🧠 AI-powered document question answering with persistent chat history, built using Groq LLMs, HuggingFace Embeddings, and LangChain RAG — deployed via Streamlit for an intuitive UI.
- 🧠 Groq LLM integration (Gemma, LLaMA3, Qwen, LLaMA-Guard)
- 🔍 Retrieval-Augmented Generation (RAG) with PDF, DOCX, TXT, MD, URLs
- 💬 Persistent Chat History using
st.session_state - 🧠 HuggingFace embedding + ChromaDB for document vector storage
- 📄 Upload limit: 10 documents or URLs
- 🧹 Clear chat and toggle RAG
- ⚙️ Streamlit UI – minimal, interactive
git clone https://github.com/Electrolight123/intellidoc-chatpro.git
cd intellidoc-chatpro
python -m venv venv
source venv/bin/activate # Windows: venv\Scripts\activate
pip install -r requirements.txtCreate a .env file in the root folder with:
GROQ_API_KEY="your_groq_api_key"
HUGGINGFACE_API_KEY="your_huggingface_api_key"
streamlit run app.pyOpen http://localhost:8501 in your browser.
- groq/gemma2-9b-it
- groq/llama-guard-4-12b
- groq/llama-3.3-70b-versatile
- groq/qwen-qwq-32b
The app stores chat messages per session, enabling ongoing conversations without losing context.
intellidoc-chatpro/
├── app.py
├── rag_methods.py
├── requirements.txt
├── .env
├── docs/
│ ├── test_rag.pdf
│ └── test_rag.docx
├── assets/
│ ├── banner.png
│ └── ui_mockup.png
├── README.mdSample files for testing the RAG engine are available in the docs/ folder:
test_rag_sample.pdftest_rag_sample.docx
MIT License. See LICENSE for details.
Pull requests welcome! For questions or feature requests, open an issue or contact me at [[email protected]].
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