readme.md

RAG System

A modular Retrieval-Augmented Generation (RAG) system for document-based question answering.

Features

• Document Processing: Extract and chunk text from PDF, DOCX, and TXT files
• Semantic Search: Embed and search documents based on meaning, not just keywords
• Flexible Architecture: Support for multiple embedding models and vector databases
• REST API: API for integrating with other applications
• Web UI: User-friendly Streamlit interface for document upload and querying

Architecture

The system consists of the following components:

• Embedding Model: Converts text to vector embeddings
• Vector Database: Stores and searches document embeddings
• Document Processor: Extracts and chunks text from documents
• RAG Engine: Combines retrieval and generation for question answering
• API: Exposes functionality through a RESTful API
• UI: Provides a user interface for interacting with the system

Installation

Prerequisites

• Python 3.8+
• pip

Setup

1. Clone the repository:

   git clone https://github.com/yourusername/rag-system.git
   cd rag-system
   

2. Install dependencies:

   pip install -r requirements.txt
   

3. Set up environment variables (optional):

   cp .env.example .env
   # Edit .env with your settings
   

Usage

API Server

Run the API server:

python app.py

The API will be available at http://localhost:8000

Streamlit UI

Run the Streamlit UI:

streamlit run ui/app.py

The UI will be available at http://localhost:8501

API Endpoints

• POST /documents: Add documents
• POST /upload: Upload and process document files
• POST /query: Query the RAG system
• GET /search: Search for documents
• DELETE /documents: Clear all documents
• GET /health: Check system health

Configuration

The system can be configured through environment variables or the config.py file:

• EMBEDDING_MODEL_NAME: Name of the embedding model
• VECTOR_DB_TYPE: Type of vector database to use
• CHUNK_SIZE: Size of document chunks
• CHUNK_OVERLAP: Overlap between chunks
• TOP_K: Number of documents to retrieve
• SEARCH_TYPE: Type of search (semantic, keyword, hybrid)
• LLM_MODEL_NAME: Name of the language model for generation
• LLM_API_KEY: API key for the language model

Extending

The modular architecture makes it easy to extend the system:

• Add new embedding models in embedding/model.py
• Add new vector databases in storage/vector_db.py
• Add support for new document types in document/processor.py
• Add new LLM integrations in llm/model.py

License

MIT License