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ChatMistralAI

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

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

Overview​

Integration details​

ClassPackageLocalSerializablePY supportPackage downloadsPackage latest
ChatMistralAI@langchain/mistralai❌❌✅NPM - DownloadsNPM - Version

Model features​

See the links in the table headers below for guides on how to use specific features.

Tool callingStructured outputJSON modeImage inputAudio inputVideo inputToken-level streamingToken usageLogprobs
✅✅✅❌❌❌✅✅❌

Setup​

To access ChatMistralAI models you’ll need to create a ChatMistralAI account, get an API key, and install the @langchain/mistralai integration package.

Credentials​

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

export MISTRAL_API_KEY="your-api-key"

If you want to get automated tracing of your model calls you can also set your LangSmith API key by uncommenting below:

# export LANGCHAIN_TRACING_V2="true"
# export LANGCHAIN_API_KEY="your-api-key"

Installation​

The LangChain ChatMistralAI integration lives in the @langchain/mistralai package:

tip

See this section for general instructions on installing integration packages.

yarn add @langchain/mistralai

Instantiation​

Now we can instantiate our model object and generate chat completions:

import { ChatMistralAI } from "@langchain/mistralai";

const llm = new ChatMistralAI({
  model: "mistral-large-latest",
  temperature: 0,
  maxRetries: 2,
  // other params...
});

Invocation​

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.

Chaining​

We can chain our model with a prompt template like so:

import { ChatPromptTemplate } from "@langchain/core/prompts";

const prompt = ChatPromptTemplate.fromMessages([
  [
    "system",
    "You are a helpful assistant that translates {input_language} to {output_language}.",
  ],
  ["human", "{input}"],
]);

const chain = prompt.pipe(llm);
await chain.invoke({
  input_language: "English",
  output_language: "German",
  input: "I love programming.",
});
AIMessage {
  "content": "Ich liebe Programmieren.",
  "additional_kwargs": {},
  "response_metadata": {
    "tokenUsage": {
      "completionTokens": 7,
      "promptTokens": 21,
      "totalTokens": 28
    },
    "finish_reason": "stop"
  },
  "tool_calls": [],
  "invalid_tool_calls": [],
  "usage_metadata": {
    "input_tokens": 21,
    "output_tokens": 7,
    "total_tokens": 28
  }
}

Tool calling​

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 { 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'
  }
]

API reference​

For detailed documentation of all ChatMistralAI features and configurations head to the API reference: https://api.js.langchain.com/classes/langchain_mistralai.ChatMistralAI.html

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