Wrapper around Baidu ERNIE large language models that use the Chat endpoint.

To use you should have the BAIDU_API_KEY and BAIDU_SECRET_KEY environment variable set.

Hierarchy

Implements

  • BaiduWenxinChatInput

Constructors

Properties

ParsedCallOptions: Omit<BaseLanguageModelCallOptions, never>
accessToken: string
apiUrl: string
caller: AsyncCaller

The async caller should be used by subclasses to make any async calls, which will thus benefit from the concurrency and retry logic.

modelName: string = "ERNIE-Bot-turbo"
streaming: boolean = false
verbose: boolean

Whether to print out response text.

baiduApiKey?: string
baiduSecretKey?: string
callbacks?: Callbacks
metadata?: Record<string, unknown>
penaltyScore?: number
prefixMessages?: WenxinMessage[]
tags?: string[]
temperature?: number
topP?: number
userId?: string

Accessors

  • get callKeys(): string[]
  • Keys that the language model accepts as call options.

    Returns string[]

Methods

  • Makes a single call to the chat model.

    Parameters

    Returns Promise<BaseMessage>

    A Promise that resolves to a BaseMessage.

  • Makes a single call to the chat model with a prompt value.

    Parameters

    Returns Promise<BaseMessage>

    A Promise that resolves to a BaseMessage.

  • Generates chat based on the input messages.

    Parameters

    Returns Promise<LLMResult>

    A Promise that resolves to an LLMResult.

  • Generates a prompt based on the input prompt values.

    Parameters

    Returns Promise<LLMResult>

    A Promise that resolves to an LLMResult.

  • Method that retrieves the access token for making requests to the Baidu API.

    Parameters

    Returns Promise<any>

    The access token for making requests to the Baidu API.

  • Parameters

    Returns Promise<number>

  • Get the identifying parameters for the model

    Returns {
        model_name: string;
        penalty_score?: number;
        stream?: boolean;
        system?: string;
        temperature?: number;
        top_p?: number;
        user_id?: string;
    }

    • model_name: string
    • Optional penalty_score?: number
    • Optional stream?: boolean
    • Optional system?: string
    • Optional temperature?: number
    • Optional top_p?: number
    • Optional user_id?: string
  • Get the parameters used to invoke the model

    Returns Omit<ChatCompletionRequest, "messages">

  • Create a new runnable sequence that runs each individual runnable in series, piping the output of one runnable into another runnable or runnable-like.

    Type Parameters

    • NewRunOutput

    Parameters

    Returns RunnableSequence<BaseLanguageModelInput, Exclude<NewRunOutput, Error>>

    A new runnable sequence.

  • Predicts the next message based on a text input.

    Parameters

    • text: string

      The text input.

    • Optional options: string[] | BaseLanguageModelCallOptions

      The call options or an array of stop sequences.

    • Optional callbacks: Callbacks

      The callbacks for the language model.

    Returns Promise<string>

    A Promise that resolves to a string.

  • Predicts the next message based on the input messages.

    Parameters

    Returns Promise<BaseMessage>

    A Promise that resolves to a BaseMessage.

  • 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>

  • Default implementation of transform, which buffers input and then calls stream. Subclasses should override this method if they can start producing output while input is still being generated.

    Parameters

    Returns AsyncGenerator<BaseMessageChunk, any, unknown>

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