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. Instead of taking in a single Run and Example, these evaluators take a list of each.

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

Python

from langsmith.schemas import Example, Run

def summary_eval(runs: list[Run], examples: list[Example]) -> dict:
    correct = 0
    for i, run in enumerate(runs):
        if run.outputs["output"] == examples[i].outputs["label"]:
            correct += 1
    if correct / len(runs) > 0.5:
        return {"key": "pass", "score": True}
    else:
        return {"key": "pass", "score": False}

TypeScript

import { Run, Example } from "langsmith/schemas";

function summaryEval(runs: Run[], examples: Example[]) {
  let correct = 0;
  
  for (let i = 0; i < runs.length; i++) {
    if (runs[i].outputs["output"] === examples[i].outputs["label"]) {
      correct += 1;
    }
  }
  
  return { key: "pass", score: correct / runs.length > 0.5 };
}

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

Python

results = evaluate(
    lambda inputs: label_query(inputs["text"]),
    data=dataset_name,
    evaluators=[correct_label],
    summary_evaluators=[summary_eval],
    experiment_prefix="Toxic Queries",
)

TypeScript

await evaluate((inputs) => labelQuery(inputs["input"]), {
  data: datasetName,
  evaluators: [correctLabel],
  summaryEvaluators: [summaryEval],
  experimentPrefix: "Toxic Queries",
});

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