agenerate() — langchain Function Reference

Source Code

libs/core/langchain_core/language_models/llms.py lines 1115–1327

    async def agenerate(
        self,
        prompts: list[str],
        stop: list[str] | None = None,
        callbacks: Callbacks | list[Callbacks] | None = None,
        *,
        tags: list[str] | list[list[str]] | None = None,
        metadata: dict[str, Any] | list[dict[str, Any]] | None = None,
        run_name: str | list[str] | None = None,
        run_id: uuid.UUID | list[uuid.UUID | None] | None = None,
        **kwargs: Any,
    ) -> LLMResult:
        """Asynchronously pass a sequence of prompts to a model and return generations.

        This method should make use of batched calls for models that expose a batched
        API.

        Use this method when you want to:

        1. Take advantage of batched calls,
        2. Need more output from the model than just the top generated value,
        3. Are building chains that are agnostic to the underlying language model
            type (e.g., pure text completion models vs chat models).

        Args:
            prompts: List of string prompts.
            stop: Stop words to use when generating.

                Model output is cut off at the first occurrence of any of these
                substrings.
            callbacks: `Callbacks` to pass through.

                Used for executing additional functionality, such as logging or
                streaming, throughout generation.
            tags: List of tags to associate with each prompt. If provided, the length
                of the list must match the length of the prompts list.
            metadata: List of metadata dictionaries to associate with each prompt. If
                provided, the length of the list must match the length of the prompts
                list.
            run_name: List of run names to associate with each prompt. If provided, the
                length of the list must match the length of the prompts list.
            run_id: List of run IDs to associate with each prompt. If provided, the
                length of the list must match the length of the prompts list.
            **kwargs: Arbitrary additional keyword arguments.

                These are usually passed to the model provider API call.

        Raises:
            ValueError: If the length of `callbacks`, `tags`, `metadata`, or
                `run_name` (if provided) does not match the length of prompts.

        Returns:
            An `LLMResult`, which contains a list of candidate `Generations` for each
                input prompt and additional model provider-specific output.
        """
        if isinstance(metadata, list):
            metadata = [
                {
                    **(meta or {}),
                    **self._get_ls_params(stop=stop, **kwargs),
                }
                for meta in metadata
            ]
        elif isinstance(metadata, dict):
            metadata = {
                **(metadata or {}),
                **self._get_ls_params(stop=stop, **kwargs),
            }
        # Create callback managers
        if isinstance(callbacks, list) and (
            isinstance(callbacks[0], (list, BaseCallbackManager))
            or callbacks[0] is None
        ):
            # We've received a list of callbacks args to apply to each input
            if len(callbacks) != len(prompts):
                msg = "callbacks must be the same length as prompts"
                raise ValueError(msg)
            if tags is not None and not (
                isinstance(tags, list) and len(tags) == len(prompts)
            ):
                msg = "tags must be a list of the same length as prompts"