LLM Providers

The LaraUtilX package provides a unified interface for interacting with multiple Large Language Model (LLM) providers. Choose between OpenAI's GPT models, Google's Gemini models, or Anthropic's Claude models through a consistent API.

Supported Providers

  1. OpenAIProvider: Interact with OpenAI's GPT models (GPT-3.5, GPT-4, etc.)
  2. GeminiProvider: Interact with Google's Gemini models (Gemini 2.0 Flash, etc.)
  3. ClaudeProvider: Interact with Anthropic's Claude models (Claude 3.5 Sonnet, etc.)

All providers support:

  • All major chat parameters (temperature, max tokens, top-p, etc.)
  • Automatic retry logic with configurable delays
  • Structured responses with usage metadata
  • JSON mode for structured output
  • Unified interface for easy provider switching

Methods

  1. generateResponse( string $modelName, array $messages, ?float $temperature = null, ?int $maxTokens = null, ?array $stop = null, ?float $topP = null, ?float $frequencyPenalty = null, ?float $presencePenalty = null, ?array $logitBias = null, ?string $user = null, ?bool $jsonMode = false, bool $fullResponse = false ): Response
    • Generates a chat completion using the specified model and parameters.

Usage

1. Using the Unified Interface

The provider is auto-bound to the LLMProviderInterface, so you can inject it anywhere in your Laravel app:

use LaraUtilX\LLMProviders\Contracts\LLMProviderInterface;

class MyController extends Controller
{
    public function ask(LLMProviderInterface $llm)
    {
        $response = $llm->generateResponse(
            'gpt-3.5-turbo', // or 'gemini-2.0-flash' or 'claude-sonnet-4-0'
            [
                ['role' => 'user', 'content' => 'What is Laravel?']
            ]
        );

        return $response->getContent();
    }
}

2. Constructor Injection

use LaraUtilX\LLMProviders\Contracts\LLMProviderInterface;

class MyController extends Controller
{
    private LLMProviderInterface $llm;

    public function __construct(LLMProviderInterface $llm)
    {
        $this->llm = $llm;
    }

    public function ask()
    {
        $response = $this->llm->generateResponse(
            'gpt-3.5-turbo',
            [
                ['role' => 'user', 'content' => 'What is Laravel?']
            ]
        );

        return $response->getContent();
    }
}

3. Generic LLM Test Endpoint

public function testLLM(Request $request)
{
    $provider = config('lara-util-x.llm.default_provider', 'openai');
    $model = match($provider) {
        'gemini' => config('lara-util-x.gemini.default_model', 'gemini-2.0-flash'),
        'claude' => config('lara-util-x.claude.default_model', 'claude-sonnet-4-0'),
        default => config('lara-util-x.openai.default_model', 'gpt-3.5-turbo')
    };

    $prompt = $request->input('prompt', 'Say hello in one short sentence.');
    $temperature = $request->input('temperature');
    $maxTokens = $request->input('max_tokens');
    $jsonMode = filter_var($request->input('json_mode', false), FILTER_VALIDATE_BOOL);

    $messages = [
        ['role' => 'user', 'content' => $prompt]
    ];

    $response = $this->llm->generateResponse(
        modelName: $model,
        messages: $messages,
        temperature: $temperature !== null ? (float) $temperature : null,
        maxTokens: $maxTokens !== null ? (int) $maxTokens : null,
        jsonMode: $jsonMode,
        fullResponse: true
    );

    return response()->json([
        'provider' => $provider,
        'model' => $response->getModel() ?? $model,
        'content' => $response->getContent(),
        'usage' => $response->getUsage(),
    ]);
}

4. Customizing Parameters

You can use all supported chat parameters:

$response = $llm->generateResponse(
    'gpt-3.5-turbo', // or 'gemini-2.0-flash' or 'claude-sonnet-4-0'
    [
        ['role' => 'user', 'content' => 'Tell me a joke.']
    ],
    temperature: 0.8,
    maxTokens: 100,
    stop: ['\n'],
    topP: 1.0,
    frequencyPenalty: 0,
    presencePenalty: 0,
    logitBias: null,
    user: 'user-123',
    jsonMode: false,
    fullResponse: true
);

