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(
"legacy/ragas_context_recall",
index=index,
data={
"input": row["input"],
"contexts": row["contexts"],
"expected_output": row["expected_output"],
},
settings={}
)[
{
"score": 123,
"passed": true,
"label": "<string>",
"details": "<string>",
"cost": {
"currency": "<string>",
"amount": 123
}
}
]RAG Quality
Ragas Context Recall
This evaluator measures the extent to which the retrieved context aligns with the annotated answer, treated as the ground truth. Higher values indicate better performance.
POST
/
legacy
/
ragas_context_recall
/
evaluate
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(
"legacy/ragas_context_recall",
index=index,
data={
"input": row["input"],
"contexts": row["contexts"],
"expected_output": row["expected_output"],
},
settings={}
)[
{
"score": 123,
"passed": true,
"label": "<string>",
"details": "<string>",
"cost": {
"currency": "<string>",
"amount": 123
}
}
]Authorizations
API key for authentication
Body
application/json
Response
Successful evaluation
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