from_llms() — langchain Function Reference

Architecture documentation for the from_llms() function in model_laboratory.py from the langchain codebase.

Function python LangChainCore LanguageModelBase called by 1

Entity Profile

LangChainCore→ LanguageModelBase→ from_llms() — langchain Function Reference

Dependency Diagram

graph TD
  962bae77_5dd8_f194_e1d3_68d9fbeb473e["from_llms()"]
  ddc55513_8bea_9f0f_9a07_33c17a0d376f["ModelLaboratory"]
  962bae77_5dd8_f194_e1d3_68d9fbeb473e -->|defined in| ddc55513_8bea_9f0f_9a07_33c17a0d376f
  faa83a0b_d5b8_28b7_b07c_725dc6ca4594["__init__()"]
  faa83a0b_d5b8_28b7_b07c_725dc6ca4594 -->|calls| 962bae77_5dd8_f194_e1d3_68d9fbeb473e
  style 962bae77_5dd8_f194_e1d3_68d9fbeb473e fill:#6366f1,stroke:#818cf8,color:#fff

Relationship Graph

Source Code

libs/langchain/langchain_classic/model_laboratory.py lines 62–81

    def from_llms(
        cls,
        llms: list[BaseLLM],
        prompt: PromptTemplate | None = None,
    ) -> ModelLaboratory:
        """Initialize the ModelLaboratory with LLMs and an optional prompt.

        Args:
            llms: A list of LLMs to experiment with.
            prompt: An optional prompt to use with the LLMs.
                If provided, the prompt must contain exactly one input variable.

        Returns:
            An instance of `ModelLaboratory` initialized with LLMs.
        """
        if prompt is None:
            prompt = PromptTemplate(input_variables=["_input"], template="{_input}")
        chains = [LLMChain(llm=llm, prompt=prompt) for llm in llms]
        names = [str(llm) for llm in llms]
        return cls(chains, names=names)