![\"Open](\"https://colab.research.google.com/assets/colab-badge.svg\"){kind=icon}
"
]
},
{
"cell_type": "markdown",
"source": [
"▶️ _View our 1.0 Launch Demo [on YouTube](https://www.youtube.com/watch?v=xhui_QDN8Ck)!_"
],
"metadata": {
"id": "Ctm_l6ZWHMuE"
}
},
{
"cell_type": "markdown",
"metadata": {
"id": "BeGeIDzAxbPS"
},
"source": [
"# Airbyte 1.0 Demo: AI ChatBot with PyAirbyte"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "S-54_kh5IBok"
},
"source": [
"This demo shows how **anyone** can quickly build a RAG ChatBot app using PyAirbyte.\n",
"\n",
"\n",
"| schema_name | \n", "
|---|
| public | \n", "
| information_schema | \n", "
| pg_catalog | \n", "
\n",
"Question:\n",
"\"How are you\"\n",
"\n",
"Calculated Vector (len=1536):\n",
"\n",
"\n"
]
},
"metadata": {}
},
{
"output_type": "execute_result",
"data": {
"text/plain": [
"[-0.016069870442152023,\n",
" -0.0027824214193969965,\n",
" -0.00472139660269022,\n",
" -0.036299899220466614,\n",
" -0.023806747049093246,\n",
" 0.016577206552028656,\n",
" -0.029070358723402023,\n",
" -0.02003977634012699,\n",
" -0.023337461054325104,\n",
" -0.005200195126235485,\n",
" 0.025303388014435768,\n",
" 0.005558501463383436,\n",
" -0.01047649048268795,\n",
" 0.003928684163838625,\n",
" -0.010134038515388966,\n",
" -0.015499117784202099,\n",
" 0.04373237118124962,\n",
" -0.009227175265550613,\n",
" 0.008003227412700653,\n",
" -0.014649329707026482,\n",
" '...']"
]
},
"metadata": {},
"execution_count": 20
}
],
"source": [
"# @title Test the vector embeddings function {\"run\":\"auto\",\"vertical-output\":true}\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",
"question_vector = get_vector(question)\n",
"\n",
"print(f\"\"\"\n",
"Question:\n",
"\"{question}\"\n",
"\n",
"Calculated Vector (len={len(question_vector)}):\n",
"\"\"\")\n",
"question_vector[:20]+[\"...\"]"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "RgKK1feQJmKh"
},
"source": [
"## B. Find relevant **document chunks** in our knowlege base"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {
"id": "6xTZ1hRTxbPV"
},
"outputs": [],
"source": [
"RAG_SQL_QUERY = \"\"\"\n",
"SELECT document_content, metadata->'number' as number\n",
"FROM issues\n",
"ORDER BY embedding <-> '{question_vector}'\n",
"LIMIT 15\n",
"\"\"\"\n",
"\n",
"# Markdown divider\n",
"HORIZONTAL_DIV = \"\\n\\n-------\\n\\n\"\n",
"\n",
"def get_related_context(question) -> str:\n",
" # Create a SQL query with our question's vector:\n",
" sql_query = RAG_SQL_QUERY.format(\n",
" question_vector=get_vector(question)\n",
" )\n",
" with engine.begin() as connection:\n",
" # Run the query and return results as a single string:\n",
" return (HORIZONTAL_DIV).join(\n",
" [\n",
" f\"Issue {row.number}: {row.document_content}\"\n",
" for row in connection.execute(text(sql_query))\n",
" ]\n",
" )\n"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {
"cellView": "form",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 1000
},
"id": "bIZKff1w9QUU",
"outputId": "bf882128-de0a-4f19-b043-940474adba2a"
},
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"\u001b[1mQuestion: \"What features have been added recently for HubSpot?\"\u001b[0m \n",
"\n",
"Related Context: \n",
"\n",
"\u001b[33m───────────────────────────────────────────────────────────────────────────────────────────────────────────────────\u001b[0m\n",
"Issue 43317: HubSpot recently began running a \"Batch Update\" process at the end of the month (to reset marketing \n",
"status, etc) which sets all affected records to the exact same timestamp. See example below: \n",
"\n",
"\u001b[33m───────────────────────────────────────────────────────────────────────────────────────────────────────────────────\u001b[0m\n",
"Issue 44481: title: ✨Source Hubspot: Add Leads Stream body: \n",
"\n",
"\u001b[33m───────────────────────────────────────────────────────────────────────────────────────────────────────────────────\u001b[0m\n",
"Issue 36435: title: ✨ Source Linkedin-ads: add new streams (Leads and Forms) body: ## Overview \n"
],
"text/html": [
"Question: \"What features have been added recently for HubSpot?\" \n", "\n", "Related Context: \n", "\n", "───────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n", "Issue 43317: HubSpot recently began running a \"Batch Update\" process at the end of the month (to reset marketing \n", "status, etc) which sets all affected records to the exact same timestamp. See example below: \n", "\n", "───────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n", "Issue 44481: title: ✨Source Hubspot: Add Leads Stream body: <!-- Thanks for your contribution! Before you submit \n", "the pull request, I'd like to kindly remind you to take a moment and read through our guidelines to ensure that \n", "your contribution aligns with the type of contributions our project accepts. All the information you need can be \n", "found here: \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", "updated time of the last 10k received and uses that timestamp as a starting point to pull the next 10k. For \n", "example, if the first query \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", "platform: timeplus.com \n", "\n", "I wrote the destination-timeplus more than 1 year ago and released 0.1.0. After that it seems that airbyte \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", "Issue 36435: ## Changes Below is a detailed overview of the changes included in this pull request: \n", "\n", " Added Form Stream \n", "\n", " • Implemented a new stream for ingesting form data, enabling us to capture and integrate user submission data \n", " directly into our system. This addition is crucial for analytics and monitoring user interactions, providing \n", " valuable insights into user behavior and preferences. \n", "\n", " Added Lead Stream \n", "\n", "───────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n", "Issue 45018: title: 🐙 source-hubspot: run up-to-date pipeline [2024-09-07] body: # Update source-hubspot \n", "\n", "This PR was autogenerated by running airbyte-ci connectors --name=source-hubspot up_to_date --pull \n", "\n", "───────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n", "Issue 44894: During the Sync \n", "\n", " Relevant information \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", "Issue 44191: Currenly working with v 3.3.4 google ads connector. \n", "\n", "Thanks! \n", "\n", "───────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n", "Issue 45391: I would like to add two new streams in source zendesk support: \n", "\n", " • Categories \n", " • Sections \n", "\n", "───────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n", "Issue 37775: - Connector version is set to 0.1.1 \n", "\n", " • Documentation updated \n", " • docs/integrations/source/partnerstack.md including changelog with an entry for the initial version. \n", " • docs/integrations/README.md \n", "\n", "\n", "───────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n", "Issue 43712: - in OAuth mode, there is also a need for TenantId field \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", "features in V3. \n", "\n", "───────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n", "Issue 39114: 1. Connect Hubspot 2. Select contacts_form_submissions 3. Launch it as full refresh The table is \n", "created in the DW with the right columns but it's empty. Our Hubspot is 6 years old and contain many form \n", "submissions. \n", "\n", " Relevant log output \n", "\n", " \n", " \n", " \n", "\n", " Contribute \n", "\n", "───────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n", "Issue 44800: ### Relevant information \n", "\n", "\n", "We can add webhook urls from the airbyte cloud. Can we also add that through powered by airbyte? Please update \n", "powered by airbyte with this endpoint as well. \n", "\n", "───────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n", "Issue 21123: <!--- We accept contributions! Don't feel pressured, but if you want to contribute we can help you by \n", "giving some tips, highlighting the necessary code change or explaining any relevant point your feature will impact.\n", "You can also send questions on #dev Slack channel. \n", "\n", "We understand if you can't submit a PR and we're tremendously grateful that you've already contributed by \n", "suggesting a new feature. --> \n", "\n", "───────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n", "Issue 36435: title: ✨ Source Linkedin-ads: add new streams (Leads and Forms) body: ## Overview \n", "\n" ] }, "metadata": {} } ], "source": [ "# @title Test the RAG lookup function {\"run\":\"auto\",\"vertical-output\":true}\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", "context = get_related_context(question)\n", "\n", "print(Markdown(f\"\"\"\n", "**Question: \"{question}\"**\n", "\n", "Related Context:\n", "\n", "{HORIZONTAL_DIV}\n", "\n", "{context}\n", "\"\"\"))\n" ] }, { "cell_type": "markdown", "metadata": { "id": "OKBTDbhCxbPV" }, "source": [ "## C. Call the **large language model** to answer our questions" ] }, { "cell_type": "markdown", "metadata": { "id": "NyOenZWrxbPW" }, "source": [ "The large language model (LLM) can answer questions if we write them out in english.\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" ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "id": "7nSWX0m8nKZT" }, "outputs": [], "source": [ "# We specify our instructions to the LLM in plain English.\n", "\n", "PROMPT_TEMPLATE = \"\"\"\n", "You are an AI assistant. You are able to find answers to the questions\n", "from the contextual passage snippets provided.\n", "\n", "Use the following pieces of information enclosed in
Question: \"What features have been added recently for HubSpot?\" \n",
"\n",
"───────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n",
"\n",
" • Added Form Stream for ingesting form data for analytics and user interaction insights (Issue 36435) \n",
" • Added Lead Stream (Issue 36435) \n",
"\n"
]
},
"metadata": {}
}
],
"source": [
"# @title Ask the AI Bot! 🤖 {\"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",
"get_answer(question)\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "5Cx1x0WqxbPW"
},
"source": [
"# ⭐ RECAP and Next Steps\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "x4jU6bm_UY4W"
},
"source": [
"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"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "hr6ICbeHRoVs"
},
"source": [
"\n",
"Connection check succeeded for `source-polygon-stock-api`.\n",
"\n"
]
},
"metadata": {}
}
]
},
{
"cell_type": "code",
"source": [
"source.select_streams(['stock_api']) # Select only issues stream\n",
"read_result: ab.ReadResult = source.read()"
],
"metadata": {
"id": "EbFErEv8kgzw",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 303
},
"outputId": "54d919d4-c155-4dd3-b0c1-19bbb26e56f5"
},
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"Completed `source-polygon-stock-api` read operation at 17:28:05.\n",
"\n"
]
},
"metadata": {}
}
]
},
{
"cell_type": "code",
"source": [
"stock_data = read_result[\"stock_api\"].to_pandas()\n",
"stock_data = stock_data.rename(columns={\n",
" 'c': 'Close',\n",
" 'h': 'High',\n",
" 'l': 'Low',\n",
" 'o': 'Open',\n",
" 't': 'Timestamp',\n",
" 'v': 'Volume',\n",
" 'vw': 'VWAP',\n",
"\n",
"})\n",
"\n",
"stock_data = stock_data[['Close', 'High', 'Low', 'Open', 'Timestamp', 'Volume', 'VWAP']]\n",
"\n",
"\n",
"display(stock_data)"
],
"metadata": {
"id": "SyFFmXBjkzcS",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 424
},
"outputId": "c14c5248-8786-415d-ccec-1a0bbc4c74d2"
},
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
" Close High Low Open Timestamp Volume VWAP\n",
"0 125.07 130.9000 124.1700 130.280 1672722000000 112117471.0 125.7250\n",
"1 126.36 128.6557 125.0800 126.890 1672808400000 89100633.0 126.6464\n",
"2 125.02 127.7700 124.7600 127.130 1672894800000 80716808.0 126.0883\n",
"3 129.62 130.2900 124.8900 126.010 1672981200000 87754715.0 128.1982\n",
"4 130.15 133.4100 129.8900 130.465 1673240400000 70790813.0 131.6292\n",
".. ... ... ... ... ... ... ...\n",
"115 185.01 186.1000 184.4100 184.410 1687233600000 49799092.0 185.2709\n",
"116 183.96 185.4100 182.5901 184.900 1687320000000 49515697.0 184.0809\n",
"117 187.00 187.0450 183.6700 183.740 1687406400000 51245327.0 186.1166\n",
"118 186.68 187.5600 185.0100 185.550 1687492800000 53112346.0 186.4952\n",
"119 185.27 188.0500 185.2300 186.830 1687752000000 48088661.0 186.3292\n",
"\n",
"[120 rows x 7 columns]"
],
"text/html": [
"\n",
" \n",
"
\n"
],
"application/vnd.google.colaboratory.intrinsic+json": {
"type": "dataframe",
"variable_name": "stock_data",
"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}"
}
},
"metadata": {}
}
]
},
{
"cell_type": "markdown",
"source": [
"## Chat with your data"
],
"metadata": {
"id": "b91ASLVSsKwx"
}
},
{
"cell_type": "code",
"source": [
"from langchain.agents.agent_types import AgentType\n",
"from langchain_experimental.agents.agent_toolkits import create_pandas_dataframe_agent\n",
"from langchain_openai import ChatOpenAI\n",
"from IPython.display import display"
],
"metadata": {
"id": "K_vft4losRiO"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"import os\n",
"\n",
"os.environ['OPENAI_API_KEY'] = ab.get_secret(\"OPENAI_API_KEY\")\n",
"agent = create_pandas_dataframe_agent(\n",
" ChatOpenAI(model=\"gpt-3.5-turbo\"),\n",
" stock_data,\n",
" verbose=True,\n",
" agent_type=AgentType.OPENAI_FUNCTIONS,\n",
" )"
],
"metadata": {
"id": "VjP_PfhEscBk"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"output = agent.invoke(\"what is the given data is about?\")\n",
"print(output['output'])"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "K8lQaOretq5S",
"outputId": "76ea1ddc-f3bb-4db6-bf7f-87b00747b804"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"\n",
"\n",
"\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\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",
"- `Close`: Closing price of a financial asset\n",
"- `High`: High price of a financial asset during a specific time period\n",
"- `Low`: Low price of a financial asset during a specific time period\n",
"- `Open`: Opening price of a financial asset\n",
"- `Timestamp`: Timestamp of the data point\n",
"- `Volume`: Volume of the financial asset traded\n",
"- `VWAP`: Volume Weighted Average Price\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",
"\u001b[1m> Finished chain.\u001b[0m\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",
"- `Close`: Closing price of a financial asset\n",
"- `High`: High price of a financial asset during a specific time period\n",
"- `Low`: Low price of a financial asset during a specific time period\n",
"- `Open`: Opening price of a financial asset\n",
"- `Timestamp`: Timestamp of the data point\n",
"- `Volume`: Volume of the financial asset traded\n",
"- `VWAP`: Volume Weighted Average Price\n",
"\n",
"Therefore, the data in the dataframe `df` seems to represent price and volume data of a financial asset over different timestamps.\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"output = agent.invoke(\"what is the yearly returns of the given stock?\")\n",
"print(output['output'])"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "zb9B_glfuYN0",
"outputId": "4f91af29-f371-4c60-b9e5-92e44884b9df"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"\n",
"\n",
"\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
"\u001b[32;1m\u001b[1;3m\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",
"\u001b[0m\u001b[36;1m\u001b[1;3mYear\n",
"2023 0.48133\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",
"\u001b[1m> Finished chain.\u001b[0m\n",
"The yearly returns of the given stock for the year 2023 are approximately 48.13%.\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"output = agent.invoke(\"what is the 50-day moving average of the entire data?\")\n",
"print(output['output'])"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "pf41wXkuumnp",
"outputId": "59088435-3391-4d64-ec84-87429e47eda5"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"\n",
"\n",
"\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
"\u001b[32;1m\u001b[1;3m\n",
"Invoking: `python_repl_ast` with `{'query': \"df['Close'].rolling(window=50).mean().iloc[-1]\"}`\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",
"\u001b[1m> Finished chain.\u001b[0m\n",
"The 50-day moving average of the entire data is approximately 174.68.\n"
]
}
]
}
]
}
================================================
FILE: pyairbyte_notebooks/PyAirbyte_Apify_Demo.ipynb
================================================
{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "3s3Jow7_BjQi"
},
"source": [
"# Scraping Web Data from Apify Source into Airbyte for LangChain\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",
"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",
"## Prerequisites\n",
"\n",
"1. **Apify Account**:\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",
"2. **Pinecone Account**:\n",
" - **Create a Pinecone Account**: Sign up for an account on the [Pinecone website](https://www.pinecone.io/).\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",
"3. **OpenAI API Key**:\n",
" - **Create an OpenAI Account**: Sign up for an account on [OpenAI](https://platform.openai.com/docs/overview).\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",
"## Install PyAirbyte and other dependencies"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "ij3THvimBjQk"
},
"outputs": [],
"source": [
"# Add virtual environment support in Google Colab\n",
"!apt-get install -qq python3.10-venv\n",
"\n",
"# First, we need to install the necessary libraries.\n",
"!pip3 install airbyte openai langchain pinecone-client langchain-openai langchain-pinecone python-dotenv langchainhub"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "8jDsEZdiBjQl"
},
"source": [
"## Setup Source Apify with PyAirbyte\n",
"\n",
"The provided code configures an Airbyte source to extract data from specific dataset in apify.\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",
"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"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "U7DxyLVUBjQl"
},
"outputs": [],
"source": [
"import airbyte as ab\n",
"\n",
"source = ab.get_source(\n",
" \"source-apify-dataset\",\n",
" config={\n",
" \"token\": ab.get_secret(\"API_TOKEN\"),\n",
" \"dataset_id\": ab.get_secret(\"DATASET_ID\"),\n",
" }\n",
")\n",
"source.check()"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "rtSCjGcQBjQl"
},
"source": [
"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."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "mlg7K8GUBjQm"
},
"outputs": [],
"source": [
"source.select_all_streams() # Select all streams\n",
"read_result = source.read() # Read the data\n",
"review_list = [doc for doc in read_result[\"item_collection\"].to_documents()] # We are only intrested in item_collection stream only\n",
"\n",
"print(str(review_list[10]))"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "L6KM7KPMBjQm"
},
"source": [
"# Use Langchain to build a RAG pipeline.\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."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "LXvSJoUSBjQm",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "eb25babf-f917-4f1d-8970-5e8acd132776"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Created 493 document chunks.\n"
]
}
],
"source": [
"\n",
"from langchain.text_splitter import RecursiveCharacterTextSplitter\n",
"\n",
"splitter = RecursiveCharacterTextSplitter(chunk_size=512, chunk_overlap=50)\n",
"chunked_docs = splitter.split_documents(review_list)\n",
"print(f\"Created {len(chunked_docs)} document chunks.\")\n",
"\n",
"for doc in chunked_docs:\n",
" for md in doc.metadata:\n",
" doc.metadata[md] = str(doc.metadata[md])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "kCF7gZTMBjQm"
},
"outputs": [],
"source": [
"from langchain_openai import OpenAIEmbeddings\n",
"import os\n",
"\n",
"os.environ['OPENAI_API_KEY'] = ab.get_secret(\"OPENAI_API_KEY\")\n",
"## Embedding Technique Of OPENAI\n",
"embeddings=OpenAIEmbeddings()"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "5TKCIAhYBjQm"
},
"source": [
"## Setting up Pinecone\n",
"\n",
"Pinecone is a managed vector database service designed for storing, indexing, and querying high-dimensional vector data efficiently."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "iXb5YPnhBjQn"
},
"outputs": [],
"source": [
"from pinecone import Pinecone, ServerlessSpec\n",
"from pinecone import Pinecone\n",
"\n",
"os.environ['PINECONE_API_KEY'] = ab.get_secret(\"PINECONE_API_KEY\")\n",
"pc = Pinecone()\n",
"index_name = \"apifyproductreview\" # Replace with your index name\n",
"\n",
"\n",
"# Uncomment this if you have not created a Pinecone index yet\n",
"\n",
"spec = ServerlessSpec(cloud=\"aws\", region=\"us-east-1\") # Replace with your cloud and region\n",
"pc.create_index(\n",
" name = index_name,\n",
" dimension=1536, # Replace with your model dimensions\n",
" metric='cosine', # Replace with your model metric\n",
" spec=spec\n",
")\n",
"\n",
"index = pc.Index(index_name)\n",
"\n",
"index.describe_index_stats()"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "6kRv6s7zBjQn"
},
"source": [
"PineconeVectorStore is a class provided by the LangChain library specifically designed for interacting with Pinecone vector stores.\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."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "3hToKOPsBjQn"
},
"outputs": [],
"source": [
"from langchain_pinecone import PineconeVectorStore\n",
"\n",
"pinecone = PineconeVectorStore.from_documents(\n",
" chunked_docs, embeddings, index_name=index_name\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "vj0hSWo2BjQn"
},
"source": [
"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."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "N0gE_LbmBjQn"
},
"outputs": [],
"source": [
"from langchain_openai import ChatOpenAI\n",
"from langchain import hub\n",
"from langchain_core.output_parsers import StrOutputParser\n",
"from langchain_core.runnables import RunnablePassthrough\n",
"\n",
"retriever = pinecone.as_retriever()\n",
"prompt = hub.pull(\"rlm/rag-prompt\")\n",
"llm = ChatOpenAI(model_name=\"gpt-3.5-turbo\", temperature=0)\n",
"\n",
"\n",
"def format_docs(docs):\n",
" return \"\\n\\n\".join(doc.page_content for doc in docs)\n",
"\n",
"rag_chain = (\n",
" {\"context\": retriever | format_docs, \"question\": RunnablePassthrough()}\n",
" | prompt\n",
" | llm\n",
" | StrOutputParser()\n",
")\n",
"print(\"Langchain RAG pipeline set up successfully.\")\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "axbwi9j8BjQn"
},
"outputs": [],
"source": [
"print(rag_chain.invoke(\"What is overall review of products\"))\n"
]
}
],
"metadata": {
"colab": {
"provenance": []
},
"kernelspec": {
"display_name": "myenv",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.11"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
================================================
FILE: pyairbyte_notebooks/PyAirbyte_Basic_Features_Demo.ipynb
================================================
{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "view-in-github"
},
"source": [
"\n",
"\n",
"\n",
" \n",
"
\n",
"
\n",
" \n",
" | \n", " | Close | \n", "High | \n", "Low | \n", "Open | \n", "Timestamp | \n", "Volume | \n", "VWAP | \n", "
|---|---|---|---|---|---|---|---|
| 0 | \n", "125.07 | \n", "130.9000 | \n", "124.1700 | \n", "130.280 | \n", "1672722000000 | \n", "112117471.0 | \n", "125.7250 | \n", "
| 1 | \n", "126.36 | \n", "128.6557 | \n", "125.0800 | \n", "126.890 | \n", "1672808400000 | \n", "89100633.0 | \n", "126.6464 | \n", "
| 2 | \n", "125.02 | \n", "127.7700 | \n", "124.7600 | \n", "127.130 | \n", "1672894800000 | \n", "80716808.0 | \n", "126.0883 | \n", "
| 3 | \n", "129.62 | \n", "130.2900 | \n", "124.8900 | \n", "126.010 | \n", "1672981200000 | \n", "87754715.0 | \n", "128.1982 | \n", "
| 4 | \n", "130.15 | \n", "133.4100 | \n", "129.8900 | \n", "130.465 | \n", "1673240400000 | \n", "70790813.0 | \n", "131.6292 | \n", "
| ... | \n", "... | \n", "... | \n", "... | \n", "... | \n", "... | \n", "... | \n", "... | \n", "
| 115 | \n", "185.01 | \n", "186.1000 | \n", "184.4100 | \n", "184.410 | \n", "1687233600000 | \n", "49799092.0 | \n", "185.2709 | \n", "
| 116 | \n", "183.96 | \n", "185.4100 | \n", "182.5901 | \n", "184.900 | \n", "1687320000000 | \n", "49515697.0 | \n", "184.0809 | \n", "
| 117 | \n", "187.00 | \n", "187.0450 | \n", "183.6700 | \n", "183.740 | \n", "1687406400000 | \n", "51245327.0 | \n", "186.1166 | \n", "
| 118 | \n", "186.68 | \n", "187.5600 | \n", "185.0100 | \n", "185.550 | \n", "1687492800000 | \n", "53112346.0 | \n", "186.4952 | \n", "
| 119 | \n", "185.27 | \n", "188.0500 | \n", "185.2300 | \n", "186.830 | \n", "1687752000000 | \n", "48088661.0 | \n", "186.3292 | \n", "
120 rows × 7 columns
\n", "\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "R8XtHKK4PujA"
},
"source": [
"# PyAirbyte Demo\n",
"\n",
"Below is a pre-release demo of PyAirbyte.\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "Lyxh2NLuQJUf"
},
"source": [
"## Install PyAirbyte\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "9DEgu1WpQNt-"
},
"outputs": [],
"source": [
"# Add virtual environment support for running in Google Colab\n",
"!apt-get install -qq python3.10-venv\n",
"\n",
"# Install PyAirbyte\n",
"%pip install --quiet airbyte"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "cXJ_cRQV7nIb"
},
"source": [
"## Locating your Data Source\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"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "tfjct5EQ7nIb"
},
"outputs": [],
"source": [
"# Import PyAirbyte\n",
"import airbyte as ab\n",
"\n",
"# Show all available connectors\n",
"ab.get_available_connectors()"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "JWWeEbTVEDFz"
},
"source": [
"## Load the Source Data using PyAirbyte\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "1PhfWpS8QVzE"
},
"source": [
"Create and install a source connector:\n"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 129
},
"id": "5BI9hIeUvxXE",
"outputId": "4d674fb3-25f1-400e-f2e6-c4160cb245e4"
},
"outputs": [
{
"data": {
"text/html": [
"Installing 'source-faker' into virtual environment '/content/.venv-source-faker'.\n", "Running 'pip install airbyte-source-faker'...\n", "\n", "\n" ], "text/plain": [ "Installing \u001b[32m'source-faker'\u001b[0m into virtual environment \u001b[32m'/content/.venv-source-faker'\u001b[0m.\n", "Running \u001b[32m'pip install airbyte-source-faker'\u001b[0m\u001b[33m...\u001b[0m\n", "\n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
Connector 'source-faker' installed successfully!\n", "For more information, see the source-faker documentation:\n", "https://docs.airbyte.com/integrations/sources/faker#reference\n", "\n", "\n" ], "text/plain": [ "Connector \u001b[32m'source-faker'\u001b[0m installed successfully!\n", "For more information, see the source-faker documentation:\n", "\u001b[4;94mhttps://docs.airbyte.com/integrations/sources/faker#reference\u001b[0m\n", "\n" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import airbyte as ab\n", "\n", "# Create and install the source:\n", "source: ab.Source = ab.get_source(\"source-faker\")" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 33 }, "id": "Ww7NA9cQQjQI", "outputId": "4c355b12-18a6-43ef-ecc7-e6986af6b73a" }, "outputs": [ { "data": { "text/html": [ "
Connection check succeeded for `source-faker`.\n",
"\n"
],
"text/plain": [
"Connection check succeeded for `source-faker`.\n"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Configure the source\n",
"source.set_config(\n",
" config={\n",
" \"count\": 50_000, # Adjust this to get a larger or smaller dataset\n",
" \"seed\": 123,\n",
" },\n",
")\n",
"# Verify the config and creds by running `check`:\n",
"source.check()"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "Que5DAqtEJu0"
},
"source": [
"## Read Data from the PyAirbyte Cache\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"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 337
},
"id": "qQVRO69c2DoA",
"outputId": "b94fa743-ed48-4242-9344-ee60cee2f225"
},
"outputs": [
{
"data": {
"text/markdown": [
"## Read Progress\n",
"\n",
"Started reading at 20:10:41.\n",
"\n",
"Read **100,100** records over **1min 17s** (1,300.0 records / second).\n",
"\n",
"Wrote **100,100** records over 11 batches.\n",
"\n",
"Finished reading at 20:11:59.\n",
"\n",
"Started finalizing streams at 20:11:59.\n",
"\n",
"Finalized **11** batches over 1 seconds.\n",
"\n",
"Completed 3 out of 3 streams:\n",
"\n",
" - users\n",
" - purchases\n",
" - products\n",
"\n",
"\n",
"Completed writing at 20:12:01. Total time elapsed: 1min 19s\n",
"\n",
"\n",
"------------------------------------------------\n"
],
"text/plain": [
"Completed `source-faker` read operation at 20:12:01.\n",
"\n"
],
"text/plain": [
"Completed `source-faker` read operation at \u001b[1;92m20:12:01\u001b[0m.\n"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Select all of the source's streams and read data into the internal cache:\n",
"source.select_all_streams()\n",
"read_result: ab.ReadResult = source.read()"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 424
},
"id": "gRuGVOoDEw1R",
"outputId": "8c221488-54b7-4eed-a882-dc6d776d7b8e"
},
"outputs": [
{
"data": {
"text/html": [
"\n",
" \n",
"
\n"
],
"text/plain": [
" id make model year price created_at \\\n",
"0 1 Mazda MX-5 2008 2869.0 2022-02-01 17:02:19 \n",
"1 2 Mercedes-Benz C-Class 2009 42397.0 2021-01-25 14:31:33 \n",
"2 3 Honda Accord Crosstour 2011 63293.0 2021-02-11 05:36:03 \n",
"3 4 GMC Jimmy 1998 34079.0 2022-01-24 03:00:03 \n",
"4 5 Infiniti FX 2004 17036.0 2021-10-02 03:55:44 \n",
".. ... ... ... ... ... ... \n",
"95 96 BMW 330 2006 14494.0 2021-09-17 20:52:48 \n",
"96 97 Audi R8 2008 17642.0 2021-09-21 11:56:24 \n",
"97 98 Cadillac CTS-V 2007 19914.0 2021-09-02 15:38:46 \n",
"98 99 GMC 1500 Club Coupe 1997 82288.0 2021-04-20 18:58:15 \n",
"99 100 Buick Somerset 1986 64148.0 2021-06-10 19:07:38 \n",
"\n",
" updated_at \n",
"0 2024-02-12 20:10:42 \n",
"1 2024-02-12 20:10:42 \n",
"2 2024-02-12 20:10:42 \n",
"3 2024-02-12 20:10:42 \n",
"4 2024-02-12 20:10:42 \n",
".. ... \n",
"95 2024-02-12 20:10:42 \n",
"96 2024-02-12 20:10:42 \n",
"97 2024-02-12 20:10:42 \n",
"98 2024-02-12 20:10:42 \n",
"99 2024-02-12 20:10:42 \n",
"\n",
"[100 rows x 7 columns]"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Display or transform the loaded data\n",
"products_df = read_result[\"products\"].to_pandas()\n",
"display(products_df)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "VJgl59hBF1rJ"
},
"source": [
"## Creating graphs\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"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 718
},
"id": "XcQcBlYjF3oO",
"outputId": "b8b2ae5f-605d-485a-a3c7-8bf72fd826a9"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Requirement already satisfied: matplotlib in /usr/local/lib/python3.10/dist-packages (3.7.1)\n",
"Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib) (1.2.0)\n",
"Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.10/dist-packages (from matplotlib) (0.12.1)\n",
"Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib) (4.48.1)\n",
"Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib) (1.4.5)\n",
"Requirement already satisfied: numpy>=1.20 in /usr/local/lib/python3.10/dist-packages (from matplotlib) (1.23.5)\n",
"Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib) (23.2)\n",
"Requirement already satisfied: pillow>=6.2.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib) (9.4.0)\n",
"Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib) (3.1.1)\n",
"Requirement already satisfied: python-dateutil>=2.7 in /usr/local/lib/python3.10/dist-packages (from matplotlib) (2.8.2)\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"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/usr/local/lib/python3.10/dist-packages/duckdb_engine/__init__.py:178: DuckDBEngineWarning: duckdb-engine doesn't yet support reflection on indices\n",
" warnings.warn(\n"
]
},
{
"data": {
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",
"text/plain": [
"\n",
"\n",
"\n",
" \n",
"
\n",
"
\n",
" \n",
" | \n", " | id | \n", "make | \n", "model | \n", "year | \n", "price | \n", "created_at | \n", "updated_at | \n", "
|---|---|---|---|---|---|---|---|
| 0 | \n", "1 | \n", "Mazda | \n", "MX-5 | \n", "2008 | \n", "2869.0 | \n", "2022-02-01 17:02:19 | \n", "2024-02-12 20:10:42 | \n", "
| 1 | \n", "2 | \n", "Mercedes-Benz | \n", "C-Class | \n", "2009 | \n", "42397.0 | \n", "2021-01-25 14:31:33 | \n", "2024-02-12 20:10:42 | \n", "
| 2 | \n", "3 | \n", "Honda | \n", "Accord Crosstour | \n", "2011 | \n", "63293.0 | \n", "2021-02-11 05:36:03 | \n", "2024-02-12 20:10:42 | \n", "
| 3 | \n", "4 | \n", "GMC | \n", "Jimmy | \n", "1998 | \n", "34079.0 | \n", "2022-01-24 03:00:03 | \n", "2024-02-12 20:10:42 | \n", "
| 4 | \n", "5 | \n", "Infiniti | \n", "FX | \n", "2004 | \n", "17036.0 | \n", "2021-10-02 03:55:44 | \n", "2024-02-12 20:10:42 | \n", "
| ... | \n", "... | \n", "... | \n", "... | \n", "... | \n", "... | \n", "... | \n", "... | \n", "
| 95 | \n", "96 | \n", "BMW | \n", "330 | \n", "2006 | \n", "14494.0 | \n", "2021-09-17 20:52:48 | \n", "2024-02-12 20:10:42 | \n", "
| 96 | \n", "97 | \n", "Audi | \n", "R8 | \n", "2008 | \n", "17642.0 | \n", "2021-09-21 11:56:24 | \n", "2024-02-12 20:10:42 | \n", "
| 97 | \n", "98 | \n", "Cadillac | \n", "CTS-V | \n", "2007 | \n", "19914.0 | \n", "2021-09-02 15:38:46 | \n", "2024-02-12 20:10:42 | \n", "
| 98 | \n", "99 | \n", "GMC | \n", "1500 Club Coupe | \n", "1997 | \n", "82288.0 | \n", "2021-04-20 18:58:15 | \n", "2024-02-12 20:10:42 | \n", "
| 99 | \n", "100 | \n", "Buick | \n", "Somerset | \n", "1986 | \n", "64148.0 | \n", "2021-06-10 19:07:38 | \n", "2024-02-12 20:10:42 | \n", "
100 rows × 7 columns
\n", ""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"%pip install matplotlib\n",
"\n",
"import matplotlib.pyplot as plt\n",
"\n",
"users_df = read_result[\"users\"].to_pandas()\n",
"\n",
"plt.hist(users_df[\"age\"], bins=10, edgecolor=\"black\")\n",
"plt.title(\"Histogram of Ages\")\n",
"plt.xlabel(\"Ages\")\n",
"plt.ylabel(\"Frequency\")\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "wSru-wGpHZrh"
},
"source": [
"## Working in SQL\n",
"\n",
"Since data is cached in a local DuckDB database, we can query the data with SQL.\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"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "xdotIOg70nuL",
"outputId": "b4ca91ec-0b36-42fd-d19e-16b8d1f7ae45"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\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",
"\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",
"\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",
"\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",
"\u001b[?25hDeploy Panel apps for free on Ploomber Cloud! Learn more: https://ploomber.io/s/signup\n"
]
}
],
"source": [
"# Install JupySQL to enable SQL cell magics\n",
"%pip install --quiet jupysql\n",
"# Load JupySQL extension\n",
"%load_ext sql\n",
"# Configure max row limit (optional)\n",
"%config SqlMagic.displaylimit = 200"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"id": "2tA6L1dHZ2w0"
},
"outputs": [],
"source": [
"# Get the SQLAlchemy 'engine' object for the cache\n",
"engine = read_result.cache.get_sql_engine()\n",
"# Pass the engine to JupySQL\n",
"%sql engine"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 34
},
"id": "0eaYnErPaFsH",
"outputId": "3c017bfc-adf3-40bc-81af-e6b1478d229f"
},
"outputs": [
{
"data": {
"text/plain": [
"['main.users', 'main.purchases']"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Get table objects for the 'users' and 'purchases' streams\n",
"users_table = read_result.cache[\"users\"].to_sql_table()\n",
"purchases_table = read_result.cache[\"purchases\"].to_sql_table()\n",
"display([users_table.fullname, purchases_table.fullname])"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 271
},
"id": "VjeTOtKHHiA5",
"outputId": "a4bef98d-0572-4535-9b63-a37dba00723d"
},
"outputs": [
{
"data": {
"text/html": [
"Running query in 'duckdb:///.cache/default_cache_db.duckdb'"
],
"text/plain": [
"Running query in 'duckdb:///.cache/default_cache_db.duckdb'"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"\n",
" \n",
"
"
],
"text/plain": [
"+-------+---------+------------+---------------------+\n",
"| id | name | product_id | purchased_at |\n",
"+-------+---------+------------+---------------------+\n",
"| 21589 | Torie | 48 | 2024-12-10 20:32:08 |\n",
"| 39842 | Kareen | 45 | 2024-11-28 11:26:13 |\n",
"| 19248 | Jerry | 41 | 2024-11-05 00:59:42 |\n",
"| 30780 | Dwana | 82 | 2024-10-20 20:09:11 |\n",
"| 4669 | Frankie | 100 | 2024-10-15 16:23:02 |\n",
"| 42204 | Lashaun | 9 | 2024-10-06 08:06:59 |\n",
"| 13251 | Charlie | 46 | 2024-09-19 17:55:52 |\n",
"| 47798 | Issac | 40 | 2024-09-13 05:13:17 |\n",
"| 34970 | Eleni | 39 | 2024-09-13 00:15:55 |\n",
"| 24782 | Jose | 40 | 2024-09-08 09:49:06 |\n",
"+-------+---------+------------+---------------------+"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"%%sql\n",
"# Show most recent purchases by purchase date:\n",
"SELECT users.id, users.name, purchases.product_id, purchases.purchased_at\n",
"FROM {{ users_table.fullname }} AS users\n",
"JOIN {{ purchases_table.fullname }} AS purchases\n",
"ON users.id = purchases.user_id\n",
"ORDER BY purchases.purchased_at DESC\n",
"LIMIT 10"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 147
},
"id": "nSTpNpRHhk7F",
"outputId": "a906b74d-9c4a-449e-fef5-08a0c5b8bf08"
},
"outputs": [
{
"data": {
"text/html": [
"\n",
" \n",
"
"
],
"text/plain": [
"+---------------------+\n",
"| Name |\n",
"+---------------------+\n",
"| products |\n",
"| purchases |\n",
"| users |\n",
"| _PyAirbyte_state |\n",
"| _PyAirbyte_streams |\n",
"+---------------------+"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Show tables for the other streams\n",
"%sqlcmd tables"
]
}
],
"metadata": {
"colab": {
"include_colab_link": true,
"provenance": []
},
"kernelspec": {
"display_name": "Python 3",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.6"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
================================================
FILE: pyairbyte_notebooks/PyAirbyte_CoinAPI_Demo.ipynb
================================================
{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "view-in-github"
},
"source": [
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "En5baAhvYE_y"
},
"source": [
"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",
"The only prerequisite is a CoinAPI [API key](https://www.coinapi.io/get-free-api-key?product_id=market-data-api).\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "8awBDcLvRW2g"
},
"source": [
"### Installing PyAirbyte\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "xrhNw5kK5Lvx"
},
"outputs": [],
"source": [
"# Add virtual environment support in Google Colab\n",
"!apt-get install -qq python3.10-venv\n",
"\n",
"# Install PyAirbyte\n",
"%pip install --quiet airbyte"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "mYsTAS1wRgO_"
},
"source": [
"### Load source data from CoinAPI.io to local cache\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",
"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",
"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"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "9em82J2Q5WzN"
},
"outputs": [],
"source": [
"import airbyte as ab\n",
"\n",
"# Create and configure the source connector:\n",
"source = ab.get_source(\n",
" \"source-coin-api\",\n",
" config={\n",
" \"api_key\": ab.get_secret(\"COIN_API_KEY\"),\n",
" \"environment\": \"production\",\n",
" \"symbol_id\": \"COINBASE_SPOT_INDEX_USD\",\n",
" \"period\": \"1DAY\",\n",
" \"start_date\": \"2023-01-01T00:00:00\",\n",
" },\n",
" streams=\"*\",\n",
")\n",
"\n",
"# Verify the config and creds by running `check`:\n",
"source.check()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "MxY9a8RlD-PY"
},
"outputs": [],
"source": [
"# Read data from the source into the default cache:\n",
"cache = ab.get_default_cache()\n",
"result = source.read(cache=cache)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "ZKBR6MilSJta"
},
"source": [
"### Read data from the cache\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"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "lFlveLjfGYof"
},
"outputs": [],
"source": [
"# Read from the cache into a pandas Dataframe:\n",
"ohlcv_df = cache[\"ohlcv_historical_data\"].to_pandas()"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "IFBo4q26SVX_"
},
"source": [
"### Run data transformations\n",
"\n",
"In this section, we're transforming our data for analysis:\n",
"\n",
"- Convert `time_period_start` to datetime for easy handling of dates.\n",
"- Convert numeric columns to numeric types for calculations.\n",
"- Calculate `daily_movement` to analyze daily price changes in the market.\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "vr37o4AgB_o6"
},
"outputs": [],
"source": [
"import pandas as pd\n",
"\n",
"# Convert 'time_period_start' to datetime format and necessary columns to numeric\n",
"ohlcv_df[\"time_period_start\"] = pd.to_datetime(ohlcv_df[\"time_period_start\"])\n",
"numeric_columns = [\n",
" \"price_open\",\n",
" \"price_high\",\n",
" \"price_low\",\n",
" \"price_close\",\n",
" \"volume_traded\",\n",
" \"trades_count\",\n",
"]\n",
"ohlcv_df[numeric_columns] = ohlcv_df[numeric_columns].apply(\n",
" pd.to_numeric, errors=\"coerce\"\n",
")\n",
"\n",
"# Calculate daily price movement\n",
"ohlcv_df[\"daily_movement\"] = ohlcv_df[\"price_close\"] - ohlcv_df[\"price_open\"]"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "p7vX1_w4SYaF"
},
"source": [
"### Analyze the data\n",
"\n",
"Now, we delve into visual analysis:\n",
"\n",
"- We plot the `daily_movement` to observe day-to-day price changes.\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",
"- We calculate and plot 7-day and 30-day moving averages alongside the closing price to identify trends and potential crossovers.\n",
"\n",
"These visualizations help us understanding market behaviors and trends in the data.\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 607
},
"id": "VMRjR836LJxj",
"outputId": "3a63df58-fd14-4fd4-8802-7a922d36622a"
},
"outputs": [
{
"data": {
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",
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| id | \n", "name | \n", "product_id | \n", "purchased_at | \n", "
|---|---|---|---|
| 21589 | \n", "Torie | \n", "48 | \n", "2024-12-10 20:32:08 | \n", "
| 39842 | \n", "Kareen | \n", "45 | \n", "2024-11-28 11:26:13 | \n", "
| 19248 | \n", "Jerry | \n", "41 | \n", "2024-11-05 00:59:42 | \n", "
| 30780 | \n", "Dwana | \n", "82 | \n", "2024-10-20 20:09:11 | \n", "
| 4669 | \n", "Frankie | \n", "100 | \n", "2024-10-15 16:23:02 | \n", "
| 42204 | \n", "Lashaun | \n", "9 | \n", "2024-10-06 08:06:59 | \n", "
| 13251 | \n", "Charlie | \n", "46 | \n", "2024-09-19 17:55:52 | \n", "
| 47798 | \n", "Issac | \n", "40 | \n", "2024-09-13 05:13:17 | \n", "
| 34970 | \n", "Eleni | \n", "39 | \n", "2024-09-13 00:15:55 | \n", "
| 24782 | \n", "Jose | \n", "40 | \n", "2024-09-08 09:49:06 | \n", "
| Name | \n", "
|---|
| products | \n", "
| purchases | \n", "
| users | \n", "
| _PyAirbyte_state | \n", "
| _PyAirbyte_streams | \n", "
\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "En5baAhvYE_y"
},
"source": [
"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",
"The only prerequisite is a CoinAPI [API key](https://www.coinapi.io/get-free-api-key?product_id=market-data-api).\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "8awBDcLvRW2g"
},
"source": [
"### Installing PyAirbyte\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "xrhNw5kK5Lvx"
},
"outputs": [],
"source": [
"# Add virtual environment support in Google Colab\n",
"!apt-get install -qq python3.10-venv\n",
"\n",
"# Install PyAirbyte\n",
"%pip install --quiet airbyte"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "mYsTAS1wRgO_"
},
"source": [
"### Load source data from CoinAPI.io to local cache\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",
"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",
"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"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "9em82J2Q5WzN"
},
"outputs": [],
"source": [
"import airbyte as ab\n",
"\n",
"# Create and configure the source connector:\n",
"source = ab.get_source(\n",
" \"source-coin-api\",\n",
" config={\n",
" \"api_key\": ab.get_secret(\"COIN_API_KEY\"),\n",
" \"environment\": \"production\",\n",
" \"symbol_id\": \"COINBASE_SPOT_INDEX_USD\",\n",
" \"period\": \"1DAY\",\n",
" \"start_date\": \"2023-01-01T00:00:00\",\n",
" },\n",
" streams=\"*\",\n",
")\n",
"\n",
"# Verify the config and creds by running `check`:\n",
"source.check()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "MxY9a8RlD-PY"
},
"outputs": [],
"source": [
"# Read data from the source into the default cache:\n",
"cache = ab.get_default_cache()\n",
"result = source.read(cache=cache)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "ZKBR6MilSJta"
},
"source": [
"### Read data from the cache\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"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "lFlveLjfGYof"
},
"outputs": [],
"source": [
"# Read from the cache into a pandas Dataframe:\n",
"ohlcv_df = cache[\"ohlcv_historical_data\"].to_pandas()"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "IFBo4q26SVX_"
},
"source": [
"### Run data transformations\n",
"\n",
"In this section, we're transforming our data for analysis:\n",
"\n",
"- Convert `time_period_start` to datetime for easy handling of dates.\n",
"- Convert numeric columns to numeric types for calculations.\n",
"- Calculate `daily_movement` to analyze daily price changes in the market.\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "vr37o4AgB_o6"
},
"outputs": [],
"source": [
"import pandas as pd\n",
"\n",
"# Convert 'time_period_start' to datetime format and necessary columns to numeric\n",
"ohlcv_df[\"time_period_start\"] = pd.to_datetime(ohlcv_df[\"time_period_start\"])\n",
"numeric_columns = [\n",
" \"price_open\",\n",
" \"price_high\",\n",
" \"price_low\",\n",
" \"price_close\",\n",
" \"volume_traded\",\n",
" \"trades_count\",\n",
"]\n",
"ohlcv_df[numeric_columns] = ohlcv_df[numeric_columns].apply(\n",
" pd.to_numeric, errors=\"coerce\"\n",
")\n",
"\n",
"# Calculate daily price movement\n",
"ohlcv_df[\"daily_movement\"] = ohlcv_df[\"price_close\"] - ohlcv_df[\"price_open\"]"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "p7vX1_w4SYaF"
},
"source": [
"### Analyze the data\n",
"\n",
"Now, we delve into visual analysis:\n",
"\n",
"- We plot the `daily_movement` to observe day-to-day price changes.\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",
"- We calculate and plot 7-day and 30-day moving averages alongside the closing price to identify trends and potential crossovers.\n",
"\n",
"These visualizations help us understanding market behaviors and trends in the data.\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 607
},
"id": "VMRjR836LJxj",
"outputId": "3a63df58-fd14-4fd4-8802-7a922d36622a"
},
"outputs": [
{
"data": {
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",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import matplotlib.pyplot as plt\n",
"\n",
"# Set the 'time_period_start' column as the index for plotting\n",
"ohlcv_df.set_index(\"time_period_start\", inplace=True)\n",
"\n",
"# Plotting the daily movement\n",
"plt.figure(figsize=(12, 6)) # Set the figure size\n",
"plt.plot(ohlcv_df[\"daily_movement\"], marker=\"o\", linestyle=\"-\")\n",
"plt.title(\"Daily Price Movement\")\n",
"plt.xlabel(\"Date\")\n",
"plt.ylabel(\"Price Movement\")\n",
"plt.grid(True)\n",
"plt.xticks(rotation=45) # Rotates the date labels for better readability\n",
"plt.tight_layout() # Adjusts the plot to ensure everything fits without overlapping\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 607
},
"id": "yRipqYxSMu3I",
"outputId": "e2f770fe-3513-44db-efe3-2a36b5b54751"
},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
"
"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Create a dual-axis plot\n",
"fig, ax1 = plt.subplots(figsize=(12, 6))\n",
"\n",
"# Plotting price close on the first axis\n",
"ax1.set_xlabel(\"Date\")\n",
"ax1.set_ylabel(\"Price Close\", color=\"green\")\n",
"ax1.plot(ohlcv_df[\"price_close\"], color=\"green\")\n",
"ax1.tick_params(axis=\"y\", labelcolor=\"green\")\n",
"\n",
"# Instantiate a second axes that shares the same x-axis\n",
"ax2 = ax1.twinx()\n",
"ax2.set_ylabel(\"Volume Traded\", color=\"blue\")\n",
"ax2.plot(ohlcv_df[\"volume_traded\"], color=\"blue\")\n",
"ax2.tick_params(axis=\"y\", labelcolor=\"blue\")\n",
"\n",
"# Title and layout\n",
"plt.title(\"Price Close and Trading Volume Over Time\")\n",
"fig.tight_layout()\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "1GzZfwEqQM0L"
},
"outputs": [],
"source": [
"# Calculate 7-day and 30-day moving averages\n",
"ohlcv_df[\"7_day_MA\"] = ohlcv_df[\"price_close\"].rolling(window=7).mean()\n",
"ohlcv_df[\"30_day_MA\"] = ohlcv_df[\"price_close\"].rolling(window=30).mean()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 607
},
"id": "Yn02gj5pQcoH",
"outputId": "5ef86567-6dea-40dd-dd86-65eb5c8f89ad"
},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
"
"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Plotting moving averages\n",
"plt.figure(figsize=(12, 6))\n",
"plt.plot(ohlcv_df[\"price_close\"], label=\"Closing Price\", color=\"skyblue\")\n",
"plt.plot(ohlcv_df[\"7_day_MA\"], label=\"7-Day Moving Average\", color=\"green\")\n",
"plt.plot(ohlcv_df[\"30_day_MA\"], label=\"30-Day Moving Average\", color=\"red\")\n",
"\n",
"plt.title(\"Closing Price and Moving Averages\")\n",
"plt.xlabel(\"Date\")\n",
"plt.ylabel(\"Price\")\n",
"plt.legend()\n",
"plt.grid(True)\n",
"plt.xticks(rotation=45)\n",
"plt.tight_layout()\n",
"plt.show()"
]
}
],
"metadata": {
"colab": {
"include_colab_link": true,
"provenance": []
},
"kernelspec": {
"display_name": "Python 3",
"name": "python3"
},
"language_info": {
"name": "python"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
================================================
FILE: pyairbyte_notebooks/PyAirbyte_Document_Creation_RAG_with_Langchain_Demo.ipynb
================================================
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
""
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "y4_-FI2aLFYP"
},
"source": [
"# PyAirbyte Demo\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",
"#### Prerequisites:\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",
"- OpenAI API Key. You can create one by signing up on https://openai.com/ and\n",
"going to the \"Keys\" tab on left sidebar."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "LZ5mUBw_g2kT"
},
"source": [
"## Install PyAirbyte and other dependencies\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "kZjfRtSGoQHJ"
},
"outputs": [],
"source": [
"# Add virtual environment support for running in Google Colab\n",
"!apt-get install -qq python3.10-venv\n",
"\n",
"# Install PyAirbyte & Langchain modules\n",
"%pip install --quiet airbyte langchain langchain_openai langchainhub chromadb"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "3MtTFRoYg6bP"
},
"source": [
"## Load the Source Data using PyAirbyte\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "TEzG3kXznVtz"
},
"outputs": [],
"source": [
"import airbyte as ab\n",
"\n",
"# Configure and read from the source\n",
"read_result = ab.get_source(\n",
" \"source-github\",\n",
" config={\n",
" \"repositories\": [\"airbytehq/pyAirbyte\"],\n",
" \"credentials\": {\n",
" \"personal_access_token\": ab.get_secret(\"GITHUB_PERSONAL_ACCESS_TOKEN\")\n",
"\n",
" }\n",
" },\n",
" streams=[\"issues\"],\n",
").read()"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "nM4mWwmkhLS_"
},
"source": [
"## Read a single record from stream to examine the fields"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "kj1u-znnuYKu"
},
"outputs": [],
"source": [
"first_record = next((record for record in read_result[\"issues\"]))\n",
"\n",
"# Print the fields list, followed by the first full record.\n",
"display(list(first_record.keys()))\n",
"display(first_record)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "ZiGGcJdUjnsf"
},
"source": [
"## Use PyAirbyte `to_documents()` method on a dataset\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",
"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",
"Note: We use `rich` to print the documents as markdown, although that's not strictly necessary.\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "TmA_bF6rjoXy"
},
"outputs": [],
"source": [
"import textwrap\n",
"from rich.console import Console\n",
"\n",
"# convert incoming stream data into documents\n",
"docs = list(read_result[\"issues\"].to_documents(\n",
" title_property=\"title\",\n",
" content_properties=[\"body\"],\n",
" metadata_properties=[\"state\", \"url\", \"number\"],\n",
" render_metadata=True,\n",
"))\n",
"\n",
"# print a doc comprising github issue\n",
"console = Console()\n",
"console.print(str(docs[10]))"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "0mkPWz8NkTbH"
},
"source": [
"## Use Langchain to build a RAG pipeline.\n",
"\n",
"Here, we just show a generic method of splitting docs. This and the following steps are copied from a generic LangChain tutorial."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "s1mFwmOGkNqe"
},
"outputs": [],
"source": [
"# Split the docs so they can be stored in vector database downstream\n",
"from langchain.text_splitter import RecursiveCharacterTextSplitter\n",
"\n",
"splitter = RecursiveCharacterTextSplitter(chunk_size=512, chunk_overlap=30)\n",
"\n",
"chunked_docs = splitter.split_documents(docs)\n",
"\n",
"print(f\"Created {len(chunked_docs)} document chunks.\")"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "SdRovb3c1eoH"
},
"source": [
"Now we can publish the chunks to a vector store. Ensure you have added your `OPENAI_API_KEY` to the secrects tab on left."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "Lj7rXrF417tX"
},
"outputs": [],
"source": [
"from langchain_community.vectorstores import Chroma\n",
"from langchain_openai import OpenAIEmbeddings\n",
"import os\n",
"\n",
"os.environ['OPENAI_API_KEY'] = ab.get_secret(\"OPENAI_API_KEY\")\n",
"\n",
"# store into vector db\n",
"vectorstore = Chroma.from_documents(documents=chunked_docs, embedding=OpenAIEmbeddings())\n",
"print(\"Chunks successfully stored in vectorstore.\")\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "MSP8TgBf38VN"
},
"source": [
"Set up a RAG application using LangChain."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "kaamV9Q93v52"
},
"outputs": [],
"source": [
"from langchain_openai import ChatOpenAI\n",
"from langchain import hub\n",
"from langchain_core.output_parsers import StrOutputParser\n",
"from langchain_core.runnables import RunnablePassthrough\n",
"\n",
"retriever = vectorstore.as_retriever()\n",
"prompt = hub.pull(\"rlm/rag-prompt\")\n",
"llm = ChatOpenAI(model_name=\"gpt-3.5-turbo\", temperature=0)\n",
"\n",
"\n",
"def format_docs(docs):\n",
" return \"\\n\\n\".join(doc.page_content for doc in docs)\n",
"\n",
"rag_chain = (\n",
" {\"context\": retriever | format_docs, \"question\": RunnablePassthrough()}\n",
" | prompt\n",
" | llm\n",
" | StrOutputParser()\n",
")\n",
"print(\"Langchain RAG pipeline set up successfully.\")"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "3eDeZ6-74DH7"
},
"source": [
"Ask a question."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "_haRAZyJ4Gjf"
},
"outputs": [],
"source": [
"console.print(rag_chain.invoke(\"Show me all documentation related issues, along with issue number, each on a new line.\"))"
]
}
],
"metadata": {
"colab": {
"provenance": []
},
"kernelspec": {
"display_name": "Python 3",
"name": "python3"
},
"language_info": {
"name": "python"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
================================================
FILE: pyairbyte_notebooks/PyAirbyte_GA4_Demo.ipynb
================================================
{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "view-in-github"
},
"source": [
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "En5baAhvYE_y"
},
"source": [
"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",
"#### Prerequisites:\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"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "8awBDcLvRW2g"
},
"source": [
"### Installing PyAirbyte\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "xrhNw5kK5Lvx"
},
"outputs": [],
"source": [
"# Add virtual environment support in Google Colab\n",
"!apt-get install -qq python3.10-venv\n",
"\n",
"# Install PyAirbyte\n",
"%pip install airbyte"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "mYsTAS1wRgO_"
},
"source": [
"### Load source data from Google Analytics 4 to local cache\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",
"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",
"Then, we proceed to read from the source into the internal DuckDB cache.\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"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 166
},
"id": "9em82J2Q5WzN",
"outputId": "f1b3fc28-cb4b-4f0f-d412-b702005c2e72"
},
"outputs": [
{
"data": {
"text/html": [
""
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "gS3oGgI0CVpn"
},
"source": [
"In this 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",
"#### Prerequisites:\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"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "bM4Te8XEWECV"
},
"outputs": [],
"source": [
"# Add virtual environment support in Google Colab\n",
"!apt-get install -qq python3.10-venv\n",
"\n",
"# Install airbyte\n",
"%pip install --quiet airbyte"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "qTmx2CsKDgXy"
},
"source": [
"### Load source data from Github to local cache\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",
"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",
"Then, we proceed to read from the source into the internal DuckDB cache.\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"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 149
},
"id": "JjFChgoNXIfE",
"outputId": "1097be03-026f-464c-da35-33d17fc4385c"
},
"outputs": [
{
"data": {
"text/html": [
""
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
""
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"
"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "y4_-FI2aLFYP"
},
"source": [
"# PyAirbyte Demo\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",
"#### Prerequisites:\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",
"- OpenAI API Key. You can create one by signing up on https://openai.com/ and\n",
"going to the \"Keys\" tab on left sidebar."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "LZ5mUBw_g2kT"
