Repository: n8n-io/self-hosted-ai-starter-kit Branch: main Commit: 9b802c62c609 Files: 8 Total size: 30.0 KB Directory structure: gitextract_s2t6drj2/ ├── .gitignore ├── CONTRIBUTING.md ├── LICENSE ├── README.md ├── docker-compose.yml └── n8n/ └── demo-data/ ├── credentials/ │ ├── sFfERYppMeBnFNeA.json │ └── xHuYe0MDGOs9IpBW.json └── workflows/ └── srOnR8PAY3u4RSwb.json ================================================ FILE CONTENTS ================================================ ================================================ FILE: .gitignore ================================================ .env ================================================ FILE: CONTRIBUTING.md ================================================ # Self-hosted AI Starter Kit - Vision & Contribution Guidelines Awesome that you're interested in contributing to the Self-hosted AI Starter Kit! These specific guidelines are in addition to the general [n8n contribution guidelines](https://github.com/n8n-io/n8n/blob/master/CONTRIBUTING.md). ## Vision Statement The Self-hosted AI Starter Kit is designed to be **the fastest path from zero to working AI workflows** for developers and organizations who want to experiment with local, private AI solutions. It provides a curated, pre-configured foundation that "just works" out of the box, enabling users to focus on building AI workflows rather than wrestling with infrastructure setup. ## Core Principles ### 1. Simplicity Over Completeness The starter kit should prioritize ease of use and quick setup over comprehensive feature coverage. It's better to do fewer things well than to attempt to solve every possible use case. ### 2. Learning-Focused, Not Production-Ready This is explicitly a **learning and experimentation platform**. Users should be able to go from `git clone` to working AI workflows in minutes, not hours. Production-grade concerns like high availability, advanced security, and scalability are intentionally out of scope. ### 3. Opinionated but Extensible We make opinionated choices about the core stack (n8n + Ollama + Qdrant + PostgreSQL) to reduce decision paralysis, while providing clear paths for users to extend and customize as they learn. ### 4. Privacy-First Local Development Everything should work completely offline and locally by default. External dependencies should be minimal and optional. ## What Belongs in the Starter Kit ### Core Components - **n8n**: The workflow automation platform - **Ollama**: Local LLM inference - **Qdrant**: Vector database for embeddings - **PostgreSQL**: Persistent data storage - **Basic networking**: Simple Docker networking to connect components ### Essential Configuration This includes: - Pre-configured environment variables with sensible defaults - Basic Docker Compose profiles for different hardware (CPU, GPU-Nvidia, GPU-AMD) - Minimal volume mounts for data persistence - Sample workflow demonstrating the core capabilities ### Getting Started Materials This includes: - Clear installation instructions for different platforms - A demo workflow showcasing AI capabilities - Basic documentation for accessing local files - Links to relevant n8n documentation and templates ## What Doesn't Belong in the Starter Kit ### Production Infrastructure Including: - Reverse proxies - SSL/TLS termination - Load balancers - Advanced monitoring and logging - Backup and recovery systems - Container orchestration beyond basic Docker Compose ### Advanced Networking Including: - Custom network configurations - VPN integrations - Multiple environment setups - Advanced security hardening ### Alternative Technology Stacks Including: - Different vector databases - Alternative workflow platforms - Multiple LLM backends beyond Ollama - Different databases for the core setup ### Enterprise Features Including: - Authentication systems - Multi-tenancy - Advanced access controls - Compliance tooling ## PR specific requirements - Small PRs Only: - Focus on a single feature or fix per PR. - Typo-Only PRs: - Typos are not sufficient justification for a PR and will be rejected. 