Chain to generate tasks.

Hierarchy

Constructors

Properties

lc_kwargs: SerializedFields
lc_serializable: boolean = true
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[]
lc_runnable: boolean = true

Accessors

  • 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;
    }

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

  • get lc_namespace(): string[]
  • A path to the module that contains the class, eg. ["langchain", "llms"] Usually should be the same as the entrypoint the class is exported from.

    Returns string[]

  • 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;
    }

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>

  • Creates a new TaskCreationChain instance. It takes an object of type LLMChainInput as input, omitting the 'prompt' field. It uses the PromptTemplate class to create a new prompt based on the task creation template and the input variables. The new TaskCreationChain instance is then created with this prompt and the remaining fields from the input object.

    Parameters

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

      An object of type LLMChainInput, omitting the 'prompt' field.

    Returns LLMChain<string, LLMType>

    A new instance of TaskCreationChain.

  • Helper method to transform an Iterator of Input values into an Iterator of Output values, with callbacks. Use this to implement stream() or transform() in Runnable subclasses.

    Type Parameters

    Parameters

    • inputGenerator: AsyncGenerator<I, any, unknown>
    • transformer: ((generator, runManager?, options?) => AsyncGenerator<O, any, unknown>)
        • (generator, runManager?, options?): AsyncGenerator<O, any, unknown>
        • Parameters

          Returns AsyncGenerator<O, any, unknown>

    • Optional options: BaseCallbackConfig & {
          runType?: string;
      }

    Returns AsyncGenerator<O, any, unknown>

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