| --- |
| title: ContextIQ |
| emoji: 🧠 |
| colorFrom: blue |
| colorTo: purple |
| sdk: docker |
| app_port: 7860 |
| pinned: false |
| --- |
| |
| # ContextIQ - A Local RAG Based Secure Document Q&A System |
|
|
| ContextIQ is a fully local Retrieval-Augmented Generation (RAG) system that allows users to upload documents and ask AI-powered questions about their content — without using external APIs. |
| ## Features |
|
|
| - **Multi-format Document Support**: PDF, Word (.docx), Excel (.xlsx), PowerPoint (.pptx), and Text (.txt) |
| - **Semantic Search**: Uses sentence transformers and FAISS for efficient vector similarity search |
| - **AI-Powered Answers**: Leverages OpenVINO LLM (Phi-3) for accurate question answering |
| - **Interactive UI**: Clean Streamlit interface with conversation history |
| - **Source Attribution**: Shows which document sections were used to generate answers |
|
|
| ## Architecture |
|
|
| ``` |
| User Upload → Document Parser → Text Chunker → Embedding Generator |
| ↓ |
| User Question → Query Embedder → FAISS Search → Context Retrieval → LLM → Answer |
| ``` |
|
|
| ## Prerequisites |
|
|
| - Python 3.8+ |
|
|
| ## Installation |
|
|
| ### 1. Clone the repository |
|
|
| ```bash |
| git clone https://github.com/satheeshbhukya/ContextIQ.git |
| cd ContextIQ |
| ``` |
|
|
| ### 2. Create virtual environment |
|
|
| ```bash |
| python -m venv venv |
| |
| # On Windows |
| venv\Scripts\activate |
| |
| # On macOS/Linux |
| source venv/bin/activate |
| ``` |
|
|
| ### 3. Install dependencies |
|
|
| ```bash |
| pip install -r requirements.txt |
| ``` |
| ### 4. Model download |
| ```bash |
| You must download OpenVINO Phi-3 model locally using python download_model.py |
| ``` |
|
|
| ## Usage |
|
|
| ### Run the application |
|
|
| ```bash |
| streamlit run app.py |
| ``` |
|
|
| The app will open in your browser at `http://localhost:8501` |
|
|
| ### Using the system |
|
|
| 1. **Upload a document** using the sidebar file uploader |
| 2. **Wait for processing** (you'll see a success message with chunk count) |
| 3. **Ask questions** in the text input field |
| 4. **View answers** with source attribution |
| 5. **Check conversation history** to see previous Q&A pairs |
|
|
| ## Project Structure |
|
|
| ``` |
| ContextIQ/ |
| │ |
| ├── app.py |
| ├── requirements.txt |
| ├── README.md |
| ├── .gitignore |
| │ |
| ├── scripts/ |
| │ ├── document_parser.py |
| │ ├── text_processor.py |
| │ ├── vector_store.py |
| │ └── rag_engine.py |
| │ |
| ├── model/ ❌ NOT included in GitHub |
| │ └── phi-3-openvino/ |
| │ |
| └── data/ (optional FAISS index storage) |
| |
| ``` |
|
|
| ## Configuration |
|
|
| ### Adjustable Parameters |
|
|
| In the sidebar, you can adjust: |
| - **Number of chunks to retrieve** (1-10): More chunks = more context but slower |
|
|
| ## Example Use Cases |
|
|
| 1. **Research Papers**: Upload PDFs and ask about methodologies, findings, conclusions |
| 2. **Legal Documents**: Query contracts, agreements for specific clauses |
| 3. **Business Reports**: Extract insights from quarterly reports, presentations |
| 4. **Technical Documentation**: Search for specific procedures, configurations |
| 5. **Academic Notes**: Ask questions about study materials, lecture slides |
|
|
| ## Technical Stack |
|
|
| - **Frontend**: Streamlit |
| - **Document Processing**: PyMuPDF, python-docx, python-pptx, pandas |
| - **Text Processing**: LangChain |
| - **Embeddings**: Hugging Face sentence-transformers (all-MiniLM-L6-v2) |
| - **Vector Store**: FAISS |
| - **LLM**: OpenVINO Phi-3 |
| - |
| ## Flow Diagram |
|
|
| ``` |
| ┌─────────────────────────────────────────────────────────┐ |
| │ QUICK REFERENCE │ |
| ├─────────────────────────────────────────────────────────┤ |
| │ Start App: streamlit run app.py │ |
| │ Upload Doc: Browse files → Select file │ |
| │ Ask Question: Type → Click Ask │ |
| │ Adjust Chunks: Drag slider (1-10) │ |
| │ Clear History: Click "Clear Chat History" │ |
| │ New Document: Click "Reset Document" │ |
| │ Stop App: Ctrl+C in terminal │ |
| └─────────────────────────────────────────────────────────┘ |
| ``` |
|
|
|
|
| ## Demo |
|
|
|  |
|
|
|  |
|
|
| ## License |
|
|
| MIT License - feel free to use this project for personal or commercial purposes. |
|
|
|
|
| ## 📧 Contact |
|
|
| For questions or support, please open an issue on GitHub or contact [satheeshbhukyaa@gmail.com] |
|
|
| --- |
|
|
| **⭐ If you find this project useful, please consider giving it a star!** |
|
|
