Ig |dZddlmZddlmZmZmZddlmZddl m Z ddl m Z m Z ddlmZddlmZGd d Zy ) !Experiment with different models.) annotations)ListOptionalSequence)BaseLLM)PromptTemplate)get_color_mapping print_text)Chain)LLMChaincBeZdZdZdddZe d ddZd dZy) ModelLaboratoryrNc|D]}}t|ts tdt|jdk7rtd|jt|j dk7sgtd|j |"t|t|k7r td||_tt|j Dcgc] }t|}}t||_ ||_ ycc}w)zyInitialize with chains to experiment with. Args: chains: list of chains to experiment with. 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 z#XJTF>BCs(s62CC%)*cS**6''D*s A Ac.td|dt|jD]n\}}|j|j|}n t |}t |d|j |}t ||jt |dpy)a3Compare model outputs on an input text. If a prompt was provided with starting the laboratory, then this text will be fed into the prompt. If no prompt was provided, then the input text is the entire prompt. Args: text: input text to run all models on. zInput:  N)endz )colorr-)print enumeraterrrr runr)rtextrrnameoutputs r comparezModelLaboratory.compareBs &tfB/0!$++. LHAuzz%zz!}5z t &YYt_F vT%6%6s1v%>F K L)N)rzSequence[Chain]rzOptional[List[str]])r)z List[BaseLLM]r'zOptional[PromptTemplate]returnr)r2rr7None)__name__ __module__ __qualname____doc__r! classmethodr*r5r6r rrs?+>EI( (*B( (( Lr6rN)r< __future__rtypingrrr#langchain_core.language_models.llmsrlangchain_core.prompts.promptr langchain_core.utils.inputr r langchain.chains.baser langchain.chains.llmr rr>r6r rFs.'"++78D')ELELr6