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test_pydantic_plugin.py
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from __future__ import annotations
import importlib.metadata
import os
from typing import TYPE_CHECKING, Any
from unittest.mock import patch
import cloudpickle
import pytest
import sqlmodel
from dirty_equals import IsInt
from inline_snapshot import snapshot
from opentelemetry.sdk.metrics.export import AggregationTemporality, InMemoryMetricReader
from opentelemetry.sdk.trace.export import SimpleSpanProcessor
from pydantic import (
AfterValidator,
BaseModel,
ConfigDict,
TypeAdapter,
ValidationError,
__version__ as pydantic_version,
field_validator,
)
from pydantic.dataclasses import dataclass as pydantic_dataclass
from pydantic_core import core_schema
from typing_extensions import Annotated
import logfire
import logfire.integrations.pydantic
from logfire._internal.config import GLOBAL_CONFIG
from logfire._internal.utils import get_version
from logfire.integrations.pydantic import (
LogfirePydanticPlugin,
get_schema_name,
)
from logfire.testing import SeededRandomIdGenerator, TestExporter
from tests.test_metrics import get_collected_metrics
pytestmark = pytest.mark.skipif(
get_version(pydantic_version) < get_version('2.5.0'),
reason='Skipping all tests for versions less than 2.5.',
)
if TYPE_CHECKING:
from pydantic.plugin import PydanticPluginProtocol, SchemaTypePath, ValidatePythonHandlerProtocol
try:
from pydantic.plugin import PydanticPluginProtocol, SchemaTypePath, ValidatePythonHandlerProtocol
except ImportError:
# it's fine, pydantic version <v2.5
pass
def test_plugin_listed():
found = True
entry_points: list[str] = []
for d in importlib.metadata.distributions():
for ep in d.entry_points:
if ep.group == 'pydantic' and ep.value == 'logfire': # pragma: no cover
found = True
entry_points.append(f'group={ep.group} value={ep.value}')
assert found, 'logfire pydantic plugin not found, entrypoints:' + '\n'.join(entry_points)
def test_check_plugin_installed():
"""Check Pydantic has found the logfire pydantic plugin."""
from pydantic.plugin import _loader
assert repr(next(iter(_loader.get_plugins()))) == 'LogfirePydanticPlugin()'
def test_disable_logfire_pydantic_plugin() -> None:
logfire.configure(
send_to_logfire=False,
metrics=logfire.MetricsOptions(additional_readers=[InMemoryMetricReader()]),
)
logfire.instrument_pydantic(record='off')
plugin = LogfirePydanticPlugin()
assert plugin.new_schema_validator(
core_schema.int_schema(), None, SchemaTypePath(module='', name=''), 'BaseModel', None, {}
) == (None, None, None)
def test_logfire_pydantic_plugin_settings_record_off() -> None:
plugin = LogfirePydanticPlugin()
assert plugin.new_schema_validator(
core_schema.int_schema(),
None,
SchemaTypePath(module='', name=''),
'BaseModel',
None,
{'logfire': {'record': 'off'}},
) == (None, None, None)
def test_logfire_pydantic_plugin_settings_record_off_on_model(exporter: TestExporter) -> None:
class MyModel(BaseModel, plugin_settings={'logfire': {'record': 'off'}}):
x: int
MyModel(x=1)
assert exporter.exported_spans_as_dict(_include_pending_spans=True) == []
def test_pydantic_plugin_settings_record_override_pydantic_plugin_record(exporter: TestExporter) -> None:
logfire.instrument_pydantic()
class MyModel(BaseModel, plugin_settings={'logfire': {'record': 'off'}}):
x: int
MyModel(x=1)
assert exporter.exported_spans_as_dict(_include_pending_spans=True) == []
@pytest.mark.parametrize(
'include,exclude,module,name,expected_to_include',
( # type: ignore
# include
({'MyModel'}, set(), '', 'MyModel', True),
({'MyModel'}, set(), 'test_module', 'MyModel', True),
({'MyModel'}, set(), '', 'TestMyModel', True),
({'MyModel'}, set(), 'test_module', 'MyModel1', False),
({'test_module::MyModel'}, set(), 'test_module', 'MyModel', True),
