ddn-metadata-bootstrap 1.0.1

AI-powered metadata enhancement for Hasura DDN schema files

DDN Metadata Bootstrap

AI-powered metadata enhancement for Hasura DDN (Data Delivery Network) schema files. Automatically generate descriptions and detect relationships in your YAML/HML schema definitions using advanced AI.

🚀 Features

• 🤖 AI-Powered Descriptions: Generate natural language descriptions for schema elements using Anthropic's Claude
• 🔗 Relationship Detection: Automatically detect and generate foreign key relationships
• 📊 Domain Analysis: Intelligent analysis of business domains and terminology
• ⚡ Batch Processing: Process entire directories of schema files efficiently
• 🎯 DDN Optimized: Specifically designed for Hasura DDN schema structures
• 🔧 Configurable: Extensive configuration options via environment variables or CLI

📦 Installation

From PyPI (Recommended)

pip install ddn-metadata-bootstrap

From Source

git clone https://github.com/hasura/ddn-metadata-bootstrap.git
cd ddn-metadata-bootstrap
pip install -e .

🏃 Quick Start

1. Set up your environment

export ANTHROPIC_API_KEY="your-anthropic-api-key"
export METADATA_BOOTSTRAP_INPUT_DIR="./input"
export METADATA_BOOTSTRAP_OUTPUT_DIR="./output"

2. Run the tool

# Process entire directory
ddn-metadata-bootstrap

# Or with CLI arguments
ddn-metadata-bootstrap --input-dir ./schema --output-dir ./enhanced --api-key YOUR_KEY

3. Or use as a Python package

from ddn_metadata_bootstrap import MetadataBootstrapper

bootstrapper = MetadataBootstrapper(
    api_key="your-anthropic-api-key",
    use_case="E-commerce platform"
)

# Process directory
bootstrapper.process_directory("./input", "./output")

# Get statistics
stats = bootstrapper.get_statistics()
print(f"Generated {stats['relationships_generated']} relationships")

📝 Example

Input HML File

kind: ObjectType
version: v1
definition:
  name: User
  fields:
    - name: id
      type: ID!
    - name: email
      type: String!
    - name: created_at
      type: String

Enhanced Output

kind: ObjectType
version: v1
definition:
  name: User
  description: |
    Represents a user account in the system with authentication
    and profile information.
  fields:
    - name: id
      type: ID!
      description: Unique identifier for the user account.
    - name: email
      type: String!
      description: User's email address for authentication and communication.
    - name: created_at
      type: String
      description: Timestamp when the user account was created.

⚙️ Configuration

Environment Variables

All configuration can be done via environment variables with the METADATA_BOOTSTRAP_ prefix:

# Required
ANTHROPIC_API_KEY=your_api_key_here

# Input/Output (choose one mode)
METADATA_BOOTSTRAP_INPUT_DIR=./input
METADATA_BOOTSTRAP_OUTPUT_DIR=./output

# OR single file mode
METADATA_BOOTSTRAP_INPUT_FILE=./schema.hml  
METADATA_BOOTSTRAP_OUTPUT_FILE=./enhanced.hml

# Optional
METADATA_BOOTSTRAP_USE_CASE="E-commerce platform"
METADATA_BOOTSTRAP_MODEL=claude-3-haiku-20240307
METADATA_BOOTSTRAP_FIELD_DESC_MAX_LENGTH=120
METADATA_BOOTSTRAP_KIND_DESC_MAX_LENGTH=250

CLI Arguments

ddn-metadata-bootstrap --help

Options:
  --input-dir PATH              Input directory containing HML files
  --output-dir PATH             Output directory for enhanced files
  --input-file PATH             Single input HML file
  --output-file PATH            Single output HML file
  --api-key TEXT                Anthropic API key
  --use-case TEXT               Business domain description
  --model TEXT                  AI model to use
  --field-max-length INTEGER    Max characters for field descriptions
  --kind-max-length INTEGER     Max characters for kind descriptions
  --verbose                     Enable verbose logging
  --dry-run                     Validate configuration without processing

🔄 What It Does

1. Description Generation

• Analyzes schema element names and types
• Generates contextual descriptions using AI
• Respects character limits and style guidelines
• Supports field-level and entity-level descriptions

2. Relationship Detection

• Detects foreign key patterns (e.g., user_id, customer_id)
• Identifies shared fields between entities
• Generates bidirectional relationship definitions
• Supports cross-subgraph relationships

3. Domain Analysis

• Extracts business terminology from schema
• Identifies domain-specific patterns
• Provides contextual AI prompts
• Supports domain-specific relationship hints

4. Schema Enhancement

• Preserves original schema structure
• Adds descriptions without breaking functionality
• Generates proper DDN relationship definitions
• Maintains YAML formatting and comments

🏗️ Architecture

The tool is built with a modular architecture:

• ai/ - AI integration and description generation
• schema/ - Schema analysis and metadata collection
• relationships/ - Relationship detection and generation
• processors/ - File and directory processing
• utils/ - Text processing, YAML handling, path utilities

🧪 Testing

# Install dev dependencies
pip install -e ".[dev]"

# Run tests
pytest

# Run with coverage
pytest --cov=ddn_metadata_bootstrap

# Type checking
mypy ddn_metadata_bootstrap/

# Code formatting
black ddn_metadata_bootstrap/

🤝 Contributing

1. Fork the repository
2. Create a feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

🆘 Support

• 📖 Documentation
• 🐛 Bug Reports
• 💬 Discussions

🏷️ Version History

See CHANGELOG.md for version history and breaking changes.

⭐ Acknowledgments

• Built for Hasura DDN
• Powered by Anthropic Claude
• Inspired by the GraphQL and OpenAPI communities

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Made with ❤️ by the Hasura team