`langchain.smith.evaluation.config`.RunEvalConfig¶

class langchain.smith.evaluation.config.RunEvalConfig[source]¶

Bases: BaseModel

Configuration for a run evaluation.

Parameters

evaluators (List[Union[EvaluatorType, EvalConfig]]) – Configurations for which evaluators to apply to the dataset run. Each can be the string of an EvaluatorType, such as EvaluatorType.QA, the evaluator type string (“qa”), or a configuration for a given evaluator (e.g., RunEvalConfig.QA).
custom_evaluators (Optional[List[Union[RunEvaluator, StringEvaluator]]]) – Custom evaluators to apply to the dataset run.
reference_key (Optional[str]) – The key in the dataset run to use as the reference string. If not provided, it will be inferred automatically.
prediction_key (Optional[str]) – The key from the traced run’s outputs dictionary to use to represent the prediction. If not provided, it will be inferred automatically.
input_key (Optional[str]) – The key from the traced run’s inputs dictionary to use to represent the input. If not provided, it will be inferred automatically.
eval_llm (Optional[BaseLanguageModel]) – The language model to pass to any evaluators that use a language model.

Create a new model by parsing and validating input data from keyword arguments.

Raises ValidationError if the input data cannot be parsed to form a valid model.

param custom_evaluators: Optional[List[Union[langsmith.evaluation.evaluator.RunEvaluator, langchain.evaluation.schema.StringEvaluator]]] = None¶: Custom evaluators to apply to the dataset run.

param eval_llm: Optional[langchain_core.language_models.base.BaseLanguageModel] = None¶: The language model to pass to any evaluators that require one.

param evaluators: List[Union[langchain.evaluation.schema.EvaluatorType, str, langchain.smith.evaluation.config.EvalConfig]] [Optional]¶: Configurations for which evaluators to apply to the dataset run. Each can be the string of an EvaluatorType, such as EvaluatorType.QA, the evaluator type string (“qa”), or a configuration for a given evaluator (e.g., RunEvalConfig.QA).

param input_key: Optional[str] = None¶: The key from the traced run’s inputs dictionary to use to represent the input. If not provided, it will be inferred automatically.

param prediction_key: Optional[str] = None¶: The key from the traced run’s outputs dictionary to use to represent the prediction. If not provided, it will be inferred automatically.

param reference_key: Optional[str] = None¶: The key in the dataset run to use as the reference string. If not provided, we will attempt to infer automatically.

class CoTQA[source]¶

Bases: SingleKeyEvalConfig

Configuration for a context-based QA evaluator.

Parameters

prompt (Optional[BasePromptTemplate]) – The prompt template to use for generating the question.
llm (Optional[BaseLanguageModel]) – The language model to use for the evaluation chain.

Create a new model by parsing and validating input data from keyword arguments.

Raises ValidationError if the input data cannot be parsed to form a valid model.

param evaluator_type: langchain.evaluation.schema.EvaluatorType = EvaluatorType.CONTEXT_QA¶

param input_key: Optional[str] = None¶: The key from the traced run’s inputs dictionary to use to represent the input. If not provided, it will be inferred automatically.

param llm: Optional[langchain_core.language_models.base.BaseLanguageModel] = None¶

param prediction_key: Optional[str] = None¶: The key from the traced run’s outputs dictionary to use to represent the prediction. If not provided, it will be inferred automatically.

param prompt: Optional[langchain_core.prompts.base.BasePromptTemplate] = None¶

param reference_key: Optional[str] = None¶: The key in the dataset run to use as the reference string. If not provided, we will attempt to infer automatically.

classmethod construct(_fields_set: Optional[SetStr] = None, **values: Any) → Model¶: Creates a new model setting __dict__ and __fields_set__ from trusted or pre-validated data. Default values are respected, but no other validation is performed. Behaves as if Config.extra = ‘allow’ was set since it adds all passed values

copy(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, update: Optional[DictStrAny] = None, deep: bool = False) → Model¶

Duplicate a model, optionally choose which fields to include, exclude and change.

Parameters

include – fields to include in new model
exclude – fields to exclude from new model, as with values this takes precedence over include
update – values to change/add in the new model. Note: the data is not validated before creating the new model: you should trust this data
deep – set to True to make a deep copy of the model

Returns

new model instance

dict(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, by_alias: bool = False, skip_defaults: Optional[bool] = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False) → DictStrAny¶: Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.

classmethod from_orm(obj: Any) → Model¶

get_kwargs() → Dict[str, Any]¶

Get the keyword arguments for the load_evaluator call.

Returns: The keyword arguments for the load_evaluator call.
Return type: Dict[str, Any]

json(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, by_alias: bool = False, skip_defaults: Optional[bool] = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False, encoder: Optional[Callable[[Any], Any]] = None, models_as_dict: bool = True, **dumps_kwargs: Any) → unicode¶

Generate a JSON representation of the model, include and exclude arguments as per dict().

encoder is an optional function to supply as default to json.dumps(), other arguments as per json.dumps().

classmethod parse_file(path: Union[str, Path], *, content_type: unicode = None, encoding: unicode = 'utf8', proto: Protocol = None, allow_pickle: bool = False) → Model¶

classmethod parse_obj(obj: Any) → Model¶

classmethod parse_raw(b: Union[str, bytes], *, content_type: unicode = None, encoding: unicode = 'utf8', proto: Protocol = None, allow_pickle: bool = False) → Model¶

classmethod schema(by_alias: bool = True, ref_template: unicode = '#/definitions/{model}') → DictStrAny¶

classmethod schema_json(*, by_alias: bool = True, ref_template: unicode = '#/definitions/{model}', **dumps_kwargs: Any) → unicode¶

classmethod update_forward_refs(**localns: Any) → None¶: Try to update ForwardRefs on fields based on this Model, globalns and localns.

classmethod validate(value: Any) → Model¶

class ContextQA[source]¶

Bases: SingleKeyEvalConfig

Configuration for a context-based QA evaluator.

