AskTheDoc is a powerful AI assistant that lets users chat with one or more PDF documents via a Streamlit web interface or CLI. It uses LangChain's RAG (Retrieval-Augmented Generation) pipeline with OpenAI LLMs, FAISS vector search, and chunked document embeddings to deliver accurate, source-backed answers.
- Multi-PDF upload and selection
- Per-file vector store creation and merging
- Real-time chat with source chunk display
- Exportable chat history
- Stats panel for embedded document analytics
- Persistent vector store (saved to disk)
- LangChain: Document loading, splitting, QA chains
- PyPDFLoader: PDF parsing
- FAISS: Fast Approximate Nearest Neighbor vector DB
- OpenAIEmbeddings: Embedding generation
- ChatOpenAI: Language model (GPT-style)
- Streamlit: Interactive web UI
- dotenv: Environment variable management
- Clone the repository
git clone https://github.com/mrp2003/askthedoc
cd askthedoc- Install dependencies
pip install -r requirements.txt- Set your OpenAI key
Create a
.envfile:
OPENAI_API_KEY=your_openai_key
streamlit run app.py- Upload one or more PDFs.
- Select the PDFs you want to query.
- Ask questions and see source-backed answers.
- View stats, reset session, and export chat logs.
Pull requests are welcome! For major changes, please open an issue first to discuss what you would like to change or improve. If you found this helpful, give it a ⭐ on GitHub!