How to define a summary evaluator

Some metrics can only be defined on the entire experiment level as opposed to the individual runs of the experiment. For example, you may want to compute the overall pass rate or f1 score of your evaluation target across all examples in the dataset. These are called summary_evaluators.

Basic example

Below, we'll implement a very simple summary evaluator that computes overall pass rate:

Python

def pass_50(outputs: list[dict], reference_outputs: list[dict]) -> bool:
    """Pass if >50% of all results are correct."""
    correct = sum([out["class"] == ref["label"] for out, ref in zip(outputs, reference_outputs)])
    return correct / len(outputs) > 0.5

TypeScript

function summaryEval({ outputs, referenceOutputs }: { outputs: Record<string, any>[], referenceOutputs?: Record<string, any>[]}) {
  let correct = 0;
  
  for (let i = 0; i < outputs.length; i++) {
    if (outputs[i]["output"] === referenceOutputs[i]["label"]) {
      correct += 1;
    }
  }
  
  return { key: "pass", score: correct / outputs.length > 0.5 };
}

You can then pass this evaluator to the evaluate method as follows:

Python

from langsmith import Client

ls_client = Client()
dataset = ls_client.clone_public_dataset(
  "https://smith.langchain.com/public/3d6831e6-1680-4c88-94df-618c8e01fc55/d"
)

def bad_classifier(inputs: dict) -> dict:
  return {"class": "Not toxic"}
  
def correct(outputs: dict, reference_outputs: dict) -> bool:
  """Row-level correctness evaluator."""
  return outputs["class"] == reference_outputs["label"]

results = ls_client.evaluate(
    bad_classified,
    data=dataset,
    evaluators=[correct],
    summary_evaluators=[pass_50],
)

TypeScript

import { Client } from "langsmith";
import { evaluate } from "langsmith/evaluation";
import type { EvaluationResult } from "langsmith/evaluation";

const client = new Client();
const datasetName = "Toxic queries";
const dataset = await client.clonePublicDataset(
  "https://smith.langchain.com/public/3d6831e6-1680-4c88-94df-618c8e01fc55/d",
  { datasetName: datasetName }
);

function correct({ outputs, referenceOutputs }: { outputs: Record<string, any>, referenceOutputs?: Record<string, any> }): EvaluationResult {
  const score = outputs["class"] === referenceOutputs?["label"];
  return { key: "correct", score };
}

function badClassifier(inputs: Record<string, any>): { class: string } {
  return { class: "Not toxic" };
}

await evaluate(badClassifier, {
  data: datasetName,
  evaluators: [correct],
  summaryEvaluators: [summaryEval],
  experimentPrefix: "Toxic Queries",
});

In the LangSmith UI, you'll the summary evaluator's score displayed with the corresponding key.

Summary evaluator args

Summary evaluator functions must have specific argument names. They can take any subset of the following arguments:

• inputs: list[dict]: A list of the inputs corresponding to a single example in a dataset.
• outputs: list[dict]: A list of the dict outputs produced by each experiment on the given inputs.
• reference_outputs/referenceOutputs: list[dict]: A list of the reference outputs associated with the example, if available.
• runs: list[Run]: A list of the full Run objects generated by the two experiments on the given example. Use this if you need access to intermediate steps or metadata about each run.
• examples: list[Example]: All of the dataset Example objects, including the example inputs, outputs (if available), and metdata (if available).

Summary evaluator output

Summary evaluators are expected to return one of the following types:

Python and JS/TS

• dict: dicts of the form {"score": ..., "name": ...} allow you to pass a numeric or boolean score and metric name.

Currently Python only

• int | float | bool: this is interepreted as an continuous metric that can be averaged, sorted, etc. The function name is used as the name of the metric.