Parameters

prompt (Optional[BasePromptTemplate]) – The prompt template to use for generating the question.
llm (Optional[BaseLanguageModel]) – The language model to use for the evaluation chain.

Create a new model by parsing and validating input data from keyword arguments.

Raises ValidationError if the input data cannot be parsed to form a valid model.

param evaluator_type: langchain.evaluation.schema.EvaluatorType = EvaluatorType.CONTEXT_QA¶

param input_key: Optional[str] = None¶: The key from the traced run’s inputs dictionary to use to represent the input. If not provided, it will be inferred automatically.

param llm: Optional[langchain_core.language_models.base.BaseLanguageModel] = None¶

param prediction_key: Optional[str] = None¶: The key from the traced run’s outputs dictionary to use to represent the prediction. If not provided, it will be inferred automatically.

param prompt: Optional[langchain_core.prompts.base.BasePromptTemplate] = None¶

param reference_key: Optional[str] = None¶: The key in the dataset run to use as the reference string. If not provided, we will attempt to infer automatically.

classmethod construct(_fields_set: Optional[SetStr] = None, **values: Any) → Model¶: Creates a new model setting __dict__ and __fields_set__ from trusted or pre-validated data. Default values are respected, but no other validation is performed. Behaves as if Config.extra = ‘allow’ was set since it adds all passed values

Duplicate a model, optionally choose which fields to include, exclude and change.

Parameters

include – fields to include in new model
exclude – fields to exclude from new model, as with values this takes precedence over include
update – values to change/add in the new model. Note: the data is not validated before creating the new model: you should trust this data
deep – set to True to make a deep copy of the model

Returns

new model instance

dict(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, by_alias: bool = False, skip_defaults: Optional[bool] = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False) → DictStrAny¶: Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.

classmethod from_orm(obj: Any) → Model¶

get_kwargs() → Dict[str, Any]¶

Get the keyword arguments for the load_evaluator call.

Returns: The keyword arguments for the load_evaluator call.
Return type: Dict[str, Any]

Generate a JSON representation of the model, include and exclude arguments as per dict().

encoder is an optional function to supply as default to json.dumps(), other arguments as per json.dumps().

classmethod parse_file(path: Union[str, Path], *, content_type: unicode = None, encoding: unicode = 'utf8', proto: Protocol = None, allow_pickle: bool = False) → Model¶

classmethod parse_obj(obj: Any) → Model¶

classmethod parse_raw(b: Union[str, bytes], *, content_type: unicode = None, encoding: unicode = 'utf8', proto: Protocol = None, allow_pickle: bool = False) → Model¶

classmethod schema(by_alias: bool = True, ref_template: unicode = '#/definitions/{model}') → DictStrAny¶

classmethod schema_json(*, by_alias: bool = True, ref_template: unicode = '#/definitions/{model}', **dumps_kwargs: Any) → unicode¶

classmethod update_forward_refs(**localns: Any) → None¶: Try to update ForwardRefs on fields based on this Model, globalns and localns.

classmethod validate(value: Any) → Model¶

class Criteria[source]¶

Bases: SingleKeyEvalConfig

Configuration for a reference-free criteria evaluator.

Parameters

criteria (Optional[CRITERIA_TYPE]) – The criteria to evaluate.
llm (Optional[BaseLanguageModel]) – The language model to use for the evaluation chain.

Create a new model by parsing and validating input data from keyword arguments.

Raises ValidationError if the input data cannot be parsed to form a valid model.

param criteria: Optional[Union[Mapping[str, str], langchain.evaluation.criteria.eval_chain.Criteria, langchain.chains.constitutional_ai.models.ConstitutionalPrinciple]] = None¶

param evaluator_type: langchain.evaluation.schema.EvaluatorType = EvaluatorType.CRITERIA¶

param input_key: Optional[str] = None¶: The key from the traced run’s inputs dictionary to use to represent the input. If not provided, it will be inferred automatically.

param llm: Optional[langchain_core.language_models.base.BaseLanguageModel] = None¶

param prediction_key: Optional[str] = None¶: The key from the traced run’s outputs dictionary to use to represent the prediction. If not provided, it will be inferred automatically.

param reference_key: Optional[str] = None¶: The key in the dataset run to use as the reference string. If not provided, we will attempt to infer automatically.

classmethod construct(_fields_set: Optional[SetStr] = None, **values: Any) → Model¶: Creates a new model setting __dict__ and __fields_set__ from trusted or pre-validated data. Default values are respected, but no other validation is performed. Behaves as if Config.extra = ‘allow’ was set since it adds all passed values

Duplicate a model, optionally choose which fields to include, exclude and change.

Parameters

include – fields to include in new model
exclude – fields to exclude from new model, as with values this takes precedence over include
update – values to change/add in the new model. Note: the data is not validated before creating the new model: you should trust this data
deep – set to True to make a deep copy of the model

Returns

new model instance

dict(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, by_alias: bool = False, skip_defaults: Optional[bool] = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False) → DictStrAny¶: Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.

classmethod from_orm(obj: Any) → Model¶

get_kwargs() → Dict[str, Any]¶

Get the keyword arguments for the load_evaluator call.

