9|h|dZddlmZddlmZmZmZddlmZddl m Z ddl m Z m Z ddlmZddlmZGd d Zy ) z!Experiment with different models.) annotations)ListOptionalSequence)BaseLLM)PromptTemplate)get_color_mapping print_text)Chain)LLMChaincBeZdZdZdddZe d ddZd dZy) ModelLaboratoryzMA utility to experiment with and compare the performance of different models.Nc|D]}}t|ts tdt|jdk7rtd|jt|j dk7sgtd|j |"t|t|k7r td||_tt|j Dcgc] }t|}}t||_ ||_ ycc}w)aInitialize the ModelLaboratory with chains to experiment with. Args: chains (Sequence[Chain]): A sequence of chains to experiment with. Each chain must have exactly one input and one output variable. names (Optional[List[str]]): Optional list of names corresponding to each chain. If provided, its length must match the number of chains. Raises: ValueError: If any chain is not an instance of `Chain`. ValueError: If a chain does not have exactly one input variable. ValueError: If a chain does not have exactly one output variable. ValueError: If the length of `names` does not match the number of chains. zModelLaboratory should now be initialized with Chains. If you want to initialize with LLMs, use the `from_llms` method instead (`ModelLaboratory.from_llms(...)`)z;Currently only support chains with one input variable, got zF K L)N)rzSequence[Chain]rzOptional[List[str]])r(z List[BaseLLM]r&zOptional[PromptTemplate]returnr)r1rr6None)__name__ __module__ __qualname____doc__r classmethodr)r4r5rrrs@W'REI( (*B( ((&Lr5rN)r; __future__rtypingrrr#langchain_core.language_models.llmsrlangchain_core.prompts.promptrlangchain_core.utils.inputr r langchain.chains.baser langchain.chains.llmr rr=r5rrEs.'"++78D')RLRLr5