Class TrajectoryEvalChain

llm

outputKey

prompt

OptionalllmKwargs

Optionalmemory

OptionalskipInputWarning

OptionalskipReferenceWarning

inputKeys

• get inputKeys(): any

• Returns any

outputKeys

• get outputKeys(): string[]

• Returns string[]

apply

• apply(inputs, config?): Promise<ChainValues[]>

• Parameters

  • inputs: ChainValues[]
  • Optionalconfig: any[]

  Returns Promise<ChainValues[]>

  Deprecated

  Use .batch() instead. Will be removed in 0.2.0.

  Call the chain on all inputs in the list

call

• call(values, config?): Promise<ChainValues>

• Run the core logic of this chain and add to output if desired.

  Wraps _call and handles memory.

  Parameters

  • values: any
  • Optionalconfig: any

  Returns Promise<ChainValues>

checkEvaluationArgs

• checkEvaluationArgs(reference?, input?): void

• Check if the evaluation arguments are valid.

  Parameters

  • Optionalreference: string

    The reference label.
  • Optionalinput: string

    The input string.

  Returns void

  Throws

  If the evaluator requires an input string but none is provided, or if the evaluator requires a reference label but none is provided.

evaluateAgentTrajectory

• evaluateAgentTrajectory(args, callOptions?, config?): Promise<ChainValues>

• Evaluate a trajectory.

  Parameters

  • args: LLMTrajectoryEvaluatorArgs
  • OptionalcallOptions: unknown
  • Optionalconfig: any

  Returns Promise<ChainValues>

  The evaluation result.

invoke

• invoke(input, options?): Promise<ChainValues>

• Invoke the chain with the provided input and returns the output.

  Parameters

  • input: ChainValues

    Input values for the chain run.
  • Optionaloptions: any

  Returns Promise<ChainValues>

  Promise that resolves with the output of the chain run.

predict

• predict(values, callbackManager?): Promise<EvalOutputType>

• Format prompt with values and pass to LLM

  Parameters

  • values: any

    keys to pass to prompt template
  • OptionalcallbackManager: any

    CallbackManager to use

  Returns Promise<EvalOutputType>

  Completion from LLM.

  Example

  llm.predict({ adjective: "funny" })
  Copy

prepOutputs

• prepOutputs(inputs, outputs, returnOnlyOutputs?): Promise<Record<string, unknown>>

• Parameters

  • inputs: Record<string, unknown>
  • outputs: Record<string, unknown>
  • returnOnlyOutputs: boolean = false

  Returns Promise<Record<string, unknown>>

run

• run(input, config?): Promise<string>

• Parameters

  • input: any
  • Optionalconfig: any

  Returns Promise<string>

  Deprecated

  Use .invoke() instead. Will be removed in 0.2.0.

serialize

• serialize(): SerializedLLMChain

• Returns SerializedLLMChain

  Deprecated

Staticdeserialize

• deserialize(data): Promise<LLMChain<string, any>>

• Load a chain from a json-like object describing it.

  Parameters

  • data: SerializedLLMChain

  Returns Promise<LLMChain<string, any>>