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| title: DocuMind-AI | |
| emoji: 📄 | |
| colorFrom: blue | |
| colorTo: purple | |
| sdk: docker | |
| pinned: false | |
| # 📄 DocuMind-AI | |
| An intelligent document assistant powered by RAG, LangChain, Groq LLaMA 3, and FAISS. Upload any PDF and chat with it using state-of-the-art AI — built for real-world enterprise use. | |
| ## 🚀 Features | |
| - Upload any PDF | |
| - Ask questions in natural language | |
| - Get accurate answers powered by LLaMA 3 | |
| - Chat history support | |
| --- | |
| title: DocuMind-AI | |
| emoji: 📄 | |
| colorFrom: blue | |
| colorTo: purple | |
| sdk: docker | |
| pinned: false | |
| --- | |
| # 📄 DocuMind-AI — Intelligent Document Assistant | |
| An enterprise-grade RAG (Retrieval Augmented Generation) chatbot that allows users to upload any PDF and interact with it using natural language. Built with LangChain, Groq LLaMA 3, FAISS, and Streamlit. | |
| 🔗 Live Demo: [huggingface.co/spaces/Yugadharshini/DocuMind-AI](https://huggingface.co/spaces/Yugadharshini/DocuMind-AI) | |
| 🔗 GitHub: [github.com/skrYugadharshini/DocuMind-AI](https://github.com/skrYugadharshini/DocuMind-AI) | |
| --- | |
| ## 🚀 Features | |
| - Upload any PDF document | |
| - Ask questions in natural language | |
| - Semantic search using FAISS vector store | |
| - Fast and accurate answers powered by Groq LLaMA 3 | |
| - Chat history support | |
| - Clean and intuitive Streamlit UI | |
| - Deployed on Hugging Face Spaces using Docker | |
| --- | |
| ## 🛠️ Tech Stack | |
| | Technology | Purpose | | |
| |---|---| | |
| | Python | Core programming language | | |
| | LangChain | LLM orchestration framework | | |
| | Groq LLaMA 3 | Large Language Model for answer generation | | |
| | FAISS | Vector store for semantic search | | |
| | HuggingFace Sentence Transformers | Text embeddings (all-MiniLM-L6-v2) | | |
| | Streamlit | Frontend UI | | |
| | PyPDF | PDF loading and parsing | | |
| | Docker | Containerization for deployment | | |
| | Hugging Face Spaces | Cloud deployment | | |
| --- | |
| ## ⚙️ Full Technical Process | |
| ### Step 1 — PDF Loading | |
| - User uploads any PDF through the Streamlit UI | |
| - PyPDF loads and extracts text from all pages | |
| ### Step 2 — Text Chunking | |
| - Document split into chunks of 500 characters | |
| - 50 character overlap between chunks to preserve context | |
| - Uses LangChain RecursiveCharacterTextSplitter | |
| ### Step 3 — Vector Embeddings | |
| - Each chunk converted to a 384-dimensional vector | |
| - Uses HuggingFace sentence-transformers (all-MiniLM-L6-v2) | |
| - Captures semantic meaning of text | |
| ### Step 4 — Vector Store | |
| - All vectors stored in FAISS index | |
| - Enables fast similarity search across all chunks | |
| - Finds most relevant chunks for any question | |
| ### Step 5 — RAG Chain | |
| - User asks a question | |
| - Question converted to vector | |
| - FAISS retrieves top 4 most relevant chunks | |
| - Chunks + question sent to Groq LLaMA 3 | |
| - LLaMA 3 generates accurate answer based on context | |
| ### Step 6 — Response | |
| - Answer displayed in Streamlit chat UI | |
| - Chat history maintained during session | |
| --- | |
| ## 🛠️ Built With | |
| - LangChain | |
| - Groq (LLaMA 3) | |
| - FAISS Vector Store | |
| - HuggingFace Embeddings | |
| - Streamlit | |
| ## ⚙️ How to Run | |
| 1. Clone the repo | |
| 2. Install dependencies: `pip install -r requirements.txt` | |
| 3. Add your Groq API key in `.env` | |
| 4. Run: `streamlit run app.py` | |
| ## 🎯 Future Improvements | |
| - Add chat history memory across sessions | |
| - Support multiple PDF uploads | |
| - Highlight source chunks in the PDF | |
| - Add support for other document types (DOCX, TXT) | |
| - Fine-tune chunk size for better accuracy | |
| - Add user authentication | |
| --- | |
| ## 👩💻 Author | |
| **Yugadharshini** | |
| - GitHub: [@skrYugadharshini](https://github.com/skrYugadharshini) | |
| - Hugging Face: [@Yugadharshini](https://huggingface.co/Yugadharshini) |