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---
title: SPARKNET
emoji: π₯
colorFrom: red
colorTo: blue
sdk: streamlit
sdk_version: 1.28.0
app_file: demo/app.py
python_version: "3.10"
pinned: false
---
# π₯ SPARKNET: AI-Powered Technology Transfer Office Automation
**Multi-agent AI platform for research valorization and IP management**
[](https://sparknet.streamlit.app)
[](https://vista-project.eu)
[](https://opensource.org/licenses/MIT)
---
## Overview
SPARKNET is an enterprise-grade **Technology Transfer Office (TTO) Automation Platform** that combines multi-agent AI orchestration with document intelligence to automate key TTO workflows. Built for the VISTA/Horizon EU project.
### π― Core TTO Scenarios
| Scenario | Status | Description |
|----------|--------|-------------|
| π‘ **Patent Wake-Up** | β
Live | Transform dormant patents into commercialization opportunities |
| βοΈ **Agreement Safety** | β
Live | AI-assisted legal document review with risk detection |
| π€ **Partner Matching** | β
Live | Intelligent stakeholder matching for technology transfer |
| π **License Compliance** | π¨ Dev | Payment tracking, milestone verification, revenue alerts |
| π **Award Identification** | π¨ Dev | Funding opportunity scanning and nomination assistance |
### π Coverage Dashboard
- **3 Fully Covered** - Production-ready scenarios
- **5 Partially Covered** - In development
- **2 Not Covered** - Planned for future
---
## Features
### π‘οΈ AI Quality Assurance
- **CriticAgent Validation**: Every AI output validated against VISTA quality standards
- **Confidence Scoring**: Automatic abstention for low-confidence results
- **Source Verification**: Hallucination mitigation with evidence grounding
- **Human-in-the-Loop**: Critical decisions require human approval
### π€ Multi-Agent Architecture
- **PlannerAgent**: Task decomposition and workflow planning
- **ExecutorAgent**: Task execution with tool usage
- **CriticAgent**: Output validation and refinement
- **MemoryAgent**: Context management and retrieval
### π Document Intelligence
- OCR with PaddleOCR/Tesseract
- Layout detection and semantic chunking
- Schema-driven field extraction
- Visual evidence grounding (bbox, page, confidence)
### π¬ RAG Q&A
- Vector search with ChromaDB
- Grounded retrieval with citations
- Multi-document querying
- Citation generation
---
## Quick Start
### Streamlit Cloud (Recommended)
The app is deployed on Streamlit Cloud. Visit:
```
https://sparknet.streamlit.app
```
### Local Installation
```bash
# Clone repository
git clone https://github.com/MHHamdan/SPARKNET.git
cd SPARKNET
# Install dependencies
pip install -r requirements.txt
# Run Streamlit app
streamlit run demo/app.py
```
### With Local LLM (Ollama)
For privacy-preserving local inference:
```bash
# Install Ollama
curl -fsSL https://ollama.com/install.sh | sh
# Pull models
ollama pull llama3.2:latest
ollama pull nomic-embed-text
# Run SPARKNET
streamlit run demo/app.py
```
---
## Configuration
### API Keys
Configure in `.streamlit/secrets.toml` or environment variables:
```toml
[auth]
password = "your-password"
GROQ_API_KEY = "your-groq-key"
GOOGLE_API_KEY = "your-google-key"
OPENROUTER_API_KEY = "your-openrouter-key"
```
See `.env.example` for all available configuration options.
### Supported LLM Providers
| Provider | Free Tier | Notes |
|----------|-----------|-------|
| Groq | 14,400 req/day | Fastest inference |
| Google Gemini | 15 req/min | Good for general use |
| OpenRouter | Many free models | Multi-model access |
| GitHub Models | Free GPT-4o | Requires GitHub token |
| HuggingFace | Thousands of models | Good for embeddings |
| Ollama | Unlimited (local) | Maximum privacy |
---
## Project Structure
```
SPARKNET/
βββ demo/ # Streamlit application
β βββ app.py # Main app
β βββ auth.py # Authentication
β βββ llm_providers.py # LLM provider management
β βββ pages/ # Multi-page app
βββ src/
β βββ agents/ # Agent implementations
β β βββ scenario1/ # Patent Wake-Up
β β βββ scenario3/ # License Compliance
β β βββ scenario4/ # Award Identification
β βββ rag/ # RAG subsystem
β βββ workflow/ # LangGraph workflows
β βββ document_intelligence/ # Document processing
βββ configs/ # Configuration files
βββ .streamlit/ # Streamlit config
βββ SECURITY.md # Security documentation
```
---
## Security & GDPR
SPARKNET supports GDPR-compliant deployments:
- **Local Inference**: Use Ollama for 100% on-premise processing
- **Data Isolation**: Configure data retention policies
- **Audit Logging**: Track all AI interactions
- **Private Deployment**: Enterprise deployment options
See [SECURITY.md](SECURITY.md) for detailed security documentation.
---
## Development
### Running Tests
```bash
pytest tests/
```
### Code Formatting
```bash
black src/
flake8 src/
```
---
## Roadmap
- [x] Patent Wake-Up workflow
- [x] Agreement Safety review
- [x] Partner Matching
- [x] CriticAgent validation
- [ ] License Compliance Monitoring (in progress)
- [ ] Award Identification (in progress)
- [ ] Grant Writing Assistant
- [ ] Negotiation Support
---
## Contributing
Contributions are welcome! Please:
1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Run tests
5. Submit a pull request
---
## Acknowledgments
- **Ollama** for local LLM inference
- **NVIDIA** for CUDA and GPU support
- **LangChain** for LLM orchestration
- **Streamlit** for the web framework
- **The open-source AI community**
---
## Support
- **GitHub Issues**: [github.com/MHHamdan/SPARKNET/issues](https://github.com/MHHamdan/SPARKNET/issues)
- **Documentation**: See `/docs` folder
---
## License
MIT License - see [LICENSE](LICENSE) file for details.
---
<p align="center">
<strong>π₯ SPARKNET</strong><br>
AI-Powered Technology Transfer Office Automation<br>
<em>VISTA/Horizon EU Project</em>
</p>
|