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
)