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See the License for the specific language governing permissions and limitations under the License. ================================================ FILE: README.md ================================================ # Self-hosted AI starter kit **Self-hosted AI Starter Kit** is an open-source Docker Compose template designed to swiftly initialize a comprehensive local AI and low-code development environment. ![n8n.io - Screenshot](https://raw.githubusercontent.com/n8n-io/self-hosted-ai-starter-kit/main/assets/n8n-demo.gif) Curated by , it combines the self-hosted n8n platform with a curated list of compatible AI products and components to quickly get started with building self-hosted AI workflows. > [!TIP] > [Read the announcement](https://blog.n8n.io/self-hosted-ai/) ### What’s included ✅ [**Self-hosted n8n**](https://n8n.io/) - Low-code platform with over 400 integrations and advanced AI components ✅ [**Ollama**](https://ollama.com/) - Cross-platform LLM platform to install and run the latest local LLMs ✅ [**Qdrant**](https://qdrant.tech/) - Open-source, high performance vector store with an comprehensive API ✅ [**PostgreSQL**](https://www.postgresql.org/) - Workhorse of the Data Engineering world, handles large amounts of data safely. ### What you can build ⭐️ **AI Agents** for scheduling appointments ⭐️ **Summarize Company PDFs** securely without data leaks ⭐️ **Smarter Slack Bots** for enhanced company communications and IT operations ⭐️ **Private Financial Document Analysis** at minimal cost ## Installation ### Cloning the Repository ```bash git clone https://github.com/n8n-io/self-hosted-ai-starter-kit.git cd self-hosted-ai-starter-kit cp .env.example .env # you should update secrets and passwords inside ``` ### Running n8n using Docker Compose #### For Nvidia GPU users ```bash git clone https://github.com/n8n-io/self-hosted-ai-starter-kit.git cd self-hosted-ai-starter-kit cp .env.example .env # you should update secrets and passwords inside docker compose --profile gpu-nvidia up ``` > [!NOTE] > If you have not used your Nvidia GPU with Docker before, please follow the > [Ollama Docker instructions](https://github.com/ollama/ollama/blob/main/docs/docker.md). ### For AMD GPU users on Linux ```bash git clone https://github.com/n8n-io/self-hosted-ai-starter-kit.git cd self-hosted-ai-starter-kit cp .env.example .env # you should update secrets and passwords inside docker compose --profile gpu-amd up ``` #### For Mac / Apple Silicon users If you’re using a Mac with an M1 or newer processor, you can't expose your GPU to the Docker instance, unfortunately. There are two options in this case: 1. Run the starter kit fully on CPU, like in the section "For everyone else" below 2. Run Ollama on your Mac for faster inference, and connect to that from the n8n instance If you want to run Ollama on your mac, check the [Ollama homepage](https://ollama.com/) for installation instructions, and run the starter kit as follows: ```bash git clone https://github.com/n8n-io/self-hosted-ai-starter-kit.git cd self-hosted-ai-starter-kit cp .env.example .env # you should update secrets and passwords inside docker compose up ``` ##### For Mac users running OLLAMA locally If you're running OLLAMA locally on your Mac (not in Docker), you need to modify the OLLAMA_HOST environment variable 1. Set OLLAMA_HOST to `host.docker.internal:11434` in your .env file. 2. Additionally, after you see "Editor is now accessible via: ": 1. Head to 2. Click on "Local Ollama service" 3. Change the base URL to "http://host.docker.internal:11434/" #### For everyone else ```bash git clone https://github.com/n8n-io/self-hosted-ai-starter-kit.git cd self-hosted-ai-starter-kit cp .env.example .env # you should update secrets and passwords inside docker compose --profile cpu up ``` ## ⚡️ Quick start and usage The core of the Self-hosted AI Starter Kit is a Docker Compose file, pre-configured with network and storage settings, minimizing the need for additional installations. After completing the installation steps above, simply follow the steps below to get started. 