[
  {
    "path": ".gitignore",
    "content": ".env\n"
  },
  {
    "path": "CONTRIBUTING.md",
    "content": "# Self-hosted AI Starter Kit - Vision & Contribution Guidelines\n\nAwesome that you're interested in contributing to the Self-hosted AI Starter\nKit! These specific guidelines are in addition to the general [n8n\ncontribution\nguidelines](https://github.com/n8n-io/n8n/blob/master/CONTRIBUTING.md).\n\n## Vision Statement\n\nThe Self-hosted AI Starter Kit is designed to be **the fastest path from zero\nto working AI workflows** for developers and organizations who want to\nexperiment with local, private AI solutions. It provides a curated,\npre-configured foundation that \"just works\" out of the box, enabling users to\nfocus on building AI workflows rather than wrestling with infrastructure\nsetup.\n\n## Core Principles\n\n### 1. Simplicity Over Completeness\n\nThe starter kit should prioritize ease of use and quick setup over\ncomprehensive feature coverage. It's better to do fewer things well than to\nattempt to solve every possible use case.\n\n### 2. Learning-Focused, Not Production-Ready\n\nThis is explicitly a **learning and experimentation platform**. Users should\nbe able to go from `git clone` to working AI workflows in minutes, not hours.\nProduction-grade concerns like high availability, advanced security, and\nscalability are intentionally out of scope.\n\n### 3. Opinionated but Extensible\n\nWe make opinionated choices about the core stack (n8n + Ollama + Qdrant +\nPostgreSQL) to reduce decision paralysis, while providing clear paths for\nusers to extend and customize as they learn.\n\n### 4. Privacy-First Local Development\n\nEverything should work completely offline and locally by default. External\ndependencies should be minimal and optional.\n\n## What Belongs in the Starter Kit\n\n### Core Components\n\n- **n8n**: The workflow automation platform\n- **Ollama**: Local LLM inference\n- **Qdrant**: Vector database for embeddings\n- **PostgreSQL**: Persistent data storage\n- **Basic networking**: Simple Docker networking to connect components\n\n### Essential Configuration\n\nThis includes:\n- Pre-configured environment variables with sensible defaults\n- Basic Docker Compose profiles for different hardware (CPU, GPU-Nvidia, GPU-AMD)\n- Minimal volume mounts for data persistence\n- Sample workflow demonstrating the core capabilities\n\n### Getting Started Materials\n\nThis includes:\n- Clear installation instructions for different platforms\n- A demo workflow showcasing AI capabilities\n- Basic documentation for accessing local files\n- Links to relevant n8n documentation and templates\n\n## What Doesn't Belong in the Starter Kit\n\n### Production Infrastructure\n\nIncluding:\n- Reverse proxies\n- SSL/TLS termination\n- Load balancers\n- Advanced monitoring and logging\n- Backup and recovery systems\n- Container orchestration beyond basic Docker Compose\n\n### Advanced Networking\n\nIncluding:\n- Custom network configurations\n- VPN integrations\n- Multiple environment setups\n- Advanced security hardening\n\n### Alternative Technology Stacks\n\nIncluding:\n- Different vector databases\n- Alternative workflow platforms\n- Multiple LLM backends beyond Ollama\n- Different databases for the core setup\n\n### Enterprise Features\n\nIncluding:\n- Authentication systems\n- Multi-tenancy\n- Advanced access controls\n- Compliance tooling\n\n## PR specific requirements\n\n- Small PRs Only:\n  - Focus on a single feature or fix per PR.\n- Typo-Only PRs:\n  - Typos are not sufficient justification for a PR and will be rejected.\n\n\nRemember: **It's better to be an excellent starting point than a mediocre\neverything-solution.**\n"
