Rector is installed

Author: Alexander Cherednikov

README.md

@@ -44,7 +44,7 @@
 ### Что нужно установить
 
 - Docker и Docker Compose
-- PHP 8.2+ с Composer
+- PHP 8.3+ с Composer
 - Make (опционально, для удобных команд)
 
 ### Запуск в одну команду

composer.json

@@ -86,7 +86,7 @@
     "require-dev": {
         "friendsofphp/php-cs-fixer": "^3.87.2",
         "phpstan/phpstan": "^1.12.28",
-        "rector/rector": "^1.2",
+        "rector/rector": "^1.2.10",
         "symfony/maker-bundle": "^1.64"
     }
 }

composer.lock

@@ -54,9 +54,9 @@ services:
     App\Contract\ResponseGeneratorInterface:
         class: App\Service\Generation\LlamaResponseGenerator
 
-    # Main RAG Service - uses refactored implementation
+    # Main RAG Service implementation
     App\Service\RAGServiceInterface:
-        class: App\Service\RefactoredRAGService
+        class: App\Service\RAGService
 
     # LlamaService configuration
     App\Service\LlamaService:
@@ -65,4 +65,4 @@ services:
 
     # HTTP Client for external services
     Symfony\Contracts\HttpClient\HttpClientInterface:
-        class: Symfony\Component\HttpClient\HttpClient
+        factory: ['Symfony\Component\HttpClient\HttpClient', 'create']

config/services.yaml

@@ -4,7 +4,7 @@
 
 namespace App\Command;
 
-use App\Service\ImprovedRAGService;
+use App\Service\RAGService;
 use Symfony\Component\Console\Attribute\AsCommand;
 use Symfony\Component\Console\Command\Command;
 use Symfony\Component\Console\Input\InputInterface;
@@ -17,10 +17,10 @@
 )]
 final class ProductsChatCommand extends Command
 {
-    private const SESSION_ID = 'chat_session';
+    private const string SESSION_ID = 'chat_session';
 
     public function __construct(
-        private readonly ImprovedRAGService $ragService,
+        private readonly RAGService $ragService,
     ) {
         parent::__construct();
     }
@@ -82,7 +82,6 @@ private function displayChatResults(array $results, string $aiResponse, SymfonyS
     {
         $io->section('Рекомендации');
 
-        // Очищаем markdown и форматируем для консоли
         $cleanResponse = strip_tags(str_replace(['**', '*', '#', '`'], '', $aiResponse));
         $io->text($cleanResponse);
 

src/Command/ProductsChatCommand.php

@@ -4,12 +4,7 @@
 
 namespace App\Command;
 
-use Codewithkyrian\Transformers\Pipelines\Pipeline;
-
-use function Codewithkyrian\Transformers\Pipelines\pipeline;
-
-use Codewithkyrian\Transformers\Pipelines\Task;
-use Codewithkyrian\Transformers\Tensor\Tensor;
+use App\Service\EmbeddingConfigService;
 use Qdrant\Config;
 use Qdrant\Http\Transport;
 use Qdrant\Models\Request\SearchRequest;
@@ -29,10 +24,11 @@
 )]
 final class ProductsSearchCommand extends Command
 {
-    private const COLLECTION_NAME = 'products';
+    private const string COLLECTION_NAME = 'products';
 
     private Qdrant $qdrantClient;
-    private Pipeline $embedder;
+
+    private EmbeddingConfigService $embeddingService;
 
     protected function configure(): void
     {
@@ -66,22 +62,15 @@ private function initializeServices(): void
         $config = new Config('http://localhost', 6333);
         $transport = new Transport(new Psr18Client(), $config);
         $this->qdrantClient = new Qdrant($transport);
-        $this->embedder = pipeline(Task::Embeddings, 'onnx-community/Qwen3-Embedding-0.6B-ONNX');
+        $this->embeddingService = new EmbeddingConfigService();
     }
 
     /**
      * @return array<int, array<string, mixed>>
      */
     private function searchProducts(string $query): array
     {
-        $embedding = ($this->embedder)($query, pooling: 'mean', normalize: true);
-
-        if (is_array($embedding)) {
-            $vector = $embedding[0];
-        } else {
-            $vector = $embedding instanceof Tensor ? $embedding[0] : [];
-        }
-
+        $vector = $this->embeddingService->createEmbedding($query);
         $searchVector = new VectorStruct($vector, 'default');
         $searchRequest = new SearchRequest($searchVector);
         $searchRequest->setLimit(5)->setWithPayload(true)->setScoreThreshold(0.5);

src/Command/ProductsSearchCommand.php

@@ -4,12 +4,7 @@
 
 namespace App\Command;
 
