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Minimax

Minimax 是一家中国初创公司，为企业和个人提供自然语言处理模型。

¥Minimax is a Chinese startup that provides natural language processing models for companies and individuals.

本示例演示如何使用 LangChain.js 与 Minimax 交互。

¥This example demonstrates using LangChain.js to interact with Minimax.

设置​

¥Setup

要使用 Minimax 模型，你需要一个 Minimax 账户、一个 API 密钥和一个组 ID。

¥To use Minimax models, you'll need a Minimax account, an API key, and a Group ID.

tip

请参阅 此部分包含有关安装集成包的一般说明。

¥See this section for general instructions on installing integration packages.

npm install @langchain/community @langchain/core
tip

我们正在统一所有软件包的模型参数。我们现在建议使用 model 代替 modelName，并使用 apiKey 作为 API 密钥。

¥We're unifying model params across all packages. We now suggest using model instead of modelName, and apiKey for API keys.

基本用法​

¥Basic usage

import { ChatMinimax } from "@langchain/community/chat_models/minimax";
import { HumanMessage } from "@langchain/core/messages";

// Use abab5.5
const abab5_5 = new ChatMinimax({
  model: "abab5.5-chat",
  botSetting: [
    {
      bot_name: "MM Assistant",
      content: "MM Assistant is an AI Assistant developed by minimax.",
    },
  ],
});
const messages = [
  new HumanMessage({
    content: "Hello",
  }),
];

const res = await abab5_5.invoke(messages);
console.log(res);

/*
AIChatMessage {
  text: 'Hello! How may I assist you today?',
  name: undefined,
  additional_kwargs: {}
  }
}
*/

// use abab5
const abab5 = new ChatMinimax({
  proVersion: false,
  model: "abab5-chat",
  minimaxGroupId: process.env.MINIMAX_GROUP_ID, // In Node.js defaults to process.env.MINIMAX_GROUP_ID
  minimaxApiKey: process.env.MINIMAX_API_KEY, // In Node.js defaults to process.env.MINIMAX_API_KEY
});

const result = await abab5.invoke([
  new HumanMessage({
    content: "Hello",
    name: "XiaoMing",
  }),
]);
console.log(result);

/*
AIMessage {
  lc_serializable: true,
  lc_kwargs: {
    content: 'Hello! Can I help you with anything?',
    additional_kwargs: { function_call: undefined }
  },
  lc_namespace: [ 'langchain', 'schema' ],
  content: 'Hello! Can I help you with anything?',
  name: undefined,
  additional_kwargs: { function_call: undefined }
}
 */

API Reference:

链模型调用​

¥Chain model calls

import { LLMChain } from "langchain/chains";
import { ChatMinimax } from "@langchain/community/chat_models/minimax";
import {
  ChatPromptTemplate,
  HumanMessagePromptTemplate,
  SystemMessagePromptTemplate,
} from "@langchain/core/prompts";

// We can also construct an LLMChain from a ChatPromptTemplate and a chat model.
const chat = new ChatMinimax({ temperature: 0.01 });

const chatPrompt = ChatPromptTemplate.fromMessages([
  SystemMessagePromptTemplate.fromTemplate(
    "You are a helpful assistant that translates {input_language} to {output_language}."
  ),
  HumanMessagePromptTemplate.fromTemplate("{text}"),
]);
const chainB = new LLMChain({
  prompt: chatPrompt,
  llm: chat,
});

const resB = await chainB.invoke({
  input_language: "English",
  output_language: "Chinese",
  text: "I love programming.",
});
console.log({ resB });

API Reference:

包含函数调用​

¥With function calls

import { ChatMinimax } from "@langchain/community/chat_models/minimax";
import { HumanMessage } from "@langchain/core/messages";

const functionSchema = {
  name: "get_weather",
  description: " Get weather information.",
  parameters: {
    type: "object",
    properties: {
      location: {
        type: "string",
        description: " The location to get the weather",
      },
    },
    required: ["location"],
  },
};

// Bind function arguments to the model.
// All subsequent invoke calls will use the bound parameters.
// "functions.parameters" must be formatted as JSON Schema
const model = new ChatMinimax({
  botSetting: [
    {
      bot_name: "MM Assistant",
      content: "MM Assistant is an AI Assistant developed by minimax.",
    },
  ],
}).withConfig({
  functions: [functionSchema],
});

const result = await model.invoke([
  new HumanMessage({
    content: " What is the weather like in NewYork tomorrow?",
    name: "I",
  }),
]);

console.log(result);