  },
  {
    "path": "LICENSE",
    "content": "                                 Apache License\n                           Version 2.0, January 2004\n                        http://www.apache.org/licenses/\n\n   TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION\n\n   1. Definitions.\n\n      \"License\" shall mean the terms and conditions for use, reproduction,\n      and distribution as defined by Sections 1 through 9 of this document.\n\n      \"Licensor\" shall mean the copyright owner or entity authorized by\n      the copyright owner that is granting the License.\n\n      \"Legal Entity\" shall mean the union of the acting entity and all\n      other entities that control, are controlled by, or are under common\n      control with that entity. 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  },
  {
    "path": "README.md",
    "content": "# Self-hosted AI starter kit\n\n**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.\n\n![n8n.io - Screenshot](https://raw.githubusercontent.com/n8n-io/self-hosted-ai-starter-kit/main/assets/n8n-demo.gif)\n\nCurated by <https://github.com/n8n-io>, it combines the self-hosted n8n\nplatform with a curated list of compatible AI products and components to\nquickly get started with building self-hosted AI workflows.\n\n> [!TIP]\n> [Read the announcement](https://blog.n8n.io/self-hosted-ai/)\n\n### What’s included\n\n✅ [**Self-hosted n8n**](https://n8n.io/) - Low-code platform with over 400\nintegrations and advanced AI components\n\n✅ [**Ollama**](https://ollama.com/) - Cross-platform LLM platform to install\nand run the latest local LLMs\n\n✅ [**Qdrant**](https://qdrant.tech/) - Open-source, high performance vector\nstore with an comprehensive API\n\n✅ [**PostgreSQL**](https://www.postgresql.org/) -  Workhorse of the Data\nEngineering world, handles large amounts of data safely.\n\n### What you can build\n\n⭐️ **AI Agents** for scheduling appointments\n\n⭐️ **Summarize Company PDFs** securely without data leaks\n\n⭐️ **Smarter Slack Bots** for enhanced company communications and IT operations\n\n⭐️ **Private Financial Document Analysis** at minimal cost\n\n## Installation\n\n### Cloning the Repository\n\n```bash\ngit clone https://github.com/n8n-io/self-hosted-ai-starter-kit.git\ncd self-hosted-ai-starter-kit\ncp .env.example .env # you should update secrets and passwords inside\n```\n\n### Running n8n using Docker Compose\n\n#### For Nvidia GPU users\n\n```bash\ngit clone https://github.com/n8n-io/self-hosted-ai-starter-kit.git\ncd self-hosted-ai-starter-kit\ncp .env.example .env # you should update secrets and passwords inside\ndocker compose --profile gpu-nvidia up\n```\n\n> [!NOTE]\n> If you have not used your Nvidia GPU with Docker before, please follow the\n> [Ollama Docker instructions](https://github.com/ollama/ollama/blob/main/docs/docker.md).