Source code for langchain_core.outputs.llm_result

from __future__ import annotations

from copy import deepcopy
from typing import Literal, Optional, Union

from pydantic import BaseModel

from langchain_core.outputs.chat_generation import ChatGeneration, ChatGenerationChunk
from langchain_core.outputs.generation import Generation, GenerationChunk
from langchain_core.outputs.run_info import RunInfo




class LLMResult(BaseModel):
    """A container for results of an LLM call.

    Both chat models and LLMs generate an LLMResult object. This object contains
    the generated outputs and any additional information that the model provider
    wants to return.
    """

    generations: list[
        list[Union[Generation, ChatGeneration, GenerationChunk, ChatGenerationChunk]]
    ]
    """Generated outputs.
    
    The first dimension of the list represents completions for different input
    prompts.
    
    The second dimension of the list represents different candidate generations
    for a given prompt.
    
    When returned from an LLM the type is List[List[Generation]].
    When returned from a chat model the type is List[List[ChatGeneration]].
    
    ChatGeneration is a subclass of Generation that has a field for a structured
    chat message.
    """
    llm_output: Optional[dict] = None
    """For arbitrary LLM provider specific output.
    
    This dictionary is a free-form dictionary that can contain any information that the
    provider wants to return. It is not standardized and is provider-specific.
    
    Users should generally avoid relying on this field and instead rely on
    accessing relevant information from standardized fields present in
    AIMessage.
    """
    run: Optional[list[RunInfo]] = None
    """List of metadata info for model call for each input."""

    type: Literal["LLMResult"] = "LLMResult"  # type: ignore[assignment]
    """Type is used exclusively for serialization purposes."""



    def flatten(self) -> list[LLMResult]:
        """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.
        """
        llm_results = []
        for i, gen_list in enumerate(self.generations):
            # Avoid double counting tokens in OpenAICallback
            if i == 0:
                llm_results.append(
                    LLMResult(
                        generations=[gen_list],
                        llm_output=self.llm_output,
                    )
                )
            else:
                if self.llm_output is not None:
                    llm_output = deepcopy(self.llm_output)
                    llm_output["token_usage"] = dict()
                else:
                    llm_output = None
                llm_results.append(
                    LLMResult(
                        generations=[gen_list],
                        llm_output=llm_output,
                    )
                )
        return llm_results


    def __eq__(self, other: object) -> bool:
        """Check for LLMResult equality by ignoring any metadata related to runs."""
        if not isinstance(other, LLMResult):
            return NotImplemented
        return (
            self.generations == other.generations
            and self.llm_output == other.llm_output
        )