Returns: The keyword arguments for the load_evaluator call.
Return type: Dict[str, Any]

Generate a JSON representation of the model, include and exclude arguments as per dict().

encoder is an optional function to supply as default to json.dumps(), other arguments as per json.dumps().

classmethod parse_file(path: Union[str, Path], *, content_type: unicode = None, encoding: unicode = 'utf8', proto: Protocol = None, allow_pickle: bool = False) → Model¶

classmethod parse_obj(obj: Any) → Model¶

classmethod parse_raw(b: Union[str, bytes], *, content_type: unicode = None, encoding: unicode = 'utf8', proto: Protocol = None, allow_pickle: bool = False) → Model¶

classmethod schema(by_alias: bool = True, ref_template: unicode = '#/definitions/{model}') → DictStrAny¶

classmethod schema_json(*, by_alias: bool = True, ref_template: unicode = '#/definitions/{model}', **dumps_kwargs: Any) → unicode¶

classmethod update_forward_refs(**localns: Any) → None¶: Try to update ForwardRefs on fields based on this Model, globalns and localns.

classmethod validate(value: Any) → Model¶

class EmbeddingDistance[source]¶

Bases: SingleKeyEvalConfig

Configuration for an embedding distance evaluator.

Parameters

embeddings (Optional[Embeddings]) – The embeddings to use for computing the distance.
distance_metric (Optional[EmbeddingDistanceEnum]) – The distance metric to use for computing the distance.

Create a new model by parsing and validating input data from keyword arguments.

Raises ValidationError if the input data cannot be parsed to form a valid model.

param distance_metric: Optional[langchain.evaluation.embedding_distance.base.EmbeddingDistance] = None¶

param embeddings: Optional[langchain_core.embeddings.Embeddings] = None¶

param evaluator_type: langchain.evaluation.schema.EvaluatorType = EvaluatorType.EMBEDDING_DISTANCE¶

param input_key: Optional[str] = None¶: The key from the traced run’s inputs dictionary to use to represent the input. If not provided, it will be inferred automatically.

param prediction_key: Optional[str] = None¶: The key from the traced run’s outputs dictionary to use to represent the prediction. If not provided, it will be inferred automatically.

param reference_key: Optional[str] = None¶: The key in the dataset run to use as the reference string. If not provided, we will attempt to infer automatically.

classmethod construct(_fields_set: Optional[SetStr] = None, **values: Any) → Model¶: Creates a new model setting __dict__ and __fields_set__ from trusted or pre-validated data. Default values are respected, but no other validation is performed. Behaves as if Config.extra = ‘allow’ was set since it adds all passed values

Duplicate a model, optionally choose which fields to include, exclude and change.

Parameters

include – fields to include in new model
exclude – fields to exclude from new model, as with values this takes precedence over include
update – values to change/add in the new model. Note: the data is not validated before creating the new model: you should trust this data
deep – set to True to make a deep copy of the model

Returns

new model instance

dict(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, by_alias: bool = False, skip_defaults: Optional[bool] = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False) → DictStrAny¶: Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.

classmethod from_orm(obj: Any) → Model¶

get_kwargs() → Dict[str, Any]¶

Get the keyword arguments for the load_evaluator call.

Returns: The keyword arguments for the load_evaluator call.
Return type: Dict[str, Any]

Generate a JSON representation of the model, include and exclude arguments as per dict().

encoder is an optional function to supply as default to json.dumps(), other arguments as per json.dumps().

classmethod parse_file(path: Union[str, Path], *, content_type: unicode = None, encoding: unicode = 'utf8', proto: Protocol = None, allow_pickle: bool = False) → Model¶

classmethod parse_obj(obj: Any) → Model¶

classmethod parse_raw(b: Union[str, bytes], *, content_type: unicode = None, encoding: unicode = 'utf8', proto: Protocol = None, allow_pickle: bool = False) → Model¶

classmethod schema(by_alias: bool = True, ref_template: unicode = '#/definitions/{model}') → DictStrAny¶

classmethod schema_json(*, by_alias: bool = True, ref_template: unicode = '#/definitions/{model}', **dumps_kwargs: Any) → unicode¶

classmethod update_forward_refs(**localns: Any) → None¶: Try to update ForwardRefs on fields based on this Model, globalns and localns.

classmethod validate(value: Any) → Model¶

class ExactMatch[source]¶

Bases: SingleKeyEvalConfig

Configuration for an exact match string evaluator.

Parameters

ignore_case (bool) – Whether to ignore case when comparing strings.
ignore_punctuation (bool) – Whether to ignore punctuation when comparing strings.
ignore_numbers (bool) – Whether to ignore numbers when comparing strings.

Create a new model by parsing and validating input data from keyword arguments.

Raises ValidationError if the input data cannot be parsed to form a valid model.

param evaluator_type: langchain.evaluation.schema.EvaluatorType = EvaluatorType.STRING_DISTANCE¶

param ignore_case: bool = False¶

param ignore_numbers: bool = False¶

param ignore_punctuation: bool = False¶

param input_key: Optional[str] = None¶: The key from the traced run’s inputs dictionary to use to represent the input. If not provided, it will be inferred automatically.

param prediction_key: Optional[str] = None¶: The key from the traced run’s outputs dictionary to use to represent the prediction. If not provided, it will be inferred automatically.

param reference_key: Optional[str] = None¶: The key in the dataset run to use as the reference string. If not provided, we will attempt to infer automatically.

classmethod construct(_fields_set: Optional[SetStr] = None, **values: Any) → Model¶: Creates a new model setting __dict__ and __fields_set__ from trusted or pre-validated data. Default values are respected, but no other validation is performed. Behaves as if Config.extra = ‘allow’ was set since it adds all passed values

Duplicate a model, optionally choose which fields to include, exclude and change.

