langchain_core.outputs.llm_result.LLMResult¶
- class langchain_core.outputs.llm_result.LLMResult[source]¶
Bases:
BaseModelClass that contains all results for a batched LLM call.
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 generations: List[List[langchain_core.outputs.generation.Generation]] [Required]¶
List of generated outputs. This is a List[List[]] because each input could have multiple candidate generations.
- param llm_output: Optional[dict] = None¶
Arbitrary LLM provider-specific output.
- param run: Optional[List[langchain_core.outputs.run_info.RunInfo]] = None¶
List of metadata info for model call for each input.
- 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.
- flatten() List[LLMResult][source]¶
Flatten generations into a single list.
- Unpack List[List[Generation]] -> List[LLMResult] where each returned LLMResult
contains only a single Generation. If token usage information is available, it is kept only for the LLMResult corresponding to the top-choice Generation, to avoid over-counting of token usage downstream.
- Returns
- List of LLMResults where each returned LLMResult contains a single
Generation.
- classmethod from_orm(obj: Any) Model¶
- 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¶