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
You can use the Mistral JavaScript client to interact with the Mistral AI API.
You can install the library in your project using:
npm install @mistralai/mistralai
You can watch a free course on using the Mistral JavaScript client here.
import MistralClient from '@mistralai/mistralai';
const apiKey = process.env.MISTRAL_API_KEY || 'your_api_key';
const client = new MistralClient(apiKey);const listModelsResponse = await client.listModels();
const listModels = listModelsResponse.data;
listModels.forEach((model) => {
console.log('Model:', model);
});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);
}
}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);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);// 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 });// 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 });You can run the examples in the examples directory by installing them locally:
cd examples
npm install .Running the examples requires a Mistral AI API key.
Get your own Mistral API Key: https://docs.mistral.ai/#api-access
MISTRAL_API_KEY='your_api_key' node chat_with_streaming.jsSet 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 ~/.zshenvYou can then run the examples without appending the API key:
node chat_with_streaming.jsAfter the env variable setup the client will find the MISTRAL_API_KEY by itself
import MistralClient from '@mistralai/mistralai';
const client = new MistralClient();