Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Add comprehensive README for Friction Flow project #28

Merged
merged 3 commits into from
Oct 27, 2024
Merged
Changes from 2 commits
Commits
File filter

Filter by extension

Filter by extension

Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
99 changes: 99 additions & 0 deletions README.md
Original file line number Diff line number Diff line change
@@ -0,0 +1,99 @@
# Friction Flow

## Project Overview

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.

## Key Features

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.

## Technical Stack

**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

## Getting Started

1. Clone the repository:

```bash
git clone https://github.com/your-organization/friction-flow.git
cd friction-flow
```

2. Install dependencies:

```bash
pip install -r requirements.txt
```

3. Set up the environment:
- Ensure you have the necessary language models and embeddings set up as specified in `src/config.py`.

4. Run the simulation:

```bash
python src/nfs_simple_lab_scenario.py
```

## Project Structure

- `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

## Development Guidelines

- 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.

## Testing

Run the test suite using pytest:

```bash
pytest tests/
```

## CI/CD

The project uses GitHub Actions for continuous integration. The workflow includes:
- Setting up Python 3.12.6
- Installing dependencies
- Running tests

## Contributing

We welcome contributions to the Friction Flow project. Please read our contributing guidelines before submitting pull requests. Key points:

- 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

## License

This project is licensed under the Apache License 2.0. See the LICENSE file for details.

## Acknowledgments

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.
Loading