Carlos Arias
1559ce017d
Clean-room copy of the reusable engines from comiida, with all instance data, secrets, dependencies, and build output excluded: - app/ Astro theme skeleton (no comiida blog posts; hero image -> placeholder) - api/ SeedProject PHP framework (no vendor/.env/config.php) - content-pipeline/ engine only (scripts/admin/prompts; empty runtime state) - astroagent.config.json + app/.astroagent/skills Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01SYHWLHihq3v9nxNwoPCKSn
749 lines
No EOL
25 KiB
PHP
749 lines
No EOL
25 KiB
PHP
<?php
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namespace App\LLM;
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use Exception;
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use Gemini as GeminiClient;
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use Gemini\Data\Content;
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use Gemini\Data\GenerationConfig;
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use Gemini\Data\Tool;
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use Gemini\Data\GoogleSearch;
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use Gemini\Enums\ResponseMimeType;
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class GeminiService {
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private $client;
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private string $defaultModel = 'gemini-3-pro-preview';
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private int $defaultMaxTokens = 8192;
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private ?string $apiKey;
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private string $templatePath;
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// Model presets for different use cases
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// - pro: Best for humanizing text, creative nuance, SEO strategy, and fact-checking
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// - flash: Best for bulk processing, initial drafts, AEO structure checks
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// - flash-lite: Cost saver for simple tasks like meta-tag generation or keyword extraction
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public const MODELS = [
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'pro' => 'gemini-3-pro-preview', // Best for humanizing, SEO strategy
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'flash' => 'gemini-3-flash-preview', // Faster, good for SEO review pass
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'flash-lite' => 'gemini-2.5-flash-lite', // Cost saver for simple tasks
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'flash-2.5' => 'gemini-2.5-flash', // Legacy flash model
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];
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// Human-readable labels for UI dropdowns
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public const MODEL_LABELS = [
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'gemini-3-pro-preview' => 'Gemini 3 Pro (Best for Humanizing)',
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'gemini-3-flash-preview' => 'Gemini 3 Flash (Fast & Smart)',
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'gemini-2.5-flash' => 'Gemini 2.5 Flash (Legacy)',
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'gemini-2.5-flash-lite' => 'Gemini 2.5 Flash Lite (Budget)',
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];
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/**
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* Constructor
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*/
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public function __construct(?string $apiKey = null, ?string $templatePath = null, int $timeout = 300)
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{
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$this->apiKey = $apiKey ?? (defined('GEMINI_API_KEY') ? GEMINI_API_KEY : null);
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if (empty($this->apiKey)) {
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throw new Exception('Gemini API key is required.');
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}
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$this->templatePath = $templatePath ?? '/www/wwwroot/appCarlos/templates/prompts';
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if (!is_dir($this->templatePath)) {
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throw new Exception("Template directory not found: {$this->templatePath}");
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}
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if (!is_readable($this->templatePath)) {
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throw new Exception("Template directory is not readable: {$this->templatePath}");
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}
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set_time_limit($timeout);
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ini_set('default_socket_timeout', (string) $timeout);
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$this->client = GeminiClient::client($this->apiKey);
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}
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// =========================================================================
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// CONNECTION & HEALTH
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// =========================================================================
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public function testConnection(): array
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{
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$startTime = microtime(true);
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try {
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$response = $this->client
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->generativeModel(model: $this->defaultModel)
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->generateContent('Reply with only: OK');
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$latency = round((microtime(true) - $startTime) * 1000);
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// Extract usage from response
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$usage = $this->extractUsage($response);
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return [
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'success' => true,
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'message' => 'Connected to Gemini API',
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'model' => $this->defaultModel,
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'response' => trim($response->text()),
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'latency_ms' => $latency,
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'usage' => $usage,
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];
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} catch (Exception $e) {
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return [
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'success' => false,
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'message' => 'Connection failed',
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'error' => $e->getMessage(),
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'error_type' => get_class($e),
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];
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}
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}
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public function isConnected(): bool
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{
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return $this->testConnection()['success'];
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}
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public function getStatus(): array
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{
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$connection = $this->testConnection();
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return [
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'connected' => $connection['success'],
