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.
Fill in the Middle: Automatically generate code by setting a starting prompt and an optional suffix, allowing the model to complete the code in between. Ideal for creating specific code segments within predefined boundaries.

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
            ...

        )

   
)

Fill in the middle

$prompt  = "Write response in php:\n";
$prompt .= "/** Calculate date + n days. Returns \DateTime object */";
$suffix  = 'return $datePlusNdays;\n}';

try {
    $result = $client->fim(
        prompt: $prompt,
        suffix: $suffix,
        params:[
            'model' => $model_name,
            'temperature' => 0.7,
            'top_p' => 1,
            'max_tokens' => 200,
            'min_tokens' => 0,
            'stop' => 'string',
            'random_seed' => 0
        ]
    );
} catch (MistralClientException $e) {
    echo $e->getMessage();
    exit(1);
}

Result :

function datePlusNdays(\DateTime $date, $n) {
 $datePlusNdays = clone $date;
 $datePlusNdays->add(new \DateInterval('P'.abs($n).'D'));

Fill in the middle in stream mode

try {
    $result = $client->fimStream(
        prompt: $prompt,
        suffix: $suffix,
        params:[
            'model' => $model_name,
            'temperature' => 0.7,
            'top_p' => 1,
            'max_tokens' => 200,
            'min_tokens' => 0,
            'stop' => 'string',
            'random_seed' => 0
        ]
    );
    foreach ($result as $chunk) {
        echo $chunk->getChunk();
    }
} catch (MistralClientException $e) {
    echo $e->getMessage();
    exit(1);
}

Result :

function datePlusNdays(\DateTime $date, $n) {
 $datePlusNdays = clone $date;
 $datePlusNdays->add(new \DateInterval('P'.abs($n).'D'));

Pixtral request

$contents = [
    ['type' => 'text', 'text' => 'Describe this image in detail please.'],
    ['type' => 'image_url', 'image_url' => 'https://s3.amazonaws.com/cms.ipressroom.com/338/files/201808/5b894ee1a138352221103195_A680%7Ejogging-edit/A680%7Ejogging-edit_hero.jpg'],
    ['type' => 'text', 'text' => 'and this one as well. Answer in French.'],
    ['type' => 'image_url', 'image_url' => 'https://www.wolframcloud.com/obj/resourcesystem/images/a0e/a0ee3983-46c6-4c92-b85d-059044639928/6af8cfb971db031b.png']
];

$messages = new Messages();
$messages->addMixedContentUserMessage($contents);

try {
    $result = $client->chat(
        $messages,
        [
            'model' => 'Pixtral-12B-2409',
            'max_tokens' => 1024,
        ]
    );
} catch (MistralClientException $e) {
    echo $e->getMessage();
    exit(1);
}

print($result->getMessage());

Result :

Dans cette image, on voit une famille de cinq personnes posant pour une photo. Ils sont tous vêtus de chemises rouges assorties et sont allongés sur le ventre sur une surface blanche devant un fond blanc. Les membres de la famille sourient et semblent être dans une pose détendue et joyeuse, les mains jointes devant eux. La famille semble former un groupe uni et heureux, et l'ambiance générale de la photo est chaleureuse et accueillante.

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:

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

vLLM inference

vLLM is the official inference alternative to MistralAi La plateforme. Starting to v0.6.0 vLLM is fully compatible with the tools/tool_choice from mistral's plateforme. To get an ISO instance locally you will need to specify the template: examples/tool_chat_template_mistral.jinja . Here is a docker-compose you can use for example:

vLLM inference for tool_call

services:
  mistral:
    image: vllm/vllm-openai:v0.6.0
    command: |
      --model mistralai/Mistral-Nemo-Instruct-2407
      --served-model-name nemo
      --max-model-len 32000
      --tensor-parallel-size 2
      --gpu-memory-utilization 1
      --trust-remote-code
      --enforce-eager
      --enable-auto-tool-choice
      --tool-call-parser mistral
      --chat-template /config/tool_chat_template_mistral.jinja
    environment:
      - HUGGING_FACE_HUB_TOKEN=*****
      - NVIDIA_VISIBLE_DEVICES=0,1
      - VLLM_WORKER_MULTIPROC_METHOD=spawn
    volumes:
      - /path_to_config/:/config
      - /path_to_cache/.cache/huggingface:/root/.cache/huggingface
    ports:
      - "40001:8000"
    deploy:
      resources:
        reservations:
          devices:
            - driver: nvidia
              capabilities: [gpu]
    runtime: nvidia
    ipc: host
    networks:
      - llm_1x2_network
networks:
  llm_1x2_network:
    driver: bridge

vLLM inference for Pixtral

services:
  mistral:
    image: vllm/vllm-openai:v0.6.1
    command: |
      --model mistralai/Pixtral-12B-2409 
      --tokenizer_mode mistral 
      --limit_mm_per_prompt 'image=4' 
      --max_num_batched_tokens 16384
      --served-model-name Pixtral-12B-2409 
      --max-model-len 16384
      --tensor-parallel-size 2
      --gpu-memory-utilization 1
      --trust-remote-code
      --enforce-eager
    environment:
      - HUGGING_FACE_HUB_TOKEN=*****
      - NVIDIA_VISIBLE_DEVICES=0,1
      - VLLM_WORKER_MULTIPROC_METHOD=spawn
    volumes:
      - /path_to_config/:/config
      - /path_to_cache/.cache/huggingface:/root/.cache/huggingface
    ports:
      - "40001:8000"
    deploy:
      resources:
        reservations:
          devices:
            - driver: nvidia
              capabilities: [gpu]
    runtime: nvidia
    ipc: host
    networks:
      - llm_1x2_network
networks:
  llm_1x2_network:
    driver: bridge

About the Response object

$client->chat() method return a Partitech\PhpMistral\Response object.

