flatten() — langchain Function Reference

Architecture documentation for the flatten() function in llm_result.py from the langchain codebase.

Function python LangChainCore ApiManagement

Entity Profile

LangChainCore→ ApiManagement→ flatten() — langchain Function Reference

Dependency Diagram

graph TD
  355ab337_7937_6907_7112_0037c8f34822["flatten()"]
  be2a68ad_6ac6_f078_3bbc_62ebfc7db505["LLMResult"]
  355ab337_7937_6907_7112_0037c8f34822 -->|defined in| be2a68ad_6ac6_f078_3bbc_62ebfc7db505
  style 355ab337_7937_6907_7112_0037c8f34822 fill:#6366f1,stroke:#818cf8,color:#fff

Relationship Graph

Source Code

libs/core/langchain_core/outputs/llm_result.py lines 59–93

    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 `LLMResult` objects 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"] = {}
                else:
                    llm_output = None
                llm_results.append(
                    LLMResult(
                        generations=[gen_list],
                        llm_output=llm_output,
                    )
                )
        return llm_results