({'test_module::MyModel'}, set(), '', 'MyModel', False),
({'test_module::MyModel'}, set(), 'other_module', 'MyModel', False),
({'.*test_module.*::MyModel'}, set(), 'my_test_module1', 'MyModel', True),
({'.*test_module.*::MyModel[1,2]'}, set(), 'my_test_module1', 'MyModel1', True),
({'.*test_module.*::MyModel[1,2]'}, set(), 'my_test_module1', 'MyModel3', False),
# exclude
(set(), {'MyModel'}, '', 'MyModel', False),
(set(), {'MyModel'}, '', 'MyModel1', True),
(set(), {'.*test_module.*::MyModel'}, 'my_test_module1', 'MyModel', False),
(set(), {'.*test_module.*::MyModel[1,2]'}, 'my_test_module1', 'MyModel3', True),
# include & exclude
({'MyModel'}, {'MyModel'}, '', 'MyModel', False),
({'MyModel'}, {'MyModel1'}, '', 'MyModel', True),
({'.*test_module.*::MyModel[1,2,3]'}, {'.*test_module.*::MyModel[1,3]'}, 'my_test_module', 'MyModel2', True),
({'.*test_module.*::MyModel[1,2,3]'}, {'.*test_module.*::MyModel[1,3]'}, 'my_test_module', 'MyModel1', False),
),
)
def test_logfire_plugin_include_exclude_models(
include: set[str], exclude: set[str], module: str, name: str, expected_to_include: bool
) -> None:
logfire.configure(
send_to_logfire=False,
metrics=logfire.MetricsOptions(additional_readers=[InMemoryMetricReader()]),
)
logfire.instrument_pydantic(record='all', include=include, exclude=exclude)
plugin = LogfirePydanticPlugin()
result = plugin.new_schema_validator(
core_schema.int_schema(), None, SchemaTypePath(module=module, name=name), 'BaseModel', None, {}
)
if expected_to_include:
assert result != (None, None, None)
else:
assert result == (None, None, None)
def test_get_schema_name():
# In particular this tests schemas with type 'definitions'
class Model1(BaseModel):
x: int | list[Model1]
class Model2(BaseModel):
x: Model1
assert get_schema_name(Model1.__pydantic_core_schema__) == 'Model1'
assert get_schema_name(Model2.__pydantic_core_schema__) == 'Model2'
def test_pydantic_plugin_python_record_failure(exporter: TestExporter, metrics_reader: InMemoryMetricReader) -> None:
class MyModel(BaseModel, plugin_settings={'logfire': {'record': 'failure'}}):
x: int
MyModel(x=1)
with pytest.raises(ValidationError):
MyModel(x='a') # type: ignore
assert exporter.exported_spans_as_dict(_include_pending_spans=True) == snapshot(
[
{
'name': 'Validation on {schema_name} failed',
'context': {'trace_id': 1, 'span_id': 1, 'is_remote': False},
'parent': None,
'start_time': 1000000000,
'end_time': 1000000000,
'attributes': {
'logfire.span_type': 'log',
'logfire.level_num': 13,
'logfire.msg_template': 'Validation on {schema_name} failed',
'logfire.msg': 'Validation on MyModel failed',
'code.filepath': 'test_pydantic_plugin.py',
'code.function': 'test_pydantic_plugin_python_record_failure',
'code.lineno': 123,
'schema_name': 'MyModel',
'error_count': 1,
'errors': '[{"type":"int_parsing","loc":["x"],"msg":"Input should be a valid integer, unable to parse string as an integer","input":"a"}]',
'logfire.json_schema': '{"type":"object","properties":{"schema_name":{},"error_count":{},"errors":{"type":"array","items":{"type":"object","properties":{"loc":{"type":"array","x-python-datatype":"tuple"}}}}}}',
},
}
]
)
metrics_collected = get_collected_metrics(metrics_reader)
assert metrics_collected == snapshot(
[
{
'name': 'pydantic.validations',
'description': '',
'unit': '',
'data': {
'data_points': [
{
'attributes': {
'validation_method': 'validate_python',
'schema_name': 'MyModel',
'success': True,
},
'start_time_unix_nano': IsInt(gt=0),
'time_unix_nano': IsInt(gt=0),
'value': 1,
'exemplars': [],
},
{
'attributes': {
'success': False,
'schema_name': 'MyModel',
'validation_method': 'validate_python',
},
'start_time_unix_nano': IsInt(gt=0),
'time_unix_nano': IsInt(gt=0),
'value': 1,
'exemplars': [],
},
],