$client = new MistralClient($apiKey);
$result = $client->chat(
        $messages,
        [
            'model' => 'mistral-large-latest',
            'temperature' => 0.7,
            'top_p' => 1,
            'max_tokens' => 250,
            'safe_prompt' => false,
            'random_seed' => null
        ]
    );

Available methods are :

$result->getChunk()
$result->getId()
$result->getChoices()
$result->getCreated()
$result->getGuidedMessage()
$result->getModel()
$result->getObject()
$result->getObject()
$result->getToolCalls()
$result->getUsage()

All of theses methods are accessors to basic response from the server :

{
    "id": "cmpl-e5cc70bb28c444948073e77776eb30ef",
    "object": "chat.completion",
    "created": 1702256327,
    "model": "mistral-large-latest",
    "choices":
    [
        {
            "index": 0,
            "message":
            {
                "role": "assistant",
                "content": "",
                "tool_calls":
                [
                    {
                        "function":
                        {
                            "name": "get_current_weather",
                            "arguments": "{\"location\": \"Paris, 75\"}"
                        }
                    }
                ]
            },
            "finish_reason": "tool_calls"
        }
    ],
    "usage":
    {
        "prompt_tokens": 118,
        "completion_tokens": 35,
        "total_tokens": 153
    }
}

Method's names and response json are understandable, but here is a basic explanation :

Get the last message response from the server. In fact, it gets the last message from the Messages class, which is a list of messages.

$result->getMessage()

example:

France is known for its diverse and high-quality cheeses, so it's challenging to single out one as the "best" because it largely depends on personal preference. However, some French cheeses are particularly renowned ...

When using streamed response, it get the last chunk of the message yelded by the server.

foreach ($client->chatStream($messages, $params) as $chunk) {
    echo $chunk->getChunk();
}

example:

France is known

Get the id's response from the server

$result->getId()

example:

cmpl-e5cc70bb28c444948073e77776eb30ef

Get array object with choices responses from the server

$result->getChoices()

example:

ArrayObject Object
(
    [storage:ArrayObject:private] => Array
        (
            [0] => Partitech\PhpMistral\Message Object
                (
                    [role:Partitech\PhpMistral\Message:private] => assistant
                    [content:Partitech\PhpMistral\Message:private] => France is known for its diverse and high-quality cheeses, so it's challenging to single out one as the "best" because it largely depends on personal preference. However, some French cheeses are particularly renowned:

1. Comté: This is a hard cheese made from unpasteurized cow's milk in the Franche-Comté region of eastern France. It has a nutty, slightly sweet flavor.

2. Brie de Meaux: Often just called Brie, this is a soft cheese with a white, edible rind. It's known for its creamy texture and mild, slightly earthy flavor.

3. Roquefort: This is a blue cheese made from sheep's milk. It has a strong, tangy flavor and is crumbly in texture.

4. Camembert: This is another soft cheese with a white, edible rind. It's similar to Brie but has a stronger, more pungent flavor.

5. Reblochon: This is a soft, washed-rind cheese from the Savoie region of France. It has a nutty
                    [chunk:Partitech\PhpMistral\Message:private] => 
                    [toolCalls:Partitech\PhpMistral\Message:private] => 
                )

        )

)

Get the created response's date (integer timestamp)

$result->getCreated()

example:

1702256327

Get the guided message object. This is the json_decoded message returned from the vllm server. Only used with vllm server.

$result->getGuidedMessage()

example:

object(stdClass)#1 (1) {
  ["foo"]=>
  string(3) "bar"
}

Get the model used, from the server response.

$result->getModel()

example:

mistral-large-latest

Get the object index, from the server response.

$result->getObject()

example:

chat.completion

get the tool_calls value from the server's response. This is an associative array with the json_decoded response from the server.

$result->getToolCalls()

example:

$toolCall = $chatResponse->getToolCalls();
$functionName = $toolCall[0]['function']['name'];
$functionParams = $toolCall[0]['function']['arguments'];

// Call the proper function
$functionResult = $namesToFunctions[$functionName]($functionParams);

print_r($functionResult);
//    Array
//    (
//        [status] => Paid
//    )

Get the usage response from the server.

$result->getUsage()

example:

(
    [prompt_tokens] => 10
    [total_tokens] => 260
    [completion_tokens] => 250
)

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

Details

README

Features

Getting Started

Installation

Usage

Chat message with La plateforme

Chat with streamed response with La plateforme

Chat with streamed response with Lama.cpp

Embeddings with La plateform

Embeddings with Lama.cpp server

Fill in the middle

Fill in the middle in stream mode

Pixtral request

Lama.cpp inference

vLLM inference

About the Response object

Documentation

Contributing

License

Support

Thanks