1. Open in your browser to set up n8n. You’ll only have to do this once. 2. Open the included workflow: 3. Click the **Chat** button at the bottom of the canvas, to start running the workflow. 4. If this is the first time you’re running the workflow, you may need to wait until Ollama finishes downloading Llama3.2. You can inspect the docker console logs to check on the progress. To open n8n at any time, visit in your browser. With your n8n instance, you’ll have access to over 400 integrations and a suite of basic and advanced AI nodes such as [AI Agent](https://docs.n8n.io/integrations/builtin/cluster-nodes/root-nodes/n8n-nodes-langchain.agent/), [Text classifier](https://docs.n8n.io/integrations/builtin/cluster-nodes/root-nodes/n8n-nodes-langchain.text-classifier/), and [Information Extractor](https://docs.n8n.io/integrations/builtin/cluster-nodes/root-nodes/n8n-nodes-langchain.information-extractor/) nodes. To keep everything local, just remember to use the Ollama node for your language model and Qdrant as your vector store. > [!NOTE] > This starter kit is designed to help you get started with self-hosted AI > workflows. While it’s not fully optimized for production environments, it > combines robust components that work well together for proof-of-concept > projects. You can customize it to meet your specific needs ## Upgrading * ### For Nvidia GPU setups: ```bash docker compose --profile gpu-nvidia pull docker compose create && docker compose --profile gpu-nvidia up ``` * ### For Mac / Apple Silicon users ```bash docker compose pull docker compose create && docker compose up ``` * ### For Non-GPU setups: ```bash docker compose --profile cpu pull docker compose create && docker compose --profile cpu up ``` ## 👓 Recommended reading n8n is full of useful content for getting started quickly with its AI concepts and nodes. If you run into an issue, go to [support](#support). - [AI agents for developers: from theory to practice with n8n](https://blog.n8n.io/ai-agents/) - [Tutorial: Build an AI workflow in n8n](https://docs.n8n.io/advanced-ai/intro-tutorial/) - [Langchain Concepts in n8n](https://docs.n8n.io/advanced-ai/langchain/langchain-n8n/) - [Demonstration of key differences between agents and chains](https://docs.n8n.io/advanced-ai/examples/agent-chain-comparison/) - [What are vector databases?](https://docs.n8n.io/advanced-ai/examples/understand-vector-databases/) ## 🎥 Video walkthrough - [Installing and using Local AI for n8n](https://www.youtube.com/watch?v=xz_X2N-hPg0) ## 🛍️ More AI templates For more AI workflow ideas, visit the [**official n8n AI template gallery**](https://n8n.io/workflows/categories/ai/). From each workflow, select the **Use workflow** button to automatically import the workflow into your local n8n instance. ### Learn AI key concepts - [AI Agent Chat](https://n8n.io/workflows/1954-ai-agent-chat/) - [AI chat with any data source (using the n8n workflow too)](https://n8n.io/workflows/2026-ai-chat-with-any-data-source-using-the-n8n-workflow-tool/) - [Chat with OpenAI Assistant (by adding a memory)](https://n8n.io/workflows/2098-chat-with-openai-assistant-by-adding-a-memory/) - [Use an open-source LLM (via Hugging Face)](https://n8n.io/workflows/1980-use-an-open-source-llm-via-huggingface/) - [Chat with PDF docs using AI (quoting