'aggregation_temporality': AggregationTemporality.DELTA,
'is_monotonic': True,
},
}
]
)
def test_pydantic_plugin_metrics(metrics_reader: InMemoryMetricReader) -> None:
class MyModel(BaseModel, plugin_settings={'logfire': {'record': 'metrics'}}):
x: int
MyModel(x=1)
MyModel(x=2)
MyModel(x=3)
with pytest.raises(ValidationError):
MyModel(x='a') # type: ignore
with pytest.raises(ValidationError):
MyModel(x='a') # type: ignore
metrics_collected = get_collected_metrics(metrics_reader)
assert metrics_collected == snapshot(
[
{
'name': 'pydantic.validations',
'description': '',
'unit': '',
'data': {
'data_points': [
{
'attributes': {
'validation_method': 'validate_python',
'schema_name': 'MyModel',
'success': True,
},
'start_time_unix_nano': IsInt(gt=0),
'time_unix_nano': IsInt(gt=0),
'value': 3,
'exemplars': [],
},
{
'attributes': {
'success': False,
'schema_name': 'MyModel',
'validation_method': 'validate_python',
},
'start_time_unix_nano': IsInt(gt=0),
'time_unix_nano': IsInt(gt=0),
'value': 2,
'exemplars': [],
},
],
'aggregation_temporality': AggregationTemporality.DELTA,
'is_monotonic': True,
},
}
]
)
def test_pydantic_plugin_python_success(exporter: TestExporter, metrics_reader: InMemoryMetricReader) -> None:
class MyModel(BaseModel, plugin_settings={'logfire': {'record': 'all'}}):
x: int
MyModel(x=1)
assert exporter.exported_spans_as_dict() == snapshot(
[
{
'name': 'pydantic.validate_python',
'context': {'trace_id': 1, 'span_id': 1, 'is_remote': False},
'parent': None,
'start_time': 1000000000,
'end_time': 2000000000,
'attributes': {
'logfire.span_type': 'span',
'logfire.msg_template': 'Pydantic {schema_name} {validation_method}',
'logfire.msg': 'Pydantic MyModel validate_python succeeded',
'code.filepath': 'test_pydantic_plugin.py',
'code.function': 'test_pydantic_plugin_python_success',
'code.lineno': 123,
'result': '{"x":1}',
'logfire.json_schema': '{"type":"object","properties":{"schema_name":{},"validation_method":{},"input_data":{"type":"object"},"success":{},"result":{"type":"object","title":"MyModel","x-python-datatype":"PydanticModel"}}}',
'success': True,
'input_data': '{"x":1}',
'schema_name': 'MyModel',
'validation_method': 'validate_python',
'logfire.level_num': 9,
},
}
]
)
metrics_collected = get_collected_metrics(metrics_reader)
assert metrics_collected == snapshot(
[
{
'name': 'pydantic.validations',
'description': '',
'unit': '',
'data': {
'data_points': [
{
'attributes': {
'validation_method': 'validate_python',
'schema_name': 'MyModel',
'success': True,
},
'start_time_unix_nano': IsInt(gt=0),
'time_unix_nano': IsInt(gt=0),
'value': 1,
'exemplars': [
{
'filtered_attributes': {},
'value': 1,
'time_unix_nano': IsInt(),
'span_id': 1,
'trace_id': 1,
}
],
}
],
'aggregation_temporality': AggregationTemporality.DELTA,
'is_monotonic': True,
},
}
]
)
def test_pydantic_plugin_python_error_record_failure(
exporter: TestExporter, metrics_reader: InMemoryMetricReader
) -> None:
class MyModel(BaseModel, plugin_settings={'logfire': {'record': 'failure'}}):
x: int
with pytest.raises(ValidationError):
MyModel(x='a') # type: ignore
with pytest.raises(ValidationError):
MyModel(x='a') # type: ignore
assert exporter.exported_spans_as_dict(_include_pending_spans=True) == snapshot(
[
{
'name': 'Validation on {schema_name} failed',
'context': {'trace_id': 1, 'span_id': 1, 'is_remote': False},
'parent': None,
'start_time': 1000000000,
'end_time': 1000000000,
'attributes': {
'logfire.span_type': 'log',
'logfire.level_num': 13,
'logfire.msg_template': 'Validation on {schema_name} failed',
'logfire.msg': 'Validation on MyModel failed',
'code.filepath': 'test_pydantic_plugin.py',
'code.function': 'test_pydantic_plugin_python_error_record_failure',
'code.lineno': 123,
'schema_name': 'MyModel',
'error_count': 1,
