Class ZeroShotAgent

Agent for the MRKL chain.

Example


const agent = new ZeroShotAgent({
  llmChain: new LLMChain({
    llm: new ChatOpenAI({ temperature: 0 }),
    prompt: ZeroShotAgent.createPrompt([new SerpAPI(), new Calculator()], {
      prefix: `Answer the following questions as best you can, but speaking as a pirate might speak. You have access to the following tools:`,
      suffix: `Begin! Remember to speak as a pirate when giving your final answer. Use lots of "Args"
Question: {input}
{agent_scratchpad}`,
      inputVariables: ["input", "agent_scratchpad"],
    }),
  }),
  allowedTools: ["search", "calculator"],
});

const result = await agent.invoke({
  input: `Who is Olivia Wilde's boyfriend? What is his current age raised to the 0.23 power?`,
});

Hierarchy

Constructors

constructor

Properties

ToolType llmChain outputParser

Accessors

allowedTools inputKeys returnValues

Methods

constructScratchPad finishToolName llmPrefix observationPrefix plan prepareForOutput returnStoppedResponse toJSON toJSONNotImplemented createPrompt deserialize fromLLMAndTools getDefaultOutputParser validateTools

Constructors

constructor

Properties

ToolType

ToolType: Tool

llmChain

llmChain: LLMChain<string, LLMType>

outputParser

outputParser: undefined | AgentActionOutputParser

Accessors

allowedTools

  • get allowedTools(): undefined | string[]

  • Returns undefined | string[]

inputKeys

  • get inputKeys(): string[]

  • Returns string[]

returnValues

  • get returnValues(): string[]

  • Returns string[]

Methods

constructScratchPad

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

    Parameters

    Returns Promise<string | BaseMessage[]>

finishToolName

  • Name of tool to use to terminate the chain.

    Returns string

llmPrefix

  • Prefix to append the LLM call with.

    Returns string

observationPrefix

  • Prefix to append the observation with.

    Returns string

plan

  • 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

  • Prepare the agent for output, if needed

    Parameters

    • _returnValues: Record<string, any>
    • _steps: AgentStep[]

    Returns Promise<Record<string, any>>

returnStoppedResponse

toJSON

toJSONNotImplemented

Static createPrompt

  • Create prompt in the style of the zero shot agent.

    Parameters

    • tools: Tool[]

      List of tools the agent will have access to, used to format the prompt.

    • Optional args: ZeroShotCreatePromptArgs

      Arguments to create the prompt with.

    Returns PromptTemplate<any, any>

Static deserialize

  • Load an agent from a json-like object describing it.

    Parameters

    • data: Object

    Returns Promise<ZeroShotAgent>

Static fromLLMAndTools

Static getDefaultOutputParser

Static validateTools

  • Validates the tools for the ZeroShotAgent. Throws an error if any tool does not have a description.

    Parameters

    • tools: Tool[]

      List of tools to validate.

    Returns void

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