Minimax

Minimax 是一家中国初创公司，为公司和个人提供自然语言处理模型。

本示例演示如何使用 LangChain.js 与 Minimax 进行交互。

设置

要使用 Minimax 模型，您需要一个 Minimax 帐户、一个 API 密钥和一个组 ID

提示

npm
Yarn
pnpm

npm install @langchain/community @langchain/core

yarn add @langchain/community @langchain/core

pnpm add @langchain/community @langchain/core

提示

我们正在统一所有包中的模型参数。我们现在建议使用 model 而不是 modelName，并使用 apiKey 用于 API 密钥。

基本用法

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 参考

ChatMinimax 来自 @langchain/community/chat_models/minimax
HumanMessage 来自 @langchain/core/messages

链模型调用

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 参考

LLMChain 来自 langchain/chains
ChatMinimax 来自 @langchain/community/chat_models/minimax
ChatPromptTemplate 来自 @langchain/core/prompts
HumanMessagePromptTemplate 来自 @langchain/core/prompts
SystemMessagePromptTemplate 来自 @langchain/core/prompts

使用函数调用

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.",
    },
  ],
}).bind({
  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 参考

ChatMinimax 来自 @langchain/community/chat_models/minimax
HumanMessage 来自 @langchain/core/messages

使用 Zod 的函数

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.",
    },
  ],
}).bind({
  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 参考

ChatMinimax 来自 @langchain/community/chat_models/minimax
HumanMessage 来自 @langchain/core/messages

使用字形

此功能可以帮助用户强制模型以请求的格式返回内容。

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.",
    },
  ],
}).bind({
  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.",
    },
  ],
}).bind({
  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 参考

ChatMinimax 来自 @langchain/community/chat_models/minimax
ChatPromptTemplate 来自 @langchain/core/prompts
HumanMessagePromptTemplate 来自 @langchain/core/prompts
HumanMessage 来自 @langchain/core/messages

使用示例消息

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。

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.",
    },
  ],
}).bind({
  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 参考

ChatMinimax 来自 @langchain/community/chat_models/minimax
AIMessage 来自 @langchain/core/messages
HumanMessage 来自 @langchain/core/messages

使用插件

此功能支持调用搜索引擎等工具来获取可以帮助模型的额外数据。

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.",
    },
  ],
}).bind({
  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 参考

ChatMinimax 来自 @langchain/community/chat_models/minimax
HumanMessage 来自 @langchain/core/messages

聊天模型概念指南
聊天模型操作指南

Minimax

设置

基本用法

API 参考

链模型调用

API 参考

使用函数调用

API 参考

使用 Zod 的函数

API 参考

使用字形

API 参考

使用示例消息

API 参考

使用插件

API 参考

此页面是否有帮助？

您也可以留下详细的反馈在 GitHub 上.

Minimax

设置​

基本用法​

API 参考

链模型调用​

API 参考

使用函数调用​

API 参考

使用 Zod 的函数​

API 参考

使用字形​

API 参考

使用示例消息​

API 参考

使用插件​

API 参考

相关​

此页面是否有帮助？

您也可以留下详细的反馈 在 GitHub 上.

设置

基本用法

链模型调用

使用函数调用

使用 Zod 的函数

使用字形

使用示例消息

使用插件

相关

您也可以留下详细的反馈在 GitHub 上.