'errors': '[{"type":"int_parsing","loc":["x"],"msg":"Input should be a valid integer, unable to parse string as an integer","input":"a"}]',
'logfire.json_schema': '{"type":"object","properties":{"schema_name":{},"error_count":{},"errors":{"type":"array","items":{"type":"object","properties":{"loc":{"type":"array","x-python-datatype":"tuple"}}}}}}',
},
},
{
'name': 'Validation on {schema_name} failed',
'context': {'trace_id': 2, 'span_id': 2, 'is_remote': False},
'parent': None,
'start_time': 2000000000,
'end_time': 2000000000,
'attributes': {
'logfire.span_type': 'log',
'logfire.level_num': 13,
'logfire.msg_template': 'Validation on {schema_name} failed',
'logfire.msg': 'Validation on MyModel failed',
'code.filepath': 'test_pydantic_plugin.py',
'code.function': 'test_pydantic_plugin_python_error_record_failure',
'code.lineno': 123,
'schema_name': 'MyModel',
'error_count': 1,
'errors': '[{"type":"int_parsing","loc":["x"],"msg":"Input should be a valid integer, unable to parse string as an integer","input":"a"}]',
'logfire.json_schema': '{"type":"object","properties":{"schema_name":{},"error_count":{},"errors":{"type":"array","items":{"type":"object","properties":{"loc":{"type":"array","x-python-datatype":"tuple"}}}}}}',
},
},
]
)
metrics_collected = get_collected_metrics(metrics_reader)
assert metrics_collected == snapshot(
[
{
'name': 'pydantic.validations',
'description': '',
'unit': '',
'data': {
'data_points': [
{
'attributes': {
'validation_method': 'validate_python',
'schema_name': 'MyModel',
'success': False,
},
'start_time_unix_nano': IsInt(gt=0),
'time_unix_nano': IsInt(gt=0),
'value': 2,
'exemplars': [],
}
],
'aggregation_temporality': AggregationTemporality.DELTA,
'is_monotonic': True,
},
}
]
)
def test_pydantic_plugin_python_error(exporter: TestExporter) -> None:
class MyModel(BaseModel, plugin_settings={'logfire': {'record': 'all'}}):
x: int
with pytest.raises(ValidationError):
MyModel(x='a') # type: ignore
assert exporter.exported_spans_as_dict() == snapshot(
[
{
'name': 'pydantic.validate_python',
'context': {'trace_id': 1, 'span_id': 1, 'is_remote': False},
'parent': None,
'start_time': 1000000000,
'end_time': 2000000000,
'attributes': {
'logfire.span_type': 'span',
'logfire.msg_template': 'Pydantic {schema_name} {validation_method}',
'logfire.msg': 'Pydantic MyModel validate_python failed',
'code.filepath': 'test_pydantic_plugin.py',
'code.function': 'test_pydantic_plugin_python_error',
'code.lineno': 123,
'schema_name': 'MyModel',
'error_count': 1,
'errors': '[{"type":"int_parsing","loc":["x"],"msg":"Input should be a valid integer, unable to parse string as an integer","input":"a"}]',
'logfire.json_schema': '{"type":"object","properties":{"schema_name":{},"validation_method":{},"input_data":{"type":"object"},"success":{},"error_count":{},"errors":{"type":"array","items":{"type":"object","properties":{"loc":{"type":"array","x-python-datatype":"tuple"}}}}}}',
'success': False,
'validation_method': 'validate_python',
'input_data': '{"x":"a"}',
'logfire.level_num': 13,
},
}
]
)
def test_pydantic_plugin_json_success(exporter: TestExporter) -> None:
class MyModel(BaseModel, plugin_settings={'logfire': {'record': 'all'}}):
x: int
MyModel.model_validate_json('{"x":1}')
assert exporter.exported_spans_as_dict() == snapshot(
[
{
'name': 'pydantic.validate_json',
'context': {'trace_id': 1, 'span_id': 1, 'is_remote': False},
'parent': None,
'start_time': 1000000000,
'end_time': 2000000000,
'attributes': {
'logfire.span_type': 'span',
'logfire.msg_template': 'Pydantic {schema_name} {validation_method}',
'logfire.msg': 'Pydantic MyModel validate_json succeeded',
'code.filepath': 'test_pydantic_plugin.py',
'code.function': 'test_pydantic_plugin_json_success',
'code.lineno': 123,
'result': '{"x":1}',