-use Codewithkyrian\Transformers\Pipelines\Pipeline;
-
-use function Codewithkyrian\Transformers\Pipelines\pipeline;
-
-use Codewithkyrian\Transformers\Pipelines\Task;
-use Codewithkyrian\Transformers\Tensor\Tensor;
+use App\Service\EmbeddingConfigService;
 use Qdrant\Config;
 use Qdrant\Http\Transport;
 use Qdrant\Models\PointsStruct;
@@ -31,11 +26,12 @@
 )]
 final class ProductsVectorizeCommand extends Command
 {
-    private const COLLECTION_NAME = 'products';
-    private const DATA_FILE = __DIR__.'/../../data/products.json';
+    private const string COLLECTION_NAME = 'products';
+    private const string DATA_FILE = __DIR__.'/../../data/products.json';
 
     private Qdrant $qdrantClient;
-    private Pipeline $embedder;
+
+    private EmbeddingConfigService $embeddingService;
 
     protected function configure(): void
     {
@@ -66,7 +62,7 @@ private function initializeServices(): void
         $config = new Config('http://localhost', 6333);
         $transport = new Transport(new Psr18Client(), $config);
         $this->qdrantClient = new Qdrant($transport);
-        $this->embedder = pipeline(Task::Embeddings, 'onnx-community/Qwen3-Embedding-0.6B-ONNX');
+        $this->embeddingService = new EmbeddingConfigService();
     }
 
     private function loadProductData(): \Generator
@@ -99,7 +95,8 @@ private function prepareCollection(): void
         }
 
         $createCollection = new CreateCollection();
-        $createCollection->addVector(new VectorParams(1024, VectorParams::DISTANCE_COSINE), 'default');
+        $vectorSize = $this->embeddingService->getVectorSize();
+        $createCollection->addVector(new VectorParams($vectorSize, VectorParams::DISTANCE_COSINE), 'default');
         $this->qdrantClient->collections(self::COLLECTION_NAME)->create($createCollection);
     }
 
@@ -141,7 +138,7 @@ private function processBatch(array $batch): void
             }
 
             $text .= ' '.$product['brand'].' '.$product['category'];
-            $embedding = ($this->embedder)($text, pooling: 'mean', normalize: true);
+            $vector = $this->embeddingService->createEmbedding($text);
 
             $payload = [
                 'name' => $product['name'],
@@ -151,12 +148,6 @@ private function processBatch(array $batch): void
                 'description' => $product['description'],
             ];
 
-            if (is_array($embedding)) {
-                $vector = $embedding[0];
-            } else {
-                $vector = $embedding instanceof Tensor ? $embedding[0] : [];
-            }
-
             $pointsStruct->addPoint(
                 new PointStruct(
                     $product['id'],

src/Command/ProductsVectorizeCommand.php

@@ -5,7 +5,7 @@
 namespace App\Command;
 
 use App\DTO\RAGSearchResult;
-use App\Service\ImprovedRAGService;
+use App\Service\RAGService;
 use Symfony\Component\Console\Attribute\AsCommand;
 use Symfony\Component\Console\Command\Command;
 use Symfony\Component\Console\Input\InputInterface;
@@ -15,12 +15,12 @@
 
 #[AsCommand(
     name: 'rag:demo',
-    description: 'Демонстрация улучшенной RAG архитектуры с четким разделением этапов'
+    description: 'Демонстрация RAG архитектуры с четким разделением этапов'
 )]
 final class RAGDemoCommand extends Command
 {
     public function __construct(
-        private readonly ImprovedRAGService $ragService,
+        private readonly RAGService $ragService,
     ) {
         parent::__construct();
     }
@@ -111,11 +111,11 @@ private function displayRAGSteps(RAGSearchResult $result, SymfonyStyle $io): voi
 
         $io->section('Retrieval');
         if ($result->hasResults()) {
-            $io->text("Найдено товаров: {$result->getDocumentCount()}");
+            $io->text("Найдено товаров: {$result->getResultCount()}");
 
             $io->table(
                 ['#', 'Товар', 'Бренд', 'Цена', 'Релевантность'],
-                array_map(function ($doc, $i) {
+                array_map(function (array $doc, int $i): array {
                     $payload = $doc['payload'];
                     $price = number_format($payload['price'] / 100, 0, '.', ' ').' ₽';
                     $relevance = round($doc['score'] * 100, 1).'%';