Class ChatMinimax

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

constructor

Properties

apiUrl modelName streaming basePath? beamWidth? botSetting? continueLastMessage? defaultBotName? defaultUserName? headers? maskSensitiveInfo? minimaxApiKey? minimaxGroupId? prefixMessages? proVersion? prompt? replyConstraints? roleMeta? skipInfoMask? temperature? tokensToGenerate? topP? useStandardSse?

Accessors

callKeys

Methods

defaultReplyConstraints fallbackBotName identifyingParams invocationParams messageToMinimaxMessage

Constructors

constructor

  • Parameters

    • Optional fields: Partial<MinimaxChatInput> & BaseLanguageModelParams & {
          configuration?: ConfigurationParameters;
      }

    Returns ChatMinimax

Properties

apiUrl

apiUrl: string

modelName

modelName: string = "abab5.5-chat"

streaming

streaming: boolean = false

Optional basePath

basePath?: string = "https://api.minimax.chat/v1"

Optional beamWidth

beamWidth?: number

Optional botSetting

botSetting?: BotSetting[]

Optional continueLastMessage

continueLastMessage?: boolean

Optional defaultBotName

defaultBotName?: string = "Assistant"

Optional defaultUserName

defaultUserName?: string = "I"

Optional headers

headers?: Record<string, string>

Optional maskSensitiveInfo

maskSensitiveInfo?: boolean

Optional minimaxApiKey

minimaxApiKey?: string

Optional minimaxGroupId

minimaxGroupId?: string

Optional prefixMessages

prefixMessages?: MinimaxChatCompletionRequestMessage[]

Optional proVersion

proVersion?: boolean = true

Optional prompt

prompt?: string

Optional replyConstraints

replyConstraints?: ReplyConstraints

Optional roleMeta

roleMeta?: RoleMeta

Optional skipInfoMask

skipInfoMask?: boolean

Optional temperature

temperature?: number = 0.9

Optional tokensToGenerate

tokensToGenerate?: number

Optional topP

topP?: number = 0.8

Optional useStandardSse

useStandardSse?: boolean

Accessors

callKeys

Methods

defaultReplyConstraints

  • Parameters

    Returns ReplyConstraints

fallbackBotName

  • Parameters

    Returns string

identifyingParams

  • 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

invocationParams

  • Get the parameters used to invoke the model

    Parameters

    Returns Omit<MinimaxChatCompletionRequest, "messages">

messageToMinimaxMessage

  • Convert a list of messages to the format expected by the model.

    Parameters

    Returns undefined | MinimaxChatCompletionRequestMessage[]

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