/*
AIMessage {
  lc_serializable: true,
  lc_kwargs: { content: '', additional_kwargs: { function_call: [Object] } },
  lc_namespace: [ 'langchain', 'schema' ],
  content: '',
  name: undefined,
  additional_kwargs: {
    function_call: { name: 'get_weather', arguments: '{"location": "NewYork"}' }
  }
}
*/

// Alternatively, you can pass function call arguments as an additional argument as a one-off:

const minimax = new ChatMinimax({
  model: "abab5.5-chat",
  botSetting: [
    {
      bot_name: "MM Assistant",
      content: "MM Assistant is an AI Assistant developed by minimax.",
    },
  ],
});

const result2 = await minimax.invoke(
  [new HumanMessage("What is the weather like in NewYork tomorrow?")],
  {
    functions: [functionSchema],
  }
);
console.log(result2);

/*
AIMessage {
  lc_serializable: true,
  lc_kwargs: { content: '', additional_kwargs: { function_call: [Object] } },
  lc_namespace: [ 'langchain', 'schema' ],
  content: '',
  name: undefined,
  additional_kwargs: {
    function_call: { name: 'get_weather', arguments: '{"location": "NewYork"}' }
  }
}
 */

API Reference:

使用 Zod 的函数​

¥Functions with Zod

import { z } from "zod";
import { zodToJsonSchema } from "zod-to-json-schema";
import { ChatMinimax } from "@langchain/community/chat_models/minimax";
import { HumanMessage } from "@langchain/core/messages";

const extractionFunctionZodSchema = z.object({
  location: z.string().describe(" The location to get the weather"),
});

// Bind function arguments to the model.
// "functions.parameters" must be formatted as JSON Schema.
// We translate the above Zod schema into JSON schema using the "zodToJsonSchema" package.

const model = new ChatMinimax({
  model: "abab5.5-chat",
  botSetting: [
    {
      bot_name: "MM Assistant",
      content: "MM Assistant is an AI Assistant developed by minimax.",
    },
  ],
}).withConfig({
  functions: [
    {
      name: "get_weather",
      description: " Get weather information.",
      parameters: zodToJsonSchema(extractionFunctionZodSchema),
    },
  ],
});

const result = await model.invoke([
  new HumanMessage({
    content: " What is the weather like in Shanghai tomorrow?",
    name: "XiaoMing",
  }),
]);

console.log(result);

/*
AIMessage {
  lc_serializable: true,
  lc_kwargs: { content: '', additional_kwargs: { function_call: [Object] } },
  lc_namespace: [ 'langchain', 'schema' ],
  content: '',
  name: undefined,
  additional_kwargs: {
    function_call: { name: 'get_weather', arguments: '{"location": "Shanghai"}' }
  }
}
*/

API Reference:

包含字形​

¥With glyph

此功能可以帮助用户强制模型以请求的格式返回内容。

¥This feature can help users force the model to return content in the requested format.

import { ChatMinimax } from "@langchain/community/chat_models/minimax";
import {
  ChatPromptTemplate,
  HumanMessagePromptTemplate,
} from "@langchain/core/prompts";
import { HumanMessage } from "@langchain/core/messages";

const model = new ChatMinimax({
  model: "abab5.5-chat",
  botSetting: [
    {
      bot_name: "MM Assistant",
      content: "MM Assistant is an AI Assistant developed by minimax.",
    },
  ],
}).withConfig({
  replyConstraints: {
    sender_type: "BOT",
    sender_name: "MM Assistant",
    glyph: {
      type: "raw",
      raw_glyph: "The translated text：{{gen 'content'}}",
    },
  },
});

const messagesTemplate = ChatPromptTemplate.fromMessages([
  HumanMessagePromptTemplate.fromTemplate(
    " Please help me translate the following sentence in English： {text}"
  ),
]);

const messages = await messagesTemplate.formatMessages({ text: "我是谁" });
const result = await model.invoke(messages);

console.log(result);

/*
AIMessage {
  lc_serializable: true,
  lc_kwargs: {
    content: 'The translated text： Who am I\x02',
    additional_kwargs: { function_call: undefined }
  },
  lc_namespace: [ 'langchain', 'schema' ],
  content: 'The translated text： Who am I\x02',
  name: undefined,
  additional_kwargs: { function_call: undefined }
}
*/

