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210 lines
8.3 KiB
Markdown
210 lines
8.3 KiB
Markdown
# Self-hosted AI starter kit
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**Self-hosted AI Starter Kit** is an open, docker compose template that
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quickly bootstraps a fully featured Local AI and Low Code development
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environment.
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Curated by <https://github.com/n8n-io>, it combines the self-hosted n8n
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platform with a curated list of compatible AI products and components to
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quickly get started with building self-hosted AI workflows.
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> [!TIP]
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> [Read the announcement](https://blog.n8n.io/self-hosted-ai/)
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### What’s included
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✅ [**Self-hosted n8n**](https://n8n.io/) - Low-code platform with over 400
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integrations and advanced AI components
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✅ [**Ollama**](https://ollama.com/) - Cross-platform LLM platform to install
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and run the latest local LLMs
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✅ [**Qdrant**](https://qdrant.tech/) - Open-source, high performance vector
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store with an comprehensive API
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✅ [**PostgreSQL**](https://www.postgresql.org/) - Workhorse of the Data
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Engineering world, handles large amounts of data safely.
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### What you can build
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⭐️ AI Agents which can schedule appointments
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⭐️ Summarise company PDFs without leaking data
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⭐️ Smarter slack bots for company comms and IT-ops
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⭐️ Analyse financial documents privately and for little cost
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## Installation
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### For Nvidia GPU users
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```
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git clone https://github.com/n8n-io/self-hosted-ai-starter-kit.git
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cd self-hosted-ai-starter-kit
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docker compose --profile gpu-nvidia up
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```
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> [!NOTE]
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> If you have not used your Nvidia GPU with Docker before, please follow the
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> [Ollama Docker instructions](https://github.com/ollama/ollama/blob/main/docs/docker.md).
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### For Mac / Apple Silicon users
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If you’re using a Mac with an M1 or newer processor, you can't expose your GPU
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to the Docker instance, unfortunately. There are two options in this case:
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1. Run the starter kit fully on CPU, like in the section "For everyone else"
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below
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2. Run Ollama on your Mac for faster inference, and connect to that from the
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n8n instance
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If you want to run Ollama on your mac, check the
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[Ollama homepage](https://ollama.com/)
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for installation instructions, and run the starter kit as follows:
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```
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git clone https://github.com/n8n-io/self-hosted-ai-starter-kit.git
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cd self-hosted-ai-starter-kit
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docker compose up
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```
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After you followed the quick start set-up below, change the Ollama credentials
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by using `http://host.docker.internal:11434/` as the host.
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### For everyone else
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```
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git clone https://github.com/n8n-io/self-hosted-ai-starter-kit.git
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cd self-hosted-ai-starter-kit
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docker compose --profile cpu up
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```
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## ⚡️ Quick start and usage
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The main component of the self-hosted AI starter kit is a docker compose file
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pre-configured with network and disk so there isn’t much else you need to
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install. After completing the installation steps above, follow the steps below
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to get started.
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1. Open <http://localhost:5678/> in your browser to set up n8n. You’ll only
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have to do this once.
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2. Open the included workflow:
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<http://localhost:5678/workflow/srOnR8PAY3u4RSwb>
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3. Select **Test workflow** to start running the workflow.
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4. If this is the first time you’re running the workflow, you may need to wait
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until Ollama finishes downloading Llama3.1. You can inspect the docker
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console logs to check on the progress.
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To open n8n at any time, visit <http://localhost:5678/> in your browser.
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With your n8n instance, you’ll have access to over 400 integrations and a
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suite of basic and advanced AI nodes such as
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[AI Agent](https://docs.n8n.io/integrations/builtin/cluster-nodes/root-nodes/n8n-nodes-langchain.agent/),
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[Text classifier](https://docs.n8n.io/integrations/builtin/cluster-nodes/root-nodes/n8n-nodes-langchain.text-classifier/),
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and [Information Extractor](https://docs.n8n.io/integrations/builtin/cluster-nodes/root-nodes/n8n-nodes-langchain.information-extractor/)
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nodes. To keep everything local, just remember to use the Ollama node for your
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language model and Qdrant as your vector store.
