[Evals] Session-level feedback (#18463)

Co-authored-by: William Fu-Hinthorn <13333726+hinthornw@users.noreply.github.com>
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Harrison Chase 2024-03-03 17:18:29 -08:00 committed by GitHub
parent b051bba1a9
commit 73d653324f
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7 changed files with 236 additions and 851 deletions

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@ -1,12 +1,14 @@
"""Configuration for run evaluators.""" """Configuration for run evaluators."""
from typing import Any, Dict, List, Optional, Union from typing import Any, Callable, Dict, List, Optional, Sequence, Union
from langchain_core.embeddings import Embeddings from langchain_core.embeddings import Embeddings
from langchain_core.language_models import BaseLanguageModel from langchain_core.language_models import BaseLanguageModel
from langchain_core.prompts import BasePromptTemplate from langchain_core.prompts import BasePromptTemplate
from langchain_core.pydantic_v1 import BaseModel, Field from langchain_core.pydantic_v1 import BaseModel, Field
from langsmith import RunEvaluator from langsmith import RunEvaluator
from langsmith.evaluation.evaluator import EvaluationResult, EvaluationResults
from langsmith.schemas import Example, Run
from langchain.evaluation.criteria.eval_chain import CRITERIA_TYPE from langchain.evaluation.criteria.eval_chain import CRITERIA_TYPE
from langchain.evaluation.embedding_distance.base import ( from langchain.evaluation.embedding_distance.base import (
@ -17,6 +19,14 @@ from langchain.evaluation.string_distance.base import (
StringDistance as StringDistanceEnum, StringDistance as StringDistanceEnum,
) )
RUN_EVALUATOR_LIKE = Callable[
[Run, Optional[Example]], Union[EvaluationResult, EvaluationResults, dict]
]
BATCH_EVALUATOR_LIKE = Callable[
[Sequence[Run], Optional[Sequence[Example]]],
Union[EvaluationResult, EvaluationResults, dict],
]
class EvalConfig(BaseModel): class EvalConfig(BaseModel):
"""Configuration for a given run evaluator. """Configuration for a given run evaluator.
@ -76,12 +86,16 @@ class SingleKeyEvalConfig(EvalConfig):
return kwargs return kwargs
CUSTOM_EVALUATOR_TYPE = Union[RUN_EVALUATOR_LIKE, RunEvaluator, StringEvaluator]
SINGLE_EVAL_CONFIG_TYPE = Union[EvaluatorType, str, EvalConfig]
class RunEvalConfig(BaseModel): class RunEvalConfig(BaseModel):
"""Configuration for a run evaluation. """Configuration for a run evaluation.
Parameters Parameters
---------- ----------
evaluators : List[Union[EvaluatorType, EvalConfig]] evaluators : List[Union[EvaluatorType, EvalConfig, RunEvaluator, Callable]]
Configurations for which evaluators to apply to the dataset run. Configurations for which evaluators to apply to the dataset run.
Each can be the string of an :class:`EvaluatorType <langchain.evaluation.schema.EvaluatorType>`, such Each can be the string of an :class:`EvaluatorType <langchain.evaluation.schema.EvaluatorType>`, such
as EvaluatorType.QA, the evaluator type string ("qa"), or a configuration for a as EvaluatorType.QA, the evaluator type string ("qa"), or a configuration for a
@ -107,9 +121,12 @@ class RunEvalConfig(BaseModel):
The language model to pass to any evaluators that use a language model. The language model to pass to any evaluators that use a language model.
""" # noqa: E501 """ # noqa: E501
evaluators: List[Union[EvaluatorType, str, EvalConfig]] = Field( evaluators: List[
default_factory=list Union[
) SINGLE_EVAL_CONFIG_TYPE,
CUSTOM_EVALUATOR_TYPE,
]
] = Field(default_factory=list)
"""Configurations for which evaluators to apply to the dataset run. """Configurations for which evaluators to apply to the dataset run.