'logfire.json_schema': '{"type":"object","properties":{"schema_name":{},"validation_method":{},"input_data":{},"success":{},"result":{"type":"object","title":"MyModel","x-python-datatype":"PydanticModel"}}}',
'success': True,
'input_data': '{"x":1}',
'schema_name': 'MyModel',
'validation_method': 'validate_json',
'logfire.level_num': 9,
},
}
]
)
def test_pydantic_plugin_json_error(exporter: TestExporter) -> None:
class MyModel(BaseModel, plugin_settings={'logfire': {'record': 'all'}}):
x: int
with pytest.raises(ValidationError):
MyModel.model_validate({'x': 'a'})
assert exporter.exported_spans_as_dict() == snapshot(
[
{
'name': 'pydantic.validate_python',
'context': {'trace_id': 1, 'span_id': 1, 'is_remote': False},
'parent': None,
'start_time': 1000000000,
'end_time': 2000000000,
'attributes': {
'logfire.span_type': 'span',
'logfire.msg_template': 'Pydantic {schema_name} {validation_method}',
'logfire.msg': 'Pydantic MyModel validate_python failed',
'code.filepath': 'test_pydantic_plugin.py',
'code.function': 'test_pydantic_plugin_json_error',
'code.lineno': 123,
'schema_name': 'MyModel',
'error_count': 1,
'errors': '[{"type":"int_parsing","loc":["x"],"msg":"Input should be a valid integer, unable to parse string as an integer","input":"a"}]',
'logfire.json_schema': '{"type":"object","properties":{"schema_name":{},"validation_method":{},"input_data":{"type":"object"},"success":{},"error_count":{},"errors":{"type":"array","items":{"type":"object","properties":{"loc":{"type":"array","x-python-datatype":"tuple"}}}}}}',
'success': False,
'validation_method': 'validate_python',
'input_data': '{"x":"a"}',
'logfire.level_num': 13,
},
}
]
)
def test_pydantic_plugin_strings_success(exporter: TestExporter) -> None:
class MyModel(BaseModel, plugin_settings={'logfire': {'record': 'all'}}):
x: int
MyModel.model_validate_strings({'x': '1'}, strict=True)
assert exporter.exported_spans_as_dict() == snapshot(
[
{
'name': 'pydantic.validate_strings',
'context': {'trace_id': 1, 'span_id': 1, 'is_remote': False},
'parent': None,
'start_time': 1000000000,
'end_time': 2000000000,
'attributes': {
'logfire.span_type': 'span',
'logfire.msg_template': 'Pydantic {schema_name} {validation_method}',
'logfire.msg': 'Pydantic MyModel validate_strings succeeded',
'code.filepath': 'test_pydantic_plugin.py',
'code.function': 'test_pydantic_plugin_strings_success',
'code.lineno': 123,
'result': '{"x":1}',
'logfire.json_schema': '{"type":"object","properties":{"schema_name":{},"validation_method":{},"input_data":{"type":"object"},"success":{},"result":{"type":"object","title":"MyModel","x-python-datatype":"PydanticModel"}}}',
'success': True,
'input_data': '{"x":"1"}',
'schema_name': 'MyModel',
'validation_method': 'validate_strings',
'logfire.level_num': 9,
},
}
]
)
def test_pydantic_plugin_strings_error(exporter: TestExporter) -> None:
class MyModel(BaseModel, plugin_settings={'logfire': {'record': 'all'}}):
x: int
with pytest.raises(ValidationError):
MyModel.model_validate_strings({'x': 'a'})
assert exporter.exported_spans_as_dict() == snapshot(
[
{
'name': 'pydantic.validate_strings',
'context': {'trace_id': 1, 'span_id': 1, 'is_remote': False},
'parent': None,
'start_time': 1000000000,
'end_time': 2000000000,
'attributes': {
'logfire.span_type': 'span',
'logfire.msg_template': 'Pydantic {schema_name} {validation_method}',
'logfire.msg': 'Pydantic MyModel validate_strings failed',
'code.filepath': 'test_pydantic_plugin.py',
'code.function': 'test_pydantic_plugin_strings_error',
'code.lineno': 123,
'schema_name': 'MyModel',
'error_count': 1,
'errors': '[{"type":"int_parsing","loc":["x"],"msg":"Input should be a valid integer, unable to parse string as an integer","input":"a"}]',
