LLM Answer Match

Uses an LLM to check if the generated output answers a question correctly the same way as the expected output, even if their style is different.

POST /langevals/llm_answer_match/evaluate

Python

Experiment

import langwatch df = langwatch.datasets.get_dataset("dataset-id").to_pandas() experiment = langwatch.experiment.init("my-experiment") for index, row in experiment.loop(df.iterrows()): # your execution code here experiment.evaluate( "langevals/llm_answer_match", index=index, data={ "output": output, "expected_output": row["expected_output"], "input": row["input"], }, settings={} )

200

[
  {
    "status": "processed",
    "score": 123,
    "passed": true,
    "label": "<string>",
    "details": "<string>",
    "cost": {
      "currency": "<string>",
      "amount": 123
    }
  }
]

Authorizations

X-Auth-Token · string · header · required

API key for authentication

Body

application/json

data · object · required

Show child attributes

trace_id · string

Optional trace ID to associate this evaluation with a trace

settings · object

Show child attributes

Response

200

application/json

Successful evaluation

status · enum<string>

Available options: processed, skipped, error

score · number

Numeric score from the evaluation

passed · boolean

Whether the evaluation passed

label · string

Label assigned by the evaluation

details · string

Additional details about the evaluation

cost · object

Show child attributes