Each can be the string of an Each can be the string of an
:class:`EvaluatorType <langchain.evaluation.schema.EvaluatorType>`, such :class:`EvaluatorType <langchain.evaluation.schema.EvaluatorType>`, such
@ -117,8 +134,15 @@ class RunEvalConfig(BaseModel):
given evaluator given evaluator
(e.g., (e.g.,
:class:`RunEvalConfig.QA <langchain.smith.evaluation.config.RunEvalConfig.QA>`).""" # noqa: E501 :class:`RunEvalConfig.QA <langchain.smith.evaluation.config.RunEvalConfig.QA>`).""" # noqa: E501
custom_evaluators: Optional[List[Union[RunEvaluator, StringEvaluator]]] = None custom_evaluators: Optional[List[CUSTOM_EVALUATOR_TYPE]] = None
"""Custom evaluators to apply to the dataset run.""" """Custom evaluators to apply to the dataset run."""
batch_evaluators: Optional[List[BATCH_EVALUATOR_LIKE]] = None
"""Evaluators that run on an aggregate/batch level.
These generate 1 or more metrics that are assigned to the full test run.
As a result, they are not associated with individual traces.
"""
reference_key: Optional[str] = None reference_key: Optional[str] = None
"""The key in the dataset run to use as the reference string. """The key in the dataset run to use as the reference string.
If not provided, we will attempt to infer automatically.""" If not provided, we will attempt to infer automatically."""

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@ -2,6 +2,7 @@
from __future__ import annotations from __future__ import annotations
import concurrent.futures
import dataclasses import dataclasses
import functools import functools
import inspect import inspect
@ -35,9 +36,15 @@ from langchain_core.tracers.evaluation import (
from langchain_core.tracers.langchain import LangChainTracer from langchain_core.tracers.langchain import LangChainTracer
from langsmith.client import Client from langsmith.client import Client
from langsmith.env import get_git_info, get_langchain_env_var_metadata from langsmith.env import get_git_info, get_langchain_env_var_metadata
from langsmith.evaluation import EvaluationResult, RunEvaluator from langsmith.evaluation import (
EvaluationResult,
RunEvaluator,
)
from langsmith.evaluation import (
run_evaluator as run_evaluator_dec,
)
from langsmith.run_helpers import as_runnable, is_traceable_function from langsmith.run_helpers import as_runnable, is_traceable_function
from langsmith.schemas import Dataset, DataType, Example, TracerSession from langsmith.schemas import Dataset, DataType, Example, Run, TracerSession
from langsmith.utils import LangSmithError from langsmith.utils import LangSmithError
from requests import HTTPError from requests import HTTPError
from typing_extensions import TypedDict from typing_extensions import TypedDict
@ -513,7 +520,10 @@ def _determine_reference_key(
def _construct_run_evaluator( def _construct_run_evaluator(
eval_config: Union[EvaluatorType, str, smith_eval_config.EvalConfig], eval_config: Union[
smith_eval_config.SINGLE_EVAL_CONFIG_TYPE,
smith_eval_config.CUSTOM_EVALUATOR_TYPE,
],
eval_llm: Optional[BaseLanguageModel], eval_llm: Optional[BaseLanguageModel],
run_type: str, run_type: str,
data_type: DataType, data_type: DataType,
@ -522,12 +532,14 @@ def _construct_run_evaluator(
input_key: Optional[str], input_key: Optional[str],
prediction_key: Optional[str], prediction_key: Optional[str],
) -> RunEvaluator: ) -> RunEvaluator:
if isinstance(eval_config, RunEvaluator):
return eval_config
if isinstance(eval_config, (EvaluatorType, str)): if isinstance(eval_config, (EvaluatorType, str)):
if not isinstance(eval_config, EvaluatorType): if not isinstance(eval_config, EvaluatorType):
eval_config = EvaluatorType(eval_config) eval_config = EvaluatorType(eval_config)
evaluator_ = load_evaluator(eval_config, llm=eval_llm) evaluator_ = load_evaluator(eval_config, llm=eval_llm)
eval_type_tag = eval_config.value eval_type_tag = eval_config.value
else: elif isinstance(eval_config, smith_eval_config.EvalConfig):
kwargs = {"llm": eval_llm, **eval_config.get_kwargs()} kwargs = {"llm": eval_llm, **eval_config.get_kwargs()}
evaluator_ = load_evaluator(eval_config.evaluator_type, **kwargs) evaluator_ = load_evaluator(eval_config.evaluator_type, **kwargs)
eval_type_tag = eval_config.evaluator_type.value eval_type_tag = eval_config.evaluator_type.value
@ -536,6 +548,11 @@ def _construct_run_evaluator(
input_key = eval_config.input_key or input_key input_key = eval_config.input_key or input_key
prediction_key = eval_config.prediction_key or prediction_key prediction_key = eval_config.prediction_key or prediction_key
reference_key = eval_config.reference_key or reference_key reference_key = eval_config.reference_key or reference_key
elif callable(eval_config):
# Assume we can decorate
return run_evaluator_dec(eval_config)
else:
raise ValueError(f"Unknown evaluator type: {type(eval_config)}")
if isinstance(evaluator_, StringEvaluator): if isinstance(evaluator_, StringEvaluator):
if evaluator_.requires_reference and reference_key is None: if evaluator_.requires_reference and reference_key is None:
@ -600,13 +617,9 @@ def _load_run_evaluators(
""" """
run_evaluators = [] run_evaluators = []
input_key, prediction_key, reference_key = None, None, None input_key, prediction_key, reference_key = None, None, None
if ( if config.evaluators or (
config.evaluators config.custom_evaluators
or any([isinstance(e, EvaluatorType) for e in config.evaluators]) and any([isinstance(e, StringEvaluator) for e in config.custom_evaluators])
or (
config.custom_evaluators
and any([isinstance(e, StringEvaluator) for e in config.custom_evaluators])
)
): ):
input_key, prediction_key, reference_key = _get_keys( input_key, prediction_key, reference_key = _get_keys(
config, run_inputs, run_outputs, example_outputs config, run_inputs, run_outputs, example_outputs
@ -638,6 +651,8 @@ def _load_run_evaluators(
reference_key=reference_key, reference_key=reference_key,
) )
) )
elif callable(custom_evaluator):
run_evaluators.append(run_evaluator_dec(custom_evaluator))
else: else:
raise ValueError( raise ValueError(
f"Unsupported custom evaluator: {custom_evaluator}." f"Unsupported custom evaluator: {custom_evaluator}."
@ -1032,6 +1047,7 @@ class _DatasetRunContainer:
wrapped_model: MCF wrapped_model: MCF
examples: List[Example] examples: List[Example]
configs: List[RunnableConfig] configs: List[RunnableConfig]
batch_evaluators: Optional[List[smith_eval_config.BATCH_EVALUATOR_LIKE]] = None
def _merge_test_outputs( def _merge_test_outputs(
self, self,
@ -1055,8 +1071,34 @@ class _DatasetRunContainer:
results[str(example.id)]["reference"] = example.outputs results[str(example.id)]["reference"] = example.outputs
return results return results
def _collect_metrics(self) -> Dict[str, _RowResult]: def _run_batch_evaluators(self, runs: Dict[str, Run]) -> List[dict]:
evaluators = self.batch_evaluators
if not evaluators:
return []
runs_list = [runs[str(example.id)] for example in self.examples]
aggregate_feedback = []
with concurrent.futures.ThreadPoolExecutor() as executor:
for evaluator in evaluators:
try:
result = evaluator(runs_list, self.examples)
if isinstance(result, EvaluationResult):
result = result.dict()
aggregate_feedback.append(cast(dict, result))
executor.submit(
self.client.create_feedback,
**result,
run_id=None,
project_id=self.project.id,
)
except Exception as e:
logger.error(
f"Error running batch evaluator {repr(evaluator)}: {e}"
)
return aggregate_feedback
def _collect_metrics(self) -> Tuple[Dict[str, _RowResult], Dict[str, Run]]:
all_eval_results: dict = {} all_eval_results: dict = {}
all_runs: dict = {}
for c in self.configs: for c in self.configs:
for callback in cast(list, c["callbacks"]): for callback in cast(list, c["callbacks"]):
if isinstance(callback, EvaluatorCallbackHandler): if isinstance(callback, EvaluatorCallbackHandler):
@ -1077,20 +1119,28 @@ class _DatasetRunContainer:
{ {
"execution_time": execution_time, "execution_time": execution_time,
"run_id": run_id, "run_id": run_id,
"run": run,
} }
) )
return cast(Dict[str, _RowResult], all_eval_results) all_runs[str(callback.example_id)] = run
return cast(Dict[str, _RowResult], all_eval_results), all_runs
def _collect_test_results( def _collect_test_results(
self, self,
batch_results: List[Union[dict, str, LLMResult, ChatResult]], batch_results: List[Union[dict, str, LLMResult, ChatResult]],
) -> TestResult: ) -> TestResult:
logger.info("Waiting for evaluators to complete.")
