satisfaction_analytics_zendesk_support/orchestration/tmpb3ctnsbk/storage/1eac78ed-12d1-4147-9c48-79b27dd586ed/compute_logs/izklbfmq.out", "content": "" }, { "path": "satisfaction_analytics_zendesk_support/setup.py", "content": "from setuptools import find_packages, setup\n\nsetup(\n name=\"airbyte-dbt-dagster\",\n packages=find_packages(),\n install_requires=[\n \"dbt-bigquery\",\n \"dagster\",\n \"dagster-cloud\",\n \"dagster-dbt\",\n \"dagster-airbyte\",\n ],\n extras_require={\"dev\": [\"dagit\", \"pytest\"]},\n)" }, { "path": "shopping_cart_analytics_shopify/.gitignore", "content": "# Byte-compiled / optimized / DLL files\n__pycache__/\n*.py[cod]\n*$py.class\n\n# C extensions\n*.so\n\n# Distribution / packaging\n.Python\nbuild/\ndevelop-eggs/\ndist/\ndownloads/\neggs/\n.eggs/\nlib/\nlib64/\nparts/\nsdist/\nvar/\nwheels/\nshare/python-wheels/\n*.egg-info/\n.installed.cfg\n*.egg\nMANIFEST\n\n# PyInstaller\n# Usually these files are written by a python script from a template\n# before PyInstaller builds the exe, so as to inject date/other infos into it.\n*.manifest\n*.spec\n\n# Installer logs\npip-log.txt\npip-delete-this-directory.txt\n\n# Unit test / coverage reports\nhtmlcov/\n.tox/\n.nox/\n.coverage\n.coverage.*\n.cache\nnosetests.xml\ncoverage.xml\n*.cover\n*.py,cover\n.hypothesis/\n.pytest_cache/\ncover/\n\n# Translations\n*.mo\n*.pot\n\n# Django stuff:\n*.log\nlocal_settings.py\ndb.sqlite3\ndb.sqlite3-journal\n\n# Flask stuff:\ninstance/\n.webassets-cache\n\n# Scrapy stuff:\n.scrapy\n\n# Sphinx documentation\ndocs/_build/\n\n# PyBuilder\n.pybuilder/\ntarget/\n\n# Jupyter Notebook\n.ipynb_checkpoints\n\n# IPython\nprofile_default/\nipython_config.py\n\n# pyenv\n# For a library or package, you might want to ignore these files since the code is\n# intended to run in multiple environments; otherwise, check them in:\n# .python-version\n\n# pipenv\n# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.\n# However, in case of collaboration, if having platform-specific dependencies or dependencies\n# having no cross-platform support, pipenv may install dependencies that don't work, or not\n# install all needed dependencies.\n#Pipfile.lock\n\n# poetry\n# Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.\n# This is especially recommended for binary packages to ensure reproducibility, and is more\n# commonly ignored for libraries.\n# https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control\n#poetry.lock\n\n# pdm\n# Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control.\n#pdm.lock\n# pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it\n# in version control.\n# https://pdm.fming.dev/#use-with-ide\n.pdm.toml\n\n# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm\n__pypackages__/\n\n# Celery stuff\ncelerybeat-schedule\ncelerybeat.pid\n\n# SageMath parsed files\n*.sage.py\n\n# Environments\n.env\n.venv\nenv/\nvenv/\nENV/\nenv.bak/\nvenv.bak/\n\n# Spyder project settings\n.spyderproject\n.spyproject\n\n# Rope project settings\n.ropeproject\n\n# mkdocs documentation\n/site\n\n# mypy\n.mypy_cache/\n.dmypy.json\ndmypy.json\n\n# Pyre type checker\n.pyre/\n\n# pytype static type analyzer\n.pytype/\n\n# Cython debug symbols\ncython_debug/\n\n# PyCharm\n# JetBrains specific template is maintained in a separate JetBrains.gitignore that can\n# be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore\n# and can be added to the global gitignore or merged into this file. For a more nuclear\n# option (not recommended) you can uncomment the following to ignore the entire idea folder.\n#.idea/\n\n#Desktop Services Store\n.DS_Store" }, { "path": "shopping_cart_analytics_shopify/README.md", "content": "# Shopping Cart Analytics Stack With Shopify, Airbyte, Dbt, Dagster and BigQuery\n\nWelcome to the \"Shopping Cart Analytics Stack\" repository! ✨ This is your go-to place to easily set up a data stack using Shopify, Airbyte, Dbt, BigQuery, and Dagster. With this setup, you can pull Shopify data, extract it using Airbyte, put it into BigQuery, and play around with it using dbt and Dagster.\n\nThis Quickstart is all about making things easy, getting you started quickly and showing you how smoothly all these tools can work together!\n\nBelow is a visual representation of how data flows through our integrated tools in this Quickstart. This comes from Dagster's global asset lineage view:\n\n![Global Asset Lineage](<./assets/Global_Asset_Lineage (8).svg>)\n\n## Table of Contents\n\n- [Prerequisites](#prerequisites)\n- [Setting an environment for your project](#1-setting-an-environment-for-your-project)\n- [Setting Up BigQuery to work with Airbyte and dbt](#2-setting-up-bigquery)\n- [Setting Up Airbyte Connectors with Terraform](#3-setting-up-airbyte-connectors-with-terraform)\n- [Setting Up the dbt Project](#4-setting-up-the-dbt-project)\n- [Orchestrating with Dagster](#5-orchestrating-with-dagster)\n- [Next Steps](#next-steps)\n\n## Prerequisites\n\nBefore you embark on this integration, ensure you have the following set up and ready:\n\n1. **Python 3.10 or later**: If not installed, download and install it from [Python's official website](https://www.python.org/downloads/).\n\n2. **Docker and Docker Compose (Docker Desktop)**: Install [Docker](https://docs.docker.com/get-docker/) following the official documentation for your specific OS.\n\n3. **Airbyte OSS version**: Deploy the open-source version of Airbyte. Follow the installation instructions from the [Airbyte Documentation](https://docs.airbyte.com/quickstart/deploy-airbyte/).\n\n4. **Terraform**: Terraform will help you provision and manage the Airbyte resources. If you haven't installed it, follow the [official Terraform installation guide](https://developer.hashicorp.com/terraform/tutorials/aws-get-started/install-cli).\n\n5. **Google Cloud account with BigQuery**: You will also need to add the necessary permissions to allow Airbyte and dbt to access the data in BigQuery. A step-by-step guide is provided [below](#2-setting-up-bigquery).\n\n## 1. Setting an environment for your project\n\nGet the project up and running on your local machine by following these steps:\n\n1. **Clone the repository (Clone only this quickstart)**:\n\n ```bash\n git clone --filter=blob:none --sparse https://github.com/airbytehq/quickstarts.git\n ```\n\n ```bash\n cd quickstarts\n ```\n\n ```bash\n git sparse-checkout add shopping_cart_analytics_shopify\n ```\n\n2. **Navigate to the directory**:\n\n ```bash\n cd shopping_cart_analytics_shopify\n ```\n\n3. **Set Up a Virtual Environment**:\n\n - For Mac:\n ```bash\n python3 -m venv venv\n source venv/bin/activate\n ```\n - For Windows:\n ```bash\n python -m venv venv\n .\\venv\\Scripts\\activate\n ```\n4. **Install Dependencies**:\n ```bash\n pip install -e \".[dev]\"\n ```\n\n## 2. Setting Up BigQuery\n\n#### 1. **Create a Google Cloud Project**\n\n- If you have a Google Cloud project, you can skip this step.\n- Go to the [Google Cloud Console](https://console.cloud.google.com/).\n- Click on the \"Select a project\" dropdown at the top right and select \"New Project\".\n- Give your project a name and follow the steps to create it.\n\n#### 2. **Create BigQuery Datasets**\n\n- In the Google Cloud Console, go to BigQuery.\n- Make two new datasets: `raw_data` for Airbyte and `transformed_data` for dbt.\n - If you pick different names, remember to change the names in the code too.\n\n**How to create a dataset:**\n\n- In the left sidebar, click on your project name.\n- Click “Create Dataset”.\n- Enter the dataset ID (either `raw_data` or `transformed_data`).\n- Click \"Create Dataset\".\n\n#### 3. **Create Service Accounts and Assign Roles**\n\n- Go to “IAM & Admin” > “Service accounts” in the Google Cloud Console.\n- Click “Create Service Account”.\n- Name your service account (like `airbyte-service-account`).\n- Assign the “BigQuery Data Editor” and “BigQuery Job User” roles to the service account.\n- Follow the same steps to make another service account for dbt (like `dbt-service-account`) and assign the roles.\n\n**How to create a service account and assign roles:**\n\n- While creating the service account, under the “Grant this service account access to project” section, click the “Role” dropdown.\n- Choose the “BigQuery Data Editor” and “BigQuery Job User” roles.\n- Finish the creation process.\n\n#### 4. **Generate JSON Keys for Service Accounts**\n\n- For both service accounts, make a JSON key to let the service accounts sign in.\n\n**How to generate JSON key:**\n\n- Find the service account in the “Service accounts” list.\n- Click on the service account name.\n- In the “Keys” section, click “Add Key” and pick JSON.\n- The key will download automatically. Keep it safe and don’t share it.\n- Do this for the other service account too.\n\n## 3. Setting Up Airbyte Connectors with Terraform\n\nAirbyte allows you to create connectors for sources and destinations, facilitating data synchronization between various platforms. In this project, we're harnessing the power of Terraform to automate the creation of these connectors and the connections between them. Here's how you can set this up:\n\n1. **Navigate to the Airbyte Configuration Directory**:\n\n Change to the relevant directory containing the Terraform configuration for Airbyte:\n\n ```bash\n cd infra/airbyte\n ```\n\n2. **Modify Configuration Files**:\n\n Within the `infra/airbyte` directory, you'll find three crucial Terraform files:\n\n - `provider.tf`: Defines the Airbyte provider.\n - `main.tf`: Contains the main configuration for creating Airbyte resources.\n - `variables.tf`: Holds various variables, including credentials.\n\n Adjust the configurations in these files to suit your project's needs. Specifically, provide credentials for your BigQuery connection. You can utilize the `variables.tf` file to manage these credentials.\n\n3. **Initialize Terraform**:\n\n This step prepares Terraform to create the resources defined in your configuration files.\n\n ```bash\n terraform init\n ```\n\n4. **Review the Plan**:\n\n Before applying any changes, review the plan to understand what Terraform will do.\n\n ```bash\n terraform plan\n ```\n\n5. **Apply Configuration**:\n\n After reviewing and confirming the plan, apply the Terraform configurations to create the necessary Airbyte resources.\n\n ```bash\n terraform apply\n ```\n\n6. **Verify in Airbyte UI**:\n\n Once Terraform completes its tasks, navigate to the [Airbyte UI](http://localhost:8000/). Here, you should see your source and destination connectors, as well as the connection between them, set up and ready to go.\n\n## 4. Setting Up the dbt Project\n\n[dbt (data build tool)](https://www.getdbt.com/) allows you to transform your data by writing, documenting, and executing SQL workflows. Setting up the dbt project requires specifying connection details for your data platform, in this case, BigQuery. Here’s a step-by-step guide to help you set this up:\n\n1. **Navigate to the dbt Project Directory**:\n\n Change to the directory containing the dbt configuration:\n\n ```bash\n cd dbt_project\n ```\n\n2. **Update Connection Details**:\n\n You'll find a `profiles.yml` file within the directory. This file contains configurations for dbt to connect with your data platform. Update this file with your BigQuery connection details.\n\n3. **Utilize Environment Variables (Optional but Recommended)**:\n\n To keep your credentials secure, you can leverage environment variables. An example is provided within the `profiles.yml` file.\n\n4. **Test the Connection**:\n\n Once you’ve updated the connection details, you can test the connection to your BigQuery instance using:\n\n ```bash\n dbt debug\n ```\n\n If everything is set up correctly, this command should report a successful connection to BigQuery.\n\n5. **Run the Models**:\n\n If you would like to run the dbt models manually at this point, you can do so by executing:\n\n ```bash\n dbt run\n ```\n\n You can verify the data has been transformed by going to BigQuery and checking the `transformed_data` dataset.\n\n## 5. Orchestrating with Dagster\n\n[Dagster](https://dagster.io/) is a modern data orchestrator designed to help you build, test, and monitor your data workflows. In this section, we'll walk you through setting up Dagster to oversee both the Airbyte and dbt workflows:\n\n1. **Navigate to the Orchestration Directory**:\n\n Switch to the directory containing the Dagster orchestration configurations:\n\n ```bash\n cd orchestration\n ```\n\n2. **Set Environment Variables**:\n\n Dagster requires certain environment variables to be set to interact with other tools like dbt and Airbyte. Set the following variables:\n\n ```bash\n export DAGSTER_DBT_PARSE_PROJECT_ON_LOAD=1\n export AIRBYTE_PASSWORD=password\n ```\n\n Note: The `AIRBYTE_PASSWORD` is set to `password` as a default for local Airbyte instances. If you've changed this during your Airbyte setup, ensure you use the appropriate password here.\n\n3. **Launch the Dagster UI**:\n\n With the environment variables in place, kick-start the Dagster UI:\n\n ```bash\n dagster dev\n ```\n\n4. **Access Dagster in Your Browser**:\n\n Open your browser and navigate to:\n\n ```\n http://127.0.0.1:3000\n ```\n\n Here, you should see assets for both Airbyte and dbt. To get an overview of how these assets interrelate, click on \"view global asset lineage\". This will give you a clear picture of the data lineage, visualizing how data flows between the tools.\n\n5. **Materialize Dagster Assets**:\n In the Dagster UI, click on \"Materialize all\". This should trigger the full pipeline. First the Airbyte sync to extract data from Shopify and load it into BigQuery, and then dbt to transform the raw data, materializing the `staging` and `marts` models.\n\n## Next Steps\n\nCongratulations on deploying and running the Shopping Cart Analytics Quistart! 🎉 Here are some suggestions on what you can explore next to dive deeper and get more out of your project:\n\n### 1. **Explore the Data and Insights**\n - Dive into the datasets in BigQuery, run some queries, and explore the data you've collected and transformed. This is your chance to uncover insights and understand the data better!\n\n### 2. **Optimize Your dbt Models**\n - Review the transformations you’ve applied using dbt. Try optimizing the models or create new ones based on your evolving needs and insights you want to extract.\n\n### 3. **Automate and Monitor Your Pipelines**\n - Explore more advanced Dagster configurations and setups to automate your pipelines further and set up monitoring and alerting to be informed of any issues immediately." }, { "path": "shopping_cart_analytics_shopify/dbt_project/.gitignore", "content": "\ntarget/\ndbt_packages/\nlogs/\n\n.user.yml" }, { "path": "shopping_cart_analytics_shopify/dbt_project/README.md", "content": "Welcome to your new dbt project!