README

PHP package for integrating and interacting with deployed Azure LLM models.

🚀 Changelog (v1.4.1)

New Features

Added DeepSeek model support.

📌 Documentation

📖 AI Agents
🔍 AI Search

Features

Simplifies managing Azure OpenAI API settings such as API keys, endpoints, deployments, and API versions.
Full support for Agents, Threads, and Vector Stores.
Integrated Azure AI Search functionalities.

Requirements

PHP 8.1+
Composer

Installation

Use the library via Composer:

composer require edgaras/azurellm

Include the Composer autoloader:

require __DIR__ . '/vendor/autoload.php';

Usage

1. Initialization

Set up your Azure OpenAI configuration:

use Edgaras\AzureLLM\LLM;

$config = new LLM([
    'apiKey' => '<YOUR-API-KEY>',
    'endpoint' => 'https://<DEPLOYMENT>.openai.azure.com',
    'deployment' => '<MODEL-DEPLOYMENT-ID>',
    'apiVersion' => '<API-VERSION>'
]);

2. Basic usage

Send requests to your Azure OpenAI deployment:

use Edgaras\AzureLLM\LLM;
use Edgaras\AzureLLM\AzureOpenAI;

$config = new LLM([
    'apiKey' => '<YOUR-API-KEY>',
    'endpoint' => 'https://<DEPLOYMENT>.openai.azure.com',
    'deployment' => '<MODEL-DEPLOYMENT-ID>',
    'apiVersion' => '<API-VERSION>'
]); 

$azureLLM = new AzureOpenAI($config);

$inputMessages = [
    ['role' => 'system', 'content' => 'You are a helpful assistant.'],
    ['role' => 'user', 'content' => 'What is the capital of Lithuania?']
];

$options = [
    "temperature" => 0.7,
    "top_p" => 0.95,
    "max_tokens" => 150 
];

$response = $azureLLM->chatCompletions($inputMessages, $options);

3. Use with Azure AI Search

Combine the Azure OpenAI service with Azure Search for contextual completions:

use Edgaras\AzureLLM\LLM;
use Edgaras\AzureLLM\AzureOpenAI;

$config = new LLM([
    'apiKey' => '<YOUR-API-KEY>',
    'endpoint' => 'https://<DEPLOYMENT>.openai.azure.com',
    'deployment' => '<MODEL-DEPLOYMENT-ID>',
    'apiVersion' => '<API-VERSION>'
]); 

$azureLLM = new AzureOpenAI($config);

$inputMessages = [
    ['role' => 'system', 'content' => 'You are a helpful assistant.'],
    ['role' => 'user', 'content' => 'Summarize your knowledgebase']
];

$options = [
    "temperature" => 0.7,
    "top_p" => 0.95,
    "max_tokens" => 150 
];

$data_sources = [[
    "type" => "azure_search",
    "parameters" => [
        "filter" => null,
        "endpoint" => 'https://<SEARCH-DEPLOYMENT>.search.windows.net',
        "index_name" => '<SEARCH-INDEX-NAME>',
        "authentication" => [
            "type" => "api_key",
            "key" => '<SEARCH-API-KEY>'
        ],
    ],
]];

$response = $azureLLM->chatCompletions($inputMessages, $options, $data_sources);

4. Initialize AI Search Configuration

use Edgaras\AzureLLM\AISearch\Auth;

$config = new Auth([
    'apiKey' => '<YOUR-API-KEY>',
    'endpoint' => 'https://<YOUR-SEARCH-SERVICE>.search.windows.net',
    'apiVersion' => '2023-07-01-Preview'
]);

5. Manage AI Search Indexes

use Edgaras\AzureLLM\AISearch\Index;

$indexService = new Index($config);

// Define Index Fields
$fields = [
    ['name' => 'id', 'type' => 'Edm.String', 'key' => true],
    ['name' => 'content', 'type' => 'Edm.String', 'searchable' => true, 'retrievable' => true]
];

// Create Index
$indexService->createIndex('test-index', $fields);

// List all indexes
$indexes = $indexService->listIndexes();
print_r($indexes);

6. Manage AI Search Indexers

use Edgaras\AzureLLM\AISearch\Indexer;

$indexerService = new Indexer($config);

// Define Indexer
$indexerConfig = [
    'dataSourceName' => 'test-data-source',
    'targetIndexName' => 'test-index',
    'schedule' => ['interval' => 'P1D']
];

// Create Indexer
$indexerService->createIndexer('test-indexer', $indexerConfig);

// Run Indexer Manually
$indexerService->runIndexer('test-indexer');

7. Manage AI Search Data Sources

use Edgaras\AzureLLM\AISearch\DataSource;

$dataSourceService = new DataSource($config);

// Define Data Source Configuration
$dataSourceConfig = [
    'type' => 'azureblob',
    'credentials' => ['connectionString' => '<YOUR-STORAGE-CONNECTION-STRING>'],
    'container' => ['name' => 'your-container']
];

// Create Data Source
$dataSourceService->createDataSource('test-data-source', $dataSourceConfig);

Full AI Search Docs

8. Agents & Threads

use Edgaras\AzureLLM\LLM; 
use Edgaras\AzureLLM\Agents\Agent;
use Edgaras\AzureLLM\Agents\Thread;

// Initialize
$config = new LLM([
    'apiKey' => '<API-KEY>',
    'endpoint' => 'https://<DEPLOYMENT-NAME>.openai.azure.com',
    'deployment' => '<MODEL>',
    'apiVersion' => '2024-05-01-preview'
]);

$agent = new Agent($config);
$thread = new Thread($config);

// Create an Agent
$agentResponse = $agent->createAgent("SupportBot", "Assist users with support queries.");
$agentId = $agentResponse['id'];

// Start a conversation thread
$threadResponse = $thread->createThread();
$threadId = $threadResponse['id'];

// Send a message
$thread->addMessageToThread($threadId, "user", "How do I reset my password?");

// Run the AI Assistant on the thread
$thread->runThread($threadId, $agentId);

Full AI Agents Docs

9. Basic usage (DeepSeek)

Send requests to your DeepSeek deployment:

use Edgaras\AzureLLM\LLM;
use Edgaras\AzureLLM\DeepSeek;

$config = new LLM([
    'apiKey' => '<YOUR-API-KEY>',
    'endpoint' => 'https://<DEPLOYMENT>.services.ai.azure.com',
    'deployment' => '<MODEL-DEPLOYMENT-ID>',
    'apiVersion' => '<API-VERSION>'
]); 

$deepSeek = new DeepSeek($config);

$inputMessages = [
    ['role' => 'system', 'content' => 'You are a helpful assistant.'],
    ['role' => 'user', 'content' => 'What is the capital of Lithuania?']
];

$options = [
    "temperature" => 0.7,
    "top_p" => 0.95,
    "max_tokens" => 150 
];

$response = $deepSeek->chatCompletions($inputMessages, $options);

edgaras / azurellm

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