\n\n### For AMD GPU users on Linux\n\n```bash\ngit clone https://github.com/n8n-io/self-hosted-ai-starter-kit.git\ncd self-hosted-ai-starter-kit\ncp .env.example .env # you should update secrets and passwords inside\ndocker compose --profile gpu-amd up\n```\n\n#### For Mac / Apple Silicon users\n\nIf you’re using a Mac with an M1 or newer processor, you can't expose your GPU\nto the Docker instance, unfortunately. There are two options in this case:\n\n1. Run the starter kit fully on CPU, like in the section \"For everyone else\"\n   below\n2. Run Ollama on your Mac for faster inference, and connect to that from the\n   n8n instance\n\nIf you want to run Ollama on your mac, check the\n[Ollama homepage](https://ollama.com/)\nfor installation instructions, and run the starter kit as follows:\n\n```bash\ngit clone https://github.com/n8n-io/self-hosted-ai-starter-kit.git\ncd self-hosted-ai-starter-kit\ncp .env.example .env # you should update secrets and passwords inside\ndocker compose up\n```\n\n##### For Mac users running OLLAMA locally\n\nIf you're running OLLAMA locally on your Mac (not in Docker), you need to modify the OLLAMA_HOST environment variable\n\n1. Set OLLAMA_HOST to `host.docker.internal:11434` in your .env file. \n2. Additionally, after you see \"Editor is now accessible via: <http://localhost:5678/>\":\n\n    1. Head to <http://localhost:5678/home/credentials>\n    2. Click on \"Local Ollama service\"\n    3. Change the base URL to \"http://host.docker.internal:11434/\"\n\n#### For everyone else\n\n```bash\ngit clone https://github.com/n8n-io/self-hosted-ai-starter-kit.git\ncd self-hosted-ai-starter-kit\ncp .env.example .env # you should update secrets and passwords inside\ndocker compose --profile cpu up\n```\n\n## ⚡️ Quick start and usage\n\nThe 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.\nAfter completing the installation steps above, simply follow the steps below to get started.\n\n1. Open <http://localhost:5678/> in your browser to set up n8n. You’ll only\n   have to do this once.\n2. Open the included workflow:\n   <http://localhost:5678/workflow/srOnR8PAY3u4RSwb>\n3. Click the **Chat** button at the bottom of the canvas, to start running the workflow.\n4. If this is the first time you’re running the workflow, you may need to wait\n   until Ollama finishes downloading Llama3.2. You can inspect the docker\n   console logs to check on the progress.\n\nTo open n8n at any time, visit <http://localhost:5678/> in your browser.\n\nWith your n8n instance, you’ll have access to over 400 integrations and a\nsuite of basic and advanced AI nodes such as\n[AI Agent](https://docs.n8n.io/integrations/builtin/cluster-nodes/root-nodes/n8n-nodes-langchain.agent/),\n[Text classifier](https://docs.n8n.io/integrations/builtin/cluster-nodes/root-nodes/n8n-nodes-langchain.text-classifier/),\nand [Information Extractor](https://docs.n8n.io/integrations/builtin/cluster-nodes/root-nodes/n8n-nodes-langchain.information-extractor/)\nnodes. To keep everything local, just remember to use the Ollama node for your\nlanguage model and Qdrant as your vector store.\n\n> [!NOTE]\n> This starter kit is designed to help you get started with self-hosted AI\n> workflows. While it’s not fully optimized for production environments, it\n> combines robust components that work well together for proof-of-concept\n> projects. You can customize it to meet your specific needs\n\n## Upgrading\n\n* ### For Nvidia GPU setups:\n\n```bash\ndocker compose --profile gpu-nvidia pull\ndocker compose create && docker compose --profile gpu-nvidia up\n```\n\n* ### For Mac / Apple Silicon users\n\n```bash\ndocker compose pull\ndocker compose create && docker compose up\n```\n\n* ### For Non-GPU setups:\n\n```bash\ndocker compose --profile cpu pull\ndocker compose create && docker compose --profile cpu up\n```\n\n## 👓 Recommended reading\n\nn8n is full of useful content for getting started quickly with its AI concepts\nand nodes. If you run into an issue, go to [support](#support).