// use json_value

const modelMinimax = new ChatMinimax({
  model: "abab5.5-chat",
  botSetting: [
    {
      bot_name: "MM Assistant",
      content: "MM Assistant is an AI Assistant developed by minimax.",
    },
  ],
}).withConfig({
  replyConstraints: {
    sender_type: "BOT",
    sender_name: "MM Assistant",
    glyph: {
      type: "json_value",
      json_properties: {
        name: {
          type: "string",
        },
        age: {
          type: "number",
        },
        is_student: {
          type: "boolean",
        },
        is_boy: {
          type: "boolean",
        },
        courses: {
          type: "object",
          properties: {
            name: {
              type: "string",
            },
            score: {
              type: "number",
            },
          },
        },
      },
    },
  },
});

const result2 = await modelMinimax.invoke([
  new HumanMessage({
    content:
      " My name is Yue Wushuang, 18 years old this year, just finished the test with 99.99 points.",
    name: "XiaoMing",
  }),
]);

console.log(result2);

/*
AIMessage {
  lc_serializable: true,
  lc_kwargs: {
    content: '{\n' +
      '  "name": "Yue Wushuang",\n' +
      '  "is_student": true,\n' +
      '  "is_boy": false,\n' +
      '  "courses":   {\n' +
      '    "name": "Mathematics",\n' +
      '    "score": 99.99\n' +
      '   },\n' +
      '  "age": 18\n' +
      ' }',
    additional_kwargs: { function_call: undefined }
  }
}

 */

API Reference:

包含示例消息​

¥With sample messages

此功能可以帮助模型更好地理解用户想要获取的返回信息，包括但不限于信息的内容、格式和响应模式。

¥This feature can help the model better understand the return information the user wants to get, including but not limited to the content, format, and response mode of the information.

import { ChatMinimax } from "@langchain/community/chat_models/minimax";
import { AIMessage, HumanMessage } from "@langchain/core/messages";

const model = new ChatMinimax({
  model: "abab5.5-chat",
  botSetting: [
    {
      bot_name: "MM Assistant",
      content: "MM Assistant is an AI Assistant developed by minimax.",
    },
  ],
}).withConfig({
  sampleMessages: [
    new HumanMessage({
      content: " Turn A5 into red and modify the content to minimax.",
    }),
    new AIMessage({
      content: "select A5 color red change minimax",
    }),
  ],
});

const result = await model.invoke([
  new HumanMessage({
    content:
      ' Please reply to my content according to the following requirements: According to the following interface list, give the order and parameters of calling the interface for the content I gave. You just need to give the order and parameters of calling the interface, and do not give any other output. The following is the available interface list: select: select specific table position, input parameter use letters and numbers to determine, for example "B13"; color: dye the selected table position, input parameters use the English name of the color, for example "red"; change: modify the selected table position, input parameters use strings.',
  }),
  new HumanMessage({
    content: " Process B6 to gray and modify the content to question.",
  }),
]);

console.log(result);

API Reference:

包含插件​

¥With plugins

此功能支持调用搜索引擎等工具来获取可辅助模型的额外数据。

¥This feature supports calling tools like a search engine to get additional data that can assist the model.

import { ChatMinimax } from "@langchain/community/chat_models/minimax";
import { HumanMessage } from "@langchain/core/messages";

const model = new ChatMinimax({
  model: "abab5.5-chat",
  botSetting: [
    {
      bot_name: "MM Assistant",
      content: "MM Assistant is an AI Assistant developed by minimax.",
    },
  ],
}).withConfig({
  plugins: ["plugin_web_search"],
});

const result = await model.invoke([
  new HumanMessage({
    content: " What is the weather like in NewYork tomorrow?",
  }),
]);

console.log(result);

/*
AIMessage {
  lc_serializable: true,
  lc_kwargs: {
    content: 'The weather in Shanghai tomorrow is expected to be hot. Please note that this is just a forecast and the actual weather conditions may vary.',
    additional_kwargs: { function_call: undefined }
  },
  lc_namespace: [ 'langchain', 'schema' ],
  content: 'The weather in Shanghai tomorrow is expected to be hot. Please note that this is just a forecast and the actual weather conditions may vary.',
  name: undefined,
  additional_kwargs: { function_call: undefined }
}
*/

API Reference:

¥Related