LangChain

Bases: PydanticEngine[PromptSignature, Result, Model, InferenceMode]

Engine for LangChain.

Source code in sieves/engines/langchain_.py

class LangChain(PydanticEngine[PromptSignature, Result, Model, InferenceMode]):
    """Engine for LangChain."""

    @override
    @property
    def inference_modes(self) -> type[InferenceMode]:
        return InferenceMode

    @override
    @override
    def build_executable(
        self,
        inference_mode: InferenceMode,
        prompt_template: str | None,  # noqa: UP007
        prompt_signature: type[PromptSignature] | PromptSignature,
        fewshot_examples: Sequence[pydantic.BaseModel] = tuple(),
    ) -> Executable[Result | None]:
        assert isinstance(prompt_signature, type)
        cls_name = self.__class__.__name__
        template = self._create_template(prompt_template)
        model = self._model.with_structured_output(prompt_signature)

        def execute(values: Sequence[dict[str, Any]]) -> Iterable[Result | None]:
            """Execute prompts with engine for given values.

            :param values: Values to inject into prompts.
            :return Iterable[Result | None]: Results for prompts. Results are None if corresponding prompt failed.
            """
            match inference_mode:
                case InferenceMode.structured:

                    def generate(prompts: list[str]) -> Iterable[Result]:
                        try:
                            yield from asyncio.run(model.abatch(prompts, **self._inference_kwargs))

                        except Exception as err:
                            raise type(err)(
                                f"Encountered problem in parsing {cls_name} output. Double-check your prompts and "
                                f"examples."
                            ) from err

                    generator = generate
                case _:
                    raise ValueError(f"Inference mode {inference_mode} not supported by {cls_name} engine.")

            yield from self._infer(generator, template, values, fewshot_examples)

        return execute

generation_settings property

Return generation settings.

Returns:

Type	Description
GenerationSettings	Generation settings.

model property

Return model instance.

Returns:

Type	Description
EngineModel	Model instance.

__init__(model, generation_settings)

Initialize engine with model and generation settings.

Parameters:

Name	Type	Description	Default
model	EngineModel	Instantiated model instance.	required
generation_settings	GenerationSettings	Generation settings.	required

Source code in sieves/engines/core.py

def __init__(self, model: EngineModel, generation_settings: GenerationSettings):
    """Initialize engine with model and generation settings.

    :param model: Instantiated model instance.
    :param generation_settings: Generation settings.
    """
    self._model = model
    self._generation_settings = generation_settings
    self._inference_kwargs = generation_settings.inference_kwargs or {}
    self._init_kwargs = generation_settings.init_kwargs or {}
    self._strict_mode = generation_settings.strict_mode

convert_fewshot_examples(fewshot_examples) staticmethod

Convert few‑shot examples to dicts.

Parameters:

Name	Type	Description	Default
fewshot_examples	Sequence[BaseModel]	Fewshot examples to convert.	required

Returns:

Type	Description
list[dict[str, Any]]	Fewshot examples as dicts.

Source code in sieves/engines/core.py

@staticmethod
def convert_fewshot_examples(fewshot_examples: Sequence[pydantic.BaseModel]) -> list[dict[str, Any]]:
    """Convert few‑shot examples to dicts.

    :param fewshot_examples: Fewshot examples to convert.
    :return: Fewshot examples as dicts.
    """
    return [fs_example.model_dump(serialize_as_any=True) for fs_example in fewshot_examples]