README

The Mistral PHP Client is a comprehensive PHP library designed to interface with the Mistral AI API.

This client support Mistral : La plateforme and Lama.cpp for local testing and development purpose.

Api is the same as the main Mistral api :

Features

Chat Completions: Generate conversational responses and complete dialogue prompts using Mistral's language models.
Chat Completions Streaming: Establish a real-time stream of chat completions, ideal for applications requiring continuous interaction.
Embeddings: Obtain numerical vector representations of text, enabling semantic search, clustering, and other machine learning applications.

Getting Started

To begin using the Mistral PHP Client in your project, ensure you have PHP installed on your system. This client library is compatible with PHP 8.3 and higher.

Installation

To install the Mistral PHP Client, run the following command:

composer require partitech/php-mistral

Usage

To use the client in your PHP application, you need to import the package and initialize a new client instance with your API key.

You can see full example in the examples directory.

Chat message with La plateforme

use Partitech\PhpMistral\MistralClient;
use Partitech\PhpMistral\Messages;

$apiKey = 'YOUR_PRIVATE_MISTRAL_KEY';
$client = new MistralClient($apiKey);

$messages = new Messages();
$messages->addUserMessage('What is the best French cheese?');
$result = $client->chat($messages,
    [
        'model' => 'mistral-large-latest',
        'temperature' => 0.7,
        'top_p' => 1,
        'max_tokens' => 16,
        'safe_prompt' => false,
        'random_seed' => null
    ]
);
print_r($result->getMessage());

Note that you can populate chat discussion with :

$messages->addSystemMessage('Add system Message');
$messages->addAssistantMessage('Any response');
$messages->addUserMessage('What is the best French cheese?');
$messages->addAssistantMessage('Any response');

To get the same with Lama.cpp local inference :

use Partitech\PhpMistral\LamaCppClient;
use Partitech\PhpMistral\Messages;

$apiKey = 'YOUR_PRIVATE_MISTRAL_KEY';
$client = new LamaCppClient($apiKey, 'http://localhost:8080');

$messages = new Messages();
$messages->addUserMessage('What is the best French cheese?');
$result = $client->chat($messages);
print_r($result->getMessage());

Chat with streamed response with La plateforme

use Partitech\PhpMistral\MistralClient;
use Partitech\PhpMistral\Messages;

$apiKey = 'YOUR_PRIVATE_MISTRAL_KEY';
$client = new MistralClient($apiKey, 'http://localhost:8080');

$messages = new Messages();
$messages->addUserMessage('What is the best French cheese?');
$params = [
        'model' => 'mistral-large-latest',
        'temperature' => 0.7,
        'top_p' => 1,
        'max_tokens' => 16,
        'safe_prompt' => false,
        'random_seed' => null
];
foreach ($client->chatStream($messages, $params)) as $chunk) {
    echo $chunk->getChunk();
}

Chat with streamed response with Lama.cpp

use Partitech\PhpMistral\LamaCppClient;
use Partitech\PhpMistral\Messages;

$apiKey = 'YOUR_PRIVATE_MISTRAL_KEY';
$client = new LamaCppClient($apiKey, 'http://localhost:8080');

$messages = new Messages();
$messages->addUserMessage('What is the best French cheese?');
foreach ($client->chatStream($messages) as $chunk) {
    echo $chunk->getChunk();
}

Embeddings with La plateform

$apiKey = 'YOUR_PRIVATE_MISTRAL_KEY';
$strings = ['Hello', 'World', 'Hello World'];

$client = new MistralClient($apiKey);
$embeddings = $client->embeddings($strings);
print_r($embeddings);

Result :

Array
(
    [id] => bfb5a084be3e4659bd67234e0e8a2dae
    [object] => list
    [data] => Array
        (
            [0] => Array
                (
                    [object] => embedding
                    [embedding] => Array
                        (
                            [0] => -0.024917602539062
                            ...
                            [1023] => -0.024826049804688
                        )

                    [index] => 0
                )

            [1] => Array
                (
                    [object] => embedding
                    [embedding] => Array
                        (
                            [0] => -0.010711669921875
                            ...
                            [1023] => -0.04107666015625
                        )

                    [index] => 1
                )

            [2] => Array
                (
                    [object] => embedding
                    [embedding] => Array
                        (
                            [0] => 0.0004112720489502
                            ...
                            [1023] => -0.032379150390625
                        )

                    [index] => 2
                )

        )

    [model] => mistral-embed
    [usage] => Array
        (
            [prompt_tokens] => 10
            [total_tokens] => 10
            [completion_tokens] => 0
        )

)

Embeddings with Lama.cpp server

$apiKey = 'YOUR_PRIVATE_MISTRAL_KEY';
$strings = ['Hello', 'World', 'Hello World'];

$client = new LamaCppClient($apiKey, 'http://localhost:8080');
$embeddings = $client->embeddings($strings);
print_r($embeddings);

Result :

Array
(
    [data] => Array
        (
            [0] => Array
                (
                    [embedding] => Array
                        (
                    [0] => 3.5081260204315
                    ...
                    [4095] => -2.5789933204651
                )
            [1] => Array
            ...

        )

   
)

Lama.cpp inference

MistralAi La plateforme is really cheap you should consider subscribing to it instead of running a local Lama.cpp instance. This bundle cost us only 0.02€ during our tests. If you really feel you need a local server, here is a docker-compose you can use for example:

version: '3.8'
services:
  server:
    image: ghcr.io/ggerganov/llama.cpp:full
    volumes:
      - ./Llm/models:/models
    ports:
      - '8080:8080'
    environment:
      - CHAT_MODEL
      - MISTRAL_API_KEY
    command: ["--server", "-m", "/models/${CHAT_MODEL}", "-c", "4500", "--host", "0.0.0.0", "--api-key", "${MISTRAL_API_KEY}", "-ngl", "99", "-t", "10", "-v", "--embedding"]
    extra_hosts:
      - "host.docker.internal:host-gateway"

with a .env file

MISTRAL_API_KEY=that_is_a_very_mysterious_key
CHAT_MODEL=mistral-7b-instruct-v0.2.Q4_K_M.gguf

Documentation

For detailed documentation on the Mistral AI API and the available endpoints, please refer to the Mistral AI API Documentation.

Contributing

Contributions are welcome! If you would like to contribute to the project, please fork the repository and submit a pull request with your changes.

License

The Mistral PHP Client is open-sourced software licensed under the MIT license.

Support

If you encounter any issues or require assistance, please file an issue on the GitHub repository issue tracker.

Thanks

This Readme.md is mostly copied from https://github.com/Gage-Technologies/mistral-go. thanks to them :)

partitech / php-mistral

Maintainers

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