An intelligent AI-powered chatbot that allows users to upload any PDF and ask questions based on its content. Built with Groq LLM, LangChain, FAISS, and Streamlit, this project extracts knowledge from documents and delivers clear, context-aware answers.
- 📁 Upload and process PDF documents
- 🧠 Ask natural language questions based on the PDF content
- 🔍 Semantic search using FAISS and SentenceTransformers
- 📊 Token usage and document statistics
- 📝 Tips for asking better questions
- 💬 Chat history with real-time LLM responses
| Tool | Description |
|---|---|
| Python | Core backend logic |
| Streamlit | Web-based UI |
| LangChain | Document loading and LLM orchestration |
| FAISS | Efficient vector similarity search |
| Groq LLM | Fast and intelligent language model |
| SentenceTransformers | Text embedding for semantic search |
🔗 Watch the project demo
- Clone the repository
git clone https://github.com/UzmaKhatun/PDF_Q-A_BOT.git cd pdf_que_ans_bot - Create a virtual environment
python -m venv venv source venv/bin/activate # On Windows: venv\Scripts\activate - Install dependencies
pip install -r requirements.txt - Run the Streamlit app
streamlit run app.py
- You can set the api_key manually or use .env for security:
GROQ_API_KEY=your_groq_key_here - Optional parameters like chunk size, overlap, embedding model, etc., can be configured in config.py or directly in app.py.
📁 PDF_Q-A_BOT/
- ├── app.py # Main Streamlit app
- ├── qa_engine.py # Core Q&A logic
- ├── requirements.txt # Dependencies
- └── README.md # Project documentation
Contributions, feedback, and suggestions are welcome! Feel free to fork the repo, create issues, or submit PRs.
Uzma Khatun AI/ML Enthusiast | BCA Student | Building intelligent tools with passion
📫 Let’s connect on LinkedIn