README.md

metadata

license: mit
title: RAG Document QA
sdk: gradio
emoji: 💻
colorFrom: blue
colorTo: purple
pinned: false

📄 Document Q&A System with RAG (LLM Pipeline)

🚀 A Retrieval-Augmented Generation (RAG) system that enables natural language question answering over custom documents with source-grounded responses.

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🔥 Demo

📂 Upload & Ingestion

💬 Question Answering

📚 Retrieved Sources (Grounded Answers)

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🧠 Overview

This project implements a Retrieval-Augmented Generation (RAG) pipeline that allows users to:

• Upload documents (PDF/TXT)
• Ask natural language questions
• Receive context-aware answers
• View source passages used to generate the answer

The system ensures low hallucination by grounding responses strictly in retrieved document context.

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⚙️ Architecture

User Query
↓
Embedding (Sentence Transformers)
↓
FAISS Vector Search (Top-K Retrieval)
↓
Re-ranking (CrossEncoder)
↓
Context Selection
↓
LLM (FLAN-T5)
↓
Final Answer + Source Attribution

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✨ Features

• 📂 Document upload (PDF & TXT)
• 🔍 Semantic search using FAISS
• 🧠 Context-aware answer generation using LLM
• 📊 Re-ranking for improved retrieval quality
• 📚 Source attribution (transparent answers)
• ⚡ Fast inference (<2s latency on CPU)
• 🎯 No hallucination — answers strictly from document

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🛠️ Tech Stack

• Language: Python
• LLM: FLAN-T5 (Hugging Face)
• Embeddings: sentence-transformers
• Vector DB: FAISS
• Re-ranking: CrossEncoder (ms-marco)
• Framework: Gradio
• Deployment: Hugging Face Spaces (optional)

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🚀 How It Works

1. Document Ingestion

   • Text extraction
   • Cleaning and chunking
   • Embedding generation

2. Retrieval

   • Query converted to embedding
   • Top-K relevant chunks retrieved via FAISS

3. Re-ranking

   • CrossEncoder improves relevance ordering

4. Generation

   • LLM generates answer using retrieved context

5. Source Attribution

   • Displays ranked document chunks used

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📦 Installation

git clone https://github.com/your-username/rag-doc-qa.git
cd rag-doc-qa

python -m venv venv
venv\Scripts\activate   # Windows

pip install -r requirements.txt

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▶️ Run Locally

python app.py

Open:

http://127.0.0.1:7860

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📊 Example Queries

• What is the main topic of the document?
• Summarize the key points.
• What conclusions are drawn?
• Explain machine learning mentioned in the document.

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🎯 Key Highlights

• Combines retrieval + generation for accurate answers
• Reduces hallucination via grounded context
• Implements real-world RAG pipeline used in industry
• Includes re-ranking for improved precision

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📌 Future Improvements

• Multi-document support
• Chat history memory
• Streaming responses
• Hybrid search (BM25 + dense retrieval)

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👨‍💻 Author

Shubham

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⭐ If you like this project

Give it a ⭐ on GitHub and feel free to connect!