The ULTIMATE Local AI Setup - LLMs, Qdrant, n8n (NO CODE!!)

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AI Summary

Tutorial Summary: Building a Local AI Agent with Open-Source Tools

Overview

• The tutorial demonstrates how to build a local AI agent using open-source AI tools.
• Tools used include Quadrant Vector Store, AMA platform, and a chat model.
• The AI agent allows for document uploads and interactions through a vector database.
• The process is conducted locally on the user’s machine.

Instructions

1. Clone the GitHub Repository
   • Visit github.com/nn-iio and locate the self-hosted AI starter kit.
   • Clone the repository to your local machine using the provided command in the terminal.
2. Set Up Docker Containers
   • Navigate to the cloned folder using cd.
   • Use Docker Desktop to manage the containers and images.
   • Run the Docker compose command to set up the necessary containers for the AI starter kit.
   • Containers include AMA, Postgres, n8n, and Quadrant Vector database.
3. Access the Local Host
   • Go to Local Host 5678 in your browser.
   • Sign in or sign up if it’s your first time.
4. Configure the Workflow
   • Rename the workflow to “local AI kit”.
   • Add a chat trigger with the option to allow file uploads.
5. Set Up the Vector Database
   • Add the Quadrant Vector Store node.
   • Configure the credentials (auto-loaded due to Docker setup).
   • Set the operation mode to insert documents by ID.
6. Embedding and Document Loader
   • Add an embedding node using the AMA platform.
   • Install new embedding models from ama.com if needed.
   • Use the Docker Desktop app to pull new models into the AMA container.
7. Test the Vector Database
   • Access the Quadrant Vector Store dashboard via Local Host 6333/dashboard.
   • Upload a document through the chat and observe the vector database update.
8. Set Up the AI Agent
   • Add a conversational agent node.
   • Connect the agent to the chat input and vector store retriever.
   • Configure the AI agent with the appropriate chat and embedding models.
9. Finalize and Test the Workflow
   • Ensure the vector store processes items only once.
   • Test the workflow by uploading a document and asking a question through the chat.
   • Observe the AI agent’s response and verify its accuracy.
10. Embed the Chat Publicly
    • Make the chat publicly available and embed it on a website if desired.

Conclusion

• The tutorial provides a method to build a powerful AI agent using free, open-source tools.
• The agent is capable of processing uploaded documents and answering questions.
• The entire setup is done locally, offering a cost-effective solution for AI interactions.

Notes

• The tutorial emphasizes a code-minimal approach, suitable for users who may be intimidated by coding.
• Docker is used extensively for managing the AI environment.
• The AI agent is built using a combination of chat triggers, vector databases, and embedding models.