'logfire.json_schema': '{"type":"object","properties":{"schema_name":{},"validation_method":{},"input_data":{"type":"object"},"success":{},"error_count":{},"errors":{"type":"array","items":{"type":"object","properties":{"loc":{"type":"array","x-python-datatype":"tuple"}}}}}}',
'success': False,
'validation_method': 'validate_strings',
'input_data': '{"x":"a"}',
'logfire.level_num': 13,
},
}
]
)
def test_pydantic_plugin_with_dataclass(exporter: TestExporter) -> None:
@pydantic_dataclass(config=ConfigDict(plugin_settings={'logfire': {'record': 'failure'}}))
class MyDataclass:
x: int
with pytest.raises(ValidationError):
MyDataclass(x='a') # type: ignore
assert exporter.exported_spans_as_dict() == snapshot(
[
{
'name': 'Validation on {schema_name} failed',
'context': {'trace_id': 1, 'span_id': 1, 'is_remote': False},
'parent': None,
'start_time': 1000000000,
'end_time': 1000000000,
'attributes': {
'logfire.span_type': 'log',
'logfire.level_num': 13,
'logfire.msg_template': 'Validation on {schema_name} failed',
'logfire.msg': 'Validation on MyDataclass failed',
'code.filepath': 'test_pydantic_plugin.py',
'code.function': 'test_pydantic_plugin_with_dataclass',
'code.lineno': 123,
'schema_name': 'MyDataclass',
'error_count': 1,
'errors': '[{"type":"int_parsing","loc":["x"],"msg":"Input should be a valid integer, unable to parse string as an integer","input":"a"}]',
'logfire.json_schema': '{"type":"object","properties":{"schema_name":{},"error_count":{},"errors":{"type":"array","items":{"type":"object","properties":{"loc":{"type":"array","x-python-datatype":"tuple"}}}}}}',
},
}
]
)
def test_pydantic_plugin_sample_rate_config(exporter: TestExporter, config_kwargs: dict[str, Any]) -> None:
config_kwargs.update(
sampling=logfire.SamplingOptions(head=0.1),
advanced=logfire.AdvancedOptions(id_generator=SeededRandomIdGenerator()),
)
logfire.configure(**config_kwargs)
class MyModel(BaseModel, plugin_settings={'logfire': {'record': 'all'}}):
x: int
for _ in range(10):
with pytest.raises(ValidationError):
MyModel.model_validate({'x': 'a'})
assert len(exporter.exported_spans_as_dict()) == 1
@pytest.mark.xfail(reason='We need to fix the nesting `trace_sample_rate` logic.')
def test_pydantic_plugin_plugin_settings_sample_rate(exporter: TestExporter) -> None:
logfire.configure(
send_to_logfire=False,
additional_span_processors=[SimpleSpanProcessor(exporter)],
advanced=logfire.AdvancedOptions(
id_generator=SeededRandomIdGenerator(),
),
metrics=logfire.MetricsOptions(additional_readers=[InMemoryMetricReader()]),
)
class MyModel(BaseModel, plugin_settings={'logfire': {'record': 'all', 'trace_sample_rate': 0.4}}):
x: int
for _ in range(10):
with pytest.raises(ValidationError):
MyModel.model_validate({'x': 'a'})
assert len(exporter.exported_spans_as_dict()) == 6
@pytest.mark.parametrize('tags', [['tag1', 'tag2'], ('tag1', 'tag2')])
def test_pydantic_plugin_plugin_settings_tags(exporter: TestExporter, tags: Any) -> None:
class MyModel(BaseModel, plugin_settings={'logfire': {'record': 'failure', 'tags': tags}}):
x: int
with pytest.raises(ValidationError):
MyModel.model_validate({'x': 'test'})
span = exporter.exported_spans_as_dict()[0]
assert span['attributes']['logfire.tags'] == ('tag1', 'tag2')
@pytest.mark.xfail(reason='We need to fix the nesting `trace_sample_rate` logic.')
def test_pydantic_plugin_plugin_settings_sample_rate_with_tag(exporter: TestExporter) -> None:
logfire.configure(
send_to_logfire=False,
additional_span_processors=[SimpleSpanProcessor(exporter)],
advanced=logfire.AdvancedOptions(
id_generator=SeededRandomIdGenerator(),
),
metrics=logfire.MetricsOptions(additional_readers=[InMemoryMetricReader()]),
)
class MyModel(
BaseModel, plugin_settings={'logfire': {'record': 'all', 'trace_sample_rate': 0.4, 'tags': 'test_tag'}}
):
x: int
for _ in range(10):
with pytest.raises(ValidationError):
MyModel.model_validate({'x': 'a'})
