Optional criterionOptional evaluationOptional llmOptional memoryOptional skipOptional config: (RunnableConfig | CallbackManager | (BaseCallbackHandler | BaseCallbackHandlerMethodsClass)[])[]Use .batch() instead. Will be removed in 0.2.0.
This feature is deprecated and will be removed in the future.
It is not recommended for use.
Call the chain on all inputs in the list
Run the core logic of this chain and add to output if desired.
Wraps _call and handles memory.
Optional config: BaseCallbackConfig | CallbackManager | (BaseCallbackHandler | BaseCallbackHandlerMethodsClass)[]Check if the evaluation arguments are valid.
Optional reference: stringThe reference label.
Optional input: stringThe input string.
If the evaluator requires an input string but none is provided, or if the evaluator requires a reference label but none is provided.
Evaluate the output string pairs.
Optional callOptions: BaseLanguageModelCallOptionsOptional config: BaseCallbackConfig | CallbackManager | (BaseCallbackHandler | BaseCallbackHandlerMethodsClass)[]A dictionary containing the preference, scores, and/or other information.
Format prompt with values and pass to LLM
keys to pass to prompt template
Optional callbackManager: CallbackManagerCallbackManager to use
Completion from LLM.
llm.predict({ adjective: "funny" })
Static deserializeStatic fromLLMCreate a new instance of the PairwiseStringEvalChain.
Optional criteria: "detail" | ConstitutionalPrinciple | { The criteria to use for evaluation.
Optional chainOptions: Partial<Omit<LLMEvalChainInput<EvalOutputType, BaseLanguageModelInterface<any, BaseLanguageModelCallOptions>>, "llm">>Options to pass to the chain.
Static resolveOptional criteria: "detail" | ConstitutionalPrinciple | { Static resolveGenerated using TypeDoc
A chain for comparing two outputs, such as the outputs of two models, prompts, or outputs of a single model on similar inputs, with labeled preferences.