sources)](https://n8n.io/workflows/2165-chat-with-pdf-docs-using-ai-quoting-sources/) - [AI agent that can scrape webpages](https://n8n.io/workflows/2006-ai-agent-that-can-scrape-webpages/) ### Local AI templates - [Tax Code Assistant](https://n8n.io/workflows/2341-build-a-tax-code-assistant-with-qdrant-mistralai-and-openai/) - [Breakdown Documents into Study Notes with MistralAI and Qdrant](https://n8n.io/workflows/2339-breakdown-documents-into-study-notes-using-templating-mistralai-and-qdrant/) - [Financial Documents Assistant using Qdrant and](https://n8n.io/workflows/2335-build-a-financial-documents-assistant-using-qdrant-and-mistralai/) [Mistral.ai](http://mistral.ai/) - [Recipe Recommendations with Qdrant and Mistral](https://n8n.io/workflows/2333-recipe-recommendations-with-qdrant-and-mistral/) ## Tips & tricks ### Accessing local files The self-hosted AI starter kit will create a shared folder (by default, located in the same directory) which is mounted to the n8n container and allows n8n to access files on disk. This folder within the n8n container is located at `/data/shared` -- this is the path you’ll need to use in nodes that interact with the local filesystem. **Nodes that interact with the local filesystem** - [Read/Write Files from Disk](https://docs.n8n.io/integrations/builtin/core-nodes/n8n-nodes-base.filesreadwrite/) - [Local File Trigger](https://docs.n8n.io/integrations/builtin/core-nodes/n8n-nodes-base.localfiletrigger/) - [Execute Command](https://docs.n8n.io/integrations/builtin/core-nodes/n8n-nodes-base.executecommand/) ## 📜 License This project is licensed under the Apache License 2.0 - see the [LICENSE](LICENSE) file for details. ## 💬 Support Join the conversation in the [n8n Forum](https://community.n8n.io/), where you can: - **Share Your Work**: Show off what you’ve built with n8n and inspire others in the community. - **Ask Questions**: Whether you’re just getting started or you’re a seasoned pro, the community and our team are ready to support with any challenges. - **Propose Ideas**: Have an idea for a feature or improvement? Let us know! We’re always eager to hear what you’d like to see next. ================================================ FILE: docker-compose.yml ================================================ volumes: n8n_storage: postgres_storage: ollama_storage: qdrant_storage: networks: demo: x-n8n: &service-n8n image: n8nio/n8n:latest networks: ['demo'] environment: - DB_TYPE=postgresdb - DB_POSTGRESDB_HOST=postgres - DB_POSTGRESDB_USER=${POSTGRES_USER} - DB_POSTGRESDB_PASSWORD=${POSTGRES_PASSWORD} - N8N_DIAGNOSTICS_ENABLED=false - N8N_PERSONALIZATION_ENABLED=false - N8N_ENCRYPTION_KEY - N8N_USER_MANAGEMENT_JWT_SECRET - OLLAMA_HOST=${OLLAMA_HOST:-ollama:11434} env_file: - path: .env required: true x-ollama: &service-ollama image: ollama/ollama:latest container_name: ollama networks: ['demo'] restart: unless-stopped ports: - 11434:11434 volumes: - ollama_storage:/root/.ollama x-init-ollama: &init-ollama image: ollama/ollama:latest networks: ['demo'] container_name: ollama-pull-llama volumes: - ollama_storage:/root/.ollama entrypoint: /bin/sh environment: - OLLAMA_HOST=ollama:11434 command: - "-c" - "sleep 3; ollama pull llama3.2" services: postgres: image: postgres:16-alpine hostname: postgres networks: ['demo'] restart: unless-stopped environment: - POSTGRES_USER - POSTGRES_PASSWORD - POSTGRES_DB volumes: - postgres_storage:/var/lib/postgresql/data healthcheck: test: ['CMD-SHELL', 'pg_isready -h localhost -U ${POSTGRES_USER} -d ${POSTGRES_DB}'] interval: 5s timeout: 5s retries: 10 n8n-import: <<: *service-n8n hostname: n8n-import container_name: n8n-import entrypoint: /bin/sh command: - "-c" - | if [ -z "$(n8n list:workflow --onlyId)" ]; then n8n import:credentials --separate --input=/demo-data/credentials && \ n8n import:workflow --separate --input=/demo-data/workflows else echo "Workflows