mcp-run-python

from __future__ import annotations as _annotations from dataclasses import dataclass, field from datetime import timedelta from typing import Any, Literal, cast from typing_extensions import TypedDict from pydantic_ai import models from pydantic_ai.settings import ModelSettings from ..otel.span_tree import SpanQuery from .context import EvaluatorContext from .evaluator import EvaluationReason, EvaluationScalar, Evaluator, EvaluatorOutput __all__ = ( 'Equals', 'EqualsExpected', 'Contains', 'IsInstance', 'MaxDuration', 'LLMJudge', 'HasMatchingSpan', 'OutputConfig', ) @dataclass(repr=False) class Equals(Evaluator[object, object, object]): """Check if the output exactly equals the provided value.""" value: Any evaluation_name: str | None = field(default=None) def evaluate(self, ctx: EvaluatorContext[object, object, object]) -> bool: return ctx.output == self.value @dataclass(repr=False) class EqualsExpected(Evaluator[object, object, object]): """Check if the output exactly equals the expected output.""" evaluation_name: str | None = field(default=None) def evaluate(self, ctx: EvaluatorContext[object, object, object]) -> bool | dict[str, bool]: if ctx.expected_output is None: return {} # Only compare if expected output is provided return ctx.output == ctx.expected_output # _MAX_REASON_LENGTH = 500 # _MAX_REASON_KEY_LENGTH = 30 def _truncated_repr(value: Any, max_length: int = 100) -> str: repr_value = repr(value) if len(repr_value) > max_length: repr_value = repr_value[: max_length // 2] + '...' + repr_value[-max_length // 2 :] return repr_value @dataclass(repr=False) class Contains(Evaluator[object, object, object]): """Check if the output contains the expected output. For strings, checks if expected_output is a substring of output. For lists/tuples, checks if expected_output is in output. For dicts, checks if all key-value pairs in expected_output are in output. Note: case_sensitive only applies when both the value and output are strings. """ value: Any case_sensitive: bool = True as_strings: bool = False evaluation_name: str | None = field(default=None) def evaluate( self, ctx: EvaluatorContext[object, object, object], ) -> EvaluationReason: # Convert objects to strings if requested failure_reason: str | None = None as_strings = self.as_strings or (isinstance(self.value, str) and isinstance(ctx.output, str)) if as_strings: output_str = str(ctx.output) expected_str = str(self.value) if not self.case_sensitive: output_str = output_str.lower() expected_str = expected_str.lower() failure_reason: str | None = None if expected_str not in output_str: output_trunc = _truncated_repr(output_str, max_length=100) expected_trunc = _truncated_repr(expected_str, max_length=100) failure_reason = f'Output string {output_trunc} does not contain expected string {expected_trunc}' return EvaluationReason(value=failure_reason is None, reason=failure_reason) try: # Handle different collection types if isinstance(ctx.output, dict): if isinstance(self.value, dict): # Cast to Any to avoid type checking issues output_dict = cast(dict[Any, Any], ctx.output) # pyright: ignore[reportUnknownMemberType] expected_dict = cast(dict[Any, Any], self.value) # pyright: ignore[reportUnknownMemberType] for k in expected_dict: if k not in output_dict: k_trunc = _truncated_repr(k, max_length=30) failure_reason = f'Output dictionary does not contain expected key {k_trunc}' break elif output_dict[k] != expected_dict[k]: k_trunc = _truncated_repr(k, max_length=30) output_v_trunc = _truncated_repr(output_dict[k], max_length=100) expected_v_trunc = _truncated_repr(expected_dict[k], max_length=100) failure_reason = f'Output dictionary has different value for key {k_trunc}: {output_v_trunc} != {expected_v_trunc}' break else: if self.value not in ctx.output: # pyright: ignore[reportUnknownMemberType] output_trunc = _truncated_repr(ctx.output, max_length=200) # pyright: ignore[reportUnknownMemberType] failure_reason = f'Output {output_trunc} does not contain provided value as a key' elif self.value not in ctx.output: # pyright: ignore[reportOperatorIssue] # will be handled by except block output_trunc = _truncated_repr(ctx.output, max_length=200) failure_reason = f'Output {output_trunc} does not contain provided value' except (TypeError, ValueError) as e: failure_reason = f'Containment check failed: {e}' return EvaluationReason(value=failure_reason is None, reason=failure_reason) @dataclass(repr=False) class IsInstance(Evaluator[object, object, object]): """Check if the output is an instance of a type with the given name.""" type_name: str evaluation_name: str | None = field(default=None) def evaluate(self, ctx: EvaluatorContext[object, object, object]) -> EvaluationReason: output = ctx.output for cls in type(output).__mro__: if cls.__name__ == self.type_name or cls.__qualname__ == self.type_name: return EvaluationReason(value=True) reason = f'output is of type {type(output).