pydantic-gitlab 0.2.5

Pydantic models for parsing and validating GitLab CI/CD YAML configuration files

Pydantic GitLab

A modern Python library for parsing and validating GitLab CI YAML files using Pydantic dataclasses.

Features

• ✅ Full support for GitLab CI YAML syntax
• 🔍 Comprehensive validation with helpful error messages
• 📦 Type-safe dataclasses for all GitLab CI structures
• 🐍 Python 3.9+ support
• 📝 Excellent IDE support with autocompletion

Installation

pip install pydantic-gitlab

Quick Start

import yaml
from pydantic_gitlab import GitLabCI

# Load your .gitlab-ci.yml file
with open(".gitlab-ci.yml", "r") as f:
    yaml_content = yaml.safe_load(f)

# Parse and validate
try:
    ci_config = GitLabCI(**yaml_content)
    print("✅ Valid GitLab CI configuration!")
    
    # Access configuration
    for job_name, job in ci_config.jobs.items():
        print(f"Job: {job_name}")
        print(f"  Stage: {job.stage}")
        print(f"  Script: {job.script}")
        
except Exception as e:
    print(f"❌ Invalid configuration: {e}")

Supported GitLab CI Features

• ✅ Jobs with all keywords (script, image, services, artifacts, etc.)
• ✅ Stages and dependencies
• ✅ Rules and conditions
• ✅ Variables (global and job-level)
• ✅ Include configurations
• ✅ Workflow rules
• ✅ Caching
• ✅ Artifacts and reports
• ✅ Environments and deployments
• ✅ Parallel jobs and matrix builds
• ✅ Trigger jobs
• ✅ Pages job

Example

from pydantic_gitlab import GitLabCI, GitLabCIJob, WhenType

# Create a job programmatically
build_job = GitLabCIJob(
    stage="build",
    script=["echo 'Building...'", "make build"],
    artifacts={
        "paths": ["dist/"],
        "expire_in": "1 week"
    }
)

# Create CI configuration
ci = GitLabCI(
    stages=["build", "test", "deploy"],
    variables={"DOCKER_DRIVER": "overlay2"}
)

# Add job to configuration
ci.add_job("build", build_job)

# Validate dependencies
errors = ci.validate_job_dependencies()
if errors:
    for error in errors:
        print(f"Error: {error}")

Why Pydantic GitLab?

Comparison with Plain YAML Parsing

Using plain YAML parsing:

import yaml

# Plain YAML - no validation, no type hints
with open(".gitlab-ci.yml") as f:
    config = yaml.safe_load(f)
    
# Risky - might fail at runtime
job_script = config["build"]["script"]  # KeyError?
job_image = config["build"]["image"]    # KeyError?

Using Pydantic GitLab:

from pydantic_gitlab import GitLabCI

# Type-safe with validation
with open(".gitlab-ci.yml") as f:
    data = yaml.safe_load(f)
ci = GitLabCI(**data)

# IDE autocompletion, type checking
if build_job := ci.get_job("build"):
    print(build_job.script)  # Guaranteed to exist
    print(build_job.image)   # Optional[str] - might be None

Benefits

• 🛡️ Validation: Catch configuration errors before running pipelines
• 🔍 Type Safety: Full type hints for better IDE support and fewer runtime errors
• 📝 Documentation: Each field is documented with GitLab CI reference
• 🚀 Productivity: Autocomplete for all GitLab CI keywords
• 🧪 Testing: Easily create and validate CI configurations in tests

Development

Setup

# Clone the repository
git clone https://github.com/johnlepikhin/pydantic-gitlab.git
cd pydantic-gitlab

# Install in development mode
pip install -e ".[dev]"

# Install pre-commit hooks
pre-commit install

Running Tests

pytest

Code Quality

# Run linting
ruff check .

# Run type checking
mypy src

# Format code
ruff format .

License

MIT

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.