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'model' => $this->defaultModel,
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'max_tokens' => $this->defaultMaxTokens,
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'api_key_preview' => substr($this->apiKey, 0, 10) . '...' . substr($this->apiKey, -4),
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'template_path' => $this->templatePath,
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'connection_details' => $connection,
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];
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}
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// =========================================================================
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// TEMPLATE LOADING
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// =========================================================================
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public function loadTemplate(string $filename): string
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{
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$path = $this->templatePath . '/' . $filename;
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if (!file_exists($path)) {
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throw new Exception("Template not found: {$path}");
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}
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$content = file_get_contents($path);
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if ($content === false) {
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throw new Exception("Failed to read template: {$path}");
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}
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return $content;
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}
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public function setTemplatePath(string $path): self
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{
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$this->templatePath = rtrim($path, '/');
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return $this;
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}
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public function getTemplatePath(): string
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{
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return $this->templatePath;
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}
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// =========================================================================
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// BASIC MESSAGING
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// =========================================================================
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public function message(string $prompt, ?string $systemPrompt = null, array $options = []): string
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{
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$response = $this->messageWithMeta($prompt, $systemPrompt, $options);
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return $response['content'];
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}
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/**
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* GEMINI SERVICE UPDATE
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*
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* Replace the existing messageWithMeta() method with this updated version
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* that supports JSON response mode via 'response_mime_type' option.
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*
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* Location: App\Components\GeminiService
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* Method: messageWithMeta()
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*/
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public function messageWithMeta(string $prompt, ?string $systemPrompt = null, array $options = []): array
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{
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$startTime = microtime(true);
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$model = $this->client->generativeModel(
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model: $options['model'] ?? $this->defaultModel
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);
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// Add system instruction if provided
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if ($systemPrompt) {
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$model = $model->withSystemInstruction(Content::parse($systemPrompt));
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}
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// Build generation config
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$configParams = [
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'maxOutputTokens' => $options['max_tokens'] ?? $this->defaultMaxTokens,
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];
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if (isset($options['response_mime_type']) && $options['response_mime_type'] === 'application/json') {
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$configParams['responseMimeType'] = ResponseMimeType::APPLICATION_JSON;
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}
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if (isset($options['temperature'])) {
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$configParams['temperature'] = $options['temperature'];
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}
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if (isset($options['stop_sequences'])) {
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$configParams['stopSequences'] = $options['stop_sequences'];
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}
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// Support JSON response mode
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if (isset($options['response_mime_type']) && $options['response_mime_type'] === 'application/json') {
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$configParams['responseMimeType'] = ResponseMimeType::APPLICATION_JSON;
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}
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$model = $model->withGenerationConfig(new GenerationConfig(...$configParams));
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// Enable Google Search if requested
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if ($options['use_search'] ?? false) {
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$model = $model->withTool(new Tool(googleSearch: new GoogleSearch()));
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}
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$response = $model->generateContent($prompt);
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$usage = $this->extractUsage($response);
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return [
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'content' => $response->text(),
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'model' => $options['model'] ?? $this->defaultModel,
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'stop_reason' => $response->candidates[0]->finishReason ?? null,
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'latency_ms' => round((microtime(true) - $startTime) * 1000),
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'usage' => $usage,
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];
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}
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// =========================================================================
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// ARTICLE GENERATION
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// =========================================================================
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/**
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* Build the dynamic variables block (topic, word count, etc.)