\n\n- [AI agents for developers: from theory to practice with n8n](https://blog.n8n.io/ai-agents/)\n- [Tutorial: Build an AI workflow in n8n](https://docs.n8n.io/advanced-ai/intro-tutorial/)\n- [Langchain Concepts in n8n](https://docs.n8n.io/advanced-ai/langchain/langchain-n8n/)\n- [Demonstration of key differences between agents and chains](https://docs.n8n.io/advanced-ai/examples/agent-chain-comparison/)\n- [What are vector databases?](https://docs.n8n.io/advanced-ai/examples/understand-vector-databases/)\n\n## 🎥 Video walkthrough\n\n- [Installing and using Local AI for n8n](https://www.youtube.com/watch?v=xz_X2N-hPg0)\n\n## 🛍️ More AI templates\n\nFor more AI workflow ideas, visit the [**official n8n AI template\ngallery**](https://n8n.io/workflows/categories/ai/). From each workflow,\nselect the **Use workflow** button to automatically import the workflow into\nyour local n8n instance.\n\n### Learn AI key concepts\n\n- [AI Agent Chat](https://n8n.io/workflows/1954-ai-agent-chat/)\n- [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/)\n- [Chat with OpenAI Assistant (by adding a memory)](https://n8n.io/workflows/2098-chat-with-openai-assistant-by-adding-a-memory/)\n- [Use an open-source LLM (via Hugging Face)](https://n8n.io/workflows/1980-use-an-open-source-llm-via-huggingface/)\n- [Chat with PDF docs using AI (quoting sources)](https://n8n.io/workflows/2165-chat-with-pdf-docs-using-ai-quoting-sources/)\n- [AI agent that can scrape webpages](https://n8n.io/workflows/2006-ai-agent-that-can-scrape-webpages/)\n\n### Local AI templates\n\n- [Tax Code Assistant](https://n8n.io/workflows/2341-build-a-tax-code-assistant-with-qdrant-mistralai-and-openai/)\n- [Breakdown Documents into Study Notes with MistralAI and Qdrant](https://n8n.io/workflows/2339-breakdown-documents-into-study-notes-using-templating-mistralai-and-qdrant/)\n- [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/)\n- [Recipe Recommendations with Qdrant and Mistral](https://n8n.io/workflows/2333-recipe-recommendations-with-qdrant-and-mistral/)\n\n## Tips & tricks\n\n### Accessing local files\n\nThe self-hosted AI starter kit will create a shared folder (by default,\nlocated in the same directory) which is mounted to the n8n container and\nallows n8n to access files on disk. This folder within the n8n container is\nlocated at `/data/shared` -- this is the path you’ll need to use in nodes that\ninteract with the local filesystem.\n\n**Nodes that interact with the local filesystem**\n\n- [Read/Write Files from Disk](https://docs.n8n.io/integrations/builtin/core-nodes/n8n-nodes-base.filesreadwrite/)\n- [Local File Trigger](https://docs.n8n.io/integrations/builtin/core-nodes/n8n-nodes-base.localfiletrigger/)\n- [Execute Command](https://docs.n8n.io/integrations/builtin/core-nodes/n8n-nodes-base.executecommand/)\n\n## 📜 License\n\nThis project is licensed under the Apache License 2.0 - see the\n[LICENSE](LICENSE) file for details.\n\n## 💬 Support\n\nJoin the conversation in the [n8n Forum](https://community.n8n.io/), where you\ncan:\n\n- **Share Your Work**: Show off what you’ve built with n8n and inspire others\n  in the community.\n- **Ask Questions**: Whether you’re just getting started or you’re a seasoned\n  pro, the community and our team are ready to support with any challenges.\n- **Propose Ideas**: Have an idea for a feature or improvement? Let us know!\n  We’re always eager to hear what you’d like to see next.\n"
  },
  {
    "path": "docker-compose.yml",
