backend/README.md

RAG Backend API (FastAPI)

This is the FastAPI backend serving the Document Assistant application. It handles user authentication, document parsing (PDF/TXT), vector embeddings creation using fastembed, and RAG-based chat using the Groq SDK.

Key Technologies

• FastAPI: Modern, fast web framework for building APIs.
• SQLAlchemy & Alembic: Database ORM and migrations helper.
• PostgreSQL + pgvector: Relational database with vector similarity search capabilities.
• FastEmbed: Lightweight, fast library for generating text embeddings (default: BAAI/bge-small-en-v1.5).
• PyMuPDF (fitz): Optimized library for parsing PDFs and extracting text.
• Groq SDK: Connects to the Groq Cloud API for fast llama3 text completions.
• FastAPI Users: Authentication system using JWT tokens.

Getting Started (Local Development)

Prerequisites

• Python 3.12+
• Running PostgreSQL database with pgvector extension enabled

Setup Steps

1. Navigate to backend directory

   cd backend
   

2. Create a virtual environment & install dependencies

   python -m venv .venv
   source .venv/bin/activate  # On Windows: .venv\Scripts\activate
   pip install -r requirements.txt
   

3. Configure Environment Variables Create a .env file based on .env.example in the root or copy/rename it:

   cp .env.example .env
   

   Modify .env to configure your DATABASE_URL, GROQ_API_KEY, and SECRET_KEY.

4. Run Database Migrations / Initialize DB The application automatically initializes database tables on startup.

5. Start Development Server

   uvicorn main:app --reload --port 8000
   

   Open http://localhost:8000/docs in your browser to view the interactive Swagger API documentation.

Testing

Run unit and integration tests using pytest:

pytest

Tests are located in the tests/ directory.