🧠 QA RAG Assistant

A Retrieval-Augmented Generation (RAG) assistant that retrieves knowledge and generates accurate, context-aware answers using LangChain + LLMs.

---

📌 Overview

This project implements a Question-Answering (QA) system powered by RAG (Retrieval-Augmented Generation).
The assistant combines document retrieval with a Large Language Model (LLM) to produce grounded, reliable responses.

---

⚙️ Key Features

• 🔎 RAG Pipeline: Combines retrieval with generative AI
• 🔗 LangChain Integration: Orchestrates prompts, retrieval, and response generation
• 📂 Custom Knowledge Base: Upload and query your own documents
• 📈 Evaluation: Reduces hallucinations and ensures factual responses

---

🏗️ Architecture

1. Document Preprocessing → Splits text into chunks and embeds into vector space
2. Retriever → Finds top-k relevant chunks using vector search
3. LLM Generator → Produces context-grounded answers
4. QA Assistant → Delivers concise, accurate responses