},
"source": [
"## Install PyAirbyte and other dependencies\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "kZjfRtSGoQHJ"
},
"outputs": [],
"source": [
"# Add virtual environment support for running in Google Colab\n",
"!apt-get install -qq python3.10-venv\n",
"\n",
"# Install PyAirbyte & Langchain modules\n",
"%pip install --quiet airbyte langchain langchain_openai langchainhub chromadb"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "3MtTFRoYg6bP"
},
"source": [
"## Load the Source Data using PyAirbyte\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "TEzG3kXznVtz"
},
"outputs": [],
"source": [
"import airbyte as ab\n",
"\n",
"# Configure and read from the source\n",
"read_result = ab.get_source(\n",
" \"source-github\",\n",
" config={\n",
" \"repositories\": [\"airbytehq/pyAirbyte\"],\n",
" \"credentials\": {\n",
" \"personal_access_token\": ab.get_secret(\"GITHUB_PERSONAL_ACCESS_TOKEN\")\n",
"\n",
" }\n",
" },\n",
" streams=[\"issues\"],\n",
").read()"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "nM4mWwmkhLS_"
},
"source": [
"## Read a single record from stream to examine the fields"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "kj1u-znnuYKu"
},
"outputs": [],
"source": [
"first_record = next((record for record in read_result[\"issues\"]))\n",
"\n",
"# Print the fields list, followed by the first full record.\n",
"display(list(first_record.keys()))\n",
"display(first_record)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "ZiGGcJdUjnsf"
},
"source": [
"## Use PyAirbyte `to_documents()` method on a dataset\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",
"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",
"Note: We use `rich` to print the documents as markdown, although that's not strictly necessary.\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "TmA_bF6rjoXy"
},
"outputs": [],
"source": [
"import textwrap\n",
"from rich.console import Console\n",
"\n",
"# convert incoming stream data into documents\n",
"docs = list(read_result[\"issues\"].to_documents(\n",
" title_property=\"title\",\n",
" content_properties=[\"body\"],\n",
" metadata_properties=[\"state\", \"url\", \"number\"],\n",
" render_metadata=True,\n",
"))\n",
"\n",
"# print a doc comprising github issue\n",
"console = Console()\n",
"console.print(str(docs[10]))"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "0mkPWz8NkTbH"
},
"source": [
"## Use Langchain to build a RAG pipeline.\n",
"\n",
"Here, we just show a generic method of splitting docs. This and the following steps are copied from a generic LangChain tutorial."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "s1mFwmOGkNqe"
},
"outputs": [],
"source": [
"# Split the docs so they can be stored in vector database downstream\n",
"from langchain.text_splitter import RecursiveCharacterTextSplitter\n",
"\n",
"splitter = RecursiveCharacterTextSplitter(chunk_size=512, chunk_overlap=30)\n",
"\n",
"chunked_docs = splitter.split_documents(docs)\n",
"\n",
"print(f\"Created {len(chunked_docs)} document chunks.\")"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "SdRovb3c1eoH"
},
"source": [
"Now we can publish the chunks to a vector store. Ensure you have added your `OPENAI_API_KEY` to the secrects tab on left."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "Lj7rXrF417tX"
},
"outputs": [],
"source": [
"from langchain_community.vectorstores import Chroma\n",
"from langchain_openai import OpenAIEmbeddings\n",
"import os\n",
"\n",
"os.environ['OPENAI_API_KEY'] = ab.get_secret(\"OPENAI_API_KEY\")\n",
"\n",
"# store into vector db\n",
"vectorstore = Chroma.from_documents(documents=chunked_docs, embedding=OpenAIEmbeddings())\n",
"print(\"Chunks successfully stored in vectorstore.\")\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "MSP8TgBf38VN"
},
"source": [
"Set up a RAG application using LangChain."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "kaamV9Q93v52"
},
"outputs": [],
"source": [
"from langchain_openai import ChatOpenAI\n",
"from langchain import hub\n",
"from langchain_core.output_parsers import StrOutputParser\n",
"from langchain_core.runnables import RunnablePassthrough\n",
"\n",
"retriever = vectorstore.as_retriever()\n",
"prompt = hub.pull(\"rlm/rag-prompt\")\n",
"llm = ChatOpenAI(model_name=\"gpt-3.5-turbo\", temperature=0)\n",
"\n",
"\n",
"def format_docs(docs):\n",
" return \"\\n\\n\".join(doc.page_content for doc in docs)\n",
"\n",
"rag_chain = (\n",
" {\"context\": retriever | format_docs, \"question\": RunnablePassthrough()}\n",
" | prompt\n",
" | llm\n",
" | StrOutputParser()\n",
")\n",
"print(\"Langchain RAG pipeline set up successfully.\")"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "3eDeZ6-74DH7"
},
"source": [
"Ask a question."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "_haRAZyJ4Gjf"
},
"outputs": [],
"source": [
"console.print(rag_chain.invoke(\"Show me all documentation related issues, along with issue number, each on a new line.\"))"
]
}
],
"metadata": {
"colab": {
"provenance": []
},
"kernelspec": {
"display_name": "Python 3",
"name": "python3"
},
"language_info": {
"name": "python"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
================================================
FILE: pyairbyte_notebooks/PyAirbyte_GA4_Demo.ipynb
================================================
{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "view-in-github"
},
"source": [
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "En5baAhvYE_y"
},
"source": [
"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",
"#### Prerequisites:\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"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "8awBDcLvRW2g"
},
"source": [
"### Installing PyAirbyte\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "xrhNw5kK5Lvx"
},
"outputs": [],
"source": [
"# Add virtual environment support in Google Colab\n",
"!apt-get install -qq python3.10-venv\n",
"\n",
"# Install PyAirbyte\n",
"%pip install airbyte"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "mYsTAS1wRgO_"
},
"source": [
"### Load source data from Google Analytics 4 to local cache\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",
"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",
"Then, we proceed to read from the source into the internal DuckDB cache.\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"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 166
},
"id": "9em82J2Q5WzN",
"outputId": "f1b3fc28-cb4b-4f0f-d412-b702005c2e72"
},
"outputs": [
{
"data": {
"text/html": [
"Installing 'source-google-analytics-data-api' into virtual environment \n", "'/content/.venv-source-google-analytics-data-api'.\n", "Running 'pip install airbyte-source-google-analytics-data-api'...\n", "\n", "\n" ], "text/plain": [ "Installing \u001b[32m'source-google-analytics-data-api'\u001b[0m into virtual environment \n", "\u001b[32m'/content/.venv-source-google-analytics-data-api'\u001b[0m.\n", "Running \u001b[32m'pip install airbyte-source-google-analytics-data-api'\u001b[0m\u001b[33m...\u001b[0m\n", "\n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
Connector 'source-google-analytics-data-api' installed successfully!\n", "For more information, see the source-google-analytics-data-api documentation:\n", "https://docs.airbyte.com/integrations/sources/google-analytics-data-api#reference\n", "\n", "\n" ], "text/plain": [ "Connector \u001b[32m'source-google-analytics-data-api'\u001b[0m installed successfully!\n", "For more information, see the source-google-analytics-data-api documentation:\n", "\u001b[4;94mhttps://docs.airbyte.com/integrations/sources/google-analytics-data-api#reference\u001b[0m\n", "\n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
Connection check succeeded for `source-google-analytics-data-api`.\n",
"\n"
],
"text/plain": [
"Connection check succeeded for `source-google-analytics-data-api`.\n"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import airbyte as ab\n",
"\n",
"# Create and configure the source connector:\n",
"source = ab.get_source(\n",
" \"source-google-analytics-data-api\",\n",
" install_if_missing=True,\n",
" config={\n",
" \"property_ids\": [ab.get_secret(\"GA4_PROPERTY_ID\")],\n",
" \"date_ranges_start_date\": \"2023-10-01\",\n",
" \"credentials\": {\n",
" \"credentials_json\": ab.get_secret(\"GA4_JSON_KEY\"),\n",
" \"auth_type\": \"Service\",\n",
" },\n",
" },\n",
")\n",
"\n",
"# Verify the config and creds by running `check`:\n",
"source.check()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "Y5H9BNlgOT6J",
"outputId": "3557acb8-6d15-4d97-9b69-5595201c0a85"
},
"outputs": [
{
"data": {
"text/plain": [
"['daily_active_users',\n",
" 'weekly_active_users',\n",
" 'four_weekly_active_users',\n",
" 'devices',\n",
" 'locations',\n",
" 'pages',\n",
" 'traffic_sources',\n",
" 'website_overview',\n",
" 'user_acquisition_first_user_medium_report',\n",
" 'user_acquisition_first_user_source_report',\n",
" 'user_acquisition_first_user_source_medium_report',\n",
" 'user_acquisition_first_user_source_platform_report',\n",
" 'user_acquisition_first_user_campaign_report',\n",
" 'user_acquisition_first_user_google_ads_ad_network_type_report',\n",
" 'user_acquisition_first_user_google_ads_ad_group_name_report',\n",
" 'traffic_acquisition_session_source_medium_report',\n",
" 'traffic_acquisition_session_medium_report',\n",
" 'traffic_acquisition_session_source_report',\n",
" 'traffic_acquisition_session_campaign_report',\n",
" 'traffic_acquisition_session_default_channel_grouping_report',\n",
" 'traffic_acquisition_session_source_platform_report',\n",
" 'events_report',\n",
" 'weekly_events_report',\n",
" 'conversions_report',\n",
" 'pages_title_and_screen_class_report',\n",
" 'pages_path_report',\n",
" 'pages_title_and_screen_name_report',\n",
" 'content_group_report',\n",
" 'ecommerce_purchases_item_name_report',\n",
" 'ecommerce_purchases_item_id_report',\n",
" 'ecommerce_purchases_item_category_report_combined',\n",
" 'ecommerce_purchases_item_category_report',\n",
" 'ecommerce_purchases_item_category_2_report',\n",
" 'ecommerce_purchases_item_category_3_report',\n",
" 'ecommerce_purchases_item_category_4_report',\n",
" 'ecommerce_purchases_item_category_5_report',\n",
" 'ecommerce_purchases_item_brand_report',\n",
" 'publisher_ads_ad_unit_report',\n",
" 'publisher_ads_page_path_report',\n",
" 'publisher_ads_ad_format_report',\n",
" 'publisher_ads_ad_source_report',\n",
" 'demographic_country_report',\n",
" 'demographic_region_report',\n",
" 'demographic_city_report',\n",
" 'demographic_language_report',\n",
" 'demographic_age_report',\n",
" 'demographic_gender_report',\n",
" 'demographic_interest_report',\n",
" 'tech_browser_report',\n",
" 'tech_device_category_report',\n",
" 'tech_device_model_report',\n",
" 'tech_screen_resolution_report',\n",
" 'tech_app_version_report',\n",
" 'tech_platform_report',\n",
" 'tech_platform_device_category_report',\n",
" 'tech_operating_system_report',\n",
" 'tech_os_with_version_report']"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# List the available streams available for GA4 source\n",
"source.get_available_streams()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 303,
"referenced_widgets": [
"733f09d5b4584b0382560faab0629b0c",
"3330876bb7d547b28191270cb53c3e57",
"42b10528846241e0aac16458a1b8655f",
"d7c54b0aaa4442a19254501027caf793",
"24a7bb9210cc4c00b19f12715f36ef8a",
"3031c476bc464ed08e90b5e91ee6393b"
]
},
"id": "4btpDgjkulF0",
"outputId": "bbedf001-9441-432d-9f69-7d9b5ae121fc"
},
"outputs": [
{
"data": {
"text/markdown": [
"## Read Progress\n",
"\n",
"Started reading at 21:14:13.\n",
"\n",
"Read **901,527** records over **8min 0s** (1,878.2 records / second).\n",
"\n",
"Wrote **901,527** records over 91 batches.\n",
"\n",
"Finished reading at 21:22:14.\n",
"\n",
"Started finalizing streams at 21:22:14.\n",
"\n",
"Finalized **91** batches over 6 seconds.\n",
"\n",
"Completed 1 out of 1 streams:\n",
"\n",
" - pages\n",
"\n",
"\n",
"Completed writing at 21:22:21. Total time elapsed: 8min 7s\n",
"\n",
"\n",
"------------------------------------------------\n"
],
"text/plain": [
"Completed `source-google-analytics-data-api` read operation at 21:22:21.\n",
"\n"
],
"text/plain": [
"Completed `source-google-analytics-data-api` read operation at \u001b[1;92m21:22:21\u001b[0m.\n"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Select the streams we are interested in loading to cache\n",
"source.select_streams([\"pages\"])\n",
"\n",
"# Read into DuckDB local default cache\n",
"cache = ab.get_default_cache()\n",
"result = source.read(cache=cache)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "Y98iL4XxtINW"
},
"source": [
"### Read data from the cache\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"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "yjzVJH6BtOpW"
},
"outputs": [],
"source": [
"# Read from the cache into a pandas Dataframe:\n",
"pages_df = cache[\"pages\"].to_pandas()"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "nUXbpYeuvKEf"
},
"source": [
"### Transform and analyze the data\n",
"\n",
"Let's take our pages data and run some analysis with `pandas` and `matplotlib`.\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "8HHsoj07vvHV"
},
"outputs": [],
"source": [
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
"\n",
"# Convert 'date' to datetime format\n",
"pages_df[\"date\"] = pd.to_datetime(pages_df[\"date\"], format=\"%Y%m%d\")\n",
"\n",
"# Convert 'screenpageviews' to integers\n",
"pages_df[\"screenpageviews\"] = pd.to_numeric(\n",
" pages_df[\"screenpageviews\"], errors=\"coerce\"\n",
")\n",
"\n",
"# Convert 'bouncerate' to floats\n",
"pages_df[\"bouncerate\"] = pd.to_numeric(pages_df[\"bouncerate\"], errors=\"coerce\")"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "DUfKikLt6bCF"
},
"source": [
"#### Trend Analysis Over Time\n",
"\n",
"Let's aggregate data by date and observe trends in total pageviews and average bounce rate over time.\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 564
},
"id": "8sMWEbrFxum0",
"outputId": "be947d1e-0656-41ea-9601-9ff15ecc0e89"
},
"outputs": [],
"source": [
"# Aggregate data by date\n",
"trends = pages_df.groupby(\"date\").agg(\n",
" total_pageviews=(\"screenpageviews\", \"sum\"),\n",
" average_bounce_rate=(\"bouncerate\", \"mean\"),\n",
")\n",
"\n",
"# Plotting the trends\n",
"fig, ax1 = plt.subplots(figsize=(12, 6))\n",
"\n",
"ax1.set_xlabel(\"Date\")\n",
"ax1.set_ylabel(\"Total Pageviews\", color=\"tab:blue\")\n",
"ax1.plot(trends.index, trends[\"total_pageviews\"], color=\"tab:blue\")\n",
"ax1.tick_params(axis=\"y\", labelcolor=\"tab:blue\")\n",
"\n",
"ax2 = ax1.twinx()\n",
"ax2.set_ylabel(\"Average Bounce Rate\", color=\"tab:green\")\n",
"ax2.plot(trends.index, trends[\"average_bounce_rate\"], color=\"tab:green\")\n",
"ax2.tick_params(axis=\"y\", labelcolor=\"tab:green\")\n",
"\n",
"plt.title(\"Trends in Total Pageviews and Average Bounce Rate Over Time\")\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "xLEqImdc8kf9"
},
"source": [
"#### Segmentation of Data by Date or Page Type\n",
"\n",
"Compare metrics on weekdays versus weekends or across different types of pages.\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 1000
},
"id": "dekYkkauAL6K",
"outputId": "40ddf88c-d2b7-4d85-dda4-31b29589b5d7"
},
"outputs": [],
"source": [
"import seaborn as sns\n",
"\n",
"# Label rows as 'Weekday' or 'Weekend'\n",
"pages_df[\"day_type\"] = pages_df[\"day_of_week\"].apply(\n",
" lambda x: \"Weekend\" if x in [\"Saturday\", \"Sunday\"] else \"Weekday\"\n",
")\n",
"\n",
"# Aggregating data for weekday vs weekend\n",
"day_type_agg = (\n",
" pages_df.groupby(\"day_type\")\n",
" .agg(\n",
" average_pageviews=(\"screenpageviews\", \"mean\"),\n",
" average_bounce_rate=(\"bouncerate\", \"mean\"),\n",
" )\n",
" .reset_index()\n",
")\n",
"\n",
"# Plotting Average Pageviews\n",
"plt.figure(figsize=(12, 6))\n",
"sns.barplot(x=\"day_type\", y=\"average_pageviews\", data=day_type_agg, palette=\"coolwarm\")\n",
"plt.title(\"Average Pageviews: Weekdays vs Weekends\")\n",
"plt.ylabel(\"Average Pageviews\")\n",
"plt.show()\n",
"\n",
"# Plotting Average Bounce Rate\n",
"plt.figure(figsize=(12, 6))\n",
"sns.barplot(x=\"day_type\", y=\"average_bounce_rate\", data=day_type_agg, palette=\"viridis\")\n",
"plt.title(\"Average Bounce Rate: Weekdays vs Weekends\")\n",
"plt.ylabel(\"Average Bounce Rate\")\n",
"plt.show()"
]
}
],
"metadata": {
"colab": {
"include_colab_link": true,
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================================================
FILE: pyairbyte_notebooks/PyAirbyte_Github_Incremental_Demo.ipynb
================================================
{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "view-in-github"
},
"source": [
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "gS3oGgI0CVpn"
},
"source": [
"In this 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",
"#### Prerequisites:\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"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "bM4Te8XEWECV"
},
"outputs": [],
"source": [
"# Add virtual environment support in Google Colab\n",
"!apt-get install -qq python3.10-venv\n",
"\n",
"# Install airbyte\n",
"%pip install --quiet airbyte"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "qTmx2CsKDgXy"
},
"source": [
"### Load source data from Github to local cache\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",
"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",
"Then, we proceed to read from the source into the internal DuckDB cache.\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"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 149
},
"id": "JjFChgoNXIfE",
"outputId": "1097be03-026f-464c-da35-33d17fc4385c"
},
"outputs": [
{
"data": {
"text/html": [
"Installing 'source-github' into virtual environment '/content/.venv-source-github'.\n", "Running 'pip install airbyte-source-github'...\n", "\n", "\n" ], "text/plain": [ "Installing \u001b[32m'source-github'\u001b[0m into virtual environment \u001b[32m'/content/.venv-source-github'\u001b[0m.\n", "Running \u001b[32m'pip install airbyte-source-github'\u001b[0m\u001b[33m...\u001b[0m\n", "\n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
Connector 'source-github' installed successfully!\n", "For more information, see the source-github documentation:\n", "https://docs.airbyte.com/integrations/sources/github#reference\n", "\n", "\n" ], "text/plain": [ "Connector \u001b[32m'source-github'\u001b[0m installed successfully!\n", "For more information, see the source-github documentation:\n", "\u001b[4;94mhttps://docs.airbyte.com/integrations/sources/github#reference\u001b[0m\n", "\n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
Connection check succeeded for `source-github`.\n",
"\n"
],
"text/plain": [
"Connection check succeeded for `source-github`.\n"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import airbyte as ab\n",
"\n",
"# Create and configure the source:\n",
"source = ab.get_source(\n",
" \"source-github\",\n",
" install_if_missing=True,\n",
" config={\n",
" \"repositories\": [\"airbytehq/quickstarts\"],\n",
" \"credentials\": {\n",
" \"personal_access_token\": ab.get_secret(\"GITHUB_PERSONAL_ACCESS_TOKEN\"),\n",
" },\n",
" },\n",
")\n",
"\n",
"# Verify the config and creds by running `check`:\n",
"source.check()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "gyn_ymCiEVD6",
"outputId": "e5ca7ea5-5082-4ddf-9234-74051853a5a3"
},
"outputs": [
{
"data": {
"text/plain": [
"['issue_timeline_events',\n",
" 'assignees',\n",
" 'branches',\n",
" 'collaborators',\n",
" 'comments',\n",
" 'commit_comment_reactions',\n",
" 'commit_comments',\n",
" 'commits',\n",
" 'contributor_activity',\n",
" 'deployments',\n",
" 'events',\n",
" 'issue_comment_reactions',\n",
" 'issue_events',\n",
" 'issue_labels',\n",
" 'issue_milestones',\n",
" 'issue_reactions',\n",
" 'issues',\n",
" 'organizations',\n",
" 'project_cards',\n",
" 'project_columns',\n",
" 'projects',\n",
" 'pull_request_comment_reactions',\n",
" 'pull_request_commits',\n",
" 'pull_request_stats',\n",
" 'projects_v2',\n",
" 'pull_requests',\n",
" 'releases',\n",
" 'repositories',\n",
" 'review_comments',\n",
" 'reviews',\n",
" 'stargazers',\n",
" 'tags',\n",
" 'teams',\n",
" 'team_members',\n",
" 'users',\n",
" 'workflows',\n",
" 'workflow_runs',\n",
" 'workflow_jobs',\n",
" 'team_memberships']"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# List the available streams available for the Github source\n",
"source.get_available_streams()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 355
},
"id": "w94J7TIFiBt1",
"outputId": "65633720-0948-472b-cf81-f587b772abb5"
},
"outputs": [
{
"data": {
"text/markdown": [
"## Read Progress\n",
"\n",
"Started reading at 22:16:25.\n",
"\n",
"Read **314** records over **6 seconds** (52.3 records / second).\n",
"\n",
"Wrote **314** records over 4 batches.\n",
"\n",
"Finished reading at 22:16:32.\n",
"\n",
"Started finalizing streams at 22:16:32.\n",
"\n",
"Finalized **4** batches over 2 seconds.\n",
"\n",
"Completed 4 out of 4 streams:\n",
"\n",
" - reviews\n",
" - stargazers\n",
" - pull_requests\n",
" - issues\n",
"\n",
"\n",
"Completed writing at 22:16:34. Total time elapsed: 8 seconds\n",
"\n",
"\n",
"------------------------------------------------\n"
],
"text/plain": [
"Completed `source-github` read operation at 22:16:34.\n",
"\n"
],
"text/plain": [
"Completed `source-github` read operation at \u001b[1;92m22:16:34\u001b[0m.\n"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Select the streams we are interested in loading to cache\n",
"source.set_streams([\"pull_requests\", \"issues\", \"reviews\", \"stargazers\"])\n",
"\n",
"# Read into DuckDB local default cache\n",
"cache = ab.get_default_cache()\n",
"result = source.read(cache=cache)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "7iFNbPbPFB3q"
},
"source": [
"### Read again to sync changes\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",
"For more information on sync modes for this source, you can refer to the [docs](https://docs.airbyte.com/integrations/sources/github).\n",
"\n",
"Let's read again, and see how no records will be loaded to cache.\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 332
},
"id": "UOm1jvefGMrR",
"outputId": "a4fc014c-0d48-4186-c21b-1ba6e890f9ae"
},
"outputs": [
{
"data": {
"text/markdown": [
"## Read Progress\n",
"\n",
"Started reading at 22:17:30.\n",
"\n",
"Read **0** records over **3 seconds** (0.0 records / second).\n",
"\n",
"Finished reading at 22:17:33.\n",
"\n",
"Started finalizing streams at 22:17:33.\n",
"\n",
"Finalized **0** batches over 0 seconds.\n",
"\n",
"Completed 4 out of 4 streams:\n",
"\n",
" - reviews\n",
" - stargazers\n",
" - pull_requests\n",
" - issues\n",
"\n",
"\n",
"Completed writing at 22:17:33. Total time elapsed: 3 seconds\n",
"\n",
"\n",
"------------------------------------------------\n"
],
"text/plain": [
"Completed `source-github` read operation at 22:17:33.\n",
"\n"
],
"text/plain": [
"Completed `source-github` read operation at \u001b[1;92m22:17:33\u001b[0m.\n"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"result = source.read(cache=cache)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "EZfsqKfJGg_Z"
},
"source": [
"### Read data from the cache\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"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "E5-ADp0dGnGR"
},
"outputs": [],
"source": [
"# Read from the cache into a pandas Dataframe:\n",
"reviews = cache[\"reviews\"].to_pandas()\n",
"stargazers = cache[\"stargazers\"].to_pandas()\n",
"pull_requests = cache[\"pull_requests\"].to_pandas()\n",
"issues = cache[\"issues\"].to_pandas()"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "EDs_pEAaGP5X"
},
"source": [
"### Transform and analyze the data\n",
"\n",
"Let's take our Github data and run some analysis with `pandas` and `matplotlib`.\n"
]
},
{
"cell_type": "code",
"execution_count": null,
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},
"source": [
"#### Pull Requests Trend Over Time\n",
"\n",
"This will visualize the number of pull requests created over time.\n"
]
},
{
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xY3Ifr3nz5jzxxBP07t2bAw44gAoVKnDCCSdw9NFHU7NmTVq1asWOO+7InDlzuO222zjuuOM2W/ehIOT1e5of5513HrfddhtnnXUWH330EbVq1eLhhx+mXLlyBZ5fkpRYLPaSpISQkpLC008/zTXXXMMTTzzBAw88QP369Rk9ejRXXHHFNj3mAQccwLBhw7jrrrt48cUXycnJYeHChQVS7CFcjv/ss89y2mmnkZ6eXiCPudF5553HwoULuf/++3nxxRc55JBDePnllznyyCML9Hm2pm/fvjz33HPceuutDBkyhAEDBrDrrrsyduzY3DnndevW5eijj+bEE0/M/bzzzz+f7OxsRo8eTd++fdlrr714+umnufrqqzd7jkcffZQLLriAG264gcqVK9O9e3cOP/xwjjrqqE3u17p1a959912GDRvGbbfdxu+//07NmjVp0aIFF1xwARBWf+/ZsyfTpk3LXXG+UaNG3HHHHVx44YW5j9WzZ09mzZrFAw88wNixY6lXrx4nnHACF1xwAY8++ig33XQTv//+O3Xq1KFXr15cddVVhfHtBcjz9zSvypUrx/Tp07nkkku49dZbKVeuHGeccQbt2rXjmGOOKej4kqQEEou76ookSdtk6tSptG/fnjfeeINDDjkk6jgJrXXr1gDMmDEj0hySJBVHzrGXJGkb3XvvvTRs2JCDDz446iiSJKkE81J8SZLy6fHHH+ezzz7jueee4+abby7U1fclSZL+jcVekqR86tKlCxUqVKB79+707Nkz6jiSJKmEc469JEmSJElJzDn2kiRJkiQlMYu9JEmSJElJrNjPsc/JyeHHH3+kYsWKLm4kSZIkSSp08XiclStXUrt2bVJSCv98erEv9j/++CN169aNOoYkSZIkqYT5/vvvqVOnTqE/T6TF/s477+TOO+9k0aJFAOy5555cc801tGvXDoA1a9ZwxRVX8Pjjj7N27Vratm3LHXfcwY477pjn56hYsSIQvqGVKlUq8K9BkiRJkqS/+vXXX2nQoEFuHy1ska6K/8wzz5Camkrjxo2Jx+M8+OCDjB49mk8++YQ999yTCy+8kOeee47x48eTkZHBxRdfTEpKCm+//Xaen2PFihVkZGSQlZVlsZckSZIkFbrMzEyqVatWZD004ba7q1q1KqNHj+aUU06hevXqTJgwgVNOOQWAr776it133513332X//znP3l6PIu9JEmSJKkoFXWxT5g59tnZ2Tz11FOsWrWKli1b8tFHH7F+/XratGmTe58mTZqw8847/2OxX7t2LWvXrs29vWLFCgDWr1/P+vXrC/eLkCRJkiSVeEXdPSMv9p9//jktW7ZkzZo1VKhQgSlTprDHHnswa9YsSpcuTeXKlTe5/4477sjSpUu3+ngjRoxg6NChmx2fNm0a5cqVK+j4kiRJkiRt4o8//ijS54u82O+2227MmjWLrKwsJk6cSNeuXXn99de3+fEGDhxI7969c2+vWLGCunXrcvTRR3spviRJkiSp0GVmZhbp80Ve7EuXLk2jRo0AaN68OTNnzuTmm2+mU6dOrFu3jt9++22Ts/Y//fQTNWvW3Orjpaenk56evtnxtLQ00tLSCjy/JEmSJEl/VdTdM6VIny0PcnJyWLt2Lc2bNyctLY3p06fnfmzu3Ll89913tGzZMsKEkiRJkiQljkjP2A8cOJB27dqx8847s3LlSiZMmMCMGTN46aWXyMjIoHv37vTu3ZuqVatSqVIlLrnkElq2bJnnFfElSZIkSSruIi32y5Yt46yzzmLJkiVkZGSw995789JLL3HUUUcBMHbsWFJSUujYsSNr166lbdu23HHHHVFGliRJkiQpoSTcPvYFzX3sJUmSJElFqaj3sU+4OfaSJEmSJCnvLPaSJEmSJCUxi70kSZIkSUnMYi9JkiRJUhKLdFV8SSVXdja8+SYsWQK1asEhh0BqatSpJEmSpORjsZdU5CZPhksvhcWL/zxWpw7cfDN06BBdLkmSJCkZeSm+pCI1eTKccsqmpR7ghx/C8cmTo8klSZIkJSuLvaQik50dztTH45t/bOOxyy4L95MkSZKUNxZ7SUXmzTc3P1P/V/E4fP99uJ8kSZKkvLHYSyoyS5YU7P0kSZIkWewlFaFatQr2fpIkSZIs9pKK0CGHhNXv/8nGre8kSZIk5Y3FXlKRSU2F4cP/+T5pabBmTdHkkSRJkooDi72kIvX55+H3UqU2PV67NmRkwHffwfnnb3nlfEmSJEmbK/Xvd5GkgrFwIdxySxj/739QvnxYKG/j5fdvvw1HHAETJkCLFtCrV6RxJUmSpKRgsZdUZK68EtatgzZt4NhjIRbb9OOHHgpjxoS97K+4Avbd1/n2kiRJ0r/xUnxJReKDD+Dxx0OZHz1681K/Ua9ecPrpsGEDnHoq/Phj0eaUJEmSko3FXlKhi8ehT58w7toV9tln6/eNxeCee2CvveCnn0K5X7euSGJKkiRJScliL6nQTZ0Kb74JZcvCsGH/fv/y5WHy5LCY3jvvQO/ehZ9RkiRJSlYWe0mFav166NcvjHv3/vd97Ddq1AgeeSSMb78dHn64cPJJkiRJyc5iL6lQ3XMPzJsHNWpA//75+9zjj4drrgnj88+HTz4p+HySJElSsrPYSyo0WVkwZEgYDx0KFSvm/zEGDw4r6K9ZAx07wq+/FmhESZIkKelZ7CUVmhtugF9+gSZN4Nxzt+0xUlLCJfkNG8LChWHF/Ozsgs0pSZIkJTOLvaRC8d13MG5cGI8aBaVKbftjVakSFtMrWxZeeunPqwAkSZIkWewlFZKrrgqXz7duHebKb69mzeDee8P4uuvg6ae3/zElSZKk4sBiL6nAffzxn6vY33hj2Ju+IJxxBlxySRj/3/+FRfkkSZKkks5iL6lAxePQp08Yn3EGNG9esI9/441w8MGwYgWcfDL8/nvBPr4kSZKUbCz2kgrU88/Da69BejoMH17wj1+6NDz5JNSsCV98ERbli8cL/nkkSZKkZGGxl1RgNmyAvn3D+LLLoF69wnmeWrVg4sSwIN8TT8DYsYXzPJIkSVIysNhLKjD33w9z5sAOO8DAgYX7XK1a/Vno+/WDGTMK9/kkSZKkRGWxl1QgVq6EwYPDePBgyMgo/Oe86CI488ywr32nTrB4ceE/pyRJkpRoLPaSCsTo0fDTT9C4MVxwQdE8ZywGd98dtsJbtgxOOQXWri2a55YkSZIShcVe0nb74YewWj3ADTeEBe6KSrlyMHkyVKkC778f5vZLkiRJJYnFXtJ2u/pqWL06zHs/+eSif/6GDeHRR8MZ/LvuggceKPoMkiRJUlQs9pK2y2efwfjxYTxmTCjXUWjXDoYODeMLL4SPPoomhyRJklTULPaStkvfvmEf+U6doEWLaLMMGgQnnBDm2XfsCL/8Em0eSZIkqShY7CVts5degmnTIC0Nrr8+6jSQkgIPPQSNGsG330KXLmHFfEmSJKk4s9hL2ibZ2eFsPcAll4R57omgcmWYMiUsqvfKK2H+vyRJklScWewlbZMHH4TPPw+r0Q8aFHWaTTVtCvffH8YjRoSiL0mSJBVXFntJ+bZqFVx1VRhfdRVUrRptni3p3BkuvzyMu3aFr76KNo8kSZJUWCz2kvJtzBhYsgQaNICLLoo6zdaNHAmHHgorV0KHDuF3SZIkqbix2EvKl6VLYdSoML7hBkhPjzbPP0lLgyefhNq1Yc4cOPvssIK/JEmSVJxY7CXly+DB4VL8Fi3g1FOjTvPvdtwRJk0KJX/SJLjxxqgTSZIkSQXLYi8pz774Au67L4zHjIFYLNo8efWf/8DNN4fxgAHw6qvR5pEkSZIKksVeUp717w85OWG+eqtWUafJnx49oFu3kL9TJ/juu6gTSZIkSQXDYi8pT6ZPh+eeg1Klwtz6ZBOLwR13wH77wS+/wCmnwJo1UaeSJEmStp/FXtK/ysmBPn3C+MILoXHjaPNsq7Jlwzz7qlVh5kzo1SvqRJIkSdL2s9hL+lePPAKzZkGlSnDNNVGn2T7168Njj4Uz+Pfe++eaAZIkSVKysthL+kerV8OgQWE8aBBUqxZtnoJw9NFw3XVhfNFF8MEH0eaRJEmStofFXtI/GjcOFi+GnXcuXpeuDxgA7dvDunVhvv3PP0edSJIkSdo2FntJW7VsGYwYEcbXXw9lykSbpyClpMCDD8Kuu8L330PnzrBhQ9SpJEmSpPyz2EvaqmuvhZUroXlz6NIl6jQFr1IlmDIFypcPe9tvnHIgSZIkJROLvaQtmjsX7rorjG+8MZzhLo722AMeeCCMR42CiROjzSNJkiTlVzF9qS5pe/XvD9nZcMIJ0Lp11GkK16mn/rmd39lnw5w50eaRJEmS8sNiL2kzb7wBU6dCaiqMHBl1mqIxYgQcfjj8/jucfDKsWBF1IkmSJClvLPaSNpGT8+fZ6/PPh913jzZPUSlVCh5/HOrUCdMQunWDeDzqVJIkSdK/s9hL2sQTT8DMmVCxIgwZEnWaolWjBkyaBKVLh0X1SsrVCpIkSUpuFntJudasgYEDw7h//1B0S5oDD4TbbgvjQYPg5ZejzSNJkiT9G4u9pFy33grffgs77QSXXx51muicdx507x6mJXTpEr4nkiRJUqKy2EsCIDMThg8P4+HDoVy5aPNE7bbbYP/9w/elQwdYvTrqRJIkSdKWWewlATBsGGRlwT77wJlnRp0memXKhPn21arBxx/DRRe5mJ4kSZISk8VeEvPnw+23h/Ho0WGbO8HOO4eV8lNS4IEH4J57ok4kSZIkbc5iL4mBA2HDBmjXDtq0iTpNYjnyyLDHPcAll8B770WbR5IkSfo7i71Uwr3zDkycGM5KjxoVdZrE1LcvdOwI69fDKafATz9FnUiSJEn6k8VeKsHicbjiijA+5xxo2jTaPIkqFguX4jdpAj/8AJ06hSscJEmSpERgsZdKsIkTw6Xl5crBtddGnSaxVawIU6aE319/Hfr3jzqRJEmSFFjspRJq3ToYMCCM+/WDWrWizZMMmjSB8ePD+Kab4IknIo0jSZIkAREX+xEjRnDAAQdQsWJFatSoQfv27Zk7d+4m92ndujWxWGyTXz169IgosVR83HEHfPNNKPR9+kSdJnl06PDn2fru3WH27GjzSJIkSZEW+9dff52LLrqI9957j5dffpn169dz9NFHs2rVqk3ud95557FkyZLcX6Nc4UvaLsuX/3np/bXXQvny0eZJNtddF3YPWLUqFP2srKgTSZIkqSQrFeWTv/jii5vcHj9+PDVq1OCjjz7i0EMPzT1erlw5atasWdTxpGLr+utDuW/aFM4+O+o0yadUKXjsMWjeHObNg7POCvPvU5zcJEmSpAhEWuz/Luv/n/aqWrXqJscfffRRHnnkEWrWrMkJJ5zA1VdfTbly5bb4GGvXrmXt2rW5t1esWAHA+vXrWb9+fSEll5LHwoVwyy2lgBgjRmwgJydOTk7UqZJPRgY88USM1q1TefrpGMOGZXPllX4jJUmSRJF3z4Qp9jk5OVx22WW0atWKpn/Zc+v000+nXr161K5dm88++4z+/fszd+5cJk+evMXHGTFiBEOHDt3s+LRp07b6ZoBUkowZ05x16+rQrNkyNmx4l+efjzpRcjvvvJ257bZ9GTo0hZycD9hvv2VRR5IkSVLE/vjjjyJ9vlg8Ho8X6TNuxYUXXsgLL7zAW2+9RZ06dbZ6v1dffZUjjzyS+fPns8suu2z28S2dsa9bty6//PILlSpVKpTsUrKYOTNGq1aliMXivP/+BvbZJ+pExUPPnincd18qVarEee+9DTRoEHUiSZIkRSkzM5NatWqRlZVVJD00Ic7YX3zxxTz77LO88cYb/1jqAVq0aAGw1WKfnp5Oenr6ZsfT0tJIS0srmMBSEorH/9zermvXGAcc4L+HgnLbbfDZZ/DBBzE6dUrj7bfBC4QkSZJKrqLunpEu9RSPx7n44ouZMmUKr776Kg3ycJpr1qxZANRy020pX6ZOhTffhLJlYdiwqNMUL+npMGkSVK8Os2ZBjx7hjRRJkiSpKERa7C+66CIeeeQRJkyYQMWKFVm6dClLly5l9erVACxYsIBhw4bx0UcfsWjRIp5++mnOOussDj30UPbee+8oo0tJZf166NcvjHv3hn+5MEbboE4deOIJSE2Fhx+GO+6IOpEkSZJKikjn2MdisS0ef+CBB+jWrRvff/89Z555JrNnz2bVqlXUrVuXk08+mauuuirP8xRWrFhBRkZGkc1tkBLR7bfDxRdDjRowfz5UrBh1ouJrzBjo0ydsiff663DQQVEnkiRJUlHLzMykWrVqRdZDE2bxvMJisVdJl5UFjRrBL7/AnXeGy8RVeOJx6NwZnnwSatWCjz+GmjWjTiVJkqSiVNTFPtJL8SUVvhtuCKW+SRM499yo0xR/sRjcfz/ssQcsWQKnnRamQkiSJEmFxWIvFWPffQdjx4bxqFHh8nAVvgoVYMoUqFQpLFjYt2/UiSRJklScWeylYuyqq2DtWmjdGo4/Puo0Jcuuu8JDD4XxzTfDhAnR5pEkSVLxZbGXiqmPPw6rswPceGO4RFxF66STYNCgMD733LDXvSRJklTQLPZSMRSPh5XZAc44A5o3jzZPSTZ0KLRtC6tXQ4cOsHx51IkkSZJU3FjspWLo+efhtdcgPR2GD486TcmWmgqPPgr168OCBXDmmZCTE3UqSZIkFScWe6mY2bDhz8XaLrsM6tWLNI6AHXaAyZOhTJnwpsuwYVEnkiRJUnFisZeKmfvvhzlzQpkcODDqNNpo333hrrvCeOhQeO65aPNIkiSp+LDYS8XIypVwzTVhPHgwZGREm0eb6toVevYMayCceWa4NF+SJEnaXhZ7qRgZPRqWLYPGjeGCC6JOoy0ZOxZatoTffoOTT4ZVq6JOJEmSpGRnsZeKiR9+CNvaAYwcCaVLR5tHW1a6NDz1FOy4I3z+OZx/fjiDL0mSJG0ri71UTFx9ddhSrVUraN8+6jT6JzvtBE8+GVbMnzABbr016kSSJElKZhZ7qRj47DMYPz6Mx4yBWCzSOMqDQw/98wqLK66AN9+MNo8kSZKSl8VeKgb69g2Xc3fqBC1aRJ1GeXXppdClS9ii8NRT4ccfo04kSZKkZGSxl5LcSy/BtGmQlgbXXx91GuVHLAb33gt77QU//RTK/bp1UaeSJElSsrHYS0ksOxv69AnjSy6Bhg2jzaP8K18eJk8OWxO+8064LF+SJEnKD4u9lMQefBBmz4YqVWDQoKjTaFs1agSPPBLGt90GDz8cbR5JkiQlF4u9lKRWrYKrrgrjq6+GqlWjzaPtc/zxcM01YXz++fDJJ9HmkSRJUvKw2EtJaswYWLIEGjSAnj2jTqOCMHgwHHssrFkDHTvCr79GnUiSJEnJwGIvJaGlS2HUqDC+4QZIT482jwpGSkq4JL9hQ1i4EM44I6yjIEmSJP0Ti72UhAYPDpfit2gRVlJX8VGlSlhMr2xZePFFGDo06kSSJElKdBZ7Kcl88QXcd18YjxkTtkxT8dKsGdxzTxgPGwZPPx1tHkmSJCU2i72UZPr1g5wc6NABWrWKOo0Ky5lnhi0MAf7v/2DevGjzSJIkKXFZ7KUkMn06PP88lCoV5tareLvxRjj4YFixIryR8/vvUSeSJElSIrLYS0kiJwf69Anjnj2hceNo86jwlS4NTz4JNWvC7Nlw7rkQj0edSpIkSYnGYi8liUcegVmzoFKlsG+9SoZatWDixHCVxhNPwLhxUSeSJElSorHYS0lg9WoYNCiMBw2CatWizaOi1aoVjB0bxn37wowZkcaRJElSgrHYS0lg3DhYvBh23hl69Yo6jaJw0UVhQb3sbOjUKfx9kCRJksBiLyW8ZctgxIgwvv56KFMm2jyKRiwGd98dtsJbtgxOPRXWro06lSRJkhKBxV5KcEOHwsqV0Lw5dOkSdRpFqVw5mDwZKleG996Dyy6LOpEkSZISgcVeSmBz54aztBC2PkvxX2yJ17AhTJgQzuDfdRc88EDUiSRJkhQ1a4KUwPr3D3OqTzwRWreOOo0SRbt24UoOgAsvhI8+ijaPJEmSomWxlxLUG2/A1KmQmgojR0adRolm0CA44YQwz75jR8jMjDqRJEmSomKxlxJQTg706RPG558PTZpEm0eJJyUFHnoIGjWCb78N6y9kZ0edSpIkSVGw2EsJ6IknYOZMqFgRhgyJOo0SVeXKYTG9cuXg5Zfh6qujTiRJkqQoWOylBLNmDQwcGMb9+0ONGtHmUWLbay+4//4wHjEC/ve/SONIkiQpAhZ7KcHcemu4tHqnneDyy6NOo2TQufOff1fOOivspiBJkqSSw2IvJZDMTBg+PIyHDw+XWEt5MXIkHHoorFwJJ58cfpckSVLJYLGXEsiwYZCVBfvsA2eeGXUaJZO0NHjySahdG+bMgXPOgXg86lSSJEkqChZ7KUHMnw+33x7Go0eHbe6k/NhxR5g4MZT8iRPhxhujTiRJkqSiYLGXEsTAgbBhA7RrB23aRJ1GyaplS7j55jAeMABefTXaPJIkSSp8FnspAbzzTjjDmpICo0ZFnUbJrkcP6NoVcnKgUyf4/vuoE0mSJKkwWeyliMXjcMUVYXzOOdC0abR5lPxiMbjzTth3X/jlF+jYMWyjKEmSpOLJYi9FbOJEeO+9sAL+tddGnUbFRdmyMHkyVK0KM2dCr15RJ5IkSVJhsdhLEVq3LsyDBujXD2rVijaPipf69eGxx8IZ/HvvhfvuizqRJEmSCoPFXorQHXfAN9+EQt+nT9RpVBwdfTRcd10YX3RROHsvSZKk4sViL0Vk+fI/L72/9looXz7aPCq+BgyAk04KV4h07Ag//xx1IkmSJBUki70UkeuvD+W+aVM4++yo06g4S0mBBx+EXXcNK+R37hy2VpQkSVLxYLGXIrBwIdxySxiPHg2pqdHmUfGXkQFTpoQrQ159FQYNijqRJEmSCorFXorAlVeGy6LbtIG2baNOo5Jijz3ggQfCeNQomDQp2jySJEkqGBZ7qYh98AE8/nhYqXz06PC7VFROPfXPhRq7dYM5cyKNI0mSpAJgsZeKUDz+Z6nq2hX22SfSOCqhRoyAww+H33+Hk0+GFSuiTiRJkqTtYbGXitDUqfDmm1C2LAwbFnUalVSlSoWrRurUgblzw5n7eDzqVJIkSdpWFnupiKxfD/36hXHv3qFUSVGpUSPMsS9dOiyqN3Jk1IkkSZK0rSz2UhG55x6YNy8Uqv79o04jwYEHwq23hvGgQfDyy9HmkSRJ0rax2EtFICsLhgwJ46FDoWLFSONIuc47D7p3h5wc6NIFvv026kSSJEnKL4u9VARuuAF++QWaNIFzz406jfSnWAxuuw323x8yM6FjR1izJupUkiRJyg+LvVTIvvsOxo4N41GjwsJlUiIpUybMt69WDT76CHr2dDE9SZKkZJLvYv/xxx/z+eef596eOnUq7du358orr2TdunUFGk4qDq66Ctauhdat4fjjo04jbdnOO4eV8lNS4IEHwpoQkiRJSg75LvYXXHABX3/9NQDffPMNnTt3ply5cjz11FP027jktyQAPv4YHn44jG+8MVz2LCWqI4+E668P40sugffeizaPJEmS8ibfxf7rr79mn332AeCpp57i0EMPZcKECYwfP55JkyYVdD4pacXj0KdPGJ9xBjRvHm0eKS/69Qvz7Nevh1NOgZ9+ijqRJEmS/k2+i308HicnJweAV155hWOPPRaAunXr8ssvvxRsOimJPf88vPYapKfD8OFRp5HyJhYLl+I3aQI//ACdO8OGDVGnkiRJ0j/Jd7Hff//9ue6663j44Yd5/fXXOe644wBYuHAhO+64Y4EHlJLRhg3Qt28YX3YZ1KsXaRwpXypWhClToEIFmDEDBgyIOpEkSZL+Sb6L/dixY/n444+5+OKLGTRoEI0aNQJg4sSJHHTQQQUeUEpG998Pc+bADjvAwIFRp5Hyr0kTePDBMB4zBp54Ito8kiRJ2rpYPF4wmxqtWbOGUqVKUSrB9vJasWIFGRkZZGVlUalSpajjqARYuRIaNYJly+CWW8IiZFKyGjAARo6E8uXDYnpNm0adSJIkKfFlZmZSrVq1Iuuh+T5j37BhQzIzMzc7vmbNGnbdddcCCSUls9GjQ6lv3BguuCDqNNL2ue46aNMGVq2CDh0gKyvqRJIkSfq7fBf7RYsWkZ2dvdnxtWvXsnjx4gIJJSWrH34I29pBOMtZunS0eaTtVaoUPPZY2Od+3jw46yz4/+unSpIkKUHk+br5p59+Onf80ksvkZGRkXs7Ozub6dOn06BBg3w9+YgRI5g8eTJfffUVZcuW5aCDDmLkyJHstttuufdZs2YNV1xxBY8//jhr166lbdu23HHHHS7Up4R09dWwejW0agXt20edRioY1arBpElw8MHw9NMwYgQMGhR1KkmSJG2U5zn2KSnh5H4sFuPvn5KWlkb9+vUZM2YMxx9/fJ6f/JhjjqFz584ccMABbNiwgSuvvJLZs2fz5ZdfUr58eQAuvPBCnnvuOcaPH09GRgYXX3wxKSkpvP3223l6DufYq6h89hnss0/Yv/6996BFi6gTSQXrv/+F7t3DlnjPPw/HHBN1IkmSpMRU1HPs8714XoMGDZg5cybVqlUr8DA///wzNWrU4PXXX+fQQw8lKyuL6tWrM2HCBE455RQAvvrqK3bffXfeffdd/vOf//zrY1rsVVTatoVp06BTJ3j88ajTSIXjggvgnnugShX46CPI54VakiRJJUJRF/t8L2G/cOHCzY799ttvVK5cebvDZP3/VZmqVq0KwEcffcT69etp06ZN7n2aNGnCzjvvvNViv3btWtauXZt7e8WKFQCsX7+e9evXb3dGaUumTYsxbVop0tLiDB26Af+qqbgaMwY++SSVmTNTOPnkOK+/voFy5aJOJUmSlFiKunvmu9iPHDmS+vXr06lTJwBOPfVUJk2aRK1atXj++edp1qzZNgXJycnhsssuo1WrVjT9//spLV26lNKlS2/2psGOO+7I0qVLt/g4I0aMYOjQoZsdnzZtGuV89alCkJ0Nl19+OFCJdu0W8NVXX/DVV1GnkgrPBReU4euvW/Ppp+mcfPISevX6hFgs6lSSJEmJ448//ijS58t3sb/rrrt49NFHAXj55Zd55ZVXePHFF3nyySfp27cv06ZN26YgF110EbNnz+att97aps/faODAgfTu3Tv39ooVK6hbty5HH320l+KrUIwfH+O770pRpUqce+6pR9Wq9aKOJBW6nXeOccwxcV57bWdOPnknevRwqXxJkqSNtrRFfGHKd7FfunQpdevWBeDZZ5/ltNNO4+ijj6Z+/fq02MbVwi6++GKeffZZ3njjDerUqZN7vGbNmqxbt26zS/1/+uknatasucXHSk9PJz09fbPjaWlppKWlbVM+aWtWrYLBg8P46qtj7Lijf8dUMhx1FIwaBX36QO/eqTRvnspBB0WdSpIkKTEUdffM9z72VapU4fvvvwfgxRdfzJ3/Ho/Ht7i//T+Jx+NcfPHFTJkyhVdffXWz7fKaN29OWloa06dPzz02d+5cvvvuO1q2bJnf6FKBGzMGliwJC4j17Bl1Gqlo9e4Np50GGzbAKafAVmZISZIkqZDl+4x9hw4dOP3002ncuDGZmZm0a9cOgE8++YRGjRrl67EuuugiJkyYwNSpU6lYsWLuvPmMjAzKli1LRkYG3bt3p3fv3lStWpVKlSpxySWX0LJlyzytiC8VpqVLwxlLgBtugC1cKCIVa7EY3H8/zJ4NX34ZSv706eDFUZIkSUUr32fsx44dy8UXX8wee+zByy+/TIUKFQBYsmQJPfN5yvLOO+8kKyuL1q1bU6tWrdxfTzzxxCbPd/zxx9OxY0cOPfRQatasyeTJk/MbWypwgweHS/FbtIBTT406jRSNChVg8mSoVAnefBP69o06kSRJUsmT733sk4372KswfPEF7L035OTAW29Bq1ZRJ5KiNXUqtG8fxo8+CqefHmkcSZKkSBX1Pvb5PmMP8PDDD3PwwQdTu3Ztvv32WwDGjRvH1KlTCzSclKj69QulvkMHS70EcNJJMGhQGJ97Lnz2WbR5JEmSSpJ8F/s777yT3r17065dO3777bfcBfMqV67MuHHjCjqflHCmT4fnn4dSpcLceknB0KHQti2sXh3e9Prtt6gTSZIklQz5Lva33nor9957L4MGDSI1NTX3+P7778/nn39eoOGkRJOTE7b3grAKfuPG0eaREklqargMv359WLAAzjwz/JuRJElS4cp3sV+4cCH77rvvZsfT09NZtWpVgYSSEtUjj8CsWWGhsKuvjjqNlHh22AEmTYIyZeC552DYsKgTSZIkFX/5LvYNGjRg1qxZmx1/8cUX2X333Qsik5SQVq/+cw7xoEFQrVq0eaREtd9+cNddYTx0aJi6IkmSpMKT733se/fuzUUXXcSaNWuIx+N88MEHPPbYY4wYMYL77ruvMDJKCWHcOFi8GHbeGXr1ijqNlNi6doUPPoA77oAzzoAPP4Rddok6lSRJUvG0TdvdPfroowwZMoQFCxYAULt2bYYOHUr37t0LPOD2crs7FYRly6BRI1i5MlyOf8YZUSeSEt+6ddC6Nbz7btge8t13oVy5qFNJkiQVvqLe7m679rH/448/+P3336lRo0ZBZipQFnsVhIsuCmcemzcPZyFTtmmjSKnk+eGHcGn+smVhb/tHHoFYLOpUkiRJhSsp9rHfqFy5cgld6qWCMHcu3H13GN94o6Veyo+ddoKnngor5k+YALfeGnUiSZKk4iffc+wbNGhA7B9Ot3zzzTfbFUhKNP37Q3Y2nHhiuKxYUv4cemh4U+zyy+GKK8IZ/IMPjjqVJElS8ZHvYn/ZZZdtcnv9+vV88sknvPjii/Tt27egckkJ4fXXYerUcLZx5Mio00jJ69JL4f334fHH4dRT4aOPoHbtqFNJkiQVD/ku9pdeeukWj99+++18+OGH2x1IShQ5OdCnTxiffz40aRJtHimZxWJw330we3b4deqp8NprULp01MkkSZKSX4HNFm7Xrh2TJk0qqIeTIvfEE2GLrooVYciQqNNIya98eZgyBTIy4J13wmX5kiRJ2n4FVuwnTpxI1apVC+rhpEitWQMDB4Zx//7gGpFSwWjUKKyMD3DbbfDww9HmkSRJKg7yfSn+vvvuu8niefF4nKVLl/Lzzz9zxx13FGg4KSq33grffhtW9L788qjTSMXL8cfDNdfAtdeGaS577QX77BN1KkmSpOSV72Lfvn37TW6npKRQvXp1WrduTRMnIasYyMyE4cPDePhwKFcu2jxScTR4MMycCS+8AB06hGkvXvQlSZK0bWLxeDwedYjCtGLFCjIyMsjKyqJSpUpRx1ESuOwyuPnmcAbxww/DiviSCt6vv8IBB8A338Axx8Czz/rvTZIkFQ+ZmZlUq1atyHpovs/Yr1ixIs/3tUgr2cybB7ffHsajR1sypMJUtSpMngwtW8KLL8LQoeHyfEmSJOVPvot95cqVN5ljvyXxeJxYLEZ2dvY2B5OiMHAgbNgA7dpBmzZRp5GKv2bN4J574P/+D4YNC2fwTzgh6lSSJEnJJd/F/oEHHmDAgAF069aNli1bAvDuu+/y4IMPMmLECOrXr1/QGaUi8c47MGkSpKTAqFFRp5FKjjPPhA8+CItWnnlmmALTuHHUqSRJkpJHvov9Qw89xE033USXLl1yj5144onstdde3HPPPcyYMaMg80lFIh7/c0/tc86Bpk2jzSOVNDfeCB9/DG+/HRbTe/ddqFAh6lSSJEnJId/72L/77rvsv//+mx3ff//9+eCDDwoklFTUJk6E994LK+A7x1cqeqVLw1NPQc2aMHs2nHtueMNNkiRJ/y7fxb5u3brce++9mx2/7777qFu3boGEkorSunUwYEAY9+sHtWpFm0cqqWrVCm+ylSoFTzwB48ZFnUiSJCk55PtS/LFjx9KxY0deeOEFWrRoAcAHH3zAvHnzmDRpUoEHlArbHXeE7bZq1YI+faJOI5VsrVrBTTdBr17Qty/stx8cdljUqSRJkhJbvs/YH3vssXz99deccMIJ/Prrr/z666+ccMIJfP311xx77LGFkVEqNMuX/3np/bXXQvny0eaRBBdfHBbRy86G006DxYujTiRJkpTYYvF48Z7FuGLFCjIyMsjKyqJSpUpRx1GC6dMHxowJi+XNmuW+9VKi+OMPOOgg+PRT+M9/YMYMSE+POpUkSVLeZGZmUq1atSLrofk+Yw/w5ptvcuaZZ3LQQQfxww8/APDwww/z1ltvFWg4qTAtXBi21wIYPdpSLyWScuVg8mSoXDksbHn55VEnkiRJSlz5LvaTJk2ibdu2lC1blo8//pi1a9cCkJWVxfXXX1/gAaXCcuWVYeG8Nm2gbduo00j6u4YNYcIEiMXgzjth/PioE0mSJCWmfBf76667jrvuuot7772XtLS03OOtWrXi448/LtBwUmH54AN4/PFQGEaPDr9LSjzt2sGQIWHco0fY616SJEmbynexnzt3LoceeuhmxzMyMvjtt98KIpNUqOLxP1e/79oV9tkn0jiS/sVVV8Hxx8PatdChA2RmRp1IkiQpseS72NesWZP58+dvdvytt96iYcOGBRJKKkxTp8Kbb0LZsjBsWNRpJP2blBR4+GFo1Ai+/Ra6dAkr5kuSJCnId7E/77zzuPTSS3n//feJxWL8+OOPPProo/Tp04cLL7ywMDJKBWb9eujXL4x794Y6daLNIylvKlcOi+mVKwcvvwzXXBN1IkmSpMRRKr+fMGDAAHJycjjyyCP5448/OPTQQ0lPT6dPnz5ccsklhZFRKjB33w3z5kGNGtC/f9RpJOXHXnvBfffB6afD9dfDAQdA+/ZRp5IkSYreNu9jv27dOubPn8/vv//OHnvsQYUKFVi9ejVly5Yt6IzbxX3stVFWVriU95dfwgrbPXpEnUjStrj8chg3DipWhJkzYbfdok4kSZK0qaTYxx6gdOnS7LHHHhx44IGkpaVx00030aBBg4LMJhWoG24Ipb5JEzj33KjTSNpWo0bBoYfCypVw8snhd0mSpJIsz8V+7dq1DBw4kP3335+DDjqI//3vfwA88MADNGjQgLFjx3L55ZcXVk5pu3z3HYwdG8ajRkGpfE9CkZQo0tLgySehdm2YMwfOOSfsdiFJklRS5bnYX3PNNdx5553Ur1+fRYsWceqpp3L++eczduxYbrrpJhYtWkR/Jy0rQV11Vdgqq3XrsG2WpOS2444wcWIo+RMnwpgxUSeSJEmKTp6L/VNPPcVDDz3ExIkTmTZtGtnZ2WzYsIFPP/2Uzp07k5qaWpg5pW328cdhqyyAG2+EWCzaPJIKRsuWcPPNYdy/P7z6arR5JEmSopLnYr948WKaN28OQNOmTUlPT+fyyy8nZktSAovH4YorwviMM+D//xWWVEz06AFdu0JODnTqBN9/H3UiSZKkopfnYp+dnU3p0qVzb5cqVYoKFSoUSiipoDz3HMyYAenpMHx41GkkFbRYLOxyse++YXHMjh1hzZqoU0mSJBWtPC8hFo/H6datG+np6QCsWbOGHj16UL58+U3uN3ny5IJNKG2jDRugX78wvuwyqFcv0jiSCknZsjB5crgiZ+ZM6NUL7rkn6lSSJElFJ8/FvmvXrpvcPvPMMws8jFSQ7r8/rJi9ww4wcGDUaSQVpvr14bHH4Jhj4N574cAD3dZSkiSVHLF4vHhvErRixQoyMjLIysqiUqVKUcdREVm5Eho1gmXL4JZb4JJLok4kqShcfz0MGgSlS8Nbb8EBB0SdSJIklUSZmZlUq1atyHponufYS8lk9OhQ6hs3hgsuiDqNpKIyYACcdBKsWxfm2//8c9SJJEmSCp/FXsXODz+Ebe0ARo4MZ+4klQwpKfDgg7DrrmGF/C5dwnobkiRJxZnFXsXO1VfD6tXQqhW0bx91GklFLSMjLKZXvjxMnx4uzZckSSrOLPYqVj79FMaPD+MxY8JWWJJKnj33hAceCONRo2DSpGjzSJIkFSaLvYqVfv0gHodOnaBFi6jTSIrSqadCnz5h3K1b2CVDkiSpOMrTdndPP/10nh/wxBNP3OYw0vZ46SWYNg3S0sLK2JI0YgR89BG89hqcfDJ88AG4QYokSSpu8lTs2+dxonIsFiM7O3t78kjbJDv7zzNzl1wCDRtGm0dSYihVCh5/HJo3h7lzw5n7SZOcpiNJkoqXPF2Kn5OTk6dflnpF5cEHYfZsqFLFhbIkbapGDZg4MeyQMWVK2C1DkiSpOHGOvZLeqlVw1VVhfPXVULVqtHkkJZ4WLeDWW8N40CB45ZVo80iSJBWkPF2Kf8stt+T5AXv16rXNYaRtMWYMLFkCDRpAz55Rp5GUqM47L8yxv/9+6Nw5zL2vVy/qVJIkSdsvFo/H4/92pwYNGuTtwWIxvvnmm+0OVZBWrFhBRkYGWVlZVHLFpGJn6VJo1CictX/iCTjttKgTSUpka9bAIYfAhx+GefdvvQVlykSdSpIkFTeZmZlUq1atyHpons7YL1y4sLBzSNtk8OBQ6lu0CFtbSdI/KVMmzLdv3jycse/ZM5zBdzE9SZKUzJxjr6T1xRdw331hPGaML8wl5U29emGl/JQUeOABuOeeqBNJkiRtnzydsf+rc8455x8//t///nebw0j50a8f5ORAhw7QqlXUaSQlkzZt4PrrYcCAsEXmPvuEK38kSZKSUb6L/fLlyze5vX79embPns1vv/3GEUccUWDBpH8yfTo8/3zYo/qGG6JOIykZ9esXFtObPBk6dgyX5u+4Y9SpJEmS8i/fxX7KlCmbHcvJyeHCCy9kl112KZBQ0j/JyYE+fcK4Z09o3DjaPJKSUywWLsX/8kv46quwUv7LL4c3DCVJkpJJgcyxT0lJoXfv3owdO7YgHk76R488ArNmQaVKYd96SdpWlSrBlClQoQLMmBEuzZckSUo2BbZ43oIFC9iwYUNBPZy0RatXw6BBYTxoEFSrFm0eScmvSRN48MEwHjMGnnwy2jySJEn5le8LDnv37r3J7Xg8zpIlS3juuefo2rVrgQWTtmTcOFi8GHbeGXr1ijqNpOKiQwfo3x9GjoRzzoE99wy/JEmSkkEsHo/H8/MJhx9++Ca3U1JSqF69OkcccQTnnHMOpRJscuKKFSvIyMggKyuLSpUqRR1H22HZMmjUCFauDJfjn3FG1IkkFScbNsAxx4TFORs3hpkzISMj6lSSJCkZZWZmUq1atSLroflu4a+99lph5JD+1dChodQ3bw5dukSdRlJxU6oUPPYY7L8/zJsHZ50V5t+nFNikNUmSpMKR55crOTk5jBw5klatWnHAAQcwYMAAVq9eXZjZpFxz58Ldd4fxjTf6QltS4aheHSZNgvR0ePppGDEi6kSSJEn/Ls/1aPjw4Vx55ZVUqFCBnXbaiZtvvpmLLrqoMLNJufr3h+xsOPFEaN066jSSirP994c77gjjq6+Gl16KNo8kSdK/yfMc+8aNG9OnTx8uuOACAF555RWOO+44Vq9eTUoCnz51jn3ye/31UOZTU2H27LCCtSQVtgsugHvugSpV4KOPoEGDqBNJkqRkUdRz7PPcyL/77juOPfbY3Ntt2rQhFovx448/bvOTv/HGG5xwwgnUrl2bWCzG//73v00+3q1bN2Kx2Ca/jjnmmG1+PiWfnBzo0yeMzz/fUi+p6NxyCxx4ICxfHlbN/+OPqBNJkiRtWZ6L/YYNGyhTpswmx9LS0li/fv02P/mqVato1qwZt99++1bvc8wxx7BkyZLcX4899tg2P5+SzxNPwIcfQsWKMGRI1GkklSTp6TBxYph3P2sWXHgh5G8fGUmSpKKR51Xx4/E43bp1Iz09PffYmjVr6NGjB+XLl889Nnny5Dw/ebt27WjXrt0/3ic9PZ2aNWvm+THXrl3L2rVrc2+vWLECgPXr12/XmxAqemvWwMCBpYAYffpkU6VKDv4RSipKNWvCo4/GOOaYVB56KMb++2fTo0dO1LEkSVKCK+rumedi37Vr182OnXnmmQUaZktmzJhBjRo1qFKlCkcccQTXXXcdO+yww1bvP2LECIYOHbrZ8WnTplGuXLnCjKoCNmVKI779dk922GE1TZpM5/nns6OOJKmEOuusXRg/vim9e8f44493adJkedSRJElSAvujiOfw5XnxvMIWi8WYMmUK7du3zz32+OOPU65cORo0aMCCBQtyV+V/9913SU1N3eLjbOmMfd26dfnll19cPC+JZGZCkyalyMqKcd99GzjrrIT4ayqphIrH4fTTU5k0KYVateK8//4G8nExmSRJKmEyMzOpVatWkS2el+cz9lHo3Llz7nivvfZi7733ZpdddmHGjBkceeSRW/yc9PT0TaYLbJSWlkZaWlqhZVXBuuEGyMqCffaBbt1KsZX3cSSpyIwfD3PmwJdfxjjjjDSmTwd/rEiSpC0p6u6ZuPvUbUHDhg2pVq0a8+fPjzqKCtG8ebBxPcXRo7HUS0oIFSrA5MlQqRK8+Sb06xd1IkmSpCCpiv3ixYtzL2lQ8TVwIGzYAO3aQZs2UaeRpD/tths89FAYjxsHbtQiSZISQaTF/vfff2fWrFnMmjULgIULFzJr1iy+++47fv/9d/r27ct7773HokWLmD59OieddBKNGjWibdu2UcZWIXrnHZg0CVJSYNSoqNNI0uZOOgmuvDKMu3eHzz6LNo8kSVKeiv1+++3H8uVhBeBrr722wFb4+/DDD9l3333Zd999Aejduzf77rsv11xzDampqXz22WeceOKJ7LrrrnTv3p3mzZvz5ptvbnEOvZJfPA5XXBHG55wDTZtGm0eStubaa+Hoo2H1aujQAX77LepEkiSpJMvTqvhly5Zl3rx51KlTh9TUVJYsWUKNGjWKIt92W7FiBRkZGUW2GqG23VNPwWmnQblyMH8+OONCUiLLzIT994dFi+D442Hq1HC1kSRJUmZmJtWqVUusVfH32Wcfzj77bA4++GDi8Tg33ngjFSpU2OJ9r7nmmgINqJJh3ToYMCCM+/Wz1EtKfDvsEKYOtWoFzz4L110H/giUJElRyNMZ+7lz5zJ48GAWLFjAxx9/zB577EGpUpu/JxCLxfj4448LJei28ox9chg3Di6/PBT6efOgfPmoE0lS3jz4IHTrBrFYKPjHHht1IkmSFLWiPmOfp2L/VykpKSxdutRL8VVgli+HXXYJv997L5x7btSJJCl/evaEO++EypXhww/D/2mSJKnkKupin+/ZgDk5OUlT6pUchg8Ppb5pUzj77KjTSFL+jRsH//lPWESvQwcooDVmJUmS8mSblvlZsGABl1xyCW3atKFNmzb06tWLBQsWFHQ2lQALF8Ktt4bx6NGQmhptHknaFqVLw8SJUKNG2P7u/PPDTh+SJElFId/F/qWXXmKPPfbggw8+YO+992bvvffm/fffZ8899+Tll18ujIwqxq68Miyc16YNtG0bdRpJ2nY77QRPPhneoHz0UbjttqgTSZKkkiLfc+z33Xdf2rZtyw033LDJ8QEDBjBt2jQXz1OeffABtGgRFpz6+GPYZ5+oE0nS9tu4GGipUvDaa3DwwVEnkiRJRS3h59jPmTOH7t27b3b8nHPO4csvvyyQUCr+4nHo0yeMu3a11EsqPi69FDp3hg0b4NRTYcmSqBNJkqTiLt/Fvnr16syaNWuz47NmzXJRPeXZ1Knw5ptQtiwMGxZ1GkkqOLEY3HdfWBB06dJQ7tetizqVJEkqzjbfjP5fnHfeeZx//vl88803HHTQQQC8/fbbjBw5kt69exd4QBU/69dDv35h3Ls31KkTbR5JKmjly8OUKbD//vD223DFFX8uFCpJklTQ8j3HPh6PM27cOMaMGcOPP/4IQO3atenbty+9evUiFosVStBt5Rz7xHPbbXDJJWH16PnzoWLFqBNJUuF49lk44YQwfugh+L//izaPJEkqGkU9xz7fxf6vVq5cCUDFBG5mFvvEkpUFjRrBL7/AnXdCjx5RJ5KkwjV4MFx7LZQpA+++65oikiSVBAm/eN5fVaxYMaFLvRLPDTeEUt+kCZx7btRpJKnwDR4M7drBmjXQoQP8+mvUiSRJUnGzXcVeyo/vvoOxY8N41KiwFZQkFXcpKfDII9CwISxcCGecAdnZUaeSJEnFicVeReaqq2DtWmjdGo4/Puo0klR0qlaFyZPDTiAvvghDh0adSJIkFScWexWJjz+Ghx8O4xtvDNtBSVJJ0qwZ3HNPGA8bBs88E20eSZJUfOSr2K9fv54jjzySefPmFVYeFUPxeNjqCcIlqM2bR5tHkqJy5plhV5CNY3+cSpKkgpCvYp+WlsZnn31WWFlUTD33HMyYAenpMHx41GkkKVo33gitWsGKFWExvVWrok4kSZKSXb4vxT/zzDO5//77CyOLiqENG6BfvzC+7DKoVy/SOJIUudKl4amnoGZNmD077BCy7RvPSpIkQb7XJd+wYQP//e9/eeWVV2jevDnly5ff5OM33XRTgYVT8rv/fpgzB3bYAQYOjDqNJCWGWrVCuT/8cHj8cTjwQLj88qhTSZKkZJXvYj979mz2228/AL7++utNPhZzRTT9xcqVcM01YTx4MGRkRJtHkhLJwQfDTTdBr17Qty/stx8cdljUqSRJUjLKd7F/7bXXCiOHiqHRo2HZMmjcGC64IOo0kpR4Lr4YPvgg7HN/2mlhB5Gddoo6lSRJSjbbvN3d/Pnzeemll1i9ejUAcScI6i9++CEsEAUwcmSYUypJ2lQsBnffHbbCW7YMTjkF1q6NOpUkSUo2+S72mZmZHHnkkey6664ce+yxLFmyBIDu3btzxcY9zVTiXX01rF4dVn5u3z7qNJKUuMqVg0mToHJleO8959pLkqT8y3exv/zyy0lLS+O7776jXLlyucc7derEiy++WKDhlJw+/RTGjw/jMWPCGSlJ0tbtsgtMmBD+v7zzzj//D5UkScqLfBf7adOmMXLkSOrUqbPJ8caNG/Ptt98WWDAlr379wtZNnTpBixZRp5Gk5NCuHQwZEsY9eoT59pIkSXmR72K/atWqTc7Ub/Trr7+Snp5eIKGUvF56CaZNC3PqR4yIOo0kJZerroLjjw/z7Dt0gMzMqBNJkqRkkO9if8ghh/DQQw/l3o7FYuTk5DBq1CgOP/zwAg2n5JKdDX36hPHFF0ODBtHmkaRkk5ICDz8cLs3/9lvo0iX83ypJkvRP8r3d3ahRozjyyCP58MMPWbduHf369eOLL77g119/5e233y6MjEoSDz4Is2dDlSowaFDUaSQpOVWuDFOmwH/+Ay+/DNdcA8OHR51KkiQlsnyfsW/atClff/01Bx98MCeddBKrVq2iQ4cOfPLJJ+yyyy6FkVFJYNWqcAkphBXxq1aNNo8kJbO99oL77gvj66+H//0v0jiSJCnBxeLFfAP6FStWkJGRQVZWFpUqVYo6TrF17bUweHC4/H7OHHC5BUnafpdfDuPGQcWKMHMm7LZb1IkkSVJeZGZmUq1atSLrofm+FB9g+fLl3H///cyZMweAPfbYg7PPPpuqnqYtkZYuhVGjwviGGyz1klRQRo0Kq+O/8UZYTO+990LJlyRJ+qt8X4r/xhtvUL9+fW655RaWL1/O8uXLueWWW2jQoAFvvPFGYWRUghs8OFyK36IFnHpq1GkkqfhIS4MnnoDateHLL+Gcc8J2opIkSX+V70vx99prL1q2bMmdd95JamoqANnZ2fTs2ZN33nmHzz//vFCCbisvxS9cX3wBe+8NOTnw1lvQqlXUiSSp+Hn3XTjsMFi/HkaP/nMHEkmSlJiK+lL8fJ+xnz9/PldccUVuqQdITU2ld+/ezJ8/v0DDKfH16xdKfYcOlnpJKiwtW8LNN4dx//7w6qvR5pEkSYkl38V+v/32y51b/1dz5syhWbNmBRJKyWH6dHj+eShVKsytlyQVnh49oGvX8GZqp07w/fdRJ5IkSYkiT4vnffbZZ7njXr16cemllzJ//nz+85//APDee+9x++23c4PtrsTIyfnzUtCePaFx42jzSFJxF4vBnXfCZ5/BJ59Ax45hUb0yZaJOJkmSopanOfYpKSnEYjH+7a6xWIzs7OwCC1cQnGNfOB56KJw5qlQJFiyAatWiTiRJJcPChbD//vDrr3DeeXDPPVEnkiRJf5eQ290tXLiwsHMoiaxeDYMGhfGgQZZ6SSpKDRrAY4/BMcfAvfeGHUm6d486lSRJilKein29evUKO4eSyLhxsHgx7Lwz9OoVdRpJKnmOPhqGDYOrroKLLgq7kxxwQNSpJElSVPJU7P/uxx9/5K233mLZsmXk5ORs8rFeNr1ibdkyGDEijEeMcG6nJEVl4ECYOROmTg3z7T/6CKpXjzqVJEmKQr6L/fjx47ngggsoXbo0O+ywA7FYLPdjsVjMYl/MDR0KK1dC8+bQuXPUaSSp5EpJgQcfhAMPhK+/hi5d4MUXw04lkiSpZMnT4nl/VbduXXr06MHAgQNJScn3bnlFzsXzCs7cubDnnpCdDa+9Bq1bR51IkvTFF2Ge/apVYY97N6iRJCl6Rb14Xr6b+R9//EHnzp2TotSrYPXvH0r9iSda6iUpUey5JzzwQBiPHAmTJkWbR5IkFb18t/Pu3bvz1FNPFUYWJbDXXw/zOFNTwwtHSVLiOPVUuOKKMO7WDebMiTSOJEkqYvm+FD87O5vjjz+e1atXs9dee5GWlrbJx2+66aYCDbi9vBR/++XkhMs8P/wQLrwQ7rgj6kSSpL/bsAGOOgpmzIDddoMPPgB/7EmSFI2E3Mf+r0aMGMFLL73EbrvtBrDZ4nkqfp54IpT6ihVhyJCo00iStqRUqfD/dfPmYU2Us8+GiRPBH82SJBV/+T5jX6VKFcaOHUu3bt0KKVLB8oz99lmzBpo0gW+/heHD4coro04kSfon778Phx4K69aFhfT69486kSRJJU/CL56Xnp5Oq1atCiOLEtCtt4ZSv9NOcNllUaeRJP2bFi3C/90Q3ox95ZVo80iSpMKX72J/6aWXcuvGVwwq1jIzw1l6CL+XKxdtHklS3px3HpxzTlgjpXPn8AatJEkqvvI9x/6DDz7g1Vdf5dlnn2XPPffcbPG8yZMnF1g4RWvYMMjKgn32gTPPjDqNJCmvYjG4/Xb47LOwRkrHjvDWW1CmTNTJJElSYch3sa9cuTIdOnQojCxKIPPmhReFAKNHh23uJEnJo0yZsHhe8+bw0Udw0UVw330upidJUnGU78Xzko2L522bU06BSZOgXTt4/vmo00iSttUrr0DbtuGy/LvvhvPPjzqRJEnFX8Ivnqfi7513QqlPSYFRo6JOI0naHm3awPXXh/HFF4dV8yVJUvGS70vxGzRo8I/71X/zzTfbFUjRisfhiivCuHt3aNo02jySpO3Xrx988AFMnhzm23/8MdSoEXUqSZJUUPJd7C/7255n69ev55NPPuHFF1+kb9++BZVLEZk4Ed57D8qXh6FDo04jSSoIsRg88AB8+SV89RV06gQvvwyl8v0qQJIkJaJ8/0i/9NJLt3j89ttv58MPP9zuQIrO2rUwYEAY9+0LtWpFm0eSVHAqVYIpU+CAA2DGjPD//Y03Rp1KkiQVhAKbY9+uXTsmTZpUUA+nCNx5J3zzTSj0ffpEnUaSVNCaNIHx48N4zBh48slI40iSpAJSYMV+4sSJVK1ataAeTkVs+XK49towvvbacCm+JKn46dgR+vcP43POgS++iDaPJEnafvm+FH/ffffdZPG8eDzO0qVL+fnnn7njjjsKNJyKzvDhodw3bQpnnx11GklSYbruOvjwQ5g+HU4+GWbOhIyMqFNJkqRtle9i3759+01up6SkUL16dVq3bk2TJk0KKpeK0MKFcOutYTx6NKSmRptHklS4SpWCxx6D/feHefOga9ewYn6Km+BKkpSUYvF4PB51iMK0YsUKMjIyyMrKolKlSlHHSUhdusDjj8NRR8FLL4XVkyVJxd+HH8LBB4fFU6+7DgYNijqRJEnFQ2ZmJtWqVSuyHup78yXc+++HUh+LhbP1lnpJKjn23x9uvz2Mr746vLkrSZKST56LfUpKCqmpqf/4q5Qb4iaVePzP1e+7doVmzaLNI0kqet27w/nnh58Jp58epmdJkqTkkucmPmXKlK1+7N133+WWW24hJyenQEKpaEydCm+9BWXLwrBhUaeRJEXllltg1iz44APo0AHeeSf8bJAkSckhz8X+pJNO2uzY3LlzGTBgAM888wxnnHEG127cL00Jb/166NcvjHv3hjp1os0jSYpOejpMnAjNm4eC36MH3H9/ePN3yRKoVQsOOcTFVSVJSlTbNMf+xx9/5LzzzmOvvfZiw4YNzJo1iwcffJB69erl63HeeOMNTjjhBGrXrk0sFuN///vfJh+Px+Ncc8011KpVi7Jly9KmTRvmzZu3LZH1N3ffHVZCrlHjz/2MJUklV9268MQTYWX8hx6C6tXh8MPD5fmHHw7164eV8yVJUuLJV7HPysqif//+NGrUiC+++ILp06fzzDPP0LRp02168lWrVtGsWTNu37hyz9+MGjWKW265hbvuuov333+f8uXL07ZtW9asWbNNz6cgKwuGDg3joUOhYsVo80iSEsPhh8OZZ4bxb79t+rEffoBTTrHcS5KUiPK83d2oUaMYOXIkNWvW5Prrr9/ipfnbFSQWY8qUKbRv3x4IZ+tr167NFVdcQZ//v8JbVlYWO+64I+PHj6dz5855ely3u9vcwIFwww3QpAl8/nnYz1iSpOzscGZ+8eItfzwWC1O3Fi70snxJkv5JUW93l+dKN2DAAMqWLUujRo148MEHefDBB7d4v8kF9Fb+woULWbp0KW3atMk9lpGRQYsWLXj33Xe3WuzXrl3L2rVrc2+vWLECgPXr17N+/foCyZbMvvsOxo4tBcS4/voNxONx/LZIkgBefz3G4sVbf2kQj8P338Nrr23gsMPydF5AkqQSqai7Z56L/VlnnUWsCDc5X7p0KQA77rjjJsd33HHH3I9tyYgRIxi68Trzv5g2bRrlypUr2JBJaOzY/Vi7ti5Nm/5MLPYOzz8fdSJJUqJ4442dgP3/9X4vvDCLVat+KPxAkiQlqT/++KNIny/PxX78+PGFGKPgDBw4kN69e+feXrFiBXXr1uXoo48u8Zfif/IJvP56GgD33VeZ/fY7NuJEkqREUr58jJtu+vf7NWu2D8ce26zwA0mSlKQyMzOL9PkSdnZ1zZo1Afjpp5+oVatW7vGffvqJffbZZ6ufl56eTnp6+mbH09LSSEtLK/CcySIe/3P1+zPOgBYtSu73QpK0ZYcfHubQ//BD+LmxNd27l2LmTLjqKvjbhXWSJAmKvHtu03Z3RaFBgwbUrFmT6dOn5x5bsWIF77//Pi1btowwWXJ67jmYMSPsVTx8eNRpJEmJKDUVbr45jP8++y4WC7+aNYMNG+C222CXXWDwYPj/y9lIkqSIRFrsf//9d2bNmsWsWbOAsGDerFmz+O6774jFYlx22WVcd911PP3003z++eecddZZ1K5dO3flfOXNhg3Qr18YX3YZ1KsXaRxJUgLr0AEmToSddtr0eJ064fisWTB9OhxwAKxaBddeGwr+zTfDX9aulSRJRSjP290VhhkzZnD44Ydvdrxr166MHz+eeDzO4MGDueeee/jtt984+OCDueOOO9h1113z/Bxudwd33w09esAOO8CCBZCREXUiSVKiy86GN9+EJUugVi045JBNt7iLx2HSJBg0CL7+OhyrXz8U/dNPdzs8SVLJVtTb3UVa7ItCSS/2K1dCo0awbBnccgtccknUiSRJxcmGDfDf/8KQIeFNAIC99oIRI+DYYze/pF+SpJKgqIt9ws6xV8EYNSqU+saN4YILok4jSSpuSpWC88+H+fNDmc/IgM8/h+OPh8MOg3ffjTqhJEnFn8W+GPvhBxgzJoxHjoTSpaPNI0kqvsqVgwED4JtvoG9fKFMmXMp/0EHQvj18+WXUCSVJKr4s9sXY1VfD6tXQqlV4USVJUmGrWjVcLTZvHnTvDikpMHVquDz/nHPg+++jTihJUvFjsS+mPv0Uxo8P4zFjnOMoSSpaderAfffB7Nlw8smQkwMPPBCmhvXpA5mZUSeUJKn4sNgXU/36hRWLO3WCFi2iTiNJKql23x0mTw5z7Q87LGyJN2YMNGwI118ftsyTJEnbx2JfDL30EkybFubUjxgRdRpJkuA//4HXXoPnn4dmzWDFirBVXqNGcNddsH591AklSUpeFvtiJjs7XOIIcPHF0KBBtHkkSdooFoN27eDjj+GRR8LPqKVL4cILYc894cknwyX7kiQpfyz2xcz48WE+Y5Uq4UyIJEmJJiUFzjgDvvoKbrkFqlcPi+116gQHHgivvBJ1QkmSkovFvhhZtSqshA/h96pVo80jSdI/KV0aLrkEFiyAIUOgQgX46CM46qjw66OPok4oSVJysNgXI2PGwJIl4dLGnj2jTiNJUt5UrAiDB4eC36sXpKWFs/b77x/O4s+bF3VCSZISm8W+mFi6NOwbDHDDDZCeHm0eSZLyq0YNuPlmmDsXzjwzzMl/8knYY48wD3/JkqgTSpKUmCz2xcTgweFS/BYt4NRTo04jSdK2a9AAHn4YPvkkLLa3YUNYOb9Ro7B+TFZW1AklSUosFvti4Isv4L77wnjMmHCGQ5KkZNesWdgeb8aMsF3eH3/A9ddDw4Zw002wZk3UCSVJSgwW+2KgX7+wPVCHDtCqVdRpJEkqWIcdBu+8A1OmwO67w6+/whVXwK67wgMPhK1eJUkqySz2SW769HA2o1SpMLdekqTiKBaD9u3hs8/g/vthp53g++/hnHNg771h6lSIx6NOKUlSNCz2SSwnB/r0CeOePaFx42jzSJJU2EqVCmV+3jwYPRqqVIEvvwyl/+CD4a23ok4oSVLRs9gnsUcegVmzoFKlP/evlySpJChbNry5/c03MHBguP3OO3DIIXDCCfD551EnlCSp6Fjsk9Tq1WFlYAi/V6sWbR5JkqJQuXJYUG/+fDj/fEhNhWefDQvvde0K334bdUJJkgqfxT5JjRsHixfDzjtDr15Rp5EkKVq1a8Pdd4edYk45Jcy3f+ihsMDe5ZfDL79EnVCSpMJjsU9Cy5bBiBFhPGIElCkTbR5JkhLFbrvBU0/BBx/AEUfAunXhzfCGDWHYMPj996gTSpJU8Cz2SWjoUFi5Epo3h86do04jSVLiOeAAeOUVeOkl2Hff8HPzmmugUSO4/fZQ+CVJKi4s9knmq6/CpYYAN94IKf4JSpK0RbEYHH00fPghPPYY7LIL/PQTXHwx7L57OJaTE3VKSZK2n7UwyQwYANnZcOKJ0Lp11GkkSUp8KSnhCrcvvwxn63fcMaymf/rpsP/+4ax+PB51SkmStp3FPom8/jpMnRpW/B05Muo0kiQll9KloWfPsIL+sGFQsSJ88gkccwwceWSYly9JUjKy2CeJnJywXy+E7XyaNIk2jyRJyapCBbjqqnDW/vLLQ+F/7TVo0SKsqD93btQJJUnKH4t9knjiiTBHsGJFGDIk6jSSJCW/atXgppvg66/DnvexGEyaBHvuGd5E/+GHqBNKkpQ3FvsksGYNDBwYxgMGQI0a0eaRJKk4qVcPxo+Hzz6DE04Ia9nce29YQX/AAFi+POqEkiT9M4t9Erj1Vvj2W9hpJ7jssqjTSJJUPDVtCk8/DW++Ca1ahTfWR44Mq+mPGgWrV0edUJKkLbPYJ7jMTBg+PIyHD4dy5aLNI0lScXfwwaHcP/10uCx/+XLo3x8aN4b77oMNG6JOKEnSpiz2CW7YMMjKgn32gTPPjDqNJEklQywWLsv/9NNwmf7OO4c59+edB3vtBZMnu0WeJClxWOwT2Lx5Yb9dgNGjwzZ3kiSp6KSmhoX15s4NC+3tsAN89RV07AgtW8KMGVEnlCTJYp/QBg4Ml/u1awdt2kSdRpKkkqtMmbA13oIFYau8cuXg/ffh8MPDz+lZs6JOKEkqySz2Ceqdd8KWOykpYcEeSZIUvYyMME1uwQLo2RNKlYIXX4R994UzzoBvvok6oSSpJLLYJ6B4HK64Ioy7dw+r9EqSpMRRs2aYLjdnDnTuHI5NmABNmsAll8CyZdHmkySVLBb7BDRxIrz3HpQvD0OHRp1GkiRtTaNG8Nhj8NFHcPTRsH493HYbNGwIQ4bAypVRJ5QklQQW+wSzdi0MGBDGfftCrVrR5pEkSf9uv/3gpZdg+nTYf39YtSq8Ob/LLnDLLeHnuyRJhcVin2DuvDPMz6tVC/r0iTqNJEnKjyOOgA8+gKeegl13hZ9/hksvDZfoP/wwZGdHnVCSVBxZ7BPI8uVw7bVhfO214VJ8SZKUXGIxOOUUmD0b7r47vFm/aBGcdVY4s//882E9HUmSCorFPoEMHx7KfdOmcPbZUaeRJEnbIy0Nzj8f5s+HESPCivqffQbHHQetW8O770adUJJUXFjsE8TChXDrrWE8ejSkpkabR5IkFYxy5cL6Od98E6bZpafDG2/AQQdB+/bw5ZdRJ5QkJTuLfYK48kpYtw6OOgrato06jSRJKmhVq4Y37+fNg3POgZQUmDoV9torbG/7/fdRJ5QkJSuLfQJ4/314/PEwJ2/06PC7JEkqnurWhfvvD3Pw27eHnBz473+hceOwI86vv0adUJKUbCz2EYvH/1z9vmtXaNYs2jySJKlo7L47TJkC77wDhx4atsS78UZo2DDMyf/jj6gTSpKShcU+YlOnwltvQdmyMGxY1GkkSVJRa9kSZsyA556DvfeGrKwwRa9Ro7Cq/vr1USeUJCU6i32E1q+Hfv3CuHdvqFMn2jySJCkasRgceyx88knY775+fViyBHr0gD33hKeecos8SdLWWewjdPfdYQGdGjWgf/+o00iSpKilpMCZZ8JXX8HNN0P16uG1wmmnwYEHwvTpUSeUJCUii31EsrJg6NAwHjoUKlaMNo8kSUoc6enQqxcsWACDB0OFCvDhh9CmDRx9NHz8cdQJJUmJxGIfkRtugF9+gSZN4Nxzo04jSZISUcWKMGRIKPiXXAJpafDyy9C8OXTuDPPnR51QkpQILPYR+O47GDs2jEeNglKlos0jSZISW40acMst4RL9M84Ic/KfeCKsrN+zJyxdGnVCSVKULPYRGDQobGnTujUcf3zUaSRJUrJo2BAeeSQssteuHWzYAHfeCbvsAlddFab6SZJKHot9Efv44/ADGcJetbFYtHkkSVLyadYMnn8+bJPXokXY83748FDwb7oJ1qyJOqEkqShZ7ItQPA5XXBHGZ5wR5sdJkiRtq8MOg3ffhcmTw7o9mZnhtcZuu8H48ZCdHXVCSVJRsNgXoeeeC++sp6eHd9UlSZK2VywGJ58Mn38O990HO+0U1vM5++xwZv/pp8PJBUlS8WWxLyIbNkC/fmF82WVQr16kcSRJUjFTqhR07x72vR81CqpUgS++gJNOgkMOgbfeijqhJKmwWOyLyP33w5w5sMMOMHBg1GkkSVJxVbYs9O0btsgbMCDcfvvtUO5POAFmz446oSSpoFnsi8DKlXDNNWE8eDBkZESbR5IkFX9VqsCIEWGv+/PPh9RUePZZ2Htv6NoVvv026oSSpIJisS8Co0bBsmXQuDFccEHUaSRJUklSuzbcfXe4LP+UU8J8+4cegl13hcsvh19+iTqhJGl7WewL2Q8/wJgxYTxyJJQuHW0eSZJUMu22Gzz1FLz/Phx+OKxbB+PGQcOGMGwY/P571AklSdvKYl/Irr4aVq+GVq2gffuo00iSpJLuwANh+nR46SXYd98/pww2agR33AHr10edUJKUXxb7QvTpp2EPWQhn7WOxSONIkiQB4TXJ0UfDhx/CY4/BLrvATz/BRRfB7rvD449DTk7UKSVJeWWxL0T9+oV5bJ06QYsWUaeRJEnaVEoKdO4MX34Jt90GNWqE1fS7dIH994dp08JrGUlSYrPYF5KXXgo/DEuXDivSSpIkJarSpcPZ+gULwnz7ihXhk0+gbVto0wZmzow6oSTpn1jsC0F2NvTpE8YXXwwNGkSbR5IkKS8qVICrroJvvgkr5pcuDa++Gubln3oqfP111AklSVtisS8E48fD7Nlh/9hBg6JOI0mSlD/VqsFNN4Uif9ZZYU7+xImwxx5h694ff4w6oSTpryz2BWzVqrASPoTfq1aNNo8kSdK2qlcPHnwwLAh8/PHhqsR77gkr6A8cCL/9FnVCSRJY7AvcmDGwZEm4/L5nz6jTSJIkbb+99oJnnoE334SDDgpb+d5wAzRsCKNHh9uSpOhY7AvQ0qUwalQY33ADpKdHm0eSJKkgHXwwvPUWTJ0Ke+4Jy5eHXYB23RXuvx82bIg6oSSVTAld7IcMGUIsFtvkV5MmTaKOtVWDB4dL8Vu0CAvMSJIkFTexGJx4Yrg8/4EHoG5dWLwYzj0X9t4bpkxxizxJKmoJXewB9txzT5YsWZL766233oo60hZ98QXcd18YjxkTfuhJkiQVV6mp0K1bWGBvzJiwrtCcOdChQ7hc//XXo04oSSVHwhf7UqVKUbNmzdxf1apVizrSFvXrBzk54YdZq1ZRp5EkSSoaZcpA795hi7xBg6BcOXjvPWjdGo49NpzZlyQVrlJRB/g38+bNo3bt2pQpU4aWLVsyYsQIdt55563ef+3ataxduzb39ooVKwBYv34969evL5SMr74a4/nnS1GqVJxhwzZQSE8jSZKUsMqVC9MSzz8frr8+hfvvT+GFF2K8+GKczp3jDBmSTYMGUaeUpKJRWN1za2LxeOLOgnrhhRf4/fff2W233ViyZAlDhw7lhx9+YPbs2VSsWHGLnzNkyBCGDh262fEJEyZQrly5As+YkwNXXHEYCxdW5vjjF3DuubML/DkkSZKSzZIl5Xn00Sa89VYdAEqVyqFt20WceupcKldeF3E6SSpcf/zxB6effjpZWVlUqlSp0J8voYv93/3222/Uq1ePm266ie7du2/xPls6Y1+3bl1++eWXQvmGPvxwjO7dS1GpUpyvvtpAgs4UkCRJisTHH8NVV6XyyithBmiFCnEuuyyHyy/PYSvnaSQp6WVmZlKrVq0iK/YJfyn+X1WuXJldd92V+fPnb/U+6enppG9hn7m0tDTS0tIKNM/q1eGSM4BBg2LUqlWwjy9JkpTsWrSAl1+G6dNhwAD48MMY112Xyt13p3LVVXDBBW4RLKn4Keju+W8SfvG8v/r9999ZsGABtWrVijoKAOPGhe1ddt4ZevWKOo0kSVLiOvJI+OADePJJaNwYfv4ZLr0UmjSBRx4J0xslSdsmoYt9nz59eP3111m0aBHvvPMOJ598MqmpqXTp0iXqaCxbBiNGhPGIEWFFWEmSJG1dLAannhq2Cb7rLqhVCxYtgv/7P9h3X3j+eUieSaKSlDgSutgvXryYLl26sNtuu3Haaaexww478N5771G9evWoozF0KKxcCc2bQ+fOUaeRJElKHmlp4RL8+fPh+ushIwM++wyOOy5sk/fee1EnlKTkklSL522LFStWkJGRUaCLFnz1FTRtCtnZ8Npr4QeQJEmStk1mJtxwA9x6K2xcA/nkk2H4cNh992izSdK2yMzMpFq1akW2eF5Cn7FPVAMGhFJ/4omWekmSpO21ww4wejTMmwfnnAMpKTBlSjiRcu658P33USeUpMRmsc+n11+HqVMhNRVGjow6jSRJUvFRty7cfz98/jm0bx8W1Lv//rDYXt++8OuvUSeUpMRksc+HnBzo0yeMzz8/rOIqSZKkgrXHHuGM/TvvwKGHhsvzb7wRGjYMl+z/8UfUCSUpsVjs8+GJJ+DDD6FiRRgyJOo0kiRJxVvLljBjBjz3HOy1F2RlwcCB4Qz+PffA+vVRJ5SkxGCxz6M1a8IPEghz7GvUiDaPJElSSRCLwbHHwiefwEMPQb168OOPYVX9pk1h4kS3yJMki30e3XorfPst7LQTXHZZ1GkkSZJKltTUsN/93LkwbhxUqwZffw2nngotWsCrr0adUJKiY7HPg8zMsN0KhN/LlYs2jyRJUkmVng6XXgrffAODB0P58jBzJhx5JLRtG87sS1JJY7HPg2HDwpyuffaBM8+MOo0kSZI2rnm0YAFcfDGkpcG0abDfftClC8yfH3VCSSo6Fvt/MW8e3H57GI8eHS4DkyRJUmLYcccwZfKrr+D008Oxxx+H3XeHiy6CpUujzSdJRcFi/y8GDoQNG6BdO2jTJuo0kiRJ2pKGDeHRR8Ol+MccE16/3XEH7LILXH01rFgRdUJJKjwW+3/w9tswaRKkpMCoUVGnkSRJ0r/ZZx944QV47TU48MCw5/1114XiP3Zs2OlIkoobi/1WxOPQp08Yd+8etlORJElScmjdGt57L5yk2W23sBhy795h/OCDkJ0ddUJJKjgW+62YODH8MChfHoYOjTqNJEmS8isWgw4dYPZsuPfesG3xd99Bt27hzP4zz4STOZKU7Cz2W7B2LQwYEMZ9+0KtWtHmkSRJ0rYrVQrOPTcsijxyJFSuHMr+iSfCIYeE6ZeSlMws9ltw551hb9Ratf68HF+SJEnJrWxZ6NcvvM7r3x/KlAml/uCDQ8mfPTvqhJK0bSz2f7N8OVx7bRhfe224FF+SJEnFR5UqcMMNYa/7884L2xk/8wzsvXe4TP/bb6NOKEn5Y7H/m+HDQ7lv2hTOPjvqNJIkSSosO+0E99wDX3wBHTuG+fYPPgi77hoW2vvll6gTSlLeWOz/YuFCuPXWMB49Orx7K0mSpOJtt93Cwsnvvw+HHw7r1oWt8XbZJWyVt2pV1Akl6Z9Z7P/iyivDf+RHHQVt20adRpIkSUXpwANh+nR48cWwav6KFXD11aHg33EHrF8fdUJJ2jKL/f/3/vvw+ONhW5TRo8PvkiRJKllisXCC56OPYMIEaNgQfvoJLroIdt89vF7MyYk6paRElp0Nb71VtIXSYk+YT7Vx9fuuXaFZs2jzSJIkKVopKdClC8yZA7fdBjVqwIIF4dgBB8DLL0edUFIimjwZ6teH9u1LFenzWuyBqVPhrbfCFijDhkWdRpIkSYmidOlwtn7BgrBjUsWK8PHHcPTR0KYNzJwZdUJJiWLyZDjlFFi8uOifu8QX+/Xrw36mEFY/rVMn2jySJElKPBUqhPn2CxbAZZeFwj99epiXf+qp8PXXUSeUFKXsbLj00nA1eBRi8XhUT100VqxYQUZGBllZWVSqVGmzj992G1xySbi8av788C6sJEmS9E8WLYLBg+Hhh8ML+dRU6N49HKtdO+p0krZXPA6//x62Qt/ar19//XP87bfw1Vd/fYQVwNZ7aEEr0cU+KyuscpqZCXfeCT16RBRSkiRJSenzz8POSs8+G26XLRvO2vXvD5UrRxpNKvHi8bBdZV7L+d9/bdiwPc9usS9Q/1TsBw6EG26AJk3Cf8qlinZ9A0mSJBUTb74Zyvy774bbVaqE15oXXxzKvqRtE4/DH39sWzFfvnz7t6ksXTr8e/77r6pVN729eDFcddVfP9NiX6C2Vuy/+w523RXWroWnn4YTTogwpCRJkpJePA7PPBMK/ZdfhmN16sDQoXDWWZ5EUskVj8Pq1dteztet277nT0vLWznf0sfLls3bVujZ2WE1/B9+2DjP3mJfoLZW7P/v/+CRR6B1a3j1VfetlyRJUsHIzg5z76+5Br7/PhzbfXe4/no46SRfdyp5ba2c/1sx//XX7S/npUptWzGvUgXKlSuaf3cbV8UHiMct9gVqS8X+44+hefPw8Q8//HMsSZIkFZQ1a+D220Oh//XXcKxlyzAV9NBDo82mkmvNmm0r5suXh6udt0dq6raX8/Llk+NNscmTwzobixdb7AvU34t9PA5HHAEzZsAZZ4Sz9pIkSVJhycqC0aNh7NgwVxjg2GNhxAjYe+9osyk5rV2bv3L+14+tWbN9z52aGhaGzGs5/+vHKlRIjnK+vbKz4dlnf6V9+x0s9gXl78X+2WfDfPr0dJg7F+rVizqhJEmSSoIlS2DYMLjnnvDCPxYLJ5quvRYaNIg6nYraunXbdtZ8+fJwSfz2SEnZtJzn9ax5lSphe/CSUM63V2ZmJtWqVbPYF5S/Fvty5Sqx115hf8H+/cNlUJIkSVJRmjcvrJ795JPhdloaXHhhOFa9erTZlD/r12/bWfPly/+8emNbxWLbV85TUgrkW6CtsNgXsL8W+8ceq0SPHrDDDrBgAWRkRJ1OkiRJJdVHH8GAAfDKK+F2hQrQpw/07h2Kl4rG+vXw22/5P2u+fHnYI317xGKhk+S3mFepApUqWc4TmcW+gG0s9osXZ7HffpVYtgxuuQUuuSTqZJIkSVIo9gMGhKIP4az91VfDBReEPbT17zZsCOU8v8V8+XL4/fftf/5tLecZGZbz4spiX8A2Fvu+fbMYPboSjRvD7Nn+JylJkqTEkZMDEyfCoEEwf3441qBBmJPfpUvJKH/Z2f9ezrf2sZUrt//5K1XKfzHfWM5TU7f/+VW8WOwL2MZin5aWxfr1lZg8GU4+OepUkiRJ0ubWr4f774ehQ2Hp0nCsWbOwgv4xx4RLt7Oz4c03w2J8tWrBIYckTrHcWM7ze9Z8+XJYsWL7n79ixW0v56VKbf/zSxsVdbEvMX99168PZ+lzcqJOIkmSJG1ZWhr06AH/939w880wciR8+mnYHu+ww0K5v/12WLz4z8+pUyfct0OHgsmQnR226MtvMV++PHze9qpYMf/FvEqVsJCc5VwlVYk5Yw9ZQCVisXCZU0H9xydJkiQVlszMcLb+ttvC3uVbsnHrsb++xs3J2Xo5/7d56FlZsL0NoUKFbS/naWnb99xSIvBS/AK2pWJfpw4sXJg4lyxJkiRJ/2ThQmja9J+3SEtLC69zf/st/NreV/nly+e/mG8s565npZLOS/ELWTwO338f5iW1bh11GkmSJOnfffvtv+97vn59eAPgr8qV27ZyXqWK5VxKJiWu2G+0ZEnUCSRJkqS8yetr1yFD4LTT/izn6emFGktSgiixxb5WragTSJIkSXmT19euhx0Gu+9euFkkJZ4SsCPmpmIxqFs3bAsiSZIkJYNDDgnz5zculPd3vsaVSrYSVew3/kc4bpwL50mSJCl5pKaGLe1g83Lva1xJJarY16njVneSJElKTh06hNeyO+206XFf40oqMXPsn30WjjnGdzElSZKUvDp0gJNOCjs8LVkS5t4fcoivcaWSrsQUe//DkyRJUnGQmuq2zZI2VaIuxZckSZIkqbix2EuSJEmSlMQs9pIkSZIkJTGLvSRJkiRJScxiL0mSJElSErPYS5IkSZKUxCz2kiRJkiQlMYu9JEmSJElJzGIvSZIkSVISs9hLkiRJkpTELPaSJEmSJCUxi70kSZIkSUnMYi9JkiRJUhIrFXWAwhaPxwFYsWJFxEkkSZIkSSXBypUrgT/7aGEr9sV+4ze0bt26ESeRJEmSJJUkmZmZZGRkFPrzxOJF9RZCRHJycth111356KOPiMViUceR9DcHHHAAM2fOjDqGpK3w36iUuPz3KSWurKwsdt55Z5YvX07lypUL/fmK/Rn7lJQUSpcuXSTvkkjKv9TUVCpVqhR1DElb4b9RKXH571NKfCkpRbOsXYlYPO+iiy6KOoKkrfDfp5TY/DcqJS7/fUraqNhfii9JkiRJUlFasWIFGRkZZGVlFcmVNSXijL0kSZIkSUUlPT2dwYMHk56eXiTP5xl7SZIkSZKSmGfsJUmSJElKYhZ7SZIkSZKSWNIW+59//pkLL7yQnXfemfT0dGrWrEnbtm15++23o44m6f/7/vvvOeecc6hduzalS5emXr16XHrppWRmZubp82fMmEEsFuO3334r3KBSCdKtWzdisRg33HDDJsf/97//EYvFIkolCf789xmLxUhLS2PHHXfkqKOO4r///S85OTlRx5OUwJK22Hfs2JFPPvmEBx98kK+//pqnn36a1q1b57kwSCpc33zzDfvvvz/z5s3jscceY/78+dx1111Mnz6dli1b8uuvv0YdUSqxypQpw8iRI1m+fHnUUST9zTHHHMOSJUtYtGgRL7zwAocffjiXXnopxx9/PBs2bIg6nqQElZTF/rfffuPNN99k5MiRHH744dSrV48DDzyQgQMHcuKJJ+be59xzz6V69epUqlSJI444gk8//TT3MYYMGcI+++zD3XffTd26dSlXrhynnXYaWVlZUX1ZUrFy0UUXUbp0aaZNm8Zhhx3GzjvvTLt27XjllVf44YcfGDRoEABr166lf//+1K1bl/T0dBo1asT999/PokWLOPzwwwGoUqUKsViMbt26RfgVScVHmzZtqFmzJiNGjNjqfSZNmsSee+5Jeno69evXZ8yYMbkfu/LKK2nRosVmn9OsWTOuvfbaQskslRQbr0Tdaaed2G+//bjyyiuZOnUqL7zwAuPHjwf+/XUuwDPPPMMBBxxAmTJlqFatGieffHIEX41UfHXr1o327dtHHSNXUhb7ChUqUKFCBf73v/+xdu3aLd7n1FNPZdmyZbzwwgt89NFH7Lfffhx55JGbnCWcP38+Tz75JM888wwvvvgin3zyCT179iyqL0Mqtn799VdeeuklevbsSdmyZTf5WM2aNTnjjDN44okniMfjnHXWWTz22GPccsstzJkzh7vvvpsKFSpQt25dJk2aBMDcuXNZsmQJN998cxRfjlTspKamcv3113PrrbeyePHizT7+0Ucfcdppp9G5c2c+//xzhgwZwtVXX51bKs444ww++OADFixYkPs5X3zxBZ999hmnn356UX0ZUolxxBFH0KxZMyZPngz8++vc5557jpNPPpljjz2WTz75hOnTp3PggQdG+SVIKmzxJDVx4sR4lSpV4mXKlIkfdNBB8YEDB8Y//fTTeDwej7/55pvxSpUqxdesWbPJ5+yyyy7xu+++Ox6Px+ODBw+Op6amxhcvXpz78RdeeCGekpISX7JkSdF9IVIx9N5778WB+JQpU7b48ZtuuikOxN9///04EH/55Ze3eL/XXnstDsSXL19eeGGlEqZr167xk046KR6Px+P/+c9/4uecc048Ho/Hp0yZEt/4suD000+PH3XUUZt8Xt++feN77LFH7u1mzZrFr7322tzbAwcOjLdo0aKQ00vF21//ff5dp06d4rvvvnueXue2bNkyfsYZZxR2XKlE++u/1xdeeCHeqlWreEZGRrxq1arx4447Lj5//vzc+y5cuDAOxCdNmhRv3bp1vGzZsvG99947/s477xRYnqQ8Yw9hjv2PP/7I008/zTHHHMOMGTPYb7/9GD9+PJ9++im///47O+ywQ+7Z/QoVKrBw4cJNzi7svPPO7LTTTrm3W7ZsSU5ODnPnzo3iS5KKnXg8/o8fX7RoEampqRx22GFFlEjSX40cOZIHH3yQOXPmbHJ8zpw5tGrVapNjrVq1Yt68eWRnZwPhrP2ECROA8G/9scce44wzziia4FIJFI/HicVieXqdO2vWLI488siIE0slx6pVq+jduzcffvgh06dPJyUlhZNPPnmzRS8HDRpEnz59mDVrFrvuuitdunQpsLUzShXIo0SkTJkyHHXUURx11FFcffXVnHvuuQwePJiePXtSq1YtZsyYsdnnVK5cuchzSiVNo0aNiMVizJkzZ4tz+ubMmUOVKlU2u0xfUtE69NBDadu2LQMHDsz3GhZdunShf//+fPzxx6xevZrvv/+eTp06FU5QScyZM4cGDRrw+++//+vrXH++SkWrY8eOm9z+73//S/Xq1fnyyy9p2rRp7vE+ffpw3HHHATB06FD23HNP5s+fT5MmTbY7Q9Kesd+SPfbYg1WrVrHffvuxdOlSSpUqRaNGjTb5Va1atdz7f/fdd/z444+5t9977z1SUlLYbbfdoogvFRs77LADRx11FHfccQerV6/e5GNLly7l0UcfpVOnTuy1117k5OTw+uuvb/FxSpcuDZB7hlBSwbvhhht45plnePfdd3OP7b777pttH/v222+z6667kpqaCkCdOnU47LDDePTRR3n00Uc56qijqFGjRpFml0qKV199lc8//5yOHTvm6XXu3nvvzfTp0yNOLZUc8+bNo0uXLjRs2JBKlSpRv359IPTNv9p7771zx7Vq1QJg2bJlBZIhKYt9ZmYmRxxxBI888gifffYZCxcu5KmnnmLUqFGcdNJJtGnThpYtW9K+fXumTZvGokWLeOeddxg0aBAffvhh7uOUKVOGrl278umnn/Lmm2/Sq1cvTjvtNGrWrBnhVycVD7fddhtr166lbdu2vPHGG3z//ff8v/buP6bKKo7j+OfC5QoooCSIJogLK8mKzFpmK0OSzBokLDNMCLdWA1pINZpuSA37wVqrGf7hAtwYUSJS2daPoaCiGSqYKFErkK1EYIDhD34MTn80n3lH+SuKXXq//rqcc57zfB82xv3sOc95vvzySz300EO6/vrrlZOTo9DQUCUmJio5OVnl5eVqampSZWWlPvnkE0nSjBkzZLPZtGPHDrW3t+vMmTOjfFXA2HPrrbcqISFB77//vtWWkZGhiooKvf766/rxxx+1ZcsWbdy4US+99JLTsQkJCSopKdHWrVtZhg+MkL6+PrW2turXX3/V4cOHtWHDBsXExOjRRx/VqlWrruh7blZWlj766CNlZWWpoaFBR48e1VtvvTXKVwaMXY899pg6Ozu1efNmHThwQAcOHJAk9ff3O43z8PCwPttsNkkatlz/mo3Y0/r/od7eXpOZmWnmzp1r/Pz8jLe3t7npppvMunXrzLlz54wxxvz+++8mLS3NTJs2zXh4eJjg4GCTkJBgWlpajDF/bp53++23m7y8PDNt2jTj6elp4uPjTWdn52heGjCmNDc3m8TERDNlyhTr7zAtLc10dHRYY86fP2/S09PN1KlTjcPhMGFhYSY/P9/qf+2110xQUJCx2WwmMTFxFK4CGFv+anOupqYm43A4zMVfC0pLS014eLjx8PAwISEhJjc3d9hcXV1dZty4ccbb29v09PT826UDY15iYqKRZCQZu91uAgICTFRUlMnPzzeDg4PWuMt9zzXGmG3btpmIiAjjcDjM5MmTzbJly0bjkoAx68L/046ODiPJ7N692+rbs2eP00bSFzbPq62ttcZ0dXUZSWbXrl0jUo/NmMvsbjVGrV+/XuXl5aqrqxvtUgAAAAAALiQpKUnd3d0qKytTYGCglixZoqysLLW0tCgzM1M1NTXavn27YmNj1dzcrJkzZ6q2tlYRERGSpO7ubk2aNEm7du3SwoUL/3E9LrkUHwAAAACA0TI0NCS73S43NzeVlJTo0KFDmjNnjtLT05Wbm/uf1+PSu+IDAAAAAPBfa2trU1hYmCQpKipKx48fd+q/eGF8aGjosNdAT5w48bKvhr4a/9ul+AAAAAAAXI2uri5VV1crPj5eJSUlio2NHe2SJHHHHgAAAACAK5KcnKyamhplZGQoJiZmtMuxcMceAAAAAAAXxuZ5AAAAAAC4MII9AAAAAAAujGAPAAAAAMBF3njjDd11113y8fFRYGCgYmNj1djY6DSmt7dXKSkpuu666zRhwgTFxcXp1KlTVv+RI0e0YsUKBQcHy8vLS7Nnz9Z77733t+esrq6W3W633nV/NQj2AAAAAABcpKqqSikpKfr222/1zTffaGBgQIsXL9bZs2etMenp6fr888+1detWVVVV6bffftOyZcus/kOHDikwMFBFRUU6duyY1q5dq1dffVUbN24cdr7u7m6tWrVKixYtuqZ62TwPAAAAAIBLaG9vV2BgoKqqqnT//ffr9OnTCggIUHFxseLj4yVJP/zwg2bPnq39+/frnnvu+ct5UlJS1NDQoJ07dzq1P/nkk5o1a5bc3d1VXl6uurq6q6qPO/YAAAAAAFzC6dOnJUn+/v6S/rwbPzAwoKioKGvMzTffrJCQEO3fv/+S81yY44KCggL98ssvysrKuub6eI89AAAAAAB/Y2hoSC+++KIWLFigOXPmSJJaW1vlcDg0ceJEp7FTpkxRa2vrX86zb98+ffzxx/riiy+stp9++kmZmZnas2eP7PZrj+cEewAAAAAA/kZKSorq6+u1d+/ea56jvr5eMTExysrK0uLFiyVJg4ODeuqpp5Sdna0bb7zxH9VIsAcAAAAA4C+kpqZqx44d2r17t6ZPn261BwUFqb+/X93d3U537U+dOqWgoCCnOY4fP65Fixbp2Wef1bp166z2np4eHTx4ULW1tUpNTZX05+oAY4zsdru+/vprRUZGXlGdBHsAAAAAAC5ijFFaWpq2b9+uyspKzZw506n/zjvvlIeHhyoqKhQXFydJamxsVEtLi+bPn2+NO3bsmCIjI5WYmKicnBynOXx9fXX06FGntry8PO3cuVOlpaXDznkpBHsAAAAAAC6SkpKi4uJiffrpp/Lx8bGem/fz85OXl5f8/Py0evVqrVmzRv7+/vL19VVaWprmz59v7YhfX1+vyMhIRUdHa82aNdYc7u7uCggIkJubm/XM/gWBgYHy9PQc1n45BHsAAAAAAC6yadMmSdLChQud2gsKCpSUlCRJevfdd+Xm5qa4uDj19fUpOjpaeXl51tjS0lK1t7erqKhIRUVFVvuMGTPU3Nw8ovXyHnsAAAAAAFwY77EHAAAAAMCFEewBAAAAAHBhBHsAAAAAAFwYwR4AAAAAABdGsAcAAAAAwIUR7AEAAAAAcGEEewAAAAAAXBjBHgAAAAAAF0awBwAAI85ms6m8vHy0ywAA4H+BYA8AwBiSlJQkm82m5557blhfSkqKbDabkpKSRux869evV0RExIjNBwAArh7BHgCAMSY4OFglJSU6f/681dbb26vi4mKFhISMYmUAAODfQLAHAGCMmTt3roKDg1VWVma1lZWVKSQkRHfccYfV1tfXpxdeeEGBgYHy9PTUfffdp5qaGqu/srJSNptNFRUVmjdvnry9vXXvvfeqsbFRklRYWKjs7GwdOXJENptNNptNhYWF1vEdHR16/PHH5e3trVmzZumzzz779y8eAID/IYI9AABjUHJysgoKCqyf8/Pz9cwzzziNeeWVV7Rt2zZt2bJFhw8fVlhYmKKjo9XZ2ek0bu3atXrnnXd08OBB2e12JScnS5KWL1+ujIwM3XLLLTp58qROnjyp5cuXW8dlZ2friSee0Pfff69HHnlECQkJw+YGAAD/HMEeAIAxaOXKldq7d69OnDihEydOqLq6WitXrrT6z549q02bNik3N1dLlixReHi4Nm/eLC8vL3344YdOc+Xk5OiBBx5QeHi4MjMztW/fPvX29srLy0sTJkyQ3W5XUFCQgoKC5OXlZR2XlJSkFStWKCwsTBs2bNCZM2f03Xff/We/AwAA/i/so10AAAAYeQEBAVq6dKkKCwtljNHSpUs1efJkq//nn3/WwMCAFixYYLV5eHjo7rvvVkNDg9Nct912m/V56tSpkqS2trbLPq9/8XHjx4+Xr6+v2tra/tF1AQCA4Qj2AACMUcnJyUpNTZUkffDBB9c8j4eHh/XZZrNJkoaGhq7quAvHXslxAADg6rAUHwCAMerhhx9Wf3+/BgYGFB0d7dR3ww03yOFwqLq62mobGBhQTU2NwsPDr/gcDodDg4ODI1YzAAC4etyxBwBgjHJ3d7eW1bu7uzv1jR8/Xs8//7xefvll+fv7KyQkRG+//bbOnTun1atXX/E5QkND1dTUpLq6Ok2fPl0+Pj4aN27ciF4HAAC4NII9AABjmK+v79/2vfnmmxoaGtLTTz+tnp4ezZs3T1999ZUmTZp0xfPHxcWprKxMDz74oLq7u1VQUKCkpKQRqBwAAFwpmzHGjHYRAAAAAADg2vCMPQAAAAAALoxgDwAAAACACyPYAwAAAADgwgj2AAAAAAC4MII9AAAAAAAujGAPAAAAAIALI9gDAAAAAODCCPYAAAAAALgwgj0AAAAAAC6MYA8AAAAAgAsj2AMAAAAA4ML+AOnBZwIXlFrZAAAAAElFTkSuQmCC",
