refactor: rename Embedder to Indexer and open input to iterable

README.md

@@ -391,22 +391,22 @@ $response = $chain->call($messages);
 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 `Embedder`, which requires an instance of an
+For populating a vector store, LLM Chain provides the service `Indexer`, 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\Platform\Bridge\OpenAI\Embeddings;
 use PhpLlm\LlmChain\Platform\Bridge\OpenAI\PlatformFactory;
 use PhpLlm\LlmChain\Store\Bridge\Pinecone\Store;
-use PhpLlm\LlmChain\Store\Embedder;
+use PhpLlm\LlmChain\Store\Indexer;
 use Probots\Pinecone\Pinecone;
 
-$embedder = new Embedder(
+$indexer = new Indexer(
     PlatformFactory::create($_ENV['OPENAI_API_KEY']),
     new Embeddings(),
     new Store(Pinecone::client($_ENV['PINECONE_API_KEY'], $_ENV['PINECONE_HOST']),
 );
-$embedder->embed($documents);
+$indexer->index($documents);
 ```
 
 The collection of `Document` instances is usually created by text input of your domain entities:

examples/store/mongodb-similarity-search.php

@@ -13,7 +13,7 @@
 use PhpLlm\LlmChain\Store\Bridge\MongoDB\Store;
 use PhpLlm\LlmChain\Store\Document\Metadata;
 use PhpLlm\LlmChain\Store\Document\TextDocument;
-use PhpLlm\LlmChain\Store\Embedder;
+use PhpLlm\LlmChain\Store\Indexer;
 use Symfony\Component\Dotenv\Dotenv;
 use Symfony\Component\Uid\Uuid;
 
@@ -52,8 +52,8 @@
 
 // create embeddings for documents
 $platform = PlatformFactory::create($_ENV['OPENAI_API_KEY']);
-$embedder = new Embedder($platform, $embeddings = new Embeddings(), $store);
-$embedder->embed($documents);
+$indexer = new Indexer($platform, $embeddings = new Embeddings(), $store);
+$indexer->index($documents);
 
 // initialize the index
 $store->initialize();

examples/store/pinecone-similarity-search.php

@@ -12,7 +12,7 @@
 use PhpLlm\LlmChain\Store\Bridge\Pinecone\Store;
 use PhpLlm\LlmChain\Store\Document\Metadata;
 use PhpLlm\LlmChain\Store\Document\TextDocument;
-use PhpLlm\LlmChain\Store\Embedder;
+use PhpLlm\LlmChain\Store\Indexer;
 use Probots\Pinecone\Pinecone;
 use Symfony\Component\Dotenv\Dotenv;
 use Symfony\Component\Uid\Uuid;
@@ -46,8 +46,8 @@
 
 // create embeddings for documents
 $platform = PlatformFactory::create($_ENV['OPENAI_API_KEY']);
-$embedder = new Embedder($platform, $embeddings = new Embeddings(), $store);
-$embedder->embed($documents);
+$indexer = new Indexer($platform, $embeddings = new Embeddings(), $store);
+$indexer->index($documents);
 
 $model = new GPT(GPT::GPT_4O_MINI);
 

src/Store/Embedder.php → src/Store/Indexer.php

@@ -17,7 +17,7 @@
 /**
  * @author Christopher Hertel <[email protected]>
  */
-final readonly class Embedder
+final readonly class Indexer
 {
     private ClockInterface $clock;
 
@@ -32,16 +32,16 @@ public function __construct(
     }
 
     /**
-     * @param TextDocument|TextDocument[] $documents
+     * @param TextDocument|iterable<TextDocument> $documents
      */
-    public function embed(TextDocument|array $documents, int $chunkSize = 0, int $sleep = 0): void
+    public function index(TextDocument|iterable $documents, int $chunkSize = 0, int $sleep = 0): void
     {
         if ($documents instanceof TextDocument) {
             $documents = [$documents];
         }
 
         if ([] === $documents) {
-            $this->logger->debug('No documents to embed');
+            $this->logger->debug('No documents to index');
 
             return;
         }

tests/Store/Document/NullVectorTest.php → tests/Platform/Vector/NullVectorTest.php

@@ -2,7 +2,7 @@
 
 declare(strict_types=1);
 
-namespace PhpLlm\LlmChain\Tests\Store\Document;
+namespace PhpLlm\LlmChain\Tests\Platform\Vector;
 
 use PhpLlm\LlmChain\Platform\Vector\NullVector;
