RAG Document Q&A System

This project implements a Retrieval-Augmented Generation (RAG) system for document-based question answering.

Features

• Document ingestion and semantic retrieval
• MVC architecture for clean separation of concerns
• Modular support for multiple LLM providers
• Multilingual support: Arabic, English, French

Models & Integrations

• Embedding and generation using OpenAI and Cohere
• Easily extensible to add new models or providers

Use Case

Ask natural language questions over your documents and receive accurate, context-aware answers powered by retrieval and generation.