assert len(exporter.exported_spans_as_dict()) == 6
# TODO(Marcelo): Why are those lines not being reached?
span = exporter.exported_spans_as_dict()[0] # pragma: no cover
assert span['attributes']['logfire.tags'] == ('test_tag',) # pragma: no cover
def test_pydantic_plugin_nested_model(exporter: TestExporter):
class Model1(BaseModel, plugin_settings={'logfire': {'record': 'all'}}):
x: int
class Model2(BaseModel, plugin_settings={'logfire': {'record': 'all'}}):
m: Model1
@field_validator('m', mode='before')
def validate_m(cls, v: Any):
return Model1.model_validate(v)
Model2.model_validate({'m': {'x': 10}})
with pytest.raises(ValidationError):
Model2.model_validate({'m': {'x': 'y'}})
assert exporter.exported_spans_as_dict() == snapshot(
[
{
'name': 'pydantic.validate_python',
'context': {'trace_id': 1, 'span_id': 3, 'is_remote': False},
'parent': {'trace_id': 1, 'span_id': 1, 'is_remote': False},
'start_time': 2000000000,
'end_time': 3000000000,
'attributes': {
'logfire.span_type': 'span',
'logfire.msg_template': 'Pydantic {schema_name} {validation_method}',
'logfire.msg': 'Pydantic Model1 validate_python succeeded',
'code.filepath': 'test_pydantic_plugin.py',
'code.function': 'validate_m',
'code.lineno': 123,
'result': '{"x":10}',
'logfire.json_schema': '{"type":"object","properties":{"schema_name":{},"validation_method":{},"input_data":{"type":"object"},"success":{},"result":{"type":"object","title":"Model1","x-python-datatype":"PydanticModel"}}}',
'success': True,
'input_data': '{"x":10}',
'schema_name': 'Model1',
'validation_method': 'validate_python',
'logfire.level_num': 9,
},
},
{
'name': 'pydantic.validate_python',
'context': {'trace_id': 1, 'span_id': 1, 'is_remote': False},
'parent': None,
'start_time': 1000000000,
'end_time': 4000000000,
'attributes': {
'code.filepath': 'test_pydantic_plugin.py',
'code.function': 'test_pydantic_plugin_nested_model',
'code.lineno': 123,
'logfire.msg_template': 'Pydantic {schema_name} {validation_method}',
'logfire.msg': 'Pydantic Model2 validate_python succeeded',
'logfire.json_schema': '{"type":"object","properties":{"schema_name":{},"validation_method":{},"input_data":{"type":"object"},"success":{},"result":{"type":"object","title":"Model2","x-python-datatype":"PydanticModel","properties":{"m":{"type":"object","title":"Model1","x-python-datatype":"PydanticModel"}}}}}',
'logfire.span_type': 'span',
'schema_name': 'Model2',
'validation_method': 'validate_python',
'input_data': '{"m":{"x":10}}',
'result': '{"m":{"x":10}}',
'success': True,
'logfire.level_num': 9,
},
},
{
'name': 'pydantic.validate_python',
'context': {'trace_id': 2, 'span_id': 7, 'is_remote': False},
'parent': {'trace_id': 2, 'span_id': 5, 'is_remote': False},
'start_time': 6000000000,
'end_time': 7000000000,
'attributes': {
'logfire.span_type': 'span',
'logfire.msg_template': 'Pydantic {schema_name} {validation_method}',
'logfire.msg': 'Pydantic Model1 validate_python failed',
'code.filepath': 'test_pydantic_plugin.py',
'code.function': 'validate_m',
'code.lineno': 123,
'logfire.json_schema': '{"type":"object","properties":{"schema_name":{},"validation_method":{},"input_data":{"type":"object"},"success":{},"error_count":{},"errors":{"type":"array","items":{"type":"object","properties":{"loc":{"type":"array","x-python-datatype":"tuple"}}}}}}',
'success': False,
'input_data': '{"x":"y"}',
'schema_name': 'Model1',
'validation_method': 'validate_python',
'error_count': 1,
'errors': '[{"type":"int_parsing","loc":["x"],"msg":"Input should be a valid integer, unable to parse string as an integer","input":"y"}]',
'logfire.level_num': 13,
},
},
{
'name': 'pydantic.validate_python',
'context': {'trace_id': 2, 'span_id': 5, 'is_remote': False},
'parent': None,
'start_time': 5000000000,
'end_time': 8000000000,
'attributes': {
'code.filepath': 'test_pydantic_plugin.py',
'code.function': 'test_pydantic_plugin_nested_model',
'code.lineno': 123,
'schema_name': 'Model2',
'logfire.msg_template': 'Pydantic {schema_name} {validation_method}',
'logfire.msg': 'Pydantic Model2 validate_python failed',
'logfire.json_schema': '{"type":"object","properties":{"schema_name":{},"validation_method":{},"input_data":{"type":"object"},"success":{},"error_count":{},"errors":{"type":"array","items":{"type":"object","properties":{"loc":{"type":"array","x-python-datatype":"tuple"}}}}}}',