"text/plain": [
"\n",
"\n",
"\n",
" \n",
"
\n",
"
\n",
" \n",
" | \n", " | repository | \n", "id | \n", "node_id | \n", "url | \n", "repository_url | \n", "labels_url | \n", "comments_url | \n", "events_url | \n", "html_url | \n", "number | \n", "... | \n", "pull_request | \n", "closed_at | \n", "created_at | \n", "updated_at | \n", "author_association | \n", "draft | \n", "reactions | \n", "timeline_url | \n", "performed_via_github_app | \n", "state_reason | \n", "
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | \n", "airbytehq/quickstarts | \n", "1917441405 | \n", "PR_kwDOKQqr2s5bcLCL | \n", "https://api.github.com/repos/airbytehq/quickst... | \n", "https://api.github.com/repos/airbytehq/quickst... | \n", "https://api.github.com/repos/airbytehq/quickst... | \n", "https://api.github.com/repos/airbytehq/quickst... | \n", "https://api.github.com/repos/airbytehq/quickst... | \n", "https://github.com/airbytehq/quickstarts/pull/35 | \n", "35 | \n", "... | \n", "{'diff_url': https://github.com/airbytehq/quic... | \n", "2023-09-28T12:35:12Z | \n", "2023-09-28 12:32:19 | \n", "2023-09-28 12:35:12 | \n", "MEMBER | \n", "false | \n", "{'+1': 0, '-1': 0, 'confused': 0, 'eyes': 0, '... | \n", "https://api.github.com/repos/airbytehq/quickst... | \n", "None | \n", "None | \n", "
| 1 | \n", "airbytehq/quickstarts | \n", "1917507008 | \n", "PR_kwDOKQqr2s5bcZuy | \n", "https://api.github.com/repos/airbytehq/quickst... | \n", "https://api.github.com/repos/airbytehq/quickst... | \n", "https://api.github.com/repos/airbytehq/quickst... | \n", "https://api.github.com/repos/airbytehq/quickst... | \n", "https://api.github.com/repos/airbytehq/quickst... | \n", "https://github.com/airbytehq/quickstarts/pull/36 | \n", "36 | \n", "... | \n", "{'diff_url': https://github.com/airbytehq/quic... | \n", "2023-09-28T13:02:56Z | \n", "2023-09-28 13:01:48 | \n", "2023-09-28 13:02:56 | \n", "MEMBER | \n", "false | \n", "{'+1': 0, '-1': 0, 'confused': 0, 'eyes': 0, '... | \n", "https://api.github.com/repos/airbytehq/quickst... | \n", "None | \n", "None | \n", "
| 2 | \n", "airbytehq/quickstarts | \n", "1930249843 | \n", "PR_kwDOKQqr2s5cHR4v | \n", "https://api.github.com/repos/airbytehq/quickst... | \n", "https://api.github.com/repos/airbytehq/quickst... | \n", "https://api.github.com/repos/airbytehq/quickst... | \n", "https://api.github.com/repos/airbytehq/quickst... | \n", "https://api.github.com/repos/airbytehq/quickst... | \n", "https://github.com/airbytehq/quickstarts/pull/38 | \n", "38 | \n", "... | \n", "{'diff_url': https://github.com/airbytehq/quic... | \n", "None | \n", "2023-10-06 14:04:27 | \n", "2023-10-12 15:20:21 | \n", "FIRST_TIME_CONTRIBUTOR | \n", "false | \n", "{'+1': 0, '-1': 0, 'confused': 0, 'eyes': 0, '... | \n", "https://api.github.com/repos/airbytehq/quickst... | \n", "None | \n", "None | \n", "
3 rows × 32 columns
\n", ""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
"\n",
"# Assuming 'pull_requests' dataframe is available and has a 'created_at' column in datetime format\n",
"pull_requests[\"created_at\"] = pd.to_datetime(pull_requests[\"created_at\"])\n",
"pr_trend = pull_requests.resample(\"M\", on=\"created_at\").size()\n",
"\n",
"plt.figure(figsize=(12, 6))\n",
"pr_trend.plot(kind=\"line\", color=\"blue\", marker=\"o\")\n",
"plt.title(\"Monthly Pull Requests Trend\")\n",
"plt.xlabel(\"Month\")\n",
"plt.ylabel(\"Number of Pull Requests\")\n",
"plt.grid(True)\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "OK5jXo0nJcnW"
},
"source": [
"#### Open vs Closed Issues\n",
"\n",
"Compare the number of open versus closed issues.\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 564
},
"id": "WFtnoUE5Jhus",
"outputId": "4ea9b4a2-1b6d-4f69-8eee-4d7df78a0109"
},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
"
"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Assuming 'issues' dataframe has a 'state' column\n",
"issue_status_count = issues[\"state\"].value_counts()\n",
"\n",
"plt.figure(figsize=(8, 6))\n",
"issue_status_count.plot(kind=\"bar\", color=[\"blue\", \"green\"])\n",
"plt.title(\"Open vs Closed Issues\")\n",
"plt.xlabel(\"State\")\n",
"plt.ylabel(\"Number of Issues\")\n",
"plt.xticks(rotation=0)\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "ETo6-qu4JsI_"
},
"source": [
"#### Star Growth Over Time\n",
"\n",
"Plot the cumulative count of stars over time.\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 580
},
"id": "NC7PthQMJxpd",
"outputId": "654b3bc8-9eaa-469a-f2d1-bdf139e5e04b"
},
"outputs": [
{
"data": {
"image/png": 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YtGhRheXffPMNN954IxA/89+2bdsKo+DfcsstNZ6re/fuAAnvVVZW9qujn0P87GIkEkk46//tt9/y5JNPVli3efPm1Sq/w4YNY8GCBfzf//0fH330UcLl1gDHHnssycnJTJw4MeHKBIif3VyyZMlG9//yyy9X2A7gueeeAxIvb68qe3JycoV9/POf/9ykqyA2ZujQofTp04e///3v5Y+H21Bl2SszcOBA0tLSmDRpUsI2d955J4WFhRx++OHl8zIyMthtt9148MEH+e677xLO5K9Zs4ZJkybRvXv3hEdArntefE2P3t6sWTPOPvtsZs+ezQUXXFBh+bPPPsvdd9/NoEGD2GOPPRKW1fb3Tn3UGI9Jkuo7z+RLUhPXvXt3pkyZwrBhw9h222058cQT6du3LyUlJbz11ls88sgj5c9fh/hAeldddRV/+MMf6NevH6+99hpfffVVjefabrvt2GOPPRg/fjxLly4lJyeHqVOnlg/otTGHH3441113HYcccggnnHACixcv5uabb6ZHjx7ll1Kvs+uuu/Lf//6X6667jvz8fLp168buu+9e5b4PO+wwWrZsydlnn01ycjKDBw9OWN69e3cuu+wyxo8fz7fffsvRRx9Ny5YtmTt3Lk888QSnnnoqZ599dpX7P/3001m9ejXHHHMMvXv3Lv/v8NBDD9G1a9eEgdaqyn7EEUdw3333kZ2dTZ8+fZgxYwb//e9/adOmza/+3W1MamoqTzzxBIMGDWKvvfbi2GOPZe+996Z58+bMnz+fp556iu+++y6hoFelXbt2jB8/nokTJ3LIIYdw5JFHMnv2bG655RZ22223Co/p23vvvbnqqqvIzs5m++23ByA3N5dtttmG2bNnJ3yPQvxS9j59+vDQQw/Rq1cvcnJy6Nu3L3379t2ivwOA888/nw8++ICrr76aGTNmMHjwYDIzM3njjTe4//772XbbbbnnnnsqbFfb3zv1UWM8Jkmq9+pwJH9JUj321VdfBaecckrQtWvXIC0tLWjZsmUwYMCA4J///Gewdu3a8vVWr14dnHzyyUF2dnbQsmXLYOjQocHixYurfITejz/+mPA+I0eODJo3b17h/ffdd99gu+22S5j3zTffBAMHDgzS09OD9u3bB3/729+CadOmbdIj9O68886gZ8+eQXp6etC7d+9g8uTJ5ZnW9+WXXwb77LNPkJmZGQDlj6Sr6hF+QRAEI0aMCIBg4MCBVf59PvbYY8Fee+0VNG/ePGjevHnQu3fvYMyYMcHs2bOr3CYIguD5558PRo8eHfTu3Tto0aJFkJaWFvTo0SM4/fTTg0WLFm1S9mXLlgUnnXRS0LZt26BFixbBoEGDgi+//DLo0qVLpY8pnDlz5kYzbWj58uXBpZdeGuy8887lGTt16hQMGTIkePrppxPW/bX3uOmmm4LevXsHqampQfv27YM///nP5Y/BW9+zzz4bABUe2faHP/whAII777yzwjZvvfVWsOuuuwZpaWkJ359VfQ9W9v1RlbKysmDy5MnBgAEDgqysrCAjIyPYbrvtgokTJwYrV66scrua+t6p7POyqTbnEXqPPPJIwnpV/Xet6nO/uZ8HSVL1RYJgE6+rkyRJkiRJ9Zr35EuSJEmS1EhY8iVJkiRJaiQs+ZIkSZIkNRKWfEmSJEmSGglLviRJkiRJjYQlX5IkSZKkRiIl7AD1QSwWY8GCBbRs2ZJIJBJ2HEmSJElSIxcEAStWrCA/P5+kpJo7/27JBxYsWECnTp3CjiFJkiRJamK+//57ttpqqxrbnyUfaNmyJRD/y83Kygo5jSRJkrTlotEoL774IgcffDCpqalhx5G0gaKiIjp16lTeR2uKJR/KL9HPysqy5EuSJKlRiEajNGvWjKysLEu+VI/V9C3jDrwnSZIkSVIjYcmXJEmSJKmRsORLkiRJktRIWPIlSZIkSWokLPmSJEmSJDUSlnxJkiRJkhoJS74kSZIkSY2EJV+SJEmSpEbCki9JkiRJUiNhyZckSZIkqZEIteS/9tpr/Pa3vyU/P59IJMKTTz5ZviwajXLeeeex/fbb07x5c/Lz8znxxBNZsGBBwj6WLl3KiBEjyMrKolWrVpx88smsXLmyjo9EkiRJkqTwhVryV61axY477sjNN99cYdnq1at5//33ueiii3j//fd5/PHHmT17NkceeWTCeiNGjOCzzz5j2rRpPPPMM7z22muceuqpdXUIkiRJkiTVG5EgCIKwQwBEIhGeeOIJjj766CrXmTlzJr/5zW+YN28enTt35osvvqBPnz7MnDmTfv36AfDCCy9w2GGH8cMPP5Cfn79J711UVER2djaFhYVkZWXVxOFIkiRJoYpGozz33HMcdthhpKamhh1H0gZqq4em1Nie6kBhYSGRSIRWrVoBMGPGDFq1alVe8AEGDhxIUlIS77zzDsccc0yl+ykuLqa4uLj8dVFRERD/QRiNRmvvACRJkqQ6su73Wn+/leqn2vpsNpiSv3btWs477zyOP/748v/LUVBQQG5ubsJ6KSkp5OTkUFBQUOW+rrzySiZOnFhh/osvvkizZs1qNrgkSZIUomnTpoUdQVIlUks/rZX9NoiSH41GGTp0KEEQcOutt27x/saPH8+4cePKXxcVFdGpUycOPvhgL9eXJElSoxCNRpk2bRoHHXSQl+tL9dDKuc/Wyn7rfclfV/DnzZvHSy+9lFDC8/LyWLx4ccL6paWlLF26lLy8vCr3mZ6eTnp6eoX5qamp/gCUJElSo+LvuFI9EQSwdhYUfwSlP5C84v5aeZt6XfLXFfyvv/6al19+mTZt2iQs79+/P8uXL2fWrFnsuuuuALz00kvEYjF23333MCJLkiRJkpqqIAalC6BkDkTn/PznN7+8jq33uPdaGgI/1JK/cuVK5syZU/567ty5fPjhh+Tk5NChQweGDBnC+++/zzPPPENZWVn5ffY5OTmkpaWx7bbbcsghh3DKKadw2223EY1GGTt2LMOHD9/kkfUlSZIkSdpkQRlEv6tY4tdNB2tDjRdqyX/vvffYf//9y1+vu09+5MiRXHLJJTz11FMA7LTTTgnbvfzyy+y3334APPDAA4wdO5YDDzyQpKQkBg8ezKRJk+okvyRJkiSpEQpKIPotlHyzwVn5OVAyF9jYyPjJkNoN0npAavf4n2k9ILVHfH7JZ/DtrrUWPdSSv99++xEEVV+jsLFl6+Tk5DBlypSajCVJkiRJauxiayA6N7HEr5uOzgNiVW8bSYPUrePFff0Sn9YDUjtDZFPGwYhQG9fs1+t78iVJkiRJ2myxlVWcjZ8DpT9sfNtIM0jrXnmRT+kIkeTNy5ScC8l5kJ4PvL95+9gIS74kSZIkqeEqW1752fiSb6CsYOPbJmVVXuLTesSLeCRS83lTt4Lu38KKtUCrGt+9JV+SJEmSVH8FAZT9VHmJj86BsiUb3z65zXqX0q9f6LtDctvaKfK/JikdIsW1smtLviRJkiQpXEEApQs3uKR+/UfPFW18++S8Su6N7x6/3D65dd0cQz1hyZckSZIk1b6gLH4ffIXHzv1c6IPVG98+pdN6JX79e+W7Q1KLujmGBsCSL0mSJEmqGUE0PjL9ukvpE0as/1/80XRVSoLUromPnis/K98NkjLr6igaNEu+JEmSJGnTxYrjj56rcI/8nPiz5SnbyMapkLZ1xRKf1gNSu8QfTactYsmXJEmSJCWKrYKS/1V87FzJHCj9no0+3z2S8UuJTzgb3wNSO23+o+e0SSz5kiRJktQUlRVWcm/8z3+WLtz4tkktqhixvgekdIBIUt0cgyqw5EuSJElSYxQEEFtascSvG/iu7MeNb5/UCtJ6Vjwbn9YdknPDefScfpUlX5IkSZIaqiCAskWVPz++ZA7Elm98++Tcyp8fn9YDknPq5BBUsyz5kiRJklSfBTEonV95iS+ZA8GqjW+f0rHyEp/aHZKz6uYYVGcs+ZIkSZIUtqAUot/9UuIT7pH/BoLijWwciY9MX+H58T0gdWtIalZnh6HwWfIlSZIkqS4EJfFHzFV6j/xcoHQjG6fEnxVfocT3iD9bPim9bo5B9Z4lX5IkSZJqSmwNRP9X+Yj10e+AWNXbRtLjZ94rG7E+tTNErG/6dX6XSJIkSVJ1lK345dFzCWfj58Tvnd+YSLPKS3xaj/i98z56TlvIki9JkiRJGypbVvlj50rmxEez35ikrPij5yo8R747JOf56DnVKku+JEmSpKYnCOLPid9wgLt1hT62dOPbJ7et5Gz8z/fLJ7exyCs0lnxJkiRJjVMQg9KFlZf46ByIrdj49ikdNjgbv/6j51rVySFI1WXJlyRJktRwBWUQ/b7iPfLrHkUXrNnIxhFI6VTJ8+N7QNrWkNSizg5DqimWfEmSJEn1WxCF6LzKB7qLzo0/mq5KyfFHzK3/6LnyP7tBUkZdHYVUJyz5kiRJksIXWxsv7BXOxs+JF3zKqt42khYv7JWNWJ/aBSKpdXYYUtgs+ZIkSZLqRmxV/BL6Dc/Gl8yB0h+AoOptI5mVn41P6wEpW0Ekuc4OQ6rPLPmSJEmSak5ZYSUl/udiX7pw49smtazibHyP+CB4jlgv/SpLviRJkqRNFwRQtqTy58dH50DZTxvfPimnkhL/84B3ye0s8tIWsuRLkiRJjVBk7Sz23OoiImvbQ+oe1ds4CKCsoPISXzIHYoUb3z65/QaX1Hf/5RF0yTmbf1CSfpUlX5IkSWqEIivvp13zTyhb+QC0rKTkB7H4ffBV3SMfrN74G6RsVclj59Y9Q75l7RyUpF9lyZckSZIai+i8ny+Xj5C08mEAklZMgYze8WfJl/4EscU/l/n/QVC8kZ0lxUemT+2ROOBdWg9I3RqSMuvkkCRVjyVfkiRJaujKlkLJbJi3Z4VFkdhSWDS2ig1T4o+e2/Ae+bQe8WfLR9JqNbakmmfJlyRJkhqCoCx+pr7ky/hX8Ze/TJf9WGH1isPXRaDZIdDyt78U+tROELESSI2Jn2hJkiSpPomtjJ+VX7/El8yGkq82fnl9ylaQ1js+ev3Pl+on6PoeZOxSe7kl1QuWfEmSJKmuBQGUzt/grPzs+HTpD1VvF0mHtF7xMp+2TfzP9N6Q2uuXwe7Wvg8rHyYgiQix8j8lNQ2WfEmSJKm2xNZCydcbnJH/+c/Yyqq3S879pcin9/55und8ILxI8sbfMzkXkvMIUjry0bzfsEOXd4mUzo/Pl9ToWfIlSZKkLREE8Xvi15X39S+zj84Fgio2TP55kLttfinx6T8X+y15lnzqVtD9W8pKI8z7+Hm263gDSSkBJKVv/j4lNRiWfEmSJGlTBFEo+d96Z+PXu9Q+tqzq7ZKyNyjx6762rr3R65PSIRKNT0cikOQo+VJTYcmXJEmS1le2/JcinzD43RygtIqNIvFL6dN6Vyz0ybnxoi1JdcCSL0mSpKYniEH0u4pn5Eu+hLJFVW8XaVbxPvm03pDWE5Iy6y6/JFXBki9JkqTGK7Yq/ui5hLPys+Nfwdqqt0vJr+Ss/Dbxx9RFkuouvyRVkyVfkiRJDVsQQOnCxLPy6wbAK/2u6u0iaZDac72z8tv88mdyVt3ll6QaZMmXJElSwxArhuicxGfKr/uKrah6u+S2FZ8rn9YbUrtCxF+HJTUu/lSTJElS/VL6U+Iz5dddZh/9HxCrYqMkSN26khHst4GUtnWZXpJCZcmXJElS3QtK48+Q3/C58iVfQtmSqrdLaln5CPap3X0OvCRhyZckSVJtKitKvLS+vNB/DUSr3i6lc8UR7NN7Q3Kej6OTpI2w5EuSJGnLBDEo/aGS58p/GR8QryqRjPUGu1v/MvuekNS87vJLUiNiyZckSdKmia2On4GvcFb+KwhWV71dcl7F++TTekNqZx9HJ0k1zJIvSZKkXwQBlC2qeEa+ZDZE5wFBFRumxM/Ar/9M+fLH0bWqwwOQpKbNki9JktQUBSVQ8s16Z+XXu28+Vlj1dkmtIX3bio+kS+0GkdS6yy9JqpQlX5IkqTErW7reWfn1ny3/DVBWxUZJ8dK+4XPl03rHnznvwHeSVG9Z8iVJkhq6oAyi3/5S5Ne/1L7sx6q3S2qROPBd+Vn5HpCUUWfxJUk1x5IvSZLUUJStgOhXldwv/zUExVVvl7JVJSPY94aUfM/KS1IjY8mXJEmqT4IASudXMoL9l/H5VYmkQ1qvimfl03rFz9hLkpoES74kSVIYYmsTH0dXXuhnQ7Cq6u2Sczc4I7/ucXRdIJJcd/klSfWSJV+SJKm2BEH8nvgKZ+VnQ3QuVT+OLhnSeiQ+V37dY+mSc+ryCCRJDYwlX5IkaUsFUSj5X+Iz5deV+tiyqrdLyoa0bTd4rnxvSNsaIml1l1+S1GhY8iVJkjZV2fLEy+rLS/0coLSKjSKQ2jWxxK+71D4514HvJEk1ypIvSZK0viAG0XkVz8iXfAlli6reLtJsvcvq1//qCUmZdZdfktSkWfIlSVLTFFsFJV9VHMG+5CsI1la9XUp+JSPY94aUjhBJqrv8kiRVwpIvSZIaryCA0oWVjGD/JZR+X/V2kTRI7VnJWflekJxVd/klSaomS74kSWr4YsUQnbPBGfmfL7ePrah6u+S2lZyV3yZ+D33EX5MkSQ2P/3pJkqSGo/Snys/KR+cCsSo2SoLU7hucld8m/pXSti7TS5JU6yz5kiSpfglK46W9+MuKj6QrW1L1dklZFc/Ip/WOF/yk9LrLL0lSiCz5kiRp8615D348F9pdA5n9qrdtWWHiY+iK101/DUSr3i6lS8Xnyqf3huQ8H0cnSWryQh0C9rXXXuO3v/0t+fn5RCIRnnzyyYTlQRBw8cUX06FDBzIzMxk4cCBff/11wjpLly5lxIgRZGVl0apVK04++WRWrlxZh0chSVITVnQvrH4Ziu6rfPm6x9Gt/A8svREKToPvDoA5+fB1K5i3OywcCUuuhJWPQ8nnQBQimZC+I7QcBm0mQP6D0PUD6LUSenwLnV6A9jdC6z9D8/0hpYMFX5IkQj6Tv2rVKnbccUdGjx7NscceW2H5Nddcw6RJk7jnnnvo1q0bF110EYMGDeLzzz8nIyMDgBEjRrBw4UKmTZtGNBrlpJNO4tRTT2XKlCl1fTiSJDUN0XlQ9hMQgaKH4vOKHoSMPSD6LZQthtKCXy6zD9ZUva/kvIoj2Kf3hpROPo5OkqTNEAmCIAg7BEAkEuGJJ57g6KOPBuJn8fPz8znrrLM4++yzASgsLKR9+/bcfffdDB8+nC+++II+ffowc+ZM+vWLXyL4wgsvcNhhh/HDDz+Qn59f6XsVFxdTXFxc/rqoqIhOnTrx008/kZXlY3EkSdqY1G/SyqcD4NfOnwekQmp3grRtCFK3IUjbBlJ7E6T2guTsWs0qNWXRaJRp06Zx0EEHkZqaGnYcSRsoKiqibdu2FBYW1mgPrbf35M+dO5eCggIGDhxYPi87O5vdd9+dGTNmMHz4cGbMmEGrVq3KCz7AwIEDSUpK4p133uGYY46pdN9XXnklEydOrDD/xRdfpFmzZjV/MJIkNSJbtfwrO3eYRFKkrELBDwJYsno7Fq3qx4qSjqws6cjqaHuCCr9y/Pjzl6TaNm3atLAjSKrE6tWra2W/9bbkFxQUANC+ffuE+e3bty9fVlBQQG5ubsLylJQUcnJyytepzPjx4xk3blz563Vn8g8++GDP5EuS9KsOI1g0H1Y+XGFJaad3yE7fGc/PS+HzTL5UvxUVFdXKfuttya9N6enppKdXfJROamqqPwAlSfo1RQ+vV/AjxC/aTwJipKakgP+WSvWKv+NK9VNtfS7r7Yg2eXl5ACxatChh/qJFi8qX5eXlsXjx4oTlpaWlLF26tHwdSZJUg4pnQ8HJ8elIc8joB+1vg4xd44PoJedufHtJklSr6m3J79atG3l5eUyfPr18XlFREe+88w79+/cHoH///ixfvpxZs2aVr/PSSy8Ri8XYfffd6zyzJEmNWmw1LBgCsZWQuS/0WARd3oHWf4z/2f1bSN0q7JSSJDVpoV6uv3LlSubMmVP+eu7cuXz44Yfk5OTQuXNnzjzzTC677DJ69uxZ/gi9/Pz88hH4t912Ww455BBOOeUUbrvtNqLRKGPHjmX48OFVjqwvSZI2QxBAwZ+h+FNIbg8dH4Tk5r8sj0QgUvFWOEmSVLdCLfnvvfce+++/f/nrdYPhjRw5krvvvptzzz2XVatWceqpp7J8+XL22msvXnjhBTIyMsq3eeCBBxg7diwHHnggSUlJDB48mEmTJtX5sUiS1KgV3glF9wJJkD8VUjqEnUiSJFUiEgRBEHaIsBUVFZGdnV3jzyeUJKlRWPshzNsDgmJodwW0GR92IkmbIBqN8txzz3HYYYc58J5UD9VWD6239+RLkqR6oGw5zB8SL/jND4ec88JOJEmSNsKSL0mSKhcEsPAkiH4DKV0g/16I+KuDJEn1mf9SS5Kkyi27HlY+CZE06PgIJOeEnUiSJP0KS74kSapo9Ruw+Nz4dO71kLlbuHkkSdImseRLkqREpYthwTCgDLKOh1Z/DjuRJEnaRJZ8SZL0i6AMFpwApQsgbVvIux0ikbBTSZKkTWTJlyRJv/jpUlg9HSLNoOOjkNQi7ESSJKkaLPmSJClu5Quw5O/x6bzbIb1PuHkkSVK1WfIlSRJEv4eFvwMCaPUnyB4RdiJJkrQZLPmSJDV1QQnMHwplSyB9l/ho+pIkqUGy5EuS1NQtPhfWvg1JrX6+Dz8j7ESSJGkzWfIlSWrKih6BZTfGpzvcA2ndws0jSZK2iCVfkqSmquQrKDg5Pp1zLrQ8Mtw8kiRpi1nyJUlqimKrYf4QiK2AzH2g3eVhJ5IkSTXAki9JUlMTBFBwGhR/AsntoeNUiKSEnUqSJNUAS74kSU1N4V1QdA+QBPkPQkqHsBNJkqQaYsmXJKkpWfshLBobn277d2i+f6hxJElSzbLkS5LUVJQVxu/DD9ZC88OgzflhJ5IkSTXMki9JUlMQBLDwJIh+AyldIP8+iPhrgCRJjY3/ukuS1BQsuwFWPgGkQseHITkn7ESSJKkWWPIlSWrsVr8Ji8+NT7e/HjJ/E24eSZJUayz5kiQ1ZqU/woJhQCm0HA6tTgs7kSRJqkWWfEmSGqugDBaMgNL5kNYb8m6HSCTsVJIkqRZZ8iVJaqx++jusngaRZtDxUUhuGXYiSZJUyyz5kiQ1Riv/A0sujU/n3Qbp24WbR5Ik1QlLviRJjU30e1g4Agig1R8h+/dhJ5IkSXXEki9JUmMSlMQH2itbAum7QO4NYSeSJEl1yJIvSVJjsvg8WDMDkrKh4yOQlBF2IkmSVIcs+ZIkNRZFj8KyG+LTHe6FtK1DjSNJkuqeJV+SpMag5GsoGB2fzjkHWh4Zbh5JkhQKS74kSQ1dbDXMHwKxFZC5N7S7POxEkiQpJJZ8SZIaukVjofhjSM6F/KkQSQ07kSRJCoklX5Kkhmz5XVA4GUiKF/zU/LATSZKkEFnyJUlqqNZ+BIvGxKfbXgrN9w83jyRJCp0lX5KkhqisMH4ffrAWmh8KbcaHnUiSJNUDlnxJkhqaIICCkyE6B1I6Q/59EPGfdEmSZMmXJKnhWXYjrHgMSIWOj0Bym7ATSZKkesKSL0lSQ7L6LVh8Tny6/XWQ+Ztw80iSpHrFki9JUkNR+iMsGAqUQsth0GpM2IkkSVI9Y8mXJKkhCMpg4e+gdD6kbQN5d0AkEnYqSZJUz1jyJUlqCJZcBqtehEgmdHwUkluGnUiSJNVDlnxJkuq7VS/CTxPj03n/gvS+4eaRJEn1liVfkqT6LPoDLBgBBNDqVMj+fdiJJElSPWbJlySpvgqi8YH2yn6C9J0h98awE0mSpHrOki9JUn21+DxYMwOSsqHjI5CUEXYiSZJUz1nyJUmqj1Y8Dsuuj093uAfSuoebR5IkNQiWfEmS6puSr2HhSfHpnLOh5VHh5pEkSQ2GJV+SpPoktgbmD4FYEWTuBe2uCDuRJElqQCz5kiTVJ4vGQvHHkJwL+Q9BJDXsRJIkqQGx5EuSVF8snwyFdwERyJ8CqflhJ5IkSQ2MJV+SpPpg7Uew6LT4dNtLofmB4eaRJEkNkiVfkqSwlRXB/OMgWAvND4U2fws7kSRJaqAs+ZIkhSkIoOBkiH4NKZ0g/z6I+M+zJEnaPP4WIUlSmJZNghWPAqnQ8RFIbhN2IkmS1IBZ8iVJCsuaGbD47Ph07rWQuXu4eSRJUoNnyZckKQylP8H8oUAptBwKrceGnUiSJDUClnxJkupaUAYLR0DpD5DWC/L+DyKRsFNJkqRGwJIvSVJdW3I5rHoRIpmQ/ygktww7kSRJaiQs+ZIk1aVV0+CnS+LTebdBxvahxpEkSY2LJV+SpLoS/QEWnAAEkP0HyD4x7ESSJKmRseRLklQXgigsGAZlP0H6TtB+UtiJJElSI1SvS35ZWRkXXXQR3bp1IzMzk+7du/P3v/+dIAjK1wmCgIsvvpgOHTqQmZnJwIED+frrr0NMLUlSJRafD2vegqRs6PgoJGWGnUiSJDVC9brkX3311dx6663cdNNNfPHFF1x99dVcc801/POf/yxf55prrmHSpEncdtttvPPOOzRv3pxBgwaxdu3aEJNLkrSeFY/Dsuvi0x3uhrTuocaRJEmNV0rYATbmrbfe4qijjuLwww8HoGvXrjz44IO8++67QPws/g033MCFF17IUUcdBcC9995L+/btefLJJxk+fHho2SVJAqBkDiw8KT6dcxa0PDrUOJIkqXGr1yV/zz335Pbbb+err76iV69efPTRR7zxxhtcd138bMjcuXMpKChg4MCB5dtkZ2ez++67M2PGjCpLfnFxMcXFxeWvi4qKAIhGo0Sj0Vo8IklSkxJbQ8r8wURiRcQy9qSs1aXgvzOS6si632v9/Vaqn2rrs1mvS/75559PUVERvXv3Jjk5mbKyMi6//HJGjBgBQEFBAQDt27dP2K59+/blyypz5ZVXMnHixArzX3zxRZo1a1aDRyBJasp2bH8zXVt9THFpNq98djJrP5oWdiRJTdC0af7skeqj1atX18p+63XJf/jhh3nggQeYMmUK2223HR9++CFnnnkm+fn5jBw5crP3O378eMaNG1f+uqioiE6dOnHwwQeTlZVVE9ElSU1cpOheUn6cRkCE5E5TOWCbA8OOJKmJiUajTJs2jYMOOojU1NSw40jawLorymtavS7555xzDueff375Zffbb7898+bN48orr2TkyJHk5eUBsGjRIjp06FC+3aJFi9hpp52q3G96ejrp6ekV5qempvoDUJK05dZ+DD+dDkCk7URSsg8JOZCkpszfcaX6qbY+l/V6dP3Vq1eTlJQYMTk5mVgsBkC3bt3Iy8tj+vTp5cuLiop455136N+/f51mlSQJgLIimD8EgjXQfBC0uSDsRJIkqQmp12fyf/vb33L55ZfTuXNntttuOz744AOuu+46Ro8eDUAkEuHMM8/ksssuo2fPnnTr1o2LLrqI/Px8jj766HDDS5KaniCAgj9A9GtI2Qo63A+Rev3/0yVJUiNTr0v+P//5Ty666CJOO+00Fi9eTH5+Pn/84x+5+OKLy9c599xzWbVqFaeeeirLly9nr7324oUXXiAjIyPE5JKkJmnZP2HFI0AKdHwEUtqGnUiSJDUxkSAIgrBDhK2oqIjs7GwKCwsdeE+StHnWvA3z9gZKIfdGyPlL2IkkNXHRaJTnnnuOww47zHvypXqotnqo1xBKkrSlSn+C+UOBUmh5HLQ+PexEkiSpibLkS5K0JYIYLPwdlH4PqT0h7/8gEgk7lSRJaqIs+ZIkbYkll8Oq/0AkEzo+Bsne9iVJksJjyZckaXOt+i/8NCE+nXcrZGwfbh5JktTkVbvkv/DCC7zxxhvlr2+++WZ22mknTjjhBJYtW1aj4SRJqrei82HBCUAA2SdD9siwE0mSJFW/5J9zzjkUFRUB8Mknn3DWWWdx2GGHMXfuXMaNG1fjASVJqneCKCwYBmU/QvqO0P6fYSeSJEkCIKW6G8ydO5c+ffoA8Nhjj3HEEUdwxRVX8P7773PYYYfVeEBJkuqdH8fDmjchKQs6PgpJmWEnkiRJAjbjTH5aWhqrV68G4L///S8HH3wwADk5OeVn+CVJarRWPAFLr41Pd5gMaT3CzSNJkrSeap/JHzBgAOPGjWPAgAG8++67PPTQQwB89dVXbLXVVjUeUJKkeqPkG1g4Kj7dehy0PDbUOJIkSRuq9pn8m2++mdTUVB599FFuvfVWOnbsCMDzzz/PIYccUuMBJUmqF2JrYP4QiBVB5p6Qe1XYiSRJkiqo1pn80tJSXnnlFe644w7y8vISll1//fU1GkySpHpl0RlQ/CEkt4P8hyGSGnYiSZKkCqp1Jj8lJYU//elPFBcX11YeSZLqn8J7oPAOIAL5UyC1Y9iJJEmSKlXty/V/85vf8MEHH9RGFkmS6p+1n0DBn+PTbS+B5gNDjSNJkrQx1R5477TTTuOss87ihx9+YNddd6V58+YJy3fYYYcaCydJUqjKVsCCIRCsgeaDoM2FYSeSJEnaqGqX/OHDhwPwl7/8pXxeJBIhCAIikQhlZWU1l06SpLAEART8AUq+gpStoMP9EKn2BXCSJEl1qtolf+7cubWRQ5Kk+mXZTbDiYSAFOj4MKW3DTiRJkvSrql3yu3TpUhs5JEmqP9a8A4vPik/n/j/I7B9uHkmSpE1U7ZK/zueff853331HSUlJwvwjjzxyi0NJkhSasiUw/zggCi0HQ+u//OomkiRJ9UW1S/7//vc/jjnmGD755JPye/Ehfl8+4D35kqSGK4jBgt9B6feQ2hPy7oKf/32TJElqCKo9gtAZZ5xBt27dWLx4Mc2aNeOzzz7jtddeo1+/frzyyiu1EFGSpDqy5ApY9QJEMqDjo5CcFXYiSZKkaqn2mfwZM2bw0ksv0bZtW5KSkkhKSmKvvfbiyiuv5C9/+QsffPBBbeSUJKl2rZoOP02IT7e/FTJ8JKwkSWp4qn0mv6ysjJYtWwLQtm1bFixYAMQH5Js9e3bNppMkqS5E58OCE4AYZI+GVqPCTiRJkrRZqn0mv2/fvnz00Ud069aN3XffnWuuuYa0tDRuv/12tt5669rIKElS7QmisGA4lC2G9B2g/U1hJ5IkSdps1S75F154IatWrQLg0ksv5YgjjmDvvfemTZs2TJ06tcYDSpJUq368ANa8AUlZ8fvwkzLDTiRJkrTZql3yBw0aVD7do0cPvvzyS5YuXUrr1q3LR9iXJKlBWPEkLP1HfLrDZEjrGWocSZKkLVXte/JHjx7NihUrEubl5OSwevVqRo8eXWPBJEmqVSXfwMJR8enWf4WWx4YaR5IkqSZUu+Tfc889rFmzpsL8NWvWcO+999ZIKEmSalVsLcw/DmKFkLkn5F4ddiJJkqQascmX6xcVFREEAUEQsGLFCjIyMsqXlZWV8dxzz5Gbm1srISVJqlGLz4DiDyC5LeQ/BJHUsBNJkiTViE0u+a1atSISiRCJROjVq1eF5ZFIhIkTJ9ZoOEmSalzhvbD8diAC+VMgdauwE0mSJNWYTS75L7/8MkEQcMABB/DYY4+Rk5NTviwtLY0uXbqQn59fKyElSaoRxZ9CwZ/i020nQPODws0jSZJUwza55O+7774AzJ07l86dOzuSviSpYSlbAfOHQLAGmh8MbS4MO5EkSVKN2+SB93766SfmzZtHly5dygv+Z599xkknncTQoUOZMmVKrYWUJGmLBAEU/AFKZkNKR+hwP0SSw04lSZJU4za55J9++ulMmjSp/PXixYvZe++9mTlzJsXFxYwaNYr77ruvVkJKkrRFlt8MKx4GUiD/YUhpF3YiSZKkWrHJJf/tt9/myCOPLH997733kpOTw4cffsi///1vrrjiCm6++eZaCSlJ0mZb8y4sGhefzv0HNNsz3DySJEm1aJNLfkFBAV27di1//dJLL3HssceSkhK/rf/II4/k66+/rvGAkiRttrIlMP84IAotB0PrM8JOJEmSVKs2ueRnZWWxfPny8tfvvvsuu+++e/nrSCRCcXFxjYaTJGmzBTFY8Hso/Q5Se0DeneCgsZIkqZHb5JK/xx57MGnSJGKxGI8++igrVqzggAMOKF/+1Vdf0alTp1oJKUlStS25ElY9D5EM6PgoJGeHnUiSJKnWbfIj9P7+979z4IEHcv/991NaWsrf/vY3WrduXb586tSp5Y/ZkyQpVKtegp8ujk+3vwUydgw3jyRJUh3Z5JK/ww478MUXX/Dmm2+Sl5eXcKk+wPDhw+nTp0+NB5QkqVqiC2DB8UAMsk+CVieFnUiSJKnObHLJB2jbti1HHXVUpcsOP/zwGgkkSdJmC0phwXAoWwzpO0D7m8JOJEmSVKc2+Z58SZLqvR8vgDWvQ1LL+H34Sc3CTiRJklSnLPmSpMZhxb9h6TXx6bzJkNYz3DySJEkhsORLkhq+kv/BwpHx6dZnQtbgUONIkiSFxZIvSWrYYmth/nEQK4TM/pB7ddiJJEmSQrNZJf+bb77hwgsv5Pjjj2fx4sUAPP/883z22Wc1Gk6SpF+1+Ewofh+S20D+QxBJCzuRJElSaKpd8l999VW233573nnnHR5//HFWrlwJwEcffcSECRNqPKAkSVUqvA+W/wuIQIcHILVT2IkkSZJCVe2Sf/7553PZZZcxbdo00tJ+OVtywAEH8Pbbb9doOEmSqlT8GRT8KT7d5mJoMSjcPJIkSfVAtUv+J598wjHHHFNhfm5uLj/99FONhJIkaaPKVsD8wRCshmYHQduLwk4kSZJUL1S75Ldq1YqFCxdWmP/BBx/QsWPHGgklSVKVggAKToWS2ZDSEfIfgEhy2KkkSZLqhWqX/OHDh3PeeedRUFBAJBIhFovx5ptvcvbZZ3PiiSfWRkZJkn6x/BZYMRVIiQ+0l9Iu7ESSJEn1RrVL/hVXXEHv3r3p1KkTK1eupE+fPuyzzz7sueeeXHjhhbWRUZKkuDXvwqK/xqdzr4FmA8LNI0mSVM+kVHeDtLQ07rjjDi666CI+/fRTVq5cyc4770zPnj1rI58kSXFlS2D+cUAUWhwLrc8MO5EkSVK9U+2S/8Ybb7DXXnvRuXNnOnfuXBuZJElKFMRgwYlQ+h2kdocOd0EkEnYqSZKkeqfal+sfcMABdOvWjb/97W98/vnntZFJkqRES6+GVc9BJAM6PgrJ2WEnkiRJqpeqXfIXLFjAWWedxauvvkrfvn3Zaaed+Mc//sEPP/xQG/kkSU3dqpfhx5/HfGl/E2TsFGocSZKk+qzaJb9t27aMHTuWN998k2+++YbjjjuOe+65h65du3LAAQfURkZJUlMVXQALhgMxyB4F2aPDTiRJklSvVbvkr69bt26cf/75XHXVVWy//fa8+uqrNZVLktTUBaWw4HgoWwzp20P7m70PX5Ik6Vdsdsl/8803Oe200+jQoQMnnHACffv25dlnn63JbJKkpuzHC2HNa5DUMn4fflKzsBNJkiTVe9UeXX/8+PFMnTqVBQsWcNBBB3HjjTdy1FFH0ayZv3xJkmrIiqfig+0B5N0Fab3CzSNJktRAVLvkv/baa5xzzjkMHTqUtm3b1kYmSVJTVjIXFo6MT7c+A7KGhJtHkiSpAal2yX/zzTdrI4ckSRBbC/OHQGw5ZOwBudeEnUiSJKlB2aSS/9RTT3HooYeSmprKU089tdF1jzzyyBoJts78+fM577zzeP7551m9ejU9evRg8uTJ9OvXD4AgCJgwYQJ33HEHy5cvZ8CAAdx666307NmzRnNIkurA4r9C8fuQ3AY6PgyRtLATSZIkNSibVPKPPvpoCgoKyM3N5eijj65yvUgkQllZWU1lY9myZQwYMID999+f559/nnbt2vH111/TunXr8nWuueYaJk2axD333EO3bt246KKLGDRoEJ9//jkZGRk1lkWSVMsKH4DltwER6HA/pHYKO5EkSVKDs0klPxaLVTpd266++mo6derE5MmTy+d169atfDoIAm644QYuvPBCjjrqKADuvfde2rdvz5NPPsnw4cPrLKskaQsUfw4Fp8an21wELQ4JN48kSVIDVe178u+9916GDRtGenp6wvySkhKmTp3KiSeeWGPhnnrqKQYNGsRxxx3Hq6++SseOHTnttNM45ZRTAJg7dy4FBQUMHDiwfJvs7Gx23313ZsyYUWXJLy4upri4uPx1UVERANFolGg0WmP5JUmbILaSlB+OJRKsJpZ5IGXZ48GfxZK0xdb9Xuvvt1L9VFufzUgQBEF1NkhOTmbhwoXk5uYmzF+yZAm5ubk1ern+usvtx40bx3HHHcfMmTM544wzuO222xg5ciRvvfUWAwYMYMGCBXTo0KF8u6FDhxKJRHjooYcq3e8ll1zCxIkTK8yfMmWKjwKUpDoVsGuH69gq63XWRHN4Zd51lJS1CjuUJElSrVu9ejUnnHAChYWFZGVl1dh+q30mPwgCIpFIhfk//PAD2dnZNRJqnVgsRr9+/bjiiisA2Hnnnfn000/LS/7mGj9+POPGjSt/XVRURKdOnTj44INr9C9XkrRxSYW3kfzT6wQkk9rlcQb23jPsSJLUaESjUaZNm8ZBBx1Eampq2HEkbWDdFeU1bZNL/s4770wkEiESiXDggQeSkvLLpmVlZcydO5dDDqnZeyg7dOhAnz59EuZtu+22PPbYYwDk5eUBsGjRooQz+YsWLWKnnXaqcr/p6ekVbjcASE1N9QegJNWVNTNhydkARHKvISVr35ADSVLj5O+4Uv1UW5/LTS7560bV//DDDxk0aBAtWrQoX5aWlkbXrl0ZPHhwjYYbMGAAs2fPTpj31Vdf0aVLFyA+CF9eXh7Tp08vL/VFRUW88847/PnPf67RLJKkGlS2FOYfB0EJtDgGWv817ESSJEmNwiaX/AkTJgDQtWtXhg0bViePp/vrX//KnnvuyRVXXMHQoUN59913uf3227n99tuB+CP7zjzzTC677DJ69uxZ/gi9/Pz8jT7qT5IUoiAGC06E0nmQ2h063AWV3AYmSZKk6qv2Pflbci98de2222488cQTjB8/nksvvZRu3bpxww03MGLEiPJ1zj33XFatWsWpp57K8uXL2WuvvXjhhRfq5H9CSJI2w9JrYNWzEEmHjo9CcquwE0mSJDUa1R5dv6ysjOuvv56HH36Y7777jpKSkoTlS5curdGAdaGoqIjs7OwaH9VQkrSBVa/A9wcCMci7A1r9IexEktRoRaNRnnvuOQ477DDvyZfqodrqoUnV3WDixIlcd911DBs2jMLCQsaNG8exxx5LUlISl1xySY0FkyQ1MqULYcFwIAZZIyH75LATSZIkNTrVLvkPPPAAd9xxB2eddRYpKSkcf/zx/N///R8XX3wxb7/9dm1klCQ1dEEpzD8eyhZB+vaQd4v34UuSJNWCapf8goICtt9+ewBatGhBYWEhAEcccQTPPvtszaaTJDUOP14Ea16FpBaQ/wgkNQs7kSRJUqNU7ZK/1VZbsXDhQgC6d+/Oiy++CMDMmTMrffa8JKmJW/E0LL0qPp13J6RvE24eSZKkRqzaJf+YY45h+vTpAJx++ulcdNFF9OzZkxNPPJHRo0fXeEBJUgNWMhcWnhifbv0XyBoabh5JkqRGrtqP0LvqqqvKp4cNG0bnzp2ZMWMGPXv25Le//W2NhpMkNWCxYlhwHMSWQ8bukPuPsBNJkiQ1etUu+Rvq378//fv3r4kskqTGZPFfYe0sSMqBjg9DJC3sRJIkSY3eJpX8p556apN3eOSRR252GElSI1E4BZbfCkQg/wFI7Rx2IkmSpCZhk0r+0UcfvUk7i0QilJWVbUkeSVJDV/w5FJwan25zIbQ4JNw8kiRJTcgmlfxYLFbbOSRJjUFsJcwfAsEqaHYgtJ0QdiJJkqQmpdqj60uSVKkggII/QskXkJIP+VMgkhx2KkmSpCal2gPvXXrppRtdfvHFF292GElSA7b8X1A0BUiG/IcgJTfsRJIkSU1OtUv+E088kfA6Go0yd+5cUlJS6N69uyVfkpqiNe/B4jPi0+2ugmZ7hZtHkiSpiap2yf/ggw8qzCsqKmLUqFEcc8wxNRJKktSAlC39+T78EmhxNOScFXYiSZKkJqtG7snPyspi4sSJXHTRRTWxO0lSQxHEYMFIKJ0HqVtDh8kQiYSdSpIkqcmqsYH3CgsLKSwsrKndSZIagqX/gFXPQCQdOj4Kya3CTiRJktSkVfty/UmTJiW8DoKAhQsXct9993HooYfWWDBJUj236hX48W/x6fb/hIydQ40jSZKkzSj5119/fcLrpKQk2rVrx8iRIxk/fnyNBZMk1WOlBbBgOBCDrBMh+w9hJ5IkSRKbUfLnzp1bGzkkSQ1FUAoLjoeyRZDeF/Ju8T58SZKkeqLG7smXJDURP10Mq1+BpBaQ/ygkNQ87kSRJkn5W7TP5a9eu5Z///Ccvv/wyixcvJhaLJSx///33ayycJKmeWfkMLLkyPp33f5C+Tbh5JEmSlKDaJf/kk0/mxRdfZMiQIfzmN78h4iWaktQ0lHwLC06MT7c+HbKGhRpHkiRJFVW75D/zzDM899xzDBgwoDbySJLqo1gxLDgOYssgY3fI/X9hJ5IkSVIlqn1PfseOHWnZsmVtZJEk1VeLx8Ha9yApBzo+DJG0sBNJkiSpEtUu+ddeey3nnXce8+bNq408kqT6pnAKLL8lPp1/P6R2DjePJEmSqlTty/X79evH2rVr2XrrrWnWrBmpqakJy5cuXVpj4SRJISv+AgpOjU+3uRBaHBpuHkmSJG1UtUv+8ccfz/z587niiito3769A+9JUmMVWwnzB0OwCpodAG0vCTuRJEmSfkW1S/5bb73FjBkz2HHHHWsjjySpPggCKPgTlHwBKR0gfwpEksNOJUmSpF9R7Xvye/fuzZo1a2ojiySpvlh+OxQ9ACRD/kOQ0j7sRJIkSdoE1S75V111FWeddRavvPIKS5YsoaioKOFLktTArZ0Fi/8Sn253JTTbO9w8kiRJ2mTVvlz/kEMOAeDAAw9MmB8EAZFIhLKysppJJkmqe2XLYP4QCEqgxVGQc3bYiSRJklQN1S75L7/8cm3kkCSFLYjBwpEQ/RZSu0GHu8HBVSVJkhqUapf8fffdtzZySJLCtvT/wcqnIZIOHR+F5FZhJ5IkSVI1Vbvkv/baaxtdvs8++2x2GElSSFa/Cj/+LT7dfhJk7BJuHkmSJG2Wapf8/fbbr8K8yHqXc3pPviQ1MKUFMH84UAZZv4fsU8JOJEmSpM1U7dH1ly1blvC1ePFiXnjhBXbbbTdefPHF2sgoSaotQSksOAHKCiBtO8i71fvwJUmSGrBqn8nPzs6uMO+ggw4iLS2NcePGMWvWrBoJJkmqAz9NgNUvQ1KL+H34Sc3DTiRJkqQtUO0z+VVp3749s2fPrqndSZJq28pnYckV8em8OyC9d7h5JEmStMWqfSb/448/TngdBAELFy7kqquuYqeddqqpXJKk2hSdBwt+H59uNRayhoebR5IkSTWi2iV/p512IhKJEARBwvw99tiDu+66q8aCSZJqSawY5h8HsWWQsRvk/r+wE0mSJKmGVLvkz507N+F1UlIS7dq1IyMjo8ZCSZJq0eKzYO1MSGoNHR+BpPSwE0mSJKmGVLvkd+nSpTZySJLqQtFUWH5zfDr/fkj1Z7okSVJjsskD77300kv06dOHoqKiCssKCwvZbrvteP3112s0nCSpBhV/AQv/EJ9ucwG0OCzcPJIkSapxm1zyb7jhBk455RSysrIqLMvOzuaPf/wj1113XY2GkyTVkNgqmD8EglXQbH9oOzHsRJIkSaoFm1zyP/roIw455JAqlx988MHMmjWrRkJJkmpQEEDBn6Dkc0jpAPlTIJIcdipJkiTVgk0u+YsWLSI1NbXK5SkpKfz44481EkqSVIMK74Ci+4FkyJ8KKXlhJ5IkSVIt2eSS37FjRz799NMql3/88cd06NChRkJJkmrI2lmw6PT4dLsroNk+4eaRJElSrdrkkn/YYYdx0UUXsXbt2grL1qxZw4QJEzjiiCNqNJwkaQuULYP5x0FQAi2OhJyzw04kSZKkWrbJj9C78MILefzxx+nVqxdjx45lm222AeDLL7/k5ptvpqysjAsuuKDWgkqSqiEIYOEoiM6F1G7Q4W6IbPL/15UkSVIDtcklv3379rz11lv8+c9/Zvz48QRBAEAkEmHQoEHcfPPNtG/fvtaCSpKqYen/g5VPQSQNOj4Cya3DTiRJkqQ6sMklH6BLly4899xzLFu2jDlz5hAEAT179qR1a395lKR6Y/Vr8OP4+HTuJMjYNdw8kiRJqjPVKvnrtG7dmt12262ms0iStlTpIlgwHCiDrN9Bq1PDTiRJkqQ65A2aktRYBGWw4HgoXQhp20HebRCJhJ1KkiRJdciSL0mNxU8TYPXLEGkevw8/qXnYiSRJklTHLPmS1BisfA6WXB6f7vB/kL5tuHkkSZIUCku+JDV00Xmw4Pfx6VZjIGt4uHkkSZIUGku+JDVksWKYPxRiSyFjN8i9NuxEkiRJCpElX5IassVnw9p3Iak15D8MSelhJ5IkSVKILPmS1FAVPQTLb4pP598HaV1DjSNJkqTwWfIlqSEq/hIK/hCfbjMeWhwebh5JkiTVC5Z8SWpoYqtg/hCIrYRm+0HbS8NOJEmSpHrCki9JDUkQQMGfoeQzSM6D/AchkhJ2KkmSJNUTDarkX3XVVUQiEc4888zyeWvXrmXMmDG0adOGFi1aMHjwYBYtWhReSEmqTYX/B0X3AcnQ8SFIyQs7kSRJkuqRBlPyZ86cyb/+9S922GGHhPl//etfefrpp3nkkUd49dVXWbBgAccee2xIKSWpFq19HxadHp9udzk02yfcPJIkSap3GkTJX7lyJSNGjOCOO+6gdevW5fMLCwu58847ue666zjggAPYddddmTx5Mm+99RZvv/12iIklqYaVLY/fhx8UQ4vfQs45YSeSJElSPdQgbuQcM2YMhx9+OAMHDuSyyy4rnz9r1iyi0SgDBw4sn9e7d286d+7MjBkz2GOPPSrdX3FxMcXFxeWvi4qKAIhGo0Sj0Vo6CknaTEFAcsGJJEXnEqR0pbTt/0FpGVAWdjJJUj227vdaf7+V6qfa+mzW+5I/depU3n//fWbOnFlhWUFBAWlpabRq1Sphfvv27SkoKKhyn1deeSUTJ06sMP/FF1+kWbNmW5xZkmpS99ZP0jf3acpiKbw+ZyyFn80IO5IkqQGZNm1a2BEkVWL16tW1st96XfK///57zjjjDKZNm0ZGRkaN7Xf8+PGMGzeu/HVRURGdOnXi4IMPJisrq8beR5K2VGTNGyQvuC/+IvcGBvQ8NdxAkqQGIxqNMm3aNA466CBSU1PDjiNpA+uuKK9p9brkz5o1i8WLF7PLLruUzysrK+O1117jpptu4j//+Q8lJSUsX7484Wz+okWLyMuresTp9PR00tPTK8xPTU31B6Ck+qN0ESweAZRB1giS25xGciQSdipJUgPj77hS/VRbn8t6XfIPPPBAPvnkk4R5J510Er179+a8886jU6dOpKamMn36dAYPHgzA7Nmz+e677+jfv38YkSWpZgRlsOAEKF0IadtC3m1gwZckSdKvqNclv2XLlvTt2zdhXvPmzWnTpk35/JNPPplx48aRk5NDVlYWp59+Ov37969y0D1JahB+ugRWvwSR5tDxMUhqEXYiSZIkNQD1uuRviuuvv56kpCQGDx5McXExgwYN4pZbbgk7liRtvpXPw5KfnySSdzukbxtuHkmSJDUYkSAIgrBDhK2oqIjs7GwKCwsdeE9SuKLfwdydIbYUWp0GeTeHnUiS1EBFo1Gee+45DjvsMO/Jl+qh2uqhSTW2J0nSlglKYP7QeMHP6Ae514WdSJIkSQ2MJV+S6ovF58DadyCpNeQ/AkkVnwIiSZIkbYwlX5Lqg6KHYdmk+HT+vZDWNdQ4kiRJapgs+ZIUtuLZUHByfDrnfGhxRLh5JEmS1GBZ8iUpTLHVsGAIxFZC5r7Q7u9hJ5IkSVIDZsmXpLAEART8GYo/heQ86DgVIg3+yaaSJEkKkSVfksJSeCcU3QskQf6DkJIXdiJJkiQ1cJZ8SQrD2g9g0dj4dLvLofl+ocaRJElS42DJl6S6VrYc5h8HQTE0PwJyzg07kSRJkhoJS74k1aUggIUnQfQbSO0K+fdAxB/FkiRJqhn+ZilJdWnpdbDySYikQf4jkJwTdiJJkiQ1IpZ8Saorq9+AH8+LT+feAJn9Qo0jSZKkxseSL0l1oXQxLBgGlEHWCdDqT2EnkiRJUiNkyZek2haUwYIToHQBpG0Lef+CSCTsVJIkSWqELPmSVNt+mgirp0OkGXR8FJJahJ1IkiRJjZQlX5Jq08oXYMll8em8OyC9T7h5JEmS1KhZ8iWptkS/g4W/A4L4PfjZJ4SdSJIkSY2cJV+SakNQAvOHQtkSyNgVcq8PO5EkSZKaAEu+JNWGxefC2ncgqRXkPwJJGWEnkiRJUhNgyZekmlb0CCy7MT7d4V5I6xZuHkmSJDUZlnxJqkklX0HByfHpnPOg5W/DzSNJkqQmxZIvSTUlthrmD4HYCsjcB9pdFnYiSZIkNTGWfEmqCUEABadB8SeQ3B46ToVIStipJEmS1MRY8iWpJhTeBUX3AEmQPxVSOoSdSJIkSU2QJV+SttTaD2HRmPh0u8ug+X5hppEkSVITZsmXpC1RVhi/Dz8ohuaHxwfbkyRJkkJiyZekzRUEsPAkiH4DKV0g/16I+GNVkiRJ4fG3UUnaXMuuh5VPQCQNOj4CyTlhJ5IkSVITZ8mXpM2x+k1Y/POl+bnXQ+Zu4eaRJEmSsORLUvWVLoYFw4BSyDoeWv057ESSJEkSYMmXpOoJymDBCCidD2m9Ie92iETCTiVJkiQBlnxJqp6f/g6r/wuRZtDxUUhqEXYiSZIkqZwlX5I21cr/wJJL49N5t0P6duHmkSRJkjZgyZekTRH9HhaOAAJo9UfIHhF2IkmSJKkCS74k/ZqgBOYPhbIlkL4L5N4QdiJJkiSpUpZ8Sfo1i8+DtW9DUquf78PPCDuRJEmSVClLviRtTNGjsOyG+HSHeyCtW6hxJEmSpI2x5EtSVUq+goLR8emcc6HlkeHmkSRJkn6FJV+SKhNbDfOPg9gKyNwH2l0ediJJkiTpV1nyJakyi8ZC8ceQ3B46ToVIStiJJEmSpF9lyZekDS2/CwonA0mQ/yCkdAg7kSRJkrRJLPmStL61H8GiMfHptn+H5vuHm0eSJEmqBku+JK1TVgjzh0CwFpofBm3ODzuRJEmSVC2WfEkCCAJYOBqicyClM+TfCxF/REqSJKlh8TdYSQJYdgOsfBxIhY6PQHKbsBNJkiRJ1WbJl6TVb8Hic+PT7a+HzN+Em0eSJEnaTJZ8SU1b6Y+wYChQCi2HQ6vTwk4kSZIkbTZLvqSmKyiDBSOgdD6kbQN5t0MkEnYqSZIkabNZ8iU1XUsug9XTINIMOj4GyS3DTiRJkiRtEUu+pKZp1Yvw08T4dN5tkL5duHkkSZKkGmDJl9T0RH+IX6ZPAK1Ohezfh51IkiRJqhGWfElNSxCND7RX9hOk7wy5N4adSJIkSaoxlnxJTcvi82DNDEjKho6PQlJG2IkkSZKkGmPJl9R0FD0Gy66PT3e4B9K2DjePJEmSVMMs+ZKahpKvoeCk+HTOOdDyqHDzSJIkSbXAki+p8YutgflDILYCMveGdpeHnUiSJEmqFZZ8SY3forFQ/DEk50L+VIikhp1IkiRJqhWWfEmN2/LJUHgXkAT5D0JqftiJJEmSpFpjyZfUeK39CBadFp9ueyk0PyDcPJIkSVIts+RLapzKCuP34Qdrofmh0GZ82IkkSZKkWmfJl9T4BAEUnAzROZDSGfLvg4g/7iRJktT41evfeq+88kp22203WrZsSW5uLkcffTSzZ89OWGft2rWMGTOGNm3a0KJFCwYPHsyiRYtCSiypXlg2CVY8BqRCx4chuU3YiSRJkqQ6Ua9L/quvvsqYMWN4++23mTZtGtFolIMPPphVq1aVr/PXv/6Vp59+mkceeYRXX32VBQsWcOyxx4aYWlKo1syAxWfHp9tfB5m7h5tHkiRJqkMpYQfYmBdeeCHh9d13301ubi6zZs1in332obCwkDvvvJMpU6ZwwAHxAbUmT57Mtttuy9tvv80ee+wRRmxJYSn9EeYPBUqh5VBoNSbsRJIkSVKdqtclf0OFhYUA5OTkADBr1iyi0SgDBw4sX6d379507tyZGTNmVFnyi4uLKS4uLn9dVFQEQDQaJRqN1lZ8SbUpKCN54QiSSn8gSO1FadtbobQ07FSSJIVm3e+1/n4r1U+19dlsMCU/Fotx5plnMmDAAPr27QtAQUEBaWlptGrVKmHd9u3bU1BQUOW+rrzySiZOnFhh/osvvkizZs1qNLekutGrzUNs23YapbE0XvtqDCs+fT3sSJIk1QvTpk0LO4KkSqxevbpW9ttgSv6YMWP49NNPeeONN7Z4X+PHj2fcuHHlr4uKiujUqRMHH3wwWVlZW7x/SXUrsvq/JC+cGn+Rdxt79/xduIEkSaoHotEo06ZN46CDDiI1NTXsOJI2sO6K8prWIEr+2LFjeeaZZ3jttdfYaqutyufn5eVRUlLC8uXLE87mL1q0iLy8vCr3l56eTnp6eoX5qamp/gCUGproD7D4RCCA7FNIyTkp7ESSJNUr/o4r1U+19bms16PrB0HA2LFjeeKJJ3jppZfo1q1bwvJdd92V1NRUpk+fXj5v9uzZfPfdd/Tv37+u40qqa0EUFgyDsp8gfWdoPynsRJIkSVKo6vWZ/DFjxjBlyhT+/e9/07Jly/L77LOzs8nMzCQ7O5uTTz6ZcePGkZOTQ1ZWFqeffjr9+/d3ZH2pKVh8Pqx5C5KyoeMjkJQRdiJJkiQpVPW65N96660A7LfffgnzJ0+ezKhRowC4/vrrSUpKYvDgwRQXFzNo0CBuueWWOk4qqc6teByWXRef7nA3pHUPNY4kSZJUH9Trkh8Ewa+uk5GRwc0338zNN99cB4kk1Qslc2Dhz/fe55wNLY8ONY4kSZJUX9Tre/IlqYLYGpg/BGJFkLkXtLsi7ESSJElSvWHJl9SwLDodij+C5HaQPxUijhYsSZIkrWPJl9RwLL8bCu8EIpD/IKR2DDuRJEmSVK9Y8iXVb2veg+8OgMIHYdFp8XltL4XmB4abS5IkSaqH6vXAe5JE0b2w+mVY+wEEa6D5odDmb2GnkiRJkuolS76k+ic6D8p+AiJQ9FB8Xmw5JLeHnLOg9HtI7RJmQkmSJKlesuRLqn++6Vr5/LJF8P3A+HTvX3/EpiRJktTUWPIlha9sGax9H9a+F/9KbvvzmfzKpECHu+synSRJktRgWPIl1a2yosRCv/Y9iH6z6dt3fQcydqm9fJIkSVIDZsmXVHtiK+MD5q1f6Eu+qnzd1O6Q0e/nr12BJPh+v/ifxNb7U5IkSVJVLPmSakZsNaz9cINC/yVQyb3zqV0TC33GLpCck7hO9AdIzoPUTpB9MhTeCdHvITm3Dg5GkiRJapgs+ZKqL7YWij9ar9DPguLPqPRMe0qn9cr8z3+mtP3190jdCrp/C5E0iESg1akQlEBSek0fjSRJktRoWPIlbVysGIo/2aDQfwqUVlw3JX+9Mr+u0Lff/Pdev9BHIhCx4EuSJEkbY8mX9IsgGi/w68r82vdg7cdAtOK6ybnrlfmfC31qfp1HliRJkvQLS77UVAWlUPz5emX+vfgl+EFxxXWT22xQ6PtBSsf42XVJkiRJ9YYlX2oKgjIomZ04KN7aDyFYU3HdpFaVnKHvYqGXJEmSGgBLvtTYBDEo+XqDQv8BBKsqrpuUVfEe+tStLfSSJElSA2XJlxqyIIDoN7+U+TXvQfH7EFtRcd1I858L/c+lPrMfpPaASFLd55YkSZJUKyz5UkMRBBD9doMz9O9DbHnFdSOZkLFz4mX3ab0gklzXqSVJkiTVIUu+VB8FAZR+n/jYujXvQWxpxXUj6ZC+0y9lPrMfpPWGiB9vSZIkqamxBUhhCwIoXbDBY+veg7IfK64bSYP0HRLP0Kf3gUhq3eeWJEmSVO9Y8qW6VlrwS5lfs67QF1SyYgqkb//L2fmMfpDeN170JUmSJKkSlnypNpX+mHh2fu17UDq/khWTIX27xFHu03eApIw6jyxJkiSp4bLkSzWlbGnFM/Sl31WyYhKkbftLmc/sB+k7QlKzOo8sSZIkqXGx5Eubo2x5fGT79c/QR+dWsmIE0rbZ4Fn0O0FSizoOLEmSJKkpsORLv6asCIo/+OXs/NpZEP268nVTeySOcp++MyRn1W1eSZIkSU2WJV9aX2wVrP0g8dF1JbOBoOK6qd0SR7nP2AWSW9V1YkmSJEkqZ8lX0xVbDcUf/XyG/ud76Uu+AGIV103pnDjKfcYukNymziNLkiRJ0sZY8tU0xNZC8ceJI90XfwaUVVw3peMGZ+h3hZR2dR5ZkiRJkqrLkq/GJyiB4k8TR7kv/gQorbhucnvI2O3nM/S7/lzoO9R5ZEmSJEmqCZZ8NWxBFIo/TxzlvvjjeNHfUHLbeKFff6T7lHyIROo+tyRJkiTVAku+Go6gFEq+XG+U+/eg+EMIiiuum5TzyzPoywt9Jwu9JEmSpEbNkq/6KSiDkq8SR7lf+wEEqyuum5S9wXPo+0FqVwu9JEmSpCbHkq/wBTGIzkl8Dn3x+xBbWXHdpBaQvsEZ+tStIZJU97klSZIkqZ6x5KtuBQFE/7fe2fmf/4wVVVw30iz+qLr1z9Cn9bTQS5IkSVIVLPmqPUEA0XmJj61bOwtiyyquG8mAjJ1/eWRdRj9I6w2R5LrPLUmSJEkNlCVfNSMIoPSHDQr9e1C2pOK6kTRI3ymx0Kf3gYjfjpIkSZK0JWxV2jzRBZUU+sWVrJgKGTskDoyXvl286EuSJEmSapQlX7+udFHFQl+6sJIVkyG9b+I99OnbQ1J6nUeWJEmSpKbIkq9EpT9VvIe+9PtKVkyKX2KfUOh3gKTMOo8sSZIkSYqz5DdlZcsqFvrot5WsGIkPgrd+oc/YEZKa13ViSZIkSdJGWPKbirJCWPt+4qProt9Uvm5ar/XK/K6QvjMkt6zbvJIkSZKkarPkN0ZlK6D4g8Sz9CVfVb5uavfEQp+xCyRn121eSZIkSVKNsOQ3dLHVsPbDxEHxSr4EgorrpnZNHOU+YxdIzqnjwJIkSZKk2mLJb0hia6D448RCX/w5EKu4bkqnDQr9rpDSts4jS5IkSZLqjiW/vooVQ/EnGxT6z4DSiuumdNhgULxdIaV9nUeWJEmSJIXLkl8fBFEo/vSXMr/mvXjBJ1px3eTcioU+Nb/OI0uSJEmS6h9Lfl0LSuOX2K//2LrijyAorrhucptKztBvBZFI3eeWJEmSJNV7lvzaFJTFB8Fb/7F1az+EYE3FdZNabTDKfT9I7WKhlyRJkiRtMkt+TQli8cfUrf/YurUfQLCq4rpJWT8X+fUGxkvd2kIvSZIkSdoilvzNEQQQ/WaDe+jfh9iKiutGmicW+sx+kNoDIkl1n1uSJEmS1KhZ8te35n3I2i9xXhBA9NvEUe7XzoJYYcXtI5mQsXPiffRpvSCSXBfpJUmSJElNnCV/fUVTIWfrxDP0a2dBbGnFdSPpkL7TL2U+sx+k9YaIf6WSJEmSpHDYSNdX+C/45l+VLEiFjB0Tz9Cn94FIap1HlCRJkiSpKpb8jcn7V/xe+rS+kJQedhpJkiRJkjbKkl+pFOhwN2SPCDuIJEmSJEmbzJJfma7vQMYuYaeQJEmSJKlafI5bAp9TL0mSJElquCz560vfGZLzIDk37CSSJEmSJFWbl+uvr/NL0DLDQfYkSZIkSQ2SZ/LXF4lY8CVJkiRJDZYlX5IkSZKkRqLRlPybb76Zrl27kpGRwe677867774bdiRJkiRJkupUoyj5Dz30EOPGjWPChAm8//777LjjjgwaNIjFixeHHU2SJEmSpDrTKAbeu+666zjllFM46aSTALjtttt49tlnueuuuzj//PMrrF9cXExxcXH566KiIgCi0SjRaLRuQkuSJEm1aN3vtf5+K9VPtfXZbPAlv6SkhFmzZjF+/PjyeUlJSQwcOJAZM2ZUus2VV17JxIkTK8x/8cUXadasWa1llSRJkuratGnTwo4gqRKrV6+ulf02+JL/008/UVZWRvv27RPmt2/fni+//LLSbcaPH8+4cePKXxcVFdGpUycOPvhgsrKyajWvJEmSVBei0SjTpk3joIMOIjU1New4kjaw7orymtbgS/7mSE9PJz294qPyUlNT/QEoSZKkRsXfcaX6qbY+lw1+4L22bduSnJzMokWLEuYvWrSIvLy8kFJJkiRJklT3GnzJT0tLY9ddd2X69Onl82KxGNOnT6d///4hJpMkSZIkqW41isv1x40bx8iRI+nXrx+/+c1vuOGGG1i1alX5aPuSJEmSJDUFjaLkDxs2jB9//JGLL76YgoICdtppJ1544YUKg/FJkiRJktSYNYqSDzB27FjGjh0bdgxJkiRJkkLT4O/JlyRJkiRJcZZ8SZIkSZIaiUZzuf6WCIIAgKKiopCTSJIkSTUjGo2yevVqioqKau153JI237r+ua6P1hRLPrBixQoAOnXqFHISSZIkSVJTsmLFCrKzs2tsf5Ggpv+3QQMUi8Xo1asXs2bNIhKJhB1H0gZ22203Zs6cGXYMSVXwMyrVT0VFRXTq1Invv/+erKyssONI2kAQBOy666589dVXJCXV3J30nskHkpKSSEtLq9H/eyKp5iQnJ/vLiVSP+RmV6resrCw/o1I9lZaWVqMFHxx4r9yYMWPCjiCpCn4+pfrNz6gkSZunNv4N9XJ9SZIkqREqKioiOzubwsJCz+RLTYhn8iVJkqRGKD09nQkTJpCenh52FEl1yDP5kiRJkiQ1Ep7JlyRJkiSpkbDkS5IkSZJUD0QiEZ588skt2kejL/nff/89o0ePJj8/n7S0NLp06cIZZ5zBkiVLNmn7V155hUgkwvLly2s3qNSEjBo1ikgkwlVXXZUw/8knnyQSiYSUStI66z6jkUiE1NRU2rdvz0EHHcRdd91FLBYLO54kSfXa+v+Orv81Z86cOnn/Rl3y//e//9GvXz++/vprHnzwQebMmcNtt93G9OnT6d+/P0uXLg07otRkZWRkcPXVV7Ns2bKwo0iqxCGHHMLChQv59ttvef7559l///0544wzOOKIIygtLQ07niRJ9dq6f0fX/+rWrVudvHejLvljxowhLS2NF198kX333ZfOnTtz6KGH8t///pf58+dzwQUXAFBcXMx5551Hp06dSE9Pp0ePHtx55518++237L///gC0bt2aSCTCqFGjQjwiqfEYOHAgeXl5XHnllVWu89hjj7HddtuRnp5O165dufbaa8uX/e1vf2P33XevsM2OO+7IpZdeWiuZpaYkPT2dvLw8OnbsyC677MLf/vY3/v3vf/P8889z9913A7B8+XL+8Ic/0K5dO7KysjjggAP46KOPEvbz9NNPs9tuu5GRkUHbtm055phjQjgaqXEaNWoURx99dNgxJFVi3b+j638lJyfz73//m1122YWMjAy23nprJk6cWOF/ni9cuJBDDz2UzMxMtt56ax599NFqvXejLflLly7lP//5D6eddhqZmZkJy/Ly8hgxYgQPPfQQQRBw4okn8uCDDzJp0iS++OIL/vWvf9GiRQs6derEY489BsDs2bNZuHAhN954YxiHIzU6ycnJXHHFFfzzn//khx9+qLB81qxZDB06lOHDh/PJJ59wySWXcNFFF5WXixEjRvDuu+/yzTfflG/z2Wef8fHHH3PCCSfU1WFITcoBBxzAjjvuyOOPPw7Acccdx+LFi3n++eeZNWsWu+yyCwceeGD5lXLPPvssxxxzDIcddhgffPAB06dP5ze/+U2YhyBJUmhef/11TjzxRM444ww+//xz/vWvf3H33Xdz+eWXJ6x30UUXMXjwYD766CNGjBjB8OHD+eKLLzb9jYJG6u233w6A4Iknnqh0+XXXXRcAwTvvvBMAwbRp0ypd7+WXXw6AYNmyZbUXVmpiRo4cGRx11FFBEATBHnvsEYwePToIgiB44okngnU/lk444YTgoIMOStjunHPOCfr06VP+escddwwuvfTS8tfjx48Pdt9991pOLzV+639GNzRs2LBg2223DV5//fUgKysrWLt2bcLy7t27B//617+CIAiC/v37ByNGjKjtuFKTtf5n9fnnnw8GDBgQZGdnBzk5OcHhhx8ezJkzp3zduXPnBkDw2GOPBfvtt1+QmZkZ7LDDDsFbb70VUnqp8Ro5cmSQnJwcNG/evPxryJAhwYEHHhhcccUVCeved999QYcOHcpfA8Gf/vSnhHV233334M9//vMmv3+jPZO/TvzvqWrffvstycnJ7LvvvnWUSNL6rr76au65554K/3fyiy++YMCAAQnzBgwYwNdff01ZWRkQP5s/ZcoUIP5Zf/DBBxkxYkTdBJeaqCAIiEQifPTRR6xcuZI2bdrQokWL8q+5c+eWX2Hz4YcfcuCBB4acWGoaVq1axbhx43jvvfeYPn06SUlJHHPMMRUGy7zgggs4++yz+fDDD+nVqxfHH3+842xItWD//ffnww8/LP+aNGkSH330EZdeemnCv5unnHIKCxcuZPXq1eXb9u/fP2Ff/fv3r9aZ/JQaO4p6pkePHkQiEb744otK7//74osvaN26dYVL+SXVrX322YdBgwYxfvz4ao95cfzxx3Peeefx/vvvs2bNGr7//nuGDRtWO0ElAfF/P7t168bKlSvp0KEDr7zySoV1WrVqBeC/sVIdGjx4cMLru+66i3bt2vH555/Tt2/f8vlnn302hx9+OAATJ05ku+22Y86cOfTu3btO80qNXfPmzenRo0fCvJUrVzJx4kSOPfbYCutnZGTU2Hs32jP5bdq04aCDDuKWW25hzZo1CcsKCgp44IEHGDZsGNtvvz2xWIxXX3210v2kpaUBlJ85lFTzrrrqKp5++mlmzJhRPm/bbbflzTffTFjvzTffpFevXiQnJwOw1VZbse+++/LAAw/wwAMPcNBBB5Gbm1un2aWm5KWXXuKTTz5h8ODB7LLLLhQUFJCSkkKPHj0Svtq2bQvADjvswPTp00NOLTUNX3/9Nccffzxbb701WVlZdO3aFYDvvvsuYb0ddtihfLpDhw4ALF68uM5ySk3ZLrvswuzZsyv8u9mjRw+Skn6p5m+//XbCdm+//TbbbrvtJr9Poz2TD3DTTTex5557MmjQIC677DK6devGZ599xjnnnEPHjh25/PLLycnJYeTIkYwePZpJkyax4447Mm/ePBYvXszQoUPp0qULkUiEZ555hsMOO4zMzExatGgR9qFJjcr222/PiBEjmDRpUvm8s846i912242///3vDBs2jBkzZnDTTTdxyy23JGw7YsQIJkyYQElJCddff31dR5careLiYgoKCigrK2PRokW88MILXHnllRxxxBGceOKJJCUl0b9/f44++miuueYaevXqxYIFC8oH2+vXrx8TJkzgwAMPpHv37gwfPpzS0lKee+45zjvvvLAPT2p0fvvb39KlSxfuuOMO8vPzicVi9O3bl5KSkoT1UlNTy6cjkQhAhUv6JdWOiy++mCOOOILOnTszZMgQkpKS+Oijj/j000+57LLLytd75JFH6NevH3vttRcPPPAA7777Lnfeeecmv0+jPZMP0LNnT9577z223nprhg4dSvfu3Tn11FPZf//9mTFjBjk5OQDceuutDBkyhNNOO43evXtzyimnsGrVKgA6duzIxIkTOf/882nfvj1jx44N85CkRuvSSy9N+CVjl1124eGHH2bq1Kn07duXiy++mEsvvbTCJf1DhgxhyZIlrF692scISTXohRdeoEOHDnTt2pVDDjmEl19+mUmTJvHvf/+b5ORkIpEIzz33HPvssw8nnXQSvXr1Yvjw4cybN4/27dsDsN9++/HII4/w1FNPsdNOO3HAAQfw7rvvhnxkUuOzZMkSZs+ezYUXXsiBBx7Itttuy7Jly8KOJWkDgwYN4plnnuHFF19kt912Y4899uD666+nS5cuCetNnDiRqVOnssMOO3Dvvffy4IMP0qdPn01+n0jwayPTSZIkSap3Ro0axfLly3n88cfJzc3l0EMPZcKECXz33Xecf/75zJw5kyeeeIKjjz6ab7/9lm7duvHBBx+w0047AbB8+XJat27Nyy+/zH777RfqsUiqOY36TL4kSZLUWMViMVJSUkhKSmLq1KnMmjWLvn378te//pV//OMfYceTFBLP5EuSJEkN0CGHHEKPHj246aabwo4iqR7xTL4kSZLUgCxbtoxnnnmGV155hYEDB4YdR1I906hH15ckSZIam9GjRzNz5kzOOussjjrqqLDjSKpnvFxfkiRJkqRGwsv1JUmSJElqJCz5kiRJkiQ1EpZ8SZIkqZ668sor2W233WjZsiW5ubkcffTRzJ49O2GdtWvXMmbMGNq0aUOLFi0YPHgwixYtKl/+0Ucfcfzxx9OpUycyMzPZdtttufHGG6t8zzfffJOUlBR22mmn2josSbXIki9JkiTVU6+++ipjxozh7bffZtq0aUSjUQ4++GBWrVpVvs5f//pXnn76aR555BFeffVVFixYwLHHHlu+fNasWeTm5nL//ffz2WefccEFFzB+/PhKH723fPlyTjzxRA488MA6OT5JNc+B9yRJkqQG4scffyQ3N5dXX32VffbZh8LCQtq1a8eUKVMYMmQIAF9++SXbbrstM2bMYI899qh0P2PGjOGLL77gpZdeSpg/fPhwevbsSXJyMk8++SQffvhhbR+SpBrmmXxJkiSpgSgsLAQgJycHiJ+lj0ajDBw4sHyd3r1707lzZ2bMmLHR/azbxzqTJ0/mf//7HxMmTKiF5JLqSkrYASRJkiT9ulgsxplnnsmAAQPo27cvAAUFBaSlpdGqVauEddu3b09BQUGl+3nrrbd46KGHePbZZ8vnff3115x//vm8/vrrpKRYEaSGzE+wJEmS1ACMGTOGTz/9lDfeeGOz9/Hpp59y1FFHMWHCBA4++GAAysrKOOGEE5g4cSK9evWqqbiSQmLJlyRJkuq5sWPH8swzz/Daa6+x1VZblc/Py8ujpKSE5cuXJ5zNX7RoEXl5eQn7+PzzzznwwAM59dRTufDCC8vnr1ixgvfee48PPviAsWPHAvGrBoIgICUlhRdffJEDDjigdg9QUo2x5EuSJEn1VBAEnH766TzxxBO88sordOvWLWH5rrvuSmpqKtOnT2fw4MEAzJ49m++++47+/fuXr/fZZ59xwAEHMHLkSC6//PKEfWRlZfHJJ58kzLvlllt46aWXePTRRyu8p6T6zZIvSZIk1VNjxoxhypQp/Pvf/6Zly5bl99lnZ2eTmZlJdnY2J598MuPGjSMnJ4esrCxOP/10+vfvXz6y/qeffsoBBxzAoEGDGDduXPk+kpOTadeuHUlJSeX3+K+Tm5tLRkZGhfmS6j9LviRJklRP3XrrrQDst99+CfMnT57MqFGjALj++utJSkpi8ODBFBcXM2jQIG655ZbydR999FF+/PFH7r//fu6///7y+V26dOHbb7+t7UOQVMciQRAEYYeQJEmSJElbLinsAJIkSZIkqWZY8iVJkiRJaiQs+ZIkSZIkNRKWfEmSJEmSGglLviRJkiRJjYQlX5IkSZKkRsKSL0mSJElSI2HJlyRJkiSpkbDkS5KkOheJRHjyySfDjiFJUqNjyZckqQkZNWoUkUiEP/3pTxWWjRkzhkgkwqhRo2rs/S655BJ22mmnGtufJEnaOEu+JElNTKdOnZg6dSpr1qwpn7d27VqmTJlC586dQ0wmSZK2lCVfkqQmZpdddqFTp048/vjj5fMef/xxOnfuzM4771w+r7i4mL/85S/k5uaSkZHBXnvtxcyZM8uXv/LKK0QiEaZPn06/fv1o1qwZe+65J7Nnzwbg7rvvZuLEiXz00UdEIhEikQh33313+fY//fQTxxxzDM2aNaNnz5489dRTtX/wkiQ1cpZ8SZKaoNGjRzN58uTy13fddRcnnXRSwjrnnnsujz32GPfccw/vv/8+PXr0YNCgQSxdujRhvQsuuIBrr72W9957j5SUFEaPHg3AsGHDOOuss9huu+1YuHAhCxcuZNiwYeXbTZw4kaFDh/Lxxx9z2GGHMWLEiAr7liRJ1WPJlySpCfrd737HG2+8wbx585g3bx5vvvkmv/vd78qXr1q1iltvvZV//OMfHHroofTp04c77riDzMxM7rzzzoR9XX755ey777706dOH888/n7feeou1a9eSmZlJixYtSElJIS8vj7y8PDIzM8u3GzVqFMcffzw9evTgiiuuYOXKlbz77rt19ncgSVJjlBJ2AEmSVPfatWvH4Ycfzt13300QBBx++OG0bdu2fPk333xDNBplwIAB5fNSU1P5zW9+wxdffJGwrx122KF8ukOHDgAsXrz4V+/vX3+75s2bk5WVxeLFi7fouCRJauos+ZIkNVGjR49m7NixANx8882bvZ/U1NTy6UgkAkAsFqvWduu23ZTtJElS1bxcX5KkJuqQQw6hpKSEaDTKoEGDEpZ1796dtLQ03nzzzfJ50WiUmTNn0qdPn01+j7S0NMrKymossyRJ2jjP5EuS1EQlJyeXX3qfnJycsKx58+b8+c9/5pxzziEnJ4fOnTtzzTXXsHr1ak4++eRNfo+uXbsyd+5cPvzwQ7baaitatmxJenp6jR6HJEn6hSVfkqQmLCsrq8plV111FbFYjN///vesWLGCfv368Z///IfWrVtv8v4HDx7M448/zv7778/y5cuZPHkyo0aNqoHkkiSpMpEgCIKwQ0iSJEmSpC3nPfmSJEmSJDUSlnxJkiRJkhoJS74kSZIkSY2EJV+SJEmSpEbCki9JkiRJUiNhyZckSZIkqZGw5EuSJEmS1EhY8iVJkiRJaiQs+ZIkSZIkNRKWfEmSJEmSGglLviRJkiRJjcT/B4/4A1T7zmMBAAAAAElFTkSuQmCC",
"text/plain": [
"
"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Assuming 'stargazers' dataframe has a 'starred_at' column in datetime format\n",
"stargazers[\"starred_at\"] = pd.to_datetime(stargazers[\"starred_at\"])\n",
"star_growth = stargazers.resample(\"M\", on=\"starred_at\").size().cumsum()\n",
"\n",
"plt.figure(figsize=(12, 6))\n",
"star_growth.plot(kind=\"line\", color=\"gold\", marker=\"*\")\n",
"plt.title(\"Cumulative Star Growth Over Time\")\n",
"plt.xlabel(\"Month\")\n",
"plt.ylabel(\"Cumulative Stars\")\n",
"plt.grid(True)\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "lwacZs7vKiTd"
},
"source": [
"#### Issue Resolution Followed by Pull Requests\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"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "STHueLOoKoZ9"
},
"outputs": [],
"source": [
"# Convert created_at and closet_at to datetime\n",
"pull_requests[\"created_at\"] = pd.to_datetime(pull_requests[\"created_at\"])\n",
"issues[\"closed_at\"] = issues[\"closed_at\"].dt.tz_localize(None)\n",
"\n",
"# Resample both dataframes to get monthly counts\n",
"monthly_closed_issues = issues.resample(\"M\", on=\"closed_at\").size()\n",
"monthly_created_prs = pull_requests.resample(\"M\", on=\"created_at\").size()\n",
"\n",
"# Combine into a single dataframe\n",
"combined_issues_prs_df = pd.concat([monthly_closed_issues, monthly_created_prs], axis=1)\n",
"combined_issues_prs_df.columns = [\"Monthly Closed Issues\", \"Monthly Created PRs\"]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 580
},
"id": "-nRXduEbKr4_",
"outputId": "5a0fe25b-0f1b-4400-d943-88da874f969d"
},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
"
"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"combined_issues_prs_df.plot(kind=\"line\", figsize=(12, 6))\n",
"plt.title(\"Monthly Closed Issues vs Monthly Created Pull Requests\")\n",
"plt.xlabel(\"Month\")\n",
"plt.ylabel(\"Count\")\n",
"plt.grid(True)\n",
"plt.show()"
]
}
],
"metadata": {
"colab": {
"include_colab_link": true,
"provenance": []
},
"kernelspec": {
"display_name": "Python 3",
"name": "python3"
},
"language_info": {
"name": "python"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
================================================
FILE: pyairbyte_notebooks/PyAirbyte_Postgres_Custom_Cache_Demo.ipynb
================================================
{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "R8XtHKK4PujA"
},
"source": [
"# PyAirbyte Custom Postgres Cache Demo\n",
"\n",
"Below is a pre-release demo of PyAirbyte, showcasing how to use Postgres as a\n",
"Cache.\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."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "Lyxh2NLuQJUf"
},
"source": [
"## Install PyAirbyte\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "9DEgu1WpQNt-"
},
"outputs": [],
"source": [
"# Add virtual environment support for running in Google Colab\n",
"!apt-get install -qq python3.10-venv\n",
"\n",
"# Install PyAirbyte\n",
"%pip install --quiet airbyte"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "QdCDkVEgfPod"
},
"source": [
"## Install and setup Postgres (optional)\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."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "xmJAMk2bfdss"
},
"outputs": [],
"source": [
"# Install postgresql server\n",
"!sudo apt-get -y -qq update\n",
"!sudo apt-get -y -qq install postgresql\n",
"!sudo service postgresql start\n",
"\n",
"# Setup a password `postgres` for username `postgres`\n",
"!sudo -u postgres psql -U postgres -c \"ALTER USER postgres PASSWORD 'postgres';\"\n",
"\n",
"# Setup a database with name `pyairbyte_demo` to be used\n",
"!sudo -u postgres psql -U postgres -c 'DROP DATABASE IF EXISTS pyairbyte_demo;'\n",
"!sudo -u postgres psql -U postgres -c 'CREATE DATABASE pyairbyte_demo;'"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "cXJ_cRQV7nIb"
},
"source": [
"## Locating your Data Source\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"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "tfjct5EQ7nIb"
},
"outputs": [],
"source": [
"# Import PyAirbyte\n",
"import airbyte as ab\n",
"\n",
"# Show all available connectors\n",
"ab.get_available_connectors()"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "JWWeEbTVEDFz"
},
"source": [
"## Load the Source Data using PyAirbyte\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "1PhfWpS8QVzE"
},
"source": [
"Create and install a source connector:\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 133
},
"id": "5BI9hIeUvxXE",
"outputId": "1fbe2871-ffe4-403b-c4a5-cb15cd4ee260"
},
"outputs": [
{
"data": {
"text/html": [
""
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"\n",
" \n",
"
"
],
"text/plain": [
"+----+-----------+------------+--------------+\n",
"| id | name | product_id | purchased_at |\n",
"+----+-----------+------------+--------------+\n",
"| 40 | Kelvin | 80 | None |\n",
"| 44 | Jen | 1 | None |\n",
"| 24 | Nestor | 20 | None |\n",
"| 19 | Marquitta | 93 | None |\n",
"| 43 | Tari | 26 | None |\n",
"| 28 | Porter | 10 | None |\n",
"| 1 | Murray | 41 | None |\n",
"| 9 | Travis | 70 | None |\n",
"| 5 | Osvaldo | 89 | None |\n",
"| 46 | Rodger | 35 | None |\n",
"+----+-----------+------------+--------------+"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"%%sql\n",
"# Show most recent purchases by purchase date:\n",
"SELECT users.id, users.name, purchases.product_id, purchases.purchased_at\n",
"FROM {{ users_table.fullname }} AS users\n",
"JOIN {{ purchases_table.fullname }} AS purchases\n",
"ON users.id = purchases.user_id\n",
"ORDER BY purchases.purchased_at DESC\n",
"LIMIT 10"
]
}
],
"metadata": {
"colab": {
"provenance": []
},
"kernelspec": {
"display_name": "Python 3",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.6"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
================================================
FILE: pyairbyte_notebooks/PyAirbyte_Shopify_Demo.ipynb
================================================
{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "En5baAhvYE_y"
},
"source": [
"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",
"### Prerequisites:\n",
"- An active [Shopify store](https://www.shopify.com/).\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)."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "8awBDcLvRW2g"
},
"source": [
"## Install PyAirbyte"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "xrhNw5kK5Lvx"
},
"outputs": [],
"source": [
"# Add virtual environment support for running in Google Colab\n",
"!apt-get install -qq python3.10-venv\n",
"\n",
"# Install PyAirbyte\n",
"%pip install --quiet airbyte"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "mYsTAS1wRgO_"
},
"source": [
"## Configure the source PyAirbyte connector\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",
"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",
"*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."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 149
},
"id": "9em82J2Q5WzN",
"outputId": "9c275156-e51d-4b7f-a8f3-9bd0e98aee42"
},
"outputs": [
{
"data": {
"text/html": [
""
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"
Installing 'source-faker' into virtual environment '/content/.venv-source-faker'.\n", "Running 'pip install airbyte-source-faker'...\n", "\n", "\n" ], "text/plain": [ "Installing \u001b[32m'source-faker'\u001b[0m into virtual environment \u001b[32m'/content/.venv-source-faker'\u001b[0m.\n", "Running \u001b[32m'pip install airbyte-source-faker'\u001b[0m\u001b[33m...\u001b[0m\n", "\n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
Connector 'source-faker' installed successfully!\n", "For more information, see the source-faker documentation:\n", "https://docs.airbyte.com/integrations/sources/faker#reference\n", "\n", "\n" ], "text/plain": [ "Connector \u001b[32m'source-faker'\u001b[0m installed successfully!\n", "For more information, see the source-faker documentation:\n", "\u001b[4;94mhttps://docs.airbyte.com/integrations/sources/faker#reference\u001b[0m\n", "\n" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import airbyte as ab\n", "\n", "# Create and install the source:\n", "source: ab.Source = ab.get_source(\"source-faker\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 34 }, "id": "Ww7NA9cQQjQI", "outputId": "894457f1-2691-456d-bda6-438c027e4376" }, "outputs": [ { "data": { "text/html": [ "
Connection check succeeded for `source-faker`.\n",
"\n"
],
"text/plain": [
"Connection check succeeded for `source-faker`.\n"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Configure the source\n",
"source.set_config(\n",
" config={\n",
" \"count\": 50_000, # Adjust this to get a larger or smaller dataset\n",
" \"seed\": 123,\n",
" },\n",
")\n",
"# Verify the config and creds by running `check`:\n",
"source.check()"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "EwSqhsrmgcQ4"
},
"source": [
"## Define a Postgres Cache\n",
"\n",
"Define a PyAirbyte Cache from the existing PosgreSQL database."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "jJeZN6phg-1v"
},
"outputs": [],
"source": [
"from airbyte.caches import PostgresCacheConfig, PostgresCache\n",
"\n",
"#Define a Postgres Cache and pass the necessary configuration\n",
"pg_cache = PostgresCache(\n",
" PostgresCacheConfig(\n",
" host=\"localhost\",\n",
" port=5432,\n",
" username=\"postgres\",\n",
" password=\"postgres\",\n",
" database=\"pyairbyte_demo\"\n",
" )\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "Que5DAqtEJu0"
},
"source": [
"## Read Data into the PostgreSQL Cache\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 338
},
"id": "qQVRO69c2DoA",
"outputId": "7bc4bb21-c302-45cd-fb23-93e8deaca239"
},
"outputs": [
{
"data": {
"text/markdown": [
"## Read Progress\n",
"\n",
"Started reading at 22:27:43.\n",
"\n",
"Read **100,100** records over **59 seconds** (1,696.6 records / second).\n",
"\n",
"Wrote **100,100** records over 13 batches.\n",
"\n",
"Finished reading at 22:28:42.\n",
"\n",
"Started finalizing streams at 22:28:42.\n",
"\n",
"Finalized **13** batches over 8 seconds.\n",
"\n",
"Completed 3 out of 3 streams:\n",
"\n",
" - users\n",
" - products\n",
" - purchases\n",
"\n",
"\n",
"Completed writing at 22:28:51. Total time elapsed: 1min 8s\n",
"\n",
"\n",
"------------------------------------------------\n"
],
"text/plain": [
"Completed `source-faker` read operation at 22:28:51.\n",
"\n"
],
"text/plain": [
"Completed `source-faker` read operation at \u001b[1;92m22:28:51\u001b[0m.\n"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Select all of the source's streams and read data into the previously defined Postgres cache:\n",
"source.select_all_streams()\n",
"read_result: ab.ReadResult = source.read(cache=pg_cache)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "wSru-wGpHZrh"
},
"source": [
"## Working in SQL\n",
"\n",
"Since data is cached in the Postgres database, we can query the data with SQL.\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."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "xdotIOg70nuL"
},
"outputs": [],
"source": [
"# Install JupySQL to enable SQL cell magics\n",
"%pip install --quiet jupysql\n",
"# Load JupySQL extension\n",
"%load_ext sql\n",
"# Configure max row limit (optional)\n",
"%config SqlMagic.displaylimit = 200"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "2tA6L1dHZ2w0"
},
"outputs": [],
"source": [
"# Get the SQLAlchemy 'engine' object for the cache\n",
"engine = read_result.cache.get_sql_engine()\n",
"# Pass the engine to JupySQL\n",
"%sql engine"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 35
},
"id": "0eaYnErPaFsH",
"outputId": "6012ea51-cf61-404a-e731-dcc68b325c1c"
},
"outputs": [
{
"data": {
"text/plain": [
"['airbyte_raw.users', 'airbyte_raw.purchases']"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Get table objects for the 'users' and 'purchases' streams\n",
"users_table = read_result.cache[\"users\"].to_sql_table()\n",
"purchases_table = read_result.cache[\"purchases\"].to_sql_table()\n",
"display([users_table.fullname, purchases_table.fullname])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 289
},
"id": "VjeTOtKHHiA5",
"outputId": "e00b7f64-33ae-424c-93da-6bd0b1e8d757"
},
"outputs": [
{
"data": {
"text/html": [
"Running query in 'postgresql+psycopg2://postgres:***@localhost:5432/pyairbyte_demo'"
],
"text/plain": [
"Running query in 'postgresql+psycopg2://postgres:***@localhost:5432/pyairbyte_demo'"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"10 rows affected."
