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.
- 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.
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-mistralTo 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($embeddings);Result :
Array
(
[data] => Array
(
[0] => Array
(
[embedding] => Array
(
[0] => 3.5081260204315
...
[4095] => -2.5789933204651
)
[1] => Array
...
)
)$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'));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'));$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.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.ggufvLLM 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: bridgevLLM 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$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
)
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 :)