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# Friction Flow | ||
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## Project Overview | ||
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Friction Flow is an advanced Python-based research project aimed at developing a framework for analyzing and simulating complex human behavior and group dynamics based on Narrative Field Dynamics. This project leverages AI and machine learning techniques, with a focus on integrating Large Language Models (LLMs) for natural language-based decision making and interactions. | ||
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## Key Features | ||
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1. **Multi-Agent Systems**: Simulates emergent behavior in complex social systems. | ||
2. **Psychological Modeling**: Incorporates advanced models of individual and group psychology. | ||
3. **LLM Integration**: Utilizes state-of-the-art language models for natural language processing and generation. | ||
4. **Distributed Computing**: Employs event-driven architectures for scalable simulations. | ||
5. **Machine Learning Components**: Includes neural networks and other ML techniques for behavior prediction and analysis. | ||
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## Technical Stack | ||
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**Python**: Core programming language (version >= 3.12 recommended) | ||
- **PyTorch**: For neural network components and tensor operations | ||
- **Transformers**: For integration with pre-trained language models | ||
- **Ray**: For distributed computing | ||
- **FastAPI**: For service endpoints | ||
- **Redis**: For state management | ||
- **Ollama**: For local LLM integration | ||
- **ChromaDB**: For vector storage and similarity search | ||
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## Getting Started | ||
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1. Clone the repository: | ||
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```bash | ||
git clone https://github.com/your-organization/friction-flow.git | ||
cd friction-flow | ||
``` | ||
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2. Install dependencies: | ||
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```bash | ||
pip install -r requirements.txt | ||
``` | ||
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3. Set up the environment: | ||
- Ensure you have the necessary language models and embeddings set up as specified in `src/config.py`. | ||
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4. Run the simulation: | ||
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```bash | ||
python src/nfs_simple_lab_scenario.py | ||
``` | ||
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## Project Structure | ||
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- `src/`: Contains the core source code | ||
- `nfs_story_waves.py`: Simulation components for narrative field dynamics | ||
- `nfs_simple_lab_scenario.py`: Example scenario implementation | ||
- `language_models.py`: Interfaces for various language models | ||
- `config.py`: Configuration settings | ||
- `tests/`: Unit and integration tests | ||
- `pocs/`: Proof of concept implementations | ||
- `.github/`: Issue templates and CI/CD workflows | ||
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## Development Guidelines | ||
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- Follow PEP 8 style guide and use Black for code formatting. | ||
- Implement type hints as per PEP 484. | ||
- Maintain a minimum of 80% test coverage. | ||
- Adhere to SOLID principles and maintain clear separation of concerns. | ||
- Use meaningful commit messages following the conventional commits format. | ||
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## Testing | ||
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Run the test suite using pytest: | ||
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```bash | ||
pytest tests/ | ||
``` | ||
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## CI/CD | ||
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The project uses GitHub Actions for continuous integration. The workflow includes: | ||
- Setting up Python 3.12.6 | ||
- Installing dependencies | ||
- Running tests | ||
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## Contributing | ||
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We welcome contributions to the Friction Flow project. Please read our contributing guidelines before submitting pull requests. Key points: | ||
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- No commented-out code in the main branch | ||
- No TODOs in the main branch | ||
- Clear variable and function naming | ||
- Adherence to DRY and SOLID principles | ||
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## License | ||
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This project is licensed under the Apache License 2.0. See the LICENSE file for details. | ||
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## Acknowledgments | ||
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This project builds upon research in cognitive science, complex systems theory, social network analysis, and organizational behavior. We acknowledge the contributions of the open-source community and the developers of the libraries and tools used in this project. |
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