Class ConversationChain

A class for conducting conversations between a human and an AI. It extends the LLMChain class.

Example

const model = new ChatOpenAI({});
const chain = new ConversationChain({ llm: model });

// Sending a greeting to the conversation chain
const res1 = await chain.call({ input: "Hi! I'm Jim." });
console.log({ res1 });

// Following up with a question in the conversation
const res2 = await chain.call({ input: "What's my name?" });
console.log({ res2 });

Hierarchy

Constructors

constructor

Properties

llm outputKey prompt llmKwargs? memory? outputParser?

Accessors

inputKeys outputKeys

Methods

apply call invoke predict prepOutputs run serialize deserialize

Constructors

constructor

Properties

llm

llm: LLMType

LLM Wrapper to use

outputKey

outputKey: string = "text"

Key to use for output, defaults to text

prompt

prompt: BasePromptTemplate<any, BasePromptValue, any>

Prompt object to use

Optional llmKwargs

llmKwargs?: any

Kwargs to pass to LLM

Optional memory

memory?: BaseMemory

Optional outputParser

outputParser?: BaseLLMOutputParser<string>

OutputParser to use

Accessors

inputKeys

  • get inputKeys(): string[]

  • Returns string[]

outputKeys

  • get outputKeys(): string[]

  • Returns string[]

Methods

apply

  • Call the chain on all inputs in the list

    Parameters

    • inputs: ChainValues[]
    • Optional config: (BaseCallbackConfig | Callbacks)[]

    Returns Promise<ChainValues[]>

call

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

    Wraps _call and handles memory.

    Parameters

    • values: any
    • Optional config: BaseCallbackConfig | Callbacks

    Returns Promise<ChainValues>

invoke

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

    Parameters

    • input: ChainValues

      Input values for the chain run.

    • Optional config: BaseCallbackConfig

      Optional configuration for the Runnable.

    Returns Promise<ChainValues>

    Promise that resolves with the output of the chain run.

predict

  • 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" })

prepOutputs

  • Parameters

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

    Returns Promise<Record<string, unknown>>

run

  • Parameters

    • input: any
    • Optional config: BaseCallbackConfig | Callbacks

    Returns Promise<string>

serialize

Static deserialize

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

    Parameters

    Returns Promise<LLMChain<string, BaseLanguageModel<any, BaseLanguageModelCallOptions>>>

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