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*/
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public function buildArticleVariables(array $config): string
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{
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$lines = [];
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if (!empty($config['topic'])) {
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$lines[] = "TOPIC: {$config['topic']}";
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}
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if (!empty($config['domain'])) {
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$lines[] = "DOMAIN: {$config['domain']}";
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}
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if (!empty($config['industry'])) {
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$lines[] = "INDUSTRY: {$config['industry']}";
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}
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if (!empty($config['target_audience'])) {
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$lines[] = "AUDIENCE: {$config['target_audience']}";
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}
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if (!empty($config['knowledge_level'])) {
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$lines[] = "KNOWLEDGE_LEVEL: {$config['knowledge_level']}";
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}
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if (!empty($config['tone'])) {
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$lines[] = "TONE: {$config['tone']}";
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}
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if (!empty($config['word_count'])) {
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$lines[] = "WORD_COUNT: {$config['word_count']}";
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}
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if (!empty($config['location'])) {
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$lines[] = "LOCATION: {$config['location']}";
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}
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return implode("\n", $lines);
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}
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/**
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* Generate article using template + variables
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* Uses Google Search for fact verification
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*/
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public function generateArticle(string $templateFile, array $config): array
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{
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$startTime = microtime(true);
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// Load template - this includes the master prompt + business profile
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$template = $this->loadTemplate($templateFile);
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// Build dynamic variables
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$variables = $this->buildArticleVariables($config);
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// Combine template with variables
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$systemPrompt = $template . "\n\n" . $variables;
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// Build the model with configuration
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$model = $this->client->generativeModel(
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model: $config['model'] ?? $this->defaultModel
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);
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// Add system instruction (template + variables)
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$model = $model->withSystemInstruction(Content::parse($systemPrompt));
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// Add generation config
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$model = $model->withGenerationConfig(new GenerationConfig(
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maxOutputTokens: $config['max_tokens'] ?? $this->defaultMaxTokens,
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));
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// Enable Google Search for fact verification
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if ($config['use_search'] ?? true) {
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$model = $model->withTool(new Tool(googleSearch: new GoogleSearch()));
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}
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// Generate content
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$userPrompt = $config['prompt'] ?? 'BEGIN GENERATION NOW.';
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$response = $model->generateContent($userPrompt);
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$usage = $this->extractUsage($response);
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$latency = round((microtime(true) - $startTime) * 1000);
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// Check if grounding was used
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$groundingUsed = $this->checkGroundingUsed($response);
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return [
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'content' => $response->text(),
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'model' => $config['model'] ?? $this->defaultModel,
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'latency_ms' => $latency,
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'grounding_used' => $groundingUsed,
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'usage' => $usage,
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];
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}
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/**
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* Generate article with structured JSON output
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*/
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public function generateArticleStructured(string $templateFile, array $config): array
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{
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$startTime = microtime(true);
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// Load template
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$template = $this->loadTemplate($templateFile);
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// Build dynamic variables
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$variables = $this->buildArticleVariables($config);
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// Define JSON structure for article
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$jsonSchema = $config['json_schema'] ?? [
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'title' => 'Article title',
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'meta_description' => 'SEO meta description',
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'content' => 'Full article content in HTML format',
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'sections' => [
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['heading' => 'Section heading', 'content' => 'Section content']
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],
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'tags' => ['relevant', 'tags'],
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'sources' => [
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['title' => 'Source title', 'url' => 'Source URL']
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],
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];
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$schemaJson = json_encode($jsonSchema, JSON_PRETTY_PRINT);
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// Combine template with variables and JSON instruction
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$systemPrompt = $template . "\n\n" . $variables . "\n\n" .
