Local AI Infrastructure and Deployment

Introduction

• Introduction to a comprehensive package for local AI by the n8n team.
• Includes:
  • Olama for LLMs (Language Learning Models)
  • Quadrant for vector database
  • Postgres for SQL database
  • n8n for workflow automation.
• Highlights the advantages of open-source models like Llama over closed-source models.

Setting Up the AI Infrastructure

Prerequisites

• Required installations:
  • Git
  • Docker
  • GitHub Desktop and Docker Desktop recommended for ease of use.

Installation Process

1. Git Clone:
   • Clone the repository from GitHub.
   • Navigate into the cloned directory.
2. Environment Setup:
   • Edit the .env file to set up credentials for Postgres and n8n.
   • Customize Postgres and n8n credentials including username, password, and database name.
3. Docker Compose Configuration:
   • Modify docker-compose.yaml to:
     • Expose the Postgres port.
     • Add embeddings model for Olama.
4. Run Docker Compose:
   • Use appropriate Docker Compose command based on your architecture (NVIDIA GPU, Mac, etc.).

Components of the AI Package

Docker Containers

• Containers involve Olama, Postgres, n8n, and Quadrant.
• Use Docker Desktop to view and manage containers.
• Commands can be executed in real-time within containers.

AI Agent Setup

1. Access n8n:
   • Navigate to localhost port 5678.
2. AI Agent Workflow:
   • Use Olama for LLMs.
   • Use Postgres for chat memory.
   • Use Quadrant for vector storage.
3. Integration Details:
   • Configure Olama, Postgres, and Quadrant settings using host.docker.internal.

Building a RAG AI Agent

Workflow for Data Ingestion

• Setup to pull files from Google Drive into Quadrant.
• Important Step: Remove old vectors to prevent duplication.
• Data Processing:
  • Google Drive triggers for file creation/update.
  • Extract and insert file data into Quadrant.

Testing the AI Agent

• Use chat widget to interact with the agent.
• Document retrieval from Quadrant for context-specific responses.

Future Enhancements

• Possible addition of Redis for caching.
• Consideration for using self-hosted Superbase instead of Postgres.
• Potential development of a full local AI tech stack including front-end integration.

Conclusion

• Encouragement to continue experimenting with local AI setups.
• Potential future improvements for better integration and ease of use.