mirror of
https://github.com/hwchase17/langchain.git
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[Evals] Session-level feedback (#18463)
Co-authored-by: William Fu-Hinthorn <13333726+hinthornw@users.noreply.github.com>
This commit is contained in:
Harrison Chase
GitHub
parent
b051bba1a9
commit
73d653324f
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@ -1,12 +1,14 @@
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"""Configuration for run evaluators."""
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from typing import Any, Dict, List, Optional, Union
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from typing import Any, Callable, Dict, List, Optional, Sequence, Union
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from langchain_core.embeddings import Embeddings
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from langchain_core.language_models import BaseLanguageModel
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from langchain_core.prompts import BasePromptTemplate
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from langchain_core.pydantic_v1 import BaseModel, Field
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from langsmith import RunEvaluator
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from langsmith.evaluation.evaluator import EvaluationResult, EvaluationResults
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from langsmith.schemas import Example, Run
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from langchain.evaluation.criteria.eval_chain import CRITERIA_TYPE
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from langchain.evaluation.embedding_distance.base import (
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@ -17,6 +19,14 @@ from langchain.evaluation.string_distance.base import (
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StringDistance as StringDistanceEnum,
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)
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RUN_EVALUATOR_LIKE = Callable[
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[Run, Optional[Example]], Union[EvaluationResult, EvaluationResults, dict]
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]
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BATCH_EVALUATOR_LIKE = Callable[
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[Sequence[Run], Optional[Sequence[Example]]],
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Union[EvaluationResult, EvaluationResults, dict],
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]
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class EvalConfig(BaseModel):
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"""Configuration for a given run evaluator.
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@ -76,12 +86,16 @@ class SingleKeyEvalConfig(EvalConfig):
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return kwargs
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CUSTOM_EVALUATOR_TYPE = Union[RUN_EVALUATOR_LIKE, RunEvaluator, StringEvaluator]
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SINGLE_EVAL_CONFIG_TYPE = Union[EvaluatorType, str, EvalConfig]
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class RunEvalConfig(BaseModel):
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"""Configuration for a run evaluation.
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Parameters
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----------
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evaluators : List[Union[EvaluatorType, EvalConfig]]
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evaluators : List[Union[EvaluatorType, EvalConfig, RunEvaluator, Callable]]
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Configurations for which evaluators to apply to the dataset run.
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Each can be the string of an :class:`EvaluatorType <langchain.evaluation.schema.EvaluatorType>`, such
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as EvaluatorType.QA, the evaluator type string ("qa"), or a configuration for a
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@ -107,9 +121,12 @@ class RunEvalConfig(BaseModel):
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The language model to pass to any evaluators that use a language model.
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""" # noqa: E501
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evaluators: List[Union[EvaluatorType, str, EvalConfig]] = Field(
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default_factory=list
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)
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evaluators: List[
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Union[
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SINGLE_EVAL_CONFIG_TYPE,
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CUSTOM_EVALUATOR_TYPE,
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]
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] = Field(default_factory=list)
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"""Configurations for which evaluators to apply to the dataset run.
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Each can be the string of an
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:class:`EvaluatorType <langchain.evaluation.schema.EvaluatorType>`, such
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@ -117,8 +134,15 @@ class RunEvalConfig(BaseModel):
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given evaluator
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(e.g.,
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:class:`RunEvalConfig.QA <langchain.smith.evaluation.config.RunEvalConfig.QA>`).""" # noqa: E501
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custom_evaluators: Optional[List[Union[RunEvaluator, StringEvaluator]]] = None
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custom_evaluators: Optional[List[CUSTOM_EVALUATOR_TYPE]] = None
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"""Custom evaluators to apply to the dataset run."""
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batch_evaluators: Optional[List[BATCH_EVALUATOR_LIKE]] = None
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"""Evaluators that run on an aggregate/batch level.
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These generate 1 or more metrics that are assigned to the full test run.
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As a result, they are not associated with individual traces.
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"""
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reference_key: Optional[str] = None
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"""The key in the dataset run to use as the reference string.
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If not provided, we will attempt to infer automatically."""