\n\n### Using the starter project\n\nTry running the following commands:\n\n- dbt run\n- dbt test\n\n### Resources:\n\n- Learn more about dbt [in the docs](https://docs.getdbt.com/docs/introduction)\n- Check out [Discourse](https://discourse.getdbt.com/) for commonly asked questions and answers\n- Join the [chat](https://community.getdbt.com/) on Slack for live discussions and support\n- Find [dbt events](https://events.getdbt.com) near you\n- Check out [the blog](https://blog.getdbt.com/) for the latest news on dbt's development and best practices\n" }, { "path": "shopping_cart_analytics_shopify/dbt_project/dbt_project.yml", "content": "\n# Name your project! Project names should contain only lowercase characters\n# and underscores. A good package name should reflect your organization's\n# name or the intended use of these models\nname: 'dbt_project'\nversion: '1.0.0'\nconfig-version: 2\n\n# This setting configures which \"profile\" dbt uses for this project.\nprofile: 'dbt_project'\n\n# These configurations specify where dbt should look for different types of files.\n# The `model-paths` config, for example, states that models in this project can be\n# found in the \"models/\" directory. You probably won't need to change these!\nmodel-paths: [\"models\"]\nanalysis-paths: [\"analyses\"]\ntest-paths: [\"tests\"]\nseed-paths: [\"seeds\"]\nmacro-paths: [\"macros\"]\nsnapshot-paths: [\"snapshots\"]\n\nclean-targets: # directories to be removed by `dbt clean`\n - \"target\"\n - \"dbt_packages\"\n\n\n# Configuring models\n# Full documentation: https://docs.getdbt.com/docs/configuring-models\n\n# In this example config, we tell dbt to build all models in the example/\n# directory as views. These settings can be overridden in the individual model\n# files using the `{{ config(...) }}` macro.\nmodels:\n dbt_project:\n # Config indicated by + and applies to all files under models/example/\n staging:\n +materialized: view\n marts:\n +materialized: view" }, { "path": "shopping_cart_analytics_shopify/dbt_project/models/marts/abandoned_checkout_ratio.sql", "content": "SELECT\n COUNTIF(completed_at IS NULL) AS abandoned_checkouts,\n COUNT(*) AS total_checkouts,\n COUNTIF(completed_at IS NULL) / COUNT(*) AS abandonment_rate\nFROM\n transformed_data.stg_abandoned_checkouts\n" }, { "path": "shopping_cart_analytics_shopify/dbt_project/models/marts/location_based_abandoned_checkouts.sql", "content": "WITH abandoned_checkouts AS (\n SELECT\n *,\n IF(billing_address IS NOT NULL, JSON_EXTRACT_SCALAR(billing_address, '$.country'), JSON_EXTRACT_SCALAR(shipping_address, '$.country')) AS checkout_country\n FROM\n transformed_data.stg_abandoned_checkouts\n)\nSELECT\n checkout_country,\n COUNTIF(completed_at IS NULL) AS abandoned_checkouts\nFROM\n abandoned_checkouts\nGROUP BY\n checkout_country\nORDER BY\n abandoned_checkouts DESC\n" }, { "path": "shopping_cart_analytics_shopify/dbt_project/models/marts/most_abandoned_products.sql", "content": "SELECT\n JSON_EXTRACT_SCALAR(line_items, '$.title') AS product_title,\n COUNTIF(completed_at IS NULL) AS abandoned_checkouts\nFROM\n transformed_data.stg_abandoned_checkouts\n CROSS JOIN UNNEST(JSON_EXTRACT_ARRAY(line_items)) AS items\nGROUP BY\n product_title\nORDER BY\n abandoned_checkouts DESC\n" }, { "path": "shopping_cart_analytics_shopify/dbt_project/models/marts/time_based.sql", "content": "SELECT\n EXTRACT(DAYOFWEEK FROM created_at) AS day_of_week,\n EXTRACT(HOUR FROM created_at) AS hour_of_day,\n COUNTIF(completed_at IS NULL) AS abandoned_checkouts\nFROM\n transformed_data.stg_abandoned_checkouts\nGROUP BY\n day_of_week,\n hour_of_day\nORDER BY\n day_of_week, hour_of_day\n" }, { "path": "shopping_cart_analytics_shopify/dbt_project/models/sources/shopify_source.yml", "content": "version: 2\n\nsources:\n - name: shopify\n # Use your BigQuery project ID\n database: \"{{ env_var('BIGQUERY_PROJECT_ID', '') }}\"\n # Use your BigQuery dataset name\n schema: shopify_airbyte\n\n tables:\n - name: abandoned_checkouts\n description: \"Simulated abandoned_checkouts data from the Shopify connector.\"\n columns:\n - name: id\n description: \"Unique identifier for the abandoned_checkouts.\"\n - name: abandoned_checkout_url\n - name: billing_address\n - name: buyer_accepts_marketing\n - name: cart_token\n - name: closed_at\n - name: completed_at\n - name: created_at\n - name: currency\n - name: customer\n - name: customer_locale\n - name: device_id\n - name: discount_codes\n - name: email\n - name: gateway\n - name: landing_site\n - name: line_items\n - name: location_id\n - name: name\n - name: note\n - name: note_attributes\n - name: phone\n - name: presentment_currency\n - name: referring_site\n - name: shipping_address\n - name: shipping_lines\n - name: shop_url\n - name: source\n - name: source_identifier\n - name: source_name\n - name: source_url\n - name: subtotal_price\n - name: tax_lines\n - name: taxes_included\n - name: token\n - name: total_discounts\n - name: total_line_items_price\n - name: total_price\n - name: total_tax\n - name: total_weight\n - name: updated_at\n - name: user_id\n\n - name: customers\n description: \"Simulated customers data from the Shopify connector.\"\n columns:\n - name: id\n description: \"Unique identifier for the customers.\"\n - name: accepts_marketing\n - name: accepts_marketing_updated_at\n - name: addresses\n - name: admin_graphql_api_id\n - name: created_at\n - name: currency\n - name: default_address\n - name: email\n - name: email_marketing_consent\n - name: first_name\n - name: last_name\n - name: last_order_id\n - name: last_order_name\n - name: marketing_opt_in_level\n - name: multipass_identifier\n - name: note\n - name: orders_count\n - name: phone\n - name: shop_url\n - name: sms_marketing_consent\n - name: state\n - name: tags\n - name: tax_exempt\n - name: tax_exemptions\n - name: total_spent\n - name: updated_at\n - name: verified_email\n\n - name: transactions\n description: \"Simulated transactions data from the Shopify connector.\"\n columns:\n - name: id\n description: \"Unique identifier for the transactions.\"\n - name: admin_graphql_api_id\n - name: amount\n - name: authorization\n - name: created_at\n - name: currency\n - name: device_id\n - name: error_code\n - name: gateway\n - name: kind\n - name: location_id\n - name: message\n - name: order_id\n - name: parent_id\n - name: payment_details\n - name: payment_id\n - name: processed_at\n - name: receipt\n - name: shop_url\n - name: source_name\n - name: status\n - name: test\n - name: total_unsettled_set\n - name: user_id\n" }, { "path": "shopping_cart_analytics_shopify/dbt_project/models/staging/stg_abandoned_checkouts.sql", "content": "select\n *\nfrom {{ source('shopify', 'abandoned_checkouts') }}" }, { "path": "shopping_cart_analytics_shopify/dbt_project/profiles.yml", "content": "dbt_project:\n outputs:\n dev:\n dataset: transformed_data\n job_execution_timeout_seconds: 300\n job_retries: 1\n # Use an env variable to indicate your JSON key file path\n keyfile: \"{{ env_var('DBT_BIGQUERY_KEYFILE_PATH', '') }}\"\n location: US\n method: service-account\n priority: interactive\n # Indicate your BigQuery project ID\n project: \"{{ env_var('BIGQUERY_PROJECT_ID', '') }}\"\n threads: 1\n type: bigquery\n target: dev" }, { "path": "shopping_cart_analytics_shopify/infra/.gitignore", "content": "# Local .terraform directories\n**/.terraform/*\n\n# .tfstate files\n*.tfstate\n*.tfstate.*\n\n# Crash log files\ncrash.log\ncrash.*.log\n\n# Exclude all .tfvars files, which are likely to contain sensitive data, such as\n# password, private keys, and other secrets. These should not be part of version \n# control as they are data points which are potentially sensitive and subject \n# to change depending on the environment.\n*.tfvars\n*.tfvars.json\n\n# Ignore override files as they are usually used to override resources locally and so\n# are not checked in\noverride.tf\noverride.tf.json\n*_override.tf\n*_override.tf.json\n\n# Include override files you do wish to add to version control using negated pattern\n# !example_override.tf\n\n# Include tfplan files to ignore the plan output of command: terraform plan -out=tfplan\n# example: *tfplan*\n\n# Ignore CLI configuration files\n.terraformrc\nterraform.rc" }, { "path": "shopping_cart_analytics_shopify/infra/airbyte/.terraform.lock.hcl", "content": "# This file is maintained automatically by \"terraform init\".\n# Manual edits may be lost in future updates.\n\nprovider \"registry.terraform.io/airbytehq/airbyte\" {\n version = \"0.3.3\"\n constraints = \"0.3.3\"\n hashes = [\n \"h1:0LmuAc5LvlMuOUPtNEaCAh9FHrV/C877bDJhm9Lz8MU=\",\n \"zh:0efa470b34d9b912b47efe4469c51713bfc3c2413e52c17e1e903f2a3cddb2f6\",\n \"zh:1bddd69fa2c2d4f3e239d60555446df9bc4ce0c0cabbe7e092fe1d44989ab004\",\n \"zh:2e20540403a0010007b53456663fb037b24e30f6c8943f65da1bcf7fa4dfc8a6\",\n \"zh:2f415369ad884e8b7115a5c5ff229d052f7af1fca27abbfc8ebef379ed11aec4\",\n \"zh:46fd9a906f4b6461112dcc5a5aa01a3fcd7a19a72d4ad0b2e37790da37701fe1\",\n \"zh:83503ebb77bb6d6941c42ba323cf22380d08a1506554a2dcc8ac54e74c0886a1\",\n \"zh:890df766e9b839623b1f0437355032a3c006226a6c200cd911e15ee1a9014e9f\",\n \"zh:8fd770eff726826d3a63b9e3733c5455b5cde004027b04ee3f75888eb8538c90\",\n \"zh:b0fc890ed4f9b077bf70ed121cc3550e7a07d16e7798ad517623274aa62ad7b0\",\n \"zh:c2a01612362da9b73cd5958f281e1aa7ff09af42182e463097d11ed78e778e72\",\n \"zh:c64b2bb1887a0367d64ba3393d4b3a16c418cf5b1792e2e7aae7c0b5413eb334\",\n \"zh:ce14ebbf0ed91913ec62655a511763dec62b5779de9a209bd6f1c336640cddc0\",\n \"zh:e0662ca837eee10f7733ea9a501d995281f56bd9b410ae13ad03eb106011db14\",\n \"zh:e103d480fc6066004bc98e9e04a141a1f55b918cc2912716beebcc6fc4c872fb\",\n \"zh:e2507049098f0f1b21cb56870f4a5ef624bcf6d3959e5612eada1f8117341648\",\n ]\n}\n" }, { "path": "shopping_cart_analytics_shopify/infra/airbyte/main.tf", "content": "// Source\nresource \"airbyte_source_shopify\" \"my_source_shopify\" {\n configuration = {\n credentials = {\n source_shopify_shopify_authorization_method_api_password = {\n api_password = var.api_password\n auth_method = \"api_password\"\n }\n }\n shop = var.shop\n source_type = \"shopify\"\n }\n name = \"Shopify\"\n workspace_id = var.workspace_id\n}\n\n// Destinations\nresource \"airbyte_destination_bigquery\" \"bigquery\" {\n configuration = {\n dataset_id = var.dataset_id\n dataset_location = \"US\"\n destination_type = \"bigquery\"\n project_id = var.project_id\n credentials_json = var.credentials_json\n loading_method = {\n destination_bigquery_loading_method_standard_inserts = {\n method = \"Standard\"\n }\n }\n }\n name = \"BigQuery\"\n workspace_id = var.workspace_id\n}\n\n// Connections\nresource \"airbyte_connection\" \"shopify_bigquery\" {\n name = \"Shopify to BigQuery\"\n source_id = airbyte_source_shopify.my_source_shopify.source_id\n destination_id = airbyte_destination_bigquery.bigquery.destination_id\n configurations = {\n streams = [\n {\n name = \"customers\"\n },\n {\n name = \"transactions\"\n },\n {\n name = \"abandoned_checkouts\"\n }\n ]\n }\n}" }, { "path": "shopping_cart_analytics_shopify/infra/airbyte/provider.tf", "content": "// Airbyte Terraform provider documentation: https://registry.terraform.io/providers/airbytehq/airbyte/latest/docs\n\nterraform {\n required_providers {\n airbyte = {\n source = \"airbytehq/airbyte\"\n version = \"0.3.3\"\n }\n }\n}\n\nprovider \"airbyte\" {\n // If running locally (Airbyte OSS) with docker-compose using the airbyte-proxy, \n // include the actual password/username you've set up (or use the defaults below)\n username = \"airbyte\"\n password = \"password\"\n\n // if running locally (Airbyte OSS), include the server url to the airbyte-api-server\n server_url = \"http://localhost:8006/v1/\"\n}\n" }, { "path": "shopping_cart_analytics_shopify/infra/airbyte/variables.tf", "content": "variable \"api_password\" {\n type = string\n}\n\nvariable \"workspace_id\" {\n type = string\n}\n\nvariable \"dataset_id\" {\n type = string\n}\n\nvariable \"project_id\" {\n type = string\n}\n\nvariable \"credentials_json\" {\n type = string\n}\n\nvariable \"shop\" {\n type = string\n}" }, { "path": "shopping_cart_analytics_shopify/orchestration/orchestration/__init__.py", "content": "" }, { "path": "shopping_cart_analytics_shopify/orchestration/orchestration/assets.py", "content": "import os\nfrom dagster import OpExecutionContext\nfrom dagster_dbt import DbtCliResource, dbt_assets\nfrom dagster_airbyte import AirbyteResource, load_assets_from_airbyte_instance\n\nfrom .constants import dbt_manifest_path\n\n\n@dbt_assets(manifest=dbt_manifest_path)\ndef dbt_project_dbt_assets(context: OpExecutionContext, dbt: DbtCliResource):\n yield from dbt.cli([\"build\"], context=context).stream()\n\n\nairbyte_instance = AirbyteResource(\n host=\"localhost\",\n port=\"8000\",\n # If using basic auth, include username and password:\n username=\"airbyte\",\n password=\"password\"\n)\n\nairbyte_assets = load_assets_from_airbyte_instance(airbyte_instance, key_prefix= \"shopify\")\n" }, { "path": "shopping_cart_analytics_shopify/orchestration/orchestration/constants.py", "content": "import os\nfrom pathlib import Path\n\nfrom dagster_dbt import DbtCliResource\n\ndbt_project_dir = Path(__file__).joinpath(\"..\", \"..\", \"..\", \"dbt_project\").resolve()\ndbt = DbtCliResource(project_dir=os.fspath(dbt_project_dir))\n\n# If DAGSTER_DBT_PARSE_PROJECT_ON_LOAD is set, a manifest will be created at runtime.\n# Otherwise, we expect a manifest to be present in the project's target directory.\nif os.getenv(\"DAGSTER_DBT_PARSE_PROJECT_ON_LOAD\"):\n dbt_parse_invocation = dbt.cli([\"parse\"], manifest={}).wait()\n dbt_manifest_path = dbt_parse_invocation.target_path.joinpath(\"manifest.json\")\nelse:\n dbt_manifest_path = dbt_project_dir.joinpath(\"target\", \"manifest.json\")" }, { "path": "shopping_cart_analytics_shopify/orchestration/orchestration/definitions.py", "content": "import os\n\nfrom dagster import Definitions\nfrom dagster_dbt import DbtCliResource\n\nfrom .assets import dbt_project_dbt_assets, airbyte_assets\nfrom .constants import dbt_project_dir\nfrom .schedules import schedules\n\ndefs = Definitions(\n assets=[dbt_project_dbt_assets, airbyte_assets],\n schedules=schedules,\n resources={\n \"dbt\": DbtCliResource(project_dir=os.fspath(dbt_project_dir)),\n },\n)\n" }, { "path": "shopping_cart_analytics_shopify/orchestration/orchestration/schedules.py", "content": "\"\"\"\nTo add a daily schedule that materializes your dbt assets, uncomment the following lines.