JsonSchemaEvaluator

class langchain.evaluation.parsing.json_schema.JsonSchemaEvaluator(**kwargs: Any)

An evaluator that validates a JSON prediction against a JSON schema reference.

This evaluator checks if a given JSON prediction conforms to the provided JSON schema. If the prediction is valid, the score is True (no errors). Otherwise, the score is False (error occurred).

requires_input

Whether the evaluator requires input.

Type:

bool

requires_reference

Whether the evaluator requires reference.

Type:

bool

evaluation_name

The name of the evaluation.

Type:

str

Examples

evaluator = JsonSchemaEvaluator() result = evaluator.evaluate_strings(

prediction=’{“name”: “John”, “age”: 30}’, reference={

“type”: “object”, “properties”: {

“name”: {“type”: “string”}, “age”: {“type”: “integer”}

}

}

) assert result[“score”] is not None

Initializes the JsonSchemaEvaluator.

Parameters:

kwargs (Any) – Additional keyword arguments.

Raises:

ImportError – If the jsonschema package is not installed.

Attributes

evaluation_name	Returns the name of the evaluation.
requires_input	Returns whether the evaluator requires input.
requires_reference	Returns whether the evaluator requires reference.

Methods

__init__(**kwargs)	Initializes the JsonSchemaEvaluator.
aevaluate_strings(*, prediction[, ...])	Asynchronously evaluate Chain or LLM output, based on optional input and label.
evaluate_strings(*, prediction[, reference, ...])	Evaluate Chain or LLM output, based on optional input and label.

__init__(**kwargs: Any) → None

Initializes the JsonSchemaEvaluator.

Parameters:

kwargs (Any) – Additional keyword arguments.

Raises:

ImportError – If the jsonschema package is not installed.

Return type:

None

async aevaluate_strings(*, prediction: str, reference: str | None = None, input: str | None = None, **kwargs: Any) → dict

Asynchronously evaluate Chain or LLM output, based on optional input and label.

Parameters:

• prediction (str) – The LLM or chain prediction to evaluate.

• reference (Optional[str], optional) – The reference label to evaluate against.

• input (Optional[str], optional) – The input to consider during evaluation.

• kwargs (Any) – Additional keyword arguments, including callbacks, tags, etc.

Returns:

The evaluation results containing the score or value.

Return type:

dict

evaluate_strings(*, prediction: str, reference: str | None = None, input: str | None = None, **kwargs: Any) → dict

Evaluate Chain or LLM output, based on optional input and label.

Parameters:

• prediction (str) – The LLM or chain prediction to evaluate.

• reference (Optional[str], optional) – The reference label to evaluate against.

• input (Optional[str], optional) – The input to consider during evaluation.

• kwargs (Any) – Additional keyword arguments, including callbacks, tags, etc.

Returns:

The evaluation results containing the score or value.

Return type:

dict