README.md

📚 PDF Q&A with Hybrid Search + LLM

🚀 Overview

This project is a Question Answering (QA) system that allows users to:

1. Upload a PDF document.
2. Automatically process and chunk the text.
3. Store embeddings in Qdrant Vector Database and build a hybrid retriever (BM25 + Qdrant).
4. Ask natural language questions, and the model will retrieve the relevant context from the PDF and generate an answer using a Large Language Model (LLM).

It combines semantic search (dense) + keyword search (BM25) for better retrieval accuracy.

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🛠️ Tech Stack

• LangChain → Orchestration of retrievers and chains.
• HuggingFace + Together API → LLM endpoint (Qwen3-235B-A22B-Instruct-2507).
• Qdrant → Vector database for storing embeddings.
• BM25 → Keyword-based retriever.
• Docling → Loader to extract text from PDF into Markdown.
• Transformers → Tokenizer for chunking text.
• Gradio → Web interface.
• dotenv → Secure API key management.

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⚙️ Workflow

1. Upload PDF

   • The file is loaded with DoclingLoader.
   • Text is split into chunks using HuggingFace tokenizer.

2. Build Hybrid Search

   • Embeddings are created using sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2.
   • Chunks are stored in Qdrant.
   • Dense retriever (embeddings) + BM25 retriever (keywords) are combined with weights 0.6 (dense) and 0.4 (BM25).

3. Ask Questions

   • User writes a question.
   • Relevant chunks are retrieved.
   • A prompt is built with context + question.
   • The LLM generates the answer (max 3 sentences).

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📋 Features

• Upload any PDF document.
• Hybrid search ensures more accurate retrieval than only embeddings or BM25.
• Context-aware Q&A answers.
• Caching retriever so you only upload once (no need to re-process for every question).
• Simple Gradio UI with upload + question box.

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🔑 Requirements

• Python 3.10+
• Install dependencies:

  pip install -r requirements.txt