exist, skipping import" fi volumes: - ./n8n/demo-data:/demo-data depends_on: postgres: condition: service_healthy n8n: <<: *service-n8n hostname: n8n container_name: n8n restart: unless-stopped ports: - 5678:5678 volumes: - n8n_storage:/home/node/.n8n - ./n8n/demo-data:/demo-data - ./shared:/data/shared depends_on: postgres: condition: service_healthy n8n-import: condition: service_completed_successfully qdrant: image: qdrant/qdrant hostname: qdrant container_name: qdrant networks: ['demo'] restart: unless-stopped ports: - 6333:6333 volumes: - qdrant_storage:/qdrant/storage ollama-cpu: profiles: ["cpu"] <<: *service-ollama ollama-gpu: profiles: ["gpu-nvidia"] <<: *service-ollama deploy: resources: reservations: devices: - driver: nvidia count: 1 capabilities: [gpu] ollama-gpu-amd: profiles: ["gpu-amd"] <<: *service-ollama image: ollama/ollama:rocm devices: - "/dev/kfd" - "/dev/dri" ollama-pull-llama-cpu: profiles: ["cpu"] <<: *init-ollama depends_on: - ollama-cpu ollama-pull-llama-gpu: profiles: ["gpu-nvidia"] <<: *init-ollama depends_on: - ollama-gpu ollama-pull-llama-gpu-amd: profiles: [gpu-amd] <<: *init-ollama image: ollama/ollama:rocm depends_on: - ollama-gpu-amd ================================================ FILE: n8n/demo-data/credentials/sFfERYppMeBnFNeA.json ================================================ { "createdAt": "2024-02-23T16:27:55.919Z", "updatedAt": "2024-02-23T16:27:55.918Z", "id": "sFfERYppMeBnFNeA", "name": "Local QdrantApi database", "data": "U2FsdGVkX18bm81Pk18TjmfyKEIbzd91Dt1O8pUPgTxVGk5v1mXp7MlE/3Fl+NHGTMBqa3u7RBS36wTQ74rijQ==", "type": "qdrantApi", "nodesAccess": [ { "nodeType": "@n8n/n8n-nodes-langchain.vectorStoreQdrant", "date": "2024-02-23T16:27:55.918Z" } ] } ================================================ FILE: n8n/demo-data/credentials/xHuYe0MDGOs9IpBW.json ================================================ { "createdAt": "2024-02-23T16:26:54.475Z", "updatedAt": "2024-02-23T16:26:58.928Z", "id": "xHuYe0MDGOs9IpBW", "name": "Local Ollama service", "data": "U2FsdGVkX18BVmjQBCdNKSrjr0GhmcTwMgG/rSWhncWtqOLPT62WnCIktky8RgM1PhH7vMkMc5EuUFIQA/eEZA==", "type": "ollamaApi", "nodesAccess": [ { "nodeType": "@n8n/n8n-nodes-langchain.lmChatOllama", "date": "2024-02-23T16:26:58.927Z" }, { "nodeType": "@n8n/n8n-nodes-langchain.lmOllama", "date": "2024-02-23T16:26:58.927Z" } ] } ================================================ FILE: n8n/demo-data/workflows/srOnR8PAY3u4RSwb.json ================================================ { "createdAt": "2024-02-23T16:58:31.616Z", "updatedAt": "2024-02-23T16:58:31.616Z", "id": "srOnR8PAY3u4RSwb", "name": "Demo workflow", "active": false, "nodes": [ { "parameters": {}, "id": "74003dcd-2ac7-4caa-a1cd-adecc5143c07", "name": "Chat Trigger", "type": "@n8n/n8n-nodes-langchain.chatTrigger", "typeVersion": 1, "position": [ 660, 340 ], "webhookId": "cdb5c076-d458-4b9d-8398-f43bd25059b1" }, { "parameters": {}, "id": "ce8c3da4-899c-4cc4-af73-8096c64eec64", "name": "Basic LLM Chain", "type": "@n8n/n8n-nodes-langchain.chainLlm", "typeVersion": 1.7, "position": [ 880, 340 ] }, { "parameters": { "model": "llama3.2:latest", "options": {} }, "id": "3dee878b-d748-4829-ac0a-cfd6705d31e5", "name": "Ollama Chat Model", "type": "@n8n/n8n-nodes-langchain.lmChatOllama", "typeVersion": 1, "position": [ 900, 560 ], "credentials": { "ollamaApi": { "id": "xHuYe0MDGOs9IpBW", "name": "Local Ollama service" } } } ], "connections": { "Chat Trigger": { "main": [ [ { "node": "Basic LLM Chain", "type": "main", "index": 0 } ] ] }, "Ollama Chat Model": { "ai_languageModel": [ [ { "node": "Basic LLM Chain", "type": "ai_languageModel", "index": 0 } ] ] } }, "settings": { "executionOrder": "v1" }, "staticData": null, "meta": { "templateCredsSetupCompleted": true }, "pinData": {}, "versionId": "4e2affe6-bb1c-4ddc-92f9-dde0b7656796", "triggerCount": 0, "tags": [] }