[Store] Extract some code from Indexer to a dedicated class + Introduce Ingester

demo/AGENTS.md

@@ -62,7 +62,7 @@ symfony console mcp:server
 - **Agents**: blog, stream, youtube, wikipedia, audio
 - **Platform**: OpenAI integration
 - **Store**: ChromaDB vector store
-- **Indexer**: Text embedding model
+- **Ingester**: Text embedding model
 
 ### Chat Pattern
 - `Chat` class: Message flow and session management
@@ -76,4 +76,4 @@ symfony console mcp:server
 - OpenAI GPT-4o-mini default model
 - ChromaDB on port 8080
 - LiveComponents for real-time UI
-- Symfony DI and best practices
+- Symfony DI and best practices

demo/CLAUDE.md

@@ -10,7 +10,7 @@ This is a Symfony 7.3 demo application showcasing AI integration capabilities us
 
 ### Core Components
 - **Chat Systems**: Multiple specialized chat implementations in `src/` (Blog, YouTube, Wikipedia, Audio, Stream)
-- **Twig LiveComponents**: Interactive UI components using Symfony UX for real-time chat interfaces  
+- **Twig LiveComponents**: Interactive UI components using Symfony UX for real-time chat interfaces
 - **AI Agents**: Configured agents with different models, tools, and system prompts
 - **Vector Store**: ChromaDB integration for embedding storage and similarity search
 - **MCP Tools**: Model Context Protocol tools for extending agent capabilities
@@ -36,7 +36,7 @@ composer install
 echo "OPENAI_API_KEY='sk-...'" > .env.local
 
 # Initialize vector store
-symfony console ai:store:index blog -vv
+symfony console ai:store:ingest blog -vv
 
 # Test vector store
 symfony console ai:store:retrieve blog "Week of Symfony"
@@ -81,7 +81,7 @@ symfony console mcp:server
 - **Agents**: Multiple pre-configured agents (blog, stream, youtube, wikipedia, audio)
 - **Platