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ã @ s$ d Z ddlmZmZmZ g d¢ZdS )aç **LangSmith** utilities.
This module provides utilities for connecting to `LangSmith `_. For more information on LangSmith, see the `LangSmith documentation `_.
**Evaluation**
LangSmith helps you evaluate Chains and other language model application components using a number of LangChain evaluators.
An example of this is shown below, assuming you've created a LangSmith dataset called ````:
.. code-block:: python
from langsmith import Client
from langchain_community.chat_models import ChatOpenAI
from langchain.chains import LLMChain
from langchain.smith import RunEvalConfig, run_on_dataset
# Chains may have memory. Passing in a constructor function lets the
# evaluation framework avoid cross-contamination between runs.
def construct_chain():
llm = ChatOpenAI(temperature=0)
chain = LLMChain.from_string(
llm,
"What's the answer to {your_input_key}"
)
return chain
# Load off-the-shelf evaluators via config or the EvaluatorType (string or enum)
evaluation_config = RunEvalConfig(
evaluators=[
"qa", # "Correctness" against a reference answer
"embedding_distance",
RunEvalConfig.Criteria("helpfulness"),
RunEvalConfig.Criteria({
"fifth-grader-score": "Do you have to be smarter than a fifth grader to answer this question?"
}),
]
)
client = Client()
run_on_dataset(
client,
"",
construct_chain,
evaluation=evaluation_config,
)
You can also create custom evaluators by subclassing the
:class:`StringEvaluator `
or LangSmith's `RunEvaluator` classes.
.. code-block:: python
from typing import Optional
from langchain.evaluation import StringEvaluator
class MyStringEvaluator(StringEvaluator):
@property
def requires_input(self) -> bool:
return False
@property
def requires_reference(self) -> bool:
return True
@property
def evaluation_name(self) -> str:
return "exact_match"
def _evaluate_strings(self, prediction, reference=None, input=None, **kwargs) -> dict:
return {"score": prediction == reference}
evaluation_config = RunEvalConfig(
custom_evaluators = [MyStringEvaluator()],
)
run_on_dataset(
client,
"",
construct_chain,
evaluation=evaluation_config,
)
**Primary Functions**
- :func:`arun_on_dataset `: Asynchronous function to evaluate a chain, agent, or other LangChain component over a dataset.
- :func:`run_on_dataset `: Function to evaluate a chain, agent, or other LangChain component over a dataset.
- :class:`RunEvalConfig `: Class representing the configuration for running evaluation. You can select evaluators by :class:`EvaluatorType ` or config, or you can pass in `custom_evaluators`
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