Your IP : 216.73.216.79
import asyncio
import uuid
from abc import abstractmethod
from typing import Callable, Dict, List, Optional, TypedDict, Union
try:
from pydantic.v1 import BaseModel, Field # type: ignore[import]
except ImportError:
from pydantic import BaseModel, Field
from functools import wraps
from langsmith.schemas import SCORE_TYPE, VALUE_TYPE, Example, Run
class EvaluationResult(BaseModel):
"""Evaluation result."""
key: str
"""The aspect, metric name, or label for this evaluation."""
score: SCORE_TYPE = None
"""The numeric score for this evaluation."""
value: VALUE_TYPE = None
"""The value for this evaluation, if not numeric."""
comment: Optional[str] = None
"""An explanation regarding the evaluation."""
correction: Optional[Dict] = None
"""What the correct value should be, if applicable."""
evaluator_info: Dict = Field(default_factory=dict)
"""Additional information about the evaluator."""
source_run_id: Optional[Union[uuid.UUID, str]] = None
"""The ID of the trace of the evaluator itself."""
target_run_id: Optional[Union[uuid.UUID, str]] = None
"""The ID of the trace this evaluation is applied to.
If none provided, the evaluation feedback is applied to the
root trace being."""
class Config:
"""Pydantic model configuration."""
allow_extra = False
class EvaluationResults(TypedDict, total=False):
"""Batch evaluation results, if your evaluator wishes
to return multiple scores."""
results: List[EvaluationResult]
"""The evaluation results."""
class RunEvaluator:
"""Evaluator interface class."""
@abstractmethod
def evaluate_run(
self, run: Run, example: Optional[Example] = None
) -> Union[EvaluationResult, EvaluationResults]:
"""Evaluate an example."""
async def aevaluate_run(
self, run: Run, example: Optional[Example] = None
) -> Union[EvaluationResult, EvaluationResults]:
"""Evaluate an example asynchronously."""
return await asyncio.get_running_loop().run_in_executor(
None, self.evaluate_run, run, example
)
class DynamicRunEvaluator(RunEvaluator):
"""
A dynamic evaluator that wraps a function and transforms it into a `RunEvaluator`.
This class is designed to be used with the `@run_evaluator` decorator, allowing
functions that take a `Run` and an optional `Example` as arguments, and return
an `EvaluationResult` or `EvaluationResults`, to be used as instances of `RunEvaluator`.
Attributes:
func (Callable): The function that is wrapped by this evaluator.
""" # noqa: E501
def __init__(
self,
func: Callable[
[Run, Optional[Example]], Union[EvaluationResult, EvaluationResults]
],
):
"""
Initialize the DynamicRunEvaluator with a given function.
Args:
func (Callable): A function that takes a `Run` and an optional `Example` as
arguments, and returns an `EvaluationResult` or `EvaluationResults`.
"""
wraps(func)(self)
self.func = func
def evaluate_run(
self, run: Run, example: Optional[Example] = None
) -> Union[EvaluationResult, EvaluationResults]:
"""
Evaluate a run using the wrapped function.
This method directly invokes the wrapped function with the provided arguments.
Args:
run (Run): The run to be evaluated.
example (Optional[Example]): An optional example to be used in the evaluation.
Returns:
Union[EvaluationResult, EvaluationResults]: The result of the evaluation.
""" # noqa: E501
return self.func(run, example)
def __call__(
self, run: Run, example: Optional[Example] = None
) -> Union[EvaluationResult, EvaluationResults]:
"""
Make the evaluator callable, allowing it to be used like a function.
This method enables the evaluator instance to be called directly, forwarding the
call to `evaluate_run`.
Args:
run (Run): The run to be evaluated.
example (Optional[Example]): An optional example to be used in the evaluation.
Returns:
Union[EvaluationResult, EvaluationResults]: The result of the evaluation.
""" # noqa: E501
return self.evaluate_run(run, example)
def run_evaluator(
func: Callable[[Run, Optional[Example]], Union[EvaluationResult, EvaluationResults]]
):
"""Decorator to create a run evaluator from a function."""
return DynamicRunEvaluator(func)