__name__}' if type(output).__qualname__ != type(output).__name__: reason += f' (qualname: {type(output).__qualname__})' return EvaluationReason(value=False, reason=reason) @dataclass(repr=False) class MaxDuration(Evaluator[object, object, object]): """Check if the execution time is under the specified maximum.""" seconds: float | timedelta def evaluate(self, ctx: EvaluatorContext[object, object, object]) -> bool: duration = timedelta(seconds=ctx.duration) seconds = self.seconds if not isinstance(seconds, timedelta): seconds = timedelta(seconds=seconds) return duration <= seconds class OutputConfig(TypedDict, total=False): """Configuration for the score and assertion outputs of the LLMJudge evaluator.""" evaluation_name: str include_reason: bool def _update_combined_output( combined_output: dict[str, EvaluationScalar | EvaluationReason], value: EvaluationScalar, reason: str | None, config: OutputConfig, default_name: str, ) -> None: name = config.get('evaluation_name') or default_name if config.get('include_reason') and reason is not None: combined_output[name] = EvaluationReason(value=value, reason=reason) else: combined_output[name] = value @dataclass(repr=False) class LLMJudge(Evaluator[object, object, object]): """Judge whether the output of a language model meets the criteria of a provided rubric. If you do not specify a model, it uses the default model for judging. This starts as 'openai:gpt-4o', but can be overridden by calling [`set_default_judge_model`][pydantic_evals.evaluators.llm_as_a_judge.set_default_judge_model]. """ rubric: str model: models.Model | models.KnownModelName | str | None = None include_input: bool = False include_expected_output: bool = False model_settings: ModelSettings | None = None score: OutputConfig | Literal[False] = False assertion: OutputConfig | Literal[False] = field(default_factory=lambda: OutputConfig(include_reason=True)) async def evaluate( self, ctx: EvaluatorContext[object, object, object], ) -> EvaluatorOutput: if self.include_input: if self.include_expected_output: from .llm_as_a_judge import judge_input_output_expected grading_output = await judge_input_output_expected( ctx.inputs, ctx.output, ctx.expected_output, self.rubric, self.model, self.model_settings ) else: from .llm_as_a_judge import judge_input_output grading_output = await judge_input_output( ctx.inputs, ctx.output, self.rubric, self.model, self.model_settings ) else: if self.include_expected_output: from .llm_as_a_judge import judge_output_expected grading_output = await judge_output_expected( ctx.output, ctx.expected_output, self.rubric, self.model, self.model_settings ) else: from .llm_as_a_judge import judge_output grading_output = await judge_output(ctx.output, self.rubric, self.model, self.model_settings) output: dict[str, EvaluationScalar | EvaluationReason] = {} include_both = self.score is not False and self.assertion is not False evaluation_name = self.get_default_evaluation_name() if self.score is not False: default_name = f'{evaluation_name}_score' if include_both else evaluation_name _update_combined_output(output, grading_output.score, grading_output.reason, self.score, default_name) if self.assertion is not False: default_name = f'{evaluation_name}_pass' if include_both else evaluation_name _update_combined_output(output, grading_output.pass_, grading_output.reason, self.assertion, default_name) return output def build_serialization_arguments(self): result = super().build_serialization_arguments() # always serialize the model as a string when present; use its name if it's a KnownModelName if (model := result.get('model')) and isinstance(model, models.Model): # pragma: no branch result['model'] = f'{model.system}:{model.model_name}' # Note: this may lead to confusion if you try to serialize-then-deserialize with a custom model. # I expect that is rare enough to be worth not solving yet, but common enough that we probably will want to # solve it eventually. I'm imagining some kind of model registry, but don't want to work out the details yet. return result @dataclass(repr=False) class HasMatchingSpan(Evaluator[object, object, object]): """Check if the span tree contains a span that matches the specified query.""" query: SpanQuery evaluation_name: str | None = field(default=None) def evaluate( self, ctx: EvaluatorContext[object, object, object], ) -> bool: return ctx.span_tree.any(self.query) DEFAULT_EVALUATORS: tuple[type[Evaluator[object, object, object]], ...] = ( Equals, EqualsExpected, Contains, IsInstance, MaxDuration, LLMJudge, HasMatchingSpan, ) def __getattr__(name: str): if name == 'Python': raise ImportError( 'The `Python` evaluator has been removed for security reasons. See https://github.com/pydantic/pydantic-ai/pull/2808 for more details and a workaround.' ) raise AttributeError(f'module {__name__!r} has no attribute {name!r}')