BioCypher CookieCutter Template

A Cookiecutter template for creating BioCypher pipeline projects.

Caution

Mostly vibe-coded for prototyping. Still needs thorough vetting.

Features

• Complete BioCypher Pipeline: Ready-to-use project structure
• Multiple Data Source Types: Support for file, API, database, and custom data sources
• Docker Support: Optional containerized deployment
• Testing Framework: Optional comprehensive test setup
• Schema Configuration: Pre-configured BioCypher schema setup (included by default)
• Git Integration: Automatic git repository initialization

Usage

Via MCP Tool (Recommended)

Use the BioCypher MCP tool in Cursor or other MCP clients:

create_biocypher_pipeline(
    project_name="my-protein-pipeline",
    project_description="Pipeline for protein data analysis",
    template_method="cookiecutter",
    data_source_type="api"
)

Direct CookieCutter Usage

# Install cookiecutter
pip install cookiecutter

# Generate project from template
cookiecutter https://github.com/biocypher/biocypher-cookiecutter-template.git

Template Variables

The template uses the following variables:

Variable	Description	Default
project_name	Name of the project	my-biocypher-pipeline
project_description	Project description	A BioCypher pipeline for biological data integration
package_name	Python package name (auto-generated)	Based on project_name
adapter_name	Adapter class name	my_resource_adapter
data_source_type	Type of data source	csv
include_docker	Include Docker configuration	y
include_tests	Include test framework	y
author_name	Author name	BioCypher User
author_email	Author email	user@example.com
version	Project version	0.1.0
license	License type	MIT
python_version	Python version requirement	3.11
biocypher_version	BioCypher version	latest (fetched from PyPI)

Generated Project Structure

my-biocypher-pipeline/
├── config/
│   ├── biocypher_config.yaml
│   └── schema_config.yaml
├── src/my_biocypher_pipeline/
│   ├── __init__.py
│   └── adapters/
│       ├── __init__.py
│       └── my_resource_adapter.py
├── tests/
│   ├── __init__.py
│   └── test_my_resource_adapter.py
├── create_knowledge_graph.py
├── docker-compose.yml
├── Dockerfile
├── pyproject.toml
├── README.md
└── .gitignore

Data Source

The template is configured for CSV data sources by default:

CSV Processing

• Pandas-based: Uses pandas for robust CSV reading and processing
• Flexible: Handles various CSV formats and structures
• Simple: Straightforward implementation that users can easily customize
• Extensible: Easy to modify for specific data requirements

The adapter assumes CSV input and provides a clean foundation that users (or the BioCypher MCP copilot) can adapt for their specific data sources and processing needs.

Post-Generation Setup

After project generation, the template automatically:

1. Creates additional directories (logs/, output/, data/)
2. Initializes git repository
3. Creates initial commit
4. Provides next steps instructions

Development

Testing the Template

# Test the template locally
cookiecutter . --no-input

# Test with custom values
cookiecutter . --no-input project_name="test-pipeline" data_source_type="api"

Contributing

1. Fork the repository
2. Make your changes
3. Test the template
4. Submit a pull request

License

MIT License - see LICENSE file for details.

Next Steps: Adapting Your Pipeline

The best way to adapt your BioCypher pipeline to your specific needs is through the BioCypher MCP Server available at https://mcp.biocypher.org. This MCP server provides:

• Interactive Guidance: Step-by-step assistance for adapter creation
• Schema Configuration: Help with BioCypher schema setup and customization
• Implementation Patterns: Best practices for different data source types
• Resource Management: Guidance on data download and caching strategies
• Decision Support: Recommendations based on your data characteristics

Using the BioCypher MCP Server

1. Install MCP Client: Use Cursor or another MCP-compatible client
2. Connect to Server: Add the BioCypher MCP server at https://mcp.biocypher.org
3. Get Guidance: Use the interactive tools to customize your pipeline
4. Implement: Follow the provided patterns and recommendations

Related Projects

• BioCypher - The main BioCypher framework
• BioCypher MCP - Interactive MCP server for BioCypher workflows