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 (view full)

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: any

LLM Wrapper to use

outputKey

outputKey: string = "text"

Key to use for output, defaults to text

prompt

prompt: BasePromptTemplate

Prompt object to use

Optional llmKwargs

llmKwargs?: any

Kwargs to pass to LLM

Optional memory

memory?: any

Optional outputParser

outputParser?: any

OutputParser to use

Accessors

inputKeys

outputKeys

Methods

apply

  • apply(inputs, config?): Promise<ChainValues[]>

  • Parameters

    • inputs: ChainValues[]
    • Optional config: any[]

    Returns Promise<ChainValues[]>

    ⚠️ Deprecated ⚠️

    Use .batch() instead. Will be removed in 0.2.0.

    This feature is deprecated and will be removed in the future.

    It is not recommended for use.

    Call the chain on all inputs in the list

call

  • call(values, config?): Promise<ChainValues>

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

    Wraps _call and handles memory.

    Parameters

    • values: any
    • Optional config: any

    Returns Promise<ChainValues>

invoke

  • invoke(input, options?): Promise<ChainValues>

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

    Parameters

    • input: ChainValues

      Input values for the chain run.

    • Optional options: any

    Returns Promise<ChainValues>

    Promise that resolves with the output of the chain run.

predict

  • predict(values, callbackManager?): Promise<string>

  • Format prompt with values and pass to LLM

    Parameters

    • values: any

      keys to pass to prompt template

    • Optional callbackManager: any

      CallbackManager to use

    Returns Promise<string>

    Completion from LLM.

    Example

    llm.predict({ adjective: "funny" })

prepOutputs

  • prepOutputs(inputs, outputs, returnOnlyOutputs?): Promise<Record<string, unknown>>

  • Parameters

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

    Returns Promise<Record<string, unknown>>

run

  • run(input, config?): Promise<string>

  • Parameters

    • input: any
    • Optional config: any

    Returns Promise<string>

    Deprecated

    Use .invoke() instead. Will be removed in 0.2.0.

serialize

Static deserialize

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