ChatMistralAI 集成

使用 LangChain JavaScript 集成 ChatMistralAI chat model。

Mistral AI is a platform that offers hosting for their powerful open source models.

This will help you getting started with Mistral chat models. For detailed documentation of all ChatMistralAI features and configurations head to the API reference.

概述

集成详情

Class	Package	Serializable	PY support	Downloads	Version
ChatMistralAI	@langchain/mistralai	❌	✅

模型功能

请参阅下表标题中的链接，了解如何使用特定功能。

Tool calling	Structured output	Image input	Audio input	Video input	Token-level streaming	Token usage	Logprobs
✅	✅	✅	❌	❌	✅	✅	❌

设置

要访问 Mistral AI models，你需要create a Mistral AI account, get an API key, and install the @langchain/mistralai integration package.

凭证

Visit the Mistral console to sign up and generate an API key. Once you’ve done this set the MISTRAL_API_KEY environment variable:

export MISTRAL_API_KEY="your-api-key"

如果你想要自动追踪模型调用，还可以设置你的 LangSmith API 密钥，取消注释以下内容：

# export LANGSMITH_TRACING="true"
# export LANGSMITH_API_KEY="your-api-key"

安装

LangChain 的 ChatMistralAI 集成位于 @langchain/mistralai 包中：

npm

npm install @langchain/mistralai @langchain/core

实例化

现在我们可以实例化模型对象并生成聊天补全：

import { ChatMistralAI } from "@langchain/mistralai"

const llm = new ChatMistralAI({
    model: "mistral-large-latest",
    temperature: 0,
    maxRetries: 2,
    // 其他参数...
})

调用

When sending chat messages to mistral, there are a few requirements to follow:

• The first message can not be an assistant (ai) message.
• Messages must alternate between user and assistant (ai) messages.
• Messages can not end with an assistant (ai) or system message.

const aiMsg = await llm.invoke([
    [
        "system",
        "You are a helpful assistant that translates English to French. Translate the user sentence.",
    ],
    ["human", "I love programming."],
])
aiMsg

AIMessage {
  "content": "J'adore la programmation.",
  "additional_kwargs": {},
  "response_metadata": {
    "tokenUsage": {
      "completionTokens": 9,
      "promptTokens": 27,
      "totalTokens": 36
    },
    "finish_reason": "stop"
  },
  "tool_calls": [],
  "invalid_tool_calls": [],
  "usage_metadata": {
    "input_tokens": 27,
    "output_tokens": 9,
    "total_tokens": 36
  }
}

console.log(aiMsg.content)

J'adore la programmation.

工具调用

Mistral’s API supports tool calling for a subset of their models. You can see which models support tool calling on this page.

The examples below demonstrates how to use it:

import { ChatMistralAI } from "@langchain/mistralai";
import { ChatPromptTemplate } from "@langchain/core/prompts";
import * as z from "zod";
import { tool } from "@langchain/core/tools";

const calculatorSchema = z.object({
  operation: z
    .enum(["add", "subtract", "multiply", "divide"])
    .describe("The type of operation to execute."),
  number1: z.number().describe("The first number to operate on."),
  number2: z.number().describe("The second number to operate on."),
});

const calculatorTool = tool((input) => {
  return JSON.stringify(input);
}, {
  name: "calculator",
  description: "A simple calculator tool",
  schema: calculatorSchema,
});

// Bind the tool to the model
const modelWithTool = new ChatMistralAI({
  model: "mistral-large-latest",
}).bindTools([calculatorTool]);


const calcToolPrompt = ChatPromptTemplate.fromMessages([
  [
    "system",
    "You are a helpful assistant who always needs to use a calculator.",
  ],
  ["human", "{input}"],
]);

// Chain your prompt, model, and output parser together
const chainWithCalcTool = calcToolPrompt.pipe(modelWithTool);

const calcToolRes = await chainWithCalcTool.invoke({
  input: "What is 2 + 2?",
});
console.log(calcToolRes.tool_calls);

[
  {
    name: 'calculator',
    args: { operation: 'add', number1: 2, number2: 2 },
    type: 'tool_call',
    id: 'DD9diCL1W'
  }
]

Hooks

Mistral AI supports custom hooks for three events: beforeRequest, requestError, and response. Examples of the function signature for each hook type can be seen below:

const beforeRequestHook = (req: Request): Request | void | Promise<Request | void> => {
    // Code to run before a request is processed by Mistral
};

const requestErrorHook = (err: unknown, req: Request): void | Promise<void> => {
    // Code to run when an error occurs as Mistral is processing a request
};

const responseHook = (res: Response, req: Request): void | Promise<void> => {
    // Code to run before Mistral sends a successful response
};

To add these hooks to the chat model, either pass them as arguments and they are automatically added:

import { ChatMistralAI } from "@langchain/mistralai"

const modelWithHooks = new ChatMistralAI({
    model: "mistral-large-latest",
    temperature: 0,
    maxRetries: 2,
    beforeRequestHooks: [ beforeRequestHook ],
    requestErrorHooks: [ requestErrorHook ],
    responseHooks: [ responseHook ],
    // 其他参数...
});

Or assign and add them manually after instantiation:

import { ChatMistralAI } from "@langchain/mistralai"

const model = new ChatMistralAI({
    model: "mistral-large-latest",
    temperature: 0,
    maxRetries: 2,
    // 其他参数...
});

model.beforeRequestHooks = [ ...model.beforeRequestHooks, beforeRequestHook ];
model.requestErrorHooks = [ ...model.requestErrorHooks, requestErrorHook ];
model.responseHooks = [ ...model.responseHooks, responseHook ];

model.addAllHooksToHttpClient();

The method addAllHooksToHttpClient clears all currently added hooks before assigning the entire updated hook lists to avoid hook duplication.

Hooks can be removed one at a time, or all hooks can be cleared from the model at once.

model.removeHookFromHttpClient(beforeRequestHook);

model.removeAllHooksFromHttpClient();

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API 参考

有关所有 ChatMistralAI 功能和配置的详细文档，请前往 API 参考。

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