\n\"\"\"\nfrom dagster_dbt import build_schedule_from_dbt_selection\n\nfrom .assets import dbt_project_dbt_assets\n\nschedules = [\n # build_schedule_from_dbt_selection(\n # [dbt_project_dbt_assets],\n # job_name=\"materialize_dbt_models\",\n # cron_schedule=\"0 0 * * *\",\n # dbt_select=\"fqn:*\",\n # ),\n]\n" }, { "path": "shopping_cart_analytics_shopify/orchestration/pyproject.toml", "content": "[build-system]\nrequires = [\"setuptools\"]\nbuild-backend = \"setuptools.build_meta\"\n\n[tool.dagster]\nmodule_name = \"orchestration.definitions\"\ncode_location_name = \"orchestration\"" }, { "path": "shopping_cart_analytics_shopify/orchestration/setup.py", "content": "from setuptools import find_packages, setup\n\nsetup(\n name=\"orchestration\",\n version=\"0.0.1\",\n packages=find_packages(),\n install_requires=[\n \"dagster\",\n \"dagster-cloud\",\n \"dagster-dbt\",\n \"dbt-core>=1.4.0\",\n \"dbt-bigquery\",\n ],\n extras_require={\n \"dev\": [\n \"dagster-webserver\",\n ]\n },\n)" }, { "path": "shopping_cart_analytics_shopify/setup.py", "content": "from setuptools import find_packages, setup\n\nsetup(\n name=\"airbyte-dbt-dagster\",\n packages=find_packages(),\n install_requires=[\n \"dbt-bigquery\",\n \"dagster\",\n \"dagster-cloud\",\n \"dagster-dbt\",\n \"dagster-airbyte\",\n ],\n extras_require={\"dev\": [\"dagit\", \"pytest\"]},\n)" }, { "path": "ticket_volume_analytics_zendesk_support/.gitignore", "content": "# Byte-compiled / optimized / DLL files\n__pycache__/\n*.py[cod]\n*$py.class\n\n# C extensions\n*.so\n\n# Distribution / packaging\n.Python\nbuild/\ndevelop-eggs/\ndist/\ndownloads/\neggs/\n.eggs/\nlib/\nlib64/\nparts/\nsdist/\nvar/\nwheels/\nshare/python-wheels/\n*.egg-info/\n.installed.cfg\n*.egg\nMANIFEST\n\n# PyInstaller\n# Usually these files are written by a python script from a template\n# before PyInstaller builds the exe, so as to inject date/other infos into it.\n*.manifest\n*.spec\n\n# Installer logs\npip-log.txt\npip-delete-this-directory.txt\n\n# Unit test / coverage reports\nhtmlcov/\n.tox/\n.nox/\n.coverage\n.coverage.*\n.cache\nnosetests.xml\ncoverage.xml\n*.cover\n*.py,cover\n.hypothesis/\n.pytest_cache/\ncover/\n\n# Translations\n*.mo\n*.pot\n\n# Django stuff:\n*.log\nlocal_settings.py\ndb.sqlite3\ndb.sqlite3-journal\n\n# Flask stuff:\ninstance/\n.webassets-cache\n\n# Scrapy stuff:\n.scrapy\n\n# Sphinx documentation\ndocs/_build/\n\n# PyBuilder\n.pybuilder/\ntarget/\n\n# Jupyter Notebook\n.ipynb_checkpoints\n\n# IPython\nprofile_default/\nipython_config.py\n\n# pyenv\n# For a library or package, you might want to ignore these files since the code is\n# intended to run in multiple environments; otherwise, check them in:\n# .python-version\n\n# pipenv\n# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.\n# However, in case of collaboration, if having platform-specific dependencies or dependencies\n# having no cross-platform support, pipenv may install dependencies that don't work, or not\n# install all needed dependencies.\n#Pipfile.lock\n\n# poetry\n# Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.\n# This is especially recommended for binary packages to ensure reproducibility, and is more\n# commonly ignored for libraries.\n# https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control\n#poetry.lock\n\n# pdm\n# Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control.\n#pdm.lock\n# pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it\n# in version control.\n# https://pdm.fming.dev/#use-with-ide\n.pdm.toml\n\n# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm\n__pypackages__/\n\n# Celery stuff\ncelerybeat-schedule\ncelerybeat.pid\n\n# SageMath parsed files\n*.sage.py\n\n# Environments\n.env\n.venv\nenv/\nvenv/\nENV/\nenv.bak/\nvenv.bak/\n\n# Spyder project settings\n.spyderproject\n.spyproject\n\n# Rope project settings\n.ropeproject\n\n# mkdocs documentation\n/site\n\n# mypy\n.mypy_cache/\n.dmypy.json\ndmypy.json\n\n# Pyre type checker\n.pyre/\n\n# pytype static type analyzer\n.pytype/\n\n# Cython debug symbols\ncython_debug/\n\n# PyCharm\n# JetBrains specific template is maintained in a separate JetBrains.gitignore that can\n# be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore\n# and can be added to the global gitignore or merged into this file. For a more nuclear\n# option (not recommended) you can uncomment the following to ignore the entire idea folder.\n#.idea/\n\n#Desktop Services Store\n.DS_Store" }, { "path": "ticket_volume_analytics_zendesk_support/README.md", "content": "# Customer Ticket Volume Analytics Stack With Zendesk Support, Airbyte, Dbt, Dagster and BigQuery\n\nWelcome to the \"Ticket Volume Analytics Stack\" repository! ✨ This is your go-to place to easily set up a data stack using Zendesk Support, Airbyte, Dbt, BigQuery, and Dagster. With this setup, you can pull Zendesk Support data, extract it using Airbyte, put it into BigQuery, and play around with it using dbt and Dagster.\n\nThis Quickstart is all about making things easy, getting you started quickly and showing you how smoothly all these tools can work together!\n\nBelow is a visual representation of how data flows through our integrated tools in this Quickstart. This comes from Dagster's global asset lineage view:\n\n![Global Asset Lineage](<./assets/Global_Asset_Lineage (4).svg>)\n\n## Table of Contents\n\n- [Prerequisites](#prerequisites)\n- [Setting an environment for your project](#1-setting-an-environment-for-your-project)\n- [Setting Up BigQuery to work with Airbyte and dbt](#2-setting-up-bigquery)\n- [Setting Up Airbyte Connectors with Terraform](#3-setting-up-airbyte-connectors-with-terraform)\n- [Setting Up the dbt Project](#4-setting-up-the-dbt-project)\n- [Orchestrating with Dagster](#5-orchestrating-with-dagster)\n- [Next Steps](#next-steps)\n\n## Prerequisites\n\nBefore you embark on this integration, ensure you have the following set up and ready:\n\n1. **Python 3.10 or later**: If not installed, download and install it from [Python's official website](https://www.python.org/downloads/).\n\n2. **Docker and Docker Compose (Docker Desktop)**: Install [Docker](https://docs.docker.com/get-docker/) following the official documentation for your specific OS.\n\n3. **Airbyte OSS version**: Deploy the open-source version of Airbyte. Follow the installation instructions from the [Airbyte Documentation](https://docs.airbyte.com/quickstart/deploy-airbyte/).\n\n4. **Terraform**: Terraform will help you provision and manage the Airbyte resources. If you haven't installed it, follow the [official Terraform installation guide](https://developer.hashicorp.com/terraform/tutorials/aws-get-started/install-cli).\n\n5. **Google Cloud account with BigQuery**: You will also need to add the necessary permissions to allow Airbyte and dbt to access the data in BigQuery. A step-by-step guide is provided [below](#2-setting-up-bigquery).\n\n## 1. Setting an environment for your project\n\nGet the project up and running on your local machine by following these steps:\n\n1. **Clone the repository (Clone only this quickstart)**:\n\n ```bash\n git clone --filter=blob:none --sparse https://github.com/airbytehq/quickstarts.git\n ```\n\n ```bash\n cd quickstarts\n ```\n\n ```bash\n git sparse-checkout add ticket_volume_analytics_zendesk_support\n ```\n\n2. **Navigate to the directory**:\n\n ```bash\n cd ticket_volume_analytics_zendesk_support\n ```\n\n3. **Set Up a Virtual Environment**:\n\n - For Mac:\n ```bash\n python3 -m venv venv\n source venv/bin/activate\n ```\n - For Windows:\n ```bash\n python -m venv venv\n .\\venv\\Scripts\\activate\n ```\n4. **Install Dependencies**:\n ```bash\n pip install -e \".[dev]\"\n ```\n\n## 2. Setting Up BigQuery\n\n#### 1. **Create a Google Cloud Project**\n\n- If you have a Google Cloud project, you can skip this step.\n- Go to the [Google Cloud Console](https://console.cloud.google.com/).\n- Click on the \"Select a project\" dropdown at the top right and select \"New Project\".\n- Give your project a name and follow the steps to create it.\n\n#### 2. **Create BigQuery Datasets**\n\n- In the Google Cloud Console, go to BigQuery.\n- Make two new datasets: `raw_data` for Airbyte and `transformed_data` for dbt.\n - If you pick different names, remember to change the names in the code too.\n\n**How to create a dataset:**\n\n- In the left sidebar, click on your project name.\n- Click “Create Dataset”.\n- Enter the dataset ID (either `raw_data` or `transformed_data`).\n- Click \"Create Dataset\".\n\n#### 3. **Create Service Accounts and Assign Roles**\n\n- Go to “IAM & Admin” > “Service accounts” in the Google Cloud Console.\n- Click “Create Service Account”.\n- Name your service account (like `airbyte-service-account`).\n- Assign the “BigQuery Data Editor” and “BigQuery Job User” roles to the service account.\n- Follow the same steps to make another service account for dbt (like `dbt-service-account`) and assign the roles.\n\n**How to create a service account and assign roles:**\n\n- While creating the service account, under the “Grant this service account access to project” section, click the “Role” dropdown.\n- Choose the “BigQuery Data Editor” and “BigQuery Job User” roles.\n- Finish the creation process.\n\n#### 4. **Generate JSON Keys for Service Accounts**\n\n- For both service accounts, make a JSON key to let the service accounts sign in.\n\n**How to generate JSON key:**\n\n- Find the service account in the “Service accounts” list.\n- Click on the service account name.\n- In the “Keys” section, click “Add Key” and pick JSON.\n- The key will download automatically. Keep it safe and don’t share it.\n- Do this for the other service account too.\n\n## 3. Setting Up Airbyte Connectors with Terraform\n\nAirbyte allows you to create connectors for sources and destinations, facilitating data synchronization between various platforms. In this project, we're harnessing the power of Terraform to automate the creation of these connectors and the connections between them. Here's how you can set this up:\n\n1. **Navigate to the Airbyte Configuration Directory**:\n\n Change to the relevant directory containing the Terraform configuration for Airbyte:\n\n ```bash\n cd infra/airbyte\n ```\n\n2. **Modify Configuration Files**:\n\n Within the `infra/airbyte` directory, you'll find three crucial Terraform files:\n\n - `provider.tf`: Defines the Airbyte provider.\n - `main.tf`: Contains the main configuration for creating Airbyte resources.\n - `variables.tf`: Holds various variables, including credentials.\n\n Adjust the configurations in these files to suit your project's needs. Specifically, provide credentials for your BigQuery connection. You can utilize the `variables.tf` file to manage these credentials.\n\n3. **Initialize Terraform**:\n\n This step prepares Terraform to create the resources defined in your configuration files.\n\n ```bash\n terraform init\n ```\n\n4. **Review the Plan**:\n\n Before applying any changes, review the plan to understand what Terraform will do.\n\n ```bash\n terraform plan\n ```\n\n5. **Apply Configuration**:\n\n After reviewing and confirming the plan, apply the Terraform configurations to create the necessary Airbyte resources.\n\n ```bash\n terraform apply\n ```\n\n6. **Verify in Airbyte UI**:\n\n Once Terraform completes its tasks, navigate to the [Airbyte UI](http://localhost:8000/). Here, you should see your source and destination connectors, as well as the connection between them, set up and ready to go.\n\n## 4. Setting Up the dbt Project\n\n[dbt (data build tool)](https://www.getdbt.com/) allows you to transform your data by writing, documenting, and executing SQL workflows. Setting up the dbt project requires specifying connection details for your data platform, in this case, BigQuery. Here’s a step-by-step guide to help you set this up:\n\n1. **Navigate to the dbt Project Directory**:\n\n Change to the directory containing the dbt configuration:\n\n ```bash\n cd dbt_project\n ```\n\n2. **Update Connection Details**:\n\n You'll find a `profiles.yml` file within the directory. This file contains configurations for dbt to connect with your data platform. Update this file with your BigQuery connection details.\n\n3. **Utilize Environment Variables (Optional but Recommended)**:\n\n To keep your credentials secure, you can leverage environment variables. An example is provided within the `profiles.yml` file.\n\n4. **Test the Connection**:\n\n Once you’ve updated the connection details, you can test the connection to your BigQuery instance using:\n\n ```bash\n dbt debug\n ```\n\n If everything is set up correctly, this command should report a successful connection to BigQuery.\n\n5. **Run the Models**:\n\n If you would like to run the dbt models manually at this point, you can do so by executing:\n\n ```bash\n dbt run\n ```\n\n You can verify the data has been transformed by going to BigQuery and checking the `transformed_data` dataset.\n\n## 5. Orchestrating with Dagster\n\n[Dagster](https://dagster.io/) is a modern data orchestrator designed to help you build, test, and monitor your data workflows. In this section, we'll walk you through setting up Dagster to oversee both the Airbyte and dbt workflows:\n\n1. **Navigate to the Orchestration Directory**:\n\n Switch to the directory containing the Dagster orchestration configurations:\n\n ```bash\n cd orchestration\n ```\n\n2. **Set Environment Variables**:\n\n Dagster requires certain environment variables to be set to interact with other tools like dbt and Airbyte. Set the following variables:\n\n ```bash\n export DAGSTER_DBT_PARSE_PROJECT_ON_LOAD=1\n export AIRBYTE_PASSWORD=password\n ```\n\n Note: The `AIRBYTE_PASSWORD` is set to `password` as a default for local Airbyte instances. If you've changed this during your Airbyte setup, ensure you use the appropriate password here.\n\n3. **Launch the Dagster UI**:\n\n With the environment variables in place, kick-start the Dagster UI:\n\n ```bash\n dagster dev\n ```\n\n4. **Access Dagster in Your Browser**:\n\n Open your browser and navigate to:\n\n ```\n http://127.0.0.1:3000\n ```\n\n Here, you should see assets for both Airbyte and dbt. To get an overview of how these assets interrelate, click on \"view global asset lineage\". This will give you a clear picture of the data lineage, visualizing how data flows between the tools.\n\n5. **Materialize Dagster Assets**:\n In the Dagster UI, click on \"Materialize all\". This should trigger the full pipeline. First the Airbyte sync to extract data from Faker and load it into BigQuery, and then dbt to transform the raw data, materializing the `staging` and `marts` models.\n\n## Next Steps\n\nCongratulations on deploying and running the Customer Satisfaction Analytics Quistart! 🎉 Here are some suggestions on what you can explore next to dive deeper and get more out of your project:\n\n### 1. **Explore the Data and Insights**\n - Dive into the datasets in BigQuery, run some queries, and explore the data you've collected and transformed. This is your chance to uncover insights and understand the data better!\n\n### 2. **Optimize Your dbt Models**\n - Review the transformations you’ve applied using dbt. Try optimizing the models or create new ones based on your evolving needs and insights you want to extract.