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@ -2,6 +2,7 @@
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from __future__ import annotations
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import concurrent.futures
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import dataclasses
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import functools
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import inspect
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@ -35,9 +36,15 @@ from langchain_core.tracers.evaluation import (
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from langchain_core.tracers.langchain import LangChainTracer
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from langsmith.client import Client
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from langsmith.env import get_git_info, get_langchain_env_var_metadata
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from langsmith.evaluation import EvaluationResult, RunEvaluator
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from langsmith.evaluation import (
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EvaluationResult,
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RunEvaluator,
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)
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from langsmith.evaluation import (
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run_evaluator as run_evaluator_dec,
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)
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from langsmith.run_helpers import as_runnable, is_traceable_function
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from langsmith.schemas import Dataset, DataType, Example, TracerSession
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from langsmith.schemas import Dataset, DataType, Example, Run, TracerSession
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from langsmith.utils import LangSmithError
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from requests import HTTPError
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from typing_extensions import TypedDict
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@ -513,7 +520,10 @@ def _determine_reference_key(
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def _construct_run_evaluator(
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eval_config: Union[EvaluatorType, str, smith_eval_config.EvalConfig],
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eval_config: Union[
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smith_eval_config.SINGLE_EVAL_CONFIG_TYPE,
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smith_eval_config.CUSTOM_EVALUATOR_TYPE,
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],
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eval_llm: Optional[BaseLanguageModel],
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run_type: str,
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data_type: DataType,
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@ -522,12 +532,14 @@ def _construct_run_evaluator(
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input_key: Optional[str],
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prediction_key: Optional[str],
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) -> RunEvaluator:
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if isinstance(eval_config, RunEvaluator):
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return eval_config
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if isinstance(eval_config, (EvaluatorType, str)):
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if not isinstance(eval_config, EvaluatorType):
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eval_config = EvaluatorType(eval_config)
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evaluator_ = load_evaluator(eval_config, llm=eval_llm)
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eval_type_tag = eval_config.value
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else:
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elif isinstance(eval_config, smith_eval_config.EvalConfig):
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kwargs = {"llm": eval_llm, **eval_config.get_kwargs()}
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evaluator_ = load_evaluator(eval_config.evaluator_type, **kwargs)
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eval_type_tag = eval_config.evaluator_type.value
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@ -536,6 +548,11 @@ def _construct_run_evaluator(
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input_key = eval_config.input_key or input_key
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prediction_key = eval_config.prediction_key or prediction_key
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reference_key = eval_config.reference_key or reference_key
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elif callable(eval_config):
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# Assume we can decorate
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return run_evaluator_dec(eval_config)
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else:
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raise ValueError(f"Unknown evaluator type: {type(eval_config)}")
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if isinstance(evaluator_, StringEvaluator):
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if evaluator_.requires_reference and reference_key is None:
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@ -600,13 +617,9 @@ def _load_run_evaluators(
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"""
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run_evaluators = []
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input_key, prediction_key, reference_key = None, None, None
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if (
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config.evaluators
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or any([isinstance(e, EvaluatorType) for e in config.evaluators])
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or (
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config.custom_evaluators
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and any([isinstance(e, StringEvaluator) for e in config.custom_evaluators])
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)
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if config.evaluators or (
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config.custom_evaluators
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and any([isinstance(e, StringEvaluator) for e in config.custom_evaluators])
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):
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input_key, prediction_key, reference_key = _get_keys(
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config, run_inputs, run_outputs, example_outputs
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@ -638,6 +651,8 @@ def _load_run_evaluators(
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reference_key=reference_key,
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)
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)
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elif callable(custom_evaluator):
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run_evaluators.append(run_evaluator_dec(custom_evaluator))
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else:
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raise ValueError(
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f"Unsupported custom evaluator: {custom_evaluator}."