 use PhpLlm\LlmChain\Platform\Vector\VectorInterface;

tests/Store/Document/VectorTest.php → tests/Platform/Vector/VectorTest.php

@@ -2,7 +2,7 @@
 
 declare(strict_types=1);
 
-namespace PhpLlm\LlmChain\Tests\Store\Document;
+namespace PhpLlm\LlmChain\Tests\Platform\Vector;
 
 use PhpLlm\LlmChain\Platform\Vector\Vector;
 use PhpLlm\LlmChain\Platform\Vector\VectorInterface;

tests/Store/EmbedderTest.php → tests/Store/IndexerTest.php

@@ -14,7 +14,7 @@
 use PhpLlm\LlmChain\Store\Document\Metadata;
 use PhpLlm\LlmChain\Store\Document\TextDocument;
 use PhpLlm\LlmChain\Store\Document\VectorDocument;
-use PhpLlm\LlmChain\Store\Embedder;
+use PhpLlm\LlmChain\Store\Indexer;
 use PhpLlm\LlmChain\Tests\Double\PlatformTestHandler;
 use PhpLlm\LlmChain\Tests\Double\TestStore;
 use PHPUnit\Framework\Attributes\CoversClass;
@@ -26,7 +26,7 @@
 use Symfony\Component\Clock\MockClock;
 use Symfony\Component\Uid\Uuid;
 
-#[CoversClass(Embedder::class)]
+#[CoversClass(Indexer::class)]
 #[Medium]
 #[UsesClass(TextDocument::class)]
 #[UsesClass(Vector::class)]
@@ -37,22 +37,22 @@
 #[UsesClass(Platform::class)]
 #[UsesClass(AsyncResponse::class)]
 #[UsesClass(VectorResponse::class)]
-final class EmbedderTest extends TestCase
+final class IndexerTest 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(
+        $indexer = new Indexer(
             PlatformTestHandler::createPlatform(new VectorResponse($vector)),
             new Embeddings(),
             $store = new TestStore(),
             new MockClock(),
         );
 
-        $embedder->embed($document);
+        $indexer->index($document);
 
         self::assertCount(1, $store->documents);
         self::assertInstanceOf(VectorDocument::class, $store->documents[0]);
@@ -64,17 +64,17 @@ public function embedSingleDocument(): void
     public function embedEmptyDocumentList(): void
     {
         $logger = self::createMock(LoggerInterface::class);
-        $logger->expects(self::once())->method('debug')->with('No documents to embed');
+        $logger->expects(self::once())->method('debug')->with('No documents to index');
 
-        $embedder = new Embedder(
+        $indexer = new Indexer(
             PlatformTestHandler::createPlatform(),
             new Embeddings(),
             $store = new TestStore(),
             new MockClock(),
             $logger,
         );
 
-        $embedder->embed([]);
+        $indexer->index([]);
 
         self::assertSame([], $store->documents);
     }
@@ -86,14 +86,14 @@ public function embedDocumentWithMetadata(): void
         $document = new TextDocument($id = Uuid::v4(), 'Test content', $metadata);
         $vector = new Vector([0.1, 0.2, 0.3]);
 
-        $embedder = new Embedder(
+        $indexer = new Indexer(
             PlatformTestHandler::createPlatform(new VectorResponse($vector)),
             new Embeddings(),
             $store = new TestStore(),
             new MockClock(),
         );
 
-        $embedder->embed($document);
+        $indexer->index($document);
 
         self::assertSame(1, $store->addCalls);
         self::assertCount(1, $store->documents);
@@ -112,14 +112,14 @@ public function embedWithSleep(): void
         $document1 = new TextDocument(Uuid::v4(), 'Test content 1');
         $document2 = new TextDocument(Uuid::v4(), 'Test content 2');
 
-        $embedder = new Embedder(
+        $indexer = new Indexer(
             PlatformTestHandler::createPlatform(new VectorResponse($vector1, $vector2)),
             new Embeddings(),
             $store = new TestStore(),
             $clock = new MockClock('2024-01-01 00:00:00'),
         );
 
-        $embedder->embed(
+        $indexer->index(
             documents: [$document1, $document2],
             sleep: 3
         );