Fix after review

Author: Iyadhfaleh

README.md

@@ -350,23 +350,23 @@ $eventDispatcher->addListener(ToolCallsExecuted::class, function (ToolCallsExecu
 LLM Chain supports document embedding and similarity search using vector stores like ChromaDB, Azure AI Search, MongoDB
 Atlas Search, or Pinecone.
 
-For populating a vector store, LLM Chain provides the service `EmbeddingProvider`, which requires an instance of an
+For populating a vector store, LLM Chain provides the service `Embedder`, which requires an instance of an
 `EmbeddingsModel` and one of `StoreInterface`, and works with a collection of `Document` objects as input:
 
 ```php
-use PhpLlm\LlmChain\EmbeddingProvider;
+use PhpLlm\LlmChain\Embedder;
 use PhpLlm\LlmChain\Bridge\OpenAI\Embeddings;
 use PhpLlm\LlmChain\Bridge\OpenAI\PlatformFactory;
 use PhpLlm\LlmChain\Bridge\Pinecone\Store;
 use Probots\Pinecone\Pinecone;
 use Symfony\Component\HttpClient\HttpClient;
 
-$embeddingProvider = new EmbeddingProvider(
+$embedder = new Embedder(
     PlatformFactory::create($_ENV['OPENAI_API_KEY']),
     new Embeddings(),
     new Store(Pinecone::client($_ENV['PINECONE_API_KEY'], $_ENV['PINECONE_HOST']),
 );
-$embeddingProvider->embed($documents);
+$embedder->embed($documents);
 ```
 
 The collection of `Document` instances is usually created by text input of your domain entities:

examples/store-mongodb-similarity-search.php

@@ -11,7 +11,7 @@
 use PhpLlm\LlmChain\Chain\Toolbox\Toolbox;
 use PhpLlm\LlmChain\Document\Metadata;
 use PhpLlm\LlmChain\Document\TextDocument;
-use PhpLlm\LlmChain\EmbeddingProvider;
+use PhpLlm\LlmChain\Embedder;
 use PhpLlm\LlmChain\Model\Message\Message;
 use PhpLlm\LlmChain\Model\Message\MessageBag;
 use Symfony\Component\Dotenv\Dotenv;
@@ -52,8 +52,8 @@
 
 // create embeddings for documents
 $platform = PlatformFactory::create($_ENV['OPENAI_API_KEY']);
-$embeddingProvider = new EmbeddingProvider($platform, $embeddings = new Embeddings(), $store);
-$embeddingProvider->embed($documents);
+$embedder = new Embedder($platform, $embeddings = new Embeddings(), $store);
+$embedder->embed($documents);
 
 // initialize the index
 $store->initialize();

examples/store-pinecone-similarity-search.php

@@ -10,7 +10,7 @@
 use PhpLlm\LlmChain\Chain\Toolbox\Toolbox;
 use PhpLlm\LlmChain\Document\Metadata;
 use PhpLlm\LlmChain\Document\TextDocument;
-use PhpLlm\LlmChain\EmbeddingProvider;
+use PhpLlm\LlmChain\Embedder;
 use PhpLlm\LlmChain\Model\Message\Message;
 use PhpLlm\LlmChain\Model\Message\MessageBag;
 use Probots\Pinecone\Pinecone;
@@ -46,8 +46,8 @@
 
 // create embeddings for documents
 $platform = PlatformFactory::create($_ENV['OPENAI_API_KEY']);
-$embeddingProvider = new EmbeddingProvider($platform, $embeddings = new Embeddings(), $store);
-$embeddingProvider->embed($documents);
+$embedder = new Embedder($platform, $embeddings = new Embeddings(), $store);
+$embedder->embed($documents);
 
 $llm = new GPT(GPT::GPT_4O_MINI);
 

src/EmbeddingProvider.php renamed to src/Embedder.php

@@ -13,7 +13,7 @@
 use Symfony\Component\Clock\Clock;
 use Symfony\Component\Clock\ClockInterface;
 