\n\n### 3. **Automate and Monitor Your Pipelines**\n - Explore more advanced Dagster configurations and setups to automate your pipelines further and set up monitoring and alerting to be informed of any issues immediately." }, { "path": "ticket_volume_analytics_zendesk_support/dbt_project/.gitignore", "content": "\ntarget/\ndbt_packages/\nlogs/\n\n.user.yml\n" }, { "path": "ticket_volume_analytics_zendesk_support/dbt_project/README.md", "content": "Welcome to your new dbt project!\n\n### Using the starter project\n\nTry running the following commands:\n\n- dbt run\n- dbt test\n\n### Resources:\n\n- Learn more about dbt [in the docs](https://docs.getdbt.com/docs/introduction)\n- Check out [Discourse](https://discourse.getdbt.com/) for commonly asked questions and answers\n- Join the [chat](https://community.getdbt.com/) on Slack for live discussions and support\n- Find [dbt events](https://events.getdbt.com) near you\n- Check out [the blog](https://blog.getdbt.com/) for the latest news on dbt's development and best practices\n" }, { "path": "ticket_volume_analytics_zendesk_support/dbt_project/dbt_project.yml", "content": "\n# Name your project! Project names should contain only lowercase characters\n# and underscores. A good package name should reflect your organization's\n# name or the intended use of these models\nname: 'dbt_project'\nversion: '1.0.0'\nconfig-version: 2\n\n# This setting configures which \"profile\" dbt uses for this project.\nprofile: 'dbt_project'\n\n# These configurations specify where dbt should look for different types of files.\n# The `model-paths` config, for example, states that models in this project can be\n# found in the \"models/\" directory. You probably won't need to change these!\nmodel-paths: [\"models\"]\nanalysis-paths: [\"analyses\"]\ntest-paths: [\"tests\"]\nseed-paths: [\"seeds\"]\nmacro-paths: [\"macros\"]\nsnapshot-paths: [\"snapshots\"]\n\nclean-targets: # directories to be removed by `dbt clean`\n - \"target\"\n - \"dbt_packages\"\n\n\n# Configuring models\n# Full documentation: https://docs.getdbt.com/docs/configuring-models\n\n# In this example config, we tell dbt to build all models in the example/\n# directory as views. These settings can be overridden in the individual model\n# files using the `{{ config(...) }}` macro.\nmodels:\n dbt_project:\n # Config indicated by + and applies to all files under models/example/\n staging:\n +materialized: view\n marts:\n +materialized: view\n" }, { "path": "ticket_volume_analytics_zendesk_support/dbt_project/models/marts/busier_day_of_week_analysis.sql", "content": "WITH ticket_day_of_week_counts AS (\n SELECT\n EXTRACT(DAYOFWEEK FROM created_at) AS day_of_week,\n COUNT(*) AS ticket_count\n FROM {{ ref('stg_tickets') }}\n GROUP BY 1\n)\nSELECT * FROM ticket_day_of_week_counts" }, { "path": "ticket_volume_analytics_zendesk_support/dbt_project/models/marts/pattern_and_trend_analysis.sql", "content": "with ticket_hourly_counts as (\n select\n extract(hour from created_at) as hour_of_day,\n count(*) as ticket_count\n from {{ ref('stg_tickets') }}\n group by 1\n)\nselect * from ticket_hourly_counts" }, { "path": "ticket_volume_analytics_zendesk_support/dbt_project/models/marts/seasonal_analysis.sql", "content": "with ticket_seasonal_counts as (\n select\n extract(month from created_at) as month,\n count(*) as ticket_count\n from {{ ref('stg_tickets') }}\n group by 1\n)\nselect * from ticket_seasonal_counts" }, { "path": "ticket_volume_analytics_zendesk_support/dbt_project/models/marts/ticket_priority_analysis.sql", "content": "with ticket_priority_counts as (\n select\n priority,\n count(*) as ticket_count\n from {{ ref('stg_tickets') }}\n group by 1\n)\nselect * from ticket_priority_counts" }, { "path": "ticket_volume_analytics_zendesk_support/dbt_project/models/marts/ticket_resolution_time_analysis.sql", "content": "with ticket_resolution_times as (\n select\n TIMESTAMP_DIFF(solved_at, created_at, HOUR) as resolution_time,\n count(*) as ticket_count\n from transformed_data.stg_ticket_metrics\n where solved_at is not null\n group by 1\n)\nselect * from ticket_resolution_times" }, { "path": "ticket_volume_analytics_zendesk_support/dbt_project/models/marts/ticket_source_analysis.sql", "content": "with source_data as (\n select\n JSON_EXTRACT_SCALAR(json_data, '$.channel') as ticket_source,\n count(*) as ticket_count\n from (\n select\n JSON_EXTRACT_ARRAY(via)[OFFSET(0)] as json_data\n from transformed_data.stg_tickets\n ) t\n group by 1\n)\nselect * from source_data" }, { "path": "ticket_volume_analytics_zendesk_support/dbt_project/models/marts/ticket_volume_analysis.sql", "content": "with ticket_counts as (\n select\n date(created_at) as ticket_date,\n count(*) as ticket_count\n from {{ ref('stg_tickets') }}\n group by 1\n)\nselect * from ticket_counts" }, { "path": "ticket_volume_analytics_zendesk_support/dbt_project/models/sources/zendesk_support_sources.yml", "content": "version: 2\n\nsources:\n - name: zendesk_support\n # Use your BigQuery project ID\n database: \"{{ env_var('BIGQUERY_PROJECT_ID', '') }}\"\n # Use your BigQuery dataset name\n schema: zendesk_airbyte_trial\n\n tables:\n - name: users\n description: \"Simulated user data from the Zendesk Support connector.\"\n columns:\n - name: id\n description: \"Unique identifier for the user.\"\n - name: active\n - name: alias\n - name: chat_only\n - name: created_at\n - name: custom_role_id\n - name: default_group_id\n - name: details\n - name: email\n - name: external_id\n - name: updated_at\n - name: iana_time_zone\n - name: last_login_at\n - name: locale\n - name: locale_id\n - name: moderator\n - name: name\n - name: notes\n - name: only_private_comments\n - name: organization_id\n - name: permanently_deleted\n - name: phone\n - name: photo\n - name: report_csv\n - name: restricted_agent\n - name: role\n - name: role_type\n - name: shared\n - name: shared_agent\n - name: shared_phone_number\n - name: signature\n - name: suspended\n - name: tags\n - name: ticket_restriction\n - name: time_zone\n - name: two_factor_auth_enabled\n - name: url\n - name: user_fields\n - name: verified\n\n - name: tickets\n description: \"Simulated tickets data from the Zendesk Support connector.\"\n columns:\n - name: id\n description: \"Unique identifier for the ticket.\"\n - name: updated_at\n - name: allow_attachments\n - name: allow_channelback\n - name: assignee_id\n - name: brand_id\n - name: collaborator_ids\n - name: created_at\n - name: custom_fields\n - name: custom_status_id\n - name: description\n - name: due_at\n - name: email_cc_ids\n - name: external_id\n - name: fields\n - name: follower_ids \n - name: followup_ids\n - name: forum_topic_id\n - name: from_messaging_channel\n - name: generated_timestamp\n - name: group_id\n - name: has_incidents\n - name: is_public\n - name: organization_id\n - name: priority\n - name: problem_id\n - name: raw_subject\n - name: recipient\n - name: requester_id\n - name: satisfaction_rating\n - name: sharing_agreement_ids\n - name: status\n - name: subject\n - name: submitter_id\n - name: tags\n - name: ticket_form_id\n - name: type\n - name: url\n - name: via\n\n - name: schedules\n description: \"Simulated schedules data from the Zendesk Support connector.\"\n columns:\n - name: id\n description: \"Unique identifier for the schedule.\"\n - name: created_at\n - name: intervals\n - name: name\n - name: time_zone\n - name: updated_at\n\n - name: ticket_metrics\n description: \"Simulated ticket_metrics data from the Zendesk Support connector.\"\n columns:\n - name: id\n description: \"Unique identifier for the ticket_metrics.\"\n - name: agent_wait_time_in_minutes\n - name: created_at\n - name: assigned_at\n - name: assignee_stations\n - name: assignee_updated_at\n - name: custom_status_updated_at\n - name: first_resolution_time_in_minutes\n - name: full_resolution_time_in_minutes\n - name: group_stations\n - name: initially_assigned_at\n - name: instance_id\n - name: latest_comment_added_at\n - name: metric\n - name: on_hold_time_in_minutes\n - name: updated_at\n - name: url\n - name: reopens\n - name: replies\n - name: reply_time_in_minutes\n - name: requester_updated_at\n - name: requester_wait_time_in_minutes\n - name: solved_at\n - name: status\n - name: status_updated_at\n - name: ticket_id\n - name: time\n - name: type" }, { "path": "ticket_volume_analytics_zendesk_support/dbt_project/models/staging/stg_schedules.sql", "content": "select\n*\nfrom {{ source('zendesk_support', 'schedules') }}" }, { "path": "ticket_volume_analytics_zendesk_support/dbt_project/models/staging/stg_ticket_metrics.sql", "content": "select\n *\nfrom {{ source('zendesk_support', 'ticket_metrics') }}" }, { "path": "ticket_volume_analytics_zendesk_support/dbt_project/models/staging/stg_tickets.sql", "content": "select\n*\nfrom {{ source('zendesk_support', 'tickets') }}\n" }, { "path": "ticket_volume_analytics_zendesk_support/dbt_project/models/staging/stg_users.sql", "content": "select\n *\nfrom {{ source('zendesk_support', 'users') }}\n" }, { "path": "ticket_volume_analytics_zendesk_support/dbt_project/profiles.yml", "content": "dbt_project:\n outputs:\n dev:\n dataset: transformed_data\n job_execution_timeout_seconds: 300\n job_retries: 1\n # Use an env variable to indicate your JSON key file path\n keyfile: \"{{ env_var('DBT_BIGQUERY_KEYFILE_PATH', '') }}\"\n location: US\n method: service-account\n priority: interactive\n # Indicate your BigQuery project ID\n project: \"{{ env_var('BIGQUERY_PROJECT_ID', '') }}\"\n threads: 1\n type: bigquery\n target: dev" }, { "path": "ticket_volume_analytics_zendesk_support/dbt_project/snapshots/.gitkeep", "content": "" }, { "path": "ticket_volume_analytics_zendesk_support/dbt_project/tests/.gitkeep", "content": "" }, { "path": "ticket_volume_analytics_zendesk_support/infra/.gitignore", "content": "# Local .terraform directories\n**/.terraform/*\n\n# .tfstate files\n*.tfstate\n*.tfstate.*\n\n# Crash log files\ncrash.log\ncrash.*.log\n\n# Exclude all .tfvars files, which are likely to contain sensitive data, such as\n# password, private keys, and other secrets. These should not be part of version \n# control as they are data points which are potentially sensitive and subject \n# to change depending on the environment.\n*.tfvars\n*.tfvars.json\n\n# Ignore override files as they are usually used to override resources locally and so\n# are not checked in\noverride.tf\noverride.tf.json\n*_override.tf\n*_override.tf.json\n\n# Include override files you do wish to add to version control using negated pattern\n# !example_override.tf\n\n# Include tfplan files to ignore the plan output of command: terraform plan -out=tfplan\n# example: *tfplan*\n\n# Ignore CLI configuration files\n.terraformrc\nterraform.rc" }, { "path": "ticket_volume_analytics_zendesk_support/infra/airbyte/.terraform.lock.hcl", "content": "# This file is maintained automatically by \"terraform init\".\n# Manual edits may be lost in future updates.\n\nprovider \"registry.terraform.io/airbytehq/airbyte\" {\n version = \"0.3.3\"\n constraints = \"0.3.3\"\n hashes = [\n \"h1:0LmuAc5LvlMuOUPtNEaCAh9FHrV/C877bDJhm9Lz8MU=\",\n \"zh:0efa470b34d9b912b47efe4469c51713bfc3c2413e52c17e1e903f2a3cddb2f6\",\n \"zh:1bddd69fa2c2d4f3e239d60555446df9bc4ce0c0cabbe7e092fe1d44989ab004\",\n \"zh:2e20540403a0010007b53456663fb037b24e30f6c8943f65da1bcf7fa4dfc8a6\",\n \"zh:2f415369ad884e8b7115a5c5ff229d052f7af1fca27abbfc8ebef379ed11aec4\",\n \"zh:46fd9a906f4b6461112dcc5a5aa01a3fcd7a19a72d4ad0b2e37790da37701fe1\",\n \"zh:83503ebb77bb6d6941c42ba323cf22380d08a1506554a2dcc8ac54e74c0886a1\",\n \"zh:890df766e9b839623b1f0437355032a3c006226a6c200cd911e15ee1a9014e9f\",\n \"zh:8fd770eff726826d3a63b9e3733c5455b5cde004027b04ee3f75888eb8538c90\",\n \"zh:b0fc890ed4f9b077bf70ed121cc3550e7a07d16e7798ad517623274aa62ad7b0\",\n \"zh:c2a01612362da9b73cd5958f281e1aa7ff09af42182e463097d11ed78e778e72\",\n \"zh:c64b2bb1887a0367d64ba3393d4b3a16c418cf5b1792e2e7aae7c0b5413eb334\",\n \"zh:ce14ebbf0ed91913ec62655a511763dec62b5779de9a209bd6f1c336640cddc0\",\n \"zh:e0662ca837eee10f7733ea9a501d995281f56bd9b410ae13ad03eb106011db14\",\n \"zh:e103d480fc6066004bc98e9e04a141a1f55b918cc2912716beebcc6fc4c872fb\",\n \"zh:e2507049098f0f1b21cb56870f4a5ef624bcf6d3959e5612eada1f8117341648\",\n ]\n}\n" }, { "path": "ticket_volume_analytics_zendesk_support/infra/airbyte/main.tf", "content": "// Airbyte Terraform provider documentation: https://registry.terraform.io/providers/airbytehq/airbyte/latest/docs\n\n// Sources\nresource \"airbyte_source_zendesk_support\" \"my_source_zendesksupport\" {\n configuration = {\n source_type = \"zendesk-support\"\n credentials = {\n source_zendesk_support_authentication_api_token = {\n credentials = \"api_token\"\n api_token = var.api_token\n email = var.email\n\n }\n\n }\n start_date = \"2020-10-15T00:00:00Z\"\n subdomain = \"self3836\"\n }\n name = \"Zendesk Support\"\n workspace_id = var.workspace_id\n}\n\n// Destinations\nresource \"airbyte_destination_bigquery\" \"bigquery\" {\n configuration = {\n dataset_id = var.dataset_id\n dataset_location = \"US\"\n destination_type = \"bigquery\"\n project_id = var.project_id\n credentials_json = var.credentials_json\n loading_method = {\n destination_bigquery_loading_method_standard_inserts = {\n method = \"Standard\"\n }\n }\n }\n name = \"BigQuery\"\n workspace_id = var.workspace_id\n}\n\n// Connections\nresource \"airbyte_connection\" \"zendesk_support_bigquery\" {\n name = \"Zendesk Support to BigQuery\"\n source_id = airbyte_source_zendesk_support.my_source_zendesksupport.source_id\n destination_id = airbyte_destination_bigquery.bigquery.destination_id\n configurations = {\n streams = [\n {\n name = \"users\"\n },\n {\n name = \"tickets\"\n },\n {\n name = \"satisfaction_ratings\"\n },\n {\n name = \"ticket_metrics\"\n },\n {\n name = \"ticket_metric_events\"\n },\n {\n name = \"ticket_comments\"\n },\n {\n name = \"ticket_audits\"\n }\n ]\n }\n}\n" }, { "path": "ticket_volume_analytics_zendesk_support/infra/airbyte/provider.tf", "content": "// Airbyte Terraform provider documentation: https://registry.terraform.io/providers/airbytehq/airbyte/latest/docs\n\nterraform {\n required_providers {\n airbyte = {\n source = \"airbytehq/airbyte\"\n version = \"0.3.3\"\n }\n }\n}\n\nprovider \"airbyte\" {\n // If running locally (Airbyte OSS) with docker-compose using the airbyte-proxy, \n // include the actual password/username you've set up (or use the defaults below)\n username = \"airbyte\"\n password = \"password\"\n\n // if running locally (Airbyte OSS), include the server url to the airbyte-api-server\n server_url = \"http://localhost:8006/v1/\"\n}\n" }, { "path": "ticket_volume_analytics_zendesk_support/infra/airbyte/variables.tf", "content": "variable \"api_token\" {\n type = string\n}\n\nvariable \"email\" {\n type = string\n}\n\nvariable \"workspace_id\" {\n type = string\n}\n\nvariable \"dataset_id\" {\n type = string\n}\n\nvariable \"project_id\" {\n type = string\n}\n\nvariable \"credentials_json\" {\n type = string\n}\n\n\n\n\n" }, { "path": "ticket_volume_analytics_zendesk_support/orchestration/orchestration/__init__.py", "content": "" }, { "path": "ticket_volume_analytics_zendesk_support/orchestration/orchestration/assets.py", "content": "import os\nfrom dagster import OpExecutionContext\nfrom dagster_dbt import DbtCliResource, dbt_assets\nfrom dagster_airbyte import AirbyteResource, load_assets_from_airbyte_instance\n\nfrom .constants import dbt_manifest_path\n\n\n@dbt_assets(manifest=dbt_manifest_path)\ndef dbt_project_dbt_assets(context: OpExecutionContext, dbt: DbtCliResource):\n yield from dbt.cli([\"build\"], context=context).stream()\n\n\nairbyte_instance = AirbyteResource(\n host=\"localhost\",\n port=\"8000\",\n # If using basic auth, include username and password:\n username=\"airbyte\",\n password=\"password\"\n)\n\nairbyte_assets = load_assets_from_airbyte_instance(airbyte_instance,)\n" }, { "path": "ticket_volume_analytics_zendesk_support/orchestration/orchestration/constants.py", "content": "import os\nfrom pathlib import Path\n\nfrom dagster_dbt import DbtCliResource\n\ndbt_project_dir = Path(__file__).joinpath(\"..