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"IMPORTANT: Respond ONLY with valid JSON using this structure:\n" . $schemaJson;
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// Build the model
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$model = $this->client->generativeModel(
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model: $config['model'] ?? $this->defaultModel
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);
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$model = $model->withSystemInstruction(Content::parse($systemPrompt));
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// Enable JSON output
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$model = $model->withGenerationConfig(new GenerationConfig(
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maxOutputTokens: $config['max_tokens'] ?? $this->defaultMaxTokens,
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responseMimeType: ResponseMimeType::APPLICATION_JSON,
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));
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// Enable Google Search
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if ($config['use_search'] ?? true) {
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$model = $model->withTool(new Tool(googleSearch: new GoogleSearch()));
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}
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$userPrompt = $config['prompt'] ?? 'BEGIN GENERATION NOW.';
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$response = $model->generateContent($userPrompt);
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$usage = $this->extractUsage($response);
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$latency = round((microtime(true) - $startTime) * 1000);
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$groundingUsed = $this->checkGroundingUsed($response);
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// Parse JSON response
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$articleData = null;
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try {
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$articleData = $response->json();
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} catch (Exception $e) {
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// Fall back to text if JSON parsing fails
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$articleData = ['content' => $response->text(), 'parse_error' => $e->getMessage()];
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}
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return [
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'content' => $response->text(),
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'article' => $articleData,
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'model' => $config['model'] ?? $this->defaultModel,
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'latency_ms' => $latency,
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'grounding_used' => $groundingUsed,
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'usage' => $usage,
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];
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}
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/**
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* Generate multiple articles
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*/
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public function generateArticleBatch(string $templateFile, array $articles): array
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{
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$results = [];
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foreach ($articles as $index => $config) {
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$results[] = [
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'index' => $index,
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'topic' => $config['topic'] ?? 'Unknown',
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'result' => $this->generateArticle($templateFile, $config),
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];
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}
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return $results;
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}
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// =========================================================================
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// ARTICLE REVIEW
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// =========================================================================
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/**
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* Review an article for quality and factual accuracy
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*/
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public function reviewArticle(string $articleContent, array $options = []): array
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{
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$startTime = microtime(true);
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$criteria = $options['criteria'] ?? [
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'grammar_spelling',
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'factual_accuracy',
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'readability',
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'seo_optimization',
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'engagement',
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'structure',
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'tone_consistency',
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];
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$criteriaList = implode(', ', $criteria);
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$systemPrompt = <<<PROMPT
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You are an expert editor and content quality analyst. Your task is to thoroughly review articles
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and provide detailed, actionable feedback. Use Google Search to verify any factual claims made
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in the article. Be thorough but constructive.
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PROMPT;
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$jsonSchema = [
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'overall_score' => 85,
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'summary' => 'Brief overall assessment',
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'criteria_scores' => [
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'example_criterion' => [
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'score' => 90,
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'feedback' => 'Detailed feedback',
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],
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],
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'strengths' => ['List of article strengths'],
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'improvements' => [
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[
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'priority' => 'high|medium|low',
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'issue' => 'Issue description',
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'suggestion' => 'How to fix it',
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],
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],
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'fact_check' => [
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[
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'claim' => 'Claim from the article',
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'verified' => true,
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'source' => 'Source or note',
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],
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],
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];
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$schemaJson = json_encode($jsonSchema, JSON_PRETTY_PRINT);
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$userPrompt = <<<PROMPT
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Review the following article and provide a comprehensive quality assessment.
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ARTICLE TO REVIEW:
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{$articleContent}
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Evaluate on these criteria: {$criteriaList}
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Use Google Search to verify factual claims.
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Respond with JSON in this structure:
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{$schemaJson}
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PROMPT;
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$model = $this->client->generativeModel(
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model: $options['model'] ?? $this->defaultModel
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);
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$model = $model->withSystemInstruction(Content::parse($systemPrompt));
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$model = $model->withGenerationConfig(new GenerationConfig(
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maxOutputTokens: $options['max_tokens'] ?? $this->defaultMaxTokens,
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responseMimeType: ResponseMimeType::APPLICATION_JSON,
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));
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// Enable Google Search for fact-checking
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$model = $model->withTool(new Tool(googleSearch: new GoogleSearch()));
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$response = $model->generateContent($userPrompt);
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$usage = $this->extractUsage($response);
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$latency = round((microtime(true) - $startTime) * 1000);
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$reviewData = null;
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try {
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$reviewData = $response->json();
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} catch (Exception $e) {
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$reviewData = ['content' => $response->text(), 'parse_error' => $e->getMessage()];
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}
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return [
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'content' => $response->text(),
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'review' => $reviewData,
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'model' => $options['model'] ?? $this->defaultModel,
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'latency_ms' => $latency,
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'usage' => $usage,
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];
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}
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|
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// =========================================================================
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// RESEARCH
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// =========================================================================
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/**
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* Research a topic using Google Search
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*/
|
|
public function research(string $query, array $options = []): array
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{
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$startTime = microtime(true);
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$depth = $options['depth'] ?? 2;
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$depthInstructions = match ($depth) {
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1 => 'Provide a quick overview with 3-5 key points.',
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2 => 'Provide a comprehensive overview with detailed findings.',
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3 => 'Provide an exhaustive analysis covering all aspects.',
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default => 'Provide a comprehensive overview.',
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};
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$systemPrompt = <<<PROMPT
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You are a research assistant. Research topics thoroughly using Google Search.