wait_for_all_evaluators() wait_for_all_evaluators()
all_eval_results = self._collect_metrics() all_eval_results, all_runs = self._collect_metrics()
aggregate_feedback = None
if self.batch_evaluators:
logger.info("Running session evaluators.")
aggregate_feedback = self._run_batch_evaluators(all_runs)
results = self._merge_test_outputs(batch_results, all_eval_results) results = self._merge_test_outputs(batch_results, all_eval_results)
return TestResult( return TestResult(
project_name=self.project.name, project_name=self.project.name,
results=results, results=results,
aggregate_metrics=aggregate_feedback,
) )
def finish(self, batch_results: list, verbose: bool = False) -> TestResult: def finish(self, batch_results: list, verbose: bool = False) -> TestResult:
@ -1140,6 +1190,9 @@ class _DatasetRunContainer:
tags = tags or [] tags = tags or []
for k, v in (project.metadata.get("git") or {}).items(): for k, v in (project.metadata.get("git") or {}).items():
tags.append(f"git:{k}={v}") tags.append(f"git:{k}={v}")
run_metadata = {"dataset_version": project.metadata["dataset_version"]}
if revision_id:
run_metadata["revision_id"] = revision_id
wrapped_model = _wrap_in_chain_factory(llm_or_chain_factory) wrapped_model = _wrap_in_chain_factory(llm_or_chain_factory)
run_evaluators = _setup_evaluation( run_evaluators = _setup_evaluation(
wrapped_model, examples, evaluation, dataset.data_type or DataType.kv wrapped_model, examples, evaluation, dataset.data_type or DataType.kv
@ -1164,7 +1217,7 @@ class _DatasetRunContainer:
], ],
tags=tags, tags=tags,
max_concurrency=concurrency_level, max_concurrency=concurrency_level,
metadata={"revision_id": revision_id} if revision_id else {}, metadata=run_metadata,
) )
for example in examples for example in examples
] ]
@ -1174,6 +1227,7 @@ class _DatasetRunContainer:
wrapped_model=wrapped_model, wrapped_model=wrapped_model,
examples=examples, examples=examples,
configs=configs, configs=configs,
batch_evaluators=evaluation.batch_evaluators if evaluation else None,
) )

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@ -3121,7 +3122,6 @@ optional = false
python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*, !=3.5.*, !=3.6.*" python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*, !=3.5.*, !=3.6.*"
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[[package]] [[package]]
@ -3525,16 +3525,17 @@ url = "../text-splitters"
[[package]] [[package]]
name = "langsmith" name = "langsmith"
version = "0.1.1" version = "0.1.14"
description = "Client library to connect to the LangSmith LLM Tracing and Evaluation Platform." description = "Client library to connect to the LangSmith LLM Tracing and Evaluation Platform."
optional = false optional = false
python-versions = ">=3.8.1,<4.0" python-versions = ">=3.8.1,<4.0"
files = [ files = [
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[package.dependencies] [package.dependencies]
orjson = ">=3.9.14,<4.0.0"
pydantic = ">=1,<3" pydantic = ">=1,<3"
requests = ">=2,<3" requests = ">=2,<3"
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[metadata] [metadata]
lock-version = "2.0" lock-version = "2.0"
python-versions = ">=3.8.1,<4.0" python-versions = ">=3.8.1,<4.0"
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View File

@ -15,7 +15,7 @@ python = ">=3.8.1,<4.0"
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langchain-text-splitters = ">=0.0.1,<0.1" langchain-text-splitters = ">=0.0.1,<0.1"
langchain-community = ">=0.0.25,<0.1" langchain-community = ">=0.0.25,<0.1"
langsmith = "^0.1.0" langsmith = "^0.1.14"
pydantic = ">=1,<3" pydantic = ">=1,<3"
SQLAlchemy = ">=1.4,<3" SQLAlchemy = ">=1.4,<3"
requests = "^2" requests = "^2"