Run ALL Your AI Locally in Minutes (LLMs, RAG, and more)

Run ALL Your AI Locally in Minutes (LLMs, RAG, and more)

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AI Summary

Summary of YouTube Video: Self-Hosted AI Starter Kit by n8n

Overview

• Introduction to a comprehensive package for setting up local AI infrastructure.
• Package includes:
  • Old Llama for LLMs.
  • Quadrant for vector database.
  • Postgres for SQL database.
  • n8n for workflow automation.

Installation Steps

1. Prerequisites:
   • Install Git and Docker (recommend GitHub Desktop and Docker Desktop).
2. Clone the Repository:
   • Use git clone to download the self-hosted AI starter kit from GitHub.
3. Edit Configuration:
   • Customize the .env file with Postgres credentials and n8n secrets.
   • Modify the Docker Compose file:
     • Add port mapping for Postgres to access it in n8n workflow.
     • Include initialization command for Llama embeddings model.
4. Run Docker Compose:
   • Execute the appropriate Docker Compose command for your system architecture (CPU or NVIDIA GPU).

Visualization and Usage

• Access Docker Desktop to visualize running containers.
• Navigate to localhost:5678 for n8n interface.
• Build a local RAG AI agent:
  • Integrate Postgres for chat memory.
  • Use Quadrant for vector storage and Llama for LLM.
  • Configure connection parameters for Llama and Postgres.

Advanced Features

• Set up workflows to ingest files from Google Drive into the knowledge base.
• Implement a custom code node to manage vector insertion and avoid duplicates in Quadrant.
• Workflow includes triggers for file creation and updates in Google Drive, processing these files into the vector database.

Conclusion

• Emphasizes the accessibility and robustness of self-hosting AI infrastructure.
• Mentions potential future expansions, including more UI functionalities and backend improvements.