Pydantic

Overview

The Pydantic Generator produces Pydantic flavored python dataclasses from a linkml model, with optional support for user-supplied jinja2 templates to generate alternate classes.

Docs

Command Line

gen-pydantic

Generate pydantic classes to represent a LinkML model

gen-pydantic [OPTIONS] YAMLFILE

Options

--template_file <template_file>: Optional jinja2 template to use for class generation

-f, --format <format>

Output format (default=pydantic)

Options: pydantic

--metadata, --no-metadata: Include metadata in output (default=–metadata)

--useuris, --metauris: Include metadata in output (default=–useuris)

-im, --importmap <importmap>: Import mapping file

--log_level <log_level>

Logging level (default=WARNING)

Options: CRITICAL | ERROR | WARNING | INFO | DEBUG

--mergeimports, --no-mergeimports: Merge imports into source file (default=mergeimports)

Arguments

YAMLFILE: Required argument

Code

class linkml.generators.pydanticgen.PydanticGenerator(schema: Union[str, TextIO, linkml_runtime.linkml_model.meta.SchemaDefinition], template_file: Optional[str] = None, format: str = 'pydantic', genmeta: bool = False, gen_classvars: bool = True, gen_slots: bool = True, **kwargs)[source]

serialize() → None[source]

Generate output in the required format

Parameters: kwargs – Generater specific parameters
Returns: Generated output

Additional Notes

LinkML contains two Python generators. The Pydantic dataclass generator is specifically useful for FastAPI, but is newer and less full featured than the standard Python generator.

Biolink Example

Begin by downloading the Biolink Model YAML and adding a virtual environment and installing linkml.

curl -OJ https://raw.githubusercontent.com/biolink/biolink-model/master/biolink-model.yaml
python3 -m venv venv
source venv/bin/activate
pip install linkml

Now generate the classes using the gen-pydantic command

gen-pydantic biolink-model.yaml > biolink-model.py