    "content": "volumes:\n  n8n_storage:\n  postgres_storage:\n  ollama_storage:\n  qdrant_storage:\n\nnetworks:\n  demo:\n\nx-n8n: &service-n8n\n  image: n8nio/n8n:latest\n  networks: ['demo']\n  environment:\n    - DB_TYPE=postgresdb\n    - DB_POSTGRESDB_HOST=postgres\n    - DB_POSTGRESDB_USER=${POSTGRES_USER}\n    - DB_POSTGRESDB_PASSWORD=${POSTGRES_PASSWORD}\n    - N8N_DIAGNOSTICS_ENABLED=false\n    - N8N_PERSONALIZATION_ENABLED=false\n    - N8N_ENCRYPTION_KEY\n    - N8N_USER_MANAGEMENT_JWT_SECRET\n    - OLLAMA_HOST=${OLLAMA_HOST:-ollama:11434}\n  env_file:\n    - path: .env\n      required: true\n\nx-ollama: &service-ollama\n  image: ollama/ollama:latest\n  container_name: ollama\n  networks: ['demo']\n  restart: unless-stopped\n  ports:\n    - 11434:11434\n  volumes:\n    - ollama_storage:/root/.ollama\n\nx-init-ollama: &init-ollama\n  image: ollama/ollama:latest\n  networks: ['demo']\n  container_name: ollama-pull-llama\n  volumes:\n    - ollama_storage:/root/.ollama\n  entrypoint: /bin/sh\n  environment:\n    - OLLAMA_HOST=ollama:11434\n  command:\n    - \"-c\"\n    - \"sleep 3; ollama pull llama3.2\"\n\nservices:\n  postgres:\n    image: postgres:16-alpine\n    hostname: postgres\n    networks: ['demo']\n    restart: unless-stopped\n    environment:\n      - POSTGRES_USER\n      - POSTGRES_PASSWORD\n      - POSTGRES_DB\n    volumes:\n      - postgres_storage:/var/lib/postgresql/data\n    healthcheck:\n      test: ['CMD-SHELL', 'pg_isready -h localhost -U ${POSTGRES_USER} -d ${POSTGRES_DB}']\n      interval: 5s\n      timeout: 5s\n      retries: 10\n\n  n8n-import:\n    <<: *service-n8n\n    hostname: n8n-import\n    container_name: n8n-import\n    entrypoint: /bin/sh\n    command:\n    - \"-c\"\n    - |\n      if [ -z \"$(n8n list:workflow --onlyId)\" ]; then\n        n8n import:credentials --separate --input=/demo-data/credentials && \\\n        n8n import:workflow --separate --input=/demo-data/workflows\n      else\n        echo \"Workflows exist, skipping import\"\n      fi\n    volumes:\n      - ./n8n/demo-data:/demo-data\n    depends_on:\n      postgres:\n        condition: service_healthy\n\n  n8n:\n    <<: *service-n8n\n    hostname: n8n\n    container_name: n8n\n    restart: unless-stopped\n    ports:\n      - 5678:5678\n    volumes:\n      - n8n_storage:/home/node/.n8n\n      - ./n8n/demo-data:/demo-data\n      - ./shared:/data/shared\n    depends_on:\n      postgres:\n        condition: service_healthy\n      n8n-import:\n        condition: service_completed_successfully\n\n  qdrant:\n    image: qdrant/qdrant\n    hostname: qdrant\n    container_name: qdrant\n    networks: ['demo']\n    restart: unless-stopped\n    ports:\n      - 6333:6333\n    volumes:\n      - qdrant_storage:/qdrant/storage\n\n  ollama-cpu:\n    profiles: [\"cpu\"]\n    <<: *service-ollama\n\n  ollama-gpu:\n    profiles: [\"gpu-nvidia\"]\n    <<: *service-ollama\n    deploy:\n      resources:\n        reservations:\n          devices:\n            - driver: nvidia\n              count: 1\n              capabilities: [gpu]\n\n  ollama-gpu-amd:\n    profiles: [\"gpu-amd\"]\n    <<: *service-ollama\n    image: ollama/ollama:rocm\n    devices:\n      - \"/dev/kfd\"\n      - \"/dev/dri\"\n\n  ollama-pull-llama-cpu:\n    profiles: [\"cpu\"]\n    <<: *init-ollama\n    depends_on:\n      - ollama-cpu\n\n  ollama-pull-llama-gpu:\n    profiles: [\"gpu-nvidia\"]\n    <<: *init-ollama\n    depends_on:\n      - ollama-gpu\n\n  ollama-pull-llama-gpu-amd:\n    profiles: [gpu-amd]\n    <<: *init-ollama\n    image: ollama/ollama:rocm\n    depends_on:\n     - ollama-gpu-amd\n"
