metadata
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
git clone https://github.com/satheeshbhukya/ContextIQ.git
cd ContextIQ
2. Create virtual environment
python -m venv venv
# On Windows
venv\Scripts\activate
# On macOS/Linux
source venv/bin/activate
3. Install dependencies
pip install -r requirements.txt
4. Model download
You must download OpenVINO Phi-3 model locally using python download_model.py
Usage
Run the application
streamlit run app.py
The app will open in your browser at http://localhost:8501
Using the system
- Upload a document using the sidebar file uploader
- Wait for processing (you'll see a success message with chunk count)
- Ask questions in the text input field
- View answers with source attribution
- 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
- Research Papers: Upload PDFs and ask about methodologies, findings, conclusions
- Legal Documents: Query contracts, agreements for specific clauses
- Business Reports: Extract insights from quarterly reports, presentations
- Technical Documentation: Search for specific procedures, configurations
- 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!