client-python

Python client library for Mistral AI platform

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The Mistral Python Client is a tool inspired by cohere-python that allows users to interact with the Mistral AI API. It provides functionalities to access and utilize the AI capabilities offered by Mistral. Users can easily install the client using pip and manage dependencies using poetry. The client includes examples demonstrating how to use the API for various tasks, such as chat interactions. To get started, users need to obtain a Mistral API Key and set it as an environment variable. Overall, the Mistral Python Client simplifies the integration of Mistral AI services into Python applications.

README:

Mistral Python Client

Migration warning

This documentation is for Mistral AI SDK v1. You can find more details on how to migrate from v0 to v1 here

API Key Setup

Before you begin, you will need a Mistral AI API key.

Get your own Mistral API Key: https://docs.mistral.ai/#api-access
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_key_here]' >> ~/.zshenv

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

Summary

Mistral AI API: Our Chat Completion and Embeddings APIs specification. Create your account on La Plateforme to get access and read the docs to learn how to use it.

Mistral Python Client
Development
- Contributions

SDK Installation

[!NOTE] Python version upgrade policy

Once a Python version reaches its official end of life date, a 3-month grace period is provided for users to upgrade. Following this grace period, the minimum python version supported in the SDK will be updated.

The SDK can be installed with either pip or poetry package managers.

PIP

PIP is the default package installer for Python, enabling easy installation and management of packages from PyPI via the command line.

pip install mistralai

Poetry

Poetry is a modern tool that simplifies dependency management and package publishing by using a single pyproject.toml file to handle project metadata and dependencies.

poetry add mistralai

SDK Example Usage

Create Chat Completions

This example shows how to create chat completions.

# Synchronous Example
from mistralai import Mistral
import os

with Mistral(
    api_key=os.getenv("MISTRAL_API_KEY", ""),
) as mistral:

    res = mistral.chat.complete(model="mistral-small-latest", messages=[
        {
            "content": "Who is the best French painter? Answer in one short sentence.",
            "role": "user",
        },
    ], stream=False)

    # Handle response
    print(res)

The same SDK client can also be used to make asychronous requests by importing asyncio.

# Asynchronous Example
import asyncio
from mistralai import Mistral
import os

async def main():
    async with Mistral(
        api_key=os.getenv("MISTRAL_API_KEY", ""),
    ) as mistral:

        res = await mistral.chat.complete_async(model="mistral-small-latest", messages=[
            {
                "content": "Who is the best French painter? Answer in one short sentence.",
                "role": "user",
            },
        ], stream=False)

        # Handle response
        print(res)

asyncio.run(main())

Upload a file

This example shows how to upload a file.

# Synchronous Example
from mistralai import Mistral
import os

with Mistral(
    api_key=os.getenv("MISTRAL_API_KEY", ""),
) as mistral:

    res = mistral.files.upload(file={
        "file_name": "example.file",
        "content": open("example.file", "rb"),
    })

    # Handle response
    print(res)

The same SDK client can also be used to make asychronous requests by importing asyncio.

# Asynchronous Example
import asyncio
from mistralai import Mistral
import os

async def main():
    async with Mistral(
        api_key=os.getenv("MISTRAL_API_KEY", ""),
    ) as mistral:

        res = await mistral.files.upload_async(file={
            "file_name": "example.file",
            "content": open("example.file", "rb"),
        })

        # Handle response
        print(res)

asyncio.run(main())

Create Agents Completions

This example shows how to create agents completions.

# Synchronous Example
from mistralai import Mistral
import os

with Mistral(
    api_key=os.getenv("MISTRAL_API_KEY", ""),
) as mistral:

    res = mistral.agents.complete(messages=[
        {
            "content": "Who is the best French painter? Answer in one short sentence.",
            "role": "user",
        },
    ], agent_id="<id>", stream=False)

    # Handle response
    print(res)

The same SDK client can also be used to make asychronous requests by importing asyncio.

# Asynchronous Example
import asyncio
from mistralai import Mistral
import os

async def main():
    async with Mistral(
        api_key=os.getenv("MISTRAL_API_KEY", ""),
    ) as mistral:

        res = await mistral.agents.complete_async(messages=[
            {
                "content": "Who is the best French painter? Answer in one short sentence.",
                "role": "user",
            },
        ], agent_id="<id>", stream=False)

        # Handle response
        print(res)

asyncio.run(main())

Create Embedding Request

This example shows how to create embedding request.