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@ -1032,6 +1047,7 @@ class _DatasetRunContainer:
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wrapped_model: MCF
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examples: List[Example]
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configs: List[RunnableConfig]
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batch_evaluators: Optional[List[smith_eval_config.BATCH_EVALUATOR_LIKE]] = None
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def _merge_test_outputs(
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self,
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@ -1055,8 +1071,34 @@ class _DatasetRunContainer:
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results[str(example.id)]["reference"] = example.outputs
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return results
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def _collect_metrics(self) -> Dict[str, _RowResult]:
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def _run_batch_evaluators(self, runs: Dict[str, Run]) -> List[dict]:
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evaluators = self.batch_evaluators
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if not evaluators:
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return []
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runs_list = [runs[str(example.id)] for example in self.examples]
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aggregate_feedback = []
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with concurrent.futures.ThreadPoolExecutor() as executor:
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for evaluator in evaluators:
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try:
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result = evaluator(runs_list, self.examples)
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if isinstance(result, EvaluationResult):
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result = result.dict()
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aggregate_feedback.append(cast(dict, result))
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executor.submit(
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self.client.create_feedback,
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**result,
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run_id=None,
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project_id=self.project.id,
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)
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except Exception as e:
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logger.error(
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f"Error running batch evaluator {repr(evaluator)}: {e}"
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)
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return aggregate_feedback
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def _collect_metrics(self) -> Tuple[Dict[str, _RowResult], Dict[str, Run]]:
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all_eval_results: dict = {}
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all_runs: dict = {}
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for c in self.configs:
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for callback in cast(list, c["callbacks"]):
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if isinstance(callback, EvaluatorCallbackHandler):
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@ -1077,20 +1119,28 @@ class _DatasetRunContainer:
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{
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"execution_time": execution_time,
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"run_id": run_id,
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"run": run,
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}
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)
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return cast(Dict[str, _RowResult], all_eval_results)
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all_runs[str(callback.example_id)] = run
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return cast(Dict[str, _RowResult], all_eval_results), all_runs
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def _collect_test_results(
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self,
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batch_results: List[Union[dict, str, LLMResult, ChatResult]],
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) -> TestResult:
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logger.info("Waiting for evaluators to complete.")
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wait_for_all_evaluators()
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all_eval_results = self._collect_metrics()
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all_eval_results, all_runs = self._collect_metrics()
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aggregate_feedback = None
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if self.batch_evaluators:
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logger.info("Running session evaluators.")
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aggregate_feedback = self._run_batch_evaluators(all_runs)
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results = self._merge_test_outputs(batch_results, all_eval_results)
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return TestResult(
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project_name=self.project.name,
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results=results,
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aggregate_metrics=aggregate_feedback,
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)
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def finish(self, batch_results: list, verbose: bool = False) -> TestResult:
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@ -1140,6 +1190,9 @@ class _DatasetRunContainer:
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tags = tags or []
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for k, v in (project.metadata.get("git") or {}).items():
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tags.append(f"git:{k}={v}")
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run_metadata = {"dataset_version": project.metadata["dataset_version"]}
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if revision_id:
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run_metadata["revision_id"] = revision_id
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wrapped_model = _wrap_in_chain_factory(llm_or_chain_factory)
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run_evaluators = _setup_evaluation(
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wrapped_model, examples, evaluation, dataset.data_type or DataType.kv
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@ -1164,7 +1217,7 @@ class _DatasetRunContainer:
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],
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tags=tags,
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max_concurrency=concurrency_level,
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metadata={"revision_id": revision_id} if revision_id else {},
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metadata=run_metadata,
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)
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for example in examples
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]
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@ -1174,6 +1227,7 @@ class _DatasetRunContainer:
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wrapped_model=wrapped_model,
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examples=examples,
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configs=configs,
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batch_evaluators=evaluation.batch_evaluators if evaluation else None,
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)
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142
libs/langchain/poetry.lock
generated
142
libs/langchain/poetry.lock
generated
@ -3038,6 +3038,7 @@ files = [
|
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|
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{file = "jq-1.6.0-cp37-cp37m-musllinux_1_1_x86_64.whl", hash = "sha256:780eb6383fbae12afa819ef676fc93e1548ae4b076c004a393af26a04b460742"},
|
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{file = "jq-1.6.0-cp38-cp38-macosx_10_9_x86_64.whl", hash = "sha256:08ded6467f4ef89fec35b2bf310f210f8cd13fbd9d80e521500889edf8d22441"},
|
||||
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|
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{file = "jq-1.6.0-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:984f33862af285ad3e41e23179ac4795f1701822473e1a26bf87ff023e5a89ea"},
|
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{file = "jq-1.6.0-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:f42264fafc6166efb5611b5d4cb01058887d050a6c19334f6a3f8a13bb369df5"},
|
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{file = "jq-1.6.0-cp38-cp38-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:a67154f150aaf76cc1294032ed588436eb002097dd4fd1e283824bf753a05080"},
|
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@ -3121,7 +3122,6 @@ optional = false
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python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*, !=3.5.*, !=3.6.*"
|
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files = [
|
||||
{file = "jsonpointer-2.4-py2.py3-none-any.whl", hash = "sha256:15d51bba20eea3165644553647711d150376234112651b4f1811022aecad7d7a"},
|
||||
{file = "jsonpointer-2.4.tar.gz", hash = "sha256:585cee82b70211fa9e6043b7bb89db6e1aa49524340dde8ad6b63206ea689d88"},
|
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]
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[[package]]
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@ -3525,16 +3525,17 @@ url = "../text-splitters"
|
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[[package]]
|
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name = "langsmith"
|
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version = "0.1.1"
|
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version = "0.1.14"
|
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description = "Client library to connect to the LangSmith LLM Tracing and Evaluation Platform."
