Kickoff Plan: AI Model Hub Service

This document outlines the plan for developing a central "hub" service that routes requests to various Large Language Models (LLMs) and uses PostgreSQL for metadata storage alongside FAISS for similarity search on vector data.

1. High-Level Architecture

The service will consist of three main components:

1. API Server: A web server that exposes endpoints to receive user prompts and return model responses. This will be the main entry point for all client applications.

2. LLM Router/Orchestrator: A core logic layer responsible for deciding which LLM (Gemini, DeepSeek, etc.) should handle a given request. It will also manage interactions with PostgreSQL and FAISS.

3. Vector Database (FAISS + PostgreSQL): A two-layered database system:

   • FAISS: Stores vectors (numerical representations of text). Handles high-performance similarity search.
   • PostgreSQL: Stores metadata such as conversation IDs, document titles, timestamps, and other relational data.

2. Technology Stack

• API Framework: FastAPI (Python) – High-performance, easy to learn, with automatic interactive documentation, ideal for testing and development.

• LLM Interaction: LangChain (or a similar abstraction library) – Simplifies communication with different LLM APIs by providing a unified interface.

• Vector Database:

  • FAISS: High-performance similarity search for vectors.
  • PostgreSQL: Stores metadata for vectors, such as document IDs, user data, timestamps, etc. Used for filtering, organizing, and managing relational data.

• Deployment: Docker – Containerizing the application for portability, ensuring easy deployment across any machine within the local network.

3. Development Roadmap

Phase 1: Core API and Model Integration (1-2 weeks)

• [X] Set up a basic FastAPI server.
• [X] Create a /chat endpoint that accepts user prompts.
• [X] Implement basic routing logic to forward requests to one hardcoded LLM (e.g., Gemini).
• [X] Connect to the LLM's API and return the response to the user.

Phase 2: PostgreSQL and FAISS Integration (2-3 weeks)

• [ ] Integrate PostgreSQL for metadata storage (document IDs, timestamps, etc.).
• [ ] Integrate FAISS for vector storage and similarity search.
• [ ] On each API call, embed the user prompt and the model's response into vectors.
• [ ] Store the vectors in FAISS and store associated metadata in PostgreSQL (such as document title, conversation ID).
• [ ] Perform a similarity search using FAISS before sending a new prompt to the LLM, and include relevant history stored in PostgreSQL as context.

Phase 3: Multi-Model Routing & RAG (1-2 weeks)

• [ ] Abstract LLM connections to easily support multiple models (Gemini, DeepSeek, etc.).
• [ ] Add logic to the /chat endpoint to allow clients to specify which model to use.
• [ ] Create a separate endpoint (e.g., /add-document) to upload text files.
• [ ] Implement a RAG pipeline:
  • When a prompt is received, search FAISS for relevant vector matches and retrieve metadata from PostgreSQL.
  • Pass the relevant document chunks along with the prompt to the selected LLM.

Phase 4: Refinement and Deployment (1 week)

• [ ] Develop a simple UI (optional, could use FastAPI's built-in docs).
• [ ] Write Dockerfiles for the application.
• [ ] Add configuration management for API keys and other settings.
• [ ] Implement basic logging and error handling.

4. PostgreSQL + FAISS Workflow

• Storing Vectors: When a document is added, its vector representation is stored in FAISS. Metadata such as document titles, timestamps, and user IDs are stored in PostgreSQL.

• Querying: For a user query, embed the query into a vector. Use FAISS to perform a similarity search and retrieve the nearest vectors. Query PostgreSQL for metadata (e.g., title, author) related to the relevant vectors.

• Syncing Data: Ensure that metadata in PostgreSQL is synchronized with vectors in FAISS for accurate and consistent retrieval.

This update to the plan leverages PostgreSQL for metadata management while FAISS handles efficient similarity search. The integration allows you to query and filter both metadata and vectors in an optimized manner, ensuring scalability and flexibility for future features.

Let me know if you'd like more specifics or adjustments!