],
"text/plain": [
"10 rows affected."
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"| id | \n", "name | \n", "product_id | \n", "purchased_at | \n", "
|---|---|---|---|
| 40 | \n", "Kelvin | \n", "80 | \n", "None | \n", "
| 44 | \n", "Jen | \n", "1 | \n", "None | \n", "
| 24 | \n", "Nestor | \n", "20 | \n", "None | \n", "
| 19 | \n", "Marquitta | \n", "93 | \n", "None | \n", "
| 43 | \n", "Tari | \n", "26 | \n", "None | \n", "
| 28 | \n", "Porter | \n", "10 | \n", "None | \n", "
| 1 | \n", "Murray | \n", "41 | \n", "None | \n", "
| 9 | \n", "Travis | \n", "70 | \n", "None | \n", "
| 5 | \n", "Osvaldo | \n", "89 | \n", "None | \n", "
| 46 | \n", "Rodger | \n", "35 | \n", "None | \n", "
Installing 'source-shopify' into virtual environment '/content/.venv-source-shopify'.\n", "Running 'pip install airbyte-source-shopify'...\n", "\n", "\n" ], "text/plain": [ "Installing \u001b[32m'source-shopify'\u001b[0m into virtual environment \u001b[32m'/content/.venv-source-shopify'\u001b[0m.\n", "Running \u001b[32m'pip install airbyte-source-shopify'\u001b[0m\u001b[33m...\u001b[0m\n", "\n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
Connector 'source-shopify' installed successfully!\n", "For more information, see the source-shopify documentation:\n", "https://docs.airbyte.com/integrations/sources/shopify#reference\n", "\n", "\n" ], "text/plain": [ "Connector \u001b[32m'source-shopify'\u001b[0m installed successfully!\n", "For more information, see the source-shopify documentation:\n", "\u001b[4;94mhttps://docs.airbyte.com/integrations/sources/shopify#reference\u001b[0m\n", "\n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
Connection check succeeded for `source-shopify`.\n",
"\n"
],
"text/plain": [
"Connection check succeeded for `source-shopify`.\n"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import airbyte as ab\n",
"from google.colab import userdata\n",
"\n",
"# Create and configure the source connector:\n",
"source = ab.get_source(\n",
" \"source-shopify\",\n",
" install_if_missing=True,\n",
" config={\n",
" \"shop\": userdata.get(\"SHOP\"),\n",
" \"credentials\": {\n",
" \"auth_method\": \"api_password\",\n",
" \"api_password\": userdata.get(\"SHOP_API_PASSWORD\")\n",
" }\n",
" }\n",
")\n",
"\n",
"# Verify the config and creds by running `check()`:\n",
"source.check()"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "-4OCBJd2tL4W"
},
"source": [
"## Load source data from Shopify to local DuckDB cache\n",
"\n",
"Now, we list the available streams for the source Shopify. This is optional but can be useful.\n",
"\n",
"Then, we select the streams we want to sync to the local cache, and proceed to `read()` to get the records."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "R4udSNyObVAv"
},
"outputs": [],
"source": [
"# List the available streams available for the Shopify source\n",
"source.get_available_streams()"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 355
},
"id": "AlqnXX6ibZqX",
"outputId": "ee680a1b-20ea-4cb6-e59d-ac95827d1151"
},
"outputs": [
{
"data": {
"text/markdown": [
"## Read Progress\n",
"\n",
"Started reading at 22:20:53.\n",
"\n",
"Read **52** records over **16 seconds** (3.2 records / second).\n",
"\n",
"Wrote **52** records over 4 batches.\n",
"\n",
"Finished reading at 22:21:09.\n",
"\n",
"Started finalizing streams at 22:21:09.\n",
"\n",
"Finalized **4** batches over 1 seconds.\n",
"\n",
"Completed 4 out of 4 streams:\n",
"\n",
" - product_variants\n",
" - products\n",
" - customers\n",
" - collections\n",
"\n",
"\n",
"Completed writing at 22:21:10. Total time elapsed: 17 seconds\n",
"\n",
"\n",
"------------------------------------------------\n"
],
"text/plain": [
"Completed `source-shopify` read operation at 22:21:10.\n",
"\n"
],
"text/plain": [
"Completed `source-shopify` read operation at \u001b[1;92m22:21:10\u001b[0m.\n"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Select the streams we are interested in loading to cache\n",
"source.select_streams([\"products\", \"product_variants\", \"collections\", \"customers\"])\n",
"\n",
"# Read into DuckDB local default cache\n",
"cache = ab.get_default_cache()\n",
"result = source.read(cache=cache)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "ZKBR6MilSJta"
},
"source": [
"## Read data from the cache\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."
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"id": "lFlveLjfGYof"
},
"outputs": [],
"source": [
"# Read from the cache into a pandas Dataframe:\n",
"products = cache[\"products\"].to_pandas()\n",
"product_variants = cache[\"product_variants\"].to_pandas()\n",
"collections = cache[\"collections\"].to_pandas()\n",
"customers = cache[\"customers\"].to_pandas()"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "p7vX1_w4SYaF"
},
"source": [
"## Data analysis\n",
"\n",
"Let's analyze the data!\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",
"### 1. Product Category and Variant Analysis\n",
"Analyze product types, variants, and their inventory levels."
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 1000
},
"id": "B8orH6pnmcy7",
"outputId": "13552705-0b38-4992-b58b-9b25cf963bdd"
},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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",
"text/plain": [
"
"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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",
"text/plain": [
"
"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
"\n",
"# Product Type Analysis\n",
"product_type_counts = products['product_type'].value_counts()\n",
"product_type_counts.plot(kind='bar')\n",
"plt.title('Product Type Distribution')\n",
"plt.xlabel('Product Type')\n",
"plt.ylabel('Count')\n",
"plt.show()\n",
"\n",
"# Variant Price Range Analysis\n",
"product_variants['price'] = pd.to_numeric(product_variants['price'], errors='coerce')\n",
"product_variants['price'].hist(bins=20)\n",
"plt.title('Product Variant Price Distribution')\n",
"plt.xlabel('Price')\n",
"plt.ylabel('Number of Variants')\n",
"plt.show()\n",
"\n",
"# Inventory Level Analysis\n",
"product_variants['inventory_quantity'] = pd.to_numeric(product_variants['inventory_quantity'], errors='coerce')\n",
"product_variants['inventory_quantity'].hist(bins=20)\n",
"plt.title('Inventory Level Distribution')\n",
"plt.xlabel('Inventory Quantity')\n",
"plt.ylabel('Number of Variants')\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "DXhO3hHNnY_1"
},
"source": [
"### 2. Customer Segmentation\n",
"Segment customers based on available attributes."
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"id": "5Mh2MiCLohWL"
},
"outputs": [],
"source": [
"import json\n",
"\n",
"# Parse JSON\n",
"def parse_json(x):\n",
" try:\n",
" return json.loads(x.replace(\"'\", \"\\\"\"))\n",
" except:\n",
" return {}\n",
"\n",
"# Apply the function to unpack JSON content\n",
"customers['email_marketing_consent'] = customers['email_marketing_consent'].apply(parse_json)\n",
"\n",
"# Extract relevant fields\n",
"customers['consent_opt_in_level'] = customers['email_marketing_consent'].apply(lambda x: x.get('opt_in_level', None))\n",
"customers['consent_state'] = customers['email_marketing_consent'].apply(lambda x: x.get('state', None))"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 1000
},
"id": "k5wYK_yWpZxY",
"outputId": "ace4b70d-12b9-466e-b89a-bb1133c06524"
},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
"
"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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",
"text/plain": [
"
"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Analyze the distribution of opt-in levels\n",
"opt_in_level_counts = customers['consent_opt_in_level'].value_counts()\n",
"\n",
"# Analyze the distribution of consent states\n",
"consent_state_counts = customers['consent_state'].value_counts()\n",
"\n",
"# Opt-in level distribution\n",
"opt_in_level_counts.plot(kind='bar')\n",
"plt.title('Distribution of Consent Opt-In Levels')\n",
"plt.xlabel('Opt-In Level')\n",
"plt.ylabel('Number of Customers')\n",
"plt.show()\n",
"\n",
"# Consent state distribution\n",
"consent_state_counts.plot(kind='bar', color='orange')\n",
"plt.title('Distribution of Consent States')\n",
"plt.xlabel('Consent State')\n",
"plt.ylabel('Number of Customers')\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "3qb7zkCOnwoH"
},
"source": [
"### 3. Collection Popularity and Strategy\n",
"Analyze collections based on the number of products."
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 538
},
"id": "V-yF_f2Anzlk",
"outputId": "593d2568-20c7-4b04-8212-8c931f5ff623"
},
"outputs": [
{
"data": {
"image/png": 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cc4qPj1d0dLTef/993XfffQoODlZiYqLeeecdHTx4UD169JAkPfXUU9qwYYOaNGmiwYMHq0iRIpo/f77S0tJyddyWyzVv3lwDBw5UTEyM9u7dq9atW6to0aI6fPiw3nnnHf33v//VP//5z1y/DvXq1dPy5cs1YsQIRUVFyd/f37VE8K9y+zkF3Ni2vxFwmazdk3fu3HnV6fr06WP5+fnleNv58+et4cOHW+XKlbOKFi1q3Xbbbdazzz7rtiulZVnWxYsXrejoaCskJMTy8/OzOnbsaB0/fjzb7smWZVnr16+3atWqZfn4+Fi333679eabb+a4K69lWdZrr71m1a1b13I6nVZwcLDVvHlza8OGDa7bf/rpJ6tDhw5WQECAJSlXuyqnpqZas2bNsqKioix/f3/Lx8fHuu2226yhQ4e67TZsWZb18ccfW40bN7Z8fX2twMBAq2PHjtb+/fuzPWZun5Mka8iQIdnuX7FiRatPnz5uY9OmTbPKly9veXl5XXNX5Svtnvzss89mmzan9+SvrrV78uX3X7VqlVW7dm2rWLFiVqVKlayZM2dar732Wo6ZV61aZTVq1Mj1ejZo0MB66623XLc3b97cioiIuObzu5r09HRr4cKFVtOmTa2goCCraNGiVsWKFa1+/fpl23V59+7dVps2bSx/f3+rePHi1t13321t27Ytx9fiarsnZ3nllVesevXqWb6+vlZAQIAVGRlpPfHEE9bJkyc9eh0uXLhg9ezZ0ypRooQlyTWvnHZPtqzcfU6zPo9nzpxxG8/6fyK3u8Lj5uCwLLZKAgAAZmIbFQAAYCyKCgAAMBZFBQAAGIuiAgAAjEVRAQAAxqKoAAAAY/2tD/iWmZmpkydPKiAg4G9zGG8AAAo7y7J0/vx5lStX7prnbvtbF5WTJ096dI4LAABgjuPHj6tChQpXneZvXVQCAgIk/flEOeU3AAB/D8nJyQoLC3P9Hb+av3VRyVrdExgYSFEBAOBvJjebbbAxLQAAMBZFBQAAGIuiAgAAjEVRAQAAxqKoAAAAY1FUAACAsSgqAADAWBQVAABgLIoKAAAwFkUFAAAYi6ICAACMRVEBAADGoqgAAABjUVQAAICxKCoAAMBYRewOAABwV2nsGrsjoAAdm9HB7ghGY4kKAAAwFkUFAAAYi6ICAACMRVEBAADGoqgAAABjUVQAAICxKCoAAMBYFBUAAGAsigoAADAWRQUAABiLogIAAIxFUQEAAMaiqAAAAGNRVAAAgLEoKgAAwFgUFQAAYCyKCgAAMBZFBQAAGIuiAgAAjEVRAQAAxqKoAAAAY1FUAACAsSgqAADAWBQVAABgLIoKAAAwFkUFAAAYi6ICAACMRVEBAADGoqgAAABjUVQAAICxKCoAAMBYFBUAAGAsigoAADAWRQUAABjL1qKSkZGhCRMmKDw8XL6+vqpSpYqmTZsmy7LsjAUAAAxRxM6Zz5w5U/PmzVNcXJwiIiL01VdfqV+/fgoKClJ0dLSd0QAAgAFsLSrbtm3T/fffrw4dOkiSKlWqpLfeeks7duywMxYAADCErat+GjVqpI0bN+r777+XJO3bt0+ff/652rVrl+P0aWlpSk5OdrsAAICbl61LVMaOHavk5GRVr15d3t7eysjI0PTp09WrV68cp4+JidGUKVMKOCUAALCLrUtU3n77bS1ZskRLly7V7t27FRcXp1mzZikuLi7H6ceNG6ekpCTX5fjx4wWcGAAAFCRbl6iMHj1aY8eOVY8ePSRJkZGR+uGHHxQTE6M+ffpkm97pdMrpdBZ0TAAAYBNbl6ikpqbKy8s9gre3tzIzM21KBAAATGLrEpWOHTtq+vTpuvXWWxUREaE9e/Zo9uzZ6t+/v52xAACAIWwtKnPnztWECRM0ePBgnT59WuXKldPAgQM1ceJEO2MBAABD2FpUAgICFBsbq9jYWDtjAAAAQ3GuHwAAYCyKCgAAMBZFBQAAGIuiAgAAjEVRAQAAxqKoAAAAY1FUAACAsSgqAADAWBQVAABgLIoKAAAwFkUFAAAYi6ICAACMRVEBAADGoqgAAABjUVQAAICxKCoAAMBYFBUAAGAsigoAADAWRQUAABiLogIAAIxFUQEAAMaiqAAAAGNRVAAAgLEoKgAAwFgUFQAAYCyKCgAAMBZFBQAAGIuiAgAAjEVRAQAAxqKoAAAAY1FUAACAsSgqAADAWBQVAABgLIoKAAAwFkUFAAAYi6ICAACMRVEBAADGoqgAAABjUVQAAICxKCoAAMBYFBUAAGAsigoAADAWRQUAABiLogIAAIxFUQEAAMaiqAAAAGNRVAAAgLEoKgAAwFgUFQAAYCyKCgAAMBZFBQAAGIuiAgAAjEVRAQAAxqKoAAAAY1FUAACAsSgqAADAWBQVAABgLIoKAAAwFkUFAAAYi6ICAACMRVEBAADGoqgAAABjUVQAAICxKCoAAMBYFBUAAGAsigoAADAWRQUAABjL9qLy448/qnfv3goJCZGvr68iIyP11Vdf2R0LAAAYoIidM//tt9/UuHFj3X333froo49UunRpHT58WMHBwXbGAgAAhrC1qMycOVNhYWFatGiRayw8PNzGRAAAwCS2rvpZtWqV6tevrwcffFChoaGqW7euFixYcMXp09LSlJyc7HYBAAA3L1uLytGjRzVv3jzddtttWrdunR599FFFR0crLi4ux+ljYmIUFBTkuoSFhRVwYgAAUJAclmVZds3cx8dH9evX17Zt21xj0dHR2rlzp7Zv355t+rS0NKWlpbmuJycnKywsTElJSQoMDCyQzACQ3yqNXWN3BBSgYzM62B2hwCUnJysoKChXf79tXaJStmxZ1axZ022sRo0aSkxMzHF6p9OpwMBAtwsAALh53XBRSU5O1ooVK3TgwAGP79u4cWMdOnTIbez7779XxYoVbzQWAAC4CXhcVLp166YXXnhBknTx4kXVr19f3bp1U+3atfXee+959FjDhw/XF198oaefflpHjhzR0qVL9corr2jIkCGexgIAADchj4vKp59+qqZNm0qSPvjgA1mWpXPnzun555/XU0895dFjRUVF6YMPPtBbb72lWrVqadq0aYqNjVWvXr08jQUAAG5CHh9HJSkpSSVLlpQkrV27Vg888ICKFy+uDh06aPTo0R4HuO+++3Tfffd5fD8AAHDz83iJSlhYmLZv366UlBStXbtWrVu3lvTnUWaLFSuW5wEBAEDh5fESlWHDhqlXr17y9/dXxYoV1aJFC0l/rhKKjIzM63wAAKAQ87ioDB48WHfddZcSExPVqlUreXn9uVCmcuXKmj59ep4HBAAAhZfHq36mTp2qGjVqqEuXLvL393eN33PPPfr444/zNBwAACjcPC4qU6ZM0YULF7KNp6amasqUKXkSCgAAQLqOomJZlhwOR7bxffv2ufYGAgAAyAu53kYlODhYDodDDodD1apVcysrGRkZunDhggYNGpQvIQEAQOGU66ISGxsry7LUv39/TZkyRUFBQa7bfHx8VKlSJTVs2DBfQgIAgMIp10WlT58+kqTw8HA1btxYRYp4vMMQAACARzzeRiUlJUUbN27MNr5u3Tp99NFHeRIKAABAuo6iMnbsWGVkZGQbtyxLY8eOzZNQAAAA0nUUlcOHD6tmzZrZxqtXr64jR47kSSgAAADpOopKUFCQjh49mm38yJEj8vPzy5NQAAAA0nUUlfvvv1/Dhg1TfHy8a+zIkSMaOXKkOnXqlKfhAABA4eZxUXnmmWfk5+en6tWrKzw8XOHh4apRo4ZCQkI0a9as/MgIAAAKKY/3MQ4KCtK2bdu0YcMG7du3T76+vqpdu7aaNWuWH/kAAEAhdl0HQ3E4HGrdurVat26d13kAAABcPC4qU6dOvertEydOvO4wAAAAl/O4qHzwwQdu1//44w8lJCSoSJEiqlKlCkUFAADkGY+Lyp49e7KNJScnq2/fvurSpUuehAIAAJCuY6+fnAQGBmrKlCmaMGFCXjwcAACApDwqKpKUlJSkpKSkvHo4AAAAz1f9PP/8827XLcvSqVOn9MYbb6hdu3Z5FgwAAMDjojJnzhy3615eXipdurT69OmjcePG5VkwAAAAj4tKQkJCfuQAAADIJs+2UQEAAMhruVqi0rVr11w/4Pvvv3/dYQAAAC6XqyUqQUFBrktgYKA2btyor776ynX7rl27tHHjRgUFBeVbUAAAUPjkaonKokWLXD+PGTNG3bp108svvyxvb29JUkZGhgYPHqzAwMD8SQkAAAolj7dRee211zRq1ChXSZEkb29vjRgxQq+99lqehgMAAIWbx0UlPT1dBw8ezDZ+8OBBZWZm5kkoAAAA6Tp2T+7Xr58GDBig+Ph4NWjQQJL05ZdfasaMGerXr1+eBwQAAIWXx0Vl1qxZuuWWW/Tcc8/p1KlTkqSyZctq9OjRGjlyZJ4HBAAAhZfHRcXLy0tPPPGEnnjiCSUnJ0sSG9ECAIB84XFRyXLmzBkdOnRIklS9enWVKlUqz0IBAABI17ExbUpKivr376+yZcuqWbNmatasmcqWLasBAwYoNTU1PzICAIBCyuOiMmLECG3ZskWrV6/WuXPndO7cOa1cuVJbtmxhGxUAAJCnPF7189577+ndd99VixYtXGPt27eXr6+vunXrpnnz5uVlPgAAUIh5vEQlNTVVZcqUyTYeGhrKqh8AAJCnPC4qDRs21KRJk/T777+7xi5evKgpU6aoYcOGeRoOAAAUbh6v+omNjVXbtm1VoUIF3XHHHZKkffv2qVixYlq3bl2eBwQAAIWXx0UlMjJShw8f1pIlS1yH0n/ooYfUq1cv+fr65nlAAABQeHlUVP744w9Vr15d//vf//TII4/kVyYAAABJHm6jUrRoUbdtUwAAAPKTxxvTDhkyRDNnzlR6enp+5AEAAHDxeBuVnTt3auPGjVq/fr0iIyPl5+fndvv777+fZ+EAAEDh5nFRKVGihB544IH8yAIAAODG46KyaNGi/MgBAACQTa63UcnMzNTMmTPVuHFjRUVFaezYsbp48WJ+ZgMAAIVcrovK9OnT9eSTT8rf31/ly5fXf//7Xw0ZMiQ/swEAgEIu10Xl9ddf10svvaR169ZpxYoVWr16tZYsWaLMzMz8zAcAAAqxXBeVxMREtW/f3nW9ZcuWcjgcOnnyZL4EAwAAyHVRSU9PV7FixdzGihYtqj/++CPPQwEAAEge7PVjWZb69u0rp9PpGvv99981aNAgt2OpcBwVAACQV3JdVPr06ZNtrHfv3nkaBgAA4HK5LiocPwUAABQ0j8/1AwAAUFAoKgAAwFgUFQAAYCyKCgAAMBZFBQAAGCtXe/2sWrUq1w/YqVOn6w4DAABwuVwVlc6dO+fqwRwOhzIyMm4kDwAAgEuuigonHgQAAHZgGxUAAGCsXB+Z9nIpKSnasmWLEhMTdenSJbfboqOj8yQYAACAx0Vlz549at++vVJTU5WSkqKSJUvq7NmzKl68uEJDQykqAAAgz3i86mf48OHq2LGjfvvtN/n6+uqLL77QDz/8oHr16mnWrFnXHWTGjBlyOBwaNmzYdT8GAAC4uXhcVPbu3auRI0fKy8tL3t7eSktLU1hYmJ555hk9+eST1xVi586dmj9/vmrXrn1d9wcAADcnj4tK0aJF5eX1591CQ0OVmJgoSQoKCtLx48c9DnDhwgX16tVLCxYsUHBwsMf3BwAANy+Pi0rdunW1c+dOSVLz5s01ceJELVmyRMOGDVOtWrU8DjBkyBB16NBBLVu2vOa0aWlpSk5OdrsAAICbl8dF5emnn1bZsmUlSdOnT1dwcLAeffRRnTlzRvPnz/fosZYtW6bdu3crJiYmV9PHxMQoKCjIdQkLC/M0PgAA+BvxeK+f+vXru34ODQ3V2rVrr2vGx48f1+OPP64NGzaoWLFiubrPuHHjNGLECNf15ORkygoAADcxj5eo3HPPPTp37ly28eTkZN1zzz25fpxdu3bp9OnTuvPOO1WkSBEVKVJEW7Zs0fPPP68iRYrkeCh+p9OpwMBAtwsAALh5ebxEZfPmzdkO8iZJv//+uz777LNcP869996rb775xm2sX79+ql69usaMGSNvb29PowEAgJtMrovK119/7fp5//79+umnn1zXMzIytHbtWpUvXz7XMw4ICMi28a2fn59CQkKua6NcAABw88l1UalTp44cDoccDkeOq3h8fX01d+7cPA0HAAAKt1wXlYSEBFmWpcqVK2vHjh0qXbq06zYfHx+Fhobe8OqazZs339D9AQDAzSXXRaVixYqSpMzMzHwLAwAAcLnrOntyfHy8YmNjdeDAAUlSzZo19fjjj6tKlSp5Gg4AABRuHu+evG7dOtWsWVM7duxQ7dq1Vbt2bX355ZeKiIjQhg0b8iMjAAAopDxeojJ27FgNHz5cM2bMyDY+ZswYtWrVKs/CAQCAws3jJSoHDhzQgAEDso33799f+/fvz5NQAAAA0nUUldKlS2vv3r3Zxvfu3avQ0NC8yAQAACDJg1U/U6dO1ahRo/TII4/o//7v/3T06FE1atRIkrR161bNnDnT7Tw8AAAAN8phWZaVmwm9vb116tQplS5dWrGxsXruued08uRJSVK5cuU0evRoRUdHy+Fw5GvgyyUnJysoKEhJSUmc9wfATaPS2DV2R0ABOjajg90RCpwnf79zvUQlq884HA4NHz5cw4cP1/nz5yX9eTh8AACAvObRXj9/XVpCQQEAAPnJo6JSrVq1a67a+fXXX28oEAAAQBaPisqUKVMUFBSUX1kAAADceFRUevTowS7IAACgwOT6OCoFuTcPAACA5EFRyeVezAAAAHkm16t+MjMz8zMHAABANh4fQh8AAKCgUFQAAICxKCoAAMBYFBUAAGAsigoAADAWRQUAABiLogIAAIxFUQEAAMaiqAAAAGNRVAAAgLEoKgAAwFgUFQAAYCyKCgAAMBZFBQAAGIuiAgAAjEVRAQAAxqKoAAAAY1FUAACAsSgqAADAWBQVAABgLIoKAAAwFkUFAAAYi6ICAACMRVEBAADGoqgAAABjUVQAAICxKCoAAMBYFBUAAGAsigoAADAWRQUAABiLogIAAIxFUQEAAMaiqAAAAGNRVAAAgLEoKgAAwFgUFQAAYCyKCgAAMBZFBQAAGIuiAgAAjEVRAQAAxqKoAAAAY1FUAACAsSgqAADAWBQVAABgLIoKAAAwFkUFAAAYi6ICAACMRVEBAADGoqgAAABjUVQAAICxKCoAAMBYthaVmJgYRUVFKSAgQKGhoercubMOHTpkZyQAAGAQW4vKli1bNGTIEH3xxRfasGGD/vjjD7Vu3VopKSl2xgIAAIYoYufM165d63Z98eLFCg0N1a5du9SsWTObUgEAAFPYWlT+KikpSZJUsmTJHG9PS0tTWlqa63pycnKB5AIAAPYwZmPazMxMDRs2TI0bN1atWrVynCYmJkZBQUGuS1hYWAGnBAAABcmYojJkyBB9++23WrZs2RWnGTdunJKSklyX48ePF2BCAABQ0IxY9fPYY4/pf//7nz799FNVqFDhitM5nU45nc4CTAYAAOxka1GxLEtDhw7VBx98oM2bNys8PNzOOAAAwDC2FpUhQ4Zo6dKlWrlypQICAvTTTz9JkoKCguTr62tnNAAAYABbt1GZN2+ekpKS1KJFC5UtW9Z1Wb58uZ2xAACAIWxf9QMAAHAlxuz1AwAA8FcUFQAAYCyKCgAAMBZFBQAAGIuiAgAAjEVRAQAAxqKoAAAAY1FUAACAsSgqAADAWBQVAABgLIoKAAAwFkUFAAAYi6ICAACMRVEBAADGoqgAAABjUVQAAICxKCoAAMBYFBUAAGAsigoAADAWRQUAABiLogIAAIxFUQEAAMaiqAAAAGNRVAAAgLEoKgAAwFgUFQAAYCyKCgAAMBZFBQAAGIuiAgAAjEVRAQAAxqKoAAAAY1FUAACAsYrYHQDXp9LYNXZHQAE6NqOD3REAwBYsUQEAAMaiqAAAAGNRVAAAgLEoKgAAwFgUFQAAYCyKCgAAMBZFBQAAGIuiAgAAjEVRAQAAxqKoAAAAY1FUAACAsSgqAADAWBQVAABgLIoKAAAwFkUFAAAYi6ICAACMRVEBAADGoqgAAABjUVQAAICxKCoAAMBYFBUAAGAsigoAADAWRQUAABiLogIAAIxFUQEAAMaiqAAAAGNRVAAAgLEoKgAAwFgUFQAAYCyKCgAAMBZFBQAAGIuiAgAAjEVRAQAAxqKoAAAAYxlRVF588UVVqlRJxYoV01133aUdO3bYHQkAABjA9qKyfPlyjRgxQpMmTdLu3bt1xx13qE2bNjp9+rTd0QAAgM1sLyqzZ8/WI488on79+qlmzZp6+eWXVbx4cb322mt2RwMAADaztahcunRJu3btUsuWLV1jXl5eatmypbZv325jMgAAYIIids787NmzysjIUJkyZdzGy5Qpo4MHD2abPi0tTWlpaa7rSUlJkqTk5OT8DWqgzLRUuyOgABXGz3hhxu934VIYf7+znrNlWdec1tai4qmYmBhNmTIl23hYWJgNaYCCExRrdwIA+aUw/36fP39eQUFBV53G1qJSqlQpeXt76+eff3Yb//nnn3XLLbdkm37cuHEaMWKE63pmZqZ+/fVXhYSEyOFw5Hte2Cs5OVlhYWE6fvy4AgMD7Y4DIA/x+124WJal8+fPq1y5ctec1tai4uPjo3r16mnjxo3q3LmzpD/Lx8aNG/XYY49lm97pdMrpdLqNlShRogCSwiSBgYH8RwbcpPj9LjyutSQli+2rfkaMGKE+ffqofv36atCggWJjY5WSkqJ+/frZHQ0AANjM9qLSvXt3nTlzRhMnTtRPP/2kOnXqaO3atdk2sAUAAIWP7UVFkh577LEcV/UAl3M6nZo0aVK21X8A/v74/caVOKzc7BsEAABgA9uPTAsAAHAlFBUAAGAsigoAADAWRQUAABiLogKjpaen6+OPP9b8+fN1/vx5SdLJkyd14cIFm5MBAAoCe/3AWD/88IPatm2rxMREpaWl6fvvv1flypX1+OOPKy0tTS+//LLdEQEA+cyI46gAOXn88cdVv3597du3TyEhIa7xLl266JFHHrExGYC8dPjwYW3atEmnT59WZmam220TJ060KRVMQVGBsT777DNt27ZNPj4+buOVKlXSjz/+aFMqAHlpwYIFevTRR1WqVCndcsstbieYdTgcFBVQVGCuzMxMZWRkZBs/ceKEAgICbEgEIK899dRTmj59usaMGWN3FBiKjWlhrNatWys2NtZ13eFw6MKFC5o0aZLat29vXzAAeea3337Tgw8+aHcMGIyNaWGsEydOqE2bNrIsS4cPH1b9+vV1+PBhlSpVSp9++qlCQ0PtjgjgBg0YMEBRUVEaNGiQ3VFgKIoKjJaenq5ly5bp66+/1oULF3TnnXeqV69e8vX1tTsagDwQExOj2bNnq0OHDoqMjFTRokXdbo+OjrYpGUxBUQEA2CY8PPyKtzkcDh09erQA08BEFBUYa9WqVTmOOxwOFStWTFWrVr3qf3IAgL8/igqM5eXlJYfDob9+RLPGHA6HmjRpohUrVig4ONimlADywqVLl5SQkKAqVaqoSBF2SMX/w14/MNaGDRsUFRWlDRs2KCkpSUlJSdqwYYPuuusu/e9//9Onn36qX375RaNGjbI7KoDrlJqaqgEDBqh48eKKiIhQYmKiJGno0KGaMWOGzelgAooKjPX4449r9uzZuvfeexUQEKCAgADde++9evbZZzV69Gg1btxYsbGx2rBhg91RAVyncePGad++fdq8ebOKFSvmGm/ZsqWWL19uYzKYguVrMFZ8fLwCAwOzjQcGBro2sLvtttt09uzZgo4GII+sWLFCy5cv1z/+8Q+3o9JGREQoPj7exmQwBUtUYKx69epp9OjROnPmjGvszJkzeuKJJxQVFSXpz3OEhIWF2RURwA06c+ZMjsdESklJcSsuKLwoKjDWq6++qoSEBFWoUEFVq1ZV1apVVaFCBR07dkwLFy6UJF24cEH/+c9/bE4K4HrVr19fa9ascV3PKicLFy5Uw4YN7YoFg7DXD4yWmZmp9evX6/vvv5ck3X777WrVqpW8vOjYwM3g888/V7t27dS7d28tXrxYAwcO1P79+7Vt2zZt2bJF9erVszsibEZRAQDYKj4+XjNmzNC+fftcR6AeM2aMIiMj7Y4GA1BUYLSUlBRt2bJFiYmJunTpktttHFobAG5+FBUYa8+ePWrfvr1SU1OVkpKikiVL6uzZsypevLhCQ0M5tDZwE0hOTs5x3OFwyOl0ysfHp4ATwTSs6Iexhg8fro4dO+q3336Tr6+vvvjiC/3www+qV6+eZs2aZXc8AHmgRIkSCg4OznYpUaKEfH19VbFiRU2aNEmZmZl2R4VNOI4KjLV3717Nnz9fXl5e8vb2VlpamipXrqxnnnlGffr0UdeuXe2OCOAGLV68WOPHj1ffvn3VoEEDSdKOHTsUFxen//znPzpz5oxmzZolp9OpJ5980ua0sANFBcYqWrSoa++e0NBQJSYmqkaNGgoKCtLx48dtTgcgL8TFxem5555Tt27dXGMdO3ZUZGSk5s+fr40bN+rWW2/V9OnTKSqFFKt+YKy6detq586dkqTmzZtr4sSJWrJkiYYNG6ZatWrZnA5AXti2bZvq1q2bbbxu3bravn27JKlJkyaucwCh8KGowFhPP/20ypYtK0maPn26goOD9eijj+rMmTN65ZVXbE4HIC+EhYXp1VdfzTb+6quvuo46/csvv3CG9EKMvX4AALZZtWqVHnzwQVWvXt11aoyvvvpKBw8e1Lvvvqv77rtP8+bN0+HDhzV79myb08IOFBUY7/Tp0zp06JAkqXr16ipdurTNiQDkpYSEBM2fP9/tCNQDBw5UpUqV7A0GI1BUYKzz589r8ODBWrZsmTIyMiRJ3t7e6t69u1588UUFBQXZnBAAkN8oKjBW9+7dtWfPHs2dO9d1crLt27fr8ccfV506dbRs2TKbEwLIC+fOndOrr76qAwcOSJIiIiLUv39/voxAEkUFBvPz89O6devUpEkTt/HPPvtMbdu2VUpKik3JAOSVr776Sm3atJGvr6/rOCo7d+7UxYsXtX79et155502J4TdOI4KjBUSEpLjN6qgoCD2AABuEsOHD1enTp20YMECFSny55+k9PR0Pfzwwxo2bJg+/fRTmxPCbixRgbFeeeUVvfPOO3rjjTd0yy23SJJ++ukn11FpBw4caHNCADfK19dXe/bsUfXq1d3G9+/fr/r16ys1NdWmZDAFS1RgrHnz5unIkSO69dZbdeutt0qSEhMT5XQ6debMGc2fP9817e7du+2KCeAGBAYGKjExMVtROX78uAICAmxKBZNQVGCszp072x0BQD7r3r27BgwYoFmzZqlRo0aSpK1bt2r06NF66KGHbE4HE7DqBwBgm0uXLmn06NF6+eWXlZ6eLunP83w9+uijmjFjhpxOp80JYTeKCgDAFhkZGdq6dasiIyPldDoVHx8vSapSpYqKFy9uczqYgqICALBNsWLFdODAAYWHh9sdBYbipIQAANvUqlVLR48etTsGDMYSFQCAbdauXatx48Zp2rRpqlevnvz8/NxuDwwMtCkZTEFRgfEuXbqkhIQEValSxXVAKAA3By+v/7dg3+FwuH62LEsOh8N1ni8UXvyvD2OlpqZq6NChiouLkyR9//33qly5soYOHary5ctr7NixNicEcKM2bdpkdwQYjqICY40bN0779u3T5s2b1bZtW9d4y5YtNXnyZIoKcBNo3ry53RFgOIoKjLVixQotX75c//jHP9wWCUdERLh2YwTw9/P111/netratWvnYxL8HVBUYKwzZ84oNDQ023hKSopbcQHw91KnTh05HA7XdihXwzYqYPdkGKt+/fpas2aN63rWf2gLFy5Uw4YN7YoF4AYlJCTo6NGjSkhI0Hvvvafw8HC99NJL2rNnj/bs2aOXXnpJVapU0XvvvWd3VBiAJSow1tNPP6127dpp//79Sk9P13//+1/t379f27Zt05YtW+yOB+A6VaxY0fXzgw8+qOeff17t27d3jdWuXVthYWGaMGEC5/wCS1RgriZNmmjv3r1KT09XZGSk1q9fr9DQUG3fvl316tWzOx6APPDNN9/keFTa8PBw7d+/34ZEMA3HUQEA2ObOO+9UrVq1tHDhQvn4+Ej689hJDz/8sL799lvt3r3b5oSwG0UFxjt9+rROnz6tzMxMt3H2BgD+/nbs2KGOHTvKsizX7/TXX38th8Oh1atXq0GDBjYnhN0oKjDWrl271KdPHx04cEB//ZhyxErg5pGSkqIlS5bo4MGDkqQaNWqoZ8+e2Q6nj8KJogJj3XHHHapSpYrGjBmjMmXKZNuN8fIN8gD8PaWkpFBIcFUUFRgrICBAe/bsUdWqVe2OAiCf+Pv7q1u3burfv7+aNGlidxwYiL1+YKx7771X+/btszsGgHz05ptv6tdff9U999yjatWqacaMGTp58qTdsWAQlqjAWGfPnlWfPn3UoEED1apVS0WLFnW7vVOnTjYlA5DXzpw5ozfeeEOLFy/WgQMH1KZNG/Xv31+dOnXirOmFHEUFxlq9erX+9a9/KTk5OdttbEwL3Lzmzp2r0aNH69KlSypVqpQGDRqksWPHqnjx4nZHgw0oKjBWpUqVdN9992nChAkqU6aM3XEA5KOff/5ZcXFxWrx4sX744Qd16dJFAwYM0IkTJzRz5kyVK1dO69evtzsmbEBRgbECAgK0d+9eValSxe4oAPLJ+++/r0WLFmndunWqWbOmHn74YfXu3VslSpRwTRMfH68aNWro0qVL9gWFbVjxB2N17dpVmzZtoqgAN7F+/fqpR48e2rp1q6KionKcply5cho/fnwBJ4MpWKICY02fPl2xsbHq0KGDIiMjs21MGx0dbVMyADcqa9uzax1HJTAwsKAiwVAUFRgrpxOVZXE4HDp69GgBpgGQl7y8vLIdxPFylmWx0TwkseoHBktISLA7AoB8smnTJtfPlmWpffv2WrhwocqXL29jKpiIJSr4W8j6mF7tGxiAv6+AgADt27dPlStXtjsKDMORaWG0119/XZGRkfL19ZWvr69q166tN954w+5YAIACwqofGGv27NmaMGGCHnvsMTVu3FiS9Pnnn2vQoEE6e/ashg8fbnNCAEB+Y9UPjBUeHq4pU6bo3//+t9t4XFycJk+ezDYswE0kICBAX3/99VU3okfhxBIVGOvUqVNq1KhRtvFGjRrp1KlTNiQCkFe6du3qdv3333/XoEGDsu2q/P777xdkLBiIogJjVa1aVW+//baefPJJt/Hly5frtttusykVgLwQFBTkdr137942JYHpWPUDY7333nvq3r27WrZs6dpGZevWrdq4caPefvttdenSxeaEAID8RlGB0Xbt2qU5c+bowIEDkqQaNWpo5MiRqlu3rs3JAAAFgaICAACMxTYqME7WOUCuhXOAAMDNjyUqMA7nAAEAZGGJCozDOUAAAFlYogLjcQ4QACi8ONcPAAAwFkUFAAAYi6KCv4WrbVwLALh5sTEtjMM5QAAAWSgqMA7nAAEAZGGvHwAAYCy2UQEAAMaiqAAAAGNRVAAAgLEoKgAAwFgUFQD5avLkyapTp47ret++fdW5c+d8neexY8fkcDi0d+/efJ0PgPxHUQFwVT/99JOGDh2qypUry+l0KiwsTB07dtTGjRvtjiYp5+ITFhamU6dOqVatWvaEApBnOI4KgCs6duyYGjdurBIlSujZZ59VZGSk/vjjD61bt05DhgzRwYMH7Y6YI29vb91yyy12xwCQB1iiAuCKBg8eLIfDoR07duiBBx5QtWrVFBERoREjRuiLL76QJCUmJur++++Xv7+/AgMD1a1bN/3888+5nkdmZqZiYmIUHh4uX19f3XHHHXr33Xfdpvnuu+903333KTAwUAEBAWratKni4+M1efJkxcXFaeXKlXI4HHI4HNq8eXOOq362bNmiBg0ayOl0qmzZsho7dqzS09Ndt7do0ULR0dF64oknVLJkSd1yyy2aPHnyDb1+AG4cRQVAjn799VetXbtWQ4YMyXb6AkkqUaKEMjMzdf/99+vXX3/Vli1btGHDBh09elTdu3fP9XxiYmL0+uuv6+WXX9Z3332n4cOHq3fv3tqyZYsk6ccff1SzZs3kdDr1ySefaNeuXerfv7/S09M1atQodevWTW3bttWpU6d06tQpNWrUKNs8fvzxR7Vv315RUVHat2+f5s2bp1dffVVPPfWU23RxcXHy8/PTl19+qWeeeUZTp07Vhg0bPHzlAOQlVv0AyNGRI0dkWZaqV69+xWk2btyob775RgkJCQoLC5Mkvf7664qIiNDOnTsVFRV11XmkpaXp6aef1scff6yGDRtKkipXrqzPP/9c8+fPV/PmzfXiiy8qKChIy5YtU9GiRSVJ1apVcz2Gr6+v0tLSrrqq56WXXlJYWJheeOEFORwOVa9eXSdPntSYMWM0ceJEeXn9+Z2tdu3amjRpkiTptttu0wsvvKCNGzeqVatWuXjFAOQHigqAHOXm7BoHDhxQWFiYq6RIUs2aNVWiRAkdOHDgmkXlyJEjSk1NzVYELl26pLp160qS9u7dq6ZNm7pKyvU4cOCAGjZs6HYW7saNG+vChQs6ceKEbr31Vkl/FpXLlS1bVqdPn77u+QK4cRQVADm67bbb5HA48nWD2QsXLkiS1qxZo/Lly7vd5nQ6Jf25xKSg/LUMORwOZWZmFtj8AWTHNioAclSyZEm1adNGL774olJSUrLdfu7cOdWoUUPHjx/X8ePHXeP79+/XuXPnVLNmzWvOo2bNmnI6nUpMTFTVqlXdLllLaWrXrq3PPvtMf/zxR46P4ePjo4yMjKvOp0aNGtq+fbvbUqKtW7cqICBAFSpUuGZOAPahqAC4ohdffFEZGRlq0KCB3nvvPR0+fFgHDhzQ888/r4YNG6ply5aKjIxUr169tHv3bu3YsUP//ve/1bx5c9WvX/+ajx8QEKBRo0Zp+PDhiouLU3x8vHbv3q25c+cqLi5OkvTYY48pOTlZPXr00FdffaXDhw/rjTfe0KFDhyRJlSpV0tdff61Dhw7p7NmzORaawYMH6/jx4xo6dKgOHjyolStXatKkSRoxYoRr+xQAZuI3FMAVVa5cWbt379bdd9+tkSNHqlatWmrVqpU2btyoefPmyeFwaOXKlQoODlazZs3UsmVLVa5cWcuXL8/1PKZNm6YJEyYoJiZGNWrUUNu2bbVmzRqFh4dLkkJCQvTJJ5/owoULat68uerVq6cFCxa4VtM88sgjuv3221W/fn2VLl1aW7duzTaP8uXL68MPP9SOHTt0xx13aNCgQRowYID+85//5M0LBSDfOKzcbDEHAABgA5aoAAAAY1FUAACAsSgqAADAWBQVAABgLIoKAAAwFkUFAAAYi6ICAACMRVEBAADGoqgAAABjUVQAAICxKCoAAMBYFBUAAGCs/w+qXXO2JYYhVgAAAABJRU5ErkJggg==",
"text/plain": [
"
"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"collections['products_count'] = pd.to_numeric(collections['products_count'], errors='coerce')\n",
"collections.groupby('title')['products_count'].sum().plot(kind='bar')\n",
"plt.title('Product Count in Each Collection')\n",
"plt.xlabel('Collection')\n",
"plt.ylabel('Total Products')\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "46eXv1UmoE07"
},
"source": [
"### 4. Inventory Analysis\n",
"Examine inventory levels across product variants."
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 572
},
"id": "gGEYdVa7oMIg",
"outputId": "63f9e5e6-340f-4749-dd10-bfb7ad0e1839"
},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
"
"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Product Variants Inventory Analysis\n",
"product_variants.groupby('product_id')['inventory_quantity'].sum().plot(kind='bar')\n",
"plt.title('Total Inventory Quantity by Product')\n",
"plt.xlabel('Product ID')\n",
"plt.ylabel('Total Inventory Quantity')\n",
"plt.show()"
]
}
],
"metadata": {
"colab": {
"provenance": []
},
"kernelspec": {
"display_name": "Python 3",
"name": "python3"
},
"language_info": {
"name": "python"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
================================================
FILE: pyairbyte_notebooks/PyAirbyte_Snowflake_Cortex_Github.ipynb
================================================
{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "n3fUVL_19YGb"
},
"source": [
"# Storing vector data into Snowflake using PyAirbyte, Snowflake Cortex\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",
"## Prerequisites\n",
"\n",
"1. **GitHub Access Token**:\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",
"2. **Snowflake**:\n",
" - To set up snowflake, follow these [instructions](https://docs.airbyte.com/integrations/destinations/snowflake#login-and-password).\n",
"\n",
"\n",
"## Install PyAirbyte and other dependencies"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "OhMxXpazUzBX"
},
"outputs": [],
"source": [
"# Add virtual environment support for running in Google Colab\n",
"!apt-get install -qq python3.10-venv\n",
"\n",
"# First, we need to install the necessary libraries.\n",
"!pip3 install airbyte snowflake-connector-python"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "KqfoQD2dAJtd"
},
"source": [
"# Setup Source Github"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "QwV29bK4VbL4"
},
"outputs": [],
"source": [
"import airbyte as ab\n",
"\n",
"source = ab.get_source(\n",
" \"source-github\",\n",
" config={\n",
" \"repositories\": [\"airbytehq/quickstarts\"],\n",
" \"credentials\": {\n",
" \"personal_access_token\": ab.get_secret(\"GITHUB_API_KEY\"),\n",
" },\n",
" },\n",
")\n",
"source.check()"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "9avgcWxs9Ura"
},
"source": [
"Reads the data from the selected issues stream, extracting the GitHub issues data for further processing."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "s8VFZEK8XbA0"
},
"outputs": [],
"source": [
"source.get_available_streams()\n",
"source.select_streams([\"issues\"]) # we are only interested in issues stream\n",
"read_result = source.read()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "MzeTFZ58YVEo"
},
"outputs": [],
"source": [
"issues = [doc for doc in read_result[\"issues\"].to_documents()] # Will be useful for vector_embedding\n",
"issue_df = read_result['issues'].to_pandas() # Converting data to pandas frame\n",
"print(str(issues[5]))"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "6fivYEfdBafd"
},
"source": [
"# Loading data into Snowflake\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."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "2EM-oHR3cXXc"
},
"outputs": [],
"source": [
"from snowflake import connector\n",
"conn = connector.connect(\n",
" account=ab.get_secret(\"SNOWFLAKE_HOST\"),\n",
" role=ab.get_secret(\"SNOWFLAKE_ROLE\"),\n",
" warehouse=ab.get_secret(\"SNOWFLAKE_WAREHOUSE\"),\n",
" database=ab.get_secret(\"SNOWFLAKE_DATABASE\"),\n",
" schema=ab.get_secret(\"SNOWFLAKE_SCHEMA\"),\n",
" user=ab.get_secret(\"SNOWFLAKE_USERNAME\"),\n",
" password=ab.get_secret(\"SNOWFLAKE_PASSWORD\"),\n",
" )\n",
"cur = conn.cursor()\n",
"\n",
"print(ab.get_secret(\"SNOWFLAKE_SCHEMA\"))"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "85uLgQ7JBgoI"
},
"source": [
"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."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "X96O3Is-i3v4"
},
"outputs": [],
"source": [
"import pandas as pd\n",
"\n",
"def create_table_from_dataframe(conn, df, table_name):\n",
" cursor = conn.cursor()\n",
" database = ab.get_secret('SNOWFLAKE_DATABASE')\n",
" print(database)\n",
" cursor.execute(f'USE DATABASE {database}')\n",
" schema_name = ab.get_secret('SNOWFLAKE_SCHEMA')\n",
" cursor.execute(f'USE SCHEMA {schema_name}')\n",
" columns = []\n",
" for column, dtype in zip(df.columns, df.dtypes):\n",
" if pd.api.types.is_integer_dtype(dtype):\n",
" snowflake_type = 'INTEGER'\n",
" elif pd.api.types.is_float_dtype(dtype):\n",
" snowflake_type = 'FLOAT'\n",
" elif pd.api.types.is_bool_dtype(dtype):\n",
" snowflake_type = 'BOOLEAN'\n",
" elif pd.api.types.is_datetime64_any_dtype(dtype):\n",
" snowflake_type = 'TIMESTAMP'\n",
" else:\n",
" snowflake_type = 'STRING'\n",
"\n",
" columns.append(f'\"{column}\" {snowflake_type}')\n",
"\n",
" create_table_sql = f'CREATE TABLE {table_name} ({\", \".join(columns)});'\n",
" cursor.execute(create_table_sql)\n",
"\n",
"# Example usage:\n",
"create_table_from_dataframe(conn, issue_df, 'github_issue') # Keep table name according to your requirments\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "RePkO21hCSZx"
},
"source": [
"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')."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "Ku61-vxBt7Xh"
},
"outputs": [],
"source": [
"from snowflake.connector.pandas_tools import write_pandas\n",
"def upload_dataframe_to_snowflake(conn, df, table_name):\n",
" success, nchunks, nrows, _ = write_pandas(conn, df, table_name)\n",
" if success:\n",
" print(f\"Successfully inserted {nrows} rows into {table_name}.\")\n",
" else:\n",
" print(\"Failed to insert data.\")\n",
"\n",
"upload_dataframe_to_snowflake(conn, issue_df, 'GITHUB_ISSUE') # Remember to use table name in uppercase\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "ppxeChRQCmm8"
},
"source": [
"# Vector Embedding the Data\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",
"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."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "5ti5uUpsu2oU"
},
"outputs": [],
"source": [
"from langchain.text_splitter import RecursiveCharacterTextSplitter\n",
"import pandas as pd\n",
"\n",
"splitter = RecursiveCharacterTextSplitter(chunk_size=512, chunk_overlap=50)\n",
"chunked_docs = splitter.split_documents(issues)\n",
"print(f\"Created {len(chunked_docs)} document chunks.\")\n",
"\n",
"\n",
"for doc in chunked_docs:\n",
" for md in doc.metadata:\n",
" doc.metadata[md] = str(doc.metadata[md])\n",
"\n",
"df = pd.DataFrame(chunked_docs, columns=['PAGE_CONTENT','META','TYPE']) # please use uppercase\n",
"# Convert all columns to string\n",
"df = df.astype(str)\n",
"print(df.head(3))\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "wfSflgGwDkjN"
},
"source": [
"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."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "qFK12XSiNej_"
},
"outputs": [],
"source": [
"create_table_from_dataframe(conn, df, 'vector_github_issues')\n",
"upload_dataframe_to_snowflake(conn, df, 'VECTOR_GITHUB_ISSUES') #use uppercase"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "1Wjq35UMECIp"
},
"source": [
"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)."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "vHHZ5fn7Rcs2"
},
"outputs": [],
"source": [
"\n",
"cur = conn.cursor()\n",
"\n",
"# Step 1: Add the new column to store the embeddings\n",
"\n",
"# We are using vector dimension 1024\n",
"alter_table_query = \"\"\"\n",
"ALTER TABLE VECTOR_GITHUB_ISSUES\n",
"ADD COLUMN embedding VECTOR(FLOAT, 1024);\n",
"\"\"\"\n",
"cur.execute(alter_table_query)\n",
"\n",
"# Step 2: Update the new column with embeddings from Cortex\n",
"# Note: Using a subquery to avoid issues with updating the same table in place\n",
"update_query = \"\"\"\n",
"UPDATE VECTOR_GITHUB_ISSUES\n",
"SET embedding = (\n",
" SELECT SNOWFLAKE.CORTEX.EMBED_TEXT_1024('nv-embed-qa-4', page_content)\n",
");\n",
"\"\"\"\n",
"cur.execute(update_query)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "1uyFarH2Fjvf"
},
"source": [
"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"
]
}
],
"metadata": {
"colab": {
"provenance": []
},
"kernelspec": {
"display_name": "Python 3",
"name": "python3"
},
"language_info": {
"name": "python"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
================================================
FILE: pyairbyte_notebooks/PyAirbyte_Snowflake_Custom_Cache_Demo.ipynb
================================================
{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "R8XtHKK4PujA"
},
"source": [
"# PyAirbyte Custom Snowflake Cache Demo\n",
"\n",
"In this demo, we use PyAirbyte to ingest cryptocurrency data from [CoinAPI.io](https://www.coinapi.io/) into Snowflake.\n",
"\n",
"### Prerequisites\n",
"- CoinAPI [API key](https://www.coinapi.io/get-free-api-key?product_id=market-data-api).\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)."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "Lyxh2NLuQJUf"
},
"source": [
"## Install PyAirbyte\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "9DEgu1WpQNt-"
},
"outputs": [],
"source": [
"# Add virtual environment support for running in Google Colab\n",
"!apt-get install -qq python3.10-venv\n",
"\n",
"# Install PyAirbyte\n",
"%pip install --quiet airbyte"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "EwSqhsrmgcQ4"
},
"source": [
"## Define a Snowflake Cache\n",
"\n",
"Define a PyAirbyte Cache for Snowflake."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "jJeZN6phg-1v"
},
"outputs": [],
"source": [
"from airbyte.caches import SnowflakeCache\n",
"from google.colab import userdata\n",
"\n",
"# Define a Snowflake Cache and pass the necessary configuration\n",
"sf_cache = SnowflakeCache(\n",
" account=userdata.get(\"SNOWFLAKE_ACCOUNT\"),\n",
" username=userdata.get(\"SNOWFLAKE_USERNAME\"),\n",
" password=userdata.get(\"SNOWFLAKE_PASSWORD\"),\n",
" warehouse=\"AIRBYTE_DEVELOP_WAREHOUSE\",\n",
" database=\"AIRBYTE_DEVELOP\",\n",
" role=\"AIRBYTE_DEVELOPER\",\n",
" schema_name=\"PYAIRBYTE_DEMO\"\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "JWWeEbTVEDFz"
},
"source": [
"## Load the Source Data using PyAirbyte\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "1PhfWpS8QVzE"
},
"source": [
"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",
"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."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "5BI9hIeUvxXE"
},
"outputs": [],
"source": [
"import airbyte as ab\n",
"\n",
"# Configure and read from the source\n",
"read_result = ab.get_source(\n",
" \"source-coin-api\",\n",
" config={\n",
" \"api_key\": userdata.get(\"API_KEY\"),\n",
" \"environment\": \"production\",\n",
" \"symbol_id\": \"COINBASE_SPOT_INDEX_USD\",\n",
" \"period\": \"1DAY\",\n",
" \"start_date\": \"2023-01-01T00:00:00\"\n",
" },\n",
" streams=[\"ohlcv_historical_data\", \"trades_historical_data\", \"quotes_historical_data\"],\n",
").read(cache=sf_cache)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "uPcaLfSCYrdo"
},
"source": [
"## Read data from Snowflake\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."
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"id": "z-nOAXR6Y-ui"
},
"outputs": [],
"source": [
"# Read from the cache into a pandas Dataframe:\n",
"ohlcv_df = read_result[\"ohlcv_historical_data\"].to_pandas()"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "b6cMu9G2ZVxK"
},
"source": [
"## Run data transformations\n",
"\n",
"- Convert `time_period_start` to datetime for easy handling of dates.\n",
"- Convert numeric columns to numeric types for calculations.\n",
"- Calculate `daily_movement` to analyze daily price changes in the market."
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"id": "hmRZZ5PsZYVW"
},
"outputs": [],
"source": [
"import pandas as pd\n",
"\n",
"# Convert 'time_period_start' to datetime format and necessary columns to numeric\n",
"ohlcv_df['time_period_start'] = pd.to_datetime(ohlcv_df['time_period_start'])\n",
"numeric_columns = ['price_open', 'price_high', 'price_low', 'price_close', 'volume_traded', 'trades_count']\n",
"ohlcv_df[numeric_columns] = ohlcv_df[numeric_columns].apply(pd.to_numeric, errors='coerce')\n",
"\n",
"# Calculate daily price movement\n",
"ohlcv_df['daily_movement'] = ohlcv_df['price_close'] - ohlcv_df['price_open']"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "knjH8Cx_zyCa"
},
"source": [
"## Write Dataframe to Snowflake\n",
"\n",
"Get a SQL engine from the Snowflake cache"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"id": "dsn6JSZzfBBw"
},
"outputs": [],
"source": [
"engine = sf_cache.get_sql_engine()"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "nhTzao0y0ObW"
},
"source": [
"Now, we can write our transformed Dataframe back to Snowflake in a new table called `daily_movement`."
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"id": "gy1TzBf-f6If"
},
"outputs": [],
"source": [
"from snowflake.connector.pandas_tools import pd_writer\n",
"\n",
"ohlcv_df.to_sql('daily_movement', engine, index=False, method=pd_writer, if_exists='replace')"
]
}
],
"metadata": {
"colab": {
"provenance": []
},
"kernelspec": {
"display_name": "Python 3",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.6"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
================================================
FILE: pyairbyte_notebooks/PyAirbyte_as_an_Orchestrator_Demo.ipynb
================================================
{
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"provenance": []
},
"kernelspec": {
"name": "python3",
"display_name": "Python 3"
},
"language_info": {
"name": "python"
}
},
"cells": [
{
"cell_type": "markdown",
"source": [
"# Using PyAirbyte as a data orchestrator for hosted Airbyte connections\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."
],
"metadata": {
"id": "zSQIhJC1wTt4"
}
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "Anj2W5pm0n8E"
},
"outputs": [],
"source": [
"# Add virtual environment support for running in Google Colab\n",
"!apt-get install -qq python3.10-venv\n",
"\n",
"# Install PyAirbyte\n",
"%pip install --quiet airbyte"
]
},
{
"cell_type": "markdown",
"source": [
"## Job execution and notifications using PyAirbyte\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",
"The code below does the following:\n",
"\n",
"- Workspace initialization: Sets up the workspace with necessary credentials.\n",
"- Slack notifications: Configures a webhook for sending job statuses to a Slack channel.\n",
"- Job data tracking: Collects and stores execution details like start time, duration, and status for visualization.\n",
"- Run syncs: Executes jobs sequentially based on dependency success and sends detailed outcomes to Slack (or simply print them)."
],
"metadata": {
"id": "QtUXa_7tuXks"
}
},
{
"cell_type": "code",
"source": [
"import requests\n",
"from airbyte import cloud\n",
"from datetime import datetime\n",
"from google.colab import userdata\n",
"\n",
"# Initialize the Airbyte Cloud workspace\n",
"workspace = cloud.CloudWorkspace(\n",
" workspace_id=userdata.get(\"AIRBYTE_WORKSPACE_ID\"),\n",
" api_key=userdata.get(\"AIRBYTE_API_KEY\"),\n",
")\n",
"\n",
"# Slack webhook URL - replace with your actual Slack webhook URL\n",
"slack_webhook_url = \"https://hooks.slack.com/services/XXX/YYY/ZZZ\"\n",
"\n",
"# Initialize job data for visualization\n",
"job_data = []\n",
"\n",
"def send_slack_message(message, webhook_url):\n",
" \"\"\"Sends a message to a Slack channel specified by the webhook URL.\"\"\"\n",
" payload = {'text': message}\n",
" response = requests.post(webhook_url, json=payload)\n",
" return response.status_code\n",
"\n",
"def trigger_sync_and_check(connection_id):\n",
" \"\"\"Triggers a sync job for a specified connection ID and checks the job status.\"\"\"\n",
" start_time = datetime.now()\n",
" connection = workspace.get_connection(connection_id=connection_id)\n",
" sync_result = connection.run_sync()\n",
" end_time = datetime.now()\n",
" duration = (end_time - start_time).total_seconds()\n",
" status = str(sync_result.get_job_status())\n",
"\n",
" # Store job data for visualization\n",
" job_data.append({\n",
" 'connection_id': connection_id,\n",
" 'start_time': start_time,\n",
" 'end_time': end_time,\n",
" 'duration': duration,\n",
" 'status': status\n",
" })\n",
"\n",
" return status, sync_result, start_time, duration\n",
"\n",
"def format_sync_details(sync_result, start_time, duration):\n",
" \"\"\"Format sync details for Slack message, including start time and duration.\"\"\"\n",
" details = (\n",
" f\"Job ID: {sync_result.job_id}\\n\"\n",
" f\"Start Time: {start_time.strftime('%Y-%m-%d %H:%M:%S')}\\n\"\n",
" f\"Duration: {duration:.2f} seconds\\n\"\n",
" f\"Records Synced: {sync_result.records_synced}\\n\"\n",
" f\"Bytes Synced: {sync_result.bytes_synced}\\n\"\n",
" f\"Job URL: {sync_result.job_url}\"\n",
" )\n",
" return details\n",
"\n",
"def run_syncs():\n",
" \"\"\"Handle sequential sync jobs where the second job depends on the success of the first.\"\"\"\n",
" # Connection IDs for the two jobs. Replace with your connection IDs\n",
" first_connection_id = \"df2898ce-b21f-43c0-8c82-bd8c6dd24931\"\n",
" second_connection_id = \"86075252-9004-41fc-b5e3-f61f20388ff5\"\n",
"\n",
" first_status, first_sync_result, first_start, first_duration = trigger_sync_and_check(first_connection_id)\n",
" if \"SUCCEEDED\" in first_status:\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",
" send_slack_message(message, slack_webhook_url)\n",
" print(message)\n",
"\n",
" second_status, second_sync_result, second_start, second_duration = trigger_sync_and_check(second_connection_id)\n",
" if \"SUCCEEDED\" in second_status:\n",
" message = f\"Second sync succeeded (Connection ID: {second_connection_id}).\\nDetails:\\n{format_sync_details(second_sync_result, second_start, second_duration)}\"\n",
" else:\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",
" else:\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",
" send_slack_message(message, slack_webhook_url)\n",
" print(message)"
],
"metadata": {
"id": "geGeKxan8jc1"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"run_syncs()"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "bOODO9mw850l",
"outputId": "b431b0db-ad1e-4e9a-eb96-823910d66b54"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"First sync succeeded (Connection ID: df2898ce-b21f-43c0-8c82-bd8c6dd24931).\n",
"Details:\n",
"Job ID: 10575770\n",
"Start Time: 2024-04-16 20:08:57\n",
"Duration: 155.78 seconds\n",
"Records Synced: 2100\n",
"Bytes Synced: 766685\n",
"Job URL: https://api.airbyte.com/v1/workspaces/ab2f3ba2-94e6-45b9-9d26-58b3f1d52295/connections/df2898ce-b21f-43c0-8c82-bd8c6dd24931/job-history/10575770\n",
"Proceeding to second sync.\n",
"Second sync succeeded (Connection ID: 86075252-9004-41fc-b5e3-f61f20388ff5).\n",
"Details:\n",
"Job ID: 10575853\n",
"Start Time: 2024-04-16 20:11:32\n",
"Duration: 123.54 seconds\n",
"Records Synced: 1\n",
"Bytes Synced: 214665\n",
"Job URL: https://api.airbyte.com/v1/workspaces/ab2f3ba2-94e6-45b9-9d26-58b3f1d52295/connections/86075252-9004-41fc-b5e3-f61f20388ff5/job-history/10575853\n"
]
}
]
},
{
"cell_type": "markdown",
"source": [
"## Job execution timeline visualization\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",
"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",
"- Job Durations: How long each job took to complete.\n",
"- Job Start Times: When each job began relative to the start of the timeline.\n",
"- Job Outcomes: The success or failure of each job."
],
"metadata": {
"id": "rPhUDRYOtPcr"
}
},
{
"cell_type": "code",
"source": [
"import matplotlib.pyplot as plt\n",
"import matplotlib.patches as patches\n",
"from datetime import datetime, timedelta\n",
"\n",
"def get_color(status):\n",
" \"\"\"Returns color based on the job status.\"\"\"\n",
" if \"SUCCEEDED\" in status:\n",
" return 'green' # Green for success\n",
" elif \"FAILED\" in status:\n",
" return 'yellow' # Yellow for failure\n",
" else:\n",
" return 'gray' # Gray for any other status\n",
"\n",
"def draw_job_graph(job_data):\n",
" \"\"\"Draws a graph of job executions where rectangles represent the duration and color indicates status.\"\"\"\n",
" fig, ax = plt.subplots(figsize=(14, 3))\n",
"\n",
" # Start position and time tracking\n",
" start_time = min(job['start_time'] for job in job_data) # Find the earliest start time\n",
" end_time = max(job['start_time'] + timedelta(seconds=job['duration']) for job in job_data) # Find the latest end time\n",
" total_duration = (end_time - start_time).total_seconds()\n",
"\n",
" for job in job_data:\n",
" start_offset = (job['start_time'] - start_time).total_seconds()\n",
" width = job['duration'] # Duration in seconds\n",
" color = get_color(job['status']) # Get color based on job status\n",
" rect = patches.Rectangle((start_offset, 0), width, 1, linewidth=1, edgecolor='black', facecolor=color)\n",
" ax.add_patch(rect)\n",
"\n",
" # Adding text inside the rectangle\n",
" ax.text(start_offset + width / 2, 0.5, f\"{job['connection_id']} ({job['duration']}s)\",\n",
" verticalalignment='center', horizontalalignment='center', color='black', fontweight='bold')\n",
"\n",
" # Set the limits and labels of the x-axis\n",
" ax.set_xlim(0, total_duration)\n",
" ax.set_ylim(-0.5, 1.5)\n",
" ax.set_xlabel(\"Time (seconds from start)\")\n",
" ax.set_title('Job Execution Timeline')\n",
" plt.xticks(range(0, int(total_duration) + 1, 20)) # Set x-ticks to appear every 20 seconds\n",
" plt.yticks([])\n",
" plt.grid(True, which='both', axis='x', linestyle='--', linewidth=0.5)\n",
" plt.show()"
],
"metadata": {
"id": "AsOSYXuMi1mS"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"## Visualize sync results"
],
"metadata": {
"id": "eWejfDkBv07x"
}
},
{
"cell_type": "code",
"source": [
"draw_job_graph(job_data)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 333
},
"id": "dGIhuTDxjAPF",
"outputId": "cf09410f-3b6b-4037-d152-2e6b5a7f0499"
},
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"
"
],
"image/png": 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}
================================================
FILE: pyairbyte_notebooks/RAG_using_github_pyairbyte_chroma.ipynb
================================================
{
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"provenance": []
},
"kernelspec": {
"name": "python3",
"display_name": "Python 3"
},
"language_info": {
"name": "python"
}
},
"cells": [
{
"cell_type": "markdown",
"source": [
"# **End-to-End RAG Tutorial Using Github, PyAirbyte, Chroma, and LangChain**\n",
"This notebook illustrates the complete setup of a Retrieval-Augmented Generation (RAG) pipeline.
\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", "## **Prerequisites**\n", "**1) OpenAI API Key**:\n", " - **Create an OpenAI Account**: Sign up for an account on [OpenAI](https://www.openai.com/).\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", "**2) Github Personal Access Token**:\n", " - **Create a Github Account**: Sign up for an account on [Github](https://www.github.com/).\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" ], "metadata": { "id": "lv1fce-k_Ul5" } }, { "cell_type": "markdown", "source": [ "# **Installing Dependencies**\n", "First Thing First !
\n", "Lets get the dependencies installed before anything else!!" ], "metadata": { "id": "mbc7jQsW-kGs" } }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true, "id": "5FFGW2iOhOqT" }, "outputs": [], "source": [ "# Add virtual environment support for running in Google Colab\n", "!apt-get install -qq python3.10-venv\n", "\n", "# First, we need to install the necessary libraries.\n", "!pip3 install airbyte langchain langchain-openai chromadb python-dotenv langchainhub langchain-chroma" ] }, { "cell_type": "markdown", "source": [ "## **Source Setup : Github with PyAirbyte**\n", "The code you see below configures an Airbyte source to pull out data from a github repository.\n", "\n", "You can also customize the configuration according to your own needs.\n", "See [this](https://docs.airbyte.com/integrations/sources/github#reference)\n", "\n", "Note that we here only fetch data from the Commits Stream
\n", "To know about all the available streams go [here](https://docs.airbyte.com/integrations/sources/github#supported-streams)\n" ], "metadata": { "id": "aBfzg42A_mhd" } }, { "cell_type": "code", "source": [ "import airbyte as ab\n", "\n", "source = ab.get_source(\n", " \"source-github\",\n", " config={\n", " \"credentials\": {\n", " \"personal_access_token\": \"your_personal_access_token\"\n", " },\n", " \"repositories\": [\"your_github_username/your_repository_ID\"]\n", " }\n", ")\n", "source.check()\n", "\n", "source.get_available_streams()\n", "source.select_streams([\"commits\"])\n", "cache = ab.get_default_cache()\n", "result = source.read(cache=cache)\n", "\n", "commits_details = [doc for doc in result[\"commits\"].to_documents()]\n", "\n", "print(str(commits_details[0]))" ], "metadata": { "collapsed": true, "id": "vMiRDXs1ii3S" }, "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "source": [ "# **Split Documents into Chunks**\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", "Here we set each chunk size to 512 characters and adjacent chunks will overlap by 50 characters to ensure continuity of context\n", "
\n", "Then the loop converts all metadata to string format to ensure consistent processing later in the pipeline." ], "metadata": { "id": "f4yYrQ9lLVRJ" } }, { "cell_type": "code", "source": [ "from langchain.text_splitter import RecursiveCharacterTextSplitter\n", "\n", "splitter = RecursiveCharacterTextSplitter(chunk_size=512, chunk_overlap=50)\n", "chunked_docs = splitter.split_documents(commits_details)\n", "\n", "for doc in chunked_docs:\n", " for md in doc.metadata:\n", " doc.metadata[md] = str(doc.metadata[md])" ], "metadata": { "collapsed": true, "id": "_shmtR8Zl6d5" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "from langchain_openai import OpenAIEmbeddings\n", "import os\n", "\n", "os.environ['OPENAI_API_KEY'] = ab.get_secret(\"YOUR_OPENAI_API_KEY\")\n", "embeddings = OpenAIEmbeddings()" ], "metadata": { "collapsed": true, "id": "CNgHPzULmB0j" }, "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "source": [ "# **Setting up Chroma**\n", "Create and configure a Chroma vector store to store the document embeddings.