|
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optional = false
|
||||
python-versions = ">=3.8.1,<4.0"
|
||||
files = [
|
||||
{file = "langsmith-0.1.1-py3-none-any.whl", hash = "sha256:10ff2b977a41e3f6351d1a4239d9bd57af0547aa909e839d2791e16cc197a6f9"},
|
||||
{file = "langsmith-0.1.1.tar.gz", hash = "sha256:09df0c2ca9085105f97a4e4f281b083e312c99d162f3fe2b2d5eefd5c3692e60"},
|
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{file = "langsmith-0.1.14-py3-none-any.whl", hash = "sha256:ecb243057d2a43c2da0524fe395585bc3421bb5d24f1cdd53eb06fbe63e43a69"},
|
||||
{file = "langsmith-0.1.14.tar.gz", hash = "sha256:b95f267d25681f4c9862bb68236fba8a57a60ec7921ecfdaa125936807e51bde"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
orjson = ">=3.9.14,<4.0.0"
|
||||
pydantic = ">=1,<3"
|
||||
requests = ">=2,<3"
|
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|
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@ -3751,16 +3752,6 @@ files = [
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||||
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||||
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||||
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||||
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||||
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||||
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|
||||
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||||
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|
||||
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|
||||
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|
||||
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||||
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@ -4723,61 +4714,61 @@ requests = ">=2,<3"
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||||
|
||||
[[package]]
|
||||
name = "orjson"
|
||||
version = "3.9.10"
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||||
version = "3.9.15"
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description = "Fast, correct Python JSON library supporting dataclasses, datetimes, and numpy"
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optional = true
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optional = false
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python-versions = ">=3.8"
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files = [
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[[package]]
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@ -5287,8 +5278,6 @@ files = [
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|
||||
{file = "PyYAML-6.0.1.tar.gz", hash = "sha256:bfdf460b1736c775f2ba9f6a92bca30bc2095067b8a9d77876d1fad6cc3b4a43"},
|
||||
@ -9158,4 +9132,4 @@ text-helpers = ["chardet"]
|
||||
[metadata]
|
||||
lock-version = "2.0"
|
||||
python-versions = ">=3.8.1,<4.0"
|
||||
content-hash = "86f58f5567562ea0058941a6066f9c687f1bc39117df7a0308b9006f190e8197"
|
||||
content-hash = "095a661dc5f767d2a3c92541d66c2d3070e86fb455b98f313e6c9a40b699b4ef"
|
||||
|
||||
@ -15,7 +15,7 @@ python = ">=3.8.1,<4.0"
|
||||
langchain-core = ">=0.1.28,<0.2"
|
||||
langchain-text-splitters = ">=0.0.1,<0.1"
|
||||
langchain-community = ">=0.0.25,<0.1"
|
||||
langsmith = "^0.1.0"
|
||||
langsmith = "^0.1.14"
|
||||
pydantic = ">=1,<3"
|
||||
SQLAlchemy = ">=1.4,<3"
|
||||
requests = "^2"
|
||||
|
||||
Loading…
Reference in New Issue
Block a user