3fitdZddlmZddlmZddlmZddlmZddl m Z m Z ddl m Z ddlmZGd d Zy ) z!Experiment with different models.) annotations)Sequence)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: A sequence of chains to experiment with. Each chain must have exactly one input and one output variable. names: 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]rzlist[str] | None)r'z list[BaseLLM]r%zPromptTemplate | Nonereturnr )r0rr5None)__name__ __module__ __qualname____doc__r classmethodr(r3r4rr r sFW)V)-((&(  ((*Lr4r N)r: __future__rcollections.abcr#langchain_core.language_models.llmsrlangchain_core.prompts.promptrlangchain_core.utils.inputrrlangchain_classic.chains.baser langchain_classic.chains.llmr r r<r4rrDs+'"$78D/1SLSLr4