🧠 Simple RAG System - Document Query Assistant

This project implements a lightweight Retrieval-Augmented Generation (RAG) system using Streamlit, FAISS, and LLMs like OpenAI and Ollama. It allows users to upload PDF, TXT, or DOCX documents, embed them into a searchable vector database, and query them using natural language. The model then returns context-aware answers based on retrieved document chunks.

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🚀 Features

• 📄 Upload PDF, TXT, or DOCX files
• 🔍 Split content into semantic chunks with overlapping context
• 📦 Vector store backed by FAISS for efficient similarity search
• 🔗 Embeddings from OpenAI, Nomic, or Chroma
• 🧠 LLM-based question answering via OpenAI (GPT-4) or Ollama (LLaMA3)
• 🧵 Streamlit UI with model selection, caching, and source highlighting

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