langchain.evaluation.exact_match.base
.ExactMatchStringEvaluator¶
- class langchain.evaluation.exact_match.base.ExactMatchStringEvaluator(*, ignore_case: bool = False, ignore_punctuation: bool = False, ignore_numbers: bool = False, **kwargs: Any)[source]¶
Compute an exact match between the prediction and the reference.
Examples
>>> evaluator = ExactMatchChain() >>> evaluator.evaluate_strings( prediction="Mindy is the CTO", reference="Mindy is the CTO", ) # This will return {'score': 1.0}
>>> evaluator.evaluate_strings( prediction="Mindy is the CTO", reference="Mindy is the CEO", ) # This will return {'score': 0.0}
Attributes
evaluation_name
Get the evaluation name.
input_keys
Get the input keys.
requires_input
This evaluator does not require input.
requires_reference
This evaluator requires a reference.
Methods
__init__
(*[, ignore_case, ...])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.
- Parameters
ignore_case (bool) –
ignore_punctuation (bool) –
ignore_numbers (bool) –
kwargs (Any) –
- __init__(*, ignore_case: bool = False, ignore_punctuation: bool = False, ignore_numbers: bool = False, **kwargs: Any)[source]¶
- Parameters
ignore_case (bool) –
ignore_punctuation (bool) –
ignore_numbers (bool) –
kwargs (Any) –
- async aevaluate_strings(*, prediction: str, reference: Optional[str] = None, input: Optional[str] = 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: Optional[str] = None, input: Optional[str] = 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