\", \"..\", \"..\", \"dbt_project\").resolve()\ndbt = DbtCliResource(project_dir=os.fspath(dbt_project_dir))\n\n# If DAGSTER_DBT_PARSE_PROJECT_ON_LOAD is set, a manifest will be created at runtime.\n# Otherwise, we expect a manifest to be present in the project's target directory.\nif os.getenv(\"DAGSTER_DBT_PARSE_PROJECT_ON_LOAD\"):\n dbt_parse_invocation = dbt.cli([\"parse\"], manifest={}).wait()\n dbt_manifest_path = dbt_parse_invocation.target_path.joinpath(\"manifest.json\")\nelse:\n dbt_manifest_path = dbt_project_dir.joinpath(\"target\", \"manifest.json\")" }, { "path": "ticket_volume_analytics_zendesk_support/orchestration/orchestration/definitions.py", "content": "import os\n\nfrom dagster import Definitions\nfrom dagster_dbt import DbtCliResource\n\nfrom .assets import dbt_project_dbt_assets, airbyte_assets\nfrom .constants import dbt_project_dir\nfrom .schedules import schedules\n\ndefs = Definitions(\n assets=[dbt_project_dbt_assets, airbyte_assets],\n schedules=schedules,\n resources={\n \"dbt\": DbtCliResource(project_dir=os.fspath(dbt_project_dir)),\n },\n)\n" }, { "path": "ticket_volume_analytics_zendesk_support/orchestration/orchestration/schedules.py", "content": "\"\"\"\nTo add a daily schedule that materializes your dbt assets, uncomment the following lines.\n\"\"\"\nfrom dagster_dbt import build_schedule_from_dbt_selection\n\nfrom .assets import dbt_project_dbt_assets\n\nschedules = [\n # build_schedule_from_dbt_selection(\n # [dbt_project_dbt_assets],\n # job_name=\"materialize_dbt_models\",\n # cron_schedule=\"0 0 * * *\",\n # dbt_select=\"fqn:*\",\n # ),\n]\n" }, { "path": "ticket_volume_analytics_zendesk_support/orchestration/pyproject.toml", "content": "[build-system]\nrequires = [\"setuptools\"]\nbuild-backend = \"setuptools.build_meta\"\n\n[tool.dagster]\nmodule_name = \"orchestration.definitions\"\ncode_location_name = \"orchestration\"" }, { "path": "ticket_volume_analytics_zendesk_support/orchestration/setup.py", "content": "from setuptools import find_packages, setup\n\nsetup(\n name=\"orchestration\",\n version=\"0.0.1\",\n packages=find_packages(),\n install_requires=[\n \"dagster\",\n \"dagster-cloud\",\n \"dagster-dbt\",\n \"dbt-core>=1.4.0\",\n \"dbt-bigquery\",\n ],\n extras_require={\n \"dev\": [\n \"dagster-webserver\",\n ]\n },\n)" }, { "path": "ticket_volume_analytics_zendesk_support/setup.py", "content": "from setuptools import find_packages, setup\n\nsetup(\n name=\"airbyte-dbt-dagster\",\n packages=find_packages(),\n install_requires=[\n \"dbt-bigquery\",\n \"dagster\",\n \"dagster-cloud\",\n \"dagster-dbt\",\n \"dagster-airbyte\",\n ],\n extras_require={\"dev\": [\"dagit\", \"pytest\"]},\n)" }, { "path": "vector_store_integration/AI_assistant_streamlit_app/.gitignore", "content": "venv/\n\n.secrets\n.streamlit\ndist/\nbuild/\n*.egg-info/\n\n.DS_STORE\n\n" }, { "path": "vector_store_integration/AI_assistant_streamlit_app/README.md", "content": "### Run Locally\n\n- The project is set to work with our database credentials and OpenAI API key. You will need to update the credentials to work with your own data.\n\n- Create a folder called `.streamlit` and create a file called `secrets.toml`.\n\n- Add the following to the `secrets.toml` file:\n ```toml\n OPENAI_API_KEY=\"YOUR_OPENAI_API_KEY\"\n SNOWFLAKE_HOST=\"YOUR_SNOWFLAKE_ACCOUNT_NAME\"\n SNOWFLAKE_ROLE=\"YOUR_SNOWFLAKE_ROLE\"\n SNOWFLAKE_WAREHOUSE=\"YOUR_SNOWFLAKE_WAREHOUSE\"\n SNOWFLAKE_DATABASE=\"YOUR_SNOWFLAKE_DATABASE\"\n SNOWFLAKE_SCHEMA=\"YOUR_SNOWFLAKE_SCHEMA\"\n SNOWFLAKE_USERNAME=\"YOUR_SNOWFLAKE_USERNAME\"\n SNOWFLAKE_PASSWORD=\"YOUR_SNOWFLAKE_PASSWORD\"\n ```\n\n- Update the table names and LLM instructions in `app.py` with data specific to your use case.\n\n- Create a virtual environment:\n ```bash\n python3 -m venv venv\n ```\n\n- Activate the virtual environment:\n ```bash\n source venv/bin/activate \n ```\n\n- Install the requirements:\n ```bash\n pip install -r requirements.txt\n ```\n\n- Run the app:\n ```bash\n streamlit run app.py\n ```\n\n- Open your browser and go to [http://localhost:8501](http://localhost:8501).\n\n### Deploy to Streamlit Community Cloud\n\n- Copy the code into its own repository.\n\n- Create an account [here](https://streamlit.io/cloud) and follow the instructions to give access to the repository you created.\n" }, { "path": "vector_store_integration/AI_assistant_streamlit_app/app.py", "content": "import streamlit as st\nimport random\nimport time\nfrom openai import OpenAI\nimport openai\nfrom snowflake import connector\nfrom langchain.embeddings import OpenAIEmbeddings\nfrom typing import List\n\n\ndef get_db_connection():\n return connector.connect(\n account=st.secrets[\"SNOWFLAKE_HOST\"],\n role=st.secrets[\"SNOWFLAKE_ROLE\"],\n warehouse=st.secrets[\"SNOWFLAKE_WAREHOUSE\"],\n database=st.secrets[\"SNOWFLAKE_DATABASE\"],\n schema=st.secrets[\"SNOWFLAKE_SCHEMA\"],\n user=st.secrets[\"SNOWFLAKE_USERNAME\"],\n password=st.secrets[\"SNOWFLAKE_PASSWORD\"],\n )\n\ndef get_similar_chunks(query_vector, table_names) -> List[str]:\n conn = get_db_connection()\n cursor = conn.cursor()\n\n chunks = [] \n for table_name in table_names:\n query = f\"\"\"\n SELECT document_content, VECTOR_COSINE_SIMILARITY(embedding, CAST({query_vector} AS VECTOR(FLOAT, 1536))) AS similarity\n FROM {table_name}\n ORDER BY similarity DESC\n LIMIT 2\n \"\"\"\n cursor.execute(query)\n result = cursor.fetchall()\n chunks += [item[0] for item in result]\n cursor.close()\n conn.close()\n\n return chunks\n\ndef get_completion(question, document_chunks: List[str], model_name: str = \"llama2-70b-chat\"):\n conn = get_db_connection()\n cur = conn.cursor()\n\n chunks = \"\\n\\n\".join(document_chunks)\n\n query = f\"\"\"\n SELECT snowflake.cortex.complete(\n '{model_name}', \n CONCAT( \n 'You are an Airbyte product assistant. Answer the question based on the context. Be concise. When returning a list of items, Please enumerate description on separate lines','Context: ',\n $$\n {chunks}\n $$,\n 'Question: ', \n $$ {question} $$,\n 'Answer: '\n )\n ) as response;\"\"\"\n cur.execute(query)\n result = cur.fetchall()\n cur.close()\n conn.close()\n # TO-DO: better parsing here \n return result[0][0].strip()\n\n\ndef get_user_intent(query):\n # this method does a simple few shots classifcation to help get user's intent\n\n examples = [\n {\"role\": \"system\", \"content\": \"You are an assistant that classifies user intents based on their messages.\"},\n {\"role\": \"user\", \"content\": \"How can I add vector data in Snowflake?\"},\n {\"role\": \"assistant\", \"content\": \"docs_question\"},\n {\"role\": \"user\", \"content\": \"How do I set up Snowflake Cortex destination?\"},\n {\"role\": \"assistant\", \"content\": \"docs_question\"},\n {\"role\": \"user\", \"content\": \"What are the upcoming features for Snowflake Cortex?\"},\n {\"role\": \"assistant\", \"content\": \"github_question\"},\n {\"role\": \"user\", \"content\": \"What are the known issues for Snowflake Cortex?\"},\n {\"role\": \"assistant\", \"content\": \"github_question\"},\n {\"role\": \"user\", \"content\": \"Which customers have requested features for Snowflake Cortex?\"},\n {\"role\": \"assistant\", \"content\": \"zendesk_question\"},\n {\"role\": \"user\", \"content\": \"Which customers have requested authorization related features for Snowflake Cortex?\"},\n {\"role\": \"assistant\", \"content\": \"zendesk_question\"},\n ]\n examples.append({\"role\": \"user\", \"content\": query})\n openai.api_key = st.secrets[\"OPENAI_API_KEY\"]\n # Call the OpenAI chat API to get the intent classification\n response = openai.chat.completions.create(\n model=\"gpt-3.5-turbo\", \n messages=examples,\n max_tokens=10,\n n=1,\n stop=[\"\\n\"]\n )\n\n intent = response.choices[0].message.content \n return intent\n\ndef get_tables_to_query(query):\n intent = get_user_intent(query)\n \n if intent == \"github_question\":\n return ([\"airbyte_github_issues\"], \"llama2-70b-chat\")\n elif intent == \"zendesk_question\":\n return ([\"airbyte_zendesk_tickets\", \"airbyte_zendesk_users\"], \"llama2-70b-chat\")\n else:\n # default \n return([\"airbyte_docs\"], \"snowflake-arctic\") \n\n\ndef get_response(query):\n # embed the query \n embeddings = OpenAIEmbeddings(openai_api_key=st.secrets[\"OPENAI_API_KEY\"])\n # get similar chunks from sources/tables in Snowflake\n [tables, model] = get_tables_to_query(query)\n chunks = get_similar_chunks(embeddings.embed_query(query), tables)\n if (len(chunks) == 0):\n return \"I am sorry, I do not have the context to answer your question.\"\n else:\n return get_completion(query, chunks, model)\n\n\ndef response_generator(query):\n response = get_response(query)\n # Split the response by spaces but preserve the new lines\n parts = []\n current_part = []\n for char in response:\n if char in ['\\n', ' ']:\n if current_part:\n parts.append(''.join(current_part))\n current_part = []\n if char == '\\n':\n parts.append('\\n')\n else:\n parts.append(' ')\n else:\n current_part.append(char)\n if current_part:\n parts.append(''.join(current_part))\n\n for part in parts:\n yield part\n time.sleep(0.01)\n\n\n\nst.title(\"AI Product assistant\")\n\n# simulate initial message from assistant\ninitial_message = \"\"\"👋 Hello! I'm here to help you with any questions you have on Airbyte's products or services. \nHow can I assist you today?\"\"\"\n\n# Display initial message from assistant\nwith st.chat_message(\"assistant\"):\n st.write(initial_message)\n\n# Recommended questions section in the sidebar\nst.sidebar.title(\"Recommended Questions\")\nrecommended_questions = [\n \"How can I add vector data in Snowflake?\",\n \"What are the upcoming features for Snowflake Cortex?\",\n \"Which customers have requested features for Snowflake Cortex?\",\n]\nfor question in recommended_questions:\n st.sidebar.markdown(f\"- {question}\")\n\n\nclient = OpenAI(api_key=st.secrets[\"OPENAI_API_KEY\"])\n\n# Set a default model\nif \"openai_model\" not in st.session_state:\n st.session_state[\"openai_model\"] = \"gpt-3.5-turbo\"\n\n# Initialize chat history\nif \"messages\" not in st.session_state:\n st.session_state.messages = []\n\n# Display chat messages from history on app rerun\nfor message in st.session_state.messages:\n with st.chat_message(message[\"role\"]):\n st.markdown(message[\"content\"])\n\nif prompt := st.chat_input(\"Ask a question?\"):\n # Display user message in chat message container\n with st.chat_message(\"user\"):\n st.markdown(prompt)\n # Add user message to chat history\n st.session_state.messages.append({\"role\": \"user\", \"content\": prompt})\n\n with st.chat_message(\"assistant\"):\n response = st.write_stream(response_generator(prompt))\n # Add assistant response to chat history\n st.session_state.messages.append({\"role\": \"assistant\", \"content\": response})\n\n" }, { "path": "vector_store_integration/AI_assistant_streamlit_app/requirements.txt", "content": "streamlit\nopenai\nsnowflake-connector-python\nlangchain\nlangchain-community \ntiktoken" }, { "path": "vector_store_integration/RAG_using_PGVector.ipynb", "content": "{\n \"nbformat\": 4,\n \"nbformat_minor\": 0,\n \"metadata\": {\n \"colab\": {\n \"provenance\": []\n },\n \"kernelspec\": {\n \"name\": \"python3\",\n \"display_name\": \"Python 3\"\n },\n \"language_info\": {\n \"name\": \"python\"\n }\n },\n \"cells\": [\n {\n \"metadata\": {},\n \"cell_type\": \"code\",\n \"outputs\": [],\n \"execution_count\": null,\n \"source\": [\n \"## Airbyte PGVector RAG Demo\\n\",\n \"\\n\",\n \"This tutorial demonstrates how to use data stored in Airbyte's PGVector destination to perform Retrieval-Augmented Generation (RAG). You should use this destination when you intend to use PGVector for LLM specific vector operations like RAG.\\n\",\n \"\\n\",\n \"As a practical example, we'll build a Assistant—an AI chatbot capable of answering questions related to simpsons episoded using data from multiple Airbyte-related sources.\\n\",\n \"\\n\",\n \"#### Prerequisites:\\n\",\n \"* Vector data stored in Postgres with Vector colums via PGVector destination. In our case we are using data from kaggle.\\n\",\n \"* Postgresql DB with PGVector enabled\\n\",\n \"* Open AI key\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"source\": [\n \"### a. Install dependencies and import secrets\\n\",\n \"\\n\"\n ],\n \"metadata\": {\n \"id\": \"7R0-uD7R3Uki\"\n }\n },\n {\n \"cell_type\": \"code\",\n \"source\": [\n \"!pip install sqlalchemy openai rich psycopg2 python-dotenv langchain-openai\"\n ],\n \"metadata\": {\n \"collapsed\": true,\n \"id\": \"HbR-po_Z3VFV\"\n },\n \"execution_count\": null,\n \"outputs\": []\n },\n {\n \"cell_type\": \"code\",\n \"source\": [\n \"import openai\\n\",\n \"import json\\n\",\n \"import rich\\n\",\n \"from langchain_openai import OpenAIEmbeddings\\n\",\n \"from sqlalchemy import create_engine\\n\",\n \"from sqlalchemy.engine import URL\\n\",\n \"from google.colab import userdata\\n\",\n \"\\n\",\n \"\\n\",\n \"OPENAI_API_KEY = userdata.get('openai_api_key')\\n\",\n \"HOST = userdata.get(\\\"db_host\\\")\\n\",\n \"USERNAME = userdata.get(\\\"db_username\\\")\\n\",\n \"PASSWORD = userdata.get(\\\"db_password\\\")\\n\",\n \"DATABASE = userdata.get(\\\"db_name\\\")\"\n ],\n \"metadata\": {\n \"id\": \"LP0QfMFQ6Flz\"\n },\n \"execution_count\": null,\n \"outputs\": []\n },\n {\n \"cell_type\": \"markdown\",\n \"source\": [\n \"### b. Initialize open AI client and DB Engine\\n\"\n ],\n \"metadata\": {\n \"id\": \"mmHB_MId7zwo\"\n }\n },\n {\n \"cell_type\": \"code\",\n \"source\": [\n \"openai.api_key = OPENAI_API_KEY\\n\",\n \"\\n\",\n \"url = URL.create(\\n\",\n \" \\\"postgresql\\\",\\n\",\n \" host=HOST,\\n\",\n \" username=USERNAME,\\n\",\n \" password=PASSWORD,\\n\",\n \" database=DATABASE,\\n\",\n \")\\n\",\n \"\\n\",\n \"engine = create_engine(url)\"\n ],\n \"metadata\": {\n \"id\": \"7Y3iCe6e7-Ra\"\n },\n \"execution_count\": null,\n \"outputs\": []\n },\n {\n \"cell_type\": \"markdown\",\n \"source\": [\n \"### d. Explore data stored in Posgresql\\n\",\n \"\\n\",\n \"We need a few methods to embed user questions and make searches in DB\\n\",\n \"\\n\",\n \"- Helper to embed the user question so we can the search for it in the DB.