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POST
/
ragas
/
context_precision
/
evaluate
import langwatch

df = langwatch.dataset.get_dataset("dataset-id").to_pandas()

evaluation = langwatch.evaluation.init("my-incredible-evaluation")

for index, row in evaluation.loop(df.iterrows()):
    # your execution code here       
    evaluation.run(
        "ragas/context_precision",
        index=index,
        data={
            "contexts": row["contexts"],
            "expected_contexts": expected_contexts,
        },
        settings={}
    )
[
  {
    "status": "processed",
    "score": 123,
    "passed": true,
    "label": "<string>",
    "details": "<string>",
    "cost": {
      "currency": "<string>",
      "amount": 123
    },
    "raw_response": {},
    "error_type": "<string>",
    "traceback": [
      "<string>"
    ]
  }
]

Authorizations

​
X-Auth-Token
string
header
required

Body

application/json
​
data
object
required
​
settings
object

Evaluator settings

Response

Successful evaluation

​
status
enum<string>
required
Available options:
processed,
skipped,
error
​
score
number

Evaluation score

​
passed
boolean

Whether the evaluation passed

​
label
string

Evaluation label

​
details
string

Additional details about the evaluation

​
cost
object
​
raw_response
object

Raw response from the evaluator

​
error_type
string

Type of error if status is 'error'

​
traceback
string[]

Error traceback if status is 'error'