Parameters

include – fields to include in new model
exclude – fields to exclude from new model, as with values this takes precedence over include
update – values to change/add in the new model. Note: the data is not validated before creating the new model: you should trust this data
deep – set to True to make a deep copy of the model

Returns

new model instance

dict(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, by_alias: bool = False, skip_defaults: Optional[bool] = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False) → DictStrAny¶: Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.

classmethod from_orm(obj: Any) → Model¶

get_kwargs() → Dict[str, Any]¶

Get the keyword arguments for the load_evaluator call.

Returns: The keyword arguments for the load_evaluator call.
Return type: Dict[str, Any]

Generate a JSON representation of the model, include and exclude arguments as per dict().

encoder is an optional function to supply as default to json.dumps(), other arguments as per json.dumps().

classmethod parse_file(path: Union[str, Path], *, content_type: unicode = None, encoding: unicode = 'utf8', proto: Protocol = None, allow_pickle: bool = False) → Model¶

classmethod parse_obj(obj: Any) → Model¶

classmethod parse_raw(b: Union[str, bytes], *, content_type: unicode = None, encoding: unicode = 'utf8', proto: Protocol = None, allow_pickle: bool = False) → Model¶

classmethod schema(by_alias: bool = True, ref_template: unicode = '#/definitions/{model}') → DictStrAny¶

classmethod schema_json(*, by_alias: bool = True, ref_template: unicode = '#/definitions/{model}', **dumps_kwargs: Any) → unicode¶

classmethod update_forward_refs(**localns: Any) → None¶: Try to update ForwardRefs on fields based on this Model, globalns and localns.

classmethod validate(value: Any) → Model¶

class JsonEqualityEvaluator[source]¶

Bases: EvalConfig

Configuration for a json equality evaluator.

Create a new model by parsing and validating input data from keyword arguments.

Raises ValidationError if the input data cannot be parsed to form a valid model.

param evaluator_type: langchain.evaluation.schema.EvaluatorType = EvaluatorType.JSON_EQUALITY¶

classmethod construct(_fields_set: Optional[SetStr] = None, **values: Any) → Model¶: Creates a new model setting __dict__ and __fields_set__ from trusted or pre-validated data. Default values are respected, but no other validation is performed. Behaves as if Config.extra = ‘allow’ was set since it adds all passed values

Duplicate a model, optionally choose which fields to include, exclude and change.

Parameters

include – fields to include in new model
exclude – fields to exclude from new model, as with values this takes precedence over include
update – values to change/add in the new model. Note: the data is not validated before creating the new model: you should trust this data
deep – set to True to make a deep copy of the model

Returns

new model instance

dict(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, by_alias: bool = False, skip_defaults: Optional[bool] = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False) → DictStrAny¶: Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.

classmethod from_orm(obj: Any) → Model¶

get_kwargs() → Dict[str, Any]¶

Get the keyword arguments for the load_evaluator call.

Returns: The keyword arguments for the load_evaluator call.
Return type: Dict[str, Any]

Generate a JSON representation of the model, include and exclude arguments as per dict().

encoder is an optional function to supply as default to json.dumps(), other arguments as per json.dumps().

classmethod parse_file(path: Union[str, Path], *, content_type: unicode = None, encoding: unicode = 'utf8', proto: Protocol = None, allow_pickle: bool = False) → Model¶

classmethod parse_obj(obj: Any) → Model¶

classmethod parse_raw(b: Union[str, bytes], *, content_type: unicode = None, encoding: unicode = 'utf8', proto: Protocol = None, allow_pickle: bool = False) → Model¶

classmethod schema(by_alias: bool = True, ref_template: unicode = '#/definitions/{model}') → DictStrAny¶

classmethod schema_json(*, by_alias: bool = True, ref_template: unicode = '#/definitions/{model}', **dumps_kwargs: Any) → unicode¶

classmethod update_forward_refs(**localns: Any) → None¶: Try to update ForwardRefs on fields based on this Model, globalns and localns.

classmethod validate(value: Any) → Model¶

class JsonValidity[source]¶

Bases: SingleKeyEvalConfig

Configuration for a json validity evaluator.

Create a new model by parsing and validating input data from keyword arguments.

Raises ValidationError if the input data cannot be parsed to form a valid model.

param evaluator_type: langchain.evaluation.schema.EvaluatorType = EvaluatorType.JSON_VALIDITY¶

param input_key: Optional[str] = None¶: The key from the traced run’s inputs dictionary to use to represent the input. If not provided, it will be inferred automatically.

param prediction_key: Optional[str] = None¶: The key from the traced run’s outputs dictionary to use to represent the prediction. If not provided, it will be inferred automatically.

param reference_key: Optional[str] = None¶: The key in the dataset run to use as the reference string. If not provided, we will attempt to infer automatically.

classmethod construct(_fields_set: Optional[SetStr] = None, **values: Any) → Model¶: Creates a new model setting __dict__ and __fields_set__ from trusted or pre-validated data. Default values are respected, but no other validation is performed. Behaves as if Config.extra = ‘allow’ was set since it adds all passed values

Duplicate a model, optionally choose which fields to include, exclude and change.

Parameters

include – fields to include in new model
exclude – fields to exclude from new model, as with values this takes precedence over include
update – values to change/add in the new model. Note: the data is not validated before creating the new model: you should trust this data
deep – set to True to make a deep copy of the model

Returns

new model instance

dict(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, by_alias: bool = False, skip_defaults: Optional[bool] = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False) → DictStrAny¶: Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.

classmethod from_orm(obj: Any) → Model¶

get_kwargs() → Dict[str, Any]¶

Get the keyword arguments for the load_evaluator call.

