Pydantic Validation¶

Pydantic Logfire has a Pydantic Validation plugin to instrumentPydantic Validation models.The plugin provides logs and metrics about model validation.

To enable the plugin, do one of the following:

• Set theLOGFIRE_PYDANTIC_PLUGIN_RECORD environment variable toall.
• Setpydantic_plugin_record inpyproject.toml, e.g:

[tool.logfire]pydantic_plugin_record="all"

• Calllogfire.instrument_pydantic with the desired configuration, e.g:

importlogfirelogfire.instrument_pydantic()# Defaults to record='all'

Note that if you only use the last option then only model classes defined and importedafter callinglogfire.instrument_pydanticwill be instrumented.

Third party modules¶

By default, third party modules are not instrumented by the plugin to avoid noise. You can enable instrumentation for thoseusing theinclude configuration.

logfire.instrument_pydantic(include={'openai'})

You can also disable instrumentation for your own modules using theexclude configuration.

logfire.instrument_pydantic(exclude={'app.api.v1'})

Model configuration¶

If you want more granular control over the plugin, you can use theplugin_settings class parameter in your Pydantic models.

fromlogfire.integrations.pydanticimportPluginSettingsfrompydanticimportBaseModelclassFoo(BaseModel,plugin_settings=PluginSettings(logfire={'record':'failure'})):...

Record¶

Therecord argument is used to configure what to record.It can be one of the following values:

• all: Send traces and metrics for all events. This is default value forlogfire.instrument_pydantic.
• failure: Send metrics for all validations and traces only for validation failures.
• metrics: Send only metrics.
• off: Disable instrumentation.

Tags¶

Tags are used to add additional information to the traces, and metrics. They can be included byadding thetags key inplugin_settings.

frompydanticimportBaseModelclassFoo(BaseModel,plugin_settings={'logfire':{'record':'all','tags':('tag1','tag2')}}):