\n", "First we initialize Chroma Client\n", "
\n", "Then we create Chroma Vector Store from Documents\n", "
\n", "Finally we use embedding function when accessing the collection\n", "
\n", "Since currently there is a waitlist for Chroma,We initialize the Chroma client in persistent mode (local file)" ], "metadata": { "id": "TwHTBa43M7fM" } }, { "cell_type": "code", "source": [ "import chromadb\n", "from langchain_chroma import Chroma\n", "from chromadb.utils import embedding_functions\n", "\n", "persist_directory = 'chroma_db'\n", "client = chromadb.PersistentClient(path=persist_directory)\n", "collection_name = \"github_commits\"\n", "\n", "openai_lc_client = Chroma.from_documents(\n", " documents=chunked_docs,\n", " embedding=embeddings,\n", " persist_directory=persist_directory,\n", " collection_name=collection_name\n", ")\n", "\n", "openai_ef = embedding_functions.OpenAIEmbeddingFunction(\n", " api_key=os.getenv(\"OPENAI_API_KEY\"),\n", " model_name=\"text-embedding-ada-002\"\n", ")\n", "collection = client.get_collection(name=collection_name, embedding_function=openai_ef)\n" ], "metadata": { "collapsed": true, "id": "PzB1caQTmbuS" }, "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "source": [ "# **Querying Chroma and RAG Pipeline**\n", "Finally we use LangChain to retrieve documents from Chroma and generate responses using an OpenAI chat model." ], "metadata": { "id": "MoCApKIWNpBd" } }, { "cell_type": "code", "source": [ "from langchain_openai import ChatOpenAI\n", "from langchain import hub\n", "from langchain_core.output_parsers import StrOutputParser\n", "from langchain_core.runnables import RunnablePassthrough\n", "\n", "# Initialize the LLM\n", "llm = ChatOpenAI(model_name=\"gpt-3.5-turbo\", temperature=0)\n", "\n", "# Set up the retriever from the Chroma vector store\n", "retriever = openai_lc_client.as_retriever()\n", "\n", "# Set up the prompt\n", "prompt = hub.pull(\"rlm/rag-prompt\")\n", "\n", "# Function to format documents\n", "def format_docs(docs):\n", " return \"\\n\\n\".join(doc.page_content for doc in docs)\n", "\n", "# Create the RAG chain\n", "rag_chain = (\n", " {\"context\": retriever | format_docs, \"question\": RunnablePassthrough()}\n", " | prompt\n", " | llm\n", " | StrOutputParser()\n", ")\n", "\n", "print(\"Langchain RAG pipeline set up successfully.\")\n", "\n", "# Example query\n", "response = rag_chain.invoke(\"Which are the commit messages of latest commits?\")\n", "print(response)\n" ], "metadata": { "id": "uDPPklkxod8-" }, "execution_count": null, "outputs": [] } ] } ================================================ FILE: pyairbyte_notebooks/README.md ================================================ # PyAirbyte Notebooks Quickstart This quickstart will help you get started quickly with PyAirbyte. ## Quickstart Quicklinks To jump right in, click on any of the below links to open a new Colab notebook from the provided quickstart template. 1. [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. 2. [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. 3. [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. 4. [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. ## How to use these Quickstarts There are three ways to use the quickstart resources here. ### Google Colab Google 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. Note: - 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`. ### Self-Hosted Jupyter If you have a self-hosted Jupyter instance, you can load any of the notebooks in this directory. ### VS Code Notebooks You 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. ## Securely Managed Secrets You can pass secrets to PyAirbyte by using the `get_secret()` function. This call will retrieve a named secret from any of the following locations: 1. Google Colab Secrets 2. Environment Variables 3. Masked User Input (via [getpass](https://docs.python.org/3/library/getpass.html)) If you are using Google Colab, we suggest using the Colab secrets feature. For other environments, you can set your secret values in environment variables. Note: 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. **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). ================================================ FILE: pyairbyte_notebooks/rag_using_gdrive_pyairbyte_pinecone.ipynb ================================================ { "cells": [ { "cell_type": "markdown", "id": "8f104c9d-933a-45fd-9d2c-57e6d56a56b1", "metadata": {}, "source": [ "# End-to-End RAG Tutorial Using GoogleDrive, PyAirbyte, Pinecone, and LangChain\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", "## Prerequisites\n", "\n", "1. **GoogleDrive**:\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", "2. **Pinecone Account**:\n", " - **Create a Pinecone Account**: Sign up for an account on the [Pinecone website](https://www.pinecone.io/).\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", "3. **OpenAI API Key**:\n", " - **Create an OpenAI Account**: Sign up for an account on [OpenAI](https://www.openai.com/).\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" ] }, { "cell_type": "markdown", "id": "8e1d3a0b-d446-43b0-b9dd-691fd837e5eb", "metadata": {}, "source": [ "## Install PyAirbyte and other dependencies" ] }, { "cell_type": "raw", "id": "ffebf71e-2b9a-4a1a-b448-9812b49f120c", "metadata": {}, "source": [ "!pip3 install airbyte openai langchain pinecone-client langchain-openai langchain-pinecone langchainhub " ] }, { "cell_type": "markdown", "id": "7b51a295-8c8e-483d-a931-24ec2590dffa", "metadata": {}, "source": [ "## Setup Source GoogleDrive with PyAirbyte\n", "\n", "The provided code configures an Airbyte source to extract data from an GoogleDrive Folder contains CSV file named NFLX.csv\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", "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" ] }, { "cell_type": "code", "execution_count": 14, "id": "6ddd2f96-8680-4a19-a02b-32fd0b703912", "metadata": {}, "outputs": [ { "name": "stdin", "output_type": "stream", "text": [ "Enter the value for secret 'service_json': ········\n" ] }, { "data": { "text/html": [ ""
]
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" tags to provide an answer to the question enclosed in tags.\n",
"\n",
"{context}\n",
" \n",
"\n",
"{question}\n",
" \n",
"\"\"\""
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Suzuki SJ 410 2023\n",
" \n"
]
}
],
"source": [
"response = openai_client.chat.completions.create(\n",
" model=\"gpt-3.5-turbo\",\n",
" messages=[\n",
" {\"role\": \"system\", \"content\": SYSTEM_PROMPT},\n",
" {\"role\": \"user\", \"content\": USER_PROMPT},\n",
" ],\n",
")\n",
"print(response.choices[0].message.content)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Summary\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."
]
}
],
"metadata": {
"kernelspec": {
"display_name": "pyairbyte-hackathon",
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"name": "python3"
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"file_extension": ".py",
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================================================
FILE: pyairbyte_notebooks/rag_with_pyairbyte_and_milvus_lite.ipynb
================================================
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This notebook demonstrates simple RAG (Retrieval-Augmented Generation) pipeline with Milvus Lite and PyAirbyte.\n",
"The focus is to showcase how to set these components for a fully local development in Python."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Prerequisites\n",
"- [PyAirbyte](https://airbyte.com/product/pyairbyte)\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",
" `source-faker`, but it's easy to set it up for other sources.\n",
"- [Milvus Lite](https://milvus.io/docs/milvus_lite.md)\n",
" \n",
" Milvus Lite is the lightweight version of [Milvus](https://github.com/milvus-io/milvus) that enables vector emdeddings and similarity search\n",
" into the Python application.\n",
"- OpenAI API Key\n",
" \n",
" Go to the [API Keys page](https://platform.openai.com/api-keys) to create the new secret key."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 1. Install and set dependencies"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"%pip install airbyte pymilvus openai milvus-model"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"import json\n",
"import os\n",
"import airbyte as ab\n",
"from openai import OpenAI\n",
"from pymilvus import MilvusClient\n",
"\n",
"\n",
"# in production, you might want to avoid putting the key here.\n",
"os.environ[\"OPENAI_API_KEY\"] = \"sk-proj-****\"\n",
"openai_client = OpenAI()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 2. Set the source"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"For simplicity, we will just use `source-faker` to generate some data."
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"text/markdown": [
"## Read Progress\n",
"\n",
"Started reading at 08:19:11.\n",
"\n",
"Read **150** records over **1 seconds** (150.0 records / second).\n",
"\n",
"Wrote **150** records over 3 batches.\n",
"\n",
"Finished reading at 08:19:13.\n",
"\n",
"Started finalizing streams at 08:19:13.\n",
"\n",
"Finalized **3** batches over 1 seconds.\n",
"\n",
"Completed 3 out of 3 streams:\n",
"\n",
" - products\n",
" - users\n",
" - purchases\n",
"\n",
"\n",
"Completed writing at 08:19:14. Total time elapsed: 3 seconds\n",
"\n",
"\n",
"------------------------------------------------\n"
],
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{
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"metadata": {},
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{
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"output_type": "stream",
"text": [
"\n",
"Suzuki SJ 410 2023\n",
" \n"
]
}
],
"source": [
"response = openai_client.chat.completions.create(\n",
" model=\"gpt-3.5-turbo\",\n",
" messages=[\n",
" {\"role\": \"system\", \"content\": SYSTEM_PROMPT},\n",
" {\"role\": \"user\", \"content\": USER_PROMPT},\n",
" ],\n",
")\n",
"print(response.choices[0].message.content)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Summary\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."
]
}
],
"metadata": {
"kernelspec": {
"display_name": "pyairbyte-hackathon",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
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"file_extension": ".py",
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"name": "python",
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================================================
FILE: pyairbyte_notebooks/sentiment_analysis_airbyte_gsheets_snowflakecortex.ipynb
================================================
{
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"provenance": []
},
"kernelspec": {
"name": "python3",
"display_name": "Python 3"
},
"language_info": {
"name": "python"
}
},
"cells": [
{
"cell_type": "markdown",
"source": [
"# Sentiment analysis using Airbyte Cloud, Google sheets, and Snowflake Cortex\n",
"\n"
],
"metadata": {
"id": "SK1X63WQ0xNk"
}
},
{
"cell_type": "markdown",
"source": [
"## Setup Airbyte Source as Google Sheets\n",
"\n",
"\n",
"\n",
"* Login to your airbyte cloud account and Select Google sheets as source\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"
],
"metadata": {
"id": "WMVZ-XWi1iVy"
}
},
{
"cell_type": "markdown",
"source": [
"### Setup Source - Google sheets Airbyte Cloud Screenshot"
],
"metadata": {
"id": "NSkQlOaP0_3M"
}
},
{
"cell_type": "markdown",
"source": [
"## Setup Airbyte Destination as Snowflake Cortex\n",
"\n",
"\n",
"* Login to your airbyte cloud account and Select Snowflake Cortex as destination\n",
"* Provide the required credentials per the airbyte documentation mentioned [here](https://docs.airbyte.com/integrations/destinations/snowflake-cortex#prerequisites)\n"
],
"metadata": {
"id": "eZp_Tar02PuC"
}
},
{
"cell_type": "markdown",
"source": [
"## Setup Destination - Snowflake Cortex Airbyte Cloud Screenshot\n",
"\n",
"\n",
"\n",
"\n",
""
],
"metadata": {
"id": "Zo_W8X5C5Rmy"
}
},
{
"cell_type": "markdown",
"source": [
"## Setting up the connector\n",
"\n",
"Setup the connection between Airbyte Google Sheets source and Snowflake Cortex destination\n",
"\n",
"\n"
],
"metadata": {
"id": "0cJlfH2E586B"
}
},
{
"cell_type": "markdown",
"source": [
"## Install PyAirbyte and other dependencies"
],
"metadata": {
"id": "O2m0TjAWkcGs"
}
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "5DC5-hZUkYSD"
},
"outputs": [],
"source": [
"!pip3 install airbyte openai langchain-openai snowflake-connector-python langchain_core"
]
},
{
"cell_type": "markdown",
"source": [
"## Query the Records from Snowflake Cortex Airbyte Destination"
],
"metadata": {
"id": "lLUe1eM8D0R4"
}
},
{
"cell_type": "code",
"source": [
"from snowflake import connector\n",
"from google.colab import userdata\n",
"from typing import List\n",
"import pandas as pd\n",
"\n",
"def get_db_connection():\n",
" return connector.connect(\n",
" account=userdata.get(\"SNOWFLAKE_HOST\"),\n",
" role=userdata.get(\"SNOWFLAKE_ROLE\"),\n",
" warehouse=userdata.get(\"SNOWFLAKE_WAREHOUSE\"),\n",
" database=userdata.get(\"SNOWFLAKE_DATABASE\"),\n",
" schema=userdata.get(\"SNOWFLAKE_SCHEMA\"),\n",
" user=userdata.get(\"SNOWFLAKE_USERNAME\"),\n",
" password=userdata.get(\"SNOWFLAKE_PASSWORD\"),\n",
" )\n",
"\n",
"def fetch_table_data(table_name, columns):\n",
" conn = get_db_connection()\n",
" cursor = conn.cursor()\n",
"\n",
" # Construct the query\n",
" columns_str = \", \".join(columns)\n",
" query = f\"SELECT {columns_str} FROM {table_name} LIMIT 5;\"\n",
"\n",
" cursor.execute(query)\n",
" result = cursor.fetchall()\n",
"\n",
" # Get column names\n",
" col_names = [desc[0] for desc in cursor.description]\n",
"\n",
" cursor.close()\n",
" conn.close()\n",
"\n",
" # Convert the result to a pandas DataFrame\n",
" df = pd.DataFrame(result, columns=col_names)\n",
" return df;\n",
"\n",
"\n",
"\n",
"data_frame = fetch_table_data(\"AMAZON_REVIEWS\", [\"DOCUMENT_ID\",\"DOCUMENT_CONTENT\"])\n",
"data_frame\n",
"\n"
],
"metadata": {
"id": "dFY3Rf6cliaU",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 206
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"outputId": "3c8d4ca9-e2b6-4f29-a9e1-2e245e10731d"
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================================================
FILE: pyairbyte_notebooks/using_langchain_airbyte_package.ipynb
================================================
{
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"provenance": []
},
"kernelspec": {
"name": "python3",
"display_name": "Python 3"
},
"language_info": {
"name": "python"
}
},
"cells": [
{
"cell_type": "markdown",
"source": [
"# **Illustrating the usage of *langchain_airbyte* package**\n",
"\n",
"The `langchain-airbyte` package integrates LangChain with Airbyte.
\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", "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" ], "metadata": { "id": "5eHLkb0FkdXz" } }, { "cell_type": "markdown", "source": [ "# **Prerequisite**\n", "**1) OpenAI API Key**:\n", " - **Create an OpenAI Account**: Sign up for an account on [OpenAI](https://www.openai.com/).\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", "**2) Github Personal Access Token**:\n", " - **Create a Github Account**: Sign up for an account on [Github](https://www.github.com/).\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": { "id": "xwF4ZyZp2ji0" } }, { "cell_type": "markdown", "source": [ "## **Installing Dependencies**\n", "Lets start by installing all the required dependencies!
\n", "First of all we will create a virtual environment and then begin installing the dependencies.\n" ], "metadata": { "id": "sC1_LEcYoU1x" } }, { "cell_type": "code", "source": [ "# Add virtual environment support for running in Google Colab\n", "!apt-get install -qq python3.10-venv\n", "\n", "#Installing the necessary libraries\n", "!pip3.10 install -qU langchain-airbyte faiss-cpu langchain-community langchain-openai" ], "metadata": { "id": "7lwuMXN3ocjE" }, "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "source": [ "## **Load Data using AirbyteLoader**\n", "Now we use `AirbyteLoader` to fetch data from the source `source-github`.
\n", "You may use any other source you require, but fetch the data accordingly!
\n", "Dont forget to add all the required fields!
\n", "Refer the guide for your source [here](https://docs.airbyte.com/integrations/sources/)\n", "\n", "For more information regarding this package [refer](https://python.langchain.com/v0.2/docs/integrations/document_loaders/airbyte/)\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." ], "metadata": { "id": "MdmoHC1Fo7fn" } }, { "cell_type": "code", "source": [ "from langchain_airbyte import AirbyteLoader\n", "from langchain.schema import Document\n", "\n", "# Configure the AirbyteLoader to load data from a GitHub repository\n", "loader = AirbyteLoader(\n", " source=\"source-github\",\n", " stream=\"commits\",\n", " config={\n", " \"credentials\": {\n", " \"personal_access_token\": \"your_personal_access_token\"\n", " },\n", " \"repositories\": [\"your_username/repository_name\"]\n", " }\n", ")\n", "\n", "# Load documents from the specified GitHub source\n", "docs = loader.load()\n", "\n", "# Convert incoming stream data into documents\n", "docs = [Document(page_content=record.page_content, metadata=record.metadata) for record in docs]\n" ], "metadata": { "id": "q-9PC6bzpQMp" }, "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "source": [ "## **Split Documents into Chunks and Store these Chunks in Vector Store using FAISS**\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", "The chunks of documents are transformed into vectors using an embedding model (OpenAI embeddings).
\n", "These vectors are then stored in a FAISS vector store, which allows for efficient similarity search.
\n", "The vector store indexes the vectors and enables fast retrieval of similar vectors based on a query." ], "metadata": { "id": "61JWR7I_p7g5" } }, { "cell_type": "code", "source": [ "from langchain.text_splitter import RecursiveCharacterTextSplitter\n", "\n", "splitter = RecursiveCharacterTextSplitter(chunk_size=512, chunk_overlap=30)\n", "chunked_docs = splitter.split_documents(docs)\n", "\n", "print(f\"Created {len(chunked_docs)} document chunks.\")\n", "\n", "# Store Chunks in Vector Store using FAISS\n", "from langchain_openai import OpenAI, OpenAIEmbeddings\n", "from langchain_community.vectorstores import FAISS\n", "import os\n", "\n", "# Set the OpenAI API Key (make sure to set your own API key here)\n", "os.environ['OPENAI_API_KEY'] = \"YOUR_OPENAI_API_KEY\"\n", "\n", "# Ensure filtered_docs is not empty\n", "if not chunked_docs:\n", " raise ValueError(\"No valid documents to store in the vector store.\")\n", "\n", "# Store document chunks in FAISS vector store\n", "embeddings = OpenAIEmbeddings(openai_api_key=os.getenv(\"OPENAI_API_KEY\"))\n", "vector_store = FAISS.from_texts([doc.page_content for doc in chunked_docs], embeddings)\n", "\n", "print(\"Chunks successfully stored in vectorstore.\")" ], "metadata": { "id": "vqI6yNtGqJC7" }, "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "source": [ "## **Perform QnA on Stored Data**\n", "Finally we perform the Question And Answer here.
\n", "\n", "When a query is made, the vector store retrieves relevant document chunks based on their vector similarity to the query.\n", "The language model (OpenAI) then generates answers based on the retrieved chunks." ], "metadata": { "id": "xUuF_RUvqVZM" } }, { "cell_type": "code", "source": [ "# Step 5: Perform QnA on Stored Data\n", "from langchain.chains.question_answering import load_qa_chain\n", "\n", "# Initialize the LLM (OpenAI)\n", "llm = OpenAI(openai_api_key=os.getenv(\"OPENAI_API_KEY\"))\n", "\n", "# Create a QnA chain\n", "qa_chain = load_qa_chain(llm=llm, chain_type=\"stuff\")\n", "\n", "# Perform a QnA\n", "query = \"What are the latest commits in the repository?\"\n", "inputs = {\"question\": query, \"input_documents\": chunked_docs}\n", "answer = qa_chain.invoke(inputs)\n", "\n", "print(\"QnA Result:\", answer)\n" ], "metadata": { "id": "tHDAkPjYqXDb" }, "execution_count": null, "outputs": [] } ] } ================================================ FILE: satisfaction_analytics_zendesk_support/.gitignore ================================================ # Byte-compiled / optimized / DLL files __pycache__/ *.py[cod] *$py.class # C extensions *.so # Distribution / packaging .Python build/ develop-eggs/ dist/ downloads/ eggs/ .eggs/ lib/ lib64/ parts/ sdist/ var/ wheels/ share/python-wheels/ *.egg-info/ .installed.cfg *.egg MANIFEST # PyInstaller # Usually these files are written by a python script from a template # before PyInstaller builds the exe, so as to inject date/other infos into it. *.manifest *.spec # Installer logs pip-log.txt pip-delete-this-directory.txt # Unit test / coverage reports htmlcov/ .tox/ .nox/ .coverage .coverage.* .cache nosetests.xml coverage.xml *.cover *.py,cover .hypothesis/ .pytest_cache/ cover/ # Translations *.mo *.pot # Django stuff: *.log local_settings.py db.sqlite3 db.sqlite3-journal # Flask stuff: instance/ .webassets-cache # Scrapy stuff: .scrapy # Sphinx documentation docs/_build/ # PyBuilder .pybuilder/ target/ # Jupyter Notebook .ipynb_checkpoints # IPython profile_default/ ipython_config.py # pyenv # For a library or package, you might want to ignore these files since the code is # intended to run in multiple environments; otherwise, check them in: # .python-version # pipenv # According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. # However, in case of collaboration, if having platform-specific dependencies or dependencies # having no cross-platform support, pipenv may install dependencies that don't work, or not # install all needed dependencies. #Pipfile.lock # poetry # Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control. # This is especially recommended for binary packages to ensure reproducibility, and is more # commonly ignored for libraries. # https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control #poetry.lock # pdm # Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control. #pdm.lock # pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it # in version control. # https://pdm.fming.dev/#use-with-ide .pdm.toml # PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm __pypackages__/ # Celery stuff celerybeat-schedule celerybeat.pid # SageMath parsed files *.sage.py # Environments .env .venv env/ venv/ ENV/ env.bak/ venv.bak/ # Spyder project settings .spyderproject .spyproject # Rope project settings .ropeproject # mkdocs documentation /site # mypy .mypy_cache/ .dmypy.json dmypy.json # Pyre type checker .pyre/ # pytype static type analyzer .pytype/ # Cython debug symbols cython_debug/ # PyCharm # JetBrains specific template is maintained in a separate JetBrains.gitignore that can # be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore # and can be added to the global gitignore or merged into this file. For a more nuclear # option (not recommended) you can uncomment the following to ignore the entire idea folder. #.idea/ #Desktop Services Store .DS_Store ================================================ FILE: satisfaction_analytics_zendesk_support/README.md ================================================ # Customer Satisfaction Analytics Stack With Zendesk Support, Airbyte, Dbt, Dagster and BigQuery Welcome 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. This Quickstart is all about making things easy, getting you started quickly and showing you how smoothly all these tools can work together! Below is a visual representation of how data flows through our integrated tools in this Quickstart. This comes from Dagster's global asset lineage view: .svg>) ## Table of Contents - [Prerequisites](#prerequisites) - [Setting an environment for your project](#1-setting-an-environment-for-your-project) - [Setting Up BigQuery to work with Airbyte and dbt](#2-setting-up-bigquery) - [Setting Up Airbyte Connectors with Terraform](#3-setting-up-airbyte-connectors-with-terraform) - [Setting Up the dbt Project](#4-setting-up-the-dbt-project) - [Orchestrating with Dagster](#5-orchestrating-with-dagster) - [Next Steps](#next-steps) ## Prerequisites Before you embark on this integration, ensure you have the following set up and ready: 1. **Python 3.10 or later**: If not installed, download and install it from [Python's official website](https://www.python.org/downloads/). 2. **Docker and Docker Compose (Docker Desktop)**: Install [Docker](https://docs.docker.com/get-docker/) following the official documentation for your specific OS. 3. **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/). 4. **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). 5. **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). ## 1. Setting an environment for your project Get the project up and running on your local machine by following these steps: 1. **Clone the repository (Clone only this quickstart)**: ```bash git clone --filter=blob:none --sparse https://github.com/airbytehq/quickstarts.git ``` ```bash cd quickstarts ``` ```bash git sparse-checkout add satisfaction_analytics_zendesk_support ``` 2. **Navigate to the directory**: ```bash cd satisfaction_analytics_zendesk_support ``` 3. **Set Up a Virtual Environment**: - For Mac: ```bash python3 -m venv venv source venv/bin/activate ``` - For Windows: ```bash python -m venv venv .\venv\Scripts\activate ``` 4. **Install Dependencies**: ```bash pip install -e ".[dev]" ``` ## 2. Setting Up BigQuery #### 1. **Create a Google Cloud Project** - If you have a Google Cloud project, you can skip this step. - Go to the [Google Cloud Console](https://console.cloud.google.com/). - Click on the "Select a project" dropdown at the top right and select "New Project". - Give your project a name and follow the steps to create it. #### 2. **Create BigQuery Datasets** - In the Google Cloud Console, go to BigQuery. - Make two new datasets: `raw_data` for Airbyte and `transformed_data` for dbt. - If you pick different names, remember to change the names in the code too. **How to create a dataset:** - In the left sidebar, click on your project name. - Click “Create Dataset”. - Enter the dataset ID (either `raw_data` or `transformed_data`). - Click "Create Dataset". #### 3. **Create Service Accounts and Assign Roles** - Go to “IAM & Admin” > “Service accounts” in the Google Cloud Console. - Click “Create Service Account”. - Name your service account (like `airbyte-service-account`). - Assign the “BigQuery Data Editor” and “BigQuery Job User” roles to the service account. - Follow the same steps to make another service account for dbt (like `dbt-service-account`) and assign the roles. **How to create a service account and assign roles:** - While creating the service account, under the “Grant this service account access to project” section, click the “Role” dropdown. - Choose the “BigQuery Data Editor” and “BigQuery Job User” roles. - Finish the creation process. #### 4. **Generate JSON Keys for Service Accounts** - For both service accounts, make a JSON key to let the service accounts sign in. **How to generate JSON key:** - Find the service account in the “Service accounts” list. - Click on the service account name. - In the “Keys” section, click “Add Key” and pick JSON. - The key will download automatically. Keep it safe and don’t share it. - Do this for the other service account too. ## 3. Setting Up Airbyte Connectors with Terraform Airbyte 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: 1. **Navigate to the Airbyte Configuration Directory**: Change to the relevant directory containing the Terraform configuration for Airbyte: ```bash cd infra/airbyte ``` 2. **Modify Configuration Files**: Within the `infra/airbyte` directory, you'll find three crucial Terraform files: - `provider.tf`: Defines the Airbyte provider. - `main.tf`: Contains the main configuration for creating Airbyte resources. - `variables.tf`: Holds various variables, including credentials. 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. 3. **Initialize Terraform**: This step prepares Terraform to create the resources defined in your configuration files. ```bash terraform init ``` 4. **Review the Plan**: Before applying any changes, review the plan to understand what Terraform will do. ```bash terraform plan ``` 5. **Apply Configuration**: After reviewing and confirming the plan, apply the Terraform configurations to create the necessary Airbyte resources. ```bash terraform apply ``` 6. **Verify in Airbyte UI**: 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. ## 4. Setting Up the dbt Project [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: 1. **Navigate to the dbt Project Directory**: Change to the directory containing the dbt configuration: ```bash cd dbt_project ``` 2. **Update Connection Details**: 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. 3. **Utilize Environment Variables (Optional but Recommended)**: To keep your credentials secure, you can leverage environment variables. An example is provided within the `profiles.yml` file. 4. **Test the Connection**: Once you’ve updated the connection details, you can test the connection to your BigQuery instance using: ```bash dbt debug ``` If everything is set up correctly, this command should report a successful connection to BigQuery. 5. **Run the Models**: If you would like to run the dbt models manually at this point, you can do so by executing: ```bash dbt run ``` You can verify the data has been transformed by going to BigQuery and checking the `transformed_data` dataset. ## 5. Orchestrating with Dagster [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: 1. **Navigate to the Orchestration Directory**: Switch to the directory containing the Dagster orchestration configurations: ```bash cd orchestration ``` 2. **Set Environment Variables**: Dagster requires certain environment variables to be set to interact with other tools like dbt and Airbyte. Set the following variables: ```bash export DAGSTER_DBT_PARSE_PROJECT_ON_LOAD=1 export AIRBYTE_PASSWORD=password ``` 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. 3. **Launch the Dagster UI**: With the environment variables in place, kick-start the Dagster UI: ```bash dagster dev ``` 4. **Access Dagster in Your Browser**: Open your browser and navigate to: ``` http://127.0.0.1:3000 ``` 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. 5. **Materialize Dagster Assets**: 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. ## Next Steps Congratulations 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: ### 1. **Explore the Data and Insights** - 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! ### 2. **Optimize Your dbt Models** - 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. ### 3. **Automate and Monitor Your Pipelines** - 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. ================================================ FILE: satisfaction_analytics_zendesk_support/dbt_project/.gitignore ================================================ target/ dbt_packages/ logs/ .user.yml ================================================ FILE: satisfaction_analytics_zendesk_support/dbt_project/README.md ================================================ Welcome to your new dbt project! ### Using the starter project Try running the following commands: - dbt run - dbt test ### Resources: - Learn more about dbt [in the docs](https://docs.getdbt.com/docs/introduction) - Check out [Discourse](https://discourse.getdbt.com/) for commonly asked questions and answers - Join the [chat](https://community.getdbt.com/) on Slack for live discussions and support - Find [dbt events](https://events.getdbt.com) near you - Check out [the blog](https://blog.getdbt.com/) for the latest news on dbt's development and best practices ================================================ FILE: satisfaction_analytics_zendesk_support/dbt_project/analysis/.gitkeep ================================================ ================================================ FILE: satisfaction_analytics_zendesk_support/dbt_project/dbt_project.yml ================================================ # Name your project! Project names should contain only lowercase characters # and underscores. A good package name should reflect your organization's # name or the intended use of these models name: 'dbt_project' version: '1.0.0' config-version: 2 # This setting configures which "profile" dbt uses for this project. profile: 'dbt_project' # These configurations specify where dbt should look for different types of files. # The `model-paths` config, for example, states that models in this project can be # found in the "models/" directory. You probably won't need to change these! model-paths: ["models"] analysis-paths: ["analyses"] test-paths: ["tests"] seed-paths: ["seeds"] macro-paths: ["macros"] snapshot-paths: ["snapshots"] clean-targets: # directories to be removed by `dbt clean` - "target" - "dbt_packages" # Configuring models # Full documentation: https://docs.getdbt.com/docs/configuring-models # In this example config, we tell dbt to build all models in the example/ # directory as views. These settings can be overridden in the individual model # files using the `{{ config(...) }}` macro. models: dbt_project: # Config indicated by + and applies to all files under models/example/ staging: +materialized: view marts: +materialized: view ================================================ FILE: satisfaction_analytics_zendesk_support/dbt_project/models/marts/analyze_satisfaction_score_over_time.sql ================================================ SELECT DATE_TRUNC(created_at, DAY) AS date, AVG(CAST(score AS FLOAT64)) AS avg_satisfaction_score FROM transformed_data.stg_satisfaction_ratings WHERE score IS NOT NULL GROUP BY date ORDER BY date ================================================ FILE: satisfaction_analytics_zendesk_support/dbt_project/models/marts/avarage_satisfaction_rating.sql ================================================ SELECT AVG(CAST(score AS FLOAT64)) AS avg_satisfaction_score FROM transformed_data.stg_satisfaction_ratings WHERE score IS NOT NULL ================================================ FILE: satisfaction_analytics_zendesk_support/dbt_project/models/marts/feedback_analysis_for_low_score.sql ================================================ SELECT reason, COUNT(*) AS count FROM transformed_data.stg_satisfaction_ratings WHERE CAST(score AS INT64) <= 2 AND reason IS NOT NULL GROUP BY reason ORDER BY count DESC ================================================ FILE: satisfaction_analytics_zendesk_support/dbt_project/models/marts/trend_analysis_by_score.sql ================================================ SELECT score, COUNT(*) AS count FROM transformed_data.stg_satisfaction_ratings WHERE score IS NOT NULL GROUP BY score ORDER BY score ================================================ FILE: satisfaction_analytics_zendesk_support/dbt_project/models/sources/zendesk_support_sources.yml ================================================ version: 2 sources: - name: zendesk_support # Use your BigQuery project ID database: "{{ env_var('BIGQUERY_PROJECT_ID', '') }}" # Use your BigQuery dataset name schema: zendesk_airbyte_trial tables: - name: users description: "Simulated user data from the Zendesk Support connector." columns: - name: id description: "Unique identifier for the user." - name: active - name: alias - name: chat_only - name: created_at - name: custom_role_id - name: default_group_id - name: details - name: email - name: external_id - name: updated_at - name: iana_time_zone - name: last_login_at - name: locale - name: locale_id - name: moderator - name: name - name: notes - name: only_private_comments - name: organization_id - name: permanently_deleted - name: phone - name: photo - name: report_csv - name: restricted_agent - name: role - name: role_type - name: shared - name: shared_agent - name: shared_phone_number - name: signature - name: suspended - name: tags - name: ticket_restriction - name: time_zone - name: two_factor_auth_enabled - name: url - name: user_fields - name: verified - name: tickets description: "Simulated tickets data from the Zendesk Support connector." columns: - name: id description: "Unique identifier for the ticket." - name: updated_at - name: allow_attachments - name: allow_channelback - name: assignee_id - name: brand_id - name: collaborator_ids - name: created_at - name: custom_fields - name: custom_status_id - name: description - name: due_at - name: email_cc_ids - name: external_id - name: fields - name: follower_ids - name: followup_ids - name: forum_topic_id - name: from_messaging_channel - name: generated_timestamp - name: group_id - name: has_incidents - name: is_public - name: organization_id - name: priority - name: problem_id - name: raw_subject - name: recipient - name: requester_id - name: satisfaction_rating - name: sharing_agreement_ids - name: status - name: subject - name: submitter_id - name: tags - name: ticket_form_id - name: type - name: url - name: via - name: satisfaction_ratings description: "Simulated satisfaction_ratings data from the Zendesk Support connector." columns: - name: id description: "Unique identifier for the satisfaction_ratings." - name: updated_at - name: created_at - name: assignee_id - name: comment - name: group_id - name: reason - name: reason_id - name: requester_id - name: score - name: ticket_id - name: url - name: brands description: "Simulated brands data from the Zendesk Support connector." columns: - name: id description: "Unique identifier for the brands." - name: updated_at - name: created_at - name: url - name: active - name: brand_url - name: default - name: has_help_center - name: help_center_state - name: host_mapping - name: is_deleted - name: logo - name: name - name: signature_template - name: subdomain - name: ticket_form_ids - name: groups description: "Simulated groups data from the Zendesk Support connector." columns: - name: id description: "Unique identifier for the groups." - name: updated_at - name: created_at - name: url - name: deleted - name: description - name: default - name: is_public - name: name - name: organizations description: "Simulated organizations data from the Zendesk Support connector." columns: - name: id description: "Unique identifier for the organizations." - name: updated_at - name: created_at - name: url - name: details - name: domain_names - name: external_id - name: group_id - name: notes - name: organization_fields - name: deleted_at - name: shared_comments - name: name - name: shared_tickets - name: tags - name: tags description: "Simulated tags data from the Zendesk Support connector." columns: - name: name - name: count - name: ticket_audits description: "Simulated ticket_audits data from the Zendesk Support connector." columns: - name: id description: "Unique identifier for the ticket_audits." - name: author_id - name: created_at - name: events - name: metadata - name: ticket_id - name: via - name: ticket_comments description: "Simulated ticket_comments data from the Zendesk Support connector." columns: - name: id description: "Unique identifier for the ticket_comments." - name: attachments - name: created_at - name: audit_id - name: author_id - name: body - name: event_type - name: html_body - name: metadata - name: plain_body - name: public - name: ticket_id - name: timestamp - name: type - name: uploads - name: via - name: via_reference_id - name: ticket_fields description: "Simulated ticket_fields data from the Zendesk Support connector." columns: - name: id description: "Unique identifier for the ticket_fields." - name: active - name: created_at - name: agent_description - name: collapsed_for_agents - name: custom_field_options - name: custom_statuses - name: description - name: editable_in_portal - name: key - name: position - name: raw_description - name: raw_title - name: raw_title_in_portal - name: regexp_for_validation - name: removable - name: required - name: required_in_portal - name: sub_type_id - name: system_field_options - name: tag - name: title - name: title_in_portal - name: type - name: updated_at - name: url - name: visible_in_portal - name: ticket_forms description: "Simulated ticket_forms data from the Zendesk Support connector." columns: - name: id description: "Unique identifier for the ticket_forms." - name: active - name: created_at - name: agent_conditions - name: default - name: display_name - name: end_user_conditions - name: end_user_visible - name: in_all_brands - name: name - name: position - name: raw_display_name - name: raw_name - name: restricted_brand_ids - name: ticket_field_ids - name: updated_at - name: url - name: ticket_metric_events description: "Simulated ticket_metric_events data from the Zendesk Support connector." columns: - name: id description: "Unique identifier for the ticket_metric_events." - name: instance_id - name: metric - name: time - name: type - name: ticket_id - name: ticket_metrics description: "Simulated ticket_metrics data from the Zendesk Support connector." columns: - name: id description: "Unique identifier for the ticket_metrics." - name: agent_wait_time_in_minutes - name: created_at - name: assigned_at - name: assignee_stations - name: assignee_updated_at - name: custom_status_updated_at - name: first_resolution_time_in_minutes - name: full_resolution_time_in_minutes - name: group_stations - name: initially_assigned_at - name: instance_id - name: latest_comment_added_at - name: metric - name: on_hold_time_in_minutes - name: updated_at - name: url - name: reopens - name: replies - name: reply_time_in_minutes - name: requester_updated_at - name: requester_wait_time_in_minutes - name: solved_at - name: status - name: status_updated_at - name: ticket_id - name: time - name: type ================================================ FILE: satisfaction_analytics_zendesk_support/dbt_project/models/staging/stg_brands.sql ================================================ select * from {{ source('zendesk_support', 'brands') }} ================================================ FILE: satisfaction_analytics_zendesk_support/dbt_project/models/staging/stg_groups.sql ================================================ select * from {{ source('zendesk_support', 'groups') }} ================================================ FILE: satisfaction_analytics_zendesk_support/dbt_project/models/staging/stg_organizations.sql ================================================ select * from {{ source('zendesk_support', 'organizations') }} ================================================ FILE: satisfaction_analytics_zendesk_support/dbt_project/models/staging/stg_satisfaction_ratings.sql ================================================ select * from {{ source('zendesk_support', 'satisfaction_ratings') }} ================================================ FILE: satisfaction_analytics_zendesk_support/dbt_project/models/staging/stg_tags.sql ================================================ select * from {{ source('zendesk_support', 'tags') }} ================================================ FILE: satisfaction_analytics_zendesk_support/dbt_project/models/staging/stg_ticket_audits.sql ================================================ select * from {{ source('zendesk_support', 'ticket_audits') }} ================================================ FILE: satisfaction_analytics_zendesk_support/dbt_project/models/staging/stg_ticket_comments.sql ================================================ select * from {{ source('zendesk_support', 'ticket_comments') }} ================================================ FILE: satisfaction_analytics_zendesk_support/dbt_project/models/staging/stg_ticket_fields.sql ================================================ select * from {{ source('zendesk_support', 'ticket_fields') }} ================================================ FILE: satisfaction_analytics_zendesk_support/dbt_project/models/staging/stg_ticket_forms.sql ================================================ select * from {{ source('zendesk_support', 'ticket_forms') }} ================================================ FILE: satisfaction_analytics_zendesk_support/dbt_project/models/staging/stg_ticket_metric_events.sql ================================================ select * from {{ source('zendesk_support', 'ticket_metric_events') }} ================================================ FILE: satisfaction_analytics_zendesk_support/dbt_project/models/staging/stg_ticket_metrics.sql ================================================ select * from {{ source('zendesk_support', 'ticket_metrics') }} ================================================ FILE: satisfaction_analytics_zendesk_support/dbt_project/models/staging/stg_tickets.sql ================================================ select * from {{ source('zendesk_support', 'tickets') }} ================================================ FILE: satisfaction_analytics_zendesk_support/dbt_project/models/staging/stg_users.sql ================================================ select * from {{ source('zendesk_support', 'users') }} ================================================ FILE: satisfaction_analytics_zendesk_support/dbt_project/profiles.yml ================================================ dbt_project: outputs: dev: dataset: transformed_data job_execution_timeout_seconds: 300 job_retries: 1 # Use an env variable to indicate your JSON key file path keyfile: "{{ env_var('DBT_BIGQUERY_KEYFILE_PATH', '') }}" location: US method: service-account priority: interactive # Indicate your BigQuery project ID project: "{{ env_var('BIGQUERY_PROJECT_ID', '') }}" threads: 1 type: bigquery target: dev ================================================ FILE: satisfaction_analytics_zendesk_support/dbt_project/tests/.gitkeep ================================================ ================================================ FILE: satisfaction_analytics_zendesk_support/infra/.gitignore ================================================ # Local .terraform directories **/.terraform/* # .tfstate files *.tfstate *.tfstate.* # Crash log files crash.log crash.*.log # Exclude all .tfvars files, which are likely to contain sensitive data, such as # password, private keys, and other secrets. These should not be part of version # control as they are data points which are potentially sensitive and subject # to change depending on the environment. *.tfvars *.tfvars.json # Ignore override files as they are usually used to override resources locally and so # are not checked in override.tf override.tf.json *_override.tf *_override.tf.json # Include override files you do wish to add to version control using negated pattern # !example_override.tf # Include tfplan files to ignore the plan output of command: terraform plan -out=tfplan # example: *tfplan* # Ignore CLI configuration files .terraformrc terraform.rc ================================================ FILE: satisfaction_analytics_zendesk_support/infra/airbyte/.terraform.lock.hcl ================================================ # This file is maintained automatically by "terraform init". # Manual edits may be lost in future updates. provider "registry.terraform.io/airbytehq/airbyte" { version = "0.3.3" constraints = "0.3.3" hashes = [ "h1:0LmuAc5LvlMuOUPtNEaCAh9FHrV/C877bDJhm9Lz8MU=", "zh:0efa470b34d9b912b47efe4469c51713bfc3c2413e52c17e1e903f2a3cddb2f6", "zh:1bddd69fa2c2d4f3e239d60555446df9bc4ce0c0cabbe7e092fe1d44989ab004", "zh:2e20540403a0010007b53456663fb037b24e30f6c8943f65da1bcf7fa4dfc8a6", "zh:2f415369ad884e8b7115a5c5ff229d052f7af1fca27abbfc8ebef379ed11aec4", "zh:46fd9a906f4b6461112dcc5a5aa01a3fcd7a19a72d4ad0b2e37790da37701fe1", "zh:83503ebb77bb6d6941c42ba323cf22380d08a1506554a2dcc8ac54e74c0886a1", "zh:890df766e9b839623b1f0437355032a3c006226a6c200cd911e15ee1a9014e9f", "zh:8fd770eff726826d3a63b9e3733c5455b5cde004027b04ee3f75888eb8538c90", "zh:b0fc890ed4f9b077bf70ed121cc3550e7a07d16e7798ad517623274aa62ad7b0", "zh:c2a01612362da9b73cd5958f281e1aa7ff09af42182e463097d11ed78e778e72", "zh:c64b2bb1887a0367d64ba3393d4b3a16c418cf5b1792e2e7aae7c0b5413eb334", "zh:ce14ebbf0ed91913ec62655a511763dec62b5779de9a209bd6f1c336640cddc0", "zh:e0662ca837eee10f7733ea9a501d995281f56bd9b410ae13ad03eb106011db14", "zh:e103d480fc6066004bc98e9e04a141a1f55b918cc2912716beebcc6fc4c872fb", "zh:e2507049098f0f1b21cb56870f4a5ef624bcf6d3959e5612eada1f8117341648", ] } ================================================ FILE: satisfaction_analytics_zendesk_support/infra/airbyte/main.tf ================================================ // Airbyte Terraform provider documentation: https://registry.terraform.io/providers/airbytehq/airbyte/latest/docs // Sources resource "airbyte_source_zendesk_support" "my_source_zendesksupport" { configuration = { source_type = "zendesk-support" credentials = { source_zendesk_support_authentication_api_token = { credentials = "api_token" api_token = var.api_token email = var.email } } start_date = "2020-10-15T00:00:00Z" subdomain = "self3836" } name = "Zendesk Support" workspace_id = var.workspace_id } // Destinations resource "airbyte_destination_bigquery" "bigquery" { configuration = { dataset_id = var.dataset_id dataset_location = "US" destination_type = "bigquery" project_id = var.project_id credentials_json = var.credentials_json loading_method = { destination_bigquery_loading_method_standard_inserts = { method = "Standard" } } } name = "BigQuery" workspace_id = var.workspace_id } // Connections resource "airbyte_connection" "zendesk_support_bigquery" { name = "Zendesk Support to BigQuery" source_id = airbyte_source_zendesk_support.my_source_zendesksupport.source_id destination_id = airbyte_destination_bigquery.bigquery.destination_id configurations = { streams = [ { name = "users" }, { name = "tickets" }, { name = "satisfaction_ratings" }, { name = "ticket_metrics" }, { name = "ticket_metric_events" }, { name = "ticket_comments" }, { name = "ticket_audits" } ] } } ================================================ FILE: satisfaction_analytics_zendesk_support/infra/airbyte/provider.tf ================================================ // Airbyte Terraform provider documentation: https://registry.terraform.io/providers/airbytehq/airbyte/latest/docs terraform { required_providers { airbyte = { source = "airbytehq/airbyte" version = "0.3.3" } } } provider "airbyte" { // If running locally (Airbyte OSS) with docker-compose using the airbyte-proxy, // include the actual password/username you've set up (or use the defaults below) username = "airbyte" password = "password" // if running locally (Airbyte OSS), include the server url to the airbyte-api-server server_url = "http://localhost:8006/v1/" } ================================================ FILE: satisfaction_analytics_zendesk_support/infra/airbyte/variables.tf ================================================ variable "api_token" { type = string } variable "email" { type = string } variable "workspace_id" { type = string } variable "dataset_id" { type = string } variable "project_id" { type = string } variable "credentials_json" { type = string } ================================================ FILE: satisfaction_analytics_zendesk_support/orchestration/orchestration/__init__.py ================================================ ================================================ FILE: satisfaction_analytics_zendesk_support/orchestration/orchestration/assets.py ================================================ import os from dagster import OpExecutionContext from dagster_dbt import DbtCliResource, dbt_assets from dagster_airbyte import AirbyteResource, load_assets_from_airbyte_instance from .constants import dbt_manifest_path @dbt_assets(manifest=dbt_manifest_path) def dbt_project_dbt_assets(context: OpExecutionContext, dbt: DbtCliResource): yield from dbt.cli(["build"], context=context).stream() airbyte_instance = AirbyteResource( host="localhost", port="8000", # If using basic auth, include username and password: username="airbyte", password="password" ) airbyte_assets = load_assets_from_airbyte_instance(airbyte_instance,) ================================================ FILE: satisfaction_analytics_zendesk_support/orchestration/orchestration/constants.py ================================================ import os from pathlib import Path from dagster_dbt import DbtCliResource dbt_project_dir = Path(__file__).joinpath("..", "..", "..", "dbt_project").resolve() dbt = DbtCliResource(project_dir=os.fspath(dbt_project_dir)) # If DAGSTER_DBT_PARSE_PROJECT_ON_LOAD is set, a manifest will be created at runtime. # Otherwise, we expect a manifest to be present in the project's target directory. if os.getenv("DAGSTER_DBT_PARSE_PROJECT_ON_LOAD"): dbt_parse_invocation = dbt.cli(["parse"], manifest={}).wait() dbt_manifest_path = dbt_parse_invocation.target_path.joinpath("manifest.json") else: dbt_manifest_path = dbt_project_dir.joinpath("target", "manifest.json") ================================================ FILE: satisfaction_analytics_zendesk_support/orchestration/orchestration/definitions.py ================================================ import os from dagster import Definitions from dagster_dbt import DbtCliResource from .assets import dbt_project_dbt_assets, airbyte_assets from .constants import dbt_project_dir from .schedules import schedules defs = Definitions( assets=[dbt_project_dbt_assets, airbyte_assets], schedules=schedules, resources={ "dbt": DbtCliResource(project_dir=os.fspath(dbt_project_dir)), }, ) ================================================ FILE: satisfaction_analytics_zendesk_support/orchestration/orchestration/schedules.py ================================================ """ To add a daily schedule that materializes your dbt assets, uncomment the following lines. """ from dagster_dbt import build_schedule_from_dbt_selection from .assets import dbt_project_dbt_assets schedules = [ # build_schedule_from_dbt_selection( # [dbt_project_dbt_assets], # job_name="materialize_dbt_models", # cron_schedule="0 0 * * *", # dbt_select="fqn:*", # ), ] ================================================ FILE: satisfaction_analytics_zendesk_support/orchestration/pyproject.toml ================================================ [build-system] requires = ["setuptools"] build-backend = "setuptools.build_meta" [tool.dagster] module_name = "orchestration.definitions" code_location_name = "orchestration" ================================================ FILE: satisfaction_analytics_zendesk_support/orchestration/setup.py ================================================ from setuptools import find_packages, setup setup( name="orchestration", version="0.0.1", packages=find_packages(), install_requires=[ "dagster", "dagster-cloud", "dagster-dbt", "dbt-core>=1.4.0", "dbt-bigquery", ], extras_require={ "dev": [ "dagster-webserver", ] }, ) ================================================ FILE: satisfaction_analytics_zendesk_support/orchestration/tmp3ks7pwhz/storage/ad21fadd-c131-4a7c-98a7-fa5ad3a929de/compute_logs/mdvhnoik.complete ================================================ ================================================ FILE: satisfaction_analytics_zendesk_support/orchestration/tmp3ks7pwhz/storage/ad21fadd-c131-4a7c-98a7-fa5ad3a929de/compute_logs/mdvhnoik.err ================================================ ================================================ FILE: satisfaction_analytics_zendesk_support/orchestration/tmp3ks7pwhz/storage/ad21fadd-c131-4a7c-98a7-fa5ad3a929de/compute_logs/mdvhnoik.out ================================================ ================================================ FILE: satisfaction_analytics_zendesk_support/orchestration/tmp3ks7pwhz/storage/ad21fadd-c131-4a7c-98a7-fa5ad3a929de/compute_logs/uzgmeijp.complete ================================================ ================================================ FILE: satisfaction_analytics_zendesk_support/orchestration/tmp3ks7pwhz/storage/ad21fadd-c131-4a7c-98a7-fa5ad3a929de/compute_logs/uzgmeijp.err ================================================ ================================================ FILE: satisfaction_analytics_zendesk_support/orchestration/tmp3ks7pwhz/storage/ad21fadd-c131-4a7c-98a7-fa5ad3a929de/compute_logs/uzgmeijp.out ================================================ ================================================ FILE: satisfaction_analytics_zendesk_support/orchestration/tmp3ks7pwhz/storage/f7507115-918d-443f-ab91-a065e84fa403/compute_logs/aeebjmfa.complete ================================================ ================================================ FILE: satisfaction_analytics_zendesk_support/orchestration/tmp3ks7pwhz/storage/f7507115-918d-443f-ab91-a065e84fa403/compute_logs/aeebjmfa.err ================================================ ================================================ FILE: satisfaction_analytics_zendesk_support/orchestration/tmp3ks7pwhz/storage/f7507115-918d-443f-ab91-a065e84fa403/compute_logs/aeebjmfa.out ================================================ ================================================ FILE: satisfaction_analytics_zendesk_support/orchestration/tmp3ks7pwhz/storage/f7507115-918d-443f-ab91-a065e84fa403/compute_logs/zqbkkiww.complete ================================================ ================================================ FILE: satisfaction_analytics_zendesk_support/orchestration/tmp3ks7pwhz/storage/f7507115-918d-443f-ab91-a065e84fa403/compute_logs/zqbkkiww.err ================================================ ================================================ FILE: satisfaction_analytics_zendesk_support/orchestration/tmp3ks7pwhz/storage/f7507115-918d-443f-ab91-a065e84fa403/compute_logs/zqbkkiww.out ================================================ ================================================ FILE: satisfaction_analytics_zendesk_support/orchestration/tmpb3ctnsbk/storage/0bc4e544-546d-44df-b79c-e75413c56ecb/compute_logs/xozgecli.complete ================================================ ================================================ FILE: satisfaction_analytics_zendesk_support/orchestration/tmpb3ctnsbk/storage/0bc4e544-546d-44df-b79c-e75413c56ecb/compute_logs/xozgecli.err ================================================ ================================================ FILE: satisfaction_analytics_zendesk_support/orchestration/tmpb3ctnsbk/storage/0bc4e544-546d-44df-b79c-e75413c56ecb/compute_logs/xozgecli.out ================================================ ================================================ FILE: satisfaction_analytics_zendesk_support/orchestration/tmpb3ctnsbk/storage/0bc4e544-546d-44df-b79c-e75413c56ecb/compute_logs/yyxjctam.complete ================================================ ================================================ FILE: satisfaction_analytics_zendesk_support/orchestration/tmpb3ctnsbk/storage/0bc4e544-546d-44df-b79c-e75413c56ecb/compute_logs/yyxjctam.err ================================================ ================================================ FILE: satisfaction_analytics_zendesk_support/orchestration/tmpb3ctnsbk/storage/0bc4e544-546d-44df-b79c-e75413c56ecb/compute_logs/yyxjctam.out ================================================ ================================================ FILE: satisfaction_analytics_zendesk_support/orchestration/tmpb3ctnsbk/storage/1eac78ed-12d1-4147-9c48-79b27dd586ed/compute_logs/iqvvuhde.complete ================================================ ================================================ FILE: satisfaction_analytics_zendesk_support/orchestration/tmpb3ctnsbk/storage/1eac78ed-12d1-4147-9c48-79b27dd586ed/compute_logs/iqvvuhde.err ================================================ ================================================ FILE: satisfaction_analytics_zendesk_support/orchestration/tmpb3ctnsbk/storage/1eac78ed-12d1-4147-9c48-79b27dd586ed/compute_logs/iqvvuhde.out ================================================ ================================================ FILE: satisfaction_analytics_zendesk_support/orchestration/tmpb3ctnsbk/storage/1eac78ed-12d1-4147-9c48-79b27dd586ed/compute_logs/izklbfmq.complete ================================================ ================================================ FILE: satisfaction_analytics_zendesk_support/orchestration/tmpb3ctnsbk/storage/1eac78ed-12d1-4147-9c48-79b27dd586ed/compute_logs/izklbfmq.err ================================================ ================================================ FILE: satisfaction_analytics_zendesk_support/orchestration/tmpb3ctnsbk/storage/1eac78ed-12d1-4147-9c48-79b27dd586ed/compute_logs/izklbfmq.out ================================================ ================================================ FILE: satisfaction_analytics_zendesk_support/setup.py ================================================ from setuptools import find_packages, setup setup( name="airbyte-dbt-dagster", packages=find_packages(), install_requires=[ "dbt-bigquery", "dagster", "dagster-cloud", "dagster-dbt", "dagster-airbyte", ], extras_require={"dev": ["dagit", "pytest"]}, ) ================================================ FILE: shopping_cart_analytics_shopify/.gitignore ================================================ # Byte-compiled / optimized / DLL files __pycache__/ *.py[cod] *$py.class # C extensions *.so # Distribution / packaging .Python build/ develop-eggs/ dist/ downloads/ eggs/ .eggs/ lib/ lib64/ parts/ sdist/ var/ wheels/ share/python-wheels/ *.egg-info/ .installed.cfg *.egg MANIFEST # PyInstaller # Usually these files are written by a python script from a template # before PyInstaller builds the exe, so as to inject date/other infos into it. *.manifest *.spec # Installer logs pip-log.txt pip-delete-this-directory.txt # Unit test / coverage reports htmlcov/ .tox/ .nox/ .coverage .coverage.* .cache nosetests.xml coverage.xml *.cover *.py,cover .hypothesis/ .pytest_cache/ cover/ # Translations *.mo *.pot # Django stuff: *.log local_settings.py db.sqlite3 db.sqlite3-journal # Flask stuff: instance/ .webassets-cache # Scrapy stuff: .scrapy # Sphinx documentation docs/_build/ # PyBuilder .pybuilder/ target/ # Jupyter Notebook .ipynb_checkpoints # IPython profile_default/ ipython_config.py # pyenv # For a library or package, you might want to ignore these files since the code is # intended to run in multiple environments; otherwise, check them in: # .python-version # pipenv # According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. # However, in case of collaboration, if having platform-specific dependencies or dependencies # having no cross-platform support, pipenv may install dependencies that don't work, or not # install all needed dependencies. #Pipfile.lock # poetry # Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control. # This is especially recommended for binary packages to ensure reproducibility, and is more # commonly ignored for libraries. # https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control #poetry.lock # pdm # Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control. #pdm.lock # pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it # in version control. # https://pdm.fming.dev/#use-with-ide .pdm.toml # PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm __pypackages__/ # Celery stuff celerybeat-schedule celerybeat.pid # SageMath parsed files *.sage.py # Environments .env .venv env/ venv/ ENV/ env.bak/ venv.bak/ # Spyder project settings .spyderproject .spyproject # Rope project settings .ropeproject # mkdocs documentation /site # mypy .mypy_cache/ .dmypy.json dmypy.json # Pyre type checker .pyre/ # pytype static type analyzer .pytype/ # Cython debug symbols cython_debug/ # PyCharm # JetBrains specific template is maintained in a separate JetBrains.gitignore that can # be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore # and can be added to the global gitignore or merged into this file. For a more nuclear # option (not recommended) you can uncomment the following to ignore the entire idea folder. #.idea/ #Desktop Services Store .DS_Store ================================================ FILE: shopping_cart_analytics_shopify/README.md ================================================ # Shopping Cart Analytics Stack With Shopify, Airbyte, Dbt, Dagster and BigQuery Welcome 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. This Quickstart is all about making things easy, getting you started quickly and showing you how smoothly all these tools can work together! Below is a visual representation of how data flows through our integrated tools in this Quickstart. This comes from Dagster's global asset lineage view: .svg>) ## Table of Contents - [Prerequisites](#prerequisites) - [Setting an environment for your project](#1-setting-an-environment-for-your-project) - [Setting Up BigQuery to work with Airbyte and dbt](#2-setting-up-bigquery) - [Setting Up Airbyte Connectors with Terraform](#3-setting-up-airbyte-connectors-with-terraform) - [Setting Up the dbt Project](#4-setting-up-the-dbt-project) - [Orchestrating with Dagster](#5-orchestrating-with-dagster) - [Next Steps](#next-steps) ## Prerequisites Before you embark on this integration, ensure you have the following set up and ready: 1. **Python 3.10 or later**: If not installed, download and install it from [Python's official website](https://www.python.org/downloads/). 2. **Docker and Docker Compose (Docker Desktop)**: Install [Docker](https://docs.docker.com/get-docker/) following the official documentation for your specific OS. 3. **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/). 4. **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). 5. **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). ## 1. Setting an environment for your project Get the project up and running on your local machine by following these steps: 1. **Clone the repository (Clone only this quickstart)**: ```bash git clone --filter=blob:none --sparse https://github.com/airbytehq/quickstarts.git ``` ```bash cd quickstarts ``` ```bash git sparse-checkout add shopping_cart_analytics_shopify ``` 2. **Navigate to the directory**: ```bash cd shopping_cart_analytics_shopify ``` 3. **Set Up a Virtual Environment**: - For Mac: ```bash python3 -m venv venv source venv/bin/activate ``` - For Windows: ```bash python -m venv venv .\venv\Scripts\activate ``` 4. **Install Dependencies**: ```bash pip install -e ".[dev]" ``` ## 2. Setting Up BigQuery #### 1. **Create a Google Cloud Project** - If you have a Google Cloud project, you can skip this step. - Go to the [Google Cloud Console](https://console.cloud.google.com/). - Click on the "Select a project" dropdown at the top right and select "New Project". - Give your project a name and follow the steps to create it. #### 2. **Create BigQuery Datasets** - In the Google Cloud Console, go to BigQuery. - Make two new datasets: `raw_data` for Airbyte and `transformed_data` for dbt. - If you pick different names, remember to change the names in the code too. **How to create a dataset:** - In the left sidebar, click on your project name. - Click “Create Dataset”. - Enter the dataset ID (either `raw_data` or `transformed_data`). - Click "Create Dataset". #### 3. **Create Service Accounts and Assign Roles** - Go to “IAM & Admin” > “Service accounts” in the Google Cloud Console. - Click “Create Service Account”. - Name your service account (like `airbyte-service-account`). - Assign the “BigQuery Data Editor” and “BigQuery Job User” roles to the service account. - Follow the same steps to make another service account for dbt (like `dbt-service-account`) and assign the roles. **How to create a service account and assign roles:** - While creating the service account, under the “Grant this service account access to project” section, click the “Role” dropdown. - Choose the “BigQuery Data Editor” and “BigQuery Job User” roles. - Finish the creation process. #### 4. **Generate JSON Keys for Service Accounts** - For both service accounts, make a JSON key to let the service accounts sign in. **How to generate JSON key:** - Find the service account in the “Service accounts” list. - Click on the service account name. - In the “Keys” section, click “Add Key” and pick JSON. - The key will download automatically. Keep it safe and don’t share it. - Do this for the other service account too. ## 3. Setting Up Airbyte Connectors with Terraform Airbyte 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: 1. **Navigate to the Airbyte Configuration Directory**: Change to the relevant directory containing the Terraform configuration for Airbyte: ```bash cd infra/airbyte ``` 2. **Modify Configuration Files**: Within the `infra/airbyte` directory, you'll find three crucial Terraform files: - `provider.tf`: Defines the Airbyte provider. - `main.tf`: Contains the main configuration for creating Airbyte resources. - `variables.tf`: Holds various variables, including credentials. 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. 3. **Initialize Terraform**: This step prepares Terraform to create the resources defined in your configuration files. ```bash terraform init ``` 4. **Review the Plan**: Before applying any changes, review the plan to understand what Terraform will do. ```bash terraform plan ``` 5. **Apply Configuration**: After reviewing and confirming the plan, apply the Terraform configurations to create the necessary Airbyte resources. ```bash terraform apply ``` 6. **Verify in Airbyte UI**: 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. ## 4. Setting Up the dbt Project [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: 1. **Navigate to the dbt Project Directory**: Change to the directory containing the dbt configuration: ```bash cd dbt_project ``` 2. **Update Connection Details**: 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. 3. **Utilize Environment Variables (Optional but Recommended)**: To keep your credentials secure, you can leverage environment variables. An example is provided within the `profiles.yml` file. 4. **Test the Connection**: Once you’ve updated the connection details, you can test the connection to your BigQuery instance using: ```bash dbt debug ``` If everything is set up correctly, this command should report a successful connection to BigQuery. 5. **Run the Models**: If you would like to run the dbt models manually at this point, you can do so by executing: ```bash dbt run ``` You can verify the data has been transformed by going to BigQuery and checking the `transformed_data` dataset. ## 5. Orchestrating with Dagster [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: 1. **Navigate to the Orchestration Directory**: Switch to the directory containing the Dagster orchestration configurations: ```bash cd orchestration ``` 2. **Set Environment Variables**: Dagster requires certain environment variables to be set to interact with other tools like dbt and Airbyte. Set the following variables: ```bash export DAGSTER_DBT_PARSE_PROJECT_ON_LOAD=1 export AIRBYTE_PASSWORD=password ``` 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. 3. **Launch the Dagster UI**: With the environment variables in place, kick-start the Dagster UI: ```bash dagster dev ``` 4. **Access Dagster in Your Browser**: Open your browser and navigate to: ``` http://127.0.0.1:3000 ``` 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. 5. **Materialize Dagster Assets**: 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. ## Next Steps Congratulations 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: ### 1. **Explore the Data and Insights** - 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! ### 2. **Optimize Your dbt Models** - 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. ### 3. **Automate and Monitor Your Pipelines** - 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. ================================================ FILE: shopping_cart_analytics_shopify/dbt_project/.gitignore ================================================ target/ dbt_packages/ logs/ .user.yml ================================================ FILE: shopping_cart_analytics_shopify/dbt_project/README.md ================================================ Welcome to your new dbt project! ### Using the starter project Try running the following commands: - dbt run - dbt test ### Resources: - Learn more about dbt [in the docs](https://docs.getdbt.com/docs/introduction) - Check out [Discourse](https://discourse.getdbt.com/) for commonly asked questions and answers - Join the [chat](https://community.getdbt.com/) on Slack for live discussions and support - Find [dbt events](https://events.getdbt.com) near you - Check out [the blog](https://blog.getdbt.com/) for the latest news on dbt's development and best practices ================================================ FILE: shopping_cart_analytics_shopify/dbt_project/dbt_project.yml ================================================ # Name your project! Project names should contain only lowercase characters # and underscores. A good package name should reflect your organization's # name or the intended use of these models name: 'dbt_project' version: '1.0.0' config-version: 2 # This setting configures which "profile" dbt uses for this project. profile: 'dbt_project' # These configurations specify where dbt should look for different types of files. # The `model-paths` config, for example, states that models in this project can be # found in the "models/" directory. You probably won't need to change these! model-paths: ["models"] analysis-paths: ["analyses"] test-paths: ["tests"] seed-paths: ["seeds"] macro-paths: ["macros"] snapshot-paths: ["snapshots"] clean-targets: # directories to be removed by `dbt clean` - "target" - "dbt_packages" # Configuring models # Full documentation: https://docs.getdbt.com/docs/configuring-models # In this example config, we tell dbt to build all models in the example/ # directory as views. These settings can be overridden in the individual model # files using the `{{ config(...) }}` macro. models: dbt_project: # Config indicated by + and applies to all files under models/example/ staging: +materialized: view marts: +materialized: view ================================================ FILE: shopping_cart_analytics_shopify/dbt_project/models/marts/abandoned_checkout_ratio.sql ================================================ SELECT COUNTIF(completed_at IS NULL) AS abandoned_checkouts, COUNT(*) AS total_checkouts, COUNTIF(completed_at IS NULL) / COUNT(*) AS abandonment_rate FROM transformed_data.stg_abandoned_checkouts ================================================ FILE: shopping_cart_analytics_shopify/dbt_project/models/marts/location_based_abandoned_checkouts.sql ================================================ WITH abandoned_checkouts AS ( SELECT *, IF(billing_address IS NOT NULL, JSON_EXTRACT_SCALAR(billing_address, '$.country'), JSON_EXTRACT_SCALAR(shipping_address, '$.country')) AS checkout_country FROM transformed_data.stg_abandoned_checkouts ) SELECT checkout_country, COUNTIF(completed_at IS NULL) AS abandoned_checkouts FROM abandoned_checkouts GROUP BY checkout_country ORDER BY abandoned_checkouts DESC ================================================ FILE: shopping_cart_analytics_shopify/dbt_project/models/marts/most_abandoned_products.sql ================================================ SELECT JSON_EXTRACT_SCALAR(line_items, '$.title') AS product_title, COUNTIF(completed_at IS NULL) AS abandoned_checkouts FROM transformed_data.stg_abandoned_checkouts CROSS JOIN UNNEST(JSON_EXTRACT_ARRAY(line_items)) AS items GROUP BY product_title ORDER BY abandoned_checkouts DESC ================================================ FILE: shopping_cart_analytics_shopify/dbt_project/models/marts/time_based.sql ================================================ SELECT EXTRACT(DAYOFWEEK FROM created_at) AS day_of_week, EXTRACT(HOUR FROM created_at) AS hour_of_day, COUNTIF(completed_at IS NULL) AS abandoned_checkouts FROM transformed_data.stg_abandoned_checkouts GROUP BY day_of_week, hour_of_day ORDER BY day_of_week, hour_of_day ================================================ FILE: shopping_cart_analytics_shopify/dbt_project/models/sources/shopify_source.yml ================================================ version: 2 sources: - name: shopify # Use your BigQuery project ID database: "{{ env_var('BIGQUERY_PROJECT_ID', '') }}" # Use your BigQuery dataset name schema: shopify_airbyte tables: - name: abandoned_checkouts description: "Simulated abandoned_checkouts data from the Shopify connector." columns: - name: id description: "Unique identifier for the abandoned_checkouts." - name: abandoned_checkout_url - name: billing_address - name: buyer_accepts_marketing - name: cart_token - name: closed_at - name: completed_at - name: created_at - name: currency - name: customer - name: customer_locale - name: device_id - name: discount_codes - name: email - name: gateway - name: landing_site - name: line_items - name: location_id - name: name - name: note - name: note_attributes - name: phone - name: presentment_currency - name: referring_site - name: shipping_address - name: shipping_lines - name: shop_url - name: source - name: source_identifier - name: source_name - name: source_url - name: subtotal_price - name: tax_lines - name: taxes_included - name: token - name: total_discounts - name: total_line_items_price - name: total_price - name: total_tax - name: total_weight - name: updated_at - name: user_id - name: customers description: "Simulated customers data from the Shopify connector." columns: - name: id description: "Unique identifier for the customers." - name: accepts_marketing - name: accepts_marketing_updated_at - name: addresses - name: admin_graphql_api_id - name: created_at - name: currency - name: default_address - name: email - name: email_marketing_consent - name: first_name - name: last_name - name: last_order_id - name: last_order_name - name: marketing_opt_in_level - name: multipass_identifier - name: note - name: orders_count - name: phone - name: shop_url - name: sms_marketing_consent - name: state - name: tags - name: tax_exempt - name: tax_exemptions - name: total_spent - name: updated_at - name: verified_email - name: transactions description: "Simulated transactions data from the Shopify connector." columns: - name: id description: "Unique identifier for the transactions." - name: admin_graphql_api_id - name: amount - name: authorization - name: created_at - name: currency - name: device_id - name: error_code - name: gateway - name: kind - name: location_id - name: message - name: order_id - name: parent_id - name: payment_details - name: payment_id - name: processed_at - name: receipt - name: shop_url - name: source_name - name: status - name: test - name: total_unsettled_set - name: user_id ================================================ FILE: shopping_cart_analytics_shopify/dbt_project/models/staging/stg_abandoned_checkouts.sql ================================================ select * from {{ source('shopify', 'abandoned_checkouts') }} ================================================ FILE: shopping_cart_analytics_shopify/dbt_project/profiles.yml ================================================ dbt_project: outputs: dev: dataset: transformed_data job_execution_timeout_seconds: 300 job_retries: 1 # Use an env variable to indicate your JSON key file path keyfile: "{{ env_var('DBT_BIGQUERY_KEYFILE_PATH', '') }}" location: US method: service-account priority: interactive # Indicate your BigQuery project ID project: "{{ env_var('BIGQUERY_PROJECT_ID', '') }}" threads: 1 type: bigquery target: dev ================================================ FILE: shopping_cart_analytics_shopify/infra/.gitignore ================================================ # Local .terraform directories **/.terraform/* # .tfstate files *.tfstate *.tfstate.* # Crash log files crash.log crash.*.log # Exclude all .tfvars files, which are likely to contain sensitive data, such as # password, private keys, and other secrets. These should not be part of version # control as they are data points which are potentially sensitive and subject # to change depending on the environment. *.tfvars *.tfvars.json # Ignore override files as they are usually used to override resources locally and so # are not checked in override.tf override.tf.json *_override.tf *_override.tf.json # Include override files you do wish to add to version control using negated pattern # !example_override.tf # Include tfplan files to ignore the plan output of command: terraform plan -out=tfplan # example: *tfplan* # Ignore CLI configuration files .terraformrc terraform.rc ================================================ FILE: shopping_cart_analytics_shopify/infra/airbyte/.terraform.lock.hcl ================================================ # This file is maintained automatically by "terraform init". # Manual edits may be lost in future updates. provider "registry.terraform.io/airbytehq/airbyte" { version = "0.3.3" constraints = "0.3.3" hashes = [ "h1:0LmuAc5LvlMuOUPtNEaCAh9FHrV/C877bDJhm9Lz8MU=", "zh:0efa470b34d9b912b47efe4469c51713bfc3c2413e52c17e1e903f2a3cddb2f6", "zh:1bddd69fa2c2d4f3e239d60555446df9bc4ce0c0cabbe7e092fe1d44989ab004", "zh:2e20540403a0010007b53456663fb037b24e30f6c8943f65da1bcf7fa4dfc8a6", "zh:2f415369ad884e8b7115a5c5ff229d052f7af1fca27abbfc8ebef379ed11aec4", "zh:46fd9a906f4b6461112dcc5a5aa01a3fcd7a19a72d4ad0b2e37790da37701fe1", "zh:83503ebb77bb6d6941c42ba323cf22380d08a1506554a2dcc8ac54e74c0886a1", "zh:890df766e9b839623b1f0437355032a3c006226a6c200cd911e15ee1a9014e9f", "zh:8fd770eff726826d3a63b9e3733c5455b5cde004027b04ee3f75888eb8538c90", "zh:b0fc890ed4f9b077bf70ed121cc3550e7a07d16e7798ad517623274aa62ad7b0", "zh:c2a01612362da9b73cd5958f281e1aa7ff09af42182e463097d11ed78e778e72", "zh:c64b2bb1887a0367d64ba3393d4b3a16c418cf5b1792e2e7aae7c0b5413eb334", "zh:ce14ebbf0ed91913ec62655a511763dec62b5779de9a209bd6f1c336640cddc0", "zh:e0662ca837eee10f7733ea9a501d995281f56bd9b410ae13ad03eb106011db14", "zh:e103d480fc6066004bc98e9e04a141a1f55b918cc2912716beebcc6fc4c872fb", "zh:e2507049098f0f1b21cb56870f4a5ef624bcf6d3959e5612eada1f8117341648", ] } ================================================ FILE: shopping_cart_analytics_shopify/infra/airbyte/main.tf ================================================ // Source resource "airbyte_source_shopify" "my_source_shopify" { configuration = { credentials = { source_shopify_shopify_authorization_method_api_password = { api_password = var.api_password auth_method = "api_password" } } shop = var.shop source_type = "shopify" } name = "Shopify" workspace_id = var.workspace_id } // Destinations resource "airbyte_destination_bigquery" "bigquery" { configuration = { dataset_id = var.dataset_id dataset_location = "US" destination_type = "bigquery" project_id = var.project_id credentials_json = var.credentials_json loading_method = { destination_bigquery_loading_method_standard_inserts = { method = "Standard" } } } name = "BigQuery" workspace_id = var.workspace_id } // Connections resource "airbyte_connection" "shopify_bigquery" { name = "Shopify to BigQuery" source_id = airbyte_source_shopify.my_source_shopify.source_id destination_id = airbyte_destination_bigquery.bigquery.destination_id configurations = { streams = [ { name = "customers" }, { name = "transactions" }, { name = "abandoned_checkouts" } ] } } ================================================ FILE: shopping_cart_analytics_shopify/infra/airbyte/provider.tf ================================================ // Airbyte Terraform provider documentation: https://registry.terraform.io/providers/airbytehq/airbyte/latest/docs terraform { required_providers { airbyte = { source = "airbytehq/airbyte" version = "0.3.3" } } } provider "airbyte" { // If running locally (Airbyte OSS) with docker-compose using the airbyte-proxy, // include the actual password/username you've set up (or use the defaults below) username = "airbyte" password = "password" // if running locally (Airbyte OSS), include the server url to the airbyte-api-server server_url = "http://localhost:8006/v1/" } ================================================ FILE: shopping_cart_analytics_shopify/infra/airbyte/variables.tf ================================================ variable "api_password" { type = string } variable "workspace_id" { type = string } variable "dataset_id" { type = string } variable "project_id" { type = string } variable "credentials_json" { type = string } variable "shop" { type = string } ================================================ FILE: shopping_cart_analytics_shopify/orchestration/orchestration/__init__.py ================================================ ================================================ FILE: shopping_cart_analytics_shopify/orchestration/orchestration/assets.py ================================================ import os from dagster import OpExecutionContext from dagster_dbt import DbtCliResource, dbt_assets from dagster_airbyte import AirbyteResource, load_assets_from_airbyte_instance from .constants import dbt_manifest_path @dbt_assets(manifest=dbt_manifest_path) def dbt_project_dbt_assets(context: OpExecutionContext, dbt: DbtCliResource): yield from dbt.cli(["build"], context=context).stream() airbyte_instance = AirbyteResource( host="localhost", port="8000", # If using basic auth, include username and password: username="airbyte", password="password" ) airbyte_assets = load_assets_from_airbyte_instance(airbyte_instance, key_prefix= "shopify") ================================================ FILE: shopping_cart_analytics_shopify/orchestration/orchestration/constants.py ================================================ import os from pathlib import Path from dagster_dbt import DbtCliResource dbt_project_dir = Path(__file__).joinpath("..", "..", "..", "dbt_project").resolve() dbt = DbtCliResource(project_dir=os.fspath(dbt_project_dir)) # If DAGSTER_DBT_PARSE_PROJECT_ON_LOAD is set, a manifest will be created at runtime. # Otherwise, we expect a manifest to be present in the project's target directory. if os.getenv("DAGSTER_DBT_PARSE_PROJECT_ON_LOAD"): dbt_parse_invocation = dbt.cli(["parse"], manifest={}).wait() dbt_manifest_path = dbt_parse_invocation.target_path.joinpath("manifest.json") else: dbt_manifest_path = dbt_project_dir.joinpath("target", "manifest.json") ================================================ FILE: shopping_cart_analytics_shopify/orchestration/orchestration/definitions.py ================================================ import os from dagster import Definitions from dagster_dbt import DbtCliResource from .assets import dbt_project_dbt_assets, airbyte_assets from .constants import dbt_project_dir from .schedules import schedules defs = Definitions( assets=[dbt_project_dbt_assets, airbyte_assets], schedules=schedules, resources={ "dbt": DbtCliResource(project_dir=os.fspath(dbt_project_dir)), }, ) ================================================ FILE: shopping_cart_analytics_shopify/orchestration/orchestration/schedules.py ================================================ """ To add a daily schedule that materializes your dbt assets, uncomment the following lines. """ from dagster_dbt import build_schedule_from_dbt_selection from .assets import dbt_project_dbt_assets schedules = [ # build_schedule_from_dbt_selection( # [dbt_project_dbt_assets], # job_name="materialize_dbt_models", # cron_schedule="0 0 * * *", # dbt_select="fqn:*", # ), ] ================================================ FILE: shopping_cart_analytics_shopify/orchestration/pyproject.toml ================================================ [build-system] requires = ["setuptools"] build-backend = "setuptools.build_meta" [tool.dagster] module_name = "orchestration.definitions" code_location_name = "orchestration" ================================================ FILE: shopping_cart_analytics_shopify/orchestration/setup.py ================================================ from setuptools import find_packages, setup setup( name="orchestration", version="0.0.1", packages=find_packages(), install_requires=[ "dagster", "dagster-cloud", "dagster-dbt", "dbt-core>=1.4.0", "dbt-bigquery", ], extras_require={ "dev": [ "dagster-webserver", ] }, ) ================================================ FILE: shopping_cart_analytics_shopify/setup.py ================================================ from setuptools import find_packages, setup setup( name="airbyte-dbt-dagster", packages=find_packages(), install_requires=[ "dbt-bigquery", "dagster", "dagster-cloud", "dagster-dbt", "dagster-airbyte", ], extras_require={"dev": ["dagit", "pytest"]}, ) ================================================ FILE: ticket_volume_analytics_zendesk_support/.gitignore ================================================ # Byte-compiled / optimized / DLL files __pycache__/ *.py[cod] *$py.class # C extensions *.so # Distribution / packaging .Python build/ develop-eggs/ dist/ downloads/ eggs/ .eggs/ lib/ lib64/ parts/ sdist/ var/ wheels/ share/python-wheels/ *.egg-info/ .installed.cfg *.egg MANIFEST # PyInstaller # Usually these files are written by a python script from a template # before PyInstaller builds the exe, so as to inject date/other infos into it. *.manifest *.spec # Installer logs pip-log.txt pip-delete-this-directory.txt # Unit test / coverage reports htmlcov/ .tox/ .nox/ .coverage .coverage.* .cache nosetests.xml coverage.xml *.cover *.py,cover .hypothesis/ .pytest_cache/ cover/ # Translations *.mo *.pot # Django stuff: *.log local_settings.py db.sqlite3 db.sqlite3-journal # Flask stuff: instance/ .webassets-cache # Scrapy stuff: .scrapy # Sphinx documentation docs/_build/ # PyBuilder .pybuilder/ target/ # Jupyter Notebook .ipynb_checkpoints # IPython profile_default/ ipython_config.py # pyenv # For a library or package, you might want to ignore these files since the code is # intended to run in multiple environments; otherwise, check them in: # .python-version # pipenv # According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. # However, in case of collaboration, if having platform-specific dependencies or dependencies # having no cross-platform support, pipenv may install dependencies that don't work, or not # install all needed dependencies. #Pipfile.lock # poetry # Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control. # This is especially recommended for binary packages to ensure reproducibility, and is more # commonly ignored for libraries. # https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control #poetry.lock # pdm # Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control. #pdm.lock # pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it # in version control. # https://pdm.fming.dev/#use-with-ide .pdm.toml # PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm __pypackages__/ # Celery stuff celerybeat-schedule celerybeat.pid # SageMath parsed files *.sage.py # Environments .env .venv env/ venv/ ENV/ env.bak/ venv.bak/ # Spyder project settings .spyderproject .spyproject # Rope project settings .ropeproject # mkdocs documentation /site # mypy .mypy_cache/ .dmypy.json dmypy.json # Pyre type checker .pyre/ # pytype static type analyzer .pytype/ # Cython debug symbols cython_debug/ # PyCharm # JetBrains specific template is maintained in a separate JetBrains.gitignore that can # be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore # and can be added to the global gitignore or merged into this file. For a more nuclear # option (not recommended) you can uncomment the following to ignore the entire idea folder. #.idea/ #Desktop Services Store .DS_Store ================================================ FILE: ticket_volume_analytics_zendesk_support/README.md ================================================ # Customer Ticket Volume Analytics Stack With Zendesk Support, Airbyte, Dbt, Dagster and BigQuery Welcome 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. This Quickstart is all about making things easy, getting you started quickly and showing you how smoothly all these tools can work together! Below is a visual representation of how data flows through our integrated tools in this Quickstart. This comes from Dagster's global asset lineage view: .svg>) ## Table of Contents - [Prerequisites](#prerequisites) - [Setting an environment for your project](#1-setting-an-environment-for-your-project) - [Setting Up BigQuery to work with Airbyte and dbt](#2-setting-up-bigquery) - [Setting Up Airbyte Connectors with Terraform](#3-setting-up-airbyte-connectors-with-terraform) - [Setting Up the dbt Project](#4-setting-up-the-dbt-project) - [Orchestrating with Dagster](#5-orchestrating-with-dagster) - [Next Steps](#next-steps) ## Prerequisites Before you embark on this integration, ensure you have the following set up and ready: 1. **Python 3.10 or later**: If not installed, download and install it from [Python's official website](https://www.python.org/downloads/). 2. **Docker and Docker Compose (Docker Desktop)**: Install [Docker](https://docs.docker.com/get-docker/) following the official documentation for your specific OS. 3. **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/). 4. **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). 5. **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). ## 1. Setting an environment for your project Get the project up and running on your local machine by following these steps: 1. **Clone the repository (Clone only this quickstart)**: ```bash git clone --filter=blob:none --sparse https://github.com/airbytehq/quickstarts.git ``` ```bash cd quickstarts ``` ```bash git sparse-checkout add ticket_volume_analytics_zendesk_support ``` 2. **Navigate to the directory**: ```bash cd ticket_volume_analytics_zendesk_support ``` 3. **Set Up a Virtual Environment**: - For Mac: ```bash python3 -m venv venv source venv/bin/activate ``` - For Windows: ```bash python -m venv venv .\venv\Scripts\activate ``` 4. **Install Dependencies**: ```bash pip install -e ".