\\n\",\n \"- Function to get the context from the database using a user question as input.\\n\",\n \"- One to get the response from the chat assistant that will use the context using the method from previous step.\"\n ],\n \"metadata\": {\n \"id\": \"ZQ0PzOfb9rtK\"\n }\n },\n {\n \"cell_type\": \"code\",\n \"source\": [\n \"from sqlalchemy import text\\n\",\n \"from openai import OpenAI\\n\",\n \"\\n\",\n \"client = OpenAI(\\n\",\n \" api_key=OPENAI_API_KEY,\\n\",\n \")\\n\",\n \"\\n\",\n \"def get_embedding_from_open_ai(question):\\n\",\n \" print(f\\\"Embedding user's query: {question}\\\")\\n\",\n \" embeddings = OpenAIEmbeddings(openai_api_key=OPENAI_API_KEY)\\n\",\n \" embedding_response = embeddings.embed_query(question)\\n\",\n \" return embedding_response\\n\",\n \"\\n\",\n \"QUERY_TEMPLATE = \\\"\\\"\\\"\\n\",\n \"SELECT document_content,\\n\",\n \" metadata->'title' as episode_title,\\n\",\n \" metadata->'script_line_number' as script_line_number,\\n\",\n \" metadata->'name' as character_name,\\n\",\n \" metadata->'spoken_words' as spoken_words\\n\",\n \"FROM episode_spoken_words\\n\",\n \"ORDER BY embedding <-> :question_vector\\n\",\n \"LIMIT 5\\n\",\n \"\\\"\\\"\\\"\\n\",\n \"\\n\",\n \"def get_context(question) -> str:\\n\",\n \" # Get the embedding from OpenAI\\n\",\n \" question_vector = get_embedding_from_open_ai(question)\\n\",\n \"\\n\",\n \" # Format the question vector as a string in the format expected by PostgreSQL\\n\",\n \" question_vector_str = '[' + ','.join(map(str, question_vector)) + ']'\\n\",\n \"\\n\",\n \" # Use the text() function for raw queries with SQLAlchemy\\n\",\n \" query = text(QUERY_TEMPLATE)\\n\",\n \"\\n\",\n \" # Execute the query, passing the vector as a bind parameter\\n\",\n \" with engine.connect() as connection:\\n\",\n \" result = connection.execute(query, {'question_vector': question_vector_str})\\n\",\n \"\\n\",\n \" # Format and return the result\\n\",\n \" return (\\\"\\\\n\\\\n\\\" + \\\"-\\\" * 8 + \\\"\\\\n\\\\n\\\").join(\\n\",\n \" [\\n\",\n \" f\\\"Episode {row.episode_title} | Line number: {row.script_line_number} | \\\"\\n\",\n \" f\\\"Spoken Words: {row.spoken_words} | Character: {row.character_name}\\\"\\n\",\n \" for row in result\\n\",\n \" ]\\n\",\n \" )\\n\",\n \"\\n\",\n \"\\n\",\n \"def get_response(question):\\n\",\n \" response = client.chat.completions.create(\\n\",\n \" model=\\\"gpt-3.5-turbo\\\",\\n\",\n \" messages=[\\n\",\n \" {\\\"role\\\": \\\"system\\\", \\\"content\\\": \\\"You are a Simpsons expert talking about Simpsons episodes.\\\"},\\n\",\n \" {\\\"role\\\": \\\"user\\\", \\\"content\\\": question},\\n\",\n \" {\\\"role\\\": \\\"assistant\\\", \\\"content\\\": f\\\"Use only this information to answer the question: {get_context(question)}. Do not search on the internet.\\\"}\\n\",\n \" ]\\n\",\n \" )\\n\",\n \" return response.choices[0].message.content\\n\",\n \"\\n\"\n ],\n \"metadata\": {\n \"id\": \"_PJ6eb5-A419\"\n },\n \"execution_count\": null,\n \"outputs\": []\n },\n {\n \"cell_type\": \"markdown\",\n \"source\": [\n \"### d. Make questions\\n\",\n \"\\n\",\n \"Finally, let's put all together and get a response from our assistant using the Simpsons database.\"\n ],\n \"metadata\": {\n \"id\": \"spQPCVe9AZKh\"\n }\n },\n {\n \"cell_type\": \"code\",\n \"source\": [\n \"question = \\\"Talking about food\\\"\\n\",\n \"response = get_response(question)\\n\",\n \"rich.print(response)\"\n ],\n \"metadata\": {\n \"id\": \"pwegM_02AgOU\"\n },\n \"execution_count\": null,\n \"outputs\": []\n }\n ]\n}\n" }, { "path": "vector_store_integration/RAG_using_Snowflake_Cortex.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"KWKEgtakJtH6\"\n },\n \"source\": [\n \"## Airbyte Snowflake Cortex RAG Demo\\n\",\n \"\\n\",\n \"This tutorial demonstrates how to use data stored in Airbyte's Snowflake Cortex destination to perform Retrieval-Augmented Generation (RAG). You should use this destination when you intend to use Snowflake for LLM specific vector operations like RAG.\\n\",\n \"\\n\",\n \"As a practical example, we'll build a Product Assistant—an AI chatbot capable of answering product-related questions using data from multiple Airbyte-related sources. With the Product Assistant, you can ask questions across all your sales enablement data in one place.\\n\",\n \"\\n\",\n \"#### Prerequisites:\\n\",\n \"* Vector data stored in Snowflake via Snowflake Cortex destination. In our case we are using data from airbyte docs, Github issues and Zendesk.\\n\",\n \"* Snowflake account with Cortex functions enabled\\n\",\n \"* Open AI key\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"gS3oGgI0CVpn\"\n },\n \"source\": [\n \"### a. Install dependencies\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"id\": \"bM4Te8XEWECV\"\n },\n \"outputs\": [],\n \"source\": [\n \"# Add virtual environment support in Google Colab\\n\",\n \"!apt-get install -qq python3.10-venv\\n\",\n \"\\n\",\n \"# Install openai\\n\",\n \"# tbd - add snowflake python connector\\n\",\n \"%pip install --quiet openai snowflake-connector-python langchain-openai tiktoken\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"nKbdq1eUcmAz\"\n },\n \"source\": [\n \"### b. Explore data stored in Snowflake.\\n\",\n \"\\n\",\n \"Let's see what document/vecto data in Snowflake looks like.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\"\n },\n \"collapsed\": true,\n \"id\": \"TKDGBUhLcfuK\",\n \"outputId\": \"1231d559-298f-43a0-95c3-a67f8cfb62ab\"\n },\n \"outputs\": [],\n \"source\": [\n \"# Fetch data from airbyte_docs table\\n\",\n \"from snowflake import connector\\n\",\n \"from google.colab import userdata\\n\",\n \"from typing import List\\n\",\n \"import pandas as pd\\n\",\n \"\\n\",\n \"def get_db_connection():\\n\",\n \" return connector.connect(\\n\",\n \" account=userdata.get(\\\"SNOWFLAKE_HOST\\\"),\\n\",\n \" role=userdata.get(\\\"SNOWFLAKE_ROLE\\\"),\\n\",\n \" warehouse=userdata.get(\\\"SNOWFLAKE_WAREHOUSE\\\"),\\n\",\n \" database=userdata.get(\\\"SNOWFLAKE_DATABASE\\\"),\\n\",\n \" schema=userdata.get(\\\"SNOWFLAKE_SCHEMA\\\"),\\n\",\n \" user=userdata.get(\\\"SNOWFLAKE_USERNAME\\\"),\\n\",\n \" password=userdata.get(\\\"SNOWFLAKE_PASSWORD\\\"),\\n\",\n \" )\\n\",\n \"\\n\",\n \"def fetch_table_data(table_name, columns):\\n\",\n \" conn = get_db_connection()\\n\",\n \" cursor = conn.cursor()\\n\",\n \"\\n\",\n \" # Form the query to select specific columns\\n\",\n \" columns_str = \\\", \\\".join(columns)\\n\",\n \" query = f\\\"SELECT {columns_str} FROM {table_name};\\\"\\n\",\n \"\\n\",\n \" cursor.execute(query)\\n\",\n \" result = cursor.fetchall()\\n\",\n \"\\n\",\n \" # Fetch the column names\\n\",\n \" col_names = [desc[0] for desc in cursor.description]\\n\",\n \"\\n\",\n \" cursor.close()\\n\",\n \" conn.close()\\n\",\n \"\\n\",\n \" # Load the result into a pandas DataFrame\\n\",\n \" df = pd.DataFrame(result, columns=col_names)\\n\",\n \" return df;\\n\",\n \"\\n\",\n \"# show data from airbtye_docs table\\n\",\n \"data_frame = fetch_table_data(\\\"airbyte_docs\\\", [\\\"document_id\\\", \\\"document_content\\\", \\\"metadata\\\", \\\"embedding\\\"])\\n\",\n \"data_frame\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"5LU0M4g6clBj\"\n },\n \"source\": []\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"SyyzczWXBXTS\"\n },\n \"source\": [\n \"### c. Build the RAG pipeline and ask a question\\n\",\n \"\\n\",\n \"Let's write the three main pieces of a RAG pipeline:\\n\",\n \"* Embedding incoming query\\n\",\n \"* Doing similarity search to find matching chunks\\n\",\n \"* Send chunks to LLM for completion\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\",\n \"height\": 265\n },\n \"id\": \"zQ6rWEV2u-3U\",\n \"outputId\": \"d364b7e3-cfca-4628-da20-3ae4f34ad143\"\n },\n \"outputs\": [],\n \"source\": [\n \"from openai import OpenAI\\n\",\n \"from snowflake import connector\\n\",\n \"from langchain_openai import OpenAIEmbeddings\\n\",\n \"from google.colab import userdata\\n\",\n \"from typing import List\\n\",\n \"from rich.console import Console\\n\",\n \"\\n\",\n \"def get_db_connection():\\n\",\n \" return connector.connect(\\n\",\n \" account=userdata.get(\\\"SNOWFLAKE_HOST\\\"),\\n\",\n \" role=userdata.get(\\\"SNOWFLAKE_ROLE\\\"),\\n\",\n \" warehouse=userdata.get(\\\"SNOWFLAKE_WAREHOUSE\\\"),\\n\",\n \" database=userdata.get(\\\"SNOWFLAKE_DATABASE\\\"),\\n\",\n \" schema=userdata.get(\\\"SNOWFLAKE_SCHEMA\\\"),\\n\",\n \" user=userdata.get(\\\"SNOWFLAKE_USERNAME\\\"),\\n\",\n \" password=userdata.get(\\\"SNOWFLAKE_PASSWORD\\\"),\\n\",\n \" )\\n\",\n \"\\n\",\n \"# convert user's query into a vector array to prep for similiary search\\n\",\n \"def get_embedding_from_openai(query)->str:\\n\",\n \" print(f\\\"Embedding user's query -> {query}...\\\")\\n\",\n \" embeddings = OpenAIEmbeddings(openai_api_key=userdata.get(\\\"OPENAI_API_KEY\\\"))\\n\",\n \" return embeddings\\n\",\n \"\\n\",\n \"# use Snowflake's Cortex in-build similarity search to find matching chunks\\n\",\n \"def get_similar_chunks_from_snowflake(query_vector, table_names) -> List[str]:\\n\",\n \" print(\\\"\\\\nRetrieving similar chunks...\\\")\\n\",\n \" conn = get_db_connection()\\n\",\n \" cursor = conn.cursor()\\n\",\n \"\\n\",\n \" chunks = []\\n\",\n \" for table_name in table_names:\\n\",\n \" query = f\\\"\\\"\\\"\\n\",\n \" SELECT document_content,\\n\",\n \" VECTOR_COSINE_SIMILARITY(embedding, CAST({query_vector} AS VECTOR(FLOAT, 1536))) AS similarity\\n\",\n \" FROM {table_name}\\n\",\n \" ORDER BY similarity DESC\\n\",\n \" LIMIT 2\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" cursor.execute(query)\\n\",\n \" result = cursor.fetchall()\\n\",\n \" print(f\\\"Found {len(result)} matching chunks in table:{table_name}!\\\")\\n\",\n \" chunks += [item[0] for item in result]\\n\",\n \" cursor.close()\\n\",\n \" conn.close()\\n\",\n \"\\n\",\n \" return chunks\\n\",\n \"\\n\",\n \"# use Snowflake's Cortex in-build completion to find matching chunks.\\n\",\n \"def get_completion_from_snowflake(question, document_chunks: List[str], model_name):\\n\",\n \" print(f\\\"\\\\nSending chunks to Snowflake (LLM: {model_name}) for completion...\\\")\\n\",\n \" conn = get_db_connection()\\n\",\n \" cur = conn.cursor()\\n\",\n \"\\n\",\n \" chunks = \\\"\\\\n\\\\n\\\".join(document_chunks)\\n\",\n \"\\n\",\n \" query = f\\\"\\\"\\\"\\n\",\n \" SELECT snowflake.cortex.complete(\\n\",\n \" '{model_name}',\\n\",\n \" CONCAT(\\n\",\n \" 'You are an Airbyte product assistant. Answer the question based on the context. Do not use any other information. Be concise. When returning a list of items, please enumerate description on separate lines','Context: ',\\n\",\n \" $$\\n\",\n \" {chunks}\\n\",\n \" $$,\\n\",\n \" 'Question: ',\\n\",\n \" $$ {question} $$,\\n\",\n \" 'Answer: '\\n\",\n \" )\\n\",\n \" ) as response;\\\"\\\"\\\"\\n\",\n \" cur.execute(query)\\n\",\n \" result = cur.fetchall()\\n\",\n \" cur.close()\\n\",\n \" conn.close()\\n\",\n \" # TO-DO: better parsing here\\n\",\n \" return result[0][0].strip()\\n\",\n \"\\n\",\n \"# Putting it all together\\n\",\n \"def get_response(query, table_names, model_name=\\\"llama2-70b-chat\\\"):\\n\",\n \" # Step 1: embed the query\\n\",\n \" embeddings = get_embedding_from_openai(query)\\n\",\n \"\\n\",\n \" # Step 2: get similar chunks from sources/tables in Snowflake\\n\",\n \" chunks = get_similar_chunks_from_snowflake(embeddings.embed_query(query), table_names)\\n\",\n \"\\n\",\n \" if (len(chunks) == 0):\\n\",\n \" return \\\"I am sorry, I do not have the context to answer your question.\\\"\\n\",\n \" else:\\n\",\n \" # Step 3: send chunks to LLM for completion\\n\",\n \" return get_completion_from_snowflake(query, chunks, model_name)\\n\",\n \"\\n\",\n \"# Ask a question\\n\",\n \"query = 'How can I store vector data in Snowflake'\\n\",\n \"response = get_response(query, [\\\"airbyte_docs\\\"], \\\"snowflake-arctic\\\")\\n\",\n \"\\n\",\n \"Console().print(f\\\"\\\\n\\\\nResponse from LLM:\\\\n\\\\n[blue]{response}[/blue]\\\")\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"G5NvV86T57-V\"\n },\n \"source\": [\n \"### d. Let's ask another question\\n\",\n \"\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\",\n \"height\": 281\n },\n \"id\": \"cPYdEs663tl8\",\n \"outputId\": \"6997984b-6ea3-4b95-d80f-ccace53b6d43\"\n },\n \"outputs\": [],\n \"source\": [\n \"query = 'What are the upcoming features for Snowflake Cortex?'\\n\",\n \"response = get_response(query, [\\\"airbyte_github_issues\\\"])\\n\",\n \"Console().print(f\\\"\\\\n\\\\nResponse from LLM:\\\\n\\\\n[blue]{response}[/blue]\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"S_CJ3_C9pvw4\"\n },\n \"source\": [\n \"### e. Closing the loop\\n\",\n \"Let's see if there are customers asking for upcoming features above.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"colab\": {\n \"base_uri\": \"https://localhost:8080/\",\n \"height\": 267\n },\n \"id\": \"mLfNlzGLqOIg\",\n \"outputId\": \"dcfa593f-dab0-44ff-fcec-e4cb671b6d1a\"\n },\n \"outputs\": [],\n \"source\": [\n \"query = 'Are there customers asking for better authorization options for Snowflake Cortex? Give me their names and email.'\\n\",\n \"response = get_response(query, [\\\"airbyte_zendesk_tickets\\\", \\\"airbyte_zendesk_users\\\"])\\n\",\n \"Console().print(f\\\"\\\\n\\\\nResponse from LLM:\\\\n\\\\n[blue]{response}[/blue]\\\")\"\n ]\n }\n ],\n \"metadata\": {\n \"colab\": {\n \"collapsed_sections\": [\n \"nKbdq1eUcmAz\"\n ],\n \"provenance\": []\n },\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"name\": \"python\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 0\n}\n" }, { "path": "vector_store_integration/RAG_using_Vectara.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"ayf6NOyRb-zn\"\n },\n \"source\": [\n \"# **Airbyte Vectara RAG Tutorial**\\n\",\n \"This tutorial showcases how to use data stored in Airbyte's Vectara destination to perform Retrieval-Augmented Generation (RAG).\\n\",\n \"## **Prerequisites**\\n\",\n \"**1) OpenAI API Key**:\\n\",\n \" - **Create an OpenAI Account**: Sign up for an account on [OpenAI](https://www.openai.com/).