Commit da94cd0
authored
Initial implementation of PydanticPrompt library
This PR establishes the foundation for the PydanticPrompt library, providing a clean, simple way to document Pydantic models for LLM interactions using standard Python docstrings.
Key accomplishments:
- Implemented the `@prompt_schema` decorator for Pydantic models
- Created docstring extraction system using Python's introspection capabilities
- Added type annotation handling with support for nested and complex types
- Implemented validation rule documentation via Pydantic's JSON schema
- Added warnings for undocumented fields
- Set up comprehensive tests with 100% functionality coverage
- Established CI workflow using GitHub Actions
- Added linting and type checking with Ruff and mypy
- Created detailed documentation with examples
The library requires Python 3.11+ and Pydantic 2.0+, focusing on modern Python practices and type safety.
This implementation allows users to document their Pydantic models once and seamlessly include that documentation in LLM prompts, improving consistency between code and AI interactions.File tree
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