[dev]" ``` ## 2. Setting Up BigQuery #### 1. **Create a Google Cloud Project** - If you have a Google Cloud project, you can skip this step. - Go to the [Google Cloud Console](https://console.cloud.google.com/). - Click on the "Select a project" dropdown at the top right and select "New Project". - Give your project a name and follow the steps to create it. #### 2. **Create BigQuery Datasets** - In the Google Cloud Console, go to BigQuery. - Make two new datasets: `raw_data` for Airbyte and `transformed_data` for dbt. - If you pick different names, remember to change the names in the code too. **How to create a dataset:** - In the left sidebar, click on your project name. - Click “Create Dataset”. - Enter the dataset ID (either `raw_data` or `transformed_data`). - Click "Create Dataset". #### 3. **Create Service Accounts and Assign Roles** - Go to “IAM & Admin” > “Service accounts” in the Google Cloud Console. - Click “Create Service Account”. - Name your service account (like `airbyte-service-account`). - Assign the “BigQuery Data Editor” and “BigQuery Job User” roles to the service account. - Follow the same steps to make another service account for dbt (like `dbt-service-account`) and assign the roles. **How to create a service account and assign roles:** - While creating the service account, under the “Grant this service account access to project” section, click the “Role” dropdown. - Choose the “BigQuery Data Editor” and “BigQuery Job User” roles. - Finish the creation process. #### 4. **Generate JSON Keys for Service Accounts** - For both service accounts, make a JSON key to let the service accounts sign in. **How to generate JSON key:** - Find the service account in the “Service accounts” list. - Click on the service account name. - In the “Keys” section, click “Add Key” and pick JSON. - The key will download automatically. Keep it safe and don’t share it. - Do this for the other service account too. ## 3. Setting Up Airbyte Connectors with Terraform Airbyte 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: 1. **Navigate to the Airbyte Configuration Directory**: Change to the relevant directory containing the Terraform configuration for Airbyte: ```bash cd infra/airbyte ``` 2. **Modify Configuration Files**: Within the `infra/airbyte` directory, you'll find three crucial Terraform files: - `provider.tf`: Defines the Airbyte provider. - `main.tf`: Contains the main configuration for creating Airbyte resources. - `variables.tf`: Holds various variables, including credentials. 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. 3. **Initialize Terraform**: This step prepares Terraform to create the resources defined in your configuration files. ```bash terraform init ``` 4. **Review the Plan**: Before applying any changes, review the plan to understand what Terraform will do. ```bash terraform plan ``` 5. **Apply Configuration**: After reviewing and confirming the plan, apply the Terraform configurations to create the necessary Airbyte resources. ```bash terraform apply ``` 6. **Verify in Airbyte UI**: 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. ## 4. Setting Up the dbt Project [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: 1. **Navigate to the dbt Project Directory**: Change to the directory containing the dbt configuration: ```bash cd dbt_project ``` 2. **Update Connection Details**: 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. 3. **Utilize Environment Variables (Optional but Recommended)**: To keep your credentials secure, you can leverage environment variables. An example is provided within the `profiles.yml` file. 4. **Test the Connection**: Once you’ve updated the connection details, you can test the connection to your BigQuery instance using: ```bash dbt debug ``` If everything is set up correctly, this command should report a successful connection to BigQuery. 5. **Run the Models**: If you would like to run the dbt models manually at this point, you can do so by executing: ```bash dbt run ``` You can verify the data has been transformed by going to BigQuery and checking the `transformed_data` dataset. ## 5. Orchestrating with Dagster [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: 1. **Navigate to the Orchestration Directory**: Switch to the directory containing the Dagster orchestration configurations: ```bash cd orchestration ``` 2. **Set Environment Variables**: Dagster requires certain environment variables to be set to interact with other tools like dbt and Airbyte. Set the following variables: ```bash export DAGSTER_DBT_PARSE_PROJECT_ON_LOAD=1 export AIRBYTE_PASSWORD=password ``` 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. 3. **Launch the Dagster UI**: With the environment variables in place, kick-start the Dagster UI: ```bash dagster dev ``` 4. **Access Dagster in Your Browser**: Open your browser and navigate to: ``` http://127.0.0.1:3000 ``` 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. 5. **Materialize Dagster Assets**: 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. ## Next Steps Congratulations 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: ### 1. **Explore the Data and Insights** - 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! ### 2. **Optimize Your dbt Models** - 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. ### 3. **Automate and Monitor Your Pipelines** - 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. ================================================ FILE: ticket_volume_analytics_zendesk_support/dbt_project/.gitignore ================================================ target/ dbt_packages/ logs/ .user.yml ================================================ FILE: ticket_volume_analytics_zendesk_support/dbt_project/README.md ================================================ Welcome to your new dbt project! ### Using the starter project Try running the following commands: - dbt run - dbt test ### Resources: - Learn more about dbt [in the docs](https://docs.getdbt.com/docs/introduction) - Check out [Discourse](https://discourse.getdbt.com/) for commonly asked questions and answers - Join the [chat](https://community.getdbt.com/) on Slack for live discussions and support - Find [dbt events](https://events.getdbt.com) near you - Check out [the blog](https://blog.getdbt.com/) for the latest news on dbt's development and best practices ================================================ FILE: ticket_volume_analytics_zendesk_support/dbt_project/dbt_project.yml ================================================ # Name your project! Project names should contain only lowercase characters # and underscores. A good package name should reflect your organization's # name or the intended use of these models name: 'dbt_project' version: '1.0.0' config-version: 2 # This setting configures which "profile" dbt uses for this project. profile: 'dbt_project' # These configurations specify where dbt should look for different types of files. # The `model-paths` config, for example, states that models in this project can be # found in the "models/" directory. You probably won't need to change these! model-paths: ["models"] analysis-paths: ["analyses"] test-paths: ["tests"] seed-paths: ["seeds"] macro-paths: ["macros"] snapshot-paths: ["snapshots"] clean-targets: # directories to be removed by `dbt clean` - "target" - "dbt_packages" # Configuring models # Full documentation: https://docs.getdbt.com/docs/configuring-models # In this example config, we tell dbt to build all models in the example/ # directory as views. These settings can be overridden in the individual model # files using the `{{ config(...) }}` macro. models: dbt_project: # Config indicated by + and applies to all files under models/example/ staging: +materialized: view marts: +materialized: view ================================================ FILE: ticket_volume_analytics_zendesk_support/dbt_project/models/marts/busier_day_of_week_analysis.sql ================================================ WITH ticket_day_of_week_counts AS ( SELECT EXTRACT(DAYOFWEEK FROM created_at) AS day_of_week, COUNT(*) AS ticket_count FROM {{ ref('stg_tickets') }} GROUP BY 1 ) SELECT * FROM ticket_day_of_week_counts ================================================ FILE: ticket_volume_analytics_zendesk_support/dbt_project/models/marts/pattern_and_trend_analysis.sql ================================================ with ticket_hourly_counts as ( select extract(hour from created_at) as hour_of_day, count(*) as ticket_count from {{ ref('stg_tickets') }} group by 1 ) select * from ticket_hourly_counts ================================================ FILE: ticket_volume_analytics_zendesk_support/dbt_project/models/marts/seasonal_analysis.sql ================================================ with ticket_seasonal_counts as ( select extract(month from created_at) as month, count(*) as ticket_count from {{ ref('stg_tickets') }} group by 1 ) select * from ticket_seasonal_counts ================================================ FILE: ticket_volume_analytics_zendesk_support/dbt_project/models/marts/ticket_priority_analysis.sql ================================================ with ticket_priority_counts as ( select priority, count(*) as ticket_count from {{ ref('stg_tickets') }} group by 1 ) select * from ticket_priority_counts ================================================ FILE: ticket_volume_analytics_zendesk_support/dbt_project/models/marts/ticket_resolution_time_analysis.sql ================================================ with ticket_resolution_times as ( select TIMESTAMP_DIFF(solved_at, created_at, HOUR) as resolution_time, count(*) as ticket_count from transformed_data.stg_ticket_metrics where solved_at is not null group by 1 ) select * from ticket_resolution_times ================================================ FILE: ticket_volume_analytics_zendesk_support/dbt_project/models/marts/ticket_source_analysis.sql ================================================ with source_data as ( select JSON_EXTRACT_SCALAR(json_data, '$.channel') as ticket_source, count(*) as ticket_count from ( select JSON_EXTRACT_ARRAY(via)[OFFSET(0)] as json_data from transformed_data.stg_tickets ) t group by 1 ) select * from source_data ================================================ FILE: ticket_volume_analytics_zendesk_support/dbt_project/models/marts/ticket_volume_analysis.sql ================================================ with ticket_counts as ( select date(created_at) as ticket_date, count(*) as ticket_count from {{ ref('stg_tickets') }} group by 1 ) select * from ticket_counts ================================================ FILE: ticket_volume_analytics_zendesk_support/dbt_project/models/sources/zendesk_support_sources.yml ================================================ version: 2 sources: - name: zendesk_support # Use your BigQuery project ID database: "{{ env_var('BIGQUERY_PROJECT_ID', '') }}" # Use your BigQuery dataset name schema: zendesk_airbyte_trial tables: - name: users description: "Simulated user data from the Zendesk Support connector." columns: - name: id description: "Unique identifier for the user." - name: active - name: alias - name: chat_only - name: created_at - name: custom_role_id - name: default_group_id - name: details - name: email - name: external_id - name: updated_at - name: iana_time_zone - name: last_login_at - name: locale - name: locale_id - name: moderator - name: name - name: notes - name: only_private_comments - name: organization_id - name: permanently_deleted - name: phone - name: photo - name: report_csv - name: restricted_agent - name: role - name: role_type - name: shared - name: shared_agent - name: shared_phone_number - name: signature - name: suspended - name: tags - name: ticket_restriction - name: time_zone - name: two_factor_auth_enabled - name: url - name: user_fields - name: verified - name: tickets description: "Simulated tickets data from the Zendesk Support connector." columns: - name: id description: "Unique identifier for the ticket." - name: updated_at - name: allow_attachments - name: allow_channelback - name: assignee_id - name: brand_id - name: collaborator_ids - name: created_at - name: custom_fields - name: custom_status_id - name: description - name: due_at - name: email_cc_ids - name: external_id - name: fields - name: follower_ids - name: followup_ids - name: forum_topic_id - name: from_messaging_channel - name: generated_timestamp - name: group_id - name: has_incidents - name: is_public - name: organization_id - name: priority - name: problem_id - name: raw_subject - name: recipient - name: requester_id - name: satisfaction_rating - name: sharing_agreement_ids - name: status - name: subject - name: submitter_id - name: tags - name: ticket_form_id - name: type - name: url - name: via - name: schedules description: "Simulated schedules data from the Zendesk Support connector." columns: - name: id description: "Unique identifier for the schedule." - name: created_at - name: intervals - name: name - name: time_zone - name: updated_at - name: ticket_metrics description: "Simulated ticket_metrics data from the Zendesk Support connector." columns: - name: id description: "Unique identifier for the ticket_metrics." - name: agent_wait_time_in_minutes - name: created_at - name: assigned_at - name: assignee_stations - name: assignee_updated_at - name: custom_status_updated_at - name: first_resolution_time_in_minutes - name: full_resolution_time_in_minutes - name: group_stations - name: initially_assigned_at - name: instance_id - name: latest_comment_added_at - name: metric - name: on_hold_time_in_minutes - name: updated_at - name: url - name: reopens - name: replies - name: reply_time_in_minutes - name: requester_updated_at - name: requester_wait_time_in_minutes - name: solved_at - name: status - name: status_updated_at - name: ticket_id - name: time - name: type ================================================ FILE: ticket_volume_analytics_zendesk_support/dbt_project/models/staging/stg_schedules.sql ================================================ select * from {{ source('zendesk_support', 'schedules') }} ================================================ FILE: ticket_volume_analytics_zendesk_support/dbt_project/models/staging/stg_ticket_metrics.sql ================================================ select * from {{ source('zendesk_support', 'ticket_metrics') }} ================================================ FILE: ticket_volume_analytics_zendesk_support/dbt_project/models/staging/stg_tickets.sql ================================================ select * from {{ source('zendesk_support', 'tickets') }} ================================================ FILE: ticket_volume_analytics_zendesk_support/dbt_project/models/staging/stg_users.sql ================================================ select * from {{ source('zendesk_support', 'users') }} ================================================ FILE: ticket_volume_analytics_zendesk_support/dbt_project/profiles.yml ================================================ dbt_project: outputs: dev: dataset: transformed_data job_execution_timeout_seconds: 300 job_retries: 1 # Use an env variable to indicate your JSON key file path keyfile: "{{ env_var('DBT_BIGQUERY_KEYFILE_PATH', '') }}" location: US method: service-account priority: interactive # Indicate your BigQuery project ID project: "{{ env_var('BIGQUERY_PROJECT_ID', '') }}" threads: 1 type: bigquery target: dev ================================================ FILE: ticket_volume_analytics_zendesk_support/dbt_project/snapshots/.gitkeep ================================================ ================================================ FILE: ticket_volume_analytics_zendesk_support/dbt_project/tests/.gitkeep ================================================ ================================================ FILE: ticket_volume_analytics_zendesk_support/infra/.gitignore ================================================ # Local .terraform directories **/.terraform/* # .tfstate files *.tfstate *.tfstate.* # Crash log files crash.log crash.*.log # Exclude all .tfvars files, which are likely to contain sensitive data, such as # password, private keys, and other secrets. These should not be part of version # control as they are data points which are potentially sensitive and subject # to change depending on the environment. *.tfvars *.tfvars.json # Ignore override files as they are usually used to override resources locally and so # are not checked in override.tf override.tf.json *_override.tf *_override.tf.json # Include override files you do wish to add to version control using negated pattern # !example_override.tf # Include tfplan files to ignore the plan output of command: terraform plan -out=tfplan # example: *tfplan* # Ignore CLI configuration files .terraformrc terraform.rc ================================================ FILE: ticket_volume_analytics_zendesk_support/infra/airbyte/.terraform.lock.hcl ================================================ # This file is maintained automatically by "terraform init". # Manual edits may be lost in future updates. provider "registry.terraform.io/airbytehq/airbyte" { version = "0.3.3" constraints = "0.3.3" hashes = [ "h1:0LmuAc5LvlMuOUPtNEaCAh9FHrV/C877bDJhm9Lz8MU=", "zh:0efa470b34d9b912b47efe4469c51713bfc3c2413e52c17e1e903f2a3cddb2f6", "zh:1bddd69fa2c2d4f3e239d60555446df9bc4ce0c0cabbe7e092fe1d44989ab004", "zh:2e20540403a0010007b53456663fb037b24e30f6c8943f65da1bcf7fa4dfc8a6", "zh:2f415369ad884e8b7115a5c5ff229d052f7af1fca27abbfc8ebef379ed11aec4", "zh:46fd9a906f4b6461112dcc5a5aa01a3fcd7a19a72d4ad0b2e37790da37701fe1", "zh:83503ebb77bb6d6941c42ba323cf22380d08a1506554a2dcc8ac54e74c0886a1", "zh:890df766e9b839623b1f0437355032a3c006226a6c200cd911e15ee1a9014e9f", "zh:8fd770eff726826d3a63b9e3733c5455b5cde004027b04ee3f75888eb8538c90", "zh:b0fc890ed4f9b077bf70ed121cc3550e7a07d16e7798ad517623274aa62ad7b0", "zh:c2a01612362da9b73cd5958f281e1aa7ff09af42182e463097d11ed78e778e72", "zh:c64b2bb1887a0367d64ba3393d4b3a16c418cf5b1792e2e7aae7c0b5413eb334", "zh:ce14ebbf0ed91913ec62655a511763dec62b5779de9a209bd6f1c336640cddc0", "zh:e0662ca837eee10f7733ea9a501d995281f56bd9b410ae13ad03eb106011db14", "zh:e103d480fc6066004bc98e9e04a141a1f55b918cc2912716beebcc6fc4c872fb", "zh:e2507049098f0f1b21cb56870f4a5ef624bcf6d3959e5612eada1f8117341648", ] } ================================================ FILE: ticket_volume_analytics_zendesk_support/infra/airbyte/main.tf ================================================ // Airbyte Terraform provider documentation: https://registry.terraform.io/providers/airbytehq/airbyte/latest/docs // Sources resource "airbyte_source_zendesk_support" "my_source_zendesksupport" { configuration = { source_type = "zendesk-support" credentials = { source_zendesk_support_authentication_api_token = { credentials = "api_token" api_token = var.api_token email = var.email } } start_date = "2020-10-15T00:00:00Z" subdomain = "self3836" } name = "Zendesk Support" workspace_id = var.workspace_id } // Destinations resource "airbyte_destination_bigquery" "bigquery" { configuration = { dataset_id = var.dataset_id dataset_location = "US" destination_type = "bigquery" project_id = var.project_id credentials_json = var.credentials_json loading_method = { destination_bigquery_loading_method_standard_inserts = { method = "Standard" } } } name = "BigQuery" workspace_id = var.workspace_id } // Connections resource "airbyte_connection" "zendesk_support_bigquery" { name = "Zendesk Support to BigQuery" source_id = airbyte_source_zendesk_support.my_source_zendesksupport.source_id destination_id = airbyte_destination_bigquery.bigquery.destination_id configurations = { streams = [ { name = "users" }, { name = "tickets" }, { name = "satisfaction_ratings" }, { name = "ticket_metrics" }, { name = "ticket_metric_events" }, { name = "ticket_comments" }, { name = "ticket_audits" } ] } } ================================================ FILE: ticket_volume_analytics_zendesk_support/infra/airbyte/provider.tf ================================================ // Airbyte Terraform provider documentation: https://registry.terraform.io/providers/airbytehq/airbyte/latest/docs terraform { required_providers { airbyte = { source = "airbytehq/airbyte" version = "0.3.3" } } } provider "airbyte" { // If running locally (Airbyte OSS) with docker-compose using the airbyte-proxy, // include the actual password/username you've set up (or use the defaults below) username = "airbyte" password = "password" // if running locally (Airbyte OSS), include the server url to the airbyte-api-server server_url = "http://localhost:8006/v1/" } ================================================ FILE: ticket_volume_analytics_zendesk_support/infra/airbyte/variables.tf ================================================ variable "api_token" { type = string } variable "email" { type = string } variable "workspace_id" { type = string } variable "dataset_id" { type = string } variable "project_id" { type = string } variable "credentials_json" { type = string } ================================================ FILE: ticket_volume_analytics_zendesk_support/orchestration/orchestration/__init__.py ================================================ ================================================ FILE: ticket_volume_analytics_zendesk_support/orchestration/orchestration/assets.py ================================================ import os from dagster import OpExecutionContext from dagster_dbt import DbtCliResource, dbt_assets from dagster_airbyte import AirbyteResource, load_assets_from_airbyte_instance from .constants import dbt_manifest_path @dbt_assets(manifest=dbt_manifest_path) def dbt_project_dbt_assets(context: OpExecutionContext, dbt: DbtCliResource): yield from dbt.cli(["build"], context=context).stream() airbyte_instance = AirbyteResource( host="localhost", port="8000", # If using basic auth, include username and password: username="airbyte", password="password" ) airbyte_assets = load_assets_from_airbyte_instance(airbyte_instance,) ================================================ FILE: ticket_volume_analytics_zendesk_support/orchestration/orchestration/constants.py ================================================ import os from pathlib import Path from dagster_dbt import DbtCliResource dbt_project_dir = Path(__file__).joinpath("..", "..", "..", "dbt_project").resolve() dbt = DbtCliResource(project_dir=os.fspath(dbt_project_dir)) # If DAGSTER_DBT_PARSE_PROJECT_ON_LOAD is set, a manifest will be created at runtime. # Otherwise, we expect a manifest to be present in the project's target directory. if os.getenv("DAGSTER_DBT_PARSE_PROJECT_ON_LOAD"): dbt_parse_invocation = dbt.cli(["parse"], manifest={}).wait() dbt_manifest_path = dbt_parse_invocation.target_path.joinpath("manifest.json") else: dbt_manifest_path = dbt_project_dir.joinpath("target", "manifest.json") ================================================ FILE: ticket_volume_analytics_zendesk_support/orchestration/orchestration/definitions.py ================================================ import os from dagster import Definitions from dagster_dbt import DbtCliResource from .assets import dbt_project_dbt_assets, airbyte_assets from .constants import dbt_project_dir from .schedules import schedules defs = Definitions( assets=[dbt_project_dbt_assets, airbyte_assets], schedules=schedules, resources={ "dbt": DbtCliResource(project_dir=os.fspath(dbt_project_dir)), }, ) ================================================ FILE: ticket_volume_analytics_zendesk_support/orchestration/orchestration/schedules.py ================================================ """ To add a daily schedule that materializes your dbt assets, uncomment the following lines. """ from dagster_dbt import build_schedule_from_dbt_selection from .assets import dbt_project_dbt_assets schedules = [ # build_schedule_from_dbt_selection( # [dbt_project_dbt_assets], # job_name="materialize_dbt_models", # cron_schedule="0 0 * * *", # dbt_select="fqn:*", # ), ] ================================================ FILE: ticket_volume_analytics_zendesk_support/orchestration/pyproject.toml ================================================ [build-system] requires = ["setuptools"] build-backend = "setuptools.build_meta" [tool.dagster] module_name = "orchestration.definitions" code_location_name = "orchestration" ================================================ FILE: ticket_volume_analytics_zendesk_support/orchestration/setup.py ================================================ from setuptools import find_packages, setup setup( name="orchestration", version="0.0.1", packages=find_packages(), install_requires=[ "dagster", "dagster-cloud", "dagster-dbt", "dbt-core>=1.4.0", "dbt-bigquery", ], extras_require={ "dev": [ "dagster-webserver", ] }, ) ================================================ FILE: ticket_volume_analytics_zendesk_support/setup.py ================================================ from setuptools import find_packages, setup setup( name="airbyte-dbt-dagster", packages=find_packages(), install_requires=[ "dbt-bigquery", "dagster", "dagster-cloud", "dagster-dbt", "dagster-airbyte", ], extras_require={"dev": ["dagit", "pytest"]}, ) ================================================ FILE: vector_store_integration/AI_assistant_streamlit_app/.gitignore ================================================ venv/ .secrets .streamlit dist/ build/ *.egg-info/ .DS_STORE ================================================ FILE: vector_store_integration/AI_assistant_streamlit_app/README.md ================================================ ### Run Locally - 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. - Create a folder called `.streamlit` and create a file called `secrets.toml`. - Add the following to the `secrets.toml` file: ```toml OPENAI_API_KEY="YOUR_OPENAI_API_KEY" SNOWFLAKE_HOST="YOUR_SNOWFLAKE_ACCOUNT_NAME" SNOWFLAKE_ROLE="YOUR_SNOWFLAKE_ROLE" SNOWFLAKE_WAREHOUSE="YOUR_SNOWFLAKE_WAREHOUSE" SNOWFLAKE_DATABASE="YOUR_SNOWFLAKE_DATABASE" SNOWFLAKE_SCHEMA="YOUR_SNOWFLAKE_SCHEMA" SNOWFLAKE_USERNAME="YOUR_SNOWFLAKE_USERNAME" SNOWFLAKE_PASSWORD="YOUR_SNOWFLAKE_PASSWORD" ``` - Update the table names and LLM instructions in `app.py` with data specific to your use case. - Create a virtual environment: ```bash python3 -m venv venv ``` - Activate the virtual environment: ```bash source venv/bin/activate ``` - Install the requirements: ```bash pip install -r requirements.txt ``` - Run the app: ```bash streamlit run app.py ``` - Open your browser and go to [http://localhost:8501](http://localhost:8501). ### Deploy to Streamlit Community Cloud - Copy the code into its own repository. - Create an account [here](https://streamlit.io/cloud) and follow the instructions to give access to the repository you created. ================================================ FILE: vector_store_integration/AI_assistant_streamlit_app/app.py ================================================ import streamlit as st import random import time from openai import OpenAI import openai from snowflake import connector from langchain.embeddings import OpenAIEmbeddings from typing import List def get_db_connection(): return connector.connect( account=st.secrets["SNOWFLAKE_HOST"], role=st.secrets["SNOWFLAKE_ROLE"], warehouse=st.secrets["SNOWFLAKE_WAREHOUSE"], database=st.secrets["SNOWFLAKE_DATABASE"], schema=st.secrets["SNOWFLAKE_SCHEMA"], user=st.secrets["SNOWFLAKE_USERNAME"], password=st.secrets["SNOWFLAKE_PASSWORD"], ) def get_similar_chunks(query_vector, table_names) -> List[str]: conn = get_db_connection() cursor = conn.cursor() chunks = [] for table_name in table_names: query = f""" SELECT document_content, VECTOR_COSINE_SIMILARITY(embedding, CAST({query_vector} AS VECTOR(FLOAT, 1536))) AS similarity FROM {table_name} ORDER BY similarity DESC LIMIT 2 """ cursor.execute(query) result = cursor.fetchall() chunks += [item[0] for item in result] cursor.close() conn.close() return chunks def get_completion(question, document_chunks: List[str], model_name: str = "llama2-70b-chat"): conn = get_db_connection() cur = conn.cursor() chunks = "\n\n".join(document_chunks) query = f""" SELECT snowflake.cortex.complete( '{model_name}', CONCAT( '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: ', $$ {chunks} $$, 'Question: ', $$ {question} $$, 'Answer: ' ) ) as response;""" cur.execute(query) result = cur.fetchall() cur.close() conn.close() # TO-DO: better parsing here return result[0][0].strip() def get_user_intent(query): # this method does a simple few shots classifcation to help get user's intent examples = [ {"role": "system", "content": "You are an assistant that classifies user intents based on their messages."}, {"role": "user", "content": "How can I add vector data in Snowflake?"}, {"role": "assistant", "content": "docs_question"}, {"role": "user", "content": "How do I set up Snowflake Cortex destination?"}, {"role": "assistant", "content": "docs_question"}, {"role": "user", "content": "What are the upcoming features for Snowflake Cortex?"}, {"role": "assistant", "content": "github_question"}, {"role": "user", "content": "What are the known issues for Snowflake Cortex?"}, {"role": "assistant", "content": "github_question"}, {"role": "user", "content": "Which customers have requested features for Snowflake Cortex?"}, {"role": "assistant", "content": "zendesk_question"}, {"role": "user", "content": "Which customers have requested authorization related features for Snowflake Cortex?"}, {"role": "assistant", "content": "zendesk_question"}, ] examples.append({"role": "user", "content": query}) openai.api_key = st.secrets["OPENAI_API_KEY"] # Call the OpenAI chat API to get the intent classification response = openai.chat.completions.create( model="gpt-3.5-turbo", messages=examples, max_tokens=10, n=1, stop=["\n"] ) intent = response.choices[0].message.content return intent def get_tables_to_query(query): intent = get_user_intent(query) if intent == "github_question": return (["airbyte_github_issues"], "llama2-70b-chat") elif intent == "zendesk_question": return (["airbyte_zendesk_tickets", "airbyte_zendesk_users"], "llama2-70b-chat") else: # default return(["airbyte_docs"], "snowflake-arctic") def get_response(query): # embed the query embeddings = OpenAIEmbeddings(openai_api_key=st.secrets["OPENAI_API_KEY"]) # get similar chunks from sources/tables in Snowflake [tables, model] = get_tables_to_query(query) chunks = get_similar_chunks(embeddings.embed_query(query), tables) if (len(chunks) == 0): return "I am sorry, I do not have the context to answer your question." else: return get_completion(query, chunks, model) def response_generator(query): response = get_response(query) # Split the response by spaces but preserve the new lines parts = [] current_part = [] for char in response: if char in ['\n', ' ']: if current_part: parts.append(''.join(current_part)) current_part = [] if char == '\n': parts.append('\n') else: parts.append(' ') else: current_part.append(char) if current_part: parts.append(''.join(current_part)) for part in parts: yield part time.sleep(0.01) st.title("AI Product assistant") # simulate initial message from assistant initial_message = """👋 Hello! I'm here to help you with any questions you have on Airbyte's products or services. How can I assist you today?""" # Display initial message from assistant with st.chat_message("assistant"): st.write(initial_message) # Recommended questions section in the sidebar st.sidebar.title("Recommended Questions") recommended_questions = [ "How can I add vector data in Snowflake?", "What are the upcoming features for Snowflake Cortex?", "Which customers have requested features for Snowflake Cortex?", ] for question in recommended_questions: st.sidebar.markdown(f"- {question}") client = OpenAI(api_key=st.secrets["OPENAI_API_KEY"]) # Set a default model if "openai_model" not in st.session_state: st.session_state["openai_model"] = "gpt-3.5-turbo" # Initialize chat history if "messages" not in st.session_state: st.session_state.messages = [] # Display chat messages from history on app rerun for message in st.session_state.messages: with st.chat_message(message["role"]): st.markdown(message["content"]) if prompt := st.chat_input("Ask a question?"): # Display user message in chat message container with st.chat_message("user"): st.markdown(prompt) # Add user message to chat history st.session_state.messages.append({"role": "user", "content": prompt}) with st.chat_message("assistant"): response = st.write_stream(response_generator(prompt)) # Add assistant response to chat history st.session_state.messages.append({"role": "assistant", "content": response}) ================================================ FILE: vector_store_integration/AI_assistant_streamlit_app/requirements.txt ================================================ streamlit openai snowflake-connector-python langchain langchain-community tiktoken ================================================ FILE: vector_store_integration/RAG_using_PGVector.ipynb ================================================ { "nbformat": 4, "nbformat_minor": 0, "metadata": { "colab": { "provenance": [] }, "kernelspec": { "name": "python3", "display_name": "Python 3" }, "language_info": { "name": "python" } }, "cells": [ { "metadata": {}, "cell_type": "code", "outputs": [], "execution_count": null, "source": [ "## Airbyte PGVector RAG Demo\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", "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", "#### Prerequisites:\n", "* Vector data stored in Postgres with Vector colums via PGVector destination. In our case we are using data from kaggle.\n", "* Postgresql DB with PGVector enabled\n", "* Open AI key\n" ] }, { "cell_type": "markdown", "source": [ "### a. Install dependencies and import secrets\n", "\n" ], "metadata": { "id": "7R0-uD7R3Uki" } }, { "cell_type": "code", "source": [ "!pip install sqlalchemy openai rich psycopg2 python-dotenv langchain-openai" ], "metadata": { "collapsed": true, "id": "HbR-po_Z3VFV" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "import openai\n", "import json\n", "import rich\n", "from langchain_openai import OpenAIEmbeddings\n", "from sqlalchemy import create_engine\n", "from sqlalchemy.engine import URL\n", "from google.colab import userdata\n", "\n", "\n", "OPENAI_API_KEY = userdata.get('openai_api_key')\n", "HOST = userdata.get(\"db_host\")\n", "USERNAME = userdata.get(\"db_username\")\n", "PASSWORD = userdata.get(\"db_password\")\n", "DATABASE = userdata.get(\"db_name\")" ], "metadata": { "id": "LP0QfMFQ6Flz" }, "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "source": [ "### b. Initialize open AI client and DB Engine\n" ], "metadata": { "id": "mmHB_MId7zwo" } }, { "cell_type": "code", "source": [ "openai.api_key = OPENAI_API_KEY\n", "\n", "url = URL.create(\n", " \"postgresql\",\n", " host=HOST,\n", " username=USERNAME,\n", " password=PASSWORD,\n", " database=DATABASE,\n", ")\n", "\n", "engine = create_engine(url)" ], "metadata": { "id": "7Y3iCe6e7-Ra" }, "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "source": [ "### d. Explore data stored in Posgresql\n", "\n", "We need a few methods to embed user questions and make searches in DB\n", "\n", "- Helper to embed the user question so we can the search for it in the DB.\n", "- Function to get the context from the database using a user question as input.\n", "- One to get the response from the chat assistant that will use the context using the method from previous step." ], "metadata": { "id": "ZQ0PzOfb9rtK" } }, { "cell_type": "code", "source": [ "from sqlalchemy import text\n", "from openai import OpenAI\n", "\n", "client = OpenAI(\n", " api_key=OPENAI_API_KEY,\n", ")\n", "\n", "def get_embedding_from_open_ai(question):\n", " print(f\"Embedding user's query: {question}\")\n", " embeddings = OpenAIEmbeddings(openai_api_key=OPENAI_API_KEY)\n", " embedding_response = embeddings.embed_query(question)\n", " return embedding_response\n", "\n", "QUERY_TEMPLATE = \"\"\"\n", "SELECT document_content,\n", " metadata->'title' as episode_title,\n", " metadata->'script_line_number' as script_line_number,\n", " metadata->'name' as character_name,\n", " metadata->'spoken_words' as spoken_words\n", "FROM episode_spoken_words\n", "ORDER BY embedding <-> :question_vector\n", "LIMIT 5\n", "\"\"\"\n", "\n", "def get_context(question) -> str:\n", " # Get the embedding from OpenAI\n", " question_vector = get_embedding_from_open_ai(question)\n", "\n", " # Format the question vector as a string in the format expected by PostgreSQL\n", " question_vector_str = '[' + ','.join(map(str, question_vector)) + ']'\n", "\n", " # Use the text() function for raw queries with SQLAlchemy\n", " query = text(QUERY_TEMPLATE)\n", "\n", " # Execute the query, passing the vector as a bind parameter\n", " with engine.connect() as connection:\n", " result = connection.execute(query, {'question_vector': question_vector_str})\n", "\n", " # Format and return the result\n", " return (\"\\n\\n\" + \"-\" * 8 + \"\\n\\n\").join(\n", " [\n", " f\"Episode {row.episode_title} | Line number: {row.script_line_number} | \"\n", " f\"Spoken Words: {row.spoken_words} | Character: {row.character_name}\"\n", " for row in result\n", " ]\n", " )\n", "\n", "\n", "def get_response(question):\n", " response = client.chat.completions.create(\n", " model=\"gpt-3.5-turbo\",\n", " messages=[\n", " {\"role\": \"system\", \"content\": \"You are a Simpsons expert talking about Simpsons episodes.\"},\n", " {\"role\": \"user\", \"content\": question},\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", " return response.choices[0].message.content\n", "\n" ], "metadata": { "id": "_PJ6eb5-A419" }, "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "source": [ "### d. Make questions\n", "\n", "Finally, let's put all together and get a response from our assistant using the Simpsons database." ], "metadata": { "id": "spQPCVe9AZKh" } }, { "cell_type": "code", "source": [ "question = \"Talking about food\"\n", "response = get_response(question)\n", "rich.print(response)" ], "metadata": { "id": "pwegM_02AgOU" }, "execution_count": null, "outputs": [] } ] } ================================================ FILE: vector_store_integration/RAG_using_Snowflake_Cortex.ipynb ================================================ { "cells": [ { "cell_type": "markdown", "metadata": { "id": "KWKEgtakJtH6" }, "source": [ "## Airbyte Snowflake Cortex RAG Demo\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", "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", "#### Prerequisites:\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", "* Snowflake account with Cortex functions enabled\n", "* Open AI key\n" ] }, { "cell_type": "markdown", "metadata": { "id": "gS3oGgI0CVpn" }, "source": [ "### a. Install dependencies\n", "\n", "\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "bM4Te8XEWECV" }, "outputs": [], "source": [ "# Add virtual environment support in Google Colab\n", "!apt-get install -qq python3.10-venv\n", "\n", "# Install openai\n", "# tbd - add snowflake python connector\n", "%pip install --quiet openai snowflake-connector-python langchain-openai tiktoken" ] }, { "cell_type": "markdown", "metadata": { "id": "nKbdq1eUcmAz" }, "source": [ "### b. Explore data stored in Snowflake.\n", "\n", "Let's see what document/vecto data in Snowflake looks like." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "collapsed": true, "id": "TKDGBUhLcfuK", "outputId": "1231d559-298f-43a0-95c3-a67f8cfb62ab" }, "outputs": [], "source": [ "# Fetch data from airbyte_docs table\n", "from snowflake import connector\n", "from google.colab import userdata\n", "from typing import List\n", "import pandas as pd\n", "\n", "def get_db_connection():\n", " return connector.connect(\n", " account=userdata.get(\"SNOWFLAKE_HOST\"),\n", " role=userdata.get(\"SNOWFLAKE_ROLE\"),\n", " warehouse=userdata.get(\"SNOWFLAKE_WAREHOUSE\"),\n", " database=userdata.get(\"SNOWFLAKE_DATABASE\"),\n", " schema=userdata.get(\"SNOWFLAKE_SCHEMA\"),\n", " user=userdata.get(\"SNOWFLAKE_USERNAME\"),\n", " password=userdata.get(\"SNOWFLAKE_PASSWORD\"),\n", " )\n", "\n", "def fetch_table_data(table_name, columns):\n", " conn = get_db_connection()\n", " cursor = conn.cursor()\n", "\n", " # Form the query to select specific columns\n", " columns_str = \", \".join(columns)\n", " query = f\"SELECT {columns_str} FROM {table_name};\"\n", "\n", " cursor.execute(query)\n", " result = cursor.fetchall()\n", "\n", " # Fetch the column names\n", " col_names = [desc[0] for desc in cursor.description]\n", "\n", " cursor.close()\n", " conn.close()\n", "\n", " # Load the result into a pandas DataFrame\n", " df = pd.DataFrame(result, columns=col_names)\n", " return df;\n", "\n", "# show data from airbtye_docs table\n", "data_frame = fetch_table_data(\"airbyte_docs\", [\"document_id\", \"document_content\", \"metadata\", \"embedding\"])\n", "data_frame" ] }, { "cell_type": "markdown", "metadata": { "id": "5LU0M4g6clBj" }, "source": [] }, { "cell_type": "markdown", "metadata": { "id": "SyyzczWXBXTS" }, "source": [ "### c. Build the RAG pipeline and ask a question\n", "\n", "Let's write the three main pieces of a RAG pipeline:\n", "* Embedding incoming query\n", "* Doing similarity search to find matching chunks\n", "* Send chunks to LLM for completion" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 265 }, "id": "zQ6rWEV2u-3U", "outputId": "d364b7e3-cfca-4628-da20-3ae4f34ad143" }, "outputs": [], "source": [ "from openai import OpenAI\n", "from snowflake import connector\n", "from langchain_openai import OpenAIEmbeddings\n", "from google.colab import userdata\n", "from typing import List\n", "from rich.console import Console\n", "\n", "def get_db_connection():\n", " return connector.connect(\n", " account=userdata.get(\"SNOWFLAKE_HOST\"),\n", " role=userdata.get(\"SNOWFLAKE_ROLE\"),\n", " warehouse=userdata.get(\"SNOWFLAKE_WAREHOUSE\"),\n", " database=userdata.get(\"SNOWFLAKE_DATABASE\"),\n", " schema=userdata.get(\"SNOWFLAKE_SCHEMA\"),\n", " user=userdata.get(\"SNOWFLAKE_USERNAME\"),\n", " password=userdata.get(\"SNOWFLAKE_PASSWORD\"),\n", " )\n", "\n", "# convert user's query into a vector array to prep for similiary search\n", "def get_embedding_from_openai(query)->str:\n", " print(f\"Embedding user's query -> {query}...\")\n", " embeddings = OpenAIEmbeddings(openai_api_key=userdata.get(\"OPENAI_API_KEY\"))\n", " return embeddings\n", "\n", "# use Snowflake's Cortex in-build similarity search to find matching chunks\n", "def get_similar_chunks_from_snowflake(query_vector, table_names) -> List[str]:\n", " print(\"\\nRetrieving similar chunks...\")\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,\n", " 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", " print(f\"Found {len(result)} matching chunks in table:{table_name}!\")\n", " chunks += [item[0] for item in result]\n", " cursor.close()\n", " conn.close()\n", "\n", " return chunks\n", "\n", "# use Snowflake's Cortex in-build completion to find matching chunks.\n", "def get_completion_from_snowflake(question, document_chunks: List[str], model_name):\n", " print(f\"\\nSending chunks to Snowflake (LLM: {model_name}) for completion...\")\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. Do not use any other information. 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", "# Putting it all together\n", "def get_response(query, table_names, model_name=\"llama2-70b-chat\"):\n", " # Step 1: embed the query\n", " embeddings = get_embedding_from_openai(query)\n", "\n", " # Step 2: get similar chunks from sources/tables in Snowflake\n", " chunks = get_similar_chunks_from_snowflake(embeddings.embed_query(query), table_names)\n", "\n", " if (len(chunks) == 0):\n", " return \"I am sorry, I do not have the context to answer your question.\"\n", " else:\n", " # Step 3: send chunks to LLM for completion\n", " return get_completion_from_snowflake(query, chunks, model_name)\n", "\n", "# Ask a question\n", "query = 'How can I store vector data in Snowflake'\n", "response = get_response(query, [\"airbyte_docs\"], \"snowflake-arctic\")\n", "\n", "Console().print(f\"\\n\\nResponse from LLM:\\n\\n[blue]{response}[/blue]\")\n" ] }, { "cell_type": "markdown", "metadata": { "id": "G5NvV86T57-V" }, "source": [ "### d. Let's ask another question\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 281 }, "id": "cPYdEs663tl8", "outputId": "6997984b-6ea3-4b95-d80f-ccace53b6d43" }, "outputs": [], "source": [ "query = 'What are the upcoming features for Snowflake Cortex?'\n", "response = get_response(query, [\"airbyte_github_issues\"])\n", "Console().print(f\"\\n\\nResponse from LLM:\\n\\n[blue]{response}[/blue]\")" ] }, { "cell_type": "markdown", "metadata": { "id": "S_CJ3_C9pvw4" }, "source": [ "### e. Closing the loop\n", "Let's see if there are customers asking for upcoming features above." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 267 }, "id": "mLfNlzGLqOIg", "outputId": "dcfa593f-dab0-44ff-fcec-e4cb671b6d1a" }, "outputs": [], "source": [ "query = 'Are there customers asking for better authorization options for Snowflake Cortex? Give me their names and email.'\n", "response = get_response(query, [\"airbyte_zendesk_tickets\", \"airbyte_zendesk_users\"])\n", "Console().print(f\"\\n\\nResponse from LLM:\\n\\n[blue]{response}[/blue]\")" ] } ], "metadata": { "colab": { "collapsed_sections": [ "nKbdq1eUcmAz" ], "provenance": [] }, "kernelspec": { "display_name": "Python 3", "name": "python3" }, "language_info": { "name": "python" } }, "nbformat": 4, "nbformat_minor": 0 } ================================================ FILE: vector_store_integration/RAG_using_Vectara.ipynb ================================================ { "cells": [ { "cell_type": "markdown", "metadata": { "id": "ayf6NOyRb-zn" }, "source": [ "# **Airbyte Vectara RAG Tutorial**\n", "This tutorial showcases how to use data stored in Airbyte's Vectara destination to perform Retrieval-Augmented Generation (RAG).\n", "## **Prerequisites**\n", "**1) OpenAI API Key**:\n", " - **Create an OpenAI Account**: Sign up for an account on [OpenAI](https://www.openai.com/).\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", "**2) Vectara Customer ID, Corpus ID ,API Key**:\n", " - **Create an Vectara Account**: Sign up for an account on [Vectara](https://vectara.com/).\n", " - **Customer ID**: Click on the profile icon on top right, and look for your customer ID [Vectara Console](https://console.vectara.com/).\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", " - **Generate an API Key**: Go here and generate a new API key. [Vectara API_Key](https://console.vectara.com/console/apiAccess/personalApiKey)." ] }, { "cell_type": "markdown", "metadata": { "id": "41LFzVLEmXT3" }, "source": [ "# **Install Dependencies**\n", "As in any other Python Code, the first step is to install the required Dependencies!" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "NSOZnJ6G9zQu" }, "outputs": [], "source": [ "# Add virtual environment support in Google Colab\n", "!apt-get install -qq python3.10-venv\n", "\n", "# Install required packages\n", "%pip install --quiet openai langchain-openai tiktoken pandas langchain_community\n" ] }, { "cell_type": "markdown", "metadata": { "id": "dQTCEicCmx2A" }, "source": [ "# **Set Up Environment Variables**\n", "We configure the required credentials here!" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "94bGodjCFfsN" }, "outputs": [], "source": [ "import os\n", "\n", "os.environ[\"VECTARA_CUSTOMER_ID\"] = \"YOUR_VECTARA_CUSTOMER_ID\"\n", "os.environ[\"VECTARA_CORPUS_ID\"] = \"YOUR_VECTARA_CORPUS_ID\"\n", "os.environ[\"VECTARA_API_KEY\"] = \"YOUR_VECTARA_API_KEY\"\n", "os.environ[\"OPENAI_API_KEY\"] = \"YOUR_OPENAI_API_KEY\"" ] }, { "cell_type": "markdown", "metadata": { "id": "QXR8wOMWnztR" }, "source": [ "# **Initialize Vectara Vector Store**\n", "We start by initializing the Vectara Vector Store and then see what the data in Vectara looks like.\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "ipg7aPJq-wZ8" }, "outputs": [], "source": [ "import pandas as pd\n", "from langchain_community.vectorstores import Vectara\n", "from google.colab import userdata\n", "\n", "# Initialize Vectara vector store\n", "vectara = Vectara(\n", " vectara_customer_id=os.getenv(\"VECTARA_CUSTOMER_ID\"),\n", " vectara_corpus_id=os.getenv(\"VECTARA_CORPUS_ID\"),\n", " vectara_api_key=os.getenv(\"VECTARA_API_KEY\")\n", ")\n", "\n", "def fetch_vectara_data():\n", " # Simulate fetching data from Vectara\n", " data = {\n", " \"document_id\": [1, 2, 3],\n", " \"document_content\": [\"Content of doc 1\", \"Content of doc 2\", \"Content of doc 3\"],\n", " \"metadata\": [\"Metadata1\", \"Metadata2\", \"Metadata3\"],\n", " \"embedding\": [\"[0.1, 0.2, ...]\", \"[0.3, 0.4, ...]\", \"[0.5, 0.6, ...]\"]\n", " }\n", " df = pd.DataFrame(data)\n", " return df\n", "\n", "# show data\n", "data_frame = fetch_vectara_data()\n", "print(data_frame)\n" ] }, { "cell_type": "markdown", "metadata": { "id": "6b0VCg4Oox4l" }, "source": [ "# **Embedding and similarity search with Vectara**\n", "Here we will convert the user's query into embeddings using OpenAI and retrieve similar chunks from Vectara based on the query" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "G6UuEXP9FpdF" }, "outputs": [], "source": [ "from openai import OpenAI\n", "from langchain_openai import OpenAIEmbeddings\n", "from typing import List\n", "from rich.console import Console\n", "\n", "\n", "client = OpenAI(api_key=os.getenv(\"OPENAI_API_KEY\"))\n", "\n", "# Convert user's query into a vector array to prep for similarity search\n", "def get_embedding_from_openai(query) -> List[float]:\n", " print(f\"Embedding user's query -> {query}...\")\n", " embeddings = OpenAIEmbeddings(openai_api_key=client.api_key)\n", " return embeddings.embed_query(query)\n", "\n", "# Use Vectara to find matching chunks\n", "def get_similar_chunks_from_vectara(query: str) -> List[str]:\n", " print(\"\\nRetrieving similar chunks...\")\n", " try:\n", " results = vectara.similarity_search(query=query)\n", " chunks = [result.page_content for result in results]\n", " print(f\"Found {len(chunks)} matching chunks!\")\n", " return chunks\n", " except Exception as e:\n", " print(f\"Error in retrieving chunks: {e}\")\n", " return []\n", "\n" ] }, { "cell_type": "markdown", "metadata": { "id": "ZEQN6yohqVSY" }, "source": [ "# **Building RAG Pipeline and asking a question**\n", "Finally we use OpenAI for querying our data!
\n", "We know the three main steps of a RAG Pipeline are :
\n", "- Embedding incoming query
\n", "- Doing similarity search to find matching chunks
\n", "- Send chunks to LLM for completion" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "zLKAuK-GqLPC" }, "outputs": [], "source": [ "# Use OpenAI to complete the response\n", "def get_completion_from_openai(question, document_chunks: List[str], model_name=\"gpt-3.5-turbo\"):\n", " print(f\"\\nSending chunks to OpenAI (LLM: {model_name}) for completion...\")\n", " chunks = \"\\n\\n\".join(document_chunks)\n", "\n", " response = client.chat.completions.create(\n", " model=model_name,\n", " messages=[\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", " {\"role\": \"user\", \"content\": f\"Context:\\n{chunks}\\n\\n{question}\\n\\nAnswer:\"}\n", " ],\n", " max_tokens=150\n", " )\n", " return response.choices[0].message.content.strip()\n", "\n", "# Putting it all together\n", "def get_response(query, model_name=\"gpt-3.5-turbo\"):\n", "\n", " chunks = get_similar_chunks_from_vectara(query)\n", "\n", " if len(chunks) == 0:\n", " return \"I am sorry, I do not have the context to answer your question.\"\n", " else:\n", "\n", " return get_completion_from_openai(query, chunks, model_name)\n", "\n", "# Ask a question\n", "query = 'What data do you have?'\n", "response = get_response(query)\n", "\n", "Console().print(f\"\\n\\nResponse from LLM:\\n\\n[blue]{response}[/blue]\")\n" ] } ], "metadata": { "colab": { "provenance": [] }, "kernelspec": { "display_name": "Python 3", "name": "python3" }, "language_info": { "name": "python" } }, "nbformat": 4, "nbformat_minor": 0 } ================================================ FILE: weather_data_stack/.gitignore ================================================ # Byte-compiled / optimized / DLL files __pycache__/ *.py[cod] *$py.class # C extensions *.so # Distribution / packaging .Python build/ develop-eggs/ dist/ downloads/ eggs/ .eggs/ lib/ lib64/ parts/ sdist/ var/ wheels/ share/python-wheels/ *.egg-info/ .installed.cfg *.egg MANIFEST # PyInstaller # Usually these files are written by a python script from a template # before PyInstaller builds the exe, so as to inject date/other infos into it. *.manifest *.spec # Installer logs pip-log.txt pip-delete-this-directory.txt # Unit test / coverage reports htmlcov/ .tox/ .nox/ .coverage .coverage.* .cache nosetests.xml coverage.xml *.cover *.py,cover .hypothesis/ .pytest_cache/ cover/ # Translations *.mo *.pot # Django stuff: *.log local_settings.py db.sqlite3 db.sqlite3-journal # Flask stuff: instance/ .webassets-cache # Scrapy stuff: .scrapy # Sphinx documentation docs/_build/ # PyBuilder .pybuilder/ target/ # Jupyter Notebook .ipynb_checkpoints # IPython profile_default/ ipython_config.py # pyenv # For a library or package, you might want to ignore these files since the code is # intended to run in multiple environments; otherwise, check them in: # .python-version # pipenv # According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. # However, in case of collaboration, if having platform-specific dependencies or dependencies # having no cross-platform support, pipenv may install dependencies that don't work, or not # install all needed dependencies. #Pipfile.lock # poetry # Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control. # This is especially recommended for binary packages to ensure reproducibility, and is more # commonly ignored for libraries. # https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control #poetry.lock # pdm # Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control. #pdm.lock # pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it # in version control. # https://pdm.fming.dev/#use-with-ide .pdm.toml # PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm __pypackages__/ # Celery stuff celerybeat-schedule celerybeat.pid # SageMath parsed files *.sage.py # Environments .env .venv env/ venv/ ENV/ env.bak/ venv.bak/ # Spyder project settings .spyderproject .spyproject # Rope project settings .ropeproject # mkdocs documentation /site # mypy .mypy_cache/ .dmypy.json dmypy.json # Pyre type checker .pyre/ # pytype static type analyzer .pytype/ # Cython debug symbols cython_debug/ # PyCharm # JetBrains specific template is maintained in a separate JetBrains.gitignore that can # be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore # and can be added to the global gitignore or merged into this file. For a more nuclear # option (not recommended) you can uncomment the following to ignore the entire idea folder. #.idea/ #Desktop Services Store .DS_Store ================================================ FILE: weather_data_stack/README.md ================================================ # Weather Data Stack with Airbyte, dbt, Dagster and BigQuery Welcome 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. This Quickstart is all about making things easy, getting you started quickly and showing you how smoothly all these tools can work together! ## Table of Contents - [Weather Data Stack with Airbyte, dbt, Dagster and BigQuery](#weather-data-stack-with-airbyte-dbt-dagster-and-bigquery) - [Table of Contents](#table-of-contents) - [Infrastracture Layout](#infrastracture-layout) - [Prerequisites](#prerequisites) - [1. Setting an environment for your project](#1-setting-an-environment-for-your-project) - [2. Weatherstack API Key](#2-weatherstack-api-key) - [1. Sign Up for a Weatherstack API Key](#1-sign-up-for-a-weatherstack-api-key) - [3. Setting Up BigQuery](#3-setting-up-bigquery) - [1. **Create a Google Cloud Project**](#1-create-a-google-cloud-project) - [2. **Create BigQuery Datasets**](#2-create-bigquery-datasets) - [3. **Create Service Accounts and Assign Roles**](#3-create-service-accounts-and-assign-roles) - [4. **Generate JSON Keys for Service Accounts**](#4-generate-json-keys-for-service-accounts) - [4. Setting Up Airbyte Connectors](#4-setting-up-airbyte-connectors) - [1. Setting Up Airbyte Connectors with AirByteUI](#1-setting-up-airbyte-connectors-with-airbyteui) - [5. Setting Up the dbt Project](#5-setting-up-the-dbt-project) - [Next Steps](#next-steps) - [1. **Explore the Data and Insights**](#1-explore-the-data-and-insights) - [2. **Optimize Your dbt Models**](#2-optimize-your-dbt-models) - [3. **Expand Your Data Sources**](#3-expand-your-data-sources) - [4. **Enhance Data Quality and Testing**](#4-enhance-data-quality-and-testing) - [5. **Automate and Monitor Your Pipelines**](#5-automate-and-monitor-your-pipelines) - [6. **Scale Your Setup**](#6-scale-your-setup) - [7. **Contribute to the Community**](#7-contribute-to-the-community) ## Infrastracture Layout  ## Prerequisites Before you embark on this integration, ensure you have the following set up and ready: 1. **Python 3.10 or later**: If not installed, download and install it from [Python's official website](https://www.python.org/downloads/). 2. **Docker and Docker Compose (Docker Desktop)**: Install [Docker](https://docs.docker.com/get-docker/) following the official documentation for your specific OS. 3. **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/). 4. **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). 5. **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 ## 1. Setting an environment for your project Get the project up and running on your local machine by following these steps: 1. **Clone the repository (Clone only this quickstart)**: ```bash git clone --filter=blob:none --sparse https://github.com/airbytehq/quickstarts.git ``` ```bash cd quickstarts ``` ```bash git sparse-checkout add weather_data_stack ``` 2. **Navigate to the directory**: ```bash cd weather_data_stack ``` 3. **Set Up a Virtual Environment**: - For Mac: ```bash python3 -m venv venv source venv/bin/activate ``` - For Windows: ```bash python -m venv venv .\venv\Scripts\activate ``` 4. **Install Dependencies**: ```bash pip install -e ".[dev]" ``` ## 2. Weatherstack API Key To extract weather data from the Weatherstack API and store the API key in its own environment variable file, you can follow these steps: #### 1. Sign Up for a Weatherstack API Key 1. Visit the [Weatherstack website](https://weatherstack.com/). 2. Sign up for an account or log in if you already have one. 3. Once logged in, go to the dashboard to obtain your API key. ## 3. Setting Up BigQuery #### 1. **Create a Google Cloud Project** - If you have a Google Cloud project, you can skip this step. - Go to the [Google Cloud Console](https://console.cloud.google.com/). - Click on the "Select a project" dropdown at the top right and select "New Project". - Give your project a name and follow the steps to create it. #### 2. **Create BigQuery Datasets** - In the Google Cloud Console, go to BigQuery. - Make two new datasets: `raw_data` for Airbyte and `transformed_data` for dbt. - If you pick different names, remember to change the names in the code too. **How to create a dataset:** - In the left sidebar, click on your project name. - Click “Create Dataset”. - Enter the dataset ID (either `raw_data` or `transformed_data`). - Click "Create Dataset". #### 3. **Create Service Accounts and Assign Roles** - Go to “IAM & Admin” > “Service accounts” in the Google Cloud Console. - Click “Create Service Account”. - Name your service account (like `airbyte-service-account`). - Assign the “BigQuery Data Editor” and “BigQuery Job User” roles to the service account. - Follow the same steps to make another service account for dbt (like `dbt-service-account`) and assign the roles. **How to create a service account and assign roles:** - While creating the service account, under the “Grant this service account access to project” section, click the “Role” dropdown. - Choose the “BigQuery Data Editor” and “BigQuery Job User” roles. - Finish the creation process. #### 4. **Generate JSON Keys for Service Accounts** - For both service accounts, make a JSON key to let the service accounts sign in. **How to generate JSON key:** - Find the service account in the “Service accounts” list. - Click on the service account name. - In the “Keys” section, click “Add Key” and pick JSON. - The key will download automatically. Keep it safe and don’t share it. - Do this for the other service account too. ## 4. Setting Up Airbyte Connectors Here, you can set up connectors with source and destination manually using the Airbyte UI. ### 1. Setting Up Airbyte Connectors with AirByteUI Use the generated public url from the previous step to manually configure using the File Option as source under public HTPPS. - Here are specific [BigQuery](https://docs.airbyte.com/integrations/destinations/bigquery) instructions. - Follow these [steps](https://docs.airbyte.com/quickstart/set-up-a-connection) for more. ## 5. Setting Up the dbt Project [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: 1. **Navigate to the dbt Project Directory**: Change to the directory containing the dbt configuration: ```bash cd dbt_project ``` 2. **Update Connection Details**: 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. 3. **Utilize Environment Variables (Optional but Recommended)**: To keep your credentials secure, you can leverage environment variables. An example is provided within the `profiles.yml` file. 4. **Test the Connection**: Once you’ve updated the connection details, you can test the connection to your BigQuery instance using: ```bash dbt debug ``` If everything is set up correctly, this command should report a successful connection to BigQuery. 5. **Run the Models**: If you would like to run the dbt models manually at this point, you can do so by executing: ```bash dbt run ``` You can verify the data has been transformed by going to BigQuery and checking the `transformed_data` dataset. ## 6. Orchestrating with Dagster [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: 1. **Navigate to the Orchestration Directory**: Switch to the directory containing the Dagster orchestration configurations: ```bash cd orchestration ``` 2. **Set Environment Variables**: Dagster requires certain environment variables to be set to interact with other tools like dbt and Airbyte. Set the following variables: ```bash export DAGSTER_DBT_PARSE_PROJECT_ON_LOAD=1 export AIRBYTE_PASSWORD=password ``` 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. 3. **Launch the Dagster UI**: With the environment variables in place, kick-start the Dagster UI: ```bash dagster dev ``` 4. **Access Dagster in Your Browser**: Open your browser and navigate to: ``` http://127.0.0.1:3000 ``` Here, you should see assets for both Airbyte and dbt. ## Next Steps Congratulations 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: ### 1. **Explore the Data and Insights** - 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! ### 2. **Optimize Your dbt Models** - 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. ### 3. **Expand Your Data Sources** - 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. ### 4. **Enhance Data Quality and Testing** - 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. ### 5. **Automate and Monitor Your Pipelines** - 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. ### 6. **Scale Your Setup** - 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. ### 7. **Contribute to the Community** - Share your learnings, optimizations, and new configurations with the community. Contribute to the respective tool’s communities and help others learn and grow. ================================================ FILE: weather_data_stack/dbt_project/.gitignore ================================================ target/ dbt_packages/ logs/ .user.yml ================================================ FILE: weather_data_stack/dbt_project/README.md ================================================ Welcome to your new dbt project! ### Using the starter project Try running the following commands: - dbt run - dbt test ### Resources: - Learn more about dbt [in the docs](https://docs.getdbt.com/docs/introduction) - Check out [Discourse](https://discourse.getdbt.com/) for commonly asked questions and answers - Join the [chat](https://community.getdbt.com/) on Slack for live discussions and support - Find [dbt events](https://events.getdbt.com) near you - Check out [the blog](https://blog.getdbt.com/) for the latest news on dbt's development and best practices ================================================ FILE: weather_data_stack/dbt_project/analyses/.gitkeep ================================================ ================================================ FILE: weather_data_stack/dbt_project/dbt_project.yml ================================================ # Name your project! Project names should contain only lowercase characters # and underscores. A good package name should reflect your organization's # name or the intended use of these models name: 'dbt_project' version: '1.0.0' config-version: 2 # This setting configures which "profile" dbt uses for this project. profile: 'dbt_project' # These configurations specify where dbt should look for different types of files. # The `model-paths` config, for example, states that models in this project can be # found in the "models/" directory. You probably won't need to change these! model-paths: ["models"] analysis-paths: ["analyses"] test-paths: ["tests"] seed-paths: ["seeds"] macro-paths: ["macros"] snapshot-paths: ["snapshots"] clean-targets: # directories to be removed by `dbt clean` - "target" - "dbt_packages" # Configuring models # Full documentation: https://docs.getdbt.com/docs/configuring-models # In this example config, we tell dbt to build all models in the example/ # directory as views. These settings can be overridden in the individual model # files using the `{{ config(...) }}` macro. models: dbt_project: # Config indicated by + and applies to all files under models/example/ staging: +materialized: view marts: +materialized: view ================================================ FILE: weather_data_stack/dbt_project/macros/.gitkeep ================================================ ================================================ FILE: weather_data_stack/dbt_project/models/marts/historial_weather_trends.sql ================================================ {% set start_date = '2023-01-01' %} {% set end_date = '2023-12-31' %} SELECT JSON_EXTRACT_SCALAR(`current`, '$.location.name') AS location_name, JSON_EXTRACT_SCALAR(`current`, '$.current.temperature') AS temperature, DATE(JSON_EXTRACT_SCALAR(`current`, '$.current.observation_time')) AS date FROM transformed_data.stg_current_weather WHERE DATE(JSON_EXTRACT_SCALAR(`current`, '$.current.observation_time')) BETWEEN '{{ start_date }}' AND '{{ end_date }}' ORDER BY date, location_name ================================================ FILE: weather_data_stack/dbt_project/models/sources/weatherstack_source.yml ================================================ version: 2 sources: - name: weatherstack # Use your BigQuery project ID database: "{{ env_var('BIGQUERY_PROJECT_ID', '') }}" # Use your BigQuery dataset name schema: weatherstack_airbyte tables: - name: current_weather description: "Simulated current_weather data from the weatherstack connector." columns: - name: current - name: location - name: request ================================================ FILE: weather_data_stack/dbt_project/models/staging/stg_current_weather.sql ================================================ select * from {{ source('weatherstack', 'current_weather') }} ================================================ FILE: weather_data_stack/dbt_project/profiles.yml ================================================ dbt_project: outputs: dev: dataset: transformed_data job_execution_timeout_seconds: 300 job_retries: 1 # Use an env variable to indicate your JSON key file path keyfile: "{{ env_var('DBT_BIGQUERY_KEYFILE_PATH', '') }}" location: US method: service-account priority: interactive # Indicate your BigQuery project ID project: your_project_id threads: 1 type: bigquery target: dev ================================================ FILE: weather_data_stack/dbt_project/seeds/.gitkeep ================================================ ================================================ FILE: weather_data_stack/dbt_project/snapshots/.gitkeep ================================================ ================================================ FILE: weather_data_stack/dbt_project/tests/.gitkeep ================================================ ================================================ FILE: weather_data_stack/orchestration/orchestration/__init__.py ================================================ ================================================ FILE: weather_data_stack/orchestration/orchestration/assets.py ================================================ import os from dagster import OpExecutionContext from dagster_dbt import DbtCliResource, dbt_assets from dagster_airbyte import AirbyteResource, load_assets_from_airbyte_instance from .constants import dbt_manifest_path @dbt_assets(manifest=dbt_manifest_path) def dbt_project_dbt_assets(context: OpExecutionContext, dbt: DbtCliResource): yield from dbt.cli(["build"], context=context).stream() airbyte_instance = AirbyteResource( host="localhost", port="8000", # If using basic auth, include username and password: username="airbyte", password=os.getenv("AIRBYTE_PASSWORD") ) airbyte_assets = load_assets_from_airbyte_instance(airbyte_instance, key_prefix="faker") ================================================ FILE: weather_data_stack/orchestration/orchestration/constants.py ================================================ import os from pathlib import Path from dagster_dbt import DbtCliResource dbt_project_dir = Path(__file__).joinpath("..", "..", "..", "dbt_project").resolve() dbt = DbtCliResource(project_dir=os.fspath(dbt_project_dir)) # If DAGSTER_DBT_PARSE_PROJECT_ON_LOAD is set, a manifest will be created at runtime. # Otherwise, we expect a manifest to be present in the project's target directory. if os.getenv("DAGSTER_DBT_PARSE_PROJECT_ON_LOAD"): dbt_parse_invocation = dbt.cli(["parse"], manifest={}).wait() dbt_manifest_path = dbt_parse_invocation.target_path.joinpath("manifest.json") else: dbt_manifest_path = dbt_project_dir.joinpath("target", "manifest.json") ================================================ FILE: weather_data_stack/orchestration/orchestration/definitions.py ================================================ import os from dagster import Definitions from dagster_dbt import DbtCliResource from .assets import dbt_project_dbt_assets, airbyte_assets from .constants import dbt_project_dir from .schedules import schedules defs = Definitions( assets=[dbt_project_dbt_assets, airbyte_assets], schedules=schedules, resources={ "dbt": DbtCliResource(project_dir=os.fspath(dbt_project_dir)), }, ) ================================================ FILE: weather_data_stack/orchestration/orchestration/schedules.py ================================================ """ To add a daily schedule that materializes your dbt assets, uncomment the following lines. """ from dagster_dbt import build_schedule_from_dbt_selection from .assets import dbt_project_dbt_assets schedules = [ # build_schedule_from_dbt_selection( # [dbt_project_dbt_assets], # job_name="materialize_dbt_models", # cron_schedule="0 0 * * *", # dbt_select="fqn:*", # ), ] ================================================ FILE: weather_data_stack/orchestration/pyproject.toml ================================================ [build-system] requires = ["setuptools"] build-backend = "setuptools.build_meta" [tool.dagster] module_name = "orchestration.definitions" code_location_name = "orchestration" ================================================ FILE: weather_data_stack/orchestration/setup.py ================================================ from setuptools import find_packages, setup setup( name="orchestration", version="0.0.1", packages=find_packages(), install_requires=[ "dagster", "dagster-cloud", "dagster-dbt", "dbt-core>=1.4.0", "dbt-bigquery", ], extras_require={ "dev": [ "dagster-webserver", ] }, ) ================================================ FILE: weather_data_stack/setup.py ================================================ from setuptools import find_packages, setup setup( name="airbyte-dbt-dagster", packages=find_packages(), install_requires=[ "dbt-bigquery", "dagster", "dagster-cloud", "dagster-dbt", "dagster-airbyte", ], extras_require={"dev": ["dagit", "pytest"]}, )
\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", "## **Prerequisites**\n", "**1) OpenAI API Key**:\n", " - **Create an OpenAI Account**: Sign up for an account on [OpenAI](https://www.openai.com/).\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", "**2) Github Personal Access Token**:\n", " - **Create a Github Account**: Sign up for an account on [Github](https://www.github.com/).\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" ], "metadata": { "id": "lv1fce-k_Ul5" } }, { "cell_type": "markdown", "source": [ "# **Installing Dependencies**\n", "First Thing First !
\n", "Lets get the dependencies installed before anything else!!" ], "metadata": { "id": "mbc7jQsW-kGs" } }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true, "id": "5FFGW2iOhOqT" }, "outputs": [], "source": [ "# Add virtual environment support for running in Google Colab\n", "!apt-get install -qq python3.10-venv\n", "\n", "# First, we need to install the necessary libraries.\n", "!pip3 install airbyte langchain langchain-openai chromadb python-dotenv langchainhub langchain-chroma" ] }, { "cell_type": "markdown", "source": [ "## **Source Setup : Github with PyAirbyte**\n", "The code you see below configures an Airbyte source to pull out data from a github repository.\n", "\n", "You can also customize the configuration according to your own needs.\n", "See [this](https://docs.airbyte.com/integrations/sources/github#reference)\n", "\n", "Note that we here only fetch data from the Commits Stream
\n", "To know about all the available streams go [here](https://docs.airbyte.com/integrations/sources/github#supported-streams)\n" ], "metadata": { "id": "aBfzg42A_mhd" } }, { "cell_type": "code", "source": [ "import airbyte as ab\n", "\n", "source = ab.get_source(\n", " \"source-github\",\n", " config={\n", " \"credentials\": {\n", " \"personal_access_token\": \"your_personal_access_token\"\n", " },\n", " \"repositories\": [\"your_github_username/your_repository_ID\"]\n", " }\n", ")\n", "source.check()\n", "\n", "source.get_available_streams()\n", "source.select_streams([\"commits\"])\n", "cache = ab.get_default_cache()\n", "result = source.read(cache=cache)\n", "\n", "commits_details = [doc for doc in result[\"commits\"].to_documents()]\n", "\n", "print(str(commits_details[0]))" ], "metadata": { "collapsed": true, "id": "vMiRDXs1ii3S" }, "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "source": [ "# **Split Documents into Chunks**\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", "Here we set each chunk size to 512 characters and adjacent chunks will overlap by 50 characters to ensure continuity of context\n", "
\n", "Then the loop converts all metadata to string format to ensure consistent processing later in the pipeline." ], "metadata": { "id": "f4yYrQ9lLVRJ" } }, { "cell_type": "code", "source": [ "from langchain.text_splitter import RecursiveCharacterTextSplitter\n", "\n", "splitter = RecursiveCharacterTextSplitter(chunk_size=512, chunk_overlap=50)\n", "chunked_docs = splitter.split_documents(commits_details)\n", "\n", "for doc in chunked_docs:\n", " for md in doc.metadata:\n", " doc.metadata[md] = str(doc.metadata[md])" ], "metadata": { "collapsed": true, "id": "_shmtR8Zl6d5" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "from langchain_openai import OpenAIEmbeddings\n", "import os\n", "\n", "os.environ['OPENAI_API_KEY'] = ab.get_secret(\"YOUR_OPENAI_API_KEY\")\n", "embeddings = OpenAIEmbeddings()" ], "metadata": { "collapsed": true, "id": "CNgHPzULmB0j" }, "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "source": [ "# **Setting up Chroma**\n", "Create and configure a Chroma vector store to store the document embeddings.
\n", "First we initialize Chroma Client\n", "
\n", "Then we create Chroma Vector Store from Documents\n", "
\n", "Finally we use embedding function when accessing the collection\n", "
\n", "Since currently there is a waitlist for Chroma,We initialize the Chroma client in persistent mode (local file)" ], "metadata": { "id": "TwHTBa43M7fM" } }, { "cell_type": "code", "source": [ "import chromadb\n", "from langchain_chroma import Chroma\n", "from chromadb.utils import embedding_functions\n", "\n", "persist_directory = 'chroma_db'\n", "client = chromadb.PersistentClient(path=persist_directory)\n", "collection_name = \"github_commits\"\n", "\n", "openai_lc_client = Chroma.from_documents(\n", " documents=chunked_docs,\n", " embedding=embeddings,\n", " persist_directory=persist_directory,\n", " collection_name=collection_name\n", ")\n", "\n", "openai_ef = embedding_functions.OpenAIEmbeddingFunction(\n", " api_key=os.getenv(\"OPENAI_API_KEY\"),\n", " model_name=\"text-embedding-ada-002\"\n", ")\n", "collection = client.get_collection(name=collection_name, embedding_function=openai_ef)\n" ], "metadata": { "collapsed": true, "id": "PzB1caQTmbuS" }, "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "source": [ "# **Querying Chroma and RAG Pipeline**\n", "Finally we use LangChain to retrieve documents from Chroma and generate responses using an OpenAI chat model." ], "metadata": { "id": "MoCApKIWNpBd" } }, { "cell_type": "code", "source": [ "from langchain_openai import ChatOpenAI\n", "from langchain import hub\n", "from langchain_core.output_parsers import StrOutputParser\n", "from langchain_core.runnables import RunnablePassthrough\n", "\n", "# Initialize the LLM\n", "llm = ChatOpenAI(model_name=\"gpt-3.5-turbo\", temperature=0)\n", "\n", "# Set up the retriever from the Chroma vector store\n", "retriever = openai_lc_client.as_retriever()\n", "\n", "# Set up the prompt\n", "prompt = hub.pull(\"rlm/rag-prompt\")\n", "\n", "# Function to format documents\n", "def format_docs(docs):\n", " return \"\\n\\n\".join(doc.page_content for doc in docs)\n", "\n", "# Create the RAG chain\n", "rag_chain = (\n", " {\"context\": retriever | format_docs, \"question\": RunnablePassthrough()}\n", " | prompt\n", " | llm\n", " | StrOutputParser()\n", ")\n", "\n", "print(\"Langchain RAG pipeline set up successfully.\")\n", "\n", "# Example query\n", "response = rag_chain.invoke(\"Which are the commit messages of latest commits?\")\n", "print(response)\n" ], "metadata": { "id": "uDPPklkxod8-" }, "execution_count": null, "outputs": [] } ] } ================================================ FILE: pyairbyte_notebooks/README.md ================================================ # PyAirbyte Notebooks Quickstart This quickstart will help you get started quickly with PyAirbyte. ## Quickstart Quicklinks To jump right in, click on any of the below links to open a new Colab notebook from the provided quickstart template. 1. [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. 2. [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. 3. [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. 4. [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. ## How to use these Quickstarts There are three ways to use the quickstart resources here. ### Google Colab Google 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. Note: - 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`. ### Self-Hosted Jupyter If you have a self-hosted Jupyter instance, you can load any of the notebooks in this directory. ### VS Code Notebooks You 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. ## Securely Managed Secrets You can pass secrets to PyAirbyte by using the `get_secret()` function. This call will retrieve a named secret from any of the following locations: 1. Google Colab Secrets 2. Environment Variables 3. Masked User Input (via [getpass](https://docs.python.org/3/library/getpass.html)) If you are using Google Colab, we suggest using the Colab secrets feature. For other environments, you can set your secret values in environment variables. Note: 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. **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). ================================================ FILE: pyairbyte_notebooks/rag_using_gdrive_pyairbyte_pinecone.ipynb ================================================ { "cells": [ { "cell_type": "markdown", "id": "8f104c9d-933a-45fd-9d2c-57e6d56a56b1", "metadata": {}, "source": [ "# End-to-End RAG Tutorial Using GoogleDrive, PyAirbyte, Pinecone, and LangChain\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", "## Prerequisites\n", "\n", "1. **GoogleDrive**:\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", "2. **Pinecone Account**:\n", " - **Create a Pinecone Account**: Sign up for an account on the [Pinecone website](https://www.pinecone.io/).\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", "3. **OpenAI API Key**:\n", " - **Create an OpenAI Account**: Sign up for an account on [OpenAI](https://www.openai.com/).\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" ] }, { "cell_type": "markdown", "id": "8e1d3a0b-d446-43b0-b9dd-691fd837e5eb", "metadata": {}, "source": [ "## Install PyAirbyte and other dependencies" ] }, { "cell_type": "raw", "id": "ffebf71e-2b9a-4a1a-b448-9812b49f120c", "metadata": {}, "source": [ "!pip3 install airbyte openai langchain pinecone-client langchain-openai langchain-pinecone langchainhub " ] }, { "cell_type": "markdown", "id": "7b51a295-8c8e-483d-a931-24ec2590dffa", "metadata": {}, "source": [ "## Setup Source GoogleDrive with PyAirbyte\n", "\n", "The provided code configures an Airbyte source to extract data from an GoogleDrive Folder contains CSV file named NFLX.csv\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", "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" ] }, { "cell_type": "code", "execution_count": 14, "id": "6ddd2f96-8680-4a19-a02b-32fd0b703912", "metadata": {}, "outputs": [ { "name": "stdin", "output_type": "stream", "text": [ "Enter the value for secret 'service_json': ········\n" ] }, { "data": { "text/html": [ "
Connection check succeeded for `source-google-drive`.\n",
"\n"
],
"text/plain": [
"Connection check succeeded for `source-google-drive`.\n"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import airbyte as ab\n",
"\n",
"service_json = ab.get_secret('service_json')\n",
"\n",
"source = ab.get_source(\n",
" \"source-google-drive\",\n",
" install_if_missing=True,\n",
" config={\n",
" \"folder_url\": \"https://drive.google.com/drive/folders/1txtyBv_mfXYjn0R_-oxV3Vg5QOi-6XaI\",\n",
" \"credentials\": {\n",
" \"auth_type\": \"Service\",\n",
" \"service_account_info\": f\"\"\"{service_json}\"\"\",\n",
" },\n",
" \"streams\": [{\n",
" \"name\": \"NFLX\",\n",
" \"globs\": [\"**/*.csv\"],\n",
" \"format\": {\n",
" \"filetype\": \"csv\"\n",
" },\n",
" \"validation_policy\": \"Emit Record\",\n",
" \"days_to_sync_if_history_is_full\": 3\n",
" }]\n",
" \n",
" },\n",
" \n",
")\n",
"\n",
"# Verify the config and creds by running `check`:\n",
"source.check()"
]
},
{
"cell_type": "markdown",
"id": "b12930b8-ff69-46e9-9daf-89b5ed49b68c",
"metadata": {},
"source": [
"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."
]
},
{
"cell_type": "code",
"execution_count": 16,
"id": "a8abe757-b263-4574-b220-da715c4d827e",
"metadata": {},
"outputs": [
{
"data": {
"text/markdown": [
"## Read Progress\n",
"\n",
"Started reading at 11:20:50.\n",
"\n",
"Read **0** records over **2 seconds** (0.0 records / second).\n",
"\n",
"Finished reading at 11:20:53.\n",
"\n",
"Started finalizing streams at 11:20:53.\n",
"\n",
"Finalized **0** batches over 0 seconds.\n",
"\n",
"\n",
"\n",
"\n",
"------------------------------------------------\n"
],
"text/plain": [
"Completed `source-google-drive` read operation at 16:50:53.\n",
"\n"
],
"text/plain": [
"Completed `source-google-drive` read operation at \u001b[1;92m16:50:53\u001b[0m.\n"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"```yaml\n",
"_ab_source_file_last_modified: '2024-06-04T04:00:24.000000Z'\n",
"_ab_source_file_url: NFLX.csv\n",
"_airbyte_extracted_at: 2024-06-07 11:20:25.946000\n",
"_airbyte_meta: {}\n",
"_airbyte_raw_id: 01HZS6VC3573GHEKRHMW1KA41T\n",
"adj_close: '254.259995'\n",
"close: '254.259995'\n",
"date: '2018-02-05'\n",
"high: '267.899994'\n",
"low: '250.029999'\n",
"open: '262.000000'\n",
"volume: '11896100'\n",
"```\n",
"\n"
]
}
],
"source": [
"# This code reads data from a Google Drive CSV source and converts it into a list of document objects.\n",
"\n",
"source.select_all_streams() # Select all streams from the Google Drive source\n",
"read_result = source.read() # Read the data from the selected streams\n",
"documents_list = []\n",
"\n",
"# Convert the read data into document objects and add them to the list\n",
"for key, value in read_result.items():\n",
" docs = value.to_documents()\n",
" for doc in docs:\n",
" documents_list.append(doc)\n",
"\n",
"# Print the Single row of the csv \n",
"print(str(documents_list[0]))"
]
},
{
"cell_type": "markdown",
"id": "a04c48f5-1801-47b9-9244-1cf1b560c291",
"metadata": {},
"source": [
"## Use Langchain to build a RAG pipeline."
]
},
{
"cell_type": "code",
"execution_count": 17,
"id": "ebef2365-3570-43ad-b05d-a9e3ef86aef1",
"metadata": {},
"outputs": [],
"source": [
"RecursiveCharacterTextSplitter from the langchain library to split documents into smaller chunks of 512 characters with a 50-character overlap.\n",
"It then converts all metadata values in each chunk to strings and prints the total number of created document chunks.\n"
]
},
{
"cell_type": "code",
"execution_count": 18,
"id": "81cada6b-c1bb-4cbf-9a40-30f2547ed15c",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Created 1009 document chunks.\n"
]
}
],
"source": [
"from langchain.text_splitter import RecursiveCharacterTextSplitter\n",
"from langchain.vectorstores.utils import filter_complex_metadata\n",
"\n",
"\n",
"\n",
"splitter = RecursiveCharacterTextSplitter(chunk_size=512, chunk_overlap=50)\n",
"chunked_docs = splitter.split_documents(documents_list)\n",
"chunked_docs = filter_complex_metadata(chunked_docs)\n",
"print(f\"Created {len(chunked_docs)} document chunks.\")\n",
"\n",
"for doc in chunked_docs:\n",
" for md in doc.metadata:\n",
" doc.metadata[md] = str(doc.metadata[md])"
]
},
{
"cell_type": "code",
"execution_count": 32,
"id": "50b756b3-745b-49ff-a57a-d1d6371243fe",
"metadata": {
"scrolled": true
},
"outputs": [],
"source": [
"from langchain_community.embeddings import HuggingFaceEmbeddings\n",
"## Create Embeddings using HuggingFace sentence-transformers/all-mpnet-base-v2 model\n",
"embeddings=HuggingFaceEmbeddings()"
]
},
{
"cell_type": "markdown",
"id": "33c36b1f-f03b-4f78-bbe1-9e9035af5a1d",
"metadata": {},
"source": [
"## Setting up Pinecone\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."
]
},
{
"cell_type": "code",
"execution_count": 43,
"id": "a61ce285-9d58-43e0-808b-786803364d8f",
"metadata": {},
"outputs": [],
"source": [
"from pinecone import Pinecone, ServerlessSpec\n",
"import os\n",
"os.environ['PINECONE_API_KEY'] = ab.get_secret(\"PINECONE_API_KEY\")\n",
"index_name = \"gdriveairbyteindex\"\n",
"\n",
"pc = Pinecone()\n",
"\n",
"# Create pinecone index if not exists otherwise skip this step\n",
"if not (pc.list_indexes()[0]['name'] == index_name):\n",
" pc.create_index(\n",
" name=index_name,\n",
" dimension=768, \n",
" metric=\"cosine\", \n",
" spec=ServerlessSpec(\n",
" cloud=\"aws\",\n",
" region=\"us-east-1\"\n",
" ) \n",
" )"
]
},
{
"cell_type": "code",
"execution_count": 44,
"id": "92f6e244-302b-4b6e-b490-3678412ba589",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'dimension': 768,\n",
" 'index_fullness': 0.0,\n",
" 'namespaces': {'': {'vector_count': 1009}},\n",
" 'total_vector_count': 1009}"
]
},
"execution_count": 44,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"index = pc.Index(index_name)\n",
"index.describe_index_stats()"
]
},
{
"cell_type": "markdown",
"id": "c2034d80-b359-4240-a695-c959619fae4a",
"metadata": {},
"source": [
"## PineconeVectorStore\n",
"PineconeVectorStore to store and index high-dimensional vectors extracted from documents, leveraging embeddings provided by Hugging Face"
]
},
{
"cell_type": "code",
"execution_count": 28,
"id": "874ddc8a-035e-436c-8a14-a7ed24fc4adb",
"metadata": {},
"outputs": [],
"source": [
"from langchain_pinecone import PineconeVectorStore\n",
"\n",
"pinecone = PineconeVectorStore.from_documents(\n",
" chunked_docs, embedding=embeddings, index_name=index_name\n",
")"
]
},
{
"cell_type": "markdown",
"id": "87e9990b-4982-44b6-9163-b12a3109e741",
"metadata": {},
"source": [
"## RAG\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",
"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"
]
},
{
"cell_type": "code",
"execution_count": 46,
"id": "b6d3490a-40e9-48d5-a8f1-5e6571f7e036",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Langchain RAG pipeline set up successfully.\n"
]
}
],
"source": [
"from langchain_openai import ChatOpenAI\n",
"from langchain import hub\n",
"from langchain_core.output_parsers import StrOutputParser\n",
"from langchain_core.runnables import RunnablePassthrough\n",
"import os\n",
"\n",
"os.environ['OPENAI_API_KEY'] = ab.get_secret(\"OPENAI_API_KEY\")\n",
"\n",
"retriever = pinecone.as_retriever()\n",
"prompt = hub.pull(\"rlm/rag-prompt\")\n",
"\n",
"llm = ChatOpenAI(model_name=\"gpt-3.5-turbo\", temperature=0)\n",
"\n",
"\n",
"def format_docs(docs):\n",
" return \"\\n\\n\".join(doc.page_content for doc in docs)\n",
"\n",
"rag_chain = (\n",
" {\"context\": retriever | format_docs, \"question\": RunnablePassthrough()}\n",
" | prompt\n",
" | llm\n",
" | StrOutputParser()\n",
")\n",
"print(\"Langchain RAG pipeline set up successfully.\")"
]
},
{
"cell_type": "code",
"execution_count": 47,
"id": "f059fe04-bf3a-4bd8-b674-1f421f3bb5f4",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"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"
]
}
],
"source": [
"print(rag_chain.invoke(\"What is the source data about?\"))"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python (gdriveenv)",
"language": "python",
"name": ".venv-source-google-drive"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.19"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
================================================
FILE: pyairbyte_notebooks/rag_using_github_pyairbyte_weaviate.ipynb
================================================
{
"cells": [
{
"cell_type": "markdown",
"source": [
"# End-to-End RAG Tutorial Using Github, PyAirbyte, Weaviate\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",
"## Prerequisites\n",
"\n",
"1. **Github**:\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",
"2. **Weaviate Account**:\n",
" - **Create a Weaviate Account**: Sign up for an account on the [Weaviate website](https://weaviate.io/).\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",
"3. **OpenAI API Key**:\n",
" - **Create an OpenAI Account**: Sign up for an acco\n",
" unt on [OpenAI](https://www.openai.com/).\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",
"## Install PyAirbyte and other dependencies"
],
"metadata": {
"id": "5pBIRIujjIw4"
}
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "4dv5cOvKi7xH"
},
"outputs": [],
"source": [
"# Add virtual environment support for running in Google Colab\n",
"!apt-get install -qq python3.10-venv\n",
"\n",
"# First, we need to install the necessary libraries.\n",
"!pip3 install airbyte weaviate-client python-dotenv"
]
},
{
"cell_type": "markdown",
"source": [
"#Setup Source Github\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."
],
"metadata": {
"id": "p-S0HQbWlqiM"
}
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "wo8v-XcGi7xI"
},
"outputs": [],
"source": [
"import airbyte as ab\n",
"\n",
"source = ab.get_source(\n",
" \"source-github\",\n",
" config={\n",
" \"repositories\": ab.get_secret('GITHUB_REPOSITORY'),\n",
" \"credentials\": {\n",
" \"personal_access_token\": ab.get_secret('GITHUB_ACCESS_TOKEN'),\n",
" },\n",
" },\n",
")\n",
"source.check()"
]
},
{
"cell_type": "markdown",
"source": [
"Reads the data from the selected issues stream, extracting the GitHub issues data for further processing."
],
"metadata": {
"id": "mScNlHAMlzpR"
}
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "FV9E_J09i7xJ"
},
"outputs": [],
"source": [
"# In this notebook we are focused on only issues stream\n",
"# checkout all stream here : https://docs.airbyte.com/integrations/sources/gitlab#supported-streams\n",
"\n",
"print(source.get_available_streams())\n",
"source.select_streams([\"issues\"])\n",
"cache = ab.get_default_cache()\n",
"result = source.read(cache=cache,force_full_refresh=True)\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "X45nwGPAi7xJ"
},
"outputs": [],
"source": [
"issues_details = result['issues'].to_pandas() #coverting data from issues stream to pandas dataframe\n",
"\n",
"print(issues_details.columns)\n",
"print(issues_details[10])"
]
},
{
"cell_type": "markdown",
"source": [
"#Setting up Weaviate\n",
"Connect to the weaviate instance, Enter your weaviate cluster url and API_KEY"
],
"metadata": {
"id": "s7Z8OTeOmFS_"
}
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "UyzpsIKPi7xK"
},
"outputs": [],
"source": [
"import weaviate\n",
"\n",
"client = weaviate.Client(\n",
" url = ab.get_secret('WCD_URL'), # Replace with your Weaviate endpoint\n",
" auth_client_secret=weaviate.auth.AuthApiKey(api_key=ab.get_secret('WCD_API_KEY')), # Replace with your Weaviate instance API key\n",
" additional_headers = {\n",
" \"X-OpenAI-Api-Key\": ab.get_secret('OPENAI_API_KEY') # Replace with your Openai API key\n",
" }\n",
")"
]
},
{
"cell_type": "markdown",
"source": [
"Weaviate stores data in collections. Each data object in a collection has a set of properties and a vector representation."
],
"metadata": {
"id": "RcUwvnBHnLO0"
}
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "CQm5WxEzi7xK"
},
"outputs": [],
"source": [
"collection_name = \"issues\" # name of collection\n",
"class_obj = {\n",
" \"class\": collection_name,\n",
" \"vectorizer\": \"text2vec-openai\", # If set to \"none\" you must always provide vectors yourself. Could be any other \"text2vec-*\" also.\n",
" \"moduleConfig\": {\n",
" \"text2vec-openai\": {},\n",
" \"generative-openai\": {} # Ensure the `generative-openai` module is used for generative queries\n",
" }\n",
"}\n",
"\n",
"client.schema.create_class(class_obj)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "vMxfH8r8i7xL"
},
"outputs": [],
"source": [
"\n",
"# Batch imports are an efficient way to add multiple data objects and cross-references.\n",
"client.batch.configure(batch_size=100) # Configure batch\n",
"\n",
"#The following example adds objects to the collection.\n",
"with client.batch as batch: # Initialize a batch process\n",
" for i,d in enumerate(issues_details): # Batch import data\n",
" properties = {\n",
" \"issue_details\": d, # You can also change property name here and also add multiple property\n",
" }\n",
" batch.add_data_object(\n",
" data_object=properties,\n",
" class_name=collection_name\n",
" )"
]
},
{
"cell_type": "markdown",
"source": [
"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",
"This makes it easier, faster and more efficient to implement generative search workflows in your application.\n",
"\n",
"You can checkout more ways of query [here](https://weaviate.io/developers/weaviate/starter-guides/generative#data-retrieval)."