Returns: The keyword arguments for the load_evaluator call.
Return type: Dict[str, Any]

Generate a JSON representation of the model, include and exclude arguments as per dict().

encoder is an optional function to supply as default to json.dumps(), other arguments as per json.dumps().

classmethod parse_file(path: Union[str, Path], *, content_type: unicode = None, encoding: unicode = 'utf8', proto: Protocol = None, allow_pickle: bool = False) → Model¶

classmethod parse_obj(obj: Any) → Model¶

classmethod parse_raw(b: Union[str, bytes], *, content_type: unicode = None, encoding: unicode = 'utf8', proto: Protocol = None, allow_pickle: bool = False) → Model¶

classmethod schema(by_alias: bool = True, ref_template: unicode = '#/definitions/{model}') → DictStrAny¶

classmethod schema_json(*, by_alias: bool = True, ref_template: unicode = '#/definitions/{model}', **dumps_kwargs: Any) → unicode¶

classmethod update_forward_refs(**localns: Any) → None¶: Try to update ForwardRefs on fields based on this Model, globalns and localns.

classmethod validate(value: Any) → Model¶

class LabeledCriteria[source]¶

Bases: SingleKeyEvalConfig

Configuration for a labeled (with references) criteria evaluator.

Parameters

criteria (Optional[CRITERIA_TYPE]) – The criteria to evaluate.
llm (Optional[BaseLanguageModel]) – The language model to use for the evaluation chain.

Create a new model by parsing and validating input data from keyword arguments.

Raises ValidationError if the input data cannot be parsed to form a valid model.

param criteria: Optional[Union[Mapping[str, str], langchain.evaluation.criteria.eval_chain.Criteria, langchain.chains.constitutional_ai.models.ConstitutionalPrinciple]] = None¶

param evaluator_type: langchain.evaluation.schema.EvaluatorType = EvaluatorType.LABELED_CRITERIA¶

param input_key: Optional[str] = None¶: The key from the traced run’s inputs dictionary to use to represent the input. If not provided, it will be inferred automatically.

param llm: Optional[langchain_core.language_models.base.BaseLanguageModel] = None¶

param prediction_key: Optional[str] = None¶: The key from the traced run’s outputs dictionary to use to represent the prediction. If not provided, it will be inferred automatically.

param reference_key: Optional[str] = None¶: The key in the dataset run to use as the reference string. If not provided, we will attempt to infer automatically.

classmethod construct(_fields_set: Optional[SetStr] = None, **values: Any) → Model¶: Creates a new model setting __dict__ and __fields_set__ from trusted or pre-validated data. Default values are respected, but no other validation is performed. Behaves as if Config.extra = ‘allow’ was set since it adds all passed values

Duplicate a model, optionally choose which fields to include, exclude and change.

Parameters

include – fields to include in new model
exclude – fields to exclude from new model, as with values this takes precedence over include
update – values to change/add in the new model. Note: the data is not validated before creating the new model: you should trust this data
deep – set to True to make a deep copy of the model

Returns

new model instance

dict(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, by_alias: bool = False, skip_defaults: Optional[bool] = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False) → DictStrAny¶: Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.

classmethod from_orm(obj: Any) → Model¶

get_kwargs() → Dict[str, Any]¶

Get the keyword arguments for the load_evaluator call.

Returns: The keyword arguments for the load_evaluator call.
Return type: Dict[str, Any]

Generate a JSON representation of the model, include and exclude arguments as per dict().

encoder is an optional function to supply as default to json.dumps(), other arguments as per json.dumps().

classmethod parse_file(path: Union[str, Path], *, content_type: unicode = None, encoding: unicode = 'utf8', proto: Protocol = None, allow_pickle: bool = False) → Model¶

classmethod parse_obj(obj: Any) → Model¶

classmethod parse_raw(b: Union[str, bytes], *, content_type: unicode = None, encoding: unicode = 'utf8', proto: Protocol = None, allow_pickle: bool = False) → Model¶

classmethod schema(by_alias: bool = True, ref_template: unicode = '#/definitions/{model}') → DictStrAny¶

classmethod schema_json(*, by_alias: bool = True, ref_template: unicode = '#/definitions/{model}', **dumps_kwargs: Any) → unicode¶

classmethod update_forward_refs(**localns: Any) → None¶: Try to update ForwardRefs on fields based on this Model, globalns and localns.

classmethod validate(value: Any) → Model¶

class LabeledScoreString[source]¶

Bases: ScoreString

Create a new model by parsing and validating input data from keyword arguments.

Raises ValidationError if the input data cannot be parsed to form a valid model.

param criteria: Optional[Union[Mapping[str, str], langchain.evaluation.criteria.eval_chain.Criteria, langchain.chains.constitutional_ai.models.ConstitutionalPrinciple]] = None¶

param evaluator_type: langchain.evaluation.schema.EvaluatorType = EvaluatorType.LABELED_SCORE_STRING¶

param input_key: Optional[str] = None¶: The key from the traced run’s inputs dictionary to use to represent the input. If not provided, it will be inferred automatically.

param llm: Optional[langchain_core.language_models.base.BaseLanguageModel] = None¶

param normalize_by: Optional[float] = None¶

param prediction_key: Optional[str] = None¶: The key from the traced run’s outputs dictionary to use to represent the prediction. If not provided, it will be inferred automatically.

param prompt: Optional[langchain_core.prompts.base.BasePromptTemplate] = None¶

param reference_key: Optional[str] = None¶: The key in the dataset run to use as the reference string. If not provided, we will attempt to infer automatically.

classmethod construct(_fields_set: Optional[SetStr] = None, **values: Any) → Model¶: Creates a new model setting __dict__ and __fields_set__ from trusted or pre-validated data. Default values are respected, but no other validation is performed. Behaves as if Config.extra = ‘allow’ was set since it adds all passed values

Duplicate a model, optionally choose which fields to include, exclude and change.

