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Python
Experiment
import langwatchdf = 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/competitor_llm", index=index, data={ "output": output, "input": row["input"], }, settings={} )
[ { "status": "processed", "score": 123, "passed": true, "label": "<string>", "details": "<string>", "cost": { "currency": "<string>", "amount": 123 } } ]
This evaluator use an LLM-as-judge to check if the conversation is related to competitors, without having to name them explicitly
API key for authentication
Show child attributes
Optional trace ID to associate this evaluation with a trace
Successful evaluation
processed
skipped
error
Numeric score from the evaluation
Whether the evaluation passed
Label assigned by the evaluation
Additional details about the evaluation
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