\\n\",\n \" - **Generate an API Key**: Go to the API section and generate a new API key. For detailed instructions, refer to the [OpenAI documentation](https://beta.openai.com/docs/quickstart).\\n\",\n \"\\n\",\n \"**2) Vectara Customer ID, Corpus ID ,API Key**:\\n\",\n \" - **Create an Vectara Account**: Sign up for an account on [Vectara](https://vectara.com/).\\n\",\n \" - **Customer ID**: Click on the profile icon on top right, and look for your customer ID [Vectara Console](https://console.vectara.com/).\\n\",\n \" - **Corpus ID**: You can see the list of Corpora you've created in your Vectara Account. Note down the required Corpus ID [Vectara Corpora](https://console.vectara.com/console/corpora).\\n\",\n \" - **Generate an API Key**: Go here and generate a new API key. [Vectara API_Key](https://console.vectara.com/console/apiAccess/personalApiKey).\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"41LFzVLEmXT3\"\n },\n \"source\": [\n \"# **Install Dependencies**\\n\",\n \"As in any other Python Code, the first step is to install the required Dependencies!\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"id\": \"NSOZnJ6G9zQu\"\n },\n \"outputs\": [],\n \"source\": [\n \"# Add virtual environment support in Google Colab\\n\",\n \"!apt-get install -qq python3.10-venv\\n\",\n \"\\n\",\n \"# Install required packages\\n\",\n \"%pip install --quiet openai langchain-openai tiktoken pandas langchain_community\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"dQTCEicCmx2A\"\n },\n \"source\": [\n \"# **Set Up Environment Variables**\\n\",\n \"We configure the required credentials here!\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"id\": \"94bGodjCFfsN\"\n },\n \"outputs\": [],\n \"source\": [\n \"import os\\n\",\n \"\\n\",\n \"os.environ[\\\"VECTARA_CUSTOMER_ID\\\"] = \\\"YOUR_VECTARA_CUSTOMER_ID\\\"\\n\",\n \"os.environ[\\\"VECTARA_CORPUS_ID\\\"] = \\\"YOUR_VECTARA_CORPUS_ID\\\"\\n\",\n \"os.environ[\\\"VECTARA_API_KEY\\\"] = \\\"YOUR_VECTARA_API_KEY\\\"\\n\",\n \"os.environ[\\\"OPENAI_API_KEY\\\"] = \\\"YOUR_OPENAI_API_KEY\\\"\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"QXR8wOMWnztR\"\n },\n \"source\": [\n \"# **Initialize Vectara Vector Store**\\n\",\n \"We start by initializing the Vectara Vector Store and then see what the data in Vectara looks like.\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"id\": \"ipg7aPJq-wZ8\"\n },\n \"outputs\": [],\n \"source\": [\n \"import pandas as pd\\n\",\n \"from langchain_community.vectorstores import Vectara\\n\",\n \"from google.colab import userdata\\n\",\n \"\\n\",\n \"# Initialize Vectara vector store\\n\",\n \"vectara = Vectara(\\n\",\n \" vectara_customer_id=os.getenv(\\\"VECTARA_CUSTOMER_ID\\\"),\\n\",\n \" vectara_corpus_id=os.getenv(\\\"VECTARA_CORPUS_ID\\\"),\\n\",\n \" vectara_api_key=os.getenv(\\\"VECTARA_API_KEY\\\")\\n\",\n \")\\n\",\n \"\\n\",\n \"def fetch_vectara_data():\\n\",\n \" # Simulate fetching data from Vectara\\n\",\n \" data = {\\n\",\n \" \\\"document_id\\\": [1, 2, 3],\\n\",\n \" \\\"document_content\\\": [\\\"Content of doc 1\\\", \\\"Content of doc 2\\\", \\\"Content of doc 3\\\"],\\n\",\n \" \\\"metadata\\\": [\\\"Metadata1\\\", \\\"Metadata2\\\", \\\"Metadata3\\\"],\\n\",\n \" \\\"embedding\\\": [\\\"[0.1, 0.2, ...]\\\", \\\"[0.3, 0.4, ...]\\\", \\\"[0.5, 0.6, ...]\\\"]\\n\",\n \" }\\n\",\n \" df = pd.DataFrame(data)\\n\",\n \" return df\\n\",\n \"\\n\",\n \"# show data\\n\",\n \"data_frame = fetch_vectara_data()\\n\",\n \"print(data_frame)\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"6b0VCg4Oox4l\"\n },\n \"source\": [\n \"# **Embedding and similarity search with Vectara**\\n\",\n \"Here we will convert the user's query into embeddings using OpenAI and retrieve similar chunks from Vectara based on the query\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"id\": \"G6UuEXP9FpdF\"\n },\n \"outputs\": [],\n \"source\": [\n \"from openai import OpenAI\\n\",\n \"from langchain_openai import OpenAIEmbeddings\\n\",\n \"from typing import List\\n\",\n \"from rich.console import Console\\n\",\n \"\\n\",\n \"\\n\",\n \"client = OpenAI(api_key=os.getenv(\\\"OPENAI_API_KEY\\\"))\\n\",\n \"\\n\",\n \"# Convert user's query into a vector array to prep for similarity search\\n\",\n \"def get_embedding_from_openai(query) -> List[float]:\\n\",\n \" print(f\\\"Embedding user's query -> {query}...\\\")\\n\",\n \" embeddings = OpenAIEmbeddings(openai_api_key=client.api_key)\\n\",\n \" return embeddings.embed_query(query)\\n\",\n \"\\n\",\n \"# Use Vectara to find matching chunks\\n\",\n \"def get_similar_chunks_from_vectara(query: str) -> List[str]:\\n\",\n \" print(\\\"\\\\nRetrieving similar chunks...\\\")\\n\",\n \" try:\\n\",\n \" results = vectara.similarity_search(query=query)\\n\",\n \" chunks = [result.page_content for result in results]\\n\",\n \" print(f\\\"Found {len(chunks)} matching chunks!\\\")\\n\",\n \" return chunks\\n\",\n \" except Exception as e:\\n\",\n \" print(f\\\"Error in retrieving chunks: {e}\\\")\\n\",\n \" return []\\n\",\n \"\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"ZEQN6yohqVSY\"\n },\n \"source\": [\n \"# **Building RAG Pipeline and asking a question**\\n\",\n \"Finally we use OpenAI for querying our data!
\\n\",\n \"We know the three main steps of a RAG Pipeline are :
\\n\",\n \"- Embedding incoming query
\\n\",\n \"- Doing similarity search to find matching chunks
\\n\",\n \"- Send chunks to LLM for completion\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"id\": \"zLKAuK-GqLPC\"\n },\n \"outputs\": [],\n \"source\": [\n \"# Use OpenAI to complete the response\\n\",\n \"def get_completion_from_openai(question, document_chunks: List[str], model_name=\\\"gpt-3.5-turbo\\\"):\\n\",\n \" print(f\\\"\\\\nSending chunks to OpenAI (LLM: {model_name}) for completion...\\\")\\n\",\n \" chunks = \\\"\\\\n\\\\n\\\".join(document_chunks)\\n\",\n \"\\n\",\n \" response = client.chat.completions.create(\\n\",\n \" model=model_name,\\n\",\n \" messages=[\\n\",\n \" {\\\"role\\\": \\\"system\\\", \\\"content\\\": \\\"You are an Airbyte product assistant. Answer the question based on the context. Do not use any other information. Be concise.\\\"},\\n\",\n \" {\\\"role\\\": \\\"user\\\", \\\"content\\\": f\\\"Context:\\\\n{chunks}\\\\n\\\\n{question}\\\\n\\\\nAnswer:\\\"}\\n\",\n \" ],\\n\",\n \" max_tokens=150\\n\",\n \" )\\n\",\n \" return response.choices[0].message.content.strip()\\n\",\n \"\\n\",\n \"# Putting it all together\\n\",\n \"def get_response(query, model_name=\\\"gpt-3.5-turbo\\\"):\\n\",\n \"\\n\",\n \" chunks = get_similar_chunks_from_vectara(query)\\n\",\n \"\\n\",\n \" if len(chunks) == 0:\\n\",\n \" return \\\"I am sorry, I do not have the context to answer your question.\\\"\\n\",\n \" else:\\n\",\n \"\\n\",\n \" return get_completion_from_openai(query, chunks, model_name)\\n\",\n \"\\n\",\n \"# Ask a question\\n\",\n \"query = 'What data do you have?'\\n\",\n \"response = get_response(query)\\n\",\n \"\\n\",\n \"Console().print(f\\\"\\\\n\\\\nResponse from LLM:\\\\n\\\\n[blue]{response}[/blue]\\\")\\n\"\n ]\n }\n ],\n \"metadata\": {\n \"colab\": {\n \"provenance\": []\n },\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"name\": \"python\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 0\n}\n" }, { "path": "weather_data_stack/.gitignore", "content": "# Byte-compiled / optimized / DLL files\n__pycache__/\n*.py[cod]\n*$py.class\n\n# C extensions\n*.so\n\n# Distribution / packaging\n.Python\nbuild/\ndevelop-eggs/\ndist/\ndownloads/\neggs/\n.eggs/\nlib/\nlib64/\nparts/\nsdist/\nvar/\nwheels/\nshare/python-wheels/\n*.egg-info/\n.installed.cfg\n*.egg\nMANIFEST\n\n# PyInstaller\n# Usually these files are written by a python script from a template\n# before PyInstaller builds the exe, so as to inject date/other infos into it.\n*.manifest\n*.spec\n\n# Installer logs\npip-log.txt\npip-delete-this-directory.txt\n\n# Unit test / coverage reports\nhtmlcov/\n.tox/\n.nox/\n.coverage\n.coverage.*\n.cache\nnosetests.xml\ncoverage.xml\n*.cover\n*.py,cover\n.hypothesis/\n.pytest_cache/\ncover/\n\n# Translations\n*.mo\n*.pot\n\n# Django stuff:\n*.log\nlocal_settings.py\ndb.sqlite3\ndb.sqlite3-journal\n\n# Flask stuff:\ninstance/\n.webassets-cache\n\n# Scrapy stuff:\n.scrapy\n\n# Sphinx documentation\ndocs/_build/\n\n# PyBuilder\n.pybuilder/\ntarget/\n\n# Jupyter Notebook\n.ipynb_checkpoints\n\n# IPython\nprofile_default/\nipython_config.py\n\n# pyenv\n# For a library or package, you might want to ignore these files since the code is\n# intended to run in multiple environments; otherwise, check them in:\n# .python-version\n\n# pipenv\n# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.\n# However, in case of collaboration, if having platform-specific dependencies or dependencies\n# having no cross-platform support, pipenv may install dependencies that don't work, or not\n# install all needed dependencies.\n#Pipfile.lock\n\n# poetry\n# Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.\n# This is especially recommended for binary packages to ensure reproducibility, and is more\n# commonly ignored for libraries.\n# https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control\n#poetry.lock\n\n# pdm\n# Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control.\n#pdm.lock\n# pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it\n# in version control.\n# https://pdm.fming.dev/#use-with-ide\n.pdm.toml\n\n# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm\n__pypackages__/\n\n# Celery stuff\ncelerybeat-schedule\ncelerybeat.pid\n\n# SageMath parsed files\n*.sage.py\n\n# Environments\n.env\n.venv\nenv/\nvenv/\nENV/\nenv.bak/\nvenv.bak/\n\n# Spyder project settings\n.spyderproject\n.spyproject\n\n# Rope project settings\n.ropeproject\n\n# mkdocs documentation\n/site\n\n# mypy\n.mypy_cache/\n.dmypy.json\ndmypy.json\n\n# Pyre type checker\n.pyre/\n\n# pytype static type analyzer\n.pytype/\n\n# Cython debug symbols\ncython_debug/\n\n# PyCharm\n# JetBrains specific template is maintained in a separate JetBrains.gitignore that can\n# be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore\n# and can be added to the global gitignore or merged into this file. For a more nuclear\n# option (not recommended) you can uncomment the following to ignore the entire idea folder.\n#.idea/\n\n#Desktop Services Store\n.DS_Store" }, { "path": "weather_data_stack/README.md", "content": "# Weather Data Stack with Airbyte, dbt, Dagster and BigQuery\n\nWelcome to the \"Weather Data Stack\" repository! 🌟 This is your go-to place to easily set up a data stack using Airbyte, dbt, BigQuery, and Dagster. With this setup, you can pull weather data from WeatherStack API, put it into BigQuery, and play around with it using dbt and Dagster.\n\nThis Quickstart is all about making things easy, getting you started quickly and showing you how smoothly all these tools can work together!\n\n## Table of Contents\n\n- [Weather Data Stack with Airbyte, dbt, Dagster and BigQuery](#weather-data-stack-with-airbyte-dbt-dagster-and-bigquery)\n - [Table of Contents](#table-of-contents)\n - [Infrastracture Layout](#infrastracture-layout)\n - [Prerequisites](#prerequisites)\n - [1. Setting an environment for your project](#1-setting-an-environment-for-your-project)\n - [2. Weatherstack API Key](#2-weatherstack-api-key)\n - [1. Sign Up for a Weatherstack API Key](#1-sign-up-for-a-weatherstack-api-key)\n - [3. Setting Up BigQuery](#3-setting-up-bigquery)\n - [1. **Create a Google Cloud Project**](#1-create-a-google-cloud-project)\n - [2. **Create BigQuery Datasets**](#2-create-bigquery-datasets)\n - [3. **Create Service Accounts and Assign Roles**](#3-create-service-accounts-and-assign-roles)\n - [4. **Generate JSON Keys for Service Accounts**](#4-generate-json-keys-for-service-accounts)\n - [4. Setting Up Airbyte Connectors](#4-setting-up-airbyte-connectors)\n - [1. Setting Up Airbyte Connectors with AirByteUI](#1-setting-up-airbyte-connectors-with-airbyteui)\n - [5. Setting Up the dbt Project](#5-setting-up-the-dbt-project)\n - [Next Steps](#next-steps)\n - [1. **Explore the Data and Insights**](#1-explore-the-data-and-insights)\n - [2. **Optimize Your dbt Models**](#2-optimize-your-dbt-models)\n - [3. **Expand Your Data Sources**](#3-expand-your-data-sources)\n - [4. **Enhance Data Quality and Testing**](#4-enhance-data-quality-and-testing)\n - [5. **Automate and Monitor Your Pipelines**](#5-automate-and-monitor-your-pipelines)\n - [6. **Scale Your Setup**](#6-scale-your-setup)\n - [7. **Contribute to the Community**](#7-contribute-to-the-community)\n\n## Infrastracture Layout\n![Infrastracture Layout](./assets/Weather.png)\n\n## Prerequisites\n\nBefore you embark on this integration, ensure you have the following set up and ready:\n\n1. **Python 3.10 or later**: If not installed, download and install it from [Python's official website](https://www.python.org/downloads/).\n\n2. **Docker and Docker Compose (Docker Desktop)**: Install [Docker](https://docs.docker.com/get-docker/) following the official documentation for your specific OS.\n\n3. **Airbyte OSS version**: Deploy the open-source version of Airbyte. Follow the installation instructions from the [Airbyte Documentation](https://docs.airbyte.com/quickstart/deploy-airbyte/).\n\n4. **Google Cloud account with BigQuery**: You will also need to add the necessary permissions to allow Airbyte and dbt to access the data in BigQuery. A step-by-step guide is provided [below](#2-setting-up-bigquery).\n\n5. **Weather Stack API**: You can grab your free weather API from [here](https://weatherstack.com/) after account opening. No Credit card is required for the starter version\n\n## 1. Setting an environment for your project\n\nGet the project up and running on your local machine by following these steps:\n\n1. **Clone the repository (Clone only this quickstart)**: \n ```bash\n git clone --filter=blob:none --sparse https://github.com/airbytehq/quickstarts.git\n ```\n\n ```bash\n cd quickstarts\n ```\n\n ```bash\n git sparse-checkout add weather_data_stack\n ```\n\n\n2. **Navigate to the directory**: \n ```bash\n cd weather_data_stack\n ```\n\n3. **Set Up a Virtual Environment**: \n - For Mac:\n ```bash\n python3 -m venv venv\n source venv/bin/activate\n ```\n - For Windows:\n ```bash\n python -m venv venv\n .\\venv\\Scripts\\activate\n ```\n\n4. **Install Dependencies**: \n ```bash\n pip install -e \".[dev]\"\n ```\n\n## 2. Weatherstack API Key\n\nTo extract weather data from the Weatherstack API and store the API key in its own environment variable file, you can follow these steps:\n\n#### 1. Sign Up for a Weatherstack API Key\n\n1. Visit the [Weatherstack website](https://weatherstack.com/).\n2. Sign up for an account or log in if you already have one.\n3. Once logged in, go to the dashboard to obtain your API key.