Parameters

include – fields to include in new model
exclude – fields to exclude from new model, as with values this takes precedence over include
update – values to change/add in the new model. Note: the data is not validated before creating the new model: you should trust this data
deep – set to True to make a deep copy of the model

Returns

new model instance

dict(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, by_alias: bool = False, skip_defaults: Optional[bool] = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False) → DictStrAny¶: Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.

classmethod from_orm(obj: Any) → Model¶

get_kwargs() → Dict[str, Any]¶

Get the keyword arguments for the load_evaluator call.

Returns: The keyword arguments for the load_evaluator call.
Return type: Dict[str, Any]

Generate a JSON representation of the model, include and exclude arguments as per dict().

encoder is an optional function to supply as default to json.dumps(), other arguments as per json.dumps().

classmethod parse_file(path: Union[str, Path], *, content_type: unicode = None, encoding: unicode = 'utf8', proto: Protocol = None, allow_pickle: bool = False) → Model¶

classmethod parse_obj(obj: Any) → Model¶

classmethod parse_raw(b: Union[str, bytes], *, content_type: unicode = None, encoding: unicode = 'utf8', proto: Protocol = None, allow_pickle: bool = False) → Model¶

classmethod schema(by_alias: bool = True, ref_template: unicode = '#/definitions/{model}') → DictStrAny¶

classmethod schema_json(*, by_alias: bool = True, ref_template: unicode = '#/definitions/{model}', **dumps_kwargs: Any) → unicode¶

classmethod update_forward_refs(**localns: Any) → None¶: Try to update ForwardRefs on fields based on this Model, globalns and localns.

classmethod validate(value: Any) → Model¶

class QA[source]¶

Bases: SingleKeyEvalConfig

Configuration for a QA evaluator.

Parameters

prompt (Optional[BasePromptTemplate]) – The prompt template to use for generating the question.
llm (Optional[BaseLanguageModel]) – The language model to use for the evaluation chain.

Create a new model by parsing and validating input data from keyword arguments.

Raises ValidationError if the input data cannot be parsed to form a valid model.

param evaluator_type: langchain.evaluation.schema.EvaluatorType = EvaluatorType.QA¶

param input_key: Optional[str] = None¶: The key from the traced run’s inputs dictionary to use to represent the input. If not provided, it will be inferred automatically.

param llm: Optional[langchain_core.language_models.base.BaseLanguageModel] = None¶

param prediction_key: Optional[str] = None¶: The key from the traced run’s outputs dictionary to use to represent the prediction. If not provided, it will be inferred automatically.

param prompt: Optional[langchain_core.prompts.base.BasePromptTemplate] = None¶

param reference_key: Optional[str] = None¶: The key in the dataset run to use as the reference string. If not provided, we will attempt to infer automatically.

classmethod construct(_fields_set: Optional[SetStr] = None, **values: Any) → Model¶: Creates a new model setting __dict__ and __fields_set__ from trusted or pre-validated data. Default values are respected, but no other validation is performed. Behaves as if Config.extra = ‘allow’ was set since it adds all passed values

Duplicate a model, optionally choose which fields to include, exclude and change.

Parameters

include – fields to include in new model
exclude – fields to exclude from new model, as with values this takes precedence over include
update – values to change/add in the new model. Note: the data is not validated before creating the new model: you should trust this data
deep – set to True to make a deep copy of the model

Returns

new model instance

dict(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, by_alias: bool = False, skip_defaults: Optional[bool] = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False) → DictStrAny¶: Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.

classmethod from_orm(obj: Any) → Model¶

get_kwargs() → Dict[str, Any]¶

Get the keyword arguments for the load_evaluator call.

Returns: The keyword arguments for the load_evaluator call.
Return type: Dict[str, Any]

Generate a JSON representation of the model, include and exclude arguments as per dict().

encoder is an optional function to supply as default to json.dumps(), other arguments as per json.dumps().

classmethod parse_file(path: Union[str, Path], *, content_type: unicode = None, encoding: unicode = 'utf8', proto: Protocol = None, allow_pickle: bool = False) → Model¶

classmethod parse_obj(obj: Any) → Model¶

classmethod parse_raw(b: Union[str, bytes], *, content_type: unicode = None, encoding: unicode = 'utf8', proto: Protocol = None, allow_pickle: bool = False) → Model¶

classmethod schema(by_alias: bool = True, ref_template: unicode = '#/definitions/{model}') → DictStrAny¶

classmethod schema_json(*, by_alias: bool = True, ref_template: unicode = '#/definitions/{model}', **dumps_kwargs: Any) → unicode¶

classmethod update_forward_refs(**localns: Any) → None¶: Try to update ForwardRefs on fields based on this Model, globalns and localns.

classmethod validate(value: Any) → Model¶

class RegexMatch[source]¶

Bases: SingleKeyEvalConfig

Configuration for a regex match string evaluator.

Parameters: flags (int) – The flags to pass to the regex. Example: re.IGNORECASE.

Create a new model by parsing and validating input data from keyword arguments.

