README.md

RAG Backend Service

This is the RAG (Retrieval-Augmented Generation) backend service for the chat application. It provides APIs for document processing, search, and chat functionality using FAISS for vector similarity search and Sentence Transformers for text embeddings.

Prerequisites

• Python 3.8 or higher
• NVIDIA GPU with CUDA support (optional, for faster processing)
• Sufficient disk space for document storage and vector indices

Setup

For Unix/macOS:

# Create and activate virtual environment
python -m venv venv
source venv/bin/activate

# Install dependencies
pip install -r requirements.txt

For Windows:

# Create and activate virtual environment
python -m venv venv
.\venv\Scripts\activate

# Install dependencies
pip install -r requirements.txt

Configuration

The service can be configured through the configuration files:

• src/config/app_config.yaml
• src/config/rag_config.yaml

Running the Service

For Unix/macOS:

# Make sure you're in the backend-rag directory
cd backend-rag

# Activate the virtual environment if not already activated
source venv/bin/activate

# Start the server
python src/app.py

For Windows:

# Make sure you're in the backend-rag directory
cd backend-rag

# Activate the virtual environment if not already activated
.\venv\Scripts\activate

# Start the server
python src\app.py

Architecture

This service implements a RAG (Retrieval-Augmented Generation) system that:

1. Processes and chunks documents
2. Generates embeddings
3. Stores vectors in a FAISS index
4. Provides semantic search capabilities