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> [!NOTE]
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> This starter kit is designed to help you get started with self-hosted AI
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> workflows. While it’s not fully optimized for production environments, it
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> combines robust components that work well together for proof-of-concept
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> projects. You can customize it to meet your specific needs
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## Upgrading
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### For Nvidia GPU users
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```
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docker compose --profile gpu-nvidia pull
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docker compose create && docker compose --profile gpu-nvidia up
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```
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### For Mac / Apple Silicon users
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```
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docker compose pull
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docker compose create && docker compose up
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```
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### For everyone else
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```
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docker compose --profile cpu pull
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docker compose create && docker compose --profile cpu up
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```
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## 👓 Recommended reading
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n8n is full of useful content for getting started quickly with its AI concepts
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and nodes. If you run into an issue, go to [support](#support).
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- [AI agents for developers: from theory to practice with n8n](https://blog.n8n.io/ai-agents/)
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- [Tutorial: Build an AI workflow in n8n](https://docs.n8n.io/advanced-ai/intro-tutorial/)
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- [Langchain Concepts in n8n](https://docs.n8n.io/advanced-ai/langchain/langchain-n8n/)
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- [Demonstration of key differences between agents and chains](https://docs.n8n.io/advanced-ai/examples/agent-chain-comparison/)
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- [What are vector databases?](https://docs.n8n.io/advanced-ai/examples/understand-vector-databases/)
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## 🎥 Video walkthrough
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- [Installing and using Local AI for n8n](https://www.youtube.com/watch?v=xz_X2N-hPg0)
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## 🛍️ More AI templates
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For more AI workflow ideas, visit the [**official n8n AI template
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gallery**](https://n8n.io/workflows/?categories=AI). From each workflow,
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select the **Use workflow** button to automatically import the workflow into
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your local n8n instance.
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### Learn AI key concepts
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- [AI Agent Chat](https://n8n.io/workflows/1954-ai-agent-chat/)
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- [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/)
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- [Chat with OpenAI Assistant (by adding a memory)](https://n8n.io/workflows/2098-chat-with-openai-assistant-by-adding-a-memory/)
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- [Use an open-source LLM (via HuggingFace)](https://n8n.io/workflows/1980-use-an-open-source-llm-via-huggingface/)
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- [Chat with PDF docs using AI (quoting sources)](https://n8n.io/workflows/2165-chat-with-pdf-docs-using-ai-quoting-sources/)
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- [AI agent that can scrape webpages](https://n8n.io/workflows/2006-ai-agent-that-can-scrape-webpages/)
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### Local AI templates
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- [Tax Code Assistant](https://n8n.io/workflows/2341-build-a-tax-code-assistant-with-qdrant-mistralai-and-openai/)
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- [Breakdown Documents into Study Notes with MistralAI and Qdrant](https://n8n.io/workflows/2339-breakdown-documents-into-study-notes-using-templating-mistralai-and-qdrant/)
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- [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/)
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- [Recipe Recommendations with Qdrant and Mistral](https://n8n.io/workflows/2333-recipe-recommendations-with-qdrant-and-mistral/)
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## Tips & tricks
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### Accessing local files
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The self-hosted AI starter kit will create a shared folder (by default,
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located in the same directory) which is mounted to the n8n container and
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allows n8n to access files on disk. This folder within the n8n container is
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located at `/data/shared` -- this is the path you’ll need to use in nodes that
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interact with the local filesystem.
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**Nodes that interact with the local filesystem**
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- [Read/Write Files from Disk](https://docs.n8n.io/integrations/builtin/core-nodes/n8n-nodes-base.filesreadwrite/)
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- [Local File Trigger](https://docs.n8n.io/integrations/builtin/core-nodes/n8n-nodes-base.localfiletrigger/)
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- [Execute Command](https://docs.n8n.io/integrations/builtin/core-nodes/n8n-nodes-base.executecommand/)
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## 📜 License
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This project is licensed under the Apache License 2.0 - see the
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[LICENSE](LICENSE) file for details.
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## 💬 Support
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Join the conversation in the [n8n Forum](https://community.n8n.io/), where you
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can:
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- **Share Your Work**: Show off what you’ve built with n8n and inspire others
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in the community.
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- **Ask Questions**: Whether you’re just getting started or you’re a seasoned
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pro, the community and our team are ready to support with any challenges.
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- **Propose Ideas**: Have an idea for a feature or improvement? Let us know!
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We’re always eager to hear what you’d like to see next.
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