Raises ValidationError if the input data cannot be parsed to form a valid model.

param evaluator_type: langchain.evaluation.schema.EvaluatorType = EvaluatorType.REGEX_MATCH¶

param flags: int = 0¶

param input_key: Optional[str] = None¶: The key from the traced run’s inputs dictionary to use to represent the input. If not provided, it will be inferred automatically.

param prediction_key: Optional[str] = None¶: The key from the traced run’s outputs dictionary to use to represent the prediction. If not provided, it will be inferred automatically.

param reference_key: Optional[str] = None¶: The key in the dataset run to use as the reference string. If not provided, we will attempt to infer automatically.

classmethod construct(_fields_set: Optional[SetStr] = None, **values: Any) → Model¶: Creates a new model setting __dict__ and __fields_set__ from trusted or pre-validated data. Default values are respected, but no other validation is performed. Behaves as if Config.extra = ‘allow’ was set since it adds all passed values

Duplicate a model, optionally choose which fields to include, exclude and change.

Parameters

include – fields to include in new model
exclude – fields to exclude from new model, as with values this takes precedence over include
update – values to change/add in the new model. Note: the data is not validated before creating the new model: you should trust this data
deep – set to True to make a deep copy of the model

Returns

new model instance

dict(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, by_alias: bool = False, skip_defaults: Optional[bool] = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False) → DictStrAny¶: Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.

classmethod from_orm(obj: Any) → Model¶

get_kwargs() → Dict[str, Any]¶

Get the keyword arguments for the load_evaluator call.

Returns: The keyword arguments for the load_evaluator call.
Return type: Dict[str, Any]

Generate a JSON representation of the model, include and exclude arguments as per dict().

encoder is an optional function to supply as default to json.dumps(), other arguments as per json.dumps().

classmethod parse_file(path: Union[str, Path], *, content_type: unicode = None, encoding: unicode = 'utf8', proto: Protocol = None, allow_pickle: bool = False) → Model¶

classmethod parse_obj(obj: Any) → Model¶

classmethod parse_raw(b: Union[str, bytes], *, content_type: unicode = None, encoding: unicode = 'utf8', proto: Protocol = None, allow_pickle: bool = False) → Model¶

classmethod schema(by_alias: bool = True, ref_template: unicode = '#/definitions/{model}') → DictStrAny¶

classmethod schema_json(*, by_alias: bool = True, ref_template: unicode = '#/definitions/{model}', **dumps_kwargs: Any) → unicode¶

classmethod update_forward_refs(**localns: Any) → None¶: Try to update ForwardRefs on fields based on this Model, globalns and localns.

classmethod validate(value: Any) → Model¶

class ScoreString[source]¶

Bases: SingleKeyEvalConfig

Configuration for a score string evaluator. This is like the criteria evaluator but it is configured by default to return a score on the scale from 1-10.

It is recommended to normalize these scores by setting normalize_by to 10.

Parameters

criteria (Optional[CRITERIA_TYPE]) – The criteria to evaluate.
llm (Optional[BaseLanguageModel]) – The language model to use for the evaluation chain.
normalize_by (Optional[int] = None) – If you want to normalize the score, the denominator to use. If not provided, the score will be between 1 and 10 (by default).
prompt (Optional[BasePromptTemplate]) –

Create a new model by parsing and validating input data from keyword arguments.

Raises ValidationError if the input data cannot be parsed to form a valid model.

param criteria: Optional[Union[Mapping[str, str], langchain.evaluation.criteria.eval_chain.Criteria, langchain.chains.constitutional_ai.models.ConstitutionalPrinciple]] = None¶

param evaluator_type: langchain.evaluation.schema.EvaluatorType = EvaluatorType.SCORE_STRING¶

param input_key: Optional[str] = None¶: The key from the traced run’s inputs dictionary to use to represent the input. If not provided, it will be inferred automatically.

param llm: Optional[langchain_core.language_models.base.BaseLanguageModel] = None¶

param normalize_by: Optional[float] = None¶

param prediction_key: Optional[str] = None¶: The key from the traced run’s outputs dictionary to use to represent the prediction. If not provided, it will be inferred automatically.

param prompt: Optional[langchain_core.prompts.base.BasePromptTemplate] = None¶

param reference_key: Optional[str] = None¶: The key in the dataset run to use as the reference string. If not provided, we will attempt to infer automatically.

classmethod construct(_fields_set: Optional[SetStr] = None, **values: Any) → Model¶: Creates a new model setting __dict__ and __fields_set__ from trusted or pre-validated data. Default values are respected, but no other validation is performed. Behaves as if Config.extra = ‘allow’ was set since it adds all passed values

Duplicate a model, optionally choose which fields to include, exclude and change.

Parameters

include – fields to include in new model
exclude – fields to exclude from new model, as with values this takes precedence over include
update – values to change/add in the new model. Note: the data is not validated before creating the new model: you should trust this data
deep – set to True to make a deep copy of the model

Returns

new model instance

dict(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, by_alias: bool = False, skip_defaults: Optional[bool] = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False) → DictStrAny¶: Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.

classmethod from_orm(obj: Any) → Model¶

get_kwargs() → Dict[str, Any]¶

Get the keyword arguments for the load_evaluator call.

Returns: The keyword arguments for the load_evaluator call.
Return type: Dict[str, Any]

Generate a JSON representation of the model, include and exclude arguments as per dict().

encoder is an optional function to supply as default to json.dumps(), other arguments as per json.dumps().

classmethod parse_file(path: Union[str, Path], *, content_type: unicode = None, encoding: unicode = 'utf8', proto: Protocol = None, allow_pickle: bool = False) → Model¶

classmethod parse_obj(obj: Any) → Model¶

classmethod parse_raw(b: Union[str, bytes], *, content_type: unicode = None, encoding: unicode = 'utf8', proto: Protocol = None, allow_pickle: bool = False) → Model¶

classmethod schema(by_alias: bool = True, ref_template: unicode = '#/definitions/{model}') → DictStrAny¶

classmethod schema_json(*, by_alias: bool = True, ref_template: unicode = '#/definitions/{model}', **dumps_kwargs: Any) → unicode¶

classmethod update_forward_refs(**localns: Any) → None¶: Try to update ForwardRefs on fields based on this Model, globalns and localns.

classmethod validate(value: Any) → Model¶

class StringDistance[source]¶

Bases: SingleKeyEvalConfig

Configuration for a string distance evaluator.

