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-# 📚 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.
-
----
-
-## 🛠️ 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.  
-
----
-
-## ⚙️ 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).  
-
----
-
-## 📋 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.  
-
----
-
-## 🔑 Requirements
-- Python 3.10+  
-- Install dependencies:  
-  ```bash
-  pip install -r requirements.txt