  },
  {
    "path": "n8n/demo-data/credentials/sFfERYppMeBnFNeA.json",
    "content": "{\n  \"createdAt\": \"2024-02-23T16:27:55.919Z\",\n  \"updatedAt\": \"2024-02-23T16:27:55.918Z\",\n  \"id\": \"sFfERYppMeBnFNeA\",\n  \"name\": \"Local QdrantApi database\",\n  \"data\": \"U2FsdGVkX18bm81Pk18TjmfyKEIbzd91Dt1O8pUPgTxVGk5v1mXp7MlE/3Fl+NHGTMBqa3u7RBS36wTQ74rijQ==\",\n  \"type\": \"qdrantApi\",\n  \"nodesAccess\": [\n    {\n      \"nodeType\": \"@n8n/n8n-nodes-langchain.vectorStoreQdrant\",\n      \"date\": \"2024-02-23T16:27:55.918Z\"\n    }\n  ]\n}\n"
  },
  {
    "path": "n8n/demo-data/credentials/xHuYe0MDGOs9IpBW.json",
    "content": "{\n  \"createdAt\": \"2024-02-23T16:26:54.475Z\",\n  \"updatedAt\": \"2024-02-23T16:26:58.928Z\",\n  \"id\": \"xHuYe0MDGOs9IpBW\",\n  \"name\": \"Local Ollama service\",\n  \"data\": \"U2FsdGVkX18BVmjQBCdNKSrjr0GhmcTwMgG/rSWhncWtqOLPT62WnCIktky8RgM1PhH7vMkMc5EuUFIQA/eEZA==\",\n  \"type\": \"ollamaApi\",\n  \"nodesAccess\": [\n    {\n      \"nodeType\": \"@n8n/n8n-nodes-langchain.lmChatOllama\",\n      \"date\": \"2024-02-23T16:26:58.927Z\"\n    },\n    {\n      \"nodeType\": \"@n8n/n8n-nodes-langchain.lmOllama\",\n      \"date\": \"2024-02-23T16:26:58.927Z\"\n    }\n  ]\n}\n"
  },
  {
    "path": "n8n/demo-data/workflows/srOnR8PAY3u4RSwb.json",
    "content": "{\n  \"createdAt\": \"2024-02-23T16:58:31.616Z\",\n  \"updatedAt\": \"2024-02-23T16:58:31.616Z\",\n  \"id\": \"srOnR8PAY3u4RSwb\",\n  \"name\": \"Demo workflow\",\n  \"active\": false,\n  \"nodes\": [\n    {\n      \"parameters\": {},\n      \"id\": \"74003dcd-2ac7-4caa-a1cd-adecc5143c07\",\n      \"name\": \"Chat Trigger\",\n      \"type\": \"@n8n/n8n-nodes-langchain.chatTrigger\",\n      \"typeVersion\": 1,\n      \"position\": [\n        660,\n        340\n      ],\n      \"webhookId\": \"cdb5c076-d458-4b9d-8398-f43bd25059b1\"\n    },\n    {\n      \"parameters\": {},\n      \"id\": \"ce8c3da4-899c-4cc4-af73-8096c64eec64\",\n      \"name\": \"Basic LLM Chain\",\n      \"type\": \"@n8n/n8n-nodes-langchain.chainLlm\",\n      \"typeVersion\": 1.7,\n      \"position\": [\n        880,\n        340\n      ]\n    },\n    {\n      \"parameters\": {\n        \"model\": \"llama3.2:latest\",\n        \"options\": {}\n      },\n      \"id\": \"3dee878b-d748-4829-ac0a-cfd6705d31e5\",\n      \"name\": \"Ollama Chat Model\",\n      \"type\": \"@n8n/n8n-nodes-langchain.lmChatOllama\",\n      \"typeVersion\": 1,\n      \"position\": [\n        900,\n        560\n      ],\n      \"credentials\": {\n        \"ollamaApi\": {\n          \"id\": \"xHuYe0MDGOs9IpBW\",\n          \"name\": \"Local Ollama service\"\n        }\n      }\n    }\n  ],\n  \"connections\": {\n    \"Chat Trigger\": {\n      \"main\": [\n        [\n          {\n            \"node\": \"Basic LLM Chain\",\n            \"type\": \"main\",\n            \"index\": 0\n          }\n        ]\n      ]\n    },\n    \"Ollama Chat Model\": {\n      \"ai_languageModel\": [\n        [\n          {\n            \"node\": \"Basic LLM Chain\",\n            \"type\": \"ai_languageModel\",\n            \"index\": 0\n          }\n        ]\n      ]\n    }\n  },\n  \"settings\": {\n    \"executionOrder\": \"v1\"\n  },\n  \"staticData\": null,\n  \"meta\": {\n    \"templateCredsSetupCompleted\": true\n  },\n  \"pinData\": {},\n  \"versionId\": \"4e2affe6-bb1c-4ddc-92f9-dde0b7656796\",\n  \"triggerCount\": 0,\n  \"tags\": []\n}\n"
  }
]