hdZddlmZddlmZddlmZddlmZddl m Z ddl m Z m Z ddlmZdd lmZGd d Zy ) z!Experiment with different models.) annotations)Sequence)Optional)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]y}t|ts d}t|t|jdk7rd|j}t|t|j dk7skd|j }{|$t|t|k7r d}t|||_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)r4r5rr r sFW)V,0(()(  ((*Lr5r N)r; __future__rcollections.abcrtypingr#langchain_core.language_models.llmsrlangchain_core.prompts.promptrlangchain_core.utils.inputrr langchain.chains.baser langchain.chains.llmr r r=r5rrFs.'"$78D')SLSLr5