Class ZeroShotAgent

lc_kwargs

lc_serializable

llmChain

outputParser

allowedTools

• get allowedTools(): undefined | string[]

• Returns undefined | string[]

inputKeys

• get inputKeys(): string[]

• Returns string[]

lc_aliases

• get lc_aliases(): undefined | {
      [key: string]: string;
  }

• A map of aliases for constructor args. Keys are the attribute names, e.g. "foo". Values are the alias that will replace the key in serialization. This is used to eg. make argument names match Python.

  Returns undefined | {
      [key: string]: string;
  }

lc_attributes

• get lc_attributes(): undefined | SerializedFields

• A map of additional attributes to merge with constructor args. Keys are the attribute names, e.g. "foo". Values are the attribute values, which will be serialized. These attributes need to be accepted by the constructor as arguments.

  Returns undefined | SerializedFields

lc_id

• get lc_id(): string[]

• The final serialized identifier for the module.

  Returns string[]

lc_secrets

• get lc_secrets(): undefined | {
      [key: string]: string;
  }

• A map of secrets, which will be omitted from serialization. Keys are paths to the secret in constructor args, e.g. "foo.bar.baz". Values are the secret ids, which will be used when deserializing.

  Returns undefined | {
      [key: string]: string;
  }

returnValues

• get returnValues(): string[]

• Returns string[]

_agentActionType

• _agentActionType(): string

• Returns string

_stop

• _stop(): string[]

• Returns string[]

constructScratchPad

• constructScratchPad(steps): Promise<string | BaseMessage[]>

• Construct a scratchpad to let the agent continue its thought process

  Parameters

  • steps: AgentStep[]

  Returns Promise<string | BaseMessage[]>

finishToolName

• finishToolName(): string

• Name of tool to use to terminate the chain.

  Returns string

plan

• plan(steps, inputs, callbackManager?): Promise<AgentAction | AgentFinish>

• Decide what to do given some input.

  Parameters

  • steps: AgentStep[]

    Steps the LLM has taken so far, along with observations from each.

  • inputs: ChainValues

    User inputs.

  • Optional callbackManager: CallbackManager

    Callback manager to use for this call.

  Returns Promise<AgentAction | AgentFinish>

  Action specifying what tool to use.

prepareForOutput

• prepareForOutput(_returnValues, _steps): Promise<Record<string, any>>

• Prepare the agent for output, if needed

  Parameters

  • _returnValues: Record<string, any>

  • _steps: AgentStep[]

  Returns Promise<Record<string, any>>

returnStoppedResponse

• returnStoppedResponse(earlyStoppingMethod, steps, inputs, callbackManager?): Promise<AgentFinish>

• Return response when agent has been stopped due to max iterations

  Parameters

  • earlyStoppingMethod: StoppingMethod

  • steps: AgentStep[]

  • inputs: ChainValues

  • Optional callbackManager: CallbackManager

  Returns Promise<AgentFinish>

toJSON

• toJSON(): Serialized

• Returns Serialized

toJSONNotImplemented

• toJSONNotImplemented(): SerializedNotImplemented

• Returns SerializedNotImplemented