\n\n\n## 3. Setting Up BigQuery\n\n#### 1. **Create a Google Cloud Project**\n - If you have a Google Cloud project, you can skip this step.\n - Go to the [Google Cloud Console](https://console.cloud.google.com/).\n - Click on the \"Select a project\" dropdown at the top right and select \"New Project\".\n - Give your project a name and follow the steps to create it.\n\n#### 2. **Create BigQuery Datasets**\n - In the Google Cloud Console, go to BigQuery.\n - Make two new datasets: `raw_data` for Airbyte and `transformed_data` for dbt.\n - If you pick different names, remember to change the names in the code too.\n \n **How to create a dataset:**\n - In the left sidebar, click on your project name.\n - Click “Create Dataset”.\n - Enter the dataset ID (either `raw_data` or `transformed_data`).\n - Click \"Create Dataset\".\n\n#### 3. **Create Service Accounts and Assign Roles**\n - Go to “IAM & Admin” > “Service accounts” in the Google Cloud Console.\n - Click “Create Service Account”.\n - Name your service account (like `airbyte-service-account`).\n - Assign the “BigQuery Data Editor” and “BigQuery Job User” roles to the service account.\n - Follow the same steps to make another service account for dbt (like `dbt-service-account`) and assign the roles.\n\n **How to create a service account and assign roles:**\n - While creating the service account, under the “Grant this service account access to project” section, click the “Role” dropdown.\n - Choose the “BigQuery Data Editor” and “BigQuery Job User” roles.\n - Finish the creation process.\n \n#### 4. **Generate JSON Keys for Service Accounts**\n - For both service accounts, make a JSON key to let the service accounts sign in.\n \n **How to generate JSON key:**\n - Find the service account in the “Service accounts” list.\n - Click on the service account name.\n - In the “Keys” section, click “Add Key” and pick JSON.\n - The key will download automatically. Keep it safe and don’t share it.\n - Do this for the other service account too.\n\n## 4. Setting Up Airbyte Connectors\nHere, you can set up connectors with source and destination manually using the Airbyte UI.\n\n### 1. Setting Up Airbyte Connectors with AirByteUI\nUse the generated public url from the previous step to manually configure using the File Option as source under public HTPPS.\n\n- Here are specific [BigQuery](https://docs.airbyte.com/integrations/destinations/bigquery) instructions.\n- Follow these [steps](https://docs.airbyte.com/quickstart/set-up-a-connection) for more.\n\n## 5. Setting Up the dbt Project\n\n[dbt (data build tool)](https://www.getdbt.com/) allows you to transform your data by writing, documenting, and executing SQL workflows. Setting up the dbt project requires specifying connection details for your data platform, in this case, BigQuery. Here’s a step-by-step guide to help you set this up:\n\n1. **Navigate to the dbt Project Directory**:\n\n Change to the directory containing the dbt configuration:\n ```bash\n cd dbt_project\n ```\n\n2. **Update Connection Details**:\n\n You'll find a `profiles.yml` file within the directory. This file contains configurations for dbt to connect with your data platform. Update this file with your BigQuery connection details.\n\n3. **Utilize Environment Variables (Optional but Recommended)**:\n\n To keep your credentials secure, you can leverage environment variables. An example is provided within the `profiles.yml` file.\n\n4. **Test the Connection**:\n\n Once you’ve updated the connection details, you can test the connection to your BigQuery instance using:\n ```bash\n dbt debug\n ```\n\n If everything is set up correctly, this command should report a successful connection to BigQuery.\n\n5. **Run the Models**:\n\n If you would like to run the dbt models manually at this point, you can do so by executing:\n ```bash\n dbt run\n ```\n\n You can verify the data has been transformed by going to BigQuery and checking the `transformed_data` dataset.\n\n## 6. Orchestrating with Dagster\n\n[Dagster](https://dagster.io/) is a modern data orchestrator designed to help you build, test, and monitor your data workflows. In this section, we'll walk you through setting up Dagster to oversee both the Airbyte and dbt workflows:\n\n1. **Navigate to the Orchestration Directory**:\n\n Switch to the directory containing the Dagster orchestration configurations:\n ```bash\n cd orchestration\n ```\n\n2. **Set Environment Variables**:\n\n Dagster requires certain environment variables to be set to interact with other tools like dbt and Airbyte. Set the following variables:\n\n ```bash\n export DAGSTER_DBT_PARSE_PROJECT_ON_LOAD=1\n export AIRBYTE_PASSWORD=password\n ```\n \n Note: The `AIRBYTE_PASSWORD` is set to `password` as a default for local Airbyte instances. If you've changed this during your Airbyte setup, ensure you use the appropriate password here.\n\n3. **Launch the Dagster UI**:\n\n With the environment variables in place, kick-start the Dagster UI:\n ```bash\n dagster dev\n ```\n\n4. **Access Dagster in Your Browser**:\n\n Open your browser and navigate to:\n ```\n http://127.0.0.1:3000\n ```\n\n Here, you should see assets for both Airbyte and dbt.\n\n## Next Steps\n\nCongratulations on deploying and running the Weather Data Stack Quistart! 🎉 Here are some suggestions on what you can explore next to dive deeper and get more out of your project:\n\n### 1. **Explore the Data and Insights**\n - Dive into the datasets in BigQuery, run some queries, and explore the data you've collected and transformed. This is your chance to uncover insights and understand the data better!\n\n### 2. **Optimize Your dbt Models**\n - Review the transformations you’ve applied using dbt. Try optimizing the models or create new ones based on your evolving needs and insights you want to extract.\n\n### 3. **Expand Your Data Sources**\n - Add more data sources to Airbyte. Explore different types of sources available, and see how they can enrich your existing datasets and broaden your analytical capabilities.\n\n### 4. **Enhance Data Quality and Testing**\n - Implement data quality tests in dbt to ensure the reliability and accuracy of your transformations. Use dbt's testing features to validate your data and catch issues early on.\n\n### 5. **Automate and Monitor Your Pipelines**\n - Explore more advanced Dagster configurations and setups to automate your pipelines further and set up monitoring and alerting to be informed of any issues immediately.\n\n### 6. **Scale Your Setup**\n - Consider scaling your setup to handle more data, more sources, and more transformations. Optimize your configurations and resources to ensure smooth and efficient processing of larger datasets.\n\n### 7. **Contribute to the Community**\n - Share your learnings, optimizations, and new configurations with the community. Contribute to the respective tool’s communities and help others learn and grow." }, { "path": "weather_data_stack/dbt_project/.gitignore", "content": "\ntarget/\ndbt_packages/\nlogs/\n\n.user.yml\n" }, { "path": "weather_data_stack/dbt_project/README.md", "content": "Welcome to your new dbt project!\n\n### Using the starter project\n\nTry running the following commands:\n- dbt run\n- dbt test\n\n\n### Resources:\n- Learn more about dbt [in the docs](https://docs.getdbt.com/docs/introduction)\n- Check out [Discourse](https://discourse.getdbt.com/) for commonly asked questions and answers\n- Join the [chat](https://community.getdbt.com/) on Slack for live discussions and support\n- Find [dbt events](https://events.getdbt.com) near you\n- Check out [the blog](https://blog.getdbt.com/) for the latest news on dbt's development and best practices\n" }, { "path": "weather_data_stack/dbt_project/analyses/.gitkeep", "content": "" }, { "path": "weather_data_stack/dbt_project/dbt_project.yml", "content": "\n# Name your project! Project names should contain only lowercase characters\n# and underscores. A good package name should reflect your organization's\n# name or the intended use of these models\nname: 'dbt_project'\nversion: '1.0.0'\nconfig-version: 2\n\n# This setting configures which \"profile\" dbt uses for this project.\nprofile: 'dbt_project'\n\n# These configurations specify where dbt should look for different types of files.\n# The `model-paths` config, for example, states that models in this project can be\n# found in the \"models/\" directory. You probably won't need to change these!\nmodel-paths: [\"models\"]\nanalysis-paths: [\"analyses\"]\ntest-paths: [\"tests\"]\nseed-paths: [\"seeds\"]\nmacro-paths: [\"macros\"]\nsnapshot-paths: [\"snapshots\"]\n\nclean-targets: # directories to be removed by `dbt clean`\n - \"target\"\n - \"dbt_packages\"\n\n\n# Configuring models\n# Full documentation: https://docs.getdbt.com/docs/configuring-models\n\n# In this example config, we tell dbt to build all models in the example/\n# directory as views. These settings can be overridden in the individual model\n# files using the `{{ config(...) }}` macro.\nmodels:\n dbt_project:\n # Config indicated by + and applies to all files under models/example/\n staging:\n +materialized: view\n marts:\n +materialized: view\n" }, { "path": "weather_data_stack/dbt_project/macros/.gitkeep", "content": "" }, { "path": "weather_data_stack/dbt_project/models/marts/historial_weather_trends.sql", "content": "{% set start_date = '2023-01-01' %}\n{% set end_date = '2023-12-31' %}\n\nSELECT\n JSON_EXTRACT_SCALAR(`current`, '$.location.name') AS location_name,\n JSON_EXTRACT_SCALAR(`current`, '$.current.temperature') AS temperature,\n DATE(JSON_EXTRACT_SCALAR(`current`, '$.current.observation_time')) AS date\nFROM\n transformed_data.stg_current_weather\nWHERE\n DATE(JSON_EXTRACT_SCALAR(`current`, '$.current.observation_time')) BETWEEN '{{ start_date }}' AND '{{ end_date }}'\nORDER BY\n date, location_name" }, { "path": "weather_data_stack/dbt_project/models/sources/weatherstack_source.yml", "content": "version: 2\n\nsources:\n - name: weatherstack\n # Use your BigQuery project ID\n database: \"{{ env_var('BIGQUERY_PROJECT_ID', '') }}\"\n # Use your BigQuery dataset name\n schema: weatherstack_airbyte\n \n tables: \n\n - name: current_weather\n description: \"Simulated current_weather data from the weatherstack connector.\"\n columns:\n - name: current \n - name: location\n - name: request" }, { "path": "weather_data_stack/dbt_project/models/staging/stg_current_weather.sql", "content": "select\n *\nfrom {{ source('weatherstack', 'current_weather') }}" }, { "path": "weather_data_stack/dbt_project/profiles.yml", "content": "dbt_project:\n outputs:\n dev:\n dataset: transformed_data\n job_execution_timeout_seconds: 300\n job_retries: 1\n # Use an env variable to indicate your JSON key file path\n keyfile: \"{{ env_var('DBT_BIGQUERY_KEYFILE_PATH', '') }}\"\n location: US\n method: service-account\n priority: interactive\n # Indicate your BigQuery project ID\n project: your_project_id\n threads: 1\n type: bigquery\n target: dev" }, { "path": "weather_data_stack/dbt_project/seeds/.gitkeep", "content": "" }, { "path": "weather_data_stack/dbt_project/snapshots/.gitkeep", "content": "" }, { "path": "weather_data_stack/dbt_project/tests/.gitkeep", "content": "" }, { "path": "weather_data_stack/orchestration/orchestration/__init__.py", "content": "" }, { "path": "weather_data_stack/orchestration/orchestration/assets.py", "content": "import os\nfrom dagster import OpExecutionContext\nfrom dagster_dbt import DbtCliResource, dbt_assets\nfrom dagster_airbyte import AirbyteResource, load_assets_from_airbyte_instance\n\nfrom .constants import dbt_manifest_path\n\n@dbt_assets(manifest=dbt_manifest_path)\ndef dbt_project_dbt_assets(context: OpExecutionContext, dbt: DbtCliResource):\n yield from dbt.cli([\"build\"], context=context).stream()\n\nairbyte_instance = AirbyteResource(\n host=\"localhost\",\n port=\"8000\",\n # If using basic auth, include username and password:\n username=\"airbyte\",\n password=os.getenv(\"AIRBYTE_PASSWORD\")\n)\n\nairbyte_assets = load_assets_from_airbyte_instance(airbyte_instance, key_prefix=\"faker\")" }, { "path": "weather_data_stack/orchestration/orchestration/constants.py", "content": "import os\nfrom pathlib import Path\n\nfrom dagster_dbt import DbtCliResource\n\ndbt_project_dir = Path(__file__).joinpath(\"..\", \"..\", \"..\", \"dbt_project\").resolve()\ndbt = DbtCliResource(project_dir=os.fspath(dbt_project_dir))\n\n# If DAGSTER_DBT_PARSE_PROJECT_ON_LOAD is set, a manifest will be created at runtime.\n# Otherwise, we expect a manifest to be present in the project's target directory.\nif os.getenv(\"DAGSTER_DBT_PARSE_PROJECT_ON_LOAD\"):\n dbt_parse_invocation = dbt.cli([\"parse\"], manifest={}).wait()\n dbt_manifest_path = dbt_parse_invocation.target_path.joinpath(\"manifest.json\")\nelse:\n dbt_manifest_path = dbt_project_dir.joinpath(\"target\", \"manifest.json\")" }, { "path": "weather_data_stack/orchestration/orchestration/definitions.py", "content": "import os\n\nfrom dagster import Definitions\nfrom dagster_dbt import DbtCliResource\n\nfrom .assets import dbt_project_dbt_assets, airbyte_assets\nfrom .constants import dbt_project_dir\nfrom .schedules import schedules\n\ndefs = Definitions(\n assets=[dbt_project_dbt_assets, airbyte_assets],\n schedules=schedules,\n resources={\n \"dbt\": DbtCliResource(project_dir=os.fspath(dbt_project_dir)),\n },\n)" }, { "path": "weather_data_stack/orchestration/orchestration/schedules.py", "content": "\"\"\"\nTo add a daily schedule that materializes your dbt assets, uncomment the following lines.\n\"\"\"\nfrom dagster_dbt import build_schedule_from_dbt_selection\n\nfrom .assets import dbt_project_dbt_assets\n\nschedules = [\n# build_schedule_from_dbt_selection(\n# [dbt_project_dbt_assets],\n# job_name=\"materialize_dbt_models\",\n# cron_schedule=\"0 0 * * *\",\n# dbt_select=\"fqn:*\",\n# ),\n]" }, { "path": "weather_data_stack/orchestration/pyproject.toml", "content": "[build-system]\nrequires = [\"setuptools\"]\nbuild-backend = \"setuptools.build_meta\"\n\n[tool.dagster]\nmodule_name = \"orchestration.definitions\"\ncode_location_name = \"orchestration\"" }, { "path": "weather_data_stack/orchestration/setup.py", "content": "from setuptools import find_packages, setup\n\nsetup(\n name=\"orchestration\",\n version=\"0.0.1\",\n packages=find_packages(),\n install_requires=[\n \"dagster\",\n \"dagster-cloud\",\n \"dagster-dbt\",\n \"dbt-core>=1.4.0\",\n \"dbt-bigquery\",\n ],\n extras_require={\n \"dev\": [\n \"dagster-webserver\",\n ]\n },\n)" }, { "path": "weather_data_stack/setup.py", "content": "from setuptools import find_packages, setup\n\nsetup(\n name=\"airbyte-dbt-dagster\",\n packages=find_packages(),\n install_requires=[\n \"dbt-bigquery\",\n \"dagster\",\n \"dagster-cloud\",\n \"dagster-dbt\",\n \"dagster-airbyte\",\n ],\n extras_require={\"dev\": [\"dagit\", \"pytest\"]},\n)" } ]