Configuration

All configurations are present in config/lara-util-x.php:

'llm' => [
    'default_provider' => env('LLM_DEFAULT_PROVIDER', 'openai'),
],

'openai' => [
    'api_key' => env('OPENAI_API_KEY'),
    'max_retries' => env('OPENAI_MAX_RETRIES', 3),
    'retry_delay' => env('OPENAI_RETRY_DELAY', 2),
    'default_model' => env('OPENAI_DEFAULT_MODEL', 'gpt-3.5-turbo'),
    'default_temperature' => env('OPENAI_DEFAULT_TEMPERATURE', 0.7),
    'default_max_tokens' => env('OPENAI_DEFAULT_MAX_TOKENS', 300),
    'default_top_p' => env('OPENAI_DEFAULT_TOP_P', 1.0),
],

'gemini' => [
    'api_key' => env('GEMINI_API_KEY'),
    'max_retries' => env('GEMINI_MAX_RETRIES', 3),
    'retry_delay' => env('GEMINI_RETRY_DELAY', 2),
    'base_url' => env('GEMINI_BASE_URL', 'https://generativelanguage.googleapis.com/v1beta'),
    'default_model' => env('GEMINI_DEFAULT_MODEL', 'gemini-2.0-flash'),
    'default_temperature' => env('GEMINI_DEFAULT_TEMPERATURE', 0.7),
    'default_max_tokens' => env('GEMINI_DEFAULT_MAX_TOKENS', 300),
    'default_top_p' => env('GEMINI_DEFAULT_TOP_P', 1.0),
],

'claude' => [
    'api_key' => env('CLAUDE_API_KEY'),
    'max_retries' => env('CLAUDE_MAX_RETRIES', 3),
    'retry_delay' => env('CLAUDE_RETRY_DELAY', 2),
    'base_url' => env('CLAUDE_BASE_URL', 'https://api.anthropic.com'),
    'default_model' => env('CLAUDE_DEFAULT_MODEL', 'claude-sonnet-4-0'),
    'default_temperature' => env('CLAUDE_DEFAULT_TEMPERATURE', 1.0),
    'default_max_tokens' => env('CLAUDE_DEFAULT_MAX_TOKENS', 1024),
    'default_top_p' => env('CLAUDE_DEFAULT_TOP_P', 1.0),
],

Add your API keys to your .env file:

OPENAI_API_KEY=sk-...
GEMINI_API_KEY=...
CLAUDE_API_KEY=sk-ant-...
LLM_DEFAULT_PROVIDER=openai

Provider Selection

Switch between providers via configuration:

// Use OpenAI
LLM_DEFAULT_PROVIDER=openai

// Use Gemini
LLM_DEFAULT_PROVIDER=gemini

// Use Claude
LLM_DEFAULT_PROVIDER=claude

Result

The method returns a response instance (either OpenAIResponse, GeminiResponse, or ClaudeResponse), which provides:

  • getContent(): The generated text from the model.
  • getModel(): The model used for the response.
  • getUsage(): Token usage information.
  • getRawResponse(): The full raw response object from the provider.

Example:

$content = $response->getContent();
$model = $response->getModel();
$usage = $response->getUsage();

Success Result:

  • The response contains the generated content and, if fullResponse is true, metadata such as model and usage.

Failure Result:

  • If the request fails, an exception is thrown. The provider will retry the request up to the configured number of times before failing.

Error Handling

  • Both providers automatically retry failed requests up to max_retries.
  • If all retries fail, an exception is thrown. You should catch exceptions in your controller or service.
  • Retry delays are configurable per provider.

Extending

To add support for other LLM providers:

  • Implement the LLMProviderInterface in your own provider class.
  • Bind your provider in the service provider if you want to swap implementations.
  • Follow the same response pattern for consistency.

This unified system simplifies the process of interacting with multiple LLM providers, providing a convenient, robust, and extensible way to generate AI-powered completions in your Laravel application.