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

SDK Installation

PIP

pip install mistralai

Poetry

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

s = Mistral(
    api_key=os.getenv("MISTRAL_API_KEY", ""),
)


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

if res is not None:
    # handle response
    pass

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():
    s = Mistral(
        api_key=os.getenv("MISTRAL_API_KEY", ""),
    )
    res = await s.chat.complete_async(model="mistral-small-latest", messages=[
        {
            "content": "Who is the best French painter? Answer in one short sentence.",
            "role": "user",
        },
    ])
    if res is not None:
        # handle response
        pass

asyncio.run(main())

Upload a file

This example shows how to upload a file.

# Synchronous Example
from mistralai import Mistral
import os

s = Mistral(
    api_key=os.getenv("MISTRAL_API_KEY", ""),
)


res = s.files.upload(file={
    "file_name": "your_file_here",
    "content": open("<file_path>", "rb"),
})

if res is not None:
    # handle response
    pass

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():
    s = Mistral(
        api_key=os.getenv("MISTRAL_API_KEY", ""),
    )
    res = await s.files.upload_async(file={
        "file_name": "your_file_here",
        "content": open("<file_path>", "rb"),
    })
    if res is not None:
        # handle response
        pass

asyncio.run(main())

Create Agents Completions

This example shows how to create agents completions.

# Synchronous Example
from mistralai import Mistral
import os

s = Mistral(
    api_key=os.getenv("MISTRAL_API_KEY", ""),
)


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

if res is not None:
    # handle response
    pass

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():
    s = Mistral(
        api_key=os.getenv("MISTRAL_API_KEY", ""),
    )
    res = await s.agents.complete_async(messages=[
        {
            "content": "Who is the best French painter? Answer in one short sentence.",
            "role": "user",
        },
    ], agent_id="<value>")
    if res is not None:
        # handle response
        pass

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

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

files

upload - Upload File
list - List Files
retrieve - Retrieve File
delete - Delete File

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

chat

complete - Chat Completion
stream - Stream chat completion

fim

complete - Fim Completion
stream - Stream fim completion

agents

complete - Agents Completion
stream - Stream Agents completion

embeddings

create - Embeddings

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.

from mistralai import Mistral
import os

s = Mistral(
    api_key=os.getenv("MISTRAL_API_KEY", ""),
)


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

if res is not None:
    for event in res:
        # 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

s = Mistral(
    api_key=os.getenv("MISTRAL_API_KEY", ""),
)


res = s.files.upload(file={
    "file_name": "your_file_here",
    "content": open("<file_path>", "rb"),
})

if res is not None:
    # handle response
    pass

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

s = Mistral(
    api_key=os.getenv("MISTRAL_API_KEY", ""),
)


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

if res is not None:
    # handle response
    pass

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

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


res = s.models.list()

if res is not None:
    # handle response
    pass

Error Handling

Handling errors in this SDK should largely match your expectations. All operations return a response object or raise an error. If Error objects are specified in your OpenAPI Spec, the SDK will raise the appropriate Error type.

Error Object	Status Code	Content Type
models.HTTPValidationError	422	application/json
models.SDKError	4xx-5xx	/

Example

from mistralai import Mistral, models
import os

s = Mistral(
    api_key=os.getenv("MISTRAL_API_KEY", ""),
)

res = None
try:
    res = s.models.list()

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

if res is not None:
    # handle response
    pass

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	Variables
`prod`	`https://api.mistral.ai`	None

Example

from mistralai import Mistral
import os

s = Mistral(
    server="prod",
    api_key=os.getenv("MISTRAL_API_KEY", ""),
)


res = s.models.list()

if res is not None:
    # handle response
    pass

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

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


res = s.models.list()

if res is not None:
    # handle response
    pass

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

s = Mistral(
    api_key=os.getenv("MISTRAL_API_KEY", ""),
)


res = s.models.list()

if res is not None:
    # handle response
    pass

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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BricksLLM is a cloud native AI gateway written in Go. Currently, it provides native support for OpenAI, Anthropic, Azure OpenAI and vLLM. BricksLLM aims to provide enterprise level infrastructure that can power any LLM production use cases. Here are some use cases for BricksLLM: * Set LLM usage limits for users on different pricing tiers * Track LLM usage on a per user and per organization basis * Block or redact requests containing PIIs * Improve LLM reliability with failovers, retries and caching * Distribute API keys with rate limits and cost limits for internal development/production use cases * Distribute API keys with rate limits and cost limits for students

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uAgents

uAgents is a Python library developed by Fetch.ai that allows for the creation of autonomous AI agents. These agents can perform various tasks on a schedule or take action on various events. uAgents are easy to create and manage, and they are connected to a fast-growing network of other uAgents. They are also secure, with cryptographically secured messages and wallets.

github

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griptape

Griptape is a modular Python framework for building AI-powered applications that securely connect to your enterprise data and APIs. It offers developers the ability to maintain control and flexibility at every step. Griptape's core components include Structures (Agents, Pipelines, and Workflows), Tasks, Tools, Memory (Conversation Memory, Task Memory, and Meta Memory), Drivers (Prompt and Embedding Drivers, Vector Store Drivers, Image Generation Drivers, Image Query Drivers, SQL Drivers, Web Scraper Drivers, and Conversation Memory Drivers), Engines (Query Engines, Extraction Engines, Summary Engines, Image Generation Engines, and Image Query Engines), and additional components (Rulesets, Loaders, Artifacts, Chunkers, and Tokenizers). Griptape enables developers to create AI-powered applications with ease and efficiency.

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