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 :
- 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.
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.
To install the Mistral PHP Client, run the following command:
composer require partitech/php-mistral
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.
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());
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();
}
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();
}
$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
)
)
$apiKey = 'YOUR_PRIVATE_MISTRAL_KEY';
$strings = ['Hello', 'World', 'Hello World'];
$client = new LamaCppClient($apiKey, 'http://localhost:8080');
$embeddings = $client->embeddings($strings);
print_r($embeddings);
Result :
Array
(
[data] => Array
(
[0] => Array
(
[embedding] => Array
(
[0] => 3.5081260204315
...
[4095] => -2.5789933204651
)
[1] => Array
...
)
)
MistralAi La plateforme is really cheap you should consider subscribing to it instead of running a local Lama.cpp instance. This bundle cost us only 0.02€ during our tests. If you really feel you need a local server, here is a docker-compose you can use for example:
version: '3.8'
services:
server:
image: ghcr.io/ggerganov/llama.cpp:full
volumes:
- ./Llm/models:/models
ports:
- '8080:8080'
environment:
- CHAT_MODEL
- MISTRAL_API_KEY
command: ["--server", "-m", "/models/${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
For detailed documentation on the Mistral AI API and the available endpoints, please refer to the Mistral AI API Documentation.
Contributions are welcome! If you would like to contribute to the project, please fork the repository and submit a pull request with your changes.
The Mistral PHP Client is open-sourced software licensed under the MIT license.
If you encounter any issues or require assistance, please file an issue on the GitHub repository issue tracker.
This Readme.md is mostly copied from https://github.com/Gage-Technologies/mistral-go. thanks to them :)