],
"metadata": {
"id": "ihPrmv3DqRL6"
}
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "4YnIQV_pi7xL",
"outputId": "287b69a9-2789-40b7-b1e5-c03754068638"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"/*\n",
"Summary of Pagination Handling in Github Connector issues in airbytehq/quickstarts repository:\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",
"- This ensures that large datasets of issues can be efficiently retrieved without overwhelming the API or the system.\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",
"- Pagination parameters such as page number and page size are typically used to control the retrieval of issues in batches.\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"
]
}
],
"source": [
"response = (\n",
" client.query\n",
" .get(class_name=collection_name, properties=[\"issue_details\"])\n",
" .with_near_text({\"concepts\": [\"title\",\"comments\"]})\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",
" .with_limit(1)\n",
" .do()\n",
")\n",
"\n",
"print(response[\"data\"][\"Get\"][\"Issues\"][0][\"_additional\"][\"generate\"][\"singleResult\"])"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.11"
},
"colab": {
"provenance": []
}
},
"nbformat": 4,
"nbformat_minor": 0
}
================================================
FILE: pyairbyte_notebooks/rag_using_gitlab_pyairbyte_qdrant.ipynb
================================================
{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "3s3Jow7_BjQi"
},
"source": [
"# End-to-End RAG Tutorial Using Gitlab, PyAirbyte, Qdrant, and LangChain\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",
"## Prerequisites\n",
"\n",
"1. **Gitlab Account**:\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",
"2. **Qdrant Account**:\n",
" - **Create a Qdrant Account**: Sign up for an account on the Qdrant [website](https://qdrant.tech/)\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",
"3. **OpenAI API Key**:\n",
" - **Create an OpenAI Account**: Sign up for an acco\n",
" unt on [OpenAI](https://www.openai.com/).\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",
"## Install PyAirbyte and other dependencies"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "XclQfDX9MQsw"
},
"outputs": [],
"source": [
"# Add virtual environment support for running in Google Colab\n",
"!apt-get install -qq python3.10-venv\n",
"\n",
"# First, we need to install the necessary libraries.\n",
"!pip3 install airbyte langchain langchain-openai qdrant-client python-dotenv langchainhub"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "Hzira-9BQq0h"
},
"source": [
"## Setup Source Gitlab with PyAirbyte\n",
"\n",
"The provided code configures an Airbyte source to extract data from a gitlab.\n",
"\n",
"To configure according to your requirements, you can refer to [this references](https://docs.airbyte.com/integrations/sources/gitlab#reference).\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"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "-2pgyG5aMGq0"
},
"outputs": [],
"source": [
"import airbyte as ab\n",
"\n",
"source = ab.get_source(\n",
" \"source-gitlab\",\n",
" config={\n",
" \"credentials\":{\n",
" \"auth_type\":\"access_token\",\n",
" \"access_token\": ab.get_secret(\"GITLAB_ACCESS_TOKEN\"),\n",
" },\n",
" \"projects\" :ab.get_secret(\"GITLAB_PROJECT\")\n",
" }\n",
")\n",
"source.check()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "_Phdo7l_MGq2"
},
"outputs": [],
"source": [
"# In this notebook we are focused on only issues stream\n",
"# checkout all stream here : https://docs.airbyte.com/integrations/sources/gitlab#supported-streams\n",
"\n",
"source.get_available_streams()\n",
"source.select_streams([\"issues\"])\n",
"cache = ab.get_default_cache()\n",
"result = source.read(cache=cache)\n",
"\n",
"issues_details = [doc for doc in result[\"issues\"].to_documents()] # Fetching data for issues stream only\n",
"\n",
"print(str(issues_details[10]))"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "jJ5Na_O2Sn1U"
},
"source": [
"# Use Langchain to build a RAG pipeline.\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."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "wravAgJhMGq3"
},
"outputs": [],
"source": [
"from langchain.text_splitter import RecursiveCharacterTextSplitter\n",
"splitter = RecursiveCharacterTextSplitter(chunk_size=512, chunk_overlap=50)\n",
"chunked_docs = splitter.split_documents(issues_details)\n",
"print(f\"Created {len(chunked_docs)} document chunks.\")\n",
"\n",
"for doc in chunked_docs:\n",
" for md in doc.metadata:\n",
" doc.metadata[md] = str(doc.metadata[md])"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"id": "tcXR48fsMGq4"
},
"outputs": [],
"source": [
"from langchain_openai import OpenAIEmbeddings\n",
"import os\n",
"\n",
"## Embedding Technique Of OPENAI\n",
"os.environ['OPENAI_API_KEY'] = ab.get_secret(\"OPENAI_API_KEY\")\n",
"embeddings=OpenAIEmbeddings()\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "Mh_lGwiJUkLg"
},
"source": [
"## Setting up Qdrant\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"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "uZXgQF-xMGq4"
},
"outputs": [],
"source": [
"from qdrant_client import QdrantClient, models\n",
"\n",
"client = QdrantClient(\n",
" location=ab.get_secret(\"QDRANT_URL\"), # As obtain above\n",
" api_key=ab.get_secret(\"QDRANT_API_KEY\"),\n",
")\n",
"\n",
"collection_name = \"gitlab_issue\" # Give collection a name\n",
"client.create_collection(\n",
" collection_name=collection_name,\n",
" vectors_config=models.VectorParams(\n",
" size=1536, # vector dimensions\n",
" distance=models.Distance.COSINE,\n",
" ),\n",
")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "nU9KjhMHMGq4"
},
"outputs": [],
"source": [
"from langchain.vectorstores.qdrant import Qdrant\n",
"\n",
"qdrant = Qdrant(\n",
" client=client,\n",
" collection_name=collection_name,\n",
" embeddings=embeddings,\n",
")\n",
"\n",
"qdrant.add_documents(chunked_docs, batch_size=20)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "8zwaoOteU7oi"
},
"source": [
"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."
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"id": "y2e-raMYMGq4",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "418f679a-dafe-4f21-d443-54e73e8c1fde"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Langchain RAG pipeline set up successfully.\n"
]
}
],
"source": [
"from langchain_openai import ChatOpenAI\n",
"from langchain import hub\n",
"from langchain_core.output_parsers import StrOutputParser\n",
"from langchain_core.runnables import RunnablePassthrough\n",
"\n",
"retriever = qdrant.as_retriever()\n",
"prompt = hub.pull(\"rlm/rag-prompt\")\n",
"llm = ChatOpenAI(model_name=\"gpt-3.5-turbo\", temperature=0)\n",
"\n",
"\n",
"def format_docs(docs):\n",
" return \"\\n\\n\".join(doc.page_content for doc in docs)\n",
"\n",
"rag_chain = (\n",
" {\"context\": retriever | format_docs, \"question\": RunnablePassthrough()}\n",
" | prompt\n",
" | llm\n",
" | StrOutputParser()\n",
")\n",
"print(\"Langchain RAG pipeline set up successfully.\")"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"id": "Ihbo8bllMGq4",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "feec51cc-2cfd-4634-e075-baa571b52b5f"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"The programming languages mentioned in the context are Java and JavaScript.\n"
]
}
],
"source": [
"print(rag_chain.invoke(\"Which programing languages are mentioned in issues most?\"))"
]
}
],
"metadata": {
"colab": {
"provenance": [],
"toc_visible": true
},
"kernelspec": {
"display_name": "myenv",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.11"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
================================================
FILE: pyairbyte_notebooks/rag_using_jira_pyairbyte_pinecone.ipynb
================================================
{
"cells": [
{
"cell_type": "markdown",
"id": "82f45520-6a6d-49b2-9dfd-ea33231e7b39",
"metadata": {},
"source": [
"# End-to-End RAG Tutorial Using Jira, PyAirbyte, Pinecone, and LangChain\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",
"## Prerequisites\n",
"\n",
"1. **Jira**:\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",
"2. **Pinecone Account**:\n",
" - **Create a Pinecone Account**: Sign up for an account on the [Pinecone website](https://www.pinecone.io/).\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",
"3. **OpenAI API Key**:\n",
" - **Create an OpenAI Account**: Sign up for an account on [OpenAI](https://www.openai.com/).\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"
]
},
{
"cell_type": "markdown",
"id": "318e598e-10b5-4fd3-891f-e316cbc4a341",
"metadata": {},
"source": [
"## Install PyAirbyte and other dependencies"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "3954735e-6c45-4d15-b2a2-4fc705c935b7",
"metadata": {},
"outputs": [],
"source": [
"!pip3 install airbyte openai langchain pinecone-client langchain-openai langchain-pinecone langchainhub "
]
},
{
"cell_type": "markdown",
"id": "b5f0ec02-f31c-44ff-ac7e-49af6f608c9e",
"metadata": {},
"source": [
"# Setup Source Jira with PyAirbyte\n",
"\n",
"The provided code configures an Airbyte source to extract issues data from jira data\n",
"\n",
"To configure according to your requirements, you can refer to [this references](https://docs.airbyte.com/integrations/sources/jira#reference).\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"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "936c79e8-2eff-4d4a-ae51-74d8f14c7005",
"metadata": {},
"outputs": [],
"source": [
"import airbyte as ab\n",
"import json\n",
"\n",
"projects = json.loads(ab.get_secret('projects_list'))\n",
"\n",
"source = ab.get_source(\n",
" \"source-jira\",\n",
" install_if_missing=True,\n",
" config={\n",
" \"api_token\": ab.get_secret('jira_api_token'),\n",
" \"domain\": ab.get_secret('jira_domain') ,\n",
" \"email\": ab.get_secret('jira_email_id'),\n",
" \"start_date\": \"2021-01-01T00:00:00Z\", # optional field, can be ignored \n",
" \"projects\": projects\n",
" },\n",
"\n",
")\n",
"\n",
"# Verify the config and creds by running `check`:\n",
"source.check()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "89205323-0676-4869-bcf6-fbbb31333a5a",
"metadata": {},
"outputs": [],
"source": [
"source.select_streams(['issues']) # Select only issues stream\n",
"read_result: ab.ReadResult = source.read()\n",
"documents_list = []\n",
"\n",
"for key, value in read_result.items():\n",
" docs = value.to_documents()\n",
" for doc in docs:\n",
" documents_list.append(doc)\n",
"\n",
"print(str(documents_list))"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "6c76d164-8894-41b6-aee6-a3de0a70aa26",
"metadata": {},
"outputs": [],
"source": [
"# store and display the issues stream in data frame\n",
"issues_df = read_result[\"issues\"].to_pandas()\n",
"display(issues_df)"
]
},
{
"cell_type": "markdown",
"id": "770f07e7-9dd6-4bd3-b180-fc322eb1e209",
"metadata": {},
"source": [
"## Use Langchain to build a RAG pipeline."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "8194e7ba-4aef-4fdf-afef-93a5dee0e36c",
"metadata": {},
"outputs": [],
"source": [
"from langchain.text_splitter import RecursiveCharacterTextSplitter\n",
"from langchain.vectorstores.utils import filter_complex_metadata\n",
"\n",
"\n",
"\n",
"splitter = RecursiveCharacterTextSplitter(chunk_size=512, chunk_overlap=50)\n",
"chunked_docs = splitter.split_documents(documents_list)\n",
"chunked_docs = filter_complex_metadata(chunked_docs)\n",
"print(f\"Created {len(chunked_docs)} document chunks.\")\n",
"\n",
"for doc in chunked_docs:\n",
" for md in doc.metadata:\n",
" doc.metadata[md] = str(doc.metadata[md])"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "01eeea04-f774-4a6b-840a-999aed9f80ec",
"metadata": {},
"outputs": [],
"source": [
"from langchain_openai import OpenAIEmbeddings\n",
"import os\n",
"\n",
"os.environ['OPENAI_API_KEY'] = ab.get_secret(\"OPENAI_API_KEY\")\n",
"\n",
"embeddings=OpenAIEmbeddings()"
]
},
{
"cell_type": "markdown",
"id": "6ac9b188-2972-42b4-a9bb-7d752b0eeaf3",
"metadata": {},
"source": [
"## Setting up Pinecone\n",
"\n",
"Pinecone is a managed vector database service designed for storing, indexing, and querying high-dimensional vector data efficiently."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "a1f11593-3ea2-4762-ad23-e21c87d44ece",
"metadata": {},
"outputs": [],
"source": [
"from pinecone import Pinecone, ServerlessSpec\n",
"os.environ['PINECONE_API_KEY'] = ab.get_secret(\"PINECONE_API_KEY\")\n",
"\n",
"index_name = \"airbytejiraindex\"\n",
"\n",
"pc = Pinecone()\n",
"\n",
"# Create pinecone index if not exists otherwise skip this step\n",
"if not (pc.list_indexes()[0]['name'] == index_name):\n",
" pc.create_index(\n",
" name=index_name,\n",
" dimension=1536,\n",
" metric=\"cosine\",\n",
" spec=ServerlessSpec(\n",
" cloud=\"aws\",\n",
" region=\"us-east-1\"\n",
" )\n",
" )\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "de7efdc6-4363-4c3e-bd6e-86b955f2e505",
"metadata": {},
"outputs": [],
"source": [
"index = pc.Index(index_name)\n",
"\n",
"index.describe_index_stats()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "98bdb85f-be90-47b0-a778-9232c81427c1",
"metadata": {},
"outputs": [],
"source": [
"from langchain_pinecone import PineconeVectorStore\n",
"\n",
"pinecone = PineconeVectorStore.from_documents(\n",
" chunked_docs, embedding=embeddings, index_name=index_name\n",
")"
]
},
{
"cell_type": "markdown",
"id": "6f5d31fa-6f88-4755-9405-6f7c84495607",
"metadata": {},
"source": [
"## RAG"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "beb7f799-3558-49f4-8c20-2a45ac61c034",
"metadata": {},
"outputs": [],
"source": [
"from langchain_openai import ChatOpenAI\n",
"from langchain import hub\n",
"from langchain_core.output_parsers import StrOutputParser\n",
"from langchain_core.runnables import RunnablePassthrough\n",
"\n",
"retriever = pinecone.as_retriever()\n",
"prompt = hub.pull(\"rlm/rag-prompt\")\n",
"\n",
"os.environ['OPENAI_API_KEY'] = ab.get_secret(\"OPENAI_API_KEY\")\n",
"\n",
"llm = ChatOpenAI(model_name=\"gpt-3.5-turbo\")\n",
"\n",
"\n",
"\n",
"def format_docs(docs):\n",
" return \"\\n\\n\".join(doc.page_content for doc in docs)\n",
"\n",
"rag_chain = (\n",
" {\"context\": retriever | format_docs, \"question\": RunnablePassthrough()}\n",
" | prompt\n",
" | llm\n",
" | StrOutputParser()\n",
")\n",
"print(\"Langchain RAG pipeline set up successfully.\")\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "ae3dd498-74a3-4868-8529-bdad7d949d46",
"metadata": {},
"outputs": [],
"source": [
"print(rag_chain.invoke(\"Summarize the issue of key IT-20\"))"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "f62b7761-c463-430d-8150-3e816897cad9",
"metadata": {},
"outputs": [],
"source": [
"print(rag_chain.invoke(\"What is the source data about?\"))"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.19"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
================================================
FILE: pyairbyte_notebooks/rag_using_s3_pyairbyte_pinecone.ipynb
================================================
{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "3s3Jow7_BjQi"
},
"source": [
"# End-to-End RAG Tutorial Using S3, PyAirbyte, Pinecone, and LangChain\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",
"## Prerequisites\n",
"\n",
"1. **AWS S3 Bucket**:\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",
"2. **Pinecone Account**:\n",
" - **Create a Pinecone Account**: Sign up for an account on the [Pinecone website](https://www.pinecone.io/).\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",
"3. **OpenAI API Key**:\n",
" - **Create an OpenAI Account**: Sign up for an account on [OpenAI](https://platform.openai.com/docs/overview).\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",
"## Install PyAirbyte and other dependencies"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "ij3THvimBjQk"
},
"outputs": [],
"source": [
"# Add virtual environment support in Google Colab\n",
"!apt-get install -qq python3.10-venv\n",
"\n",
"# First, we need to install the necessary libraries.\n",
"!pip3 install airbyte openai langchain pinecone-client langchain-openai langchain-pinecone python-dotenv langchainhub"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "8jDsEZdiBjQl"
},
"source": [
"## Setup Source S3 with PyAirbyte\n",
"\n",
"The provided code configures an Airbyte source to extract data from an Amazon S3 bucket containing CSV files.\n",
"\n",
"To configure according to your requirements, you can refer to [this references](https://docs.airbyte.com/integrations/sources/s3#reference).\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"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "U7DxyLVUBjQl"
},
"outputs": [],
"source": [
"import airbyte as ab\n",
"\n",
"source = ab.get_source(\n",
" \"source-s3\",\n",
" config={\n",
" \"streams\": [\n",
" {\n",
" \"name\": \"\",\n",
" \"format\": {\n",
" \"filetype\": \"csv\",\n",
" \"ignore_errors_on_fields_mismatch\": True,\n",
" },\n",
" \"globs\": [\"**\"],\n",
" \"legacy_prefix\": \"\",\n",
" \"validation_policy\": \"Emit Record\",\n",
" }\n",
" ],\n",
" \"bucket\": ab.get_secret(\"S3_BUCKET_NAME\"),\n",
" \"aws_access_key_id\": ab.get_secret(\"AWS_ACCESS_KEY\"),\n",
" \"aws_secret_access_key\": ab.get_secret(\"AWS_SECRET_KEY\"),\n",
" \"region_name\": ab.get_secret(\"AWS_REGION\")\n",
" }\n",
")\n",
"source.check()"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "rtSCjGcQBjQl"
},
"source": [
"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."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "mlg7K8GUBjQm"
},
"outputs": [],
"source": [
"source.select_all_streams() # Select all streams\n",
"read_result = source.read() # Read the data\n",
"documents_list = [doc for value in read_result.values() for doc in value.to_documents()]\n",
"\n",
"print(str(documents_list[10]))"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "L6KM7KPMBjQm"
},
"source": [
"# Use Langchain to build a RAG pipeline.\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."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "LXvSJoUSBjQm"
},
"outputs": [],
"source": [
"\n",
"from langchain.text_splitter import RecursiveCharacterTextSplitter\n",
"\n",
"splitter = RecursiveCharacterTextSplitter(chunk_size=512, chunk_overlap=50)\n",
"chunked_docs = splitter.split_documents(documents_list)\n",
"print(f\"Created {len(chunked_docs)} document chunks.\")\n",
"\n",
"for doc in chunked_docs:\n",
" for md in doc.metadata:\n",
" doc.metadata[md] = str(doc.metadata[md])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "kCF7gZTMBjQm"
},
"outputs": [],
"source": [
"from langchain_openai import OpenAIEmbeddings\n",
"import os\n",
"\n",
"os.environ['OPENAI_API_KEY'] = ab.get_secret(\"OPENAI_API_KEY\")\n",
"## Embedding Technique Of OPENAI\n",
"embeddings=OpenAIEmbeddings()"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "5TKCIAhYBjQm"
},
"source": [
"## Setting up Pinecone\n",
"\n",
"Pinecone is a managed vector database service designed for storing, indexing, and querying high-dimensional vector data efficiently."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "iXb5YPnhBjQn"
},
"outputs": [],
"source": [
"from pinecone import Pinecone, ServerlessSpec\n",
"from pinecone import Pinecone\n",
"\n",
"os.environ['PINECONE_API_KEY'] = ab.get_secret(\"PINECONE_API_KEY\")\n",
"pc = Pinecone()\n",
"index_name = \"s3-quickstarts-index\" # Replace with your index name\n",
"\n",
"\n",
"# Uncomment this if you have not created a Pinecone index yet\n",
"\n",
"# spec = ServerlessSpec(cloud=\"aws\", region=\"us-east-1\") // Replace with your cloud and region\n",
"# pc.create_index(\n",
"# \"quickstarts\",\n",
"# dimension=1536, // Replace with your model dimensions\n",
"# metric='cosine', // Replace with your model metric\n",
"# spec=spec\n",
"# )\n",
"\n",
"index = pc.Index(index_name)\n",
"\n",
"index.describe_index_stats()"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "6kRv6s7zBjQn"
},
"source": [
"PineconeVectorStore is a class provided by the LangChain library specifically designed for interacting with Pinecone vector stores.\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."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "3hToKOPsBjQn"
},
"outputs": [],
"source": [
"from langchain_pinecone import PineconeVectorStore\n",
"\n",
"pinecone = PineconeVectorStore.from_documents(\n",
" chunked_docs, embeddings, index_name=index_name\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "vj0hSWo2BjQn"
},
"source": [
"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."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "N0gE_LbmBjQn"
},
"outputs": [],
"source": [
"from langchain_openai import ChatOpenAI\n",
"from langchain import hub\n",
"from langchain_core.output_parsers import StrOutputParser\n",
"from langchain_core.runnables import RunnablePassthrough\n",
"\n",
"retriever = pinecone.as_retriever()\n",
"prompt = hub.pull(\"rlm/rag-prompt\")\n",
"llm = ChatOpenAI(model_name=\"gpt-3.5-turbo\", temperature=0)\n",
"\n",
"\n",
"def format_docs(docs):\n",
" return \"\\n\\n\".join(doc.page_content for doc in docs)\n",
"\n",
"rag_chain = (\n",
" {\"context\": retriever | format_docs, \"question\": RunnablePassthrough()}\n",
" | prompt\n",
" | llm\n",
" | StrOutputParser()\n",
")\n",
"print(\"Langchain RAG pipeline set up successfully.\")\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "axbwi9j8BjQn"
},
"outputs": [],
"source": [
"print(rag_chain.invoke(\"What are some best documentaries to watch?\"))\n"
]
}
],
"metadata": {
"colab": {
"provenance": []
},
"kernelspec": {
"display_name": "myenv",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.11"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
================================================
FILE: pyairbyte_notebooks/rag_using_shopify_pyairbyte_langchain.ipynb
================================================
{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "3s3Jow7_BjQi"
},
"source": [
"# End-to-End RAG Tutorial Using Shopify, PyAirbyte, Pinecone, and LangChain\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",
"## Prerequisites\n",
"\n",
"1. **Shopify**:\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",
"2. **Pinecone Account**:\n",
" - **Create a Pinecone Account**: Sign up for an account on the [Pinecone website](https://www.pinecone.io/).\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",
"3. **OpenAI API Key**:\n",
" - **Create an OpenAI Account**: Sign up for an acco\n",
" unt on [OpenAI](https://www.openai.com/).\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",
"## Install PyAirbyte and other dependencies"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "XclQfDX9MQsw"
},
"outputs": [],
"source": [
"# Add virtual environment support for running in Google Colab\n",
"!apt-get install -qq python3.10-venv\n",
"\n",
"# First, we need to install the necessary libraries.\n",
"!pip3 install airbyte openai langchain pinecone-client langchain-openai langchain-pinecone python-dotenv langchainhub"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "Hzira-9BQq0h"
},
"source": [
"## Setup Source Shopify with PyAirbyte\n",
"\n",
"The provided code configures an Airbyte source to extract data from a Shopify store.\n",
"\n",
"To configure according to your requirements, you can refer to [this references](https://docs.airbyte.com/integrations/sources/shopify#reference).\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"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "-2pgyG5aMGq0",
"outputId": "f20c47bf-1dcb-42bd-ddbb-a7b1ab939afe"
},
"outputs": [],
"source": [
"import airbyte as ab\n",
"\n",
"source = ab.get_source(\n",
" \"source-shopify\",\n",
" config={\n",
"\n",
" \"credentials\":{\n",
" # There are two methods available for authentication 'api_password' and 'oauth2.0',\n",
" # Choose one of them (https://docs.airbyte.com/integrations/sources/shopify#airbyte-open-source)\n",
" \"auth_method\": \"api_password\",\n",
" \"api_password\": ab.get_secret(\"API_PASSWORD\")\n",
" },\n",
" \"shop\":ab.get_secret(\"STORE_NAME\"),\n",
" \"start_date\": ab.get_secret(\"START_DATE\"),\n",
" \"bulk_window_in_days\": 30, # change this according to your requirement (Defines what would be a date range per single BULK Job)\n",
" \"fetch_transactions_user_id\": False\n",
" }\n",
")\n",
"source.check()"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "GO0QI-qNRNyz"
},
"source": [
"This is a basic process of fetching data from an using Airbyte and converting it into a format suitable for further processing or analysis."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "_Phdo7l_MGq2"
},
"outputs": [],
"source": [
"# List the available streams available for the Shopify source\n",
"# Available Stream for shopify here(https://docs.airbyte.com/integrations/sources/shopify#supported-streams)\n",
"source.get_available_streams()\n",
"# Select the streams we are interested in loading to cache\n",
"source.select_streams([\"products\", \"product_variants\", \"collections\", \"customers\"])\n",
"cache = ab.get_default_cache()\n",
"result = source.read(cache=cache)\n",
"\n",
"product_details = [doc for doc in result[\"products\"].to_documents()] # Fetching data for products stream only\n",
"\n",
"print(str(product_details[10]))"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "jJ5Na_O2Sn1U"
},
"source": [
"# Use Langchain to build a RAG pipeline.\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."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "wravAgJhMGq3"
},
"outputs": [],
"source": [
"from langchain.text_splitter import RecursiveCharacterTextSplitter\n",
"splitter = RecursiveCharacterTextSplitter(chunk_size=512, chunk_overlap=50)\n",
"chunked_docs = splitter.split_documents(product_details)\n",
"print(f\"Created {len(chunked_docs)} document chunks.\")\n",
"\n",
"for doc in chunked_docs:\n",
" for md in doc.metadata:\n",
" doc.metadata[md] = str(doc.metadata[md])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "tcXR48fsMGq4"
},
"outputs": [],
"source": [
"from langchain_openai import OpenAIEmbeddings\n",
"\n",
"## Embedding Technique Of OPENAI\n",
"os.environ['OPENAI_API_KEY'] = ab.get_secret(\"OPENAI_API_KEY\")\n",
"embeddings=OpenAIEmbeddings()\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "Mh_lGwiJUkLg"
},
"source": [
"## Setting up Pinecone\n",
"\n",
"Pinecone is a managed vector database service designed for storing, indexing, and querying high-dimensional vector data efficiently.\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "uZXgQF-xMGq4"
},
"outputs": [],
"source": [
"from pinecone import Pinecone, ServerlessSpec\n",
"from pinecone import Pinecone\n",
"import os\n",
"\n",
"os.environ['PINECONE_API_KEY'] = ab.get_secret(\"PINECONE_API_KEY\")\n",
"pc = Pinecone()\n",
"index_name = \"shopifyproductsindex\" # Replace with your index name\n",
"\n",
"\n",
"# Uncomment this if you have not created a Pinecone index yet\n",
"\n",
"# spec = ServerlessSpec(cloud=\"aws\", region=\"us-east-1\") #Replace with your cloud and region\n",
"# pc.create_index(\n",
"# name=index_name,\n",
"# dimension=1536,\n",
"# metric='cosine',\n",
"# spec=spec\n",
"# )\n",
"\n",
"index = pc.Index(index_name)\n",
"\n",
"index.describe_index_stats()"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "evdVEEsTUwhE"
},
"source": [
"PineconeVectorStore is a class provided by the LangChain library specifically designed for interacting with Pinecone vector stores.\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."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "nU9KjhMHMGq4"
},
"outputs": [],
"source": [
"from langchain_pinecone import PineconeVectorStore\n",
"\n",
"pinecone = PineconeVectorStore.from_documents(\n",
" chunked_docs, embeddings, index_name=index_name\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "8zwaoOteU7oi"
},
"source": [
"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."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "y2e-raMYMGq4"
},
"outputs": [],
"source": [
"from langchain_openai import ChatOpenAI\n",
"from langchain import hub\n",
"from langchain_core.output_parsers import StrOutputParser\n",
"from langchain_core.runnables import RunnablePassthrough\n",
"\n",
"retriever = pinecone.as_retriever()\n",
"prompt = hub.pull(\"rlm/rag-prompt\")\n",
"llm = ChatOpenAI(model_name=\"gpt-3.5-turbo\", temperature=0)\n",
"\n",
"\n",
"def format_docs(docs):\n",
" return \"\\n\\n\".join(doc.page_content for doc in docs)\n",
"\n",
"rag_chain = (\n",
" {\"context\": retriever | format_docs, \"question\": RunnablePassthrough()}\n",
" | prompt\n",
" | llm\n",
" | StrOutputParser()\n",
")\n",
"print(\"Langchain RAG pipeline set up successfully.\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "Ihbo8bllMGq4"
},
"outputs": [],
"source": [
"print(rag_chain.invoke(\"What type of products do we sell?\"))"
]
}
],
"metadata": {
"colab": {
"provenance": []
},
"kernelspec": {
"display_name": "myenv",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.11"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
================================================
FILE: pyairbyte_notebooks/rag_with_fb_marketing_milvus_lite.ipynb
================================================
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This notebook demonstrates simple RAG (Retrieval-Augmented Generation) pipeline with Facebook Marketing, Milvus Lite and PyAirbyte."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Prerequisites\n",
"- [PyAirbyte](https://airbyte.com/product/pyairbyte)\n",
" \n",
" PyAirbyte is an open-source that packages Airbyte connectors and makes them available in Python. We will connect to \n",
" `source-facebook-marketing`, and retrive its streams\n",
"- [Milvus Lite](https://milvus.io/docs/milvus_lite.md)\n",
" \n",
" Milvus Lite is the lightweight version of [Milvus](https://github.com/milvus-io/milvus) that enables vector emdeddings and similarity search\n",
" into the Python application.\n",
"- OpenAI API Key\n",
" \n",
" Go to the [API Keys page](https://platform.openai.com/api-keys) to create the new secret key."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 1. Install and set dependencies"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"%pip install airbyte pymilvus openai milvus-model"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"import json\n",
"import os\n",
"import airbyte as ab\n",
"from openai import OpenAI\n",
"from pymilvus import MilvusClient\n",
"\n",
"\n",
"# in production, you might want to avoid putting the key here.\n",
"os.environ[\"OPENAI_API_KEY\"] = \"sk-proj-****\"\n",
"openai_client = OpenAI()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 2. Set the source"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Connect `source-facebook-marketing` to fetch streams"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"source = ab.get_source(\n",
" \"source-facebook-marketing\",\n",
" config={\n",
" \"start_date\": \"2024-06-01T00:00:00Z\",\n",
" \"account_id\": \"account\",\n",
" \"access_token\": \"token\"\n",
" },\n",
" install_if_missing=True,\n",
")\n",
"source.check()\n",
"source.select_all_streams()\n",
"result = source.read()\n",
"\n",
"for name, records in result.streams.items():\n",
" print(f\"Streams of {name} has {len(records)} records.\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 3. Milvus Lite & Text Embedding"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"{\"type\": \"DEBUG\", \"message\": \"Created new connection using: 5a7d992d451b41db831d254213b64892\", \"data\": {}}\n"
]
}
],
"source": [
"milvus_client = MilvusClient(\"./milvus_source_fake.db\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"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."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's focused to get the `products` data. We will keep it simple by just getting the relevant fields:"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"data = []\n",
"for record in result.streams[\"products\"]:\n",
" make = record[\"make\"]\n",
" model = record[\"model\"]\n",
" year = record[\"year\"]\n",
"\n",
" text = f\"{make} {model} {year}\"\n",
" data.append(text)\n"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"['Mazda MX-5 2023',\n",
" 'Mercedes-Benz C-Class 2023',\n",
" 'Honda Accord Crosstour 2023',\n",
" 'GMC Jimmy 2023',\n",
" 'Infiniti FX 2023']"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data[:5]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from pymilvus import model\n",
"\n",
"openai_ef = model.dense.OpenAIEmbeddingFunction(\n",
" model_name='text-embedding-3-large', # Specify the model name\n",
" api_key=os.environ[\"OPENAI_API_KEY\"], # Provide your OpenAI API key\n",
" dimensions=512 # Set the embedding dimensionality\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
"embedded_data = openai_ef.encode_documents(data)"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"{\"type\": \"DEBUG\", \"message\": \"Successfully created collection: products\", \"data\": {}}\n",
"{\"type\": \"DEBUG\", \"message\": \"Successfully created an index on collection: products\", \"data\": {}}\n"
]
}
],
"source": [
"milvus_client.create_collection(\"products\", dimension=512)"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [],
"source": [
"embedded_docs = []\n",
"for _id, embedded_text in enumerate(embedded_data):\n",
" embedded_docs.append({\"id\": _id+1, \"vector\": embedded_text, \"text\": data[_id]})"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'insert_count': 50,\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",
" 'cost': 0}"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"milvus_client.insert(collection_name=\"products\", data=embedded_docs)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 4. Inspect the search results"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [],
"source": [
"question = \"Give list of products from Suzuki\""
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [],
"source": [
"search_res = milvus_client.search(\n",
" collection_name=\"products\",\n",
" data=[\n",
" openai_ef.encode_documents([question])[0]\n",
" ], # Use the `emb_text` function to convert the question to an embedding vector\n",
" limit=3, # Return top 3 results\n",
" search_params={\"metric_type\": \"COSINE\", \"params\": {}}, # Inner product distance\n",
" output_fields=[\"text\"], # Return the text field\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[\n",
" [\n",
" \"Suzuki SJ 410 2023\",\n",
" 0.5219288468360901\n",
" ],\n",
" [\n",
" \"Isuzu VehiCROSS 2023\",\n",
" 0.38782158493995667\n",
" ],\n",
" [\n",
" \"Jaguar S-Type 2023\",\n",
" 0.35628464818000793\n",
" ]\n",
"]\n"
]
}
],
"source": [
"retrieved_lines_with_distances = [\n",
" (res[\"entity\"][\"text\"], res[\"distance\"]) for res in search_res[0]\n",
"]\n",
"print(json.dumps(retrieved_lines_with_distances, indent=4))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 5. Use OpenAI ChatGPT to get the RAG response"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's define the system and user prompts for the Language Model. This prompt is assembled with the retrieved documents from Milvus.\n",
"\n",
"We also use OpenAI ChatGPT to generate a response based on the prompts."
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [],
"source": [
"context = \"\\n\".join(\n",
" [line_with_distance[0] for line_with_distance in retrieved_lines_with_distances]\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [],
"source": [
"SYSTEM_PROMPT = \"\"\"\n",
"Human: You are an AI assistant. You are able to find answers to the questions from the contextual passage snippets provided.\n",
"\"\"\"\n",
"USER_PROMPT = f\"\"\"\n",
"Use the following pieces of information enclosed in Completed `source-faker` read operation at 15:19:14.\n",
"\n"
],
"text/plain": [
"Completed `source-faker` read operation at \u001b[1;92m15:19:14\u001b[0m.\n"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Streams of products has 50 records.\n",
"Streams of users has 50 records.\n",
"Streams of purchases has 50 records.\n"
]
}
],
"source": [
"source = ab.get_source(\n",
" \"source-faker\",\n",
" config={\"count\": 5_0},\n",
" install_if_missing=True,\n",
")\n",
"source.check()\n",
"source.select_all_streams()\n",
"result = source.read()\n",
"\n",
"for name, records in result.streams.items():\n",
" print(f\"Streams of {name} has {len(records)} records.\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Here we can see there are streams of `products`, `users`, and `purchases`. All of them has 50 records."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 3. Milvus Lite & Text Embedding"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"{\"type\": \"DEBUG\", \"message\": \"Created new connection using: 5a7d992d451b41db831d254213b64892\", \"data\": {}}\n"
]
}
],
"source": [
"milvus_client = MilvusClient(\"./milvus_source_fake.db\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"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."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's focused to get the `products` data. We will keep it simple by just getting the relevant fields:"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"data = []\n",
"for record in result.streams[\"products\"]:\n",
" make = record[\"make\"]\n",
" model = record[\"model\"]\n",
" year = record[\"year\"]\n",
"\n",
" text = f\"{make} {model} {year}\"\n",
" data.append(text)\n"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"['Mazda MX-5 2023',\n",
" 'Mercedes-Benz C-Class 2023',\n",
" 'Honda Accord Crosstour 2023',\n",
" 'GMC Jimmy 2023',\n",
" 'Infiniti FX 2023']"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data[:5]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from pymilvus import model\n",
"\n",
"openai_ef = model.dense.OpenAIEmbeddingFunction(\n",
" model_name='text-embedding-3-large', # Specify the model name\n",
" api_key=os.environ[\"OPENAI_API_KEY\"], # Provide your OpenAI API key\n",
" dimensions=512 # Set the embedding dimensionality\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
"embedded_data = openai_ef.encode_documents(data)"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"{\"type\": \"DEBUG\", \"message\": \"Successfully created collection: products\", \"data\": {}}\n",
"{\"type\": \"DEBUG\", \"message\": \"Successfully created an index on collection: products\", \"data\": {}}\n"
]
}
],
"source": [
"milvus_client.create_collection(\"products\", dimension=512)"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [],
"source": [
"embedded_docs = []\n",
"for _id, embedded_text in enumerate(embedded_data):\n",
" embedded_docs.append({\"id\": _id+1, \"vector\": embedded_text, \"text\": data[_id]})"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'insert_count': 50,\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",
" 'cost': 0}"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"milvus_client.insert(collection_name=\"products\", data=embedded_docs)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 4. Inspect the search results"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [],
"source": [
"question = \"Give list of products from Suzuki\""
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [],
"source": [
"search_res = milvus_client.search(\n",
" collection_name=\"products\",\n",
" data=[\n",
" openai_ef.encode_documents([question])[0]\n",
" ], # Use the `emb_text` function to convert the question to an embedding vector\n",
" limit=3, # Return top 3 results\n",
" search_params={\"metric_type\": \"COSINE\", \"params\": {}}, # Inner product distance\n",
" output_fields=[\"text\"], # Return the text field\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[\n",
" [\n",
" \"Suzuki SJ 410 2023\",\n",
" 0.5219288468360901\n",
" ],\n",
" [\n",
" \"Isuzu VehiCROSS 2023\",\n",
" 0.38782158493995667\n",
" ],\n",
" [\n",
" \"Jaguar S-Type 2023\",\n",
" 0.35628464818000793\n",
" ]\n",
"]\n"
]
}
],
"source": [
"retrieved_lines_with_distances = [\n",
" (res[\"entity\"][\"text\"], res[\"distance\"]) for res in search_res[0]\n",
"]\n",
"print(json.dumps(retrieved_lines_with_distances, indent=4))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 5. Use OpenAI ChatGPT to get the RAG response"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's define the system and user prompts for the Language Model. This prompt is assembled with the retrieved documents from Milvus.\n",
"\n",
"We also use OpenAI ChatGPT to generate a response based on the prompts."
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [],
"source": [
"context = \"\\n\".join(\n",
" [line_with_distance[0] for line_with_distance in retrieved_lines_with_distances]\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [],
"source": [
"SYSTEM_PROMPT = \"\"\"\n",
"Human: You are an AI assistant. You are able to find answers to the questions from the contextual passage snippets provided.\n",
"\"\"\"\n",
"USER_PROMPT = f\"\"\"\n",
"Use the following pieces of information enclosed in \n",
"
\n"
],
"application/vnd.google.colaboratory.intrinsic+json": {
"type": "dataframe",
"variable_name": "data_frame",
"summary": "{\n \"name\": \"data_frame\",\n \"rows\": 5,\n \"fields\": [\n {\n \"column\": \"DOCUMENT_ID\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 5,\n \"samples\": [\n \"310610605518759482714931752273988313361\",\n \"320500296319786972506727202798756081031\",\n \"321811899600750519985540060710072527880\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"DOCUMENT_CONTENT\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 5,\n \"samples\": [\n \"\\\"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\\\"\",\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}"
}
},
"metadata": {},
"execution_count": 2
}
]
},
{
"cell_type": "markdown",
"source": [
"## Run Sentiment analysis from Snowflake Cortex Airbyte Destination Records"
],
"metadata": {
"id": "fqXE5ed4GqBD"
}
},
{
"cell_type": "code",
"source": [
"from snowflake import connector\n",
"from IPython.display import display, HTML\n",
"\n",
"def get_db_connection():\n",
" return connector.connect(\n",
" account=userdata.get(\"SNOWFLAKE_HOST\"),\n",
" role=userdata.get(\"SNOWFLAKE_ROLE\"),\n",
" warehouse=userdata.get(\"SNOWFLAKE_WAREHOUSE\"),\n",
" database=userdata.get(\"SNOWFLAKE_DATABASE\"),\n",
" schema=userdata.get(\"SNOWFLAKE_SCHEMA\"),\n",
" user=userdata.get(\"SNOWFLAKE_USERNAME\"),\n",
" password=userdata.get(\"SNOWFLAKE_PASSWORD\"),\n",
" )\n",
"\n",
"\n",
"def get_sentiment_analysis_from_snowflake(table_name):\n",
" conn = get_db_connection()\n",
" cur = conn.cursor()\n",
"\n",
"\n",
" query = f\"\"\"\n",
" SELECT\n",
" DOCUMENT_CONTENT,\n",
" CASE\n",
" WHEN SENTIMENT_SCORE > 0 THEN 'Positive'\n",
" WHEN SENTIMENT_SCORE < 0 THEN 'Negative'\n",
" ELSE 'Neutral'\n",
" END AS SENTIMENT\n",
" FROM (\n",
" SELECT\n",
" SNOWFLAKE.CORTEX.SENTIMENT(DOCUMENT_CONTENT) AS SENTIMENT_SCORE,\n",
" DOCUMENT_CONTENT\n",
" FROM {table_name}\n",
" LIMIT 5\n",
" );\n",
" \"\"\"\n",
" cur.execute(query)\n",
" result = cur.fetchall()\n",
" col_names = [desc[0] for desc in cur.description]\n",
"\n",
" df = pd.DataFrame(result,columns=col_names)\n",
"\n",
" return df\n",
"\n",
"\n",
"\n",
"\n",
"response = get_sentiment_analysis_from_snowflake(\"amazon_reviews\")\n",
"\n",
"display(HTML(response.to_html(index=False)))"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 345
},
"id": "yGn9pM7cr_6h",
"outputId": "d69ac85e-bcc6-457d-855e-3a12b57c91ee"
},
"execution_count": 5,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"\n",
"\n",
"\n",
" \n",
"
\n",
"
\n",
" \n",
" | \n", " | DOCUMENT_ID | \n", "DOCUMENT_CONTENT | \n", "
|---|---|---|
| 0 | \n", "147706135155296294706750175152529247047 | \n", "\"reviewerName: 0mie\\noverall: 5\\nreviewText: P... | \n", "
| 1 | \n", "310610605518759482714931752273988313361 | \n", "\"reviewerName: 1K3\\noverall: 4\\nreviewText: it... | \n", "
| 2 | \n", "321811899600750519985540060710072527880 | \n", "\"reviewerName: 1m2\\noverall: 5\\nreviewText: Th... | \n", "
| 3 | \n", "161308189025596278679660362756706325714 | \n", "\"reviewerName: 2&1/2Men\\noverall: 5\\nrevie... | \n", "
| 4 | \n", "320500296319786972506727202798756081031 | \n", "\"reviewerName: 2Cents!\\noverall: 5\\nreviewText... | \n", "
| DOCUMENT_CONTENT | \n", "SENTIMENT | \n", "
|---|---|
| \"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\" | \n", "Negative | \n", "
| \"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\" | \n", "Negative | \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", "Positive | \n", "
| \"reviewerName: 2&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\" | \n", "Negative | \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", "Negative | \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", "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" ], "metadata": { "id": "5eHLkb0FkdXz" } }, { "cell_type": "markdown", "source": [ "# **Prerequisite**\n", "**1) OpenAI API Key**:\n", " - **Create an OpenAI Account**: Sign up for an account on [OpenAI](https://www.openai.com/).\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", "**2) Github Personal Access Token**:\n", " - **Create a Github Account**: Sign up for an account on [Github](https://www.github.com/).\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": { "id": "xwF4ZyZp2ji0" } }, { "cell_type": "markdown", "source": [ "## **Installing Dependencies**\n", "Lets start by installing all the required dependencies!
\n", "First of all we will create a virtual environment and then begin installing the dependencies.\n" ], "metadata": { "id": "sC1_LEcYoU1x" } }, { "cell_type": "code", "source": [ "# Add virtual environment support for running in Google Colab\n", "!apt-get install -qq python3.10-venv\n", "\n", "#Installing the necessary libraries\n", "!pip3.10 install -qU langchain-airbyte faiss-cpu langchain-community langchain-openai" ], "metadata": { "id": "7lwuMXN3ocjE" }, "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "source": [ "## **Load Data using AirbyteLoader**\n", "Now we use `AirbyteLoader` to fetch data from the source `source-github`.
\n", "You may use any other source you require, but fetch the data accordingly!
\n", "Dont forget to add all the required fields!
\n", "Refer the guide for your source [here](https://docs.airbyte.com/integrations/sources/)\n", "\n", "For more information regarding this package [refer](https://python.langchain.com/v0.2/docs/integrations/document_loaders/airbyte/)\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." ], "metadata": { "id": "MdmoHC1Fo7fn" } }, { "cell_type": "code", "source": [ "from langchain_airbyte import AirbyteLoader\n", "from langchain.schema import Document\n", "\n", "# Configure the AirbyteLoader to load data from a GitHub repository\n", "loader = AirbyteLoader(\n", " source=\"source-github\",\n", " stream=\"commits\",\n", " config={\n", " \"credentials\": {\n", " \"personal_access_token\": \"your_personal_access_token\"\n", " },\n", " \"repositories\": [\"your_username/repository_name\"]\n", " }\n", ")\n", "\n", "# Load documents from the specified GitHub source\n", "docs = loader.load()\n", "\n", "# Convert incoming stream data into documents\n", "docs = [Document(page_content=record.page_content, metadata=record.metadata) for record in docs]\n" ], "metadata": { "id": "q-9PC6bzpQMp" }, "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "source": [ "## **Split Documents into Chunks and Store these Chunks in Vector Store using FAISS**\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", "The chunks of documents are transformed into vectors using an embedding model (OpenAI embeddings).
\n", "These vectors are then stored in a FAISS vector store, which allows for efficient similarity search.
\n", "The vector store indexes the vectors and enables fast retrieval of similar vectors based on a query." ], "metadata": { "id": "61JWR7I_p7g5" } }, { "cell_type": "code", "source": [ "from langchain.text_splitter import RecursiveCharacterTextSplitter\n", "\n", "splitter = RecursiveCharacterTextSplitter(chunk_size=512, chunk_overlap=30)\n", "chunked_docs = splitter.split_documents(docs)\n", "\n", "print(f\"Created {len(chunked_docs)} document chunks.\")\n", "\n", "# Store Chunks in Vector Store using FAISS\n", "from langchain_openai import OpenAI, OpenAIEmbeddings\n", "from langchain_community.vectorstores import FAISS\n", "import os\n", "\n", "# Set the OpenAI API Key (make sure to set your own API key here)\n", "os.environ['OPENAI_API_KEY'] = \"YOUR_OPENAI_API_KEY\"\n", "\n", "# Ensure filtered_docs is not empty\n", "if not chunked_docs:\n", " raise ValueError(\"No valid documents to store in the vector store.\")\n", "\n", "# Store document chunks in FAISS vector store\n", "embeddings = OpenAIEmbeddings(openai_api_key=os.getenv(\"OPENAI_API_KEY\"))\n", "vector_store = FAISS.from_texts([doc.page_content for doc in chunked_docs], embeddings)\n", "\n", "print(\"Chunks successfully stored in vectorstore.\")" ], "metadata": { "id": "vqI6yNtGqJC7" }, "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "source": [ "## **Perform QnA on Stored Data**\n", "Finally we perform the Question And Answer here.
\n", "\n", "When a query is made, the vector store retrieves relevant document chunks based on their vector similarity to the query.\n", "The language model (OpenAI) then generates answers based on the retrieved chunks." ], "metadata": { "id": "xUuF_RUvqVZM" } }, { "cell_type": "code", "source": [ "# Step 5: Perform QnA on Stored Data\n", "from langchain.chains.question_answering import load_qa_chain\n", "\n", "# Initialize the LLM (OpenAI)\n", "llm = OpenAI(openai_api_key=os.getenv(\"OPENAI_API_KEY\"))\n", "\n", "# Create a QnA chain\n", "qa_chain = load_qa_chain(llm=llm, chain_type=\"stuff\")\n", "\n", "# Perform a QnA\n", "query = \"What are the latest commits in the repository?\"\n", "inputs = {\"question\": query, \"input_documents\": chunked_docs}\n", "answer = qa_chain.invoke(inputs)\n", "\n", "print(\"QnA Result:\", answer)\n" ], "metadata": { "id": "tHDAkPjYqXDb" }, "execution_count": null, "outputs": [] } ] } ================================================ FILE: satisfaction_analytics_zendesk_support/.gitignore ================================================ # Byte-compiled / optimized / DLL files __pycache__/ *.py[cod] *$py.class # C extensions *.so # Distribution / packaging .Python build/ develop-eggs/ dist/ downloads/ eggs/ .eggs/ lib/ lib64/ parts/ sdist/ var/ wheels/ share/python-wheels/ *.egg-info/ .installed.cfg *.egg MANIFEST # PyInstaller # Usually these files are written by a python script from a template # before PyInstaller builds the exe, so as to inject date/other infos into it. *.manifest *.spec # Installer logs pip-log.txt pip-delete-this-directory.txt # Unit test / coverage reports htmlcov/ .tox/ .nox/ .coverage .coverage.* .cache nosetests.xml coverage.xml *.cover *.py,cover .hypothesis/ .pytest_cache/ cover/ # Translations *.mo *.pot # Django stuff: *.log local_settings.py db.sqlite3 db.sqlite3-journal # Flask stuff: instance/ .webassets-cache # Scrapy stuff: .scrapy # Sphinx documentation docs/_build/ # PyBuilder .pybuilder/ target/ # Jupyter Notebook .ipynb_checkpoints # IPython profile_default/ ipython_config.py # pyenv # For a library or package, you might want to ignore these files since the code is # intended to run in multiple environments; otherwise, check them in: # .python-version # pipenv # According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. # However, in case of collaboration, if having platform-specific dependencies or dependencies # having no cross-platform support, pipenv may install dependencies that don't work, or not # install all needed dependencies. #Pipfile.lock # poetry # Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control. # This is especially recommended for binary packages to ensure reproducibility, and is more # commonly ignored for libraries. # https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control #poetry.lock # pdm # Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control. #pdm.lock # pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it # in version control. # https://pdm.fming.dev/#use-with-ide .pdm.toml # PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm __pypackages__/ # Celery stuff celerybeat-schedule celerybeat.pid # SageMath parsed files *.sage.py # Environments .env .venv env/ venv/ ENV/ env.bak/ venv.bak/ # Spyder project settings .spyderproject .spyproject # Rope project settings .ropeproject # mkdocs documentation /site # mypy .mypy_cache/ .dmypy.json dmypy.json # Pyre type checker .pyre/ # pytype static type analyzer .pytype/ # Cython debug symbols cython_debug/ # PyCharm # JetBrains specific template is maintained in a separate JetBrains.gitignore that can # be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore # and can be added to the global gitignore or merged into this file. For a more nuclear # option (not recommended) you can uncomment the following to ignore the entire idea folder. #.idea/ #Desktop Services Store .DS_Store ================================================ FILE: satisfaction_analytics_zendesk_support/README.md ================================================ # Customer Satisfaction Analytics Stack With Zendesk Support, Airbyte, Dbt, Dagster and BigQuery Welcome 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. This Quickstart is all about making things easy, getting you started quickly and showing you how smoothly all these tools can work together! Below is a visual representation of how data flows through our integrated tools in this Quickstart. This comes from Dagster's global asset lineage view: .svg>) ## Table of Contents - [Prerequisites](#prerequisites) - [Setting an environment for your project](#1-setting-an-environment-for-your-project) - [Setting Up BigQuery to work with Airbyte and dbt](#2-setting-up-bigquery) - [Setting Up Airbyte Connectors with Terraform](#3-setting-up-airbyte-connectors-with-terraform) - [Setting Up the dbt Project](#4-setting-up-the-dbt-project) - [Orchestrating with Dagster](#5-orchestrating-with-dagster) - [Next Steps](#next-steps) ## Prerequisites Before you embark on this integration, ensure you have the following set up and ready: 1. **Python 3.10 or later**: If not installed, download and install it from [Python's official website](https://www.python.org/downloads/). 2. **Docker and Docker Compose (Docker Desktop)**: Install [Docker](https://docs.docker.com/get-docker/) following the official documentation for your specific OS. 3. **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/). 4. **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). 5. **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). ## 1. Setting an environment for your project Get the project up and running on your local machine by following these steps: 1. **Clone the repository (Clone only this quickstart)**: ```bash git clone --filter=blob:none --sparse https://github.com/airbytehq/quickstarts.git ``` ```bash cd quickstarts ``` ```bash git sparse-checkout add satisfaction_analytics_zendesk_support ``` 2. **Navigate to the directory**: ```bash cd satisfaction_analytics_zendesk_support ``` 3. **Set Up a Virtual Environment**: - For Mac: ```bash python3 -m venv venv source venv/bin/activate ``` - For Windows: ```bash python -m venv venv .\venv\Scripts\activate ``` 4. **Install Dependencies**: ```bash pip install -e ".[dev]" ``` ## 2. Setting Up BigQuery #### 1. **Create a Google Cloud Project** - If you have a Google Cloud project, you can skip this step. - Go to the [Google Cloud Console](https://console.cloud.google.com/). - Click on the "Select a project" dropdown at the top right and select "New Project". - Give your project a name and follow the steps to create it. #### 2. **Create BigQuery Datasets** - In the Google Cloud Console, go to BigQuery. - Make two new datasets: `raw_data` for Airbyte and `transformed_data` for dbt. - If you pick different names, remember to change the names in the code too. **How to create a dataset:** - In the left sidebar, click on your project name. - Click “Create Dataset”. - Enter the dataset ID (either `raw_data` or `transformed_data`). - Click "Create Dataset". #### 3. **Create Service Accounts and Assign Roles** - Go to “IAM & Admin” > “Service accounts” in the Google Cloud Console. - Click “Create Service Account”. - Name your service account (like `airbyte-service-account`). - Assign the “BigQuery Data Editor” and “BigQuery Job User” roles to the service account. - Follow the same steps to make another service account for dbt (like `dbt-service-account`) and assign the roles. **How to create a service account and assign roles:** - While creating the service account, under the “Grant this service account access to project” section, click the “Role” dropdown. - Choose the “BigQuery Data Editor” and “BigQuery Job User” roles. - Finish the creation process. #### 4. **Generate JSON Keys for Service Accounts** - For both service accounts, make a JSON key to let the service accounts sign in. **How to generate JSON key:** - Find the service account in the “Service accounts” list. - Click on the service account name. - In the “Keys” section, click “Add Key” and pick JSON. - The key will download automatically. Keep it safe and don’t share it. - Do this for the other service account too. ## 3. Setting Up Airbyte Connectors with Terraform Airbyte 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: 1. **Navigate to the Airbyte Configuration Directory**: Change to the relevant directory containing the Terraform configuration for Airbyte: ```bash cd infra/airbyte ``` 2. **Modify Configuration Files**: Within the `infra/airbyte` directory, you'll find three crucial Terraform files: - `provider.tf`: Defines the Airbyte provider. - `main.tf`: Contains the main configuration for creating Airbyte resources. - `variables.tf`: Holds various variables, including credentials. 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. 3. **Initialize Terraform**: This step prepares Terraform to create the resources defined in your configuration files. ```bash terraform init ``` 4. **Review the Plan**: Before applying any changes, review the plan to understand what Terraform will do. ```bash terraform plan ``` 5. **Apply Configuration**: After reviewing and confirming the plan, apply the Terraform configurations to create the necessary Airbyte resources. ```bash terraform apply ``` 6. **Verify in Airbyte UI**: 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. ## 4. Setting Up the dbt Project [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: 1. **Navigate to the dbt Project Directory**: Change to the directory containing the dbt configuration: ```bash cd dbt_project ``` 2. **Update Connection Details**: 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. 3. **Utilize Environment Variables (Optional but Recommended)**: To keep your credentials secure, you can leverage environment variables. An example is provided within the `profiles.yml` file. 4. **Test the Connection**: Once you’ve updated the connection details, you can test the connection to your BigQuery instance using: ```bash dbt debug ``` If everything is set up correctly, this command should report a successful connection to BigQuery. 5. **Run the Models**: If you would like to run the dbt models manually at this point, you can do so by executing: ```bash dbt run ``` You can verify the data has been transformed by going to BigQuery and checking the `transformed_data` dataset. ## 5. Orchestrating with Dagster [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: 1. **Navigate to the Orchestration Directory**: Switch to the directory containing the Dagster orchestration configurations: ```bash cd orchestration ``` 2. **Set Environment Variables**: Dagster requires certain environment variables to be set to interact with other tools like dbt and Airbyte. Set the following variables: ```bash export DAGSTER_DBT_PARSE_PROJECT_ON_LOAD=1 export AIRBYTE_PASSWORD=password ``` 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. 3. **Launch the Dagster UI**: With the environment variables in place, kick-start the Dagster UI: ```bash dagster dev ``` 4. **Access Dagster in Your Browser**: Open your browser and navigate to: ``` http://127.0.0.1:3000 ``` 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. 5. **Materialize Dagster Assets**: 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. ## Next Steps Congratulations 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: ### 1. **Explore the Data and Insights** - 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! ### 2. **Optimize Your dbt Models** - 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. ### 3. **Automate and Monitor Your Pipelines** - 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. ================================================ FILE: satisfaction_analytics_zendesk_support/dbt_project/.gitignore ================================================ target/ dbt_packages/ logs/ .user.yml ================================================ FILE: satisfaction_analytics_zendesk_support/dbt_project/README.md ================================================ Welcome to your new dbt project! ### Using the starter project Try running the following commands: - dbt run - dbt test ### Resources: - Learn more about dbt [in the docs](https://docs.getdbt.com/docs/introduction) - Check out [Discourse](https://discourse.getdbt.com/) for commonly asked questions and answers - Join the [chat](https://community.getdbt.com/) on Slack for live discussions and support - Find [dbt events](https://events.getdbt.com) near you - Check out [the blog](https://blog.getdbt.com/) for the latest news on dbt's development and best practices ================================================ FILE: satisfaction_analytics_zendesk_support/dbt_project/analysis/.gitkeep ================================================ ================================================ FILE: satisfaction_analytics_zendesk_support/dbt_project/dbt_project.yml ================================================ # Name your project! Project names should contain only lowercase characters # and underscores. A good package name should reflect your organization's # name or the intended use of these models name: 'dbt_project' version: '1.0.0' config-version: 2 # This setting configures which "profile" dbt uses for this project. profile: 'dbt_project' # These configurations specify where dbt should look for different types of files. # The `model-paths` config, for example, states that models in this project can be # found in the "models/" directory. You probably won't need to change these! model-paths: ["models"] analysis-paths: ["analyses"] test-paths: ["tests"] seed-paths: ["seeds"] macro-paths: ["macros"] snapshot-paths: ["snapshots"] clean-targets: # directories to be removed by `dbt clean` - "target" - "dbt_packages" # Configuring models # Full documentation: https://docs.getdbt.com/docs/configuring-models # In this example config, we tell dbt to build all models in the example/ # directory as views. These settings can be overridden in the individual model # files using the `{{ config(...) }}` macro. models: dbt_project: # Config indicated by + and applies to all files under models/example/ staging: +materialized: view marts: +materialized: view ================================================ FILE: satisfaction_analytics_zendesk_support/dbt_project/models/marts/analyze_satisfaction_score_over_time.sql ================================================ SELECT DATE_TRUNC(created_at, DAY) AS date, AVG(CAST(score AS FLOAT64)) AS avg_satisfaction_score FROM transformed_data.stg_satisfaction_ratings WHERE score IS NOT NULL GROUP BY date ORDER BY date ================================================ FILE: satisfaction_analytics_zendesk_support/dbt_project/models/marts/avarage_satisfaction_rating.sql ================================================ SELECT AVG(CAST(score AS FLOAT64)) AS avg_satisfaction_score FROM transformed_data.stg_satisfaction_ratings WHERE score IS NOT NULL ================================================ FILE: satisfaction_analytics_zendesk_support/dbt_project/models/marts/feedback_analysis_for_low_score.sql ================================================ SELECT reason, COUNT(*) AS count FROM transformed_data.stg_satisfaction_ratings WHERE CAST(score AS INT64) <= 2 AND reason IS NOT NULL GROUP BY reason ORDER BY count DESC ================================================ FILE: satisfaction_analytics_zendesk_support/dbt_project/models/marts/trend_analysis_by_score.sql ================================================ SELECT score, COUNT(*) AS count FROM transformed_data.stg_satisfaction_ratings WHERE score IS NOT NULL GROUP BY score ORDER BY score ================================================ FILE: satisfaction_analytics_zendesk_support/dbt_project/models/sources/zendesk_support_sources.yml ================================================ version: 2 sources: - name: zendesk_support # Use your BigQuery project ID database: "{{ env_var('BIGQUERY_PROJECT_ID', '') }}" # Use your BigQuery dataset name schema: zendesk_airbyte_trial tables: - name: users description: "Simulated user data from the Zendesk Support connector." columns: - name: id description: "Unique identifier for the user." - name: active - name: alias - name: chat_only - name: created_at - name: custom_role_id - name: default_group_id - name: details - name: email - name: external_id - name: updated_at - name: iana_time_zone - name: last_login_at - name: locale - name: locale_id - name: moderator - name: name - name: notes - name: only_private_comments - name: organization_id - name: permanently_deleted - name: phone - name: photo - name: report_csv - name: restricted_agent - name: role - name: role_type - name: shared - name: shared_agent - name: shared_phone_number - name: signature - name: suspended - name: tags - name: ticket_restriction - name: time_zone - name: two_factor_auth_enabled - name: url - name: user_fields - name: verified - name: tickets description: "Simulated tickets data from the Zendesk Support connector." columns: - name: id description: "Unique identifier for the ticket." - name: updated_at - name: allow_attachments - name: allow_channelback - name: assignee_id - name: brand_id - name: collaborator_ids - name: created_at - name: custom_fields - name: custom_status_id - name: description - name: due_at - name: email_cc_ids - name: external_id - name: fields - name: follower_ids - name: followup_ids - name: forum_topic_id - name: from_messaging_channel - name: generated_timestamp - name: group_id - name: has_incidents - name: is_public - name: organization_id - name: priority - name: problem_id - name: raw_subject - name: recipient - name: requester_id - name: satisfaction_rating - name: sharing_agreement_ids - name: status - name: subject - name: submitter_id - name: tags - name: ticket_form_id - name: type - name: url - name: via - name: satisfaction_ratings description: "Simulated satisfaction_ratings data from the Zendesk Support connector." columns: - name: id description: "Unique identifier for the satisfaction_ratings." - name: updated_at - name: created_at - name: assignee_id - name: comment - name: group_id - name: reason - name: reason_id - name: requester_id - name: score - name: ticket_id - name: url - name: brands description: "Simulated brands data from the Zendesk Support connector." columns: - name: id description: "Unique identifier for the brands." - name: updated_at - name: created_at - name: url - name: active - name: brand_url - name: default - name: has_help_center - name: help_center_state - name: host_mapping - name: is_deleted - name: logo - name: name - name: signature_template - name: subdomain - name: ticket_form_ids - name: groups description: "Simulated groups data from the Zendesk Support connector." columns: - name: id description: "Unique identifier for the groups." - name: updated_at - name: created_at - name: url - name: deleted - name: description - name: default - name: is_public - name: name - name: organizations description: "Simulated organizations data from the Zendesk Support connector." columns: - name: id description: "Unique identifier for the organizations." - name: updated_at - name: created_at - name: url - name: details - name: domain_names - name: external_id - name: group_id - name: notes - name: organization_fields - name: deleted_at - name: shared_comments - name: name - name: shared_tickets - name: tags - name: tags description: "Simulated tags data from the Zendesk Support connector." columns: - name: name - name: count - name: ticket_audits description: "Simulated ticket_audits data from the Zendesk Support connector." columns: - name: id description: "Unique identifier for the ticket_audits." - name: author_id - name: created_at - name: events - name: metadata - name: ticket_id - name: via - name: ticket_comments description: "Simulated ticket_comments data from the Zendesk Support connector." columns: - name: id description: "Unique identifier for the ticket_comments." - name: attachments - name: created_at - name: audit_id - name: author_id - name: body - name: event_type - name: html_body - name: metadata - name: plain_body - name: public - name: ticket_id - name: timestamp - name: type - name: uploads - name: via - name: via_reference_id - name: ticket_fields description: "Simulated ticket_fields data from the Zendesk Support connector." columns: - name: id description: "Unique identifier for the ticket_fields." - name: active - name: created_at - name: agent_description - name: collapsed_for_agents - name: custom_field_options - name: custom_statuses - name: description - name: editable_in_portal - name: key - name: position - name: raw_description - name: raw_title - name: raw_title_in_portal - name: regexp_for_validation - name: removable - name: required - name: required_in_portal - name: sub_type_id - name: system_field_options - name: tag - name: title - name: title_in_portal - name: type - name: updated_at - name: url - name: visible_in_portal - name: ticket_forms description: "Simulated ticket_forms data from the Zendesk Support connector." columns: - name: id description: "Unique identifier for the ticket_forms." - name: active - name: created_at - name: agent_conditions - name: default - name: display_name - name: end_user_conditions - name: end_user_visible - name: in_all_brands - name: name - name: position - name: raw_display_name - name: raw_name - name: restricted_brand_ids - name: ticket_field_ids - name: updated_at - name: url - name: ticket_metric_events description: "Simulated ticket_metric_events data from the Zendesk Support connector." columns: - name: id description: "Unique identifier for the ticket_metric_events." - name: instance_id - name: metric - name: time - name: type - name: ticket_id - name: ticket_metrics description: "Simulated ticket_metrics data from the Zendesk Support connector." columns: - name: id description: "Unique identifier for the ticket_metrics." - name: agent_wait_time_in_minutes - name: created_at - name: assigned_at - name: assignee_stations - name: assignee_updated_at - name: custom_status_updated_at - name: first_resolution_time_in_minutes - name: full_resolution_time_in_minutes - name: group_stations - name: initially_assigned_at - name: instance_id - name: latest_comment_added_at - name: metric - name: on_hold_time_in_minutes - name: updated_at - name: url - name: reopens - name: replies - name: reply_time_in_minutes - name: requester_updated_at - name: requester_wait_time_in_minutes - name: solved_at - name: status - name: status_updated_at - name: ticket_id - name: time - name: type ================================================ FILE: satisfaction_analytics_zendesk_support/dbt_project/models/staging/stg_brands.sql ================================================ select * from {{ source('zendesk_support', 'brands') }} ================================================ FILE: satisfaction_analytics_zendesk_support/dbt_project/models/staging/stg_groups.sql ================================================ select * from {{ source('zendesk_support', 'groups') }} ================================================ FILE: satisfaction_analytics_zendesk_support/dbt_project/models/staging/stg_organizations.sql ================================================ select * from {{ source('zendesk_support', 'organizations') }} ================================================ FILE: satisfaction_analytics_zendesk_support/dbt_project/models/staging/stg_satisfaction_ratings.sql ================================================ select * from {{ source('zendesk_support', 'satisfaction_ratings') }} ================================================ FILE: satisfaction_analytics_zendesk_support/dbt_project/models/staging/stg_tags.sql ================================================ select * from {{ source('zendesk_support', 'tags') }} ================================================ FILE: satisfaction_analytics_zendesk_support/dbt_project/models/staging/stg_ticket_audits.sql ================================================ select * from {{ source('zendesk_support', 'ticket_audits') }} ================================================ FILE: satisfaction_analytics_zendesk_support/dbt_project/models/staging/stg_ticket_comments.sql ================================================ select * from {{ source('zendesk_support', 'ticket_comments') }} ================================================ FILE: satisfaction_analytics_zendesk_support/dbt_project/models/staging/stg_ticket_fields.sql ================================================ select * from {{ source('zendesk_support', 'ticket_fields') }} ================================================ FILE: satisfaction_analytics_zendesk_support/dbt_project/models/staging/stg_ticket_forms.sql ================================================ select * from {{ source('zendesk_support', 'ticket_forms') }} ================================================ FILE: satisfaction_analytics_zendesk_support/dbt_project/models/staging/stg_ticket_metric_events.sql ================================================ select * from {{ source('zendesk_support', 'ticket_metric_events') }} ================================================ FILE: satisfaction_analytics_zendesk_support/dbt_project/models/staging/stg_ticket_metrics.sql ================================================ select * from {{ source('zendesk_support', 'ticket_metrics') }} ================================================ FILE: satisfaction_analytics_zendesk_support/dbt_project/models/staging/stg_tickets.sql ================================================ select * from {{ source('zendesk_support', 'tickets') }} ================================================ FILE: satisfaction_analytics_zendesk_support/dbt_project/models/staging/stg_users.sql ================================================ select * from {{ source('zendesk_support', 'users') }} ================================================ FILE: satisfaction_analytics_zendesk_support/dbt_project/profiles.yml ================================================ dbt_project: outputs: dev: dataset: transformed_data job_execution_timeout_seconds: 300 job_retries: 1 # Use an env variable to indicate your JSON key file path keyfile: "{{ env_var('DBT_BIGQUERY_KEYFILE_PATH', '') }}" location: US method: service-account priority: interactive # Indicate your BigQuery project ID project: "{{ env_var('BIGQUERY_PROJECT_ID', '') }}" threads: 1 type: bigquery target: dev ================================================ FILE: satisfaction_analytics_zendesk_support/dbt_project/tests/.gitkeep ================================================ ================================================ FILE: satisfaction_analytics_zendesk_support/infra/.gitignore ================================================ # Local .terraform directories **/.terraform/* # .tfstate files *.tfstate *.tfstate.* # Crash log files crash.log crash.*.log # Exclude all .tfvars files, which are likely to contain sensitive data, such as # password, private keys, and other secrets. These should not be part of version # control as they are data points which are potentially sensitive and subject # to change depending on the environment. *.tfvars *.tfvars.json # Ignore override files as they are usually used to override resources locally and so # are not checked in override.tf override.tf.json *_override.tf *_override.tf.json # Include override files you do wish to add to version control using negated pattern # !example_override.tf # Include tfplan files to ignore the plan output of command: terraform plan -out=tfplan # example: *tfplan* # Ignore CLI configuration files .terraformrc terraform.rc ================================================ FILE: satisfaction_analytics_zendesk_support/infra/airbyte/.terraform.lock.hcl ================================================ # This file is maintained automatically by "terraform init". # Manual edits may be lost in future updates. provider "registry.terraform.io/airbytehq/airbyte" { version = "0.3.3" constraints = "0.3.3" hashes = [ "h1:0LmuAc5LvlMuOUPtNEaCAh9FHrV/C877bDJhm9Lz8MU=", "zh:0efa470b34d9b912b47efe4469c51713bfc3c2413e52c17e1e903f2a3cddb2f6", "zh:1bddd69fa2c2d4f3e239d60555446df9bc4ce0c0cabbe7e092fe1d44989ab004", "zh:2e20540403a0010007b53456663fb037b24e30f6c8943f65da1bcf7fa4dfc8a6", "zh:2f415369ad884e8b7115a5c5ff229d052f7af1fca27abbfc8ebef379ed11aec4", "zh:46fd9a906f4b6461112dcc5a5aa01a3fcd7a19a72d4ad0b2e37790da37701fe1", "zh:83503ebb77bb6d6941c42ba323cf22380d08a1506554a2dcc8ac54e74c0886a1", "zh:890df766e9b839623b1f0437355032a3c006226a6c200cd911e15ee1a9014e9f", "zh:8fd770eff726826d3a63b9e3733c5455b5cde004027b04ee3f75888eb8538c90", "zh:b0fc890ed4f9b077bf70ed121cc3550e7a07d16e7798ad517623274aa62ad7b0", "zh:c2a01612362da9b73cd5958f281e1aa7ff09af42182e463097d11ed78e778e72", "zh:c64b2bb1887a0367d64ba3393d4b3a16c418cf5b1792e2e7aae7c0b5413eb334", "zh:ce14ebbf0ed91913ec62655a511763dec62b5779de9a209bd6f1c336640cddc0", "zh:e0662ca837eee10f7733ea9a501d995281f56bd9b410ae13ad03eb106011db14", "zh:e103d480fc6066004bc98e9e04a141a1f55b918cc2912716beebcc6fc4c872fb", "zh:e2507049098f0f1b21cb56870f4a5ef624bcf6d3959e5612eada1f8117341648", ] } ================================================ FILE: satisfaction_analytics_zendesk_support/infra/airbyte/main.tf ================================================ // Airbyte Terraform provider documentation: https://registry.terraform.io/providers/airbytehq/airbyte/latest/docs // Sources resource "airbyte_source_zendesk_support" "my_source_zendesksupport" { configuration = { source_type = "zendesk-support" credentials = { source_zendesk_support_authentication_api_token = { credentials = "api_token" api_token = var.api_token email = var.email } } start_date = "2020-10-15T00:00:00Z" subdomain = "self3836" } name = "Zendesk Support" workspace_id = var.workspace_id } // Destinations resource "airbyte_destination_bigquery" "bigquery" { configuration = { dataset_id = var.dataset_id dataset_location = "US" destination_type = "bigquery" project_id = var.project_id credentials_json = var.credentials_json loading_method = { destination_bigquery_loading_method_standard_inserts = { method = "Standard" } } } name = "BigQuery" workspace_id = var.workspace_id } // Connections resource "airbyte_connection" "zendesk_support_bigquery" { name = "Zendesk Support to BigQuery" source_id = airbyte_source_zendesk_support.my_source_zendesksupport.source_id destination_id = airbyte_destination_bigquery.bigquery.destination_id configurations = { streams = [ { name = "users" }, { name = "tickets" }, { name = "satisfaction_ratings" }, { name = "ticket_metrics" }, { name = "ticket_metric_events" }, { name = "ticket_comments" }, { name = "ticket_audits" } ] } } ================================================ FILE: satisfaction_analytics_zendesk_support/infra/airbyte/provider.tf ================================================ // Airbyte Terraform provider documentation: https://registry.terraform.io/providers/airbytehq/airbyte/latest/docs terraform { required_providers { airbyte = { source = "airbytehq/airbyte" version = "0.3.3" } } } provider "airbyte" { // If running locally (Airbyte OSS) with docker-compose using the airbyte-proxy, // include the actual password/username you've set up (or use the defaults below) username = "airbyte" password = "password" // if running locally (Airbyte OSS), include the server url to the airbyte-api-server server_url = "http://localhost:8006/v1/" } ================================================ FILE: satisfaction_analytics_zendesk_support/infra/airbyte/variables.tf ================================================ variable "api_token" { type = string } variable "email" { type = string } variable "workspace_id" { type = string } variable "dataset_id" { type = string } variable "project_id" { type = string } variable "credentials_json" { type = string } ================================================ FILE: satisfaction_analytics_zendesk_support/orchestration/orchestration/__init__.py ================================================ ================================================ FILE: satisfaction_analytics_zendesk_support/orchestration/orchestration/assets.py ================================================ import os from dagster import OpExecutionContext from dagster_dbt import DbtCliResource, dbt_assets from dagster_airbyte import AirbyteResource, load_assets_from_airbyte_instance from .constants import dbt_manifest_path @dbt_assets(manifest=dbt_manifest_path) def dbt_project_dbt_assets(context: OpExecutionContext, dbt: DbtCliResource): yield from dbt.cli(["build"], context=context).stream() airbyte_instance = AirbyteResource( host="localhost", port="8000", # If using basic auth, include username and password: username="airbyte", password="password" ) airbyte_assets = load_assets_from_airbyte_instance(airbyte_instance,) ================================================ FILE: satisfaction_analytics_zendesk_support/orchestration/orchestration/constants.py ================================================ import os from pathlib import Path from dagster_dbt import DbtCliResource dbt_project_dir = Path(__file__).joinpath("..", "..", "..", "dbt_project").resolve() dbt = DbtCliResource(project_dir=os.fspath(dbt_project_dir)) # If DAGSTER_DBT_PARSE_PROJECT_ON_LOAD is set, a manifest will be created at runtime. # Otherwise, we expect a manifest to be present in the project's target directory. if os.getenv("DAGSTER_DBT_PARSE_PROJECT_ON_LOAD"): dbt_parse_invocation = dbt.cli(["parse"], manifest={}).wait() dbt_manifest_path = dbt_parse_invocation.target_path.joinpath("manifest.json") else: dbt_manifest_path = dbt_project_dir.joinpath("target", "manifest.json") ================================================ FILE: satisfaction_analytics_zendesk_support/orchestration/orchestration/definitions.py ================================================ import os from dagster import Definitions from dagster_dbt import DbtCliResource from .assets import dbt_project_dbt_assets, airbyte_assets from .constants import dbt_project_dir from .schedules import schedules defs = Definitions( assets=[dbt_project_dbt_assets, airbyte_assets], schedules=schedules, resources={ "dbt": DbtCliResource(project_dir=os.fspath(dbt_project_dir)), }, ) ================================================ FILE: satisfaction_analytics_zendesk_support/orchestration/orchestration/schedules.py ================================================ """ To add a daily schedule that materializes your dbt assets, uncomment the following lines. """ from dagster_dbt import build_schedule_from_dbt_selection from .assets import dbt_project_dbt_assets schedules = [ # build_schedule_from_dbt_selection( # [dbt_project_dbt_assets], # job_name="materialize_dbt_models", # cron_schedule="0 0 * * *", # dbt_select="fqn:*", # ), ] ================================================ FILE: satisfaction_analytics_zendesk_support/orchestration/pyproject.toml ================================================ [build-system] requires = ["setuptools"] build-backend = "setuptools.build_meta" [tool.dagster] module_name = "orchestration.definitions" code_location_name = "orchestration" ================================================ FILE: satisfaction_analytics_zendesk_support/orchestration/setup.py ================================================ from setuptools import find_packages, setup setup( name="orchestration", version="0.0.1", packages=find_packages(), install_requires=[ "dagster", "dagster-cloud", "dagster-dbt", "dbt-core>=1.4.0", 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================================================ ================================================ FILE: satisfaction_analytics_zendesk_support/setup.py ================================================ from setuptools import find_packages, setup setup( name="airbyte-dbt-dagster", packages=find_packages(), install_requires=[ "dbt-bigquery", "dagster", "dagster-cloud", "dagster-dbt", "dagster-airbyte", ], extras_require={"dev": ["dagit", "pytest"]}, ) ================================================ FILE: shopping_cart_analytics_shopify/.gitignore ================================================ # Byte-compiled / optimized / DLL files __pycache__/ *.py[cod] *$py.class # C extensions *.so # Distribution / packaging .Python build/ develop-eggs/ dist/ downloads/ eggs/ .eggs/ lib/ lib64/ parts/ sdist/ var/ wheels/ share/python-wheels/ *.egg-info/ .installed.cfg *.egg MANIFEST # PyInstaller # Usually these files are written by a python script from a template # before PyInstaller builds the exe, so as to inject date/other infos into it. *.manifest *.spec # Installer logs pip-log.txt pip-delete-this-directory.txt # Unit test / coverage reports htmlcov/ .tox/ .nox/ .coverage .coverage.* .cache nosetests.xml coverage.xml *.cover *.py,cover .hypothesis/ .pytest_cache/ cover/ # Translations *.mo *.pot # Django stuff: *.log local_settings.py db.sqlite3 db.sqlite3-journal # Flask stuff: instance/ .webassets-cache # Scrapy stuff: .scrapy # Sphinx documentation docs/_build/ # PyBuilder .pybuilder/ target/ # Jupyter Notebook .ipynb_checkpoints # IPython profile_default/ ipython_config.py # pyenv # For a library or package, you might want to ignore these files since the code is # intended to run in multiple environments; otherwise, check them in: # .python-version # pipenv # According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. # However, in case of collaboration, if having platform-specific dependencies or dependencies # having no cross-platform support, pipenv may install dependencies that don't work, or not # install all needed dependencies. #Pipfile.lock # poetry # Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control. # This is especially recommended for binary packages to ensure reproducibility, and is more # commonly ignored for libraries. # https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control #poetry.lock # pdm # Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control. #pdm.lock # pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it # in version control. # https://pdm.fming.dev/#use-with-ide .pdm.toml # PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm __pypackages__/ # Celery stuff celerybeat-schedule celerybeat.pid # SageMath parsed files *.sage.py # Environments .env .venv env/ venv/ ENV/ env.bak/ venv.bak/ # Spyder project settings .spyderproject .spyproject # Rope project settings .ropeproject # mkdocs documentation /site # mypy .mypy_cache/ .dmypy.json dmypy.json # Pyre type checker .pyre/ # pytype static type analyzer .pytype/ # Cython debug symbols cython_debug/ # PyCharm # JetBrains specific template is maintained in a separate JetBrains.gitignore that can # be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore # and can be added to the global gitignore or merged into this file. For a more nuclear # option (not recommended) you can uncomment the following to ignore the entire idea folder. #.idea/ #Desktop Services Store .DS_Store ================================================ FILE: shopping_cart_analytics_shopify/README.md ================================================ # Shopping Cart Analytics Stack With Shopify, Airbyte, Dbt, Dagster and BigQuery Welcome 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. This Quickstart is all about making things easy, getting you started quickly and showing you how smoothly all these tools can work together! Below is a visual representation of how data flows through our integrated tools in this Quickstart. This comes from Dagster's global asset lineage view: .svg>) ## Table of Contents - [Prerequisites](#prerequisites) - [Setting an environment for your project](#1-setting-an-environment-for-your-project) - [Setting Up BigQuery to work with Airbyte and dbt](#2-setting-up-bigquery) - [Setting Up Airbyte Connectors with Terraform](#3-setting-up-airbyte-connectors-with-terraform) - [Setting Up the dbt Project](#4-setting-up-the-dbt-project) - [Orchestrating with Dagster](#5-orchestrating-with-dagster) - [Next Steps](#next-steps) ## Prerequisites Before you embark on this integration, ensure you have the following set up and ready: 1. **Python 3.10 or later**: If not installed, download and install it from [Python's official website](https://www.python.org/downloads/). 2. **Docker and Docker Compose (Docker Desktop)**: Install [Docker](https://docs.docker.com/get-docker/) following the official documentation for your specific OS. 3. **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/). 4. **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). 5. **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). ## 1. Setting an environment for your project Get the project up and running on your local machine by following these steps: 1. **Clone the repository (Clone only this quickstart)**: ```bash git clone --filter=blob:none --sparse https://github.com/airbytehq/quickstarts.git ``` ```bash cd quickstarts ``` ```bash git sparse-checkout add shopping_cart_analytics_shopify ``` 2. **Navigate to the directory**: ```bash cd shopping_cart_analytics_shopify ``` 3. **Set Up a Virtual Environment**: - For Mac: ```bash python3 -m venv venv source venv/bin/activate ``` - For Windows: ```bash python -m venv venv .\venv\Scripts\activate ``` 4. **Install Dependencies**: ```bash pip install -e ".[dev]" ``` ## 2. Setting Up BigQuery #### 1. **Create a Google Cloud Project** - If you have a Google Cloud project, you can skip this step. - Go to the [Google Cloud Console](https://console.cloud.google.com/). - Click on the "Select a project" dropdown at the top right and select "New Project". - Give your project a name and follow the steps to create it. #### 2. **Create BigQuery Datasets** - In the Google Cloud Console, go to BigQuery. - Make two new datasets: `raw_data` for Airbyte and `transformed_data` for dbt. - If you pick different names, remember to change the names in the code too. **How to create a dataset:** - In the left sidebar, click on your project name. - Click “Create Dataset”. - Enter the dataset ID (either `raw_data` or `transformed_data`). - Click "Create Dataset". #### 3. **Create Service Accounts and Assign Roles** - Go to “IAM & Admin” > “Service accounts” in the Google Cloud Console. - Click “Create Service Account”. - Name your service account (like `airbyte-service-account`). - Assign the “BigQuery Data Editor” and “BigQuery Job User” roles to the service account. - Follow the same steps to make another service account for dbt (like `dbt-service-account`) and assign the roles. **How to create a service account and assign roles:** - While creating the service account, under the “Grant this service account access to project” section, click the “Role” dropdown. - Choose the “BigQuery Data Editor” and “BigQuery Job User” roles. - Finish the creation process. #### 4. **Generate JSON Keys for Service Accounts** - For both service accounts, make a JSON key to let the service accounts sign in. **How to generate JSON key:** - Find the service account in the “Service accounts” list. - Click on the service account name. - In the “Keys” section, click “Add Key” and pick JSON. - The key will download automatically. Keep it safe and don’t share it. - Do this for the other service account too. ## 3. Setting Up Airbyte Connectors with Terraform Airbyte 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: 1. **Navigate to the Airbyte Configuration Directory**: Change to the relevant directory containing the Terraform configuration for Airbyte: ```bash cd infra/airbyte ``` 2. **Modify Configuration Files**: Within the `infra/airbyte` directory, you'll find three crucial Terraform files: - `provider.tf`: Defines the Airbyte provider. - `main.tf`: Contains the main configuration for creating Airbyte resources. - `variables.tf`: Holds various variables, including credentials. 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. 3. **Initialize Terraform**: This step prepares Terraform to create the resources defined in your configuration files. ```bash terraform init ``` 4. **Review the Plan**: Before applying any changes, review the plan to understand what Terraform will do. ```bash terraform plan ``` 5. **Apply Configuration**: After reviewing and confirming the plan, apply the Terraform configurations to create the necessary Airbyte resources. ```bash terraform apply ``` 6. **Verify in Airbyte UI**: 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. ## 4. Setting Up the dbt Project [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: 1. **Navigate to the dbt Project Directory**: Change to the directory containing the dbt configuration: ```bash cd dbt_project ``` 2. **Update Connection Details**: 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. 3. **Utilize Environment Variables (Optional but Recommended)**: To keep your credentials secure, you can leverage environment variables. An example is provided within the `profiles.yml` file. 4. **Test the Connection**: Once you’ve updated the connection details, you can test the connection to your BigQuery instance using: ```bash dbt debug ``` If everything is set up correctly, this command should report a successful connection to BigQuery. 5. **Run the Models**: If you would like to run the dbt models manually at this point, you can do so by executing: ```bash dbt run ``` You can verify the data has been transformed by going to BigQuery and checking the `transformed_data` dataset. ## 5. Orchestrating with Dagster [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: 1. **Navigate to the Orchestration Directory**: Switch to the directory containing the Dagster orchestration configurations: ```bash cd orchestration ``` 2. **Set Environment Variables**: Dagster requires certain environment variables to be set to interact with other tools like dbt and Airbyte. Set the following variables: ```bash export DAGSTER_DBT_PARSE_PROJECT_ON_LOAD=1 export AIRBYTE_PASSWORD=password ``` 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. 