-final readonly class EmbeddingProvider
+final readonly class Embedder
 {
     private ClockInterface $clock;
 

tests/EmbedderTest.php

@@ -0,0 +1,131 @@
+<?php
+
+declare(strict_types=1);
+
+namespace PhpLlm\LlmChain\Tests;
+
+use PhpLlm\LlmChain\Bridge\OpenAI\Embeddings;
+use PhpLlm\LlmChain\Document\Metadata;
+use PhpLlm\LlmChain\Document\TextDocument;
+use PhpLlm\LlmChain\Document\Vector;
+use PhpLlm\LlmChain\Document\VectorDocument;
+use PhpLlm\LlmChain\Embedder;
+use PhpLlm\LlmChain\Model\Message\ToolCallMessage;
+use PhpLlm\LlmChain\Model\Response\AsyncResponse;
+use PhpLlm\LlmChain\Model\Response\ToolCall;
+use PhpLlm\LlmChain\Model\Response\VectorResponse;
+use PhpLlm\LlmChain\Platform;
+use PhpLlm\LlmChain\Tests\Double\PlatformTestHandler;
+use PhpLlm\LlmChain\Tests\Double\TestStore;
+use PHPUnit\Framework\Attributes\CoversClass;
+use PHPUnit\Framework\Attributes\Medium;
+use PHPUnit\Framework\Attributes\Test;
+use PHPUnit\Framework\Attributes\UsesClass;
+use PHPUnit\Framework\TestCase;
+use Psr\Log\LoggerInterface;
+use Symfony\Component\Clock\MockClock;
+use Symfony\Component\Uid\Uuid;
+
+#[CoversClass(Embedder::class)]
+#[Medium]
+#[UsesClass(TextDocument::class)]
+#[UsesClass(Vector::class)]
+#[UsesClass(VectorDocument::class)]
+#[UsesClass(ToolCallMessage::class)]
+#[UsesClass(ToolCall::class)]
+#[UsesClass(Embeddings::class)]
+#[UsesClass(Platform::class)]
+#[UsesClass(AsyncResponse::class)]
+#[UsesClass(VectorResponse::class)]
+final class EmbedderTest extends TestCase
+{
+    #[Test]
+    public function embedSingleDocument(): void
+    {
+        $document = new TextDocument($id = Uuid::v4(), 'Test content');
+        $vector = new Vector([0.1, 0.2, 0.3]);
+
+        $embedder = new Embedder(
+            PlatformTestHandler::createPlatform(new VectorResponse($vector)),
+            new Embeddings(),
+            $store = new TestStore(),
+            new MockClock(),
+        );
+
+        $embedder->embed($document);
+
+        self::assertCount(1, $store->documents);
+        self::assertInstanceOf(VectorDocument::class, $store->documents[0]);
+        self::assertSame($id, $store->documents[0]->id);
+        self::assertSame($vector, $store->documents[0]->vector);
+    }
+
+    #[Test]
+    public function embedEmptyDocumentList(): void
+    {
+        $logger = $this->createMock(LoggerInterface::class);
+        $logger->expects($this->once())->method('debug')->with('No documents to embed');
+
+        $embedder = new Embedder(
+            PlatformTestHandler::createPlatform(),
+            new Embeddings(),
+            $store = new TestStore(),
+            new MockClock(),
+            $logger,
+        );
+
+        $embedder->embed([]);
+
+        self::assertSame([], $store->documents);
+    }
+
+    #[Test]
+    public function embedDocumentWithMetadata(): void
+    {
+        $metadata = new Metadata(['key' => 'value']);
+        $document = new TextDocument($id = Uuid::v4(), 'Test content', $metadata);
+        $vector = new Vector([0.1, 0.2, 0.3]);
+
+        $embedder = new Embedder(
+            PlatformTestHandler::createPlatform(new VectorResponse($vector)),
+            new Embeddings(),
+            $store = new TestStore(),
+            new MockClock(),
+        );
+
+        $embedder->embed($document);
+
+        self::assertSame(1, $store->addCalls);
+        self::assertCount(1, $store->documents);
+        self::assertInstanceOf(VectorDocument::class, $store->documents[0]);
+        self::assertSame($id, $store->documents[0]->id);
+        self::assertSame($vector, $store->documents[0]->vector);
+        self::assertSame(['key' => 'value'], $store->documents[0]->metadata->getArrayCopy());
+    }
+
+    #[Test]
+    public function embedWithSleep(): void
+    {
+        $vector1 = new Vector([0.1, 0.2, 0.3]);
+        $vector2 = new Vector([0.4, 0.5, 0.6]);
+
+        $document1 = new TextDocument(Uuid::v4(), 'Test content 1');
+        $document2 = new TextDocument(Uuid::v4(), 'Test content 2');
+
+        $embedder = new Embedder(
+            PlatformTestHandler::createPlatform(new VectorResponse($vector1, $vector2)),
+            new Embeddings(),
+            $store = new TestStore(),
+            $clock = new MockClock('2024-01-01 00:00:00'),
+        );
+
+        $embedder->embed(
+            documents: [$document1, $document2],
+            sleep: 3
+        );
+
+        self::assertSame(1, $store->addCalls);
+        self::assertCount(2, $store->documents);
+        self::assertSame('2024-01-01 00:00:03', $clock->now()->format('Y-m-d H:i:s'));
+    }
+}