'logfire.span_type': 'span',
'input_data': '{"m":{"x":"y"}}',
'validation_method': 'validate_python',
'success': False,
'error_count': 1,
'errors': '[{"type":"int_parsing","loc":["m","x"],"msg":"Input should be a valid integer, unable to parse string as an integer","input":"y"}]',
'logfire.level_num': 13,
},
},
]
)
def test_pydantic_plugin_python_exception(exporter: TestExporter) -> None:
class MyModel(BaseModel, plugin_settings={'logfire': {'record': 'all'}}):
x: int
@field_validator('x')
def validate_x(cls, v: Any) -> Any:
raise TypeError('My error')
with pytest.raises(TypeError):
MyModel(x=1)
assert exporter.exported_spans_as_dict() == snapshot(
[
{
'name': 'pydantic.validate_python',
'context': {'trace_id': 1, 'span_id': 1, 'is_remote': False},
'parent': None,
'start_time': 1000000000,
'end_time': 3000000000,
'attributes': {
'logfire.span_type': 'span',
'logfire.level_num': 17,
'logfire.msg_template': 'Pydantic {schema_name} {validation_method}',
'logfire.msg': 'Pydantic MyModel validate_python raised TypeError',
'code.filepath': 'test_pydantic_plugin.py',
'code.function': 'test_pydantic_plugin_python_exception',
'code.lineno': 123,
'schema_name': 'MyModel',
'logfire.json_schema': '{"type":"object","properties":{"schema_name":{},"validation_method":{},"input_data":{"type":"object"},"success":{}}}',
'validation_method': 'validate_python',
'input_data': '{"x":1}',
'success': False,
},
'events': [
{
'name': 'exception',
'timestamp': 2000000000,
'attributes': {
'exception.type': 'TypeError',
'exception.message': 'My error',
'exception.stacktrace': 'TypeError: My error',
'exception.escaped': 'True',
'logfire.exception_first_recorded': True,
},
}
],
}
]
)
def test_pydantic_plugin_python_exception_record_failure(exporter: TestExporter) -> None:
class MyModel(BaseModel, plugin_settings={'logfire': {'record': 'failure'}}):
x: int
@field_validator('x')
def validate_x(cls, v: Any) -> Any:
raise TypeError('My error')
with pytest.raises(TypeError):
MyModel(x=1)
assert exporter.exported_spans_as_dict() == snapshot(
[
{
'name': 'Validation on {schema_name} raised {exception_type}',
'context': {'trace_id': 1, 'span_id': 1, 'is_remote': False},
'parent': None,
'start_time': 1000000000,
'end_time': 1000000000,
'attributes': {
'logfire.span_type': 'log',
'logfire.level_num': 17,
'logfire.msg_template': 'Validation on {schema_name} raised {exception_type}',
'logfire.msg': 'Validation on MyModel raised TypeError',
'code.filepath': 'test_pydantic_plugin.py',
'code.function': 'test_pydantic_plugin_python_exception_record_failure',
'code.lineno': 123,
'schema_name': 'MyModel',
'logfire.json_schema': '{"type":"object","properties":{"schema_name":{},"exception_type":{}}}',
'exception_type': 'TypeError',
},
'events': [
{
'name': 'exception',
'timestamp': 2000000000,
'attributes': {
'exception.type': 'TypeError',
'exception.message': 'My error',
'exception.stacktrace': 'TypeError: My error',
'exception.escaped': 'False',
'logfire.exception_first_recorded': True,
},
}
],
}
]
)
def test_old_plugin_style(exporter: TestExporter) -> None:
# Test that plugins for the old API still work together with the logfire plugin.
events: list[str] = []
class Handler(ValidatePythonHandlerProtocol):
def on_success(self, result: Any) -> None:
events.append('success')
def on_error(self, error: ValidationError) -> None:
events.append('error')
def on_exception(self, exception: Exception) -> None:
events.append('exception')
class OldPlugin(PydanticPluginProtocol):
def new_schema_validator(self, *_: Any, **__: Any) -> Any:
return Handler(), None, None
class DummyPlugin:
def new_schema_validator(self, *_: Any, **__: Any) -> Any:
# Test returning a class that has none of the expected methods
# and is also not callable (i.e. also doesn't satisfy the new API).
return DummyPlugin(), None, None
from pydantic.plugin import _loader
_loader.get_plugins()
_loader._plugins['old'] = OldPlugin() # type: ignore
_loader._plugins['dummy'] = DummyPlugin() # type: ignore
try:
class MyModel(BaseModel, plugin_settings={'logfire': {'record': 'all'}}):
x: int
@field_validator('x')
def validate_x(cls, v: Any) -> Any:
if v == 1:
return v
raise TypeError('My error')
MyModel(x=1)
with pytest.raises(TypeError):
MyModel(x=2)