Chain to execute tasks.

Hierarchy

Constructors

Properties

llm: LLMType

LLM Wrapper to use

outputKey: string = "text"

Key to use for output, defaults to text

Prompt object to use

verbose: boolean

Whether to print out response text.

callbacks?: Callbacks
llmKwargs?: any

Kwargs to pass to LLM

memory?: BaseMemory
metadata?: Record<string, unknown>
outputParser?: BaseLLMOutputParser<string>

OutputParser to use

tags?: string[]

Accessors

Methods

  • Format prompt with values and pass to LLM

    Parameters

    • values: any

      keys to pass to prompt template

    • Optional callbackManager: CallbackManager

      CallbackManager to use

    Returns Promise<string>

    Completion from LLM.

    Example

    llm.predict({ adjective: "funny" })
    
  • Stream all output from a runnable, as reported to the callback system. This includes all inner runs of LLMs, Retrievers, Tools, etc. Output is streamed as Log objects, which include a list of jsonpatch ops that describe how the state of the run has changed in each step, and the final state of the run. The jsonpatch ops can be applied in order to construct state.

    Parameters

    Returns AsyncGenerator<RunLogPatch, any, unknown>

  • A static factory method that creates an instance of TaskExecutionChain. It constructs a prompt template for task execution, which is then used to create a new instance of TaskExecutionChain. The prompt template instructs an AI to perform a task based on a given objective, taking into account previously completed tasks.

    Parameters

    • fields: Omit<LLMChainInput<string, LLMType>, "prompt">

      An object of type LLMChainInput, excluding the "prompt" field.

    Returns LLMChain<string, LLMType>

    An instance of LLMChain.

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