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Verify information across multiple sources. Be accurate and cite sources.
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PROMPT;
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|
|
|
$jsonSchema = [
|
|
'topic' => 'The research topic',
|
|
'summary' => 'Executive summary',
|
|
'key_findings' => [
|
|
['finding' => 'Key finding', 'confidence' => 'high|medium|low'],
|
|
],
|
|
'statistics' => [
|
|
['stat' => 'Statistic', 'source' => 'Source'],
|
|
],
|
|
'sources' => [
|
|
['title' => 'Source title', 'url' => 'URL', 'credibility' => 'high|medium|low'],
|
|
],
|
|
];
|
|
|
|
$schemaJson = json_encode($jsonSchema, JSON_PRETTY_PRINT);
|
|
|
|
$userPrompt = <<<PROMPT
|
|
Research: {$query}
|
|
|
|
{$depthInstructions}
|
|
|
|
Use Google Search to find current and accurate information.
|
|
|
|
Respond with JSON:
|
|
{$schemaJson}
|
|
PROMPT;
|
|
|
|
$model = $this->client->generativeModel(
|
|
model: $options['model'] ?? $this->defaultModel
|
|
);
|
|
|
|
$model = $model->withSystemInstruction(Content::parse($systemPrompt));
|
|
|
|
$model = $model->withGenerationConfig(new GenerationConfig(
|
|
maxOutputTokens: $options['max_tokens'] ?? $this->defaultMaxTokens,
|
|
responseMimeType: ResponseMimeType::APPLICATION_JSON,
|
|
));
|
|
|
|
$model = $model->withTool(new Tool(googleSearch: new GoogleSearch()));
|
|
|
|
$response = $model->generateContent($userPrompt);
|
|
|
|
$usage = $this->extractUsage($response);
|
|
$latency = round((microtime(true) - $startTime) * 1000);
|
|
|
|
$researchData = null;
|
|
try {
|
|
$researchData = $response->json();
|
|
} catch (Exception $e) {
|
|
$researchData = ['content' => $response->text(), 'parse_error' => $e->getMessage()];
|
|
}
|
|
|
|
return [
|
|
'content' => $response->text(),
|
|
'research' => $researchData,
|
|
'model' => $options['model'] ?? $this->defaultModel,
|
|
'latency_ms' => $latency,
|
|
'usage' => $usage,
|
|
];
|
|
}
|
|
|
|
// =========================================================================
|
|
// UTILITY METHODS
|
|
// =========================================================================
|
|
|
|
/**
|
|
* Extract usage information from response
|
|
*/
|
|
private function extractUsage($response): array
|
|
{
|
|
$usage = [
|
|
'input_tokens' => 0,
|
|
'output_tokens' => 0,
|
|
'total_tokens' => 0,
|
|
];
|
|
|
|
// Try to get usage metadata from response
|
|
if (isset($response->usageMetadata)) {
|
|
$usage['input_tokens'] = $response->usageMetadata->promptTokenCount ?? 0;
|
|
$usage['output_tokens'] = $response->usageMetadata->candidatesTokenCount ?? 0;
|
|
$usage['total_tokens'] = $response->usageMetadata->totalTokenCount ??