3. **Launch the Dagster UI**: With the environment variables in place, kick-start the Dagster UI: ```bash dagster dev ``` 4. **Access Dagster in Your Browser**: Open your browser and navigate to: ``` http://127.0.0.1:3000 ``` 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. 5. **Materialize Dagster Assets**: 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. ## Next Steps Congratulations 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: ### 1. **Explore the Data and Insights** - 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! ### 2. **Optimize Your dbt Models** - 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. ### 3. **Automate and Monitor Your Pipelines** - 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. ================================================ FILE: shopping_cart_analytics_shopify/dbt_project/.gitignore ================================================ target/ dbt_packages/ logs/ .user.yml ================================================ FILE: shopping_cart_analytics_shopify/dbt_project/README.md ================================================ Welcome to your new dbt project! ### Using the starter project Try running the following commands: - dbt run - dbt test ### Resources: - Learn more about dbt [in the docs](https://docs.getdbt.com/docs/introduction) - Check out [Discourse](https://discourse.getdbt.com/) for commonly asked questions and answers - Join the [chat](https://community.getdbt.com/) on Slack for live discussions and support - Find [dbt events](https://events.getdbt.com) near you - Check out [the blog](https://blog.getdbt.com/) for the latest news on dbt's development and best practices ================================================ FILE: shopping_cart_analytics_shopify/dbt_project/dbt_project.yml ================================================ # Name your project! Project names should contain only lowercase characters # and underscores. A good package name should reflect your organization's # name or the intended use of these models name: 'dbt_project' version: '1.0.0' config-version: 2 # This setting configures which "profile" dbt uses for this project. profile: 'dbt_project' # These configurations specify where dbt should look for different types of files. # The `model-paths` config, for example, states that models in this project can be # found in the "models/" directory. You probably won't need to change these! model-paths: ["models"] analysis-paths: ["analyses"] test-paths: ["tests"] seed-paths: ["seeds"] macro-paths: ["macros"] snapshot-paths: ["snapshots"] clean-targets: # directories to be removed by `dbt clean` - "target" - "dbt_packages" # Configuring models # Full documentation: https://docs.getdbt.com/docs/configuring-models # In this example config, we tell dbt to build all models in the example/ # directory as views. These settings can be overridden in the individual model # files using the `{{ config(...) }}` macro. models: dbt_project: # Config indicated by + and applies to all files under models/example/ staging: +materialized: view marts: +materialized: view ================================================ FILE: shopping_cart_analytics_shopify/dbt_project/models/marts/abandoned_checkout_ratio.sql ================================================ SELECT COUNTIF(completed_at IS NULL) AS abandoned_checkouts, COUNT(*) AS total_checkouts, COUNTIF(completed_at IS NULL) / COUNT(*) AS abandonment_rate FROM transformed_data.stg_abandoned_checkouts ================================================ FILE: shopping_cart_analytics_shopify/dbt_project/models/marts/location_based_abandoned_checkouts.sql ================================================ WITH abandoned_checkouts AS ( SELECT *, IF(billing_address IS NOT NULL, JSON_EXTRACT_SCALAR(billing_address, '$.country'), JSON_EXTRACT_SCALAR(shipping_address, '$.country')) AS checkout_country FROM transformed_data.stg_abandoned_checkouts ) SELECT checkout_country, COUNTIF(completed_at IS NULL) AS abandoned_checkouts FROM abandoned_checkouts GROUP BY checkout_country ORDER BY abandoned_checkouts DESC ================================================ FILE: shopping_cart_analytics_shopify/dbt_project/models/marts/most_abandoned_products.sql ================================================ SELECT JSON_EXTRACT_SCALAR(line_items, '$.title') AS product_title, COUNTIF(completed_at IS NULL) AS abandoned_checkouts FROM transformed_data.stg_abandoned_checkouts CROSS JOIN UNNEST(JSON_EXTRACT_ARRAY(line_items)) AS items GROUP BY product_title ORDER BY abandoned_checkouts DESC ================================================ FILE: shopping_cart_analytics_shopify/dbt_project/models/marts/time_based.sql ================================================ SELECT EXTRACT(DAYOFWEEK FROM created_at) AS day_of_week, EXTRACT(HOUR FROM created_at) AS hour_of_day, COUNTIF(completed_at IS NULL) AS abandoned_checkouts FROM transformed_data.stg_abandoned_checkouts GROUP BY day_of_week, hour_of_day ORDER BY day_of_week, hour_of_day ================================================ FILE: shopping_cart_analytics_shopify/dbt_project/models/sources/shopify_source.yml ================================================ version: 2 sources: - name: shopify # Use your BigQuery project ID database: "{{ env_var('BIGQUERY_PROJECT_ID', '') }}" # Use your BigQuery dataset name schema: shopify_airbyte tables: - name: abandoned_checkouts description: "Simulated abandoned_checkouts data from the Shopify connector." columns: - name: id description: "Unique identifier for the abandoned_checkouts." - name: abandoned_checkout_url - name: billing_address - name: buyer_accepts_marketing - name: cart_token - name: closed_at - name: completed_at - name: created_at - name: currency - name: customer - name: customer_locale - name: device_id - name: discount_codes - name: email - name: gateway - name: landing_site - name: line_items - name: location_id - name: name - name: note - name: note_attributes - name: phone - name: presentment_currency - name: referring_site - name: shipping_address - name: shipping_lines - name: shop_url - name: source - name: source_identifier - name: source_name - name: source_url - name: subtotal_price - name: tax_lines - name: taxes_included - name: token - name: total_discounts - name: total_line_items_price - name: total_price - name: total_tax - name: total_weight - name: updated_at - name: user_id - name: customers description: "Simulated customers data from the Shopify connector." columns: - name: id description: "Unique identifier for the customers." - name: accepts_marketing - name: accepts_marketing_updated_at - name: addresses - name: admin_graphql_api_id - name: created_at - name: currency - name: default_address - name: email - name: email_marketing_consent - name: first_name - name: last_name - name: last_order_id - name: last_order_name - name: marketing_opt_in_level - name: multipass_identifier - name: note - name: orders_count - name: phone - name: shop_url - name: sms_marketing_consent - name: state - name: tags - name: tax_exempt - name: tax_exemptions - name: total_spent - name: updated_at - name: verified_email - name: transactions description: "Simulated transactions data from the Shopify connector." columns: - name: id description: "Unique identifier for the transactions." - name: admin_graphql_api_id - name: amount - name: authorization - name: created_at - name: currency - name: device_id - name: error_code - name: gateway - name: kind - name: location_id - name: message - name: order_id - name: parent_id - name: payment_details - name: payment_id - name: processed_at - name: receipt - name: shop_url - name: source_name - name: status - name: test - name: total_unsettled_set - name: user_id ================================================ FILE: shopping_cart_analytics_shopify/dbt_project/models/staging/stg_abandoned_checkouts.sql ================================================ select * from {{ source('shopify', 'abandoned_checkouts') }} ================================================ FILE: shopping_cart_analytics_shopify/dbt_project/profiles.yml ================================================ dbt_project: outputs: dev: dataset: transformed_data job_execution_timeout_seconds: 300 job_retries: 1 # Use an env variable to indicate your JSON key file path keyfile: "{{ env_var('DBT_BIGQUERY_KEYFILE_PATH', '') }}" location: US method: service-account priority: interactive # Indicate your BigQuery project ID project: "{{ env_var('BIGQUERY_PROJECT_ID', '') }}" threads: 1 type: bigquery target: dev ================================================ FILE: shopping_cart_analytics_shopify/infra/.gitignore ================================================ # Local .terraform directories **/.terraform/* # .tfstate files *.tfstate *.tfstate.* # Crash log files crash.log crash.*.log # Exclude all .tfvars files, which are likely to contain sensitive data, such as # password, private keys, and other secrets. These should not be part of version # control as they are data points which are potentially sensitive and subject # to change depending on the environment. *.tfvars *.tfvars.json # Ignore override files as they are usually used to override resources locally and so # are not checked in override.tf override.tf.json *_override.tf *_override.tf.json # Include override files you do wish to add to version control using negated pattern # !example_override.tf # Include tfplan files to ignore the plan output of command: terraform plan -out=tfplan # example: *tfplan* # Ignore CLI configuration files .terraformrc terraform.rc ================================================ FILE: shopping_cart_analytics_shopify/infra/airbyte/.terraform.lock.hcl ================================================ # This file is maintained automatically by "terraform init". # Manual edits may be lost in future updates. provider "registry.terraform.io/airbytehq/airbyte" { version = "0.3.3" constraints = "0.3.3" hashes 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"zh:e103d480fc6066004bc98e9e04a141a1f55b918cc2912716beebcc6fc4c872fb", "zh:e2507049098f0f1b21cb56870f4a5ef624bcf6d3959e5612eada1f8117341648", ] } ================================================ FILE: shopping_cart_analytics_shopify/infra/airbyte/main.tf ================================================ // Source resource "airbyte_source_shopify" "my_source_shopify" { configuration = { credentials = { source_shopify_shopify_authorization_method_api_password = { api_password = var.api_password auth_method = "api_password" } } shop = var.shop source_type = "shopify" } name = "Shopify" workspace_id = var.workspace_id } // Destinations resource "airbyte_destination_bigquery" "bigquery" { configuration = { dataset_id = var.dataset_id dataset_location = "US" destination_type = "bigquery" project_id = var.project_id credentials_json = var.credentials_json loading_method = { destination_bigquery_loading_method_standard_inserts = { method = "Standard" } } } name = "BigQuery" workspace_id = var.workspace_id } // Connections resource "airbyte_connection" "shopify_bigquery" { name = "Shopify to BigQuery" source_id = airbyte_source_shopify.my_source_shopify.source_id destination_id = airbyte_destination_bigquery.bigquery.destination_id configurations = { streams = [ { name = "customers" }, { name = "transactions" }, { name = "abandoned_checkouts" } ] } } ================================================ FILE: shopping_cart_analytics_shopify/infra/airbyte/provider.tf ================================================ // Airbyte Terraform provider documentation: https://registry.terraform.io/providers/airbytehq/airbyte/latest/docs terraform { required_providers { airbyte = { source = "airbytehq/airbyte" version = "0.3.3" } } } provider "airbyte" { // If running locally (Airbyte OSS) with docker-compose using the airbyte-proxy, // include the actual password/username you've set up (or use the defaults below) username = "airbyte" password = "password" // if running locally (Airbyte OSS), include the server url to the airbyte-api-server server_url = "http://localhost:8006/v1/" } ================================================ FILE: shopping_cart_analytics_shopify/infra/airbyte/variables.tf ================================================ variable "api_password" { type = string } variable "workspace_id" { type = string } variable "dataset_id" { type = string } variable "project_id" { type = string } variable "credentials_json" { type = string } variable "shop" { type = string } ================================================ FILE: shopping_cart_analytics_shopify/orchestration/orchestration/__init__.py ================================================ ================================================ FILE: shopping_cart_analytics_shopify/orchestration/orchestration/assets.py ================================================ import os from dagster import OpExecutionContext from dagster_dbt import DbtCliResource, dbt_assets from dagster_airbyte import AirbyteResource, load_assets_from_airbyte_instance from .constants import dbt_manifest_path @dbt_assets(manifest=dbt_manifest_path) def dbt_project_dbt_assets(context: OpExecutionContext, dbt: DbtCliResource): yield from dbt.cli(["build"], context=context).stream() airbyte_instance = AirbyteResource( host="localhost", port="8000", # If using basic auth, include username and password: username="airbyte", password="password" ) airbyte_assets = load_assets_from_airbyte_instance(airbyte_instance, key_prefix= "shopify") ================================================ FILE: shopping_cart_analytics_shopify/orchestration/orchestration/constants.py ================================================ import os from pathlib import Path from dagster_dbt import DbtCliResource dbt_project_dir = Path(__file__).joinpath("..", "..", "..", "dbt_project").resolve() dbt = DbtCliResource(project_dir=os.fspath(dbt_project_dir)) # If DAGSTER_DBT_PARSE_PROJECT_ON_LOAD is set, a manifest will be created at runtime. # Otherwise, we expect a manifest to be present in the project's target directory. if os.getenv("DAGSTER_DBT_PARSE_PROJECT_ON_LOAD"): dbt_parse_invocation = dbt.cli(["parse"], manifest={}).wait() dbt_manifest_path = dbt_parse_invocation.target_path.joinpath("manifest.json") else: dbt_manifest_path = dbt_project_dir.joinpath("target", "manifest.json") ================================================ FILE: shopping_cart_analytics_shopify/orchestration/orchestration/definitions.py ================================================ import os from dagster import Definitions from dagster_dbt import DbtCliResource from .assets import dbt_project_dbt_assets, airbyte_assets from .constants import dbt_project_dir from .schedules import schedules defs = Definitions( assets=[dbt_project_dbt_assets, airbyte_assets], schedules=schedules, resources={ "dbt": DbtCliResource(project_dir=os.fspath(dbt_project_dir)), }, ) ================================================ FILE: shopping_cart_analytics_shopify/orchestration/orchestration/schedules.py ================================================ """ To add a daily schedule that materializes your dbt assets, uncomment the following lines. """ from dagster_dbt import build_schedule_from_dbt_selection from .assets import dbt_project_dbt_assets schedules = [ # build_schedule_from_dbt_selection( # [dbt_project_dbt_assets], # job_name="materialize_dbt_models", # cron_schedule="0 0 * * *", # dbt_select="fqn:*", # ), ] ================================================ FILE: shopping_cart_analytics_shopify/orchestration/pyproject.toml ================================================ [build-system] requires = ["setuptools"] build-backend = "setuptools.build_meta" [tool.dagster] module_name = "orchestration.definitions" code_location_name = "orchestration" ================================================ FILE: shopping_cart_analytics_shopify/orchestration/setup.py ================================================ from setuptools import find_packages, setup setup( name="orchestration", version="0.0.1", packages=find_packages(), install_requires=[ "dagster", "dagster-cloud", "dagster-dbt", "dbt-core>=1.4.0", "dbt-bigquery", ], extras_require={ "dev": [ "dagster-webserver", ] }, ) ================================================ FILE: shopping_cart_analytics_shopify/setup.py ================================================ from setuptools import find_packages, setup setup( name="airbyte-dbt-dagster", packages=find_packages(), install_requires=[ "dbt-bigquery", "dagster", "dagster-cloud", "dagster-dbt", "dagster-airbyte", ], extras_require={"dev": ["dagit", "pytest"]}, ) ================================================ FILE: ticket_volume_analytics_zendesk_support/.gitignore ================================================ # Byte-compiled / optimized / DLL files __pycache__/ *.py[cod] *$py.class # C extensions *.so # Distribution / packaging .Python build/ develop-eggs/ dist/ downloads/ eggs/ .eggs/ lib/ lib64/ parts/ sdist/ var/ wheels/ share/python-wheels/ *.egg-info/ .installed.cfg *.egg MANIFEST # PyInstaller # Usually these files are written by a python script from a template # before PyInstaller builds the exe, so as to inject date/other infos into it. *.manifest *.spec # Installer logs pip-log.txt pip-delete-this-directory.txt # Unit test / coverage reports htmlcov/ .tox/ .nox/ .coverage .coverage.* .cache nosetests.xml coverage.xml *.cover *.py,cover .hypothesis/ .pytest_cache/ cover/ # Translations *.mo *.pot # Django stuff: *.log local_settings.py db.sqlite3 db.sqlite3-journal # Flask stuff: instance/ .webassets-cache # Scrapy stuff: .scrapy # Sphinx documentation docs/_build/ # PyBuilder .pybuilder/ target/ # Jupyter Notebook .ipynb_checkpoints # IPython profile_default/ ipython_config.py # pyenv # For a library or package, you might want to ignore these files since the code is # intended to run in multiple environments; otherwise, check them in: # .python-version # pipenv # According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. # However, in case of collaboration, if having platform-specific dependencies or dependencies # having no cross-platform support, pipenv may install dependencies that don't work, or not # install all needed dependencies. #Pipfile.lock # poetry # Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control. # This is especially recommended for binary packages to ensure reproducibility, and is more # commonly ignored for libraries. # https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control #poetry.lock # pdm # Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control. #pdm.lock # pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it # in version control. # https://pdm.fming.dev/#use-with-ide .pdm.toml # PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm __pypackages__/ # Celery stuff celerybeat-schedule celerybeat.pid # SageMath parsed files *.sage.py # Environments .env .venv env/ venv/ ENV/ env.bak/ venv.bak/ # Spyder project settings .spyderproject .spyproject # Rope project settings .ropeproject # mkdocs documentation /site # mypy .mypy_cache/ .dmypy.json dmypy.json # Pyre type checker .pyre/ # pytype static type analyzer .pytype/ # Cython debug symbols cython_debug/ # PyCharm # JetBrains specific template is maintained in a separate JetBrains.gitignore that can # be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore # and can be added to the global gitignore or merged into this file. For a more nuclear # option (not recommended) you can uncomment the following to ignore the entire idea folder. #.idea/ #Desktop Services Store .DS_Store ================================================ FILE: ticket_volume_analytics_zendesk_support/README.md ================================================ # Customer Ticket Volume Analytics Stack With Zendesk Support, Airbyte, Dbt, Dagster and BigQuery Welcome 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. This Quickstart is all about making things easy, getting you started quickly and showing you how smoothly all these tools can work together! Below is a visual representation of how data flows through our integrated tools in this Quickstart. This comes from Dagster's global asset lineage view: .svg>) ## Table of Contents - [Prerequisites](#prerequisites) - [Setting an environment for your project](#1-setting-an-environment-for-your-project) - [Setting Up BigQuery to work with Airbyte and dbt](#2-setting-up-bigquery) - [Setting Up Airbyte Connectors with Terraform](#3-setting-up-airbyte-connectors-with-terraform) - [Setting Up the dbt Project](#4-setting-up-the-dbt-project) - [Orchestrating with Dagster](#5-orchestrating-with-dagster) - [Next Steps](#next-steps) ## Prerequisites Before you embark on this integration, ensure you have the following set up and ready: 1. **Python 3.10 or later**: If not installed, download and install it from [Python's official website](https://www.python.org/downloads/). 2. **Docker and Docker Compose (Docker Desktop)**: Install [Docker](https://docs.docker.com/get-docker/) following the official documentation for your specific OS. 3. **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/). 4. **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). 5. **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). ## 1. Setting an environment for your project Get the project up and running on your local machine by following these steps: 1. **Clone the repository (Clone only this quickstart)**: ```bash git clone --filter=blob:none --sparse https://github.com/airbytehq/quickstarts.git ``` ```bash cd quickstarts ``` ```bash git sparse-checkout add ticket_volume_analytics_zendesk_support ``` 2. **Navigate to the directory**: ```bash cd ticket_volume_analytics_zendesk_support ``` 3. **Set Up a Virtual Environment**: - For Mac: ```bash python3 -m venv venv source venv/bin/activate ``` - For Windows: ```bash python -m venv venv .\venv\Scripts\activate ``` 4. **Install Dependencies**: ```bash pip install -e ".[dev]" ``` ## 2. Setting Up BigQuery #### 1. **Create a Google Cloud Project** - If you have a Google Cloud project, you can skip this step. - Go to the [Google Cloud Console](https://console.cloud.google.com/). - Click on the "Select a project" dropdown at the top right and select "New Project". - Give your project a name and follow the steps to create it. #### 2. **Create BigQuery Datasets** - In the Google Cloud Console, go to BigQuery. - Make two new datasets: `raw_data` for Airbyte and `transformed_data` for dbt. - If you pick different names, remember to change the names in the code too. **How to create a dataset:** - In the left sidebar, click on your project name. - Click “Create Dataset”. - Enter the dataset ID (either `raw_data` or `transformed_data`). - Click "Create Dataset". #### 3. **Create Service Accounts and Assign Roles** - Go to “IAM & Admin” > “Service accounts” in the Google Cloud Console. - Click “Create Service Account”. - Name your service account (like `airbyte-service-account`). - Assign the “BigQuery Data Editor” and “BigQuery Job User” roles to the service account. - Follow the same steps to make another service account for dbt (like `dbt-service-account`) and assign the roles. **How to create a service account and assign roles:** - While creating the service account, under the “Grant this service account access to project” section, click the “Role” dropdown. - Choose the “BigQuery Data Editor” and “BigQuery Job User” roles. - Finish the creation process. #### 4. **Generate JSON Keys for Service Accounts** - For both service accounts, make a JSON key to let the service accounts sign in. **How to generate JSON key:** - Find the service account in the “Service accounts” list. - Click on the service account name. - In the “Keys” section, click “Add Key” and pick JSON. - The key will download automatically. Keep it safe and don’t share it. - Do this for the other service account too. ## 3. Setting Up Airbyte Connectors with Terraform Airbyte 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: 1. **Navigate to the Airbyte Configuration Directory**: Change to the relevant directory containing the Terraform configuration for Airbyte: ```bash cd infra/airbyte ``` 2. **Modify Configuration Files**: Within the `infra/airbyte` directory, you'll find three crucial Terraform files: - `provider.tf`: Defines the Airbyte provider. - `main.tf`: Contains the main configuration for creating Airbyte resources. - `variables.tf`: Holds various variables, including credentials. 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. 3. **Initialize Terraform**: This step prepares Terraform to create the resources defined in your configuration files. ```bash terraform init ``` 4. **Review the Plan**: Before applying any changes, review the plan to understand what Terraform will do. ```bash terraform plan ``` 5. **Apply Configuration**: After reviewing and confirming the plan, apply the Terraform configurations to create the necessary Airbyte resources. ```bash terraform apply ``` 6. **Verify in Airbyte UI**: 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. ## 4. Setting Up the dbt Project [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: 1. **Navigate to the dbt Project Directory**: Change to the directory containing the dbt configuration: ```bash cd dbt_project ``` 2. **Update Connection Details**: 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. 3. **Utilize Environment Variables (Optional but Recommended)**: To keep your credentials secure, you can leverage environment variables. An example is provided within the `profiles.yml` file. 4. **Test the Connection**: Once you’ve updated the connection details, you can test the connection to your BigQuery instance using: ```bash dbt debug ``` If everything is set up correctly, this command should report a successful connection to BigQuery. 5. **Run the Models**: If you would like to run the dbt models manually at this point, you can do so by executing: ```bash dbt run ``` You can verify the data has been transformed by going to BigQuery and checking the `transformed_data` dataset. ## 5. Orchestrating with Dagster [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: 1. **Navigate to the Orchestration Directory**: Switch to the directory containing the Dagster orchestration configurations: ```bash cd orchestration ``` 2. **Set Environment Variables**: Dagster requires certain environment variables to be set to interact with other tools like dbt and Airbyte. Set the following variables: ```bash export DAGSTER_DBT_PARSE_PROJECT_ON_LOAD=1 export AIRBYTE_PASSWORD=password ``` 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. 3. **Launch the Dagster UI**: With the environment variables in place, kick-start the Dagster UI: ```bash dagster dev ``` 4. **Access Dagster in Your Browser**: Open your browser and navigate to: ``` http://127.0.0.1:3000 ``` 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. 5. **Materialize Dagster Assets**: 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. ## Next Steps Congratulations 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: ### 1. **Explore the Data and Insights** - 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! ### 2. **Optimize Your dbt Models** - 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. ### 3. **Automate and Monitor Your Pipelines** - 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. ================================================ FILE: ticket_volume_analytics_zendesk_support/dbt_project/.gitignore ================================================ target/ dbt_packages/ logs/ .user.yml ================================================ FILE: ticket_volume_analytics_zendesk_support/dbt_project/README.md ================================================ Welcome to your new dbt project! ### Using the starter project Try running the following commands: - dbt run - dbt test ### Resources: - Learn more about dbt [in the docs](https://docs.getdbt.com/docs/introduction) - Check out [Discourse](https://discourse.getdbt.com/) for commonly asked questions and answers - Join the [chat](https://community.getdbt.com/) on Slack for live discussions and support - Find [dbt events](https://events.getdbt.com) near you - Check out [the blog](https://blog.getdbt.com/) for the latest news on dbt's development and best practices ================================================ FILE: ticket_volume_analytics_zendesk_support/dbt_project/dbt_project.yml ================================================ # Name your project! Project names should contain only lowercase characters # and underscores. A good package name should reflect your organization's # name or the intended use of these models name: 'dbt_project' version: '1.0.0' config-version: 2 # This setting configures which "profile" dbt uses for this project. profile: 'dbt_project' # These configurations specify where dbt should look for different types of files. # The `model-paths` config, for example, states that models in this project can be # found in the "models/" directory. You probably won't need to change these! model-paths: ["models"] analysis-paths: ["analyses"] test-paths: ["tests"] seed-paths: ["seeds"] macro-paths: ["macros"] snapshot-paths: ["snapshots"] clean-targets: # directories to be removed by `dbt clean` - "target" - "dbt_packages" # Configuring models # Full documentation: https://docs.getdbt.com/docs/configuring-models # In this example config, we tell dbt to build all models in the example/ # directory as views. These settings can be overridden in the individual model # files using the `{{ config(...) }}` macro. models: dbt_project: # Config indicated by + and applies to all files under models/example/ staging: +materialized: view marts: +materialized: view ================================================ FILE: ticket_volume_analytics_zendesk_support/dbt_project/models/marts/busier_day_of_week_analysis.sql ================================================ WITH ticket_day_of_week_counts AS ( SELECT EXTRACT(DAYOFWEEK FROM created_at) AS day_of_week, COUNT(*) AS ticket_count FROM {{ ref('stg_tickets') }} GROUP BY 1 ) SELECT * FROM ticket_day_of_week_counts ================================================ FILE: ticket_volume_analytics_zendesk_support/dbt_project/models/marts/pattern_and_trend_analysis.sql ================================================ with ticket_hourly_counts as ( select extract(hour from created_at) as hour_of_day, count(*) as ticket_count from {{ ref('stg_tickets') }} group by 1 ) select * from ticket_hourly_counts ================================================ FILE: ticket_volume_analytics_zendesk_support/dbt_project/models/marts/seasonal_analysis.sql ================================================ with ticket_seasonal_counts as ( select extract(month from created_at) as month, count(*) as ticket_count from {{ ref('stg_tickets') }} group by 1 ) select * from ticket_seasonal_counts ================================================ FILE: ticket_volume_analytics_zendesk_support/dbt_project/models/marts/ticket_priority_analysis.sql ================================================ with ticket_priority_counts as ( select priority, count(*) as ticket_count from {{ ref('stg_tickets') }} group by 1 ) select * from ticket_priority_counts ================================================ FILE: ticket_volume_analytics_zendesk_support/dbt_project/models/marts/ticket_resolution_time_analysis.sql ================================================ with ticket_resolution_times as ( select TIMESTAMP_DIFF(solved_at, created_at, HOUR) as resolution_time, count(*) as ticket_count from transformed_data.stg_ticket_metrics where solved_at is not null group by 1 ) select * from ticket_resolution_times ================================================ FILE: ticket_volume_analytics_zendesk_support/dbt_project/models/marts/ticket_source_analysis.sql ================================================ with source_data as ( select JSON_EXTRACT_SCALAR(json_data, '$.channel') as ticket_source, count(*) as ticket_count from ( select JSON_EXTRACT_ARRAY(via)[OFFSET(0)] as json_data from transformed_data.stg_tickets ) t group by 1 ) select * from source_data ================================================ FILE: ticket_volume_analytics_zendesk_support/dbt_project/models/marts/ticket_volume_analysis.sql ================================================ with ticket_counts as ( select date(created_at) as ticket_date, count(*) as ticket_count from {{ ref('stg_tickets') }} group by 1 ) select * from ticket_counts ================================================ FILE: ticket_volume_analytics_zendesk_support/dbt_project/models/sources/zendesk_support_sources.yml ================================================ version: 2 sources: - name: zendesk_support # Use your BigQuery project ID database: "{{ env_var('BIGQUERY_PROJECT_ID', '') }}" # Use your BigQuery dataset name schema: zendesk_airbyte_trial tables: - name: users description: "Simulated user data from the Zendesk Support connector." columns: - name: id description: "Unique identifier for the user." - name: active - name: alias - name: chat_only - name: created_at - name: custom_role_id - name: default_group_id - name: details - name: email - name: external_id - name: updated_at - name: iana_time_zone - name: last_login_at - name: locale - name: locale_id - name: moderator - name: name - name: notes - name: only_private_comments - name: organization_id - name: permanently_deleted - name: phone - name: photo - name: report_csv - name: restricted_agent - name: role - name: role_type - name: shared - name: shared_agent - name: shared_phone_number - name: signature - name: suspended - name: tags - name: ticket_restriction - name: time_zone - name: two_factor_auth_enabled - name: url - name: user_fields - name: verified - name: tickets description: "Simulated tickets data from the Zendesk Support connector." columns: - name: id description: "Unique identifier for the ticket." - name: updated_at - name: allow_attachments - name: allow_channelback - name: assignee_id - name: brand_id - name: collaborator_ids - name: created_at - name: custom_fields - name: custom_status_id - name: description - name: due_at - name: email_cc_ids - name: external_id - name: fields - name: follower_ids - name: followup_ids - name: forum_topic_id - name: from_messaging_channel - name: generated_timestamp - name: group_id - name: has_incidents - name: is_public - name: organization_id - name: priority - name: problem_id - name: raw_subject - name: recipient - name: requester_id - name: satisfaction_rating - name: sharing_agreement_ids - name: status - name: subject - name: submitter_id - name: tags - name: ticket_form_id - name: type - name: url - name: via - name: schedules description: "Simulated schedules data from the Zendesk Support connector." columns: - name: id description: "Unique identifier for the schedule." - name: created_at - name: intervals - name: name - name: time_zone - name: updated_at - name: ticket_metrics description: "Simulated ticket_metrics data from the Zendesk Support connector." columns: - name: id description: "Unique identifier for the ticket_metrics." - name: agent_wait_time_in_minutes - name: created_at - name: assigned_at - name: assignee_stations - name: assignee_updated_at - name: custom_status_updated_at - name: first_resolution_time_in_minutes - name: full_resolution_time_in_minutes - name: group_stations - name: initially_assigned_at - name: instance_id - name: latest_comment_added_at - name: metric - name: on_hold_time_in_minutes - name: updated_at - name: url - name: reopens - name: replies - name: reply_time_in_minutes - name: requester_updated_at - name: requester_wait_time_in_minutes - name: solved_at - name: status - name: status_updated_at - name: ticket_id - name: time - name: type ================================================ FILE: ticket_volume_analytics_zendesk_support/dbt_project/models/staging/stg_schedules.sql ================================================ select * from {{ source('zendesk_support', 'schedules') }} ================================================ FILE: ticket_volume_analytics_zendesk_support/dbt_project/models/staging/stg_ticket_metrics.sql ================================================ select * from {{ source('zendesk_support', 'ticket_metrics') }} ================================================ FILE: ticket_volume_analytics_zendesk_support/dbt_project/models/staging/stg_tickets.sql ================================================ select * from {{ source('zendesk_support', 'tickets') }} ================================================ FILE: ticket_volume_analytics_zendesk_support/dbt_project/models/staging/stg_users.sql ================================================ select * from {{ source('zendesk_support', 'users') }} ================================================ FILE: ticket_volume_analytics_zendesk_support/dbt_project/profiles.yml ================================================ dbt_project: outputs: dev: dataset: transformed_data job_execution_timeout_seconds: 300 job_retries: 1 # Use an env variable to indicate your JSON key file path keyfile: "{{ env_var('DBT_BIGQUERY_KEYFILE_PATH', '') }}" location: US method: service-account priority: interactive # Indicate your BigQuery project ID project: "{{ env_var('BIGQUERY_PROJECT_ID', '') }}" threads: 1 type: bigquery target: dev ================================================ FILE: ticket_volume_analytics_zendesk_support/dbt_project/snapshots/.gitkeep ================================================ ================================================ FILE: ticket_volume_analytics_zendesk_support/dbt_project/tests/.gitkeep ================================================ ================================================ FILE: ticket_volume_analytics_zendesk_support/infra/.gitignore ================================================ # Local .terraform directories **/.terraform/* # .tfstate files *.tfstate *.tfstate.* # Crash log files crash.log crash.*.log # Exclude all .tfvars files, which are likely to contain sensitive data, such as # password, private keys, and other secrets. These should not be part of version # control as they are data points which are potentially sensitive and subject # to change depending on the environment. *.tfvars *.tfvars.json # Ignore override files as they are usually used to override resources locally and so # are not checked in override.tf override.tf.json *_override.tf *_override.tf.json # Include override files you do wish to add to version control using negated pattern # !example_override.tf # Include tfplan files to ignore the plan output of command: terraform plan -out=tfplan # example: *tfplan* # Ignore CLI configuration files .terraformrc terraform.rc ================================================ FILE: ticket_volume_analytics_zendesk_support/infra/airbyte/.terraform.lock.hcl ================================================ # This file is maintained automatically by "terraform init". # Manual edits may be lost in future updates. provider "registry.terraform.io/airbytehq/airbyte" { version = "0.3.3" constraints = "0.3.3" hashes = [ "h1:0LmuAc5LvlMuOUPtNEaCAh9FHrV/C877bDJhm9Lz8MU=", "zh:0efa470b34d9b912b47efe4469c51713bfc3c2413e52c17e1e903f2a3cddb2f6", "zh:1bddd69fa2c2d4f3e239d60555446df9bc4ce0c0cabbe7e092fe1d44989ab004", "zh:2e20540403a0010007b53456663fb037b24e30f6c8943f65da1bcf7fa4dfc8a6", "zh:2f415369ad884e8b7115a5c5ff229d052f7af1fca27abbfc8ebef379ed11aec4", "zh:46fd9a906f4b6461112dcc5a5aa01a3fcd7a19a72d4ad0b2e37790da37701fe1", "zh:83503ebb77bb6d6941c42ba323cf22380d08a1506554a2dcc8ac54e74c0886a1", "zh:890df766e9b839623b1f0437355032a3c006226a6c200cd911e15ee1a9014e9f", "zh:8fd770eff726826d3a63b9e3733c5455b5cde004027b04ee3f75888eb8538c90", "zh:b0fc890ed4f9b077bf70ed121cc3550e7a07d16e7798ad517623274aa62ad7b0", "zh:c2a01612362da9b73cd5958f281e1aa7ff09af42182e463097d11ed78e778e72", "zh:c64b2bb1887a0367d64ba3393d4b3a16c418cf5b1792e2e7aae7c0b5413eb334", "zh:ce14ebbf0ed91913ec62655a511763dec62b5779de9a209bd6f1c336640cddc0", "zh:e0662ca837eee10f7733ea9a501d995281f56bd9b410ae13ad03eb106011db14", "zh:e103d480fc6066004bc98e9e04a141a1f55b918cc2912716beebcc6fc4c872fb", "zh:e2507049098f0f1b21cb56870f4a5ef624bcf6d3959e5612eada1f8117341648", ] } ================================================ FILE: ticket_volume_analytics_zendesk_support/infra/airbyte/main.tf ================================================ // Airbyte Terraform provider documentation: https://registry.terraform.io/providers/airbytehq/airbyte/latest/docs // Sources resource "airbyte_source_zendesk_support" "my_source_zendesksupport" { configuration = { source_type = "zendesk-support" credentials = { source_zendesk_support_authentication_api_token = { credentials = "api_token" api_token = var.api_token email = var.email } } start_date = "2020-10-15T00:00:00Z" subdomain = "self3836" } name = "Zendesk Support" workspace_id = var.workspace_id } // Destinations resource "airbyte_destination_bigquery" "bigquery" { configuration = { dataset_id = var.dataset_id dataset_location = "US" destination_type = "bigquery" project_id = var.project_id credentials_json = var.credentials_json loading_method = { destination_bigquery_loading_method_standard_inserts = { method = "Standard" } } } name = "BigQuery" workspace_id = var.workspace_id } // Connections resource "airbyte_connection" "zendesk_support_bigquery" { name = "Zendesk Support to BigQuery" source_id = airbyte_source_zendesk_support.my_source_zendesksupport.source_id destination_id = airbyte_destination_bigquery.bigquery.destination_id configurations = { streams = [ { name = "users" }, { name = "tickets" }, { name = "satisfaction_ratings" }, { name = "ticket_metrics" }, { name = "ticket_metric_events" }, { name = "ticket_comments" }, { name = "ticket_audits" } ] } } ================================================ FILE: ticket_volume_analytics_zendesk_support/infra/airbyte/provider.tf ================================================ // Airbyte Terraform provider documentation: https://registry.terraform.io/providers/airbytehq/airbyte/latest/docs terraform { required_providers { airbyte = { source = "airbytehq/airbyte" version = "0.3.3" } } } provider "airbyte" { // If running locally (Airbyte OSS) with docker-compose using the airbyte-proxy, // include the actual password/username you've set up (or use the defaults below) username = "airbyte" password = "password" // if running locally (Airbyte OSS), include the server url to the airbyte-api-server server_url = "http://localhost:8006/v1/" } ================================================ FILE: ticket_volume_analytics_zendesk_support/infra/airbyte/variables.tf ================================================ variable "api_token" { type = string } variable "email" { type = string } variable "workspace_id" { type = string } variable "dataset_id" { type = string } variable "project_id" { type = string } variable "credentials_json" { type = string } ================================================ FILE: ticket_volume_analytics_zendesk_support/orchestration/orchestration/__init__.py ================================================ ================================================ FILE: ticket_volume_analytics_zendesk_support/orchestration/orchestration/assets.py ================================================ import os from dagster import OpExecutionContext from dagster_dbt import DbtCliResource, dbt_assets from dagster_airbyte import AirbyteResource, load_assets_from_airbyte_instance from .constants import dbt_manifest_path @dbt_assets(manifest=dbt_manifest_path) def dbt_project_dbt_assets(context: OpExecutionContext, dbt: DbtCliResource): yield from dbt.cli(["build"], context=context).stream() airbyte_instance = AirbyteResource( host="localhost", port="8000", # If using basic auth, include username and password: username="airbyte", password="password" ) airbyte_assets = load_assets_from_airbyte_instance(airbyte_instance,) ================================================ FILE: ticket_volume_analytics_zendesk_support/orchestration/orchestration/constants.py ================================================ import os from pathlib import Path from dagster_dbt import DbtCliResource dbt_project_dir = Path(__file__).joinpath("..", "..", "..", "dbt_project").resolve() dbt = DbtCliResource(project_dir=os.fspath(dbt_project_dir)) # If DAGSTER_DBT_PARSE_PROJECT_ON_LOAD is set, a manifest will be created at runtime. # Otherwise, we expect a manifest to be present in the project's target directory. if os.getenv("DAGSTER_DBT_PARSE_PROJECT_ON_LOAD"): dbt_parse_invocation = dbt.cli(["parse"], manifest={}).wait() dbt_manifest_path = dbt_parse_invocation.target_path.joinpath("manifest.json") else: dbt_manifest_path = dbt_project_dir.joinpath("target", "manifest.json") ================================================ FILE: ticket_volume_analytics_zendesk_support/orchestration/orchestration/definitions.py ================================================ import os from dagster import Definitions from dagster_dbt import DbtCliResource from .assets import dbt_project_dbt_assets, airbyte_assets from .constants import dbt_project_dir from .schedules import schedules defs = Definitions( assets=[dbt_project_dbt_assets, airbyte_assets], schedules=schedules, resources={ "dbt": DbtCliResource(project_dir=os.fspath(dbt_project_dir)), }, ) ================================================ FILE: ticket_volume_analytics_zendesk_support/orchestration/orchestration/schedules.py ================================================ """ To add a daily schedule that materializes your dbt assets, uncomment the following lines. """ from dagster_dbt import build_schedule_from_dbt_selection from .assets import dbt_project_dbt_assets schedules = [ # build_schedule_from_dbt_selection( # [dbt_project_dbt_assets], # job_name="materialize_dbt_models", # cron_schedule="0 0 * * *", # dbt_select="fqn:*", # ), ] ================================================ FILE: ticket_volume_analytics_zendesk_support/orchestration/pyproject.toml ================================================ [build-system] requires = ["setuptools"] build-backend = "setuptools.build_meta" [tool.dagster] module_name = "orchestration.definitions" code_location_name = "orchestration" ================================================ FILE: ticket_volume_analytics_zendesk_support/orchestration/setup.py ================================================ from setuptools import find_packages, setup setup( name="orchestration", version="0.0.1", packages=find_packages(), install_requires=[ "dagster", "dagster-cloud", "dagster-dbt", "dbt-core>=1.4.0", "dbt-bigquery", ], extras_require={ "dev": [ "dagster-webserver", ] }, ) ================================================ FILE: ticket_volume_analytics_zendesk_support/setup.py ================================================ from setuptools import find_packages, setup setup( name="airbyte-dbt-dagster", packages=find_packages(), install_requires=[ "dbt-bigquery", "dagster", "dagster-cloud", "dagster-dbt", "dagster-airbyte", ], extras_require={"dev": ["dagit", "pytest"]}, ) ================================================ FILE: vector_store_integration/AI_assistant_streamlit_app/.gitignore ================================================ venv/ .secrets .streamlit dist/ build/ *.egg-info/ .DS_STORE ================================================ FILE: vector_store_integration/AI_assistant_streamlit_app/README.md ================================================ ### Run Locally - 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. - Create a folder called `.streamlit` and create a file called `secrets.toml`. - Add the following to the `secrets.toml` file: ```toml OPENAI_API_KEY="YOUR_OPENAI_API_KEY" SNOWFLAKE_HOST="YOUR_SNOWFLAKE_ACCOUNT_NAME" SNOWFLAKE_ROLE="YOUR_SNOWFLAKE_ROLE" SNOWFLAKE_WAREHOUSE="YOUR_SNOWFLAKE_WAREHOUSE" SNOWFLAKE_DATABASE="YOUR_SNOWFLAKE_DATABASE" SNOWFLAKE_SCHEMA="YOUR_SNOWFLAKE_SCHEMA" SNOWFLAKE_USERNAME="YOUR_SNOWFLAKE_USERNAME" SNOWFLAKE_PASSWORD="YOUR_SNOWFLAKE_PASSWORD" ``` - Update the table names and LLM instructions in `app.py` with data specific to your use case. - Create a virtual environment: ```bash python3 -m venv venv ``` - Activate the virtual environment: ```bash source venv/bin/activate ``` - Install the requirements: ```bash pip install -r requirements.txt ``` - Run the app: ```bash streamlit run app.py ``` - Open your browser and go to [http://localhost:8501](http://localhost:8501). ### Deploy to Streamlit Community Cloud - Copy the code into its own repository. - Create an account [here](https://streamlit.io/cloud) and follow the instructions to give access to the repository you created. ================================================ FILE: vector_store_integration/AI_assistant_streamlit_app/app.py ================================================ import streamlit as st import random import time from openai import OpenAI import openai from snowflake import connector from langchain.embeddings import OpenAIEmbeddings from typing import List def get_db_connection(): return connector.connect( account=st.secrets["SNOWFLAKE_HOST"], role=st.secrets["SNOWFLAKE_ROLE"], warehouse=st.secrets["SNOWFLAKE_WAREHOUSE"], database=st.secrets["SNOWFLAKE_DATABASE"], schema=st.secrets["SNOWFLAKE_SCHEMA"], user=st.secrets["SNOWFLAKE_USERNAME"], password=st.secrets["SNOWFLAKE_PASSWORD"], ) def get_similar_chunks(query_vector, table_names) -> List[str]: conn = get_db_connection() cursor = conn.cursor() chunks = [] for table_name in table_names: query = f""" SELECT document_content, VECTOR_COSINE_SIMILARITY(embedding, CAST({query_vector} AS VECTOR(FLOAT, 1536))) AS similarity FROM {table_name} ORDER BY similarity DESC LIMIT 2 """ cursor.execute(query) result = cursor.fetchall() chunks += [item[0] for item in result] cursor.close() conn.close() return chunks def get_completion(question, document_chunks: List[str], model_name: str = "llama2-70b-chat"): conn = get_db_connection() cur = conn.cursor() chunks = "\n\n".join(document_chunks) query = f""" SELECT snowflake.cortex.complete( '{model_name}', CONCAT( '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: ', $$ {chunks} $$, 'Question: ', $$ {question} $$, 'Answer: ' ) ) as response;""" cur.execute(query) result = cur.fetchall() cur.close() conn.close() # TO-DO: better parsing here return result[0][0].strip() def get_user_intent(query): # this method does a simple few shots classifcation to help get user's intent examples = [ {"role": "system", "content": "You are an assistant that classifies user intents based on their messages."}, {"role": "user", "content": "How can I add vector data in Snowflake?"}, {"role": "assistant", "content": "docs_question"}, {"role": "user", "content": "How do I set up Snowflake Cortex destination?"}, {"role": "assistant", "content": "docs_question"}, {"role": "user", "content": "What are the upcoming features for Snowflake Cortex?"}, {"role": "assistant", "content": "github_question"}, {"role": "user", "content": "What are the known issues for Snowflake Cortex?"}, {"role": "assistant", "content": "github_question"}, {"role": "user", "content": "Which customers have requested features for Snowflake Cortex?"}, {"role": "assistant", "content": "zendesk_question"}, {"role": "user", "content": "Which customers have requested authorization related features for Snowflake Cortex?"}, {"role": "assistant", "content": "zendesk_question"}, ] examples.append({"role": "user", "content": query}) openai.api_key = st.secrets["OPENAI_API_KEY"] # Call the OpenAI chat API to get the intent classification response = openai.chat.completions.create( model="gpt-3.5-turbo", messages=examples, max_tokens=10, n=1, stop=["\n"] ) intent = response.choices[0].message.content return intent def get_tables_to_query(query): intent = get_user_intent(query) if intent == "github_question": return (["airbyte_github_issues"], "llama2-70b-chat") elif intent == "zendesk_question": return (["airbyte_zendesk_tickets", "airbyte_zendesk_users"], "llama2-70b-chat") else: # default return(["airbyte_docs"], "snowflake-arctic") def get_response(query): # embed the query embeddings = OpenAIEmbeddings(openai_api_key=st.secrets["OPENAI_API_KEY"]) # get similar chunks from sources/tables in Snowflake [tables, model] = get_tables_to_query(query) chunks = get_similar_chunks(embeddings.embed_query(query), tables) if (len(chunks) == 0): return "I am sorry, I do not have the context to answer your question." else: return get_completion(query, chunks, model) def response_generator(query): response = get_response(query) # Split the response by spaces but preserve the new lines parts = [] current_part = [] for char in response: if char in ['\n', ' ']: if current_part: parts.append(''.join(current_part)) current_part = [] if char == '\n': parts.append('\n') else: parts.append(' ') else: current_part.append(char) if current_part: parts.append(''.join(current_part)) for part in parts: yield part time.sleep(0.01) st.title("AI Product assistant") # simulate initial message from assistant initial_message = """👋 Hello! I'm here to help you with any questions you have on Airbyte's products or services. How can I assist you today?""" # Display initial message from assistant with st.chat_message("assistant"): st.write(initial_message) # Recommended questions section in the sidebar st.sidebar.title("Recommended Questions") recommended_questions = [ "How can I add vector data in Snowflake?", "What are the upcoming features for Snowflake Cortex?", "Which customers have requested features for Snowflake Cortex?", ] for question in recommended_questions: st.sidebar.markdown(f"- {question}") client = OpenAI(api_key=st.secrets["OPENAI_API_KEY"]) # Set a default model if "openai_model" not in st.session_state: st.session_state["openai_model"] = "gpt-3.5-turbo" # Initialize chat history if "messages" not in st.session_state: st.session_state.messages = [] # Display chat messages from history on app rerun for message in st.session_state.messages: with st.chat_message(message["role"]): st.markdown(message["content"]) if prompt := st.chat_input("Ask a question?"): # Display user message in chat message container with st.chat_message("user"): st.markdown(prompt) # Add user message to chat history st.session_state.messages.append({"role": "user", "content": prompt}) with st.chat_message("assistant"): response = st.write_stream(response_generator(prompt)) # Add assistant response to chat history st.session_state.messages.append({"role": "assistant", "content": response}) ================================================ FILE: vector_store_integration/AI_assistant_streamlit_app/requirements.txt ================================================ streamlit openai snowflake-connector-python langchain langchain-community tiktoken ================================================ FILE: vector_store_integration/RAG_using_PGVector.ipynb ================================================ { "nbformat": 4, "nbformat_minor": 0, "metadata": { "colab": { "provenance": [] }, "kernelspec": { "name": "python3", "display_name": "Python 3" }, "language_info": { "name": "python" } }, "cells": [ { "metadata": {}, "cell_type": "code", "outputs": [], "execution_count": null, "source": [ "## Airbyte PGVector RAG Demo\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", "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", "#### Prerequisites:\n", "* Vector data stored in Postgres with Vector colums via PGVector destination. In our case we are using data from kaggle.\n", "* Postgresql DB with PGVector enabled\n", "* Open AI key\n" ] }, { "cell_type": "markdown", "source": [ "### a. Install dependencies and import secrets\n", "\n" ], "metadata": { "id": "7R0-uD7R3Uki" } }, { "cell_type": "code", "source": [ "!pip install sqlalchemy openai rich psycopg2 python-dotenv langchain-openai" ], "metadata": { "collapsed": true, "id": "HbR-po_Z3VFV" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "import openai\n", "import json\n", "import rich\n", "from langchain_openai import OpenAIEmbeddings\n", "from sqlalchemy import create_engine\n", "from sqlalchemy.engine import URL\n", "from google.colab import userdata\n", "\n", "\n", "OPENAI_API_KEY = userdata.get('openai_api_key')\n", "HOST = userdata.get(\"db_host\")\n", "USERNAME = userdata.get(\"db_username\")\n", "PASSWORD = userdata.get(\"db_password\")\n", "DATABASE = userdata.get(\"db_name\")" ], "metadata": { "id": "LP0QfMFQ6Flz" }, "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "source": [ "### b. Initialize open AI client and DB Engine\n" ], "metadata": { "id": "mmHB_MId7zwo" } }, { "cell_type": "code", "source": [ "openai.api_key = OPENAI_API_KEY\n", "\n", "url = URL.create(\n", " \"postgresql\",\n", " host=HOST,\n", " username=USERNAME,\n", " password=PASSWORD,\n", " database=DATABASE,\n", ")\n", "\n", "engine = create_engine(url)" ], "metadata": { "id": "7Y3iCe6e7-Ra" }, "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "source": [ "### d. Explore data stored in Posgresql\n", "\n", "We need a few methods to embed user questions and make searches in DB\n", "\n", "- Helper to embed the user question so we can the search for it in the DB.\n", "- Function to get the context from the database using a user question as input.\n", "- One to get the response from the chat assistant that will use the context using the method from previous step." ], "metadata": { "id": "ZQ0PzOfb9rtK" } }, { "cell_type": "code", "source": [ "from sqlalchemy import text\n", "from openai import OpenAI\n", "\n", "client = OpenAI(\n", " api_key=OPENAI_API_KEY,\n", ")\n", "\n", "def get_embedding_from_open_ai(question):\n", " print(f\"Embedding user's query: {question}\")\n", " embeddings = OpenAIEmbeddings(openai_api_key=OPENAI_API_KEY)\n", " embedding_response = embeddings.embed_query(question)\n", " return embedding_response\n", "\n", "QUERY_TEMPLATE = \"\"\"\n", "SELECT document_content,\n", " metadata->'title' as episode_title,\n", " metadata->'script_line_number' as script_line_number,\n", " metadata->'name' as character_name,\n", " metadata->'spoken_words' as spoken_words\n", "FROM episode_spoken_words\n", "ORDER BY embedding <-> :question_vector\n", "LIMIT 5\n", "\"\"\"\n", "\n", "def get_context(question) -> str:\n", " # Get the embedding from OpenAI\n", " question_vector = get_embedding_from_open_ai(question)\n", "\n", " # Format the question vector as a string in the format expected by PostgreSQL\n", " question_vector_str = '[' + ','.join(map(str, question_vector)) + ']'\n", "\n", " # Use the text() function for raw queries with SQLAlchemy\n", " query = text(QUERY_TEMPLATE)\n", "\n", " # Execute the query, passing the vector as a bind parameter\n", " with engine.connect() as connection:\n", " result = connection.execute(query, {'question_vector': question_vector_str})\n", "\n", " # Format and return the result\n", " return (\"\\n\\n\" + \"-\" * 8 + \"\\n\\n\").join(\n", " [\n", " f\"Episode {row.episode_title} | Line number: {row.script_line_number} | \"\n", " f\"Spoken Words: {row.spoken_words} | Character: {row.character_name}\"\n", " for row in result\n", " ]\n", " )\n", "\n", "\n", "def get_response(question):\n", " response = client.chat.completions.create(\n", " model=\"gpt-3.5-turbo\",\n", " messages=[\n", " {\"role\": \"system\", \"content\": \"You are a Simpsons expert talking about Simpsons episodes.\"},\n", " {\"role\": \"user\", \"content\": question},\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", " return response.choices[0].message.content\n", "\n" ], "metadata": { "id": "_PJ6eb5-A419" }, "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "source": [ "### d. Make questions\n", "\n", "Finally, let's put all together and get a response from our assistant using the Simpsons database." ], "metadata": { "id": "spQPCVe9AZKh" } }, { "cell_type": "code", "source": [ "question = \"Talking about food\"\n", "response = get_response(question)\n", "rich.print(response)" ], "metadata": { "id": "pwegM_02AgOU" }, "execution_count": null, "outputs": [] } ] } ================================================ FILE: vector_store_integration/RAG_using_Snowflake_Cortex.ipynb ================================================ { "cells": [ { "cell_type": "markdown", "metadata": { "id": "KWKEgtakJtH6" }, "source": [ "## Airbyte Snowflake Cortex RAG Demo\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", "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", "#### Prerequisites:\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", "* Snowflake account with Cortex functions enabled\n", "* Open AI key\n" ] }, { "cell_type": "markdown", "metadata": { "id": "gS3oGgI0CVpn" }, "source": [ "### a. Install dependencies\n", "\n", "\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "bM4Te8XEWECV" }, "outputs": [], "source": [ "# Add virtual environment support in Google Colab\n", "!apt-get install -qq python3.10-venv\n", "\n", "# Install openai\n", "# tbd - add snowflake python connector\n", "%pip install --quiet openai snowflake-connector-python langchain-openai tiktoken" ] }, { "cell_type": "markdown", "metadata": { "id": "nKbdq1eUcmAz" }, "source": [ "### b. Explore data stored in Snowflake.\n", "\n", "Let's see what document/vecto data in Snowflake looks like." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "collapsed": true, "id": "TKDGBUhLcfuK", "outputId": "1231d559-298f-43a0-95c3-a67f8cfb62ab" }, "outputs": [], "source": [ "# Fetch data from airbyte_docs table\n", "from snowflake import connector\n", "from google.colab import userdata\n", "from typing import List\n", "import pandas as pd\n", "\n", "def get_db_connection():\n", " return connector.connect(\n", " account=userdata.get(\"SNOWFLAKE_HOST\"),\n", " role=userdata.get(\"SNOWFLAKE_ROLE\"),\n", " warehouse=userdata.get(\"SNOWFLAKE_WAREHOUSE\"),\n", " database=userdata.get(\"SNOWFLAKE_DATABASE\"),\n", " schema=userdata.get(\"SNOWFLAKE_SCHEMA\"),\n", " user=userdata.get(\"SNOWFLAKE_USERNAME\"),\n", " password=userdata.get(\"SNOWFLAKE_PASSWORD\"),\n", " )\n", "\n", "def fetch_table_data(table_name, columns):\n", " conn = get_db_connection()\n", " cursor = conn.cursor()\n", "\n", " # Form the query to select specific columns\n", " columns_str = \", \".join(columns)\n", " query = f\"SELECT {columns_str} FROM {table_name};\"\n", "\n", " cursor.execute(query)\n", " result = cursor.fetchall()\n", "\n", " # Fetch the column names\n", " col_names = [desc[0] for desc in cursor.description]\n", "\n", " cursor.close()\n", " conn.close()\n", "\n", " # Load the result into a pandas DataFrame\n", " df = pd.DataFrame(result, columns=col_names)\n", " return df;\n", "\n", "# show data from airbtye_docs table\n", "data_frame = fetch_table_data(\"airbyte_docs\", [\"document_id\", \"document_content\", \"metadata\", \"embedding\"])\n", "data_frame" ] }, { "cell_type": "markdown", "metadata": { "id": "5LU0M4g6clBj" }, "source": [] }, { "cell_type": "markdown", "metadata": { "id": "SyyzczWXBXTS" }, "source": [ "### c. Build the RAG pipeline and ask a question\n", "\n", "Let's write the three main pieces of a RAG pipeline:\n", "* Embedding incoming query\n", "* Doing similarity search to find matching chunks\n", "* Send chunks to LLM for completion" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 265 }, "id": "zQ6rWEV2u-3U", "outputId": "d364b7e3-cfca-4628-da20-3ae4f34ad143" }, "outputs": [], "source": [ "from openai import OpenAI\n", "from snowflake import connector\n", "from langchain_openai import OpenAIEmbeddings\n", "from google.colab import userdata\n", "from typing import List\n", "from rich.console import Console\n", "\n", "def get_db_connection():\n", " return connector.connect(\n", " account=userdata.get(\"SNOWFLAKE_HOST\"),\n", " role=userdata.get(\"SNOWFLAKE_ROLE\"),\n", " warehouse=userdata.get(\"SNOWFLAKE_WAREHOUSE\"),\n", " database=userdata.get(\"SNOWFLAKE_DATABASE\"),\n", " schema=userdata.get(\"SNOWFLAKE_SCHEMA\"),\n", " user=userdata.get(\"SNOWFLAKE_USERNAME\"),\n", " password=userdata.get(\"SNOWFLAKE_PASSWORD\"),\n", " )\n", "\n", "# convert user's query into a vector array to prep for similiary search\n", "def get_embedding_from_openai(query)->str:\n", " print(f\"Embedding user's query -> {query}...\")\n", " embeddings = OpenAIEmbeddings(openai_api_key=userdata.get(\"OPENAI_API_KEY\"))\n", " return embeddings\n", "\n", "# use Snowflake's Cortex in-build similarity search to find matching chunks\n", "def get_similar_chunks_from_snowflake(query_vector, table_names) -> List[str]:\n", " print(\"\\nRetrieving similar chunks...\")\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,\n", " 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", " print(f\"Found {len(result)} matching chunks in table:{table_name}!\")\n", " chunks += [item[0] for item in result]\n", " cursor.close()\n", " conn.close()\n", "\n", " return chunks\n", "\n", "# use Snowflake's Cortex in-build completion to find matching chunks.\n", "def get_completion_from_snowflake(question, document_chunks: List[str], model_name):\n", " print(f\"\\nSending chunks to Snowflake (LLM: {model_name}) for completion...\")\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. Do not use any other information. 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", "# Putting it all together\n", "def get_response(query, table_names, model_name=\"llama2-70b-chat\"):\n", " # Step 1: embed the query\n", " embeddings = get_embedding_from_openai(query)\n", "\n", " # Step 2: get similar chunks from sources/tables in Snowflake\n", " chunks = get_similar_chunks_from_snowflake(embeddings.embed_query(query), table_names)\n", "\n", " if (len(chunks) == 0):\n", " return \"I am sorry, I do not have the context to answer your question.\"\n", " else:\n", " # Step 3: send chunks to LLM for completion\n", " return get_completion_from_snowflake(query, chunks, model_name)\n", "\n", "# Ask a question\n", "query = 'How can I store vector data in Snowflake'\n", "response = get_response(query, [\"airbyte_docs\"], \"snowflake-arctic\")\n", "\n", "Console().print(f\"\\n\\nResponse from LLM:\\n\\n[blue]{response}[/blue]\")\n" ] }, { "cell_type": "markdown", "metadata": { "id": "G5NvV86T57-V" }, "source": [ "### d. Let's ask another question\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 281 }, "id": "cPYdEs663tl8", "outputId": "6997984b-6ea3-4b95-d80f-ccace53b6d43" }, "outputs": [], "source": [ "query = 'What are the upcoming features for Snowflake Cortex?'\n", "response = get_response(query, [\"airbyte_github_issues\"])\n", "Console().print(f\"\\n\\nResponse from LLM:\\n\\n[blue]{response}[/blue]\")" ] }, { "cell_type": "markdown", "metadata": { "id": "S_CJ3_C9pvw4" }, "source": [ "### e. Closing the loop\n", "Let's see if there are customers asking for upcoming features above." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 267 }, "id": "mLfNlzGLqOIg", "outputId": "dcfa593f-dab0-44ff-fcec-e4cb671b6d1a" }, "outputs": [], "source": [ "query = 'Are there customers asking for better authorization options for Snowflake Cortex? Give me their names and email.'\n", "response = get_response(query, [\"airbyte_zendesk_tickets\", \"airbyte_zendesk_users\"])\n", "Console().print(f\"\\n\\nResponse from LLM:\\n\\n[blue]{response}[/blue]\")" ] } ], "metadata": { "colab": { "collapsed_sections": [ "nKbdq1eUcmAz" ], "provenance": [] }, "kernelspec": { "display_name": "Python 3", "name": "python3" }, "language_info": { "name": "python" } }, "nbformat": 4, "nbformat_minor": 0 } ================================================ FILE: vector_store_integration/RAG_using_Vectara.ipynb ================================================ { "cells": [ { "cell_type": "markdown", "metadata": { "id": "ayf6NOyRb-zn" }, "source": [ "# **Airbyte Vectara RAG Tutorial**\n", "This tutorial showcases how to use data stored in Airbyte's Vectara destination to perform Retrieval-Augmented Generation (RAG).\n", "## **Prerequisites**\n", "**1) OpenAI API Key**:\n", " - **Create an OpenAI Account**: Sign up for an account on [OpenAI](https://www.openai.com/).\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", "**2) Vectara Customer ID, Corpus ID ,API Key**:\n", " - **Create an Vectara Account**: Sign up for an account on [Vectara](https://vectara.com/).\n", " - **Customer ID**: Click on the profile icon on top right, and look for your customer ID [Vectara Console](https://console.vectara.com/).\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", " - **Generate an API Key**: Go here and generate a new API key. [Vectara API_Key](https://console.vectara.com/console/apiAccess/personalApiKey)." ] }, { "cell_type": "markdown", "metadata": { "id": "41LFzVLEmXT3" }, "source": [ "# **Install Dependencies**\n", "As in any other Python Code, the first step is to install the required Dependencies!" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "NSOZnJ6G9zQu" }, "outputs": [], "source": [ "# Add virtual environment support in Google Colab\n", "!apt-get install -qq python3.10-venv\n", "\n", "# Install required packages\n", "%pip install --quiet openai langchain-openai tiktoken pandas langchain_community\n" ] }, { "cell_type": "markdown", "metadata": { "id": "dQTCEicCmx2A" }, "source": [ "# **Set Up Environment Variables**\n", "We configure the required credentials here!" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "94bGodjCFfsN" }, "outputs": [], "source": [ "import os\n", "\n", "os.environ[\"VECTARA_CUSTOMER_ID\"] = \"YOUR_VECTARA_CUSTOMER_ID\"\n", "os.environ[\"VECTARA_CORPUS_ID\"] = \"YOUR_VECTARA_CORPUS_ID\"\n", "os.environ[\"VECTARA_API_KEY\"] = \"YOUR_VECTARA_API_KEY\"\n", "os.environ[\"OPENAI_API_KEY\"] = \"YOUR_OPENAI_API_KEY\"" ] }, { "cell_type": "markdown", "metadata": { "id": "QXR8wOMWnztR" }, "source": [ "# **Initialize Vectara Vector Store**\n", "We start by initializing the Vectara Vector Store and then see what the data in Vectara looks like.\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "ipg7aPJq-wZ8" }, "outputs": [], "source": [ "import pandas as pd\n", "from langchain_community.vectorstores import Vectara\n", "from google.colab import userdata\n", "\n", "# Initialize Vectara vector store\n", "vectara = Vectara(\n", " vectara_customer_id=os.getenv(\"VECTARA_CUSTOMER_ID\"),\n", " vectara_corpus_id=os.getenv(\"VECTARA_CORPUS_ID\"),\n", " vectara_api_key=os.getenv(\"VECTARA_API_KEY\")\n", ")\n", "\n", "def fetch_vectara_data():\n", " # Simulate fetching data from Vectara\n", " data = {\n", " \"document_id\": [1, 2, 3],\n", " \"document_content\": [\"Content of doc 1\", \"Content of doc 2\", \"Content of doc 3\"],\n", " \"metadata\": [\"Metadata1\", \"Metadata2\", \"Metadata3\"],\n", " \"embedding\": [\"[0.1, 0.2, ...]\", \"[0.3, 0.4, ...]\", \"[0.5, 0.6, ...]\"]\n", " }\n", " df = pd.DataFrame(data)\n", " return df\n", "\n", "# show data\n", "data_frame = fetch_vectara_data()\n", "print(data_frame)\n" ] }, { "cell_type": "markdown", "metadata": { "id": "6b0VCg4Oox4l" }, "source": [ "# **Embedding and similarity search with Vectara**\n", "Here we will convert the user's query into embeddings using OpenAI and retrieve similar chunks from Vectara based on the query" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "G6UuEXP9FpdF" }, "outputs": [], "source": [ "from openai import OpenAI\n", "from langchain_openai import OpenAIEmbeddings\n", "from typing import List\n", "from rich.console import Console\n", "\n", "\n", "client = OpenAI(api_key=os.getenv(\"OPENAI_API_KEY\"))\n", "\n", "# Convert user's query into a vector array to prep for similarity search\n", "def get_embedding_from_openai(query) -> List[float]:\n", " print(f\"Embedding user's query -> {query}...\")\n", " embeddings = OpenAIEmbeddings(openai_api_key=client.api_key)\n", " return embeddings.embed_query(query)\n", "\n", "# Use Vectara to find matching chunks\n", "def get_similar_chunks_from_vectara(query: str) -> List[str]:\n", " print(\"\\nRetrieving similar chunks...\")\n", " try:\n", " results = vectara.similarity_search(query=query)\n", " chunks = [result.page_content for result in results]\n", " print(f\"Found {len(chunks)} matching chunks!\")\n", " return chunks\n", " except Exception as e:\n", " print(f\"Error in retrieving chunks: {e}\")\n", " return []\n", "\n" ] }, { "cell_type": "markdown", "metadata": { "id": "ZEQN6yohqVSY" }, "source": [ "# **Building RAG Pipeline and asking a question**\n", "Finally we use OpenAI for querying our data!
\n", "We know the three main steps of a RAG Pipeline are :
\n", "- Embedding incoming query
\n", "- Doing similarity search to find matching chunks
\n", "- Send chunks to LLM for completion" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "zLKAuK-GqLPC" }, "outputs": [], "source": [ "# Use OpenAI to complete the response\n", "def get_completion_from_openai(question, document_chunks: List[str], model_name=\"gpt-3.5-turbo\"):\n", " print(f\"\\nSending chunks to OpenAI (LLM: {model_name}) for completion...\")\n", " chunks = \"\\n\\n\".join(document_chunks)\n", "\n", " response = client.chat.completions.create(\n", " model=model_name,\n", " messages=[\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", " {\"role\": \"user\", \"content\": f\"Context:\\n{chunks}\\n\\n{question}\\n\\nAnswer:\"}\n", " ],\n", " max_tokens=150\n", " )\n", " return response.choices[0].message.content.strip()\n", "\n", "# Putting it all together\n", "def get_response(query, model_name=\"gpt-3.5-turbo\"):\n", "\n", " chunks = get_similar_chunks_from_vectara(query)\n", "\n", " if len(chunks) == 0:\n", " return \"I am sorry, I do not have the context to answer your question.\"\n", " else:\n", "\n", " return get_completion_from_openai(query, chunks, model_name)\n", "\n", "# Ask a question\n", "query = 'What data do you have?'\n", "response = get_response(query)\n", "\n", "Console().print(f\"\\n\\nResponse from LLM:\\n\\n[blue]{response}[/blue]\")\n" ] } ], "metadata": { "colab": { "provenance": [] }, "kernelspec": { "display_name": "Python 3", "name": "python3" }, "language_info": { "name": "python" } }, "nbformat": 4, "nbformat_minor": 0 } ================================================ FILE: weather_data_stack/.gitignore ================================================ # Byte-compiled / optimized / DLL files __pycache__/ *.py[cod] *$py.class # C extensions *.so # Distribution / packaging .Python build/ develop-eggs/ dist/ downloads/ eggs/ .eggs/ lib/ lib64/ parts/ sdist/ var/ wheels/ share/python-wheels/ *.egg-info/ .installed.cfg *.egg MANIFEST # PyInstaller # Usually these files are written by a python script from a template # before PyInstaller builds the exe, so as to inject date/other infos into it. *.manifest *.spec # Installer logs pip-log.txt pip-delete-this-directory.txt # Unit test / coverage reports htmlcov/ .tox/ .nox/ .coverage .coverage.* .cache nosetests.xml coverage.xml *.cover *.py,cover .hypothesis/ .pytest_cache/ cover/ # Translations *.mo *.pot # Django stuff: *.log local_settings.py db.sqlite3 db.sqlite3-journal # Flask stuff: instance/ .webassets-cache # Scrapy stuff: .scrapy # Sphinx documentation docs/_build/ # PyBuilder .pybuilder/ target/ # Jupyter Notebook .ipynb_checkpoints # IPython profile_default/ ipython_config.py # pyenv # For a library or package, you might want to ignore these files since the code is # intended to run in multiple environments; otherwise, check them in: # .python-version # pipenv # According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. # However, in case of collaboration, if having platform-specific dependencies or dependencies # having no cross-platform support, pipenv may install dependencies that don't work, or not # install all needed dependencies. #Pipfile.lock # poetry # Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control. # This is especially recommended for binary packages to ensure reproducibility, and is more # commonly ignored for libraries. # https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control #poetry.lock # pdm # Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control. #pdm.lock # pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it # in version control. # https://pdm.fming.dev/#use-with-ide .pdm.toml # PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm __pypackages__/ # Celery stuff celerybeat-schedule celerybeat.pid # SageMath parsed files *.sage.py # Environments .env .venv env/ venv/ ENV/ env.bak/ venv.bak/ # Spyder project settings .spyderproject .spyproject # Rope project settings .ropeproject # mkdocs documentation /site # mypy .mypy_cache/ .dmypy.json dmypy.json # Pyre type checker .pyre/ # pytype static type analyzer .pytype/ # Cython debug symbols cython_debug/ # PyCharm # JetBrains specific template is maintained in a separate JetBrains.gitignore that can # be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore # and can be added to the global gitignore or merged into this file. For a more nuclear # option (not recommended) you can uncomment the following to ignore the entire idea folder. #.idea/ #Desktop Services Store .DS_Store ================================================ FILE: weather_data_stack/README.md ================================================ # Weather Data Stack with Airbyte, dbt, Dagster and BigQuery Welcome 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. This Quickstart is all about making things easy, getting you started quickly and showing you how smoothly all these tools can work together! ## Table of Contents - [Weather Data Stack with Airbyte, dbt, Dagster and BigQuery](#weather-data-stack-with-airbyte-dbt-dagster-and-bigquery) - [Table of Contents](#table-of-contents) - [Infrastracture Layout](#infrastracture-layout) - [Prerequisites](#prerequisites) - [1. Setting an environment for your project](#1-setting-an-environment-for-your-project) - [2. Weatherstack API Key](#2-weatherstack-api-key) - [1. Sign Up for a Weatherstack API Key](#1-sign-up-for-a-weatherstack-api-key) - [3. Setting Up BigQuery](#3-setting-up-bigquery) - [1. **Create a Google Cloud Project**](#1-create-a-google-cloud-project) - [2. **Create BigQuery Datasets**](#2-create-bigquery-datasets) - [3. **Create Service Accounts and Assign Roles**](#3-create-service-accounts-and-assign-roles) - [4. **Generate JSON Keys for Service Accounts**](#4-generate-json-keys-for-service-accounts) - [4. Setting Up Airbyte Connectors](#4-setting-up-airbyte-connectors) - [1. Setting Up Airbyte Connectors with AirByteUI](#1-setting-up-airbyte-connectors-with-airbyteui) - [5. Setting Up the dbt Project](#5-setting-up-the-dbt-project) - [Next Steps](#next-steps) - [1. **Explore the Data and Insights**](#1-explore-the-data-and-insights) - [2. **Optimize Your dbt Models**](#2-optimize-your-dbt-models) - [3. **Expand Your Data Sources**](#3-expand-your-data-sources) - [4. **Enhance Data Quality and Testing**](#4-enhance-data-quality-and-testing) - [5. **Automate and Monitor Your Pipelines**](#5-automate-and-monitor-your-pipelines) - [6. **Scale Your Setup**](#6-scale-your-setup) - [7. **Contribute to the Community**](#7-contribute-to-the-community) ## Infrastracture Layout  ## Prerequisites Before you embark on this integration, ensure you have the following set up and ready: 1. **Python 3.10 or later**: If not installed, download and install it from [Python's official website](https://www.python.org/downloads/). 2. **Docker and Docker Compose (Docker Desktop)**: Install [Docker](https://docs.docker.com/get-docker/) following the official documentation for your specific OS. 3. **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/). 4. **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). 5. **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 ## 1. Setting an environment for your project Get the project up and running on your local machine by following these steps: 1. **Clone the repository (Clone only this quickstart)**: ```bash git clone --filter=blob:none --sparse https://github.com/airbytehq/quickstarts.git ``` ```bash cd quickstarts ``` ```bash git sparse-checkout add weather_data_stack ``` 2. **Navigate to the directory**: ```bash cd weather_data_stack ``` 3. **Set Up a Virtual Environment**: - For Mac: ```bash python3 -m venv venv source venv/bin/activate ``` - For Windows: ```bash python -m venv venv .\venv\Scripts\activate ``` 4. **Install Dependencies**: ```bash pip install -e ".[dev]" ``` ## 2. Weatherstack API Key To extract weather data from the Weatherstack API and store the API key in its own environment variable file, you can follow these steps: #### 1. Sign Up for a Weatherstack API Key 1. Visit the [Weatherstack website](https://weatherstack.com/). 2. Sign up for an account or log in if you already have one. 3. Once logged in, go to the dashboard to obtain your API key. ## 3. Setting Up BigQuery #### 1. **Create a Google Cloud Project** - If you have a Google Cloud project, you can skip this step. - Go to the [Google Cloud Console](https://console.cloud.google.com/). - Click on the "Select a project" dropdown at the top right and select "New Project". - Give your project a name and follow the steps to create it. #### 2. **Create BigQuery Datasets** - In the Google Cloud Console, go to BigQuery. - Make two new datasets: `raw_data` for Airbyte and `transformed_data` for dbt. - If you pick different names, remember to change the names in the code too. **How to create a dataset:** - In the left sidebar, click on your project name. - Click “Create Dataset”. - Enter the dataset ID (either `raw_data` or `transformed_data`). - Click "Create Dataset". #### 3. **Create Service Accounts and Assign Roles** - Go to “IAM & Admin” > “Service accounts” in the Google Cloud Console. - Click “Create Service Account”. - Name your service account (like `airbyte-service-account`). - Assign the “BigQuery Data Editor” and “BigQuery Job User” roles to the service account. - Follow the same steps to make another service account for dbt (like `dbt-service-account`) and assign the roles. **How to create a service account and assign roles:** - While creating the service account, under the “Grant this service account access to project” section, click the “Role” dropdown. - Choose the “BigQuery Data Editor” and “BigQuery Job User” roles. - Finish the creation process. #### 4. **Generate JSON Keys for Service Accounts** - For both service accounts, make a JSON key to let the service accounts sign in. **How to generate JSON key:** - Find the service account in the “Service accounts” list. - Click on the service account name. - In the “Keys” section, click “Add Key” and pick JSON. - The key will download automatically. Keep it safe and don’t share it. - Do this for the other service account too. ## 4. Setting Up Airbyte Connectors Here, you can set up connectors with source and destination manually using the Airbyte UI. ### 1. Setting Up Airbyte Connectors with AirByteUI Use the generated public url from the previous step to manually configure using the File Option as source under public HTPPS. - Here are specific [BigQuery](https://docs.airbyte.com/integrations/destinations/bigquery) instructions. - Follow these [steps](https://docs.airbyte.com/quickstart/set-up-a-connection) for more. ## 5. Setting Up the dbt Project [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: 1. **Navigate to the dbt Project Directory**: Change to the directory containing the dbt configuration: ```bash cd dbt_project ``` 2. **Update Connection Details**: 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. 3. **Utilize Environment Variables (Optional but Recommended)**: To keep your credentials secure, you can leverage environment variables. An example is provided within the `profiles.yml` file. 4. **Test the Connection**: Once you’ve updated the connection details, you can test the connection to your BigQuery instance using: ```bash dbt debug ``` If everything is set up correctly, this command should report a successful connection to BigQuery. 5. **Run the Models**: If you would like to run the dbt models manually at this point, you can do so by executing: ```bash dbt run ``` You can verify the data has been transformed by going to BigQuery and checking the `transformed_data` dataset. ## 6. Orchestrating with Dagster [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: 1. **Navigate to the Orchestration Directory**: Switch to the directory containing the Dagster orchestration configurations: ```bash cd orchestration ``` 2. **Set Environment Variables**: Dagster requires certain environment variables to be set to interact with other tools like dbt and Airbyte. Set the following variables: ```bash export DAGSTER_DBT_PARSE_PROJECT_ON_LOAD=1 export AIRBYTE_PASSWORD=password ``` 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. 3. **Launch the Dagster UI**: With the environment variables in place, kick-start the Dagster UI: ```bash dagster dev ``` 4. **Access Dagster in Your Browser**: Open your browser and navigate to: ``` http://127.0.0.1:3000 ``` Here, you should see assets for both Airbyte and dbt. ## Next Steps Congratulations 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: ### 1. **Explore the Data and Insights** - 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! ### 2. **Optimize Your dbt Models** - 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. ### 3. **Expand Your Data Sources** - 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. ### 4. **Enhance Data Quality and Testing** - 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. ### 5. **Automate and Monitor Your Pipelines** - 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. ### 6. **Scale Your Setup** - 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. ### 7. **Contribute to the Community** - Share your learnings, optimizations, and new configurations with the community. Contribute to the respective tool’s communities and help others learn and grow. ================================================ FILE: weather_data_stack/dbt_project/.gitignore ================================================ target/ dbt_packages/ logs/ .user.yml ================================================ FILE: weather_data_stack/dbt_project/README.md ================================================ Welcome to your new dbt project! ### Using the starter project Try running the following commands: - dbt run - dbt test ### Resources: - Learn more about dbt [in the docs](https://docs.getdbt.com/docs/introduction) - Check out [Discourse](https://discourse.getdbt.com/) for commonly asked questions and answers - Join the [chat](https://community.getdbt.com/) on Slack for live discussions and support - Find [dbt events](https://events.getdbt.com) near you - Check out [the blog](https://blog.getdbt.com/) for the latest news on dbt's development and best practices ================================================ FILE: weather_data_stack/dbt_project/analyses/.gitkeep ================================================ ================================================ FILE: weather_data_stack/dbt_project/dbt_project.yml ================================================ # Name your project! Project names should contain only lowercase characters # and underscores. A good package name should reflect your organization's # name or the intended use of these models name: 'dbt_project' version: '1.0.0' config-version: 2 # This setting configures which "profile" dbt uses for this project. profile: 'dbt_project' # These configurations specify where dbt should look for different types of files. # The `model-paths` config, for example, states that models in this project can be # found in the "models/" directory. You probably won't need to change these! model-paths: ["models"] analysis-paths: ["analyses"] test-paths: ["tests"] seed-paths: ["seeds"] macro-paths: ["macros"] snapshot-paths: ["snapshots"] clean-targets: # directories to be removed by `dbt clean` - "target" - "dbt_packages" # Configuring models # Full documentation: https://docs.getdbt.com/docs/configuring-models # In this example config, we tell dbt to build all models in the example/ # directory as views. These settings can be overridden in the individual model # files using the `{{ config(...) }}` macro. models: dbt_project: # Config indicated by + and applies to all files under models/example/ staging: +materialized: view marts: +materialized: view ================================================ FILE: weather_data_stack/dbt_project/macros/.gitkeep ================================================ ================================================ FILE: weather_data_stack/dbt_project/models/marts/historial_weather_trends.sql ================================================ {% set start_date = '2023-01-01' %} {% set end_date = '2023-12-31' %} SELECT JSON_EXTRACT_SCALAR(`current`, '$.location.name') AS location_name, JSON_EXTRACT_SCALAR(`current`, '$.current.temperature') AS temperature, DATE(JSON_EXTRACT_SCALAR(`current`, '$.current.observation_time')) AS date FROM transformed_data.stg_current_weather WHERE DATE(JSON_EXTRACT_SCALAR(`current`, '$.current.observation_time')) BETWEEN '{{ start_date }}' AND '{{ end_date }}' ORDER BY date, location_name ================================================ FILE: weather_data_stack/dbt_project/models/sources/weatherstack_source.yml ================================================ version: 2 sources: - name: weatherstack # Use your BigQuery project ID database: "{{ env_var('BIGQUERY_PROJECT_ID', '') }}" # Use your BigQuery dataset name schema: weatherstack_airbyte tables: - name: current_weather description: "Simulated current_weather data from the weatherstack connector." columns: - name: current - name: location - name: request ================================================ FILE: weather_data_stack/dbt_project/models/staging/stg_current_weather.sql ================================================ select * from {{ source('weatherstack', 'current_weather') }} ================================================ FILE: weather_data_stack/dbt_project/profiles.yml ================================================ dbt_project: outputs: dev: dataset: transformed_data job_execution_timeout_seconds: 300 job_retries: 1 # Use an env variable to indicate your JSON key file path keyfile: "{{ env_var('DBT_BIGQUERY_KEYFILE_PATH', '') }}" location: US method: service-account priority: interactive # Indicate your BigQuery project ID project: your_project_id threads: 1 type: bigquery target: dev ================================================ FILE: weather_data_stack/dbt_project/seeds/.gitkeep ================================================ ================================================ FILE: weather_data_stack/dbt_project/snapshots/.gitkeep ================================================ ================================================ FILE: weather_data_stack/dbt_project/tests/.gitkeep ================================================ ================================================ FILE: weather_data_stack/orchestration/orchestration/__init__.py ================================================ ================================================ FILE: weather_data_stack/orchestration/orchestration/assets.py ================================================ import os from dagster import OpExecutionContext from dagster_dbt import DbtCliResource, dbt_assets from dagster_airbyte import AirbyteResource, load_assets_from_airbyte_instance from .constants import dbt_manifest_path @dbt_assets(manifest=dbt_manifest_path) def dbt_project_dbt_assets(context: OpExecutionContext, dbt: DbtCliResource): yield from dbt.cli(["build"], context=context).stream() airbyte_instance = AirbyteResource( host="localhost", port="8000", # If using basic auth, include username and password: username="airbyte", password=os.getenv("AIRBYTE_PASSWORD") ) airbyte_assets = load_assets_from_airbyte_instance(airbyte_instance, key_prefix="faker") ================================================ FILE: weather_data_stack/orchestration/orchestration/constants.py ================================================ import os from pathlib import Path from dagster_dbt import DbtCliResource dbt_project_dir = Path(__file__).joinpath("..", "..", "..", "dbt_project").resolve() dbt = DbtCliResource(project_dir=os.fspath(dbt_project_dir)) # If DAGSTER_DBT_PARSE_PROJECT_ON_LOAD is set, a manifest will be created at runtime. # Otherwise, we expect a manifest to be present in the project's target directory. if os.getenv("DAGSTER_DBT_PARSE_PROJECT_ON_LOAD"): dbt_parse_invocation = dbt.cli(["parse"], manifest={}).wait() dbt_manifest_path = dbt_parse_invocation.target_path.joinpath("manifest.json") else: dbt_manifest_path = dbt_project_dir.joinpath("target", "manifest.json") ================================================ FILE: weather_data_stack/orchestration/orchestration/definitions.py ================================================ import os from dagster import Definitions from dagster_dbt import DbtCliResource from .assets import dbt_project_dbt_assets, airbyte_assets from .constants import dbt_project_dir from .schedules import schedules defs = Definitions( assets=[dbt_project_dbt_assets, airbyte_assets], schedules=schedules, resources={ "dbt": DbtCliResource(project_dir=os.fspath(dbt_project_dir)), }, ) ================================================ FILE: weather_data_stack/orchestration/orchestration/schedules.py ================================================ """ To add a daily schedule that materializes your dbt assets, uncomment the following lines. """ from dagster_dbt import build_schedule_from_dbt_selection from .assets import dbt_project_dbt_assets schedules = [ # build_schedule_from_dbt_selection( # [dbt_project_dbt_assets], # job_name="materialize_dbt_models", # cron_schedule="0 0 * * *", # dbt_select="fqn:*", # ), ] ================================================ FILE: weather_data_stack/orchestration/pyproject.toml ================================================ [build-system] requires = ["setuptools"] build-backend = "setuptools.build_meta" [tool.dagster] module_name = "orchestration.definitions" code_location_name = "orchestration" ================================================ FILE: weather_data_stack/orchestration/setup.py ================================================ from setuptools import find_packages, setup setup( name="orchestration", version="0.0.1", packages=find_packages(), install_requires=[ "dagster", "dagster-cloud", "dagster-dbt", "dbt-core>=1.4.0", "dbt-bigquery", ], extras_require={ "dev": [ "dagster-webserver", ] }, ) ================================================ FILE: weather_data_stack/setup.py ================================================ from setuptools import find_packages, setup setup( name="airbyte-dbt-dagster", packages=find_packages(), install_requires=[ "dbt-bigquery", "dagster", "dagster-cloud", "dagster-dbt", "dagster-airbyte", ], extras_require={"dev": ["dagit", "pytest"]}, )