Parameters: distance (Optional[StringDistanceEnum]) – The string distance metric to use.

Create a new model by parsing and validating input data from keyword arguments.

Raises ValidationError if the input data cannot be parsed to form a valid model.

param distance: Optional[langchain.evaluation.string_distance.base.StringDistance] = None¶: The string distance metric to use. damerau_levenshtein: The Damerau-Levenshtein distance. levenshtein: The Levenshtein distance. jaro: The Jaro distance. jaro_winkler: The Jaro-Winkler distance.

param evaluator_type: langchain.evaluation.schema.EvaluatorType = EvaluatorType.STRING_DISTANCE¶

param input_key: Optional[str] = None¶: The key from the traced run’s inputs dictionary to use to represent the input. If not provided, it will be inferred automatically.

param normalize_score: bool = True¶: Whether to normalize the distance to between 0 and 1. Applies only to the Levenshtein and Damerau-Levenshtein distances.

param prediction_key: Optional[str] = None¶: The key from the traced run’s outputs dictionary to use to represent the prediction. If not provided, it will be inferred automatically.

param reference_key: Optional[str] = None¶: The key in the dataset run to use as the reference string. If not provided, we will attempt to infer automatically.

classmethod construct(_fields_set: Optional[SetStr] = None, **values: Any) → Model¶: Creates a new model setting __dict__ and __fields_set__ from trusted or pre-validated data. Default values are respected, but no other validation is performed. Behaves as if Config.extra = ‘allow’ was set since it adds all passed values

Duplicate a model, optionally choose which fields to include, exclude and change.

Parameters

include – fields to include in new model
exclude – fields to exclude from new model, as with values this takes precedence over include
update – values to change/add in the new model. Note: the data is not validated before creating the new model: you should trust this data
deep – set to True to make a deep copy of the model

Returns

new model instance

dict(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, by_alias: bool = False, skip_defaults: Optional[bool] = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False) → DictStrAny¶: Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.

classmethod from_orm(obj: Any) → Model¶

get_kwargs() → Dict[str, Any]¶

Get the keyword arguments for the load_evaluator call.

Returns: The keyword arguments for the load_evaluator call.
Return type: Dict[str, Any]

Generate a JSON representation of the model, include and exclude arguments as per dict().

encoder is an optional function to supply as default to json.dumps(), other arguments as per json.dumps().

classmethod parse_file(path: Union[str, Path], *, content_type: unicode = None, encoding: unicode = 'utf8', proto: Protocol = None, allow_pickle: bool = False) → Model¶

classmethod parse_obj(obj: Any) → Model¶

classmethod parse_raw(b: Union[str, bytes], *, content_type: unicode = None, encoding: unicode = 'utf8', proto: Protocol = None, allow_pickle: bool = False) → Model¶

classmethod schema(by_alias: bool = True, ref_template: unicode = '#/definitions/{model}') → DictStrAny¶

classmethod schema_json(*, by_alias: bool = True, ref_template: unicode = '#/definitions/{model}', **dumps_kwargs: Any) → unicode¶

classmethod update_forward_refs(**localns: Any) → None¶: Try to update ForwardRefs on fields based on this Model, globalns and localns.

classmethod validate(value: Any) → Model¶

classmethod construct(_fields_set: Optional[SetStr] = None, **values: Any) → Model¶: Creates a new model setting __dict__ and __fields_set__ from trusted or pre-validated data. Default values are respected, but no other validation is performed. Behaves as if Config.extra = ‘allow’ was set since it adds all passed values

Duplicate a model, optionally choose which fields to include, exclude and change.

Parameters

include – fields to include in new model
exclude – fields to exclude from new model, as with values this takes precedence over include
update – values to change/add in the new model. Note: the data is not validated before creating the new model: you should trust this data
deep – set to True to make a deep copy of the model

Returns

new model instance

dict(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, by_alias: bool = False, skip_defaults: Optional[bool] = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False) → DictStrAny¶: Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.

classmethod from_orm(obj: Any) → Model¶

Generate a JSON representation of the model, include and exclude arguments as per dict().

encoder is an optional function to supply as default to json.dumps(), other arguments as per json.dumps().

classmethod parse_file(path: Union[str, Path], *, content_type: unicode = None, encoding: unicode = 'utf8', proto: Protocol = None, allow_pickle: bool = False) → Model¶

classmethod parse_obj(obj: Any) → Model¶

classmethod parse_raw(b: Union[str, bytes], *, content_type: unicode = None, encoding: unicode = 'utf8', proto: Protocol = None, allow_pickle: bool = False) → Model¶

classmethod schema(by_alias: bool = True, ref_template: unicode = '#/definitions/{model}') → DictStrAny¶

classmethod schema_json(*, by_alias: bool = True, ref_template: unicode = '#/definitions/{model}', **dumps_kwargs: Any) → unicode¶

classmethod update_forward_refs(**localns: Any) → None¶: Try to update ForwardRefs on fields based on this Model, globalns and localns.

classmethod validate(value: Any) → Model¶

Examples using RunEvalConfig¶

LangSmith Walkthrough

langchain.smith.evaluation.config.RunEvalConfig¶

Examples using RunEvalConfig¶

`langchain.smith.evaluation.config`.RunEvalConfig¶