Wrapper around Minimax large language models that use the Chat endpoint.

To use you should have the MINIMAX_GROUP_ID and MINIMAX_API_KEY environment variable set.

Example

// Define a chat prompt with a system message setting the context for translation
const chatPrompt = ChatPromptTemplate.fromMessages([
SystemMessagePromptTemplate.fromTemplate(
"You are a helpful assistant that translates {input_language} to {output_language}.",
),
HumanMessagePromptTemplate.fromTemplate("{text}"),
]);

// Create a new LLMChain with the chat model and the defined prompt
const chainB = new LLMChain({
prompt: chatPrompt,
llm: new ChatMinimax({ temperature: 0.01 }),
});

// Call the chain with the input language, output language, and the text to translate
const resB = await chainB.call({
input_language: "English",
output_language: "Chinese",
text: "I love programming.",
});

// Log the result
console.log({ resB });

Hierarchy

Implements

  • MinimaxChatInput

Constructors

Properties

ParsedCallOptions: Omit<ChatMinimaxCallOptions, never>
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 = "abab5.5-chat"
streaming: boolean = false
verbose: boolean

Whether to print out response text.

basePath?: string = "https://api.minimax.chat/v1"
beamWidth?: number
botSetting?: BotSetting[]
callbacks?: Callbacks
continueLastMessage?: boolean
defaultBotName?: string = "Assistant"
defaultUserName?: string = "I"
headers?: Record<string, string>
maskSensitiveInfo?: boolean
metadata?: Record<string, unknown>
minimaxApiKey?: string
minimaxGroupId?: string
prefixMessages?: MinimaxChatCompletionRequestMessage[]
proVersion?: boolean = true
prompt?: string
replyConstraints?: ReplyConstraints
roleMeta?: RoleMeta
skipInfoMask?: boolean
tags?: string[]
temperature?: number = 0.9
tokensToGenerate?: number
topP?: number = 0.8
useStandardSse?: boolean

Accessors

Methods

  • Makes a single call to the chat model.

    Parameters

    • messages: BaseMessageLike[]

      An array of BaseMessage instances.

    • Optional options: string[] | ChatMinimaxCallOptions

      The call options or an array of stop sequences.

    • Optional callbacks: Callbacks

      The callbacks for the language model.

    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

    • messages: BaseMessageLike[][]

      An array of arrays of BaseMessage instances.

    • Optional options: string[] | ChatMinimaxCallOptions

      The call options or an array of stop sequences.

    • Optional callbacks: Callbacks

      The callbacks for the language model.

    Returns Promise<LLMResult>

    A Promise that resolves to an LLMResult.

  • Generates a prompt based on the input prompt values.

    Parameters

    • promptValues: BasePromptValue[]

      An array of BasePromptValue instances.

    • Optional options: string[] | ChatMinimaxCallOptions

      The call options or an array of stop sequences.

    • Optional callbacks: Callbacks

      The callbacks for the language model.

    Returns Promise<LLMResult>

    A Promise that resolves to an LLMResult.

  • Parameters

    Returns Promise<number>

  • Get the identifying parameters for the model

    Returns {
        model: string;
        beam_width?: number;
        bot_setting?: BotSetting[];
        functions?: Function[];
        mask_sensitive_info?: boolean;
        plugins?: string[];
        prompt?: string;
        reply_constraints?: ReplyConstraints;
        role_meta?: RoleMeta;
        sample_messages?: MinimaxChatCompletionRequestMessage[];
        skip_info_mask?: boolean;
        stream?: boolean;
        temperature?: number;
        tokens_to_generate?: number;
        top_p?: number;
        use_standard_sse?: boolean;
    }

    • model: string
    • Optional beam_width?: number
    • Optional bot_setting?: BotSetting[]
    • Optional functions?: Function[]

      A list of functions the model may generate JSON inputs for.

    • Optional mask_sensitive_info?: boolean
    • Optional plugins?: string[]
    • Optional prompt?: string
    • Optional reply_constraints?: ReplyConstraints
    • Optional role_meta?: RoleMeta
    • Optional sample_messages?: MinimaxChatCompletionRequestMessage[]
    • Optional skip_info_mask?: boolean
    • Optional stream?: boolean
    • Optional temperature?: number
    • Optional tokens_to_generate?: number
    • Optional top_p?: number
    • Optional use_standard_sse?: boolean
  • 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[] | ChatMinimaxCallOptions

      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

    • messages: BaseMessage[]

      An array of BaseMessage instances.

    • Optional options: string[] | ChatMinimaxCallOptions

      The call options or an array of stop sequences.

    • Optional callbacks: Callbacks

      The callbacks for the language model.

    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>

  • Bind lifecycle listeners to a Runnable, returning a new Runnable. The Run object contains information about the run, including its id, type, input, output, error, startTime, endTime, and any tags or metadata added to the run.

    Parameters

    • params: {
          onEnd?: ((run, config?) => void | Promise<void>);
          onError?: ((run, config?) => void | Promise<void>);
          onStart?: ((run, config?) => void | Promise<void>);
      }

      The object containing the callback functions.

      • Optional onEnd?: ((run, config?) => void | Promise<void>)
          • (run, config?): void | Promise<void>
          • Called after the runnable finishes running, with the Run object.

            Parameters

            Returns void | Promise<void>

      • Optional onError?: ((run, config?) => void | Promise<void>)
          • (run, config?): void | Promise<void>
          • Called if the runnable throws an error, with the Run object.

            Parameters

            Returns void | Promise<void>

      • Optional onStart?: ((run, config?) => void | Promise<void>)
          • (run, config?): void | Promise<void>
          • Called before the runnable starts running, with the Run object.

            Parameters

            Returns void | Promise<void>

    Returns Runnable<BaseLanguageModelInput, BaseMessageChunk, ChatMinimaxCallOptions>

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