README.md

📢🚨 This repository is now archived 📢🚨

We have deprecated this package in favor of mistralai/client-ts, which is the new official Mistral client, compatibile with both Typescript and Javascript.

You can find all installation information here.

This change is effective starting with version 1.0.0 of the npm package.

---

This JavaScript client is inspired from cohere-typescript

Mistral JavaScript Client

You can use the Mistral JavaScript client to interact with the Mistral AI API.

Installing

You can install the library in your project using:

npm install @mistralai/mistralai

Usage

You can watch a free course on using the Mistral JavaScript client here.

Set up

import MistralClient from '@mistralai/mistralai';

const apiKey = process.env.MISTRAL_API_KEY || 'your_api_key';

const client = new MistralClient(apiKey);

List models

const listModelsResponse = await client.listModels();
const listModels = listModelsResponse.data;
listModels.forEach((model) => {
  console.log('Model:', model);
});

Chat with streaming

const chatStreamResponse = await client.chatStream({
  model: 'mistral-tiny',
  messages: [{role: 'user', content: 'What is the best French cheese?'}],
});

console.log('Chat Stream:');
for await (const chunk of chatStreamResponse) {
  if (chunk.choices[0].delta.content !== undefined) {
    const streamText = chunk.choices[0].delta.content;
    process.stdout.write(streamText);
  }
}

Chat without streaming

const chatResponse = await client.chat({
  model: 'mistral-tiny',
  messages: [{role: 'user', content: 'What is the best French cheese?'}],
});

console.log('Chat:', chatResponse.choices[0].message.content);

Embeddings

const input = [];
for (let i = 0; i < 1; i++) {
  input.push('What is the best French cheese?');
}

const embeddingsBatchResponse = await client.embeddings({
  model: 'mistral-embed',
  input: input,
});

console.log('Embeddings Batch:', embeddingsBatchResponse.data);

Files

// Create a new file
const file = fs.readFileSync('file.jsonl');
const createdFile = await client.files.create({ file });

// List files
const files = await client.files.list();

// Retrieve a file
const retrievedFile = await client.files.retrieve({ fileId: createdFile.id });

// Delete a file
const deletedFile = await client.files.delete({ fileId: createdFile.id });

Fine-tuning Jobs

// Create a new job
const createdJob = await client.jobs.create({
  model: 'open-mistral-7B',
  trainingFiles: [trainingFile.id],
  validationFiles: [validationFile.id],
  hyperparameters: {
    trainingSteps: 10,
    learningRate: 0.0001,
  },
});

// List jobs
const jobs = await client.jobs.list();

// Retrieve a job
const retrievedJob = await client.jobs.retrieve({ jobId: createdJob.id });

// Cancel a job
const canceledJob = await client.jobs.cancel({ jobId: createdJob.id });

Run examples

You can run the examples in the examples directory by installing them locally:

cd examples
npm install .

API key setup

Running the examples requires a Mistral AI API key.

Get your own Mistral API Key: https://docs.mistral.ai/#api-access

Run the examples

MISTRAL_API_KEY='your_api_key' node chat_with_streaming.js

Persisting the API key in environment

Set your Mistral API Key as an environment variable. You only need to do this once.

# set Mistral API Key (using zsh for example)
$ echo 'export MISTRAL_API_KEY=[your_api_key]' >> ~/.zshenv

# reload the environment (or just quit and open a new terminal)
$ source ~/.zshenv

You can then run the examples without appending the API key:

node chat_with_streaming.js

After the env variable setup the client will find the MISTRAL_API_KEY by itself

import MistralClient from '@mistralai/mistralai';

const client = new MistralClient();