|
|
($usage['input_tokens'] + $usage['output_tokens']);
|
|
}
|
|
|
|
return $usage;
|
|
}
|
|
|
|
/**
|
|
* Check if grounding/search was used in the response
|
|
*/
|
|
private function checkGroundingUsed($response): bool
|
|
{
|
|
// Check for grounding metadata in response
|
|
if (isset($response->candidates[0]->groundingMetadata)) {
|
|
return true;
|
|
}
|
|
|
|
return false;
|
|
}
|
|
|
|
public function estimateTokens(string $text): int
|
|
{
|
|
return (int) ceil(strlen($text) / 4);
|
|
}
|
|
|
|
public function estimateCost(array $usage, ?string $model = null): array
|
|
{
|
|
$model = $model ?? $this->defaultModel;
|
|
|
|
// Gemini pricing per 1M tokens (as of January 2026)
|
|
$pricing = [
|
|
'gemini-3-pro-preview' => ['input' => 2.00, 'output' => 12.00],
|
|
'gemini-3-flash-preview' => ['input' => 0.20, 'output' => 0.80],
|
|
'gemini-2.5-flash' => ['input' => 0.15, 'output' => 0.60],
|
|
'gemini-2.5-flash-lite' => ['input' => 0.075, 'output' => 0.30],
|
|
];
|
|
|
|
$rates = $pricing[$model] ?? $pricing['gemini-3-pro-preview'];
|
|
|
|
$inputCost = (($usage['input_tokens'] ?? 0) / 1_000_000) * $rates['input'];
|
|
$outputCost = (($usage['output_tokens'] ?? 0) / 1_000_000) * $rates['output'];
|
|
|
|
return [
|
|
'input_cost' => round($inputCost, 6),
|
|
'output_cost' => round($outputCost, 6),
|
|
'total_cost' => round($inputCost + $outputCost, 6),
|
|
'model' => $model,
|
|
];
|
|
}
|
|
|
|
public function formatCost(array $usage): string
|
|
{
|
|
$cost = $this->estimateCost($usage);
|
|
|
|
$output = "Cost Breakdown:\n";
|
|
$output .= " Input: $" . number_format($cost['input_cost'], 4) . "\n";
|
|
$output .= " Output: $" . number_format($cost['output_cost'], 4) . "\n";
|
|
$output .= " ─────────────\n";
|
|
$output .= " Total: $" . number_format($cost['total_cost'], 4) . "\n";
|
|
|
|
return $output;
|
|
}
|
|
|
|
|
|
|
|
|
|
// =========================================================================
|
|
// CONFIGURATION
|
|
// =========================================================================
|
|
|
|
public function setModel(string $model): self
|
|
{
|
|
$this->defaultModel = $model;
|
|
return $this;
|
|
}
|
|
|
|
public function useModel(string $preset): self
|
|
{
|
|
if (isset(self::MODELS[$preset])) {
|
|
$this->defaultModel = self::MODELS[$preset];
|
|
}
|
|
return $this;
|
|
}
|
|
|
|
public function setMaxTokens(int $tokens): self
|
|
{
|
|
$this->defaultMaxTokens = $tokens;
|
|
return $this;
|
|
}
|
|
|
|
public function getModel(): string
|
|
{
|
|
return $this->defaultModel;
|
|
}
|
|
|
|
public function getClient()
|
|
{
|
|
return $this->client;
|
|
}
|
|
|
|
public function getAvailableModels(): array
|
|
{
|
|
return self::MODELS;
|
|
}
|
|
|
|
/**
|
|
* Get model labels for UI dropdowns
|
|
*/
|
|
public static function getModelLabels(): array
|
|
{
|
|
return self::MODEL_LABELS;
|
|
}
|
|
|
|
/**
|
|
* Get model ID from preset name
|
|
*/
|
|
public static function getModelId(string $preset): string
|
|
{
|
|
return self::MODELS[$preset] ?? $preset;
|
|
}
|
|
} |