AI-Powered RAG-Based Question Answering System

A simple Retrieval-Augmented Generation (RAG) Question Answering system built in Python with a Streamlit web app interface.
It retrieves relevant context from a document database using FAISS and generates accurate answers using a language model (Hugging Face).

Features

• Load your documents.
• Retrieve relevant context for each query.
• Generate answers using a connected LLM.
• Fast search with FAISS vector store.
• Easy to customize for different datasets.
• User-friendly Streamlit web interface.

Tech Stack

• Python
• Streamlit for the web app
• FAISS for vector search
• LangChain for orchestration
• Hugging Face API for LLM integration

License

MIT License