README.md

metadata

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

🔗 GitHub: 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
• Hugging Face: @Yugadharshini