Welcome to AskMyDocs — an AI-powered document chatbot that allows you to upload documents and interact with them conversationally. Built using Retrieval-Augmented Generation (RAG), it combines document retrieval, embeddings, and large language models to deliver contextual answers directly from your files.
👉 Click the image above to watch the full demo video.
AskMyDocs simplifies document analysis using AI:
- Upload multiple files
- Ask questions naturally
- Receive contextual AI-generated answers
- Quickly extract insights without manual searching
This project demonstrates how modern AI pipelines can transform document interaction and knowledge retrieval.
- Supports PDF, TXT, CSV, DOCX files
- Automatic text extraction
- Efficient document chunking and embedding
- Conversational Q&A from documents
- Context-aware responses
- Retrieval-Augmented Generation pipeline
- Optional prompt refinement
- Document summarization
- Fast inference powered by Groq LLM
- Streamlit interactive dashboard
- Simple upload & chat workflow
- Clean, responsive design
.
├── icons/
│ └── ico.png
├── src/
│ ├── analytics.py
│ ├── app.py
│ ├── conversation.py
│ ├── document_utils.py
│ ├── prompt_refiner.py
│ └── text_processing.py
├── README.md
├── pyproject.toml
├── setup.sh
└── uv.lock
- Python 3.9+
- UV package manager recommended
Install uv:
pip install uv
Create .env file in root:
GROQ_API_KEY=your_api_key_here
Required for AI inference.
git clone https://github.com/hindav/AskMyDocs.git
cd AskMyDocs
Using uv:
uv run streamlit run src/app.py
Or:
bash setup.sh
Open browser:
http://localhost:8501
- Upload documents
- System converts them into embeddings
- Ask questions naturally
- Get contextual AI responses
Optional:
- Enable prompt refinement
- Generate document summaries
- Python
- Streamlit
- LangChain
- FAISS Vector Database
- HuggingFace Embeddings
- Groq LLM (Llama models)
- Research document analysis
- Study material Q&A
- Knowledge base assistant
- Business document exploration
- Personal document AI assistant
Hindav Deshmukh AI • Data Engineering • Machine Learning
GitHub: https://github.com/hindav LinkedIn: https://www.linkedin.com/in/hindav/
MIT License — free to use, modify, and distribute with attribution.
⭐ If you find this project useful, consider starring the repository!
