Source code for langchain_core.outputs.llm_result

from __future__ import annotations

from copy import deepcopy
from typing import List, Optional

from langchain_core.outputs.generation import Generation
from langchain_core.outputs.run_info import RunInfo
from langchain_core.pydantic_v1 import BaseModel


[docs]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[Generation]] """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."""
[docs] 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 )