Context F1

Balances between precision and recall for context retrieval, increasing it means a better signal-to-noise ratio. Uses traditional string distance metrics.

POST /ragas/context_f1/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(
        "ragas/context_f1",
        index=index,
        data={
            "contexts": row["contexts"],
            "expected_contexts": row["expected_contexts"],
        },
        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

contexts · string[] · required

Array of context strings used for RAG evaluation

expected_contexts · string[] · required

The expected contexts for comparison

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