# Synchronous Example
from mistralai import Mistral
import os

with Mistral(
    api_key=os.getenv("MISTRAL_API_KEY", ""),
) as mistral:

    res = mistral.embeddings.create(inputs=[
        "Embed this sentence.",
        "As well as this one.",
    ], model="mistral-embed")

    # Handle response
    print(res)

The same SDK client can also be used to make asychronous requests by importing asyncio.

# Asynchronous Example
import asyncio
from mistralai import Mistral
import os

async def main():
    async with Mistral(
        api_key=os.getenv("MISTRAL_API_KEY", ""),
    ) as mistral:

        res = await mistral.embeddings.create_async(inputs=[
            "Embed this sentence.",
            "As well as this one.",
        ], model="mistral-embed")

        # Handle response
        print(res)

asyncio.run(main())

More examples

You can run the examples in the examples/ directory using poetry run or by entering the virtual environment using poetry shell.

Providers' SDKs Example Usage

Azure AI

Prerequisites

Before you begin, ensure you have AZUREAI_ENDPOINT and an AZURE_API_KEY. To obtain these, you will need to deploy Mistral on Azure AI. See instructions for deploying Mistral on Azure AI here.

Here's a basic example to get you started. You can also run the example in the examples directory.

import asyncio
import os

from mistralai_azure import MistralAzure

client = MistralAzure(
    azure_api_key=os.getenv("AZURE_API_KEY", ""),
    azure_endpoint=os.getenv("AZURE_ENDPOINT", "")
)

async def main() -> None:
    res = await client.chat.complete_async( 
        max_tokens= 100,
        temperature= 0.5,
        messages= [
            {
                "content": "Hello there!",
                "role": "user"
            }
        ]
    )
    print(res)

asyncio.run(main())

The documentation for the Azure SDK is available here.

Google Cloud

Prerequisites

Before you begin, you will need to create a Google Cloud project and enable the Mistral API. To do this, follow the instructions here.

To run this locally you will also need to ensure you are authenticated with Google Cloud. You can do this by running

gcloud auth application-default login

Step 1: Install

Install the extras dependencies specific to Google Cloud:

pip install mistralai[gcp]

Step 2: Example Usage

Here's a basic example to get you started.

import asyncio
from mistralai_gcp import MistralGoogleCloud

client = MistralGoogleCloud()


async def main() -> None:
    res = await client.chat.complete_async(
        model= "mistral-small-2402",
        messages= [
            {
                "content": "Hello there!",
                "role": "user"
            }
        ]
    )
    print(res)

asyncio.run(main())

The documentation for the GCP SDK is available here.

Available Resources and Operations

Available methods

agents

complete - Agents Completion
stream - Stream Agents completion

batch

batch.jobs

list - Get Batch Jobs
create - Create Batch Job
get - Get Batch Job
cancel - Cancel Batch Job

chat

complete - Chat Completion
stream - Stream chat completion

classifiers

moderate - Moderations
moderate_chat - Moderations Chat

embeddings

create - Embeddings

files

upload - Upload File
list - List Files
retrieve - Retrieve File
delete - Delete File
download - Download File
get_signed_url - Get Signed Url

fim

complete - Fim Completion
stream - Stream fim completion

fine_tuning

fine_tuning.jobs

list - Get Fine Tuning Jobs
create - Create Fine Tuning Job
get - Get Fine Tuning Job
cancel - Cancel Fine Tuning Job
start - Start Fine Tuning Job

models

list - List Models
retrieve - Retrieve Model
delete - Delete Model
update - Update Fine Tuned Model
archive - Archive Fine Tuned Model
unarchive - Unarchive Fine Tuned Model

Server-sent event streaming

Server-sent events are used to stream content from certain operations. These operations will expose the stream as Generator that can be consumed using a simple for loop. The loop will terminate when the server no longer has any events to send and closes the underlying connection.

The stream is also a Context Manager and can be used with the with statement and will close the underlying connection when the context is exited.

from mistralai import Mistral
import os

with Mistral(
    api_key=os.getenv("MISTRAL_API_KEY", ""),
) as mistral:

    res = mistral.chat.stream(model="mistral-small-latest", messages=[
        {
            "content": "Who is the best French painter? Answer in one short sentence.",
            "role": "user",
        },
    ], stream=True)

    with res as event_stream:
        for event in event_stream:
            # handle event
            print(event, flush=True)

File uploads

Certain SDK methods accept file objects as part of a request body or multi-part request. It is possible and typically recommended to upload files as a stream rather than reading the entire contents into memory. This avoids excessive memory consumption and potentially crashing with out-of-memory errors when working with very large files. The following example demonstrates how to attach a file stream to a request.

[!TIP]

For endpoints that handle file uploads bytes arrays can also be used. However, using streams is recommended for large files.

from mistralai import Mistral
import os

with Mistral(
    api_key=os.getenv("MISTRAL_API_KEY", ""),
) as mistral:

    res = mistral.files.upload(file={
        "file_name": "example.file",
        "content": open("example.file", "rb"),
    })

    # Handle response
    print(res)

Retries

Some of the endpoints in this SDK support retries. If you use the SDK without any configuration, it will fall back to the default retry strategy provided by the API. However, the default retry strategy can be overridden on a per-operation basis, or across the entire SDK.

To change the default retry strategy for a single API call, simply provide a RetryConfig object to the call:

from mistralai import Mistral
from mistralai.utils import BackoffStrategy, RetryConfig
import os

with Mistral(
    api_key=os.getenv("MISTRAL_API_KEY", ""),
) as mistral:

    res = mistral.models.list(,
        RetryConfig("backoff", BackoffStrategy(1, 50, 1.1, 100), False))

    # Handle response
    print(res)

If you'd like to override the default retry strategy for all operations that support retries, you can use the retry_config optional parameter when initializing the SDK:

from mistralai import Mistral
from mistralai.utils import BackoffStrategy, RetryConfig
import os

with Mistral(
    retry_config=RetryConfig("backoff", BackoffStrategy(1, 50, 1.1, 100), False),
    api_key=os.getenv("MISTRAL_API_KEY", ""),
) as mistral:

    res = mistral.models.list()

    # Handle response
    print(res)

Error Handling

Handling errors in this SDK should largely match your expectations. All operations return a response object or raise an exception.

By default, an API error will raise a models.SDKError exception, which has the following properties:

Property	Type	Description
`.status_code`	int	The HTTP status code
`.message`	str	The error message
`.raw_response`	httpx.Response	The raw HTTP response
`.body`	str	The response content

When custom error responses are specified for an operation, the SDK may also raise their associated exceptions. You can refer to respective Errors tables in SDK docs for more details on possible exception types for each operation. For example, the list_async method may raise the following exceptions:

Error Type	Status Code	Content Type
models.HTTPValidationError	422	application/json
models.SDKError	4XX, 5XX	/

Example

from mistralai import Mistral, models
import os

with Mistral(
    api_key=os.getenv("MISTRAL_API_KEY", ""),
) as mistral:
    res = None
    try:

        res = mistral.models.list()

        # Handle response
        print(res)

    except models.HTTPValidationError as e:
        # handle e.data: models.HTTPValidationErrorData
        raise(e)
    except models.SDKError as e:
        # handle exception
        raise(e)

Server Selection

Select Server by Name

You can override the default server globally by passing a server name to the server: str optional parameter when initializing the SDK client instance. The selected server will then be used as the default on the operations that use it. This table lists the names associated with the available servers:

Name	Server
`eu`	`https://api.mistral.ai`

Example

from mistralai import Mistral
import os

with Mistral(
    server="eu",
    api_key=os.getenv("MISTRAL_API_KEY", ""),
) as mistral:

    res = mistral.models.list()

    # Handle response
    print(res)

Override Server URL Per-Client

The default server can also be overridden globally by passing a URL to the server_url: str optional parameter when initializing the SDK client instance. For example:

from mistralai import Mistral
import os

with Mistral(
    server_url="https://api.mistral.ai",
    api_key=os.getenv("MISTRAL_API_KEY", ""),
) as mistral:

    res = mistral.models.list()

    # Handle response
    print(res)

Custom HTTP Client

The Python SDK makes API calls using the httpx HTTP library. In order to provide a convenient way to configure timeouts, cookies, proxies, custom headers, and other low-level configuration, you can initialize the SDK client with your own HTTP client instance. Depending on whether you are using the sync or async version of the SDK, you can pass an instance of HttpClient or AsyncHttpClient respectively, which are Protocol's ensuring that the client has the necessary methods to make API calls. This allows you to wrap the client with your own custom logic, such as adding custom headers, logging, or error handling, or you can just pass an instance of httpx.Client or httpx.AsyncClient directly.

For example, you could specify a header for every request that this sdk makes as follows:

from mistralai import Mistral
import httpx

http_client = httpx.Client(headers={"x-custom-header": "someValue"})
s = Mistral(client=http_client)

or you could wrap the client with your own custom logic:

from mistralai import Mistral
from mistralai.httpclient import AsyncHttpClient
import httpx

class CustomClient(AsyncHttpClient):
    client: AsyncHttpClient

    def __init__(self, client: AsyncHttpClient):
        self.client = client

    async def send(
        self,
        request: httpx.Request,
        *,
        stream: bool = False,
        auth: Union[
            httpx._types.AuthTypes, httpx._client.UseClientDefault, None
        ] = httpx.USE_CLIENT_DEFAULT,
        follow_redirects: Union[
            bool, httpx._client.UseClientDefault
        ] = httpx.USE_CLIENT_DEFAULT,
    ) -> httpx.Response:
        request.headers["Client-Level-Header"] = "added by client"

        return await self.client.send(
            request, stream=stream, auth=auth, follow_redirects=follow_redirects
        )

    def build_request(
        self,
        method: str,
        url: httpx._types.URLTypes,
        *,
        content: Optional[httpx._types.RequestContent] = None,
        data: Optional[httpx._types.RequestData] = None,
        files: Optional[httpx._types.RequestFiles] = None,
        json: Optional[Any] = None,
        params: Optional[httpx._types.QueryParamTypes] = None,
        headers: Optional[httpx._types.HeaderTypes] = None,
        cookies: Optional[httpx._types.CookieTypes] = None,
        timeout: Union[
            httpx._types.TimeoutTypes, httpx._client.UseClientDefault
        ] = httpx.USE_CLIENT_DEFAULT,
        extensions: Optional[httpx._types.RequestExtensions] = None,
    ) -> httpx.Request:
        return self.client.build_request(
            method,
            url,
            content=content,
            data=data,
            files=files,
            json=json,
            params=params,
            headers=headers,
            cookies=cookies,
            timeout=timeout,
            extensions=extensions,
        )

s = Mistral(async_client=CustomClient(httpx.AsyncClient()))

Authentication

Per-Client Security Schemes

This SDK supports the following security scheme globally:

Name	Type	Scheme	Environment Variable
`api_key`	http	HTTP Bearer	`MISTRAL_API_KEY`

To authenticate with the API the api_key parameter must be set when initializing the SDK client instance. For example:

from mistralai import Mistral
import os

with Mistral(
    api_key=os.getenv("MISTRAL_API_KEY", ""),
) as mistral:

    res = mistral.models.list()

    # Handle response
    print(res)

Resource Management

The Mistral class implements the context manager protocol and registers a finalizer function to close the underlying sync and async HTTPX clients it uses under the hood. This will close HTTP connections, release memory and free up other resources held by the SDK. In short-lived Python programs and notebooks that make a few SDK method calls, resource management may not be a concern. However, in longer-lived programs, it is beneficial to create a single SDK instance via a context manager and reuse it across the application.

from mistralai import Mistral
import os
def main():
    with Mistral(
        api_key=os.getenv("MISTRAL_API_KEY", ""),
    ) as mistral:
        # Rest of application here...


# Or when using async:
async def amain():
    async with Mistral(
        api_key=os.getenv("MISTRAL_API_KEY", ""),
    ) as mistral:
        # Rest of application here...

Debugging

You can setup your SDK to emit debug logs for SDK requests and responses.

You can pass your own logger class directly into your SDK.

from mistralai import Mistral
import logging

logging.basicConfig(level=logging.DEBUG)
s = Mistral(debug_logger=logging.getLogger("mistralai"))

You can also enable a default debug logger by setting an environment variable MISTRAL_DEBUG to true.

IDE Support

PyCharm

Generally, the SDK will work well with most IDEs out of the box. However, when using PyCharm, you can enjoy much better integration with Pydantic by installing an additional plugin.

PyCharm Pydantic Plugin

Development

Contributions

While we value open-source contributions to this SDK, this library is generated programmatically. Any manual changes added to internal files will be overwritten on the next generation. We look forward to hearing your feedback. Feel free to open a PR or an issue with a proof of concept and we'll do our best to include it in a future release.

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client-python

README:

Mistral Python Client

Migration warning

API Key Setup

Summary

Table of Contents

SDK Installation

PIP

Poetry

SDK Example Usage

Create Chat Completions

Upload a file

Create Agents Completions

Create Embedding Request

More examples

Providers' SDKs Example Usage

Azure AI

Google Cloud

Available Resources and Operations

Server-sent event streaming

File uploads

Retries

Error Handling

Example

Server Selection

Select Server by Name

Example

Override Server URL Per-Client

Custom HTTP Client

Authentication

Per-Client Security Schemes

Resource Management

Debugging

IDE Support

PyCharm

Development

Contributions

For Tasks:

For Jobs:

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