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.

// 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 (view full)

Implements

  • MinimaxChatInput

Constructors

Properties

apiUrl: string
model: string = "abab5.5-chat"

Model name to use

"abab5.5-chat"
modelName: string = "abab5.5-chat"

Model name to use Alias for model

"abab5.5-chat"
streaming: boolean = false

Whether to stream the results or not. Defaults to false.

apiKey?: string

Secret key to use when making requests. Defaults to the value of MINIMAX_API_KEY environment variable.

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

How many results to generate; the default is 1 and the maximum is not more than 4. Because beamWidth generates multiple results, it will consume more tokens.

botSetting?: BotSetting[]

Setting for each robot, only available for pro version.

continueLastMessage?: boolean

If it is true, this indicates that the current request is set to continuation mode, and the response is a continuation of the last sentence in the incoming messages; at this time, the last sender is not limited to USER, it can also be BOT. Assuming the last sentence of incoming messages is {"sender_type": " U S E R", "text": "ċ¤İ生ĉˆ‘ĉ"}, the completion of the reply may be "It must be useful."

defaultBotName?: string = "Assistant"

Default bot name

defaultUserName?: string = "I"

Default user name

headers?: Record<string, string>
maskSensitiveInfo?: boolean

For the text information in the output that may involve privacy issues, code masking is currently included but not limited to emails, domains, links, ID numbers, home addresses, etc., with the default being true, that is, code masking is enabled.

minimaxApiKey?: string

Secret key to use when making requests. Defaults to the value of MINIMAX_API_KEY environment variable. Alias for apiKey

minimaxGroupId?: string

API key to use when making requests. Defaults to the value of MINIMAX_GROUP_ID environment variable.

prefixMessages?: MinimaxChatCompletionRequestMessage[]
proVersion?: boolean = true

Enable Chatcompletion pro

prompt?: string

Dialogue setting, characters, or functionality setting.

replyConstraints?: ReplyConstraints
roleMeta?: RoleMeta

Dialogue Metadata

skipInfoMask?: boolean

Sensitize text information in the output that may involve privacy issues, currently including but not limited to emails, domain names, links, ID numbers, home addresses, etc. Default false, ie. enable sensitization.

temperature?: number = 0.9

Amount of randomness injected into the response. Ranges from 0 to 1 (0 is not included). Use temp closer to 0 for analytical / multiple choice, and temp closer to 1 for creative and generative tasks. Defaults to 0.95.

tokensToGenerate?: number

Pay attention to the maximum number of tokens generated, this parameter does not affect the generation effect of the model itself, but only realizes the function by truncating the tokens exceeding the limit. It is necessary to ensure that the number of tokens of the input context plus this value is less than 6144 or 16384, otherwise the request will fail.

topP?: number = 0.8

The smaller the sampling method, the more determinate the result; the larger the number, the more random the result.

useStandardSse?: boolean

Whether to use the standard SSE format, when set to true, the streaming results will be separated by two line breaks. This parameter only takes effect when stream is set to true.

Accessors

Methods

  • 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
    • Optionalbeam_width?: number
    • Optionalbot_setting?: BotSetting[]
    • Optionalfunctions?: Function[]

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

    • Optionalmask_sensitive_info?: boolean
    • Optionalplugins?: string[]
    • Optionalprompt?: string
    • Optionalreply_constraints?: ReplyConstraints
    • Optionalrole_meta?: RoleMeta
    • Optionalsample_messages?: MinimaxChatCompletionRequestMessage[]
    • Optionalskip_info_mask?: boolean
    • Optionalstream?: boolean
    • Optionaltemperature?: number
    • Optionaltokens_to_generate?: number
    • Optionaltop_p?: number
    • Optionaluse_standard_sse?: boolean
  • Convert a list of messages to the format expected by the model.

    Parameters

    • Optionalmessages: BaseMessage[]
    • Optionaloptions: unknown

    Returns undefined | MinimaxChatCompletionRequestMessage[]

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