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| title: PDF Chatbot – RAG Pipeline | |
| emoji: 📄 | |
| colorFrom: indigo | |
| colorTo: purple | |
| sdk: streamlit | |
| sdk_version: "1.35.0" | |
| app_file: app.py | |
| pinned: false | |
| Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference | |
| # 📄 PDF Chatbot – RAG Pipeline | |
| This Space hosts an end-to-end **Retrieval-Augmented Generation (RAG)** pipeline that allows users to upload PDFs and ask questions about their content. | |
| The system extracts text, chunks it intelligently, embeds it into a vector database, and retrieves relevant context to answer queries using a large language model (LLM). | |
| --- | |
| ## 🚀 Features | |
| - 🔹 PDF upload support | |
| - 🔹 Automatic text extraction | |
| - 🔹 Smart document chunking | |
| - 🔹 Vector storage using ChromaDB | |
| - 🔹 LLM-powered question answering | |
| - 🔹 Streamlit-based interface | |
| - 🔹 Clean RAG pipeline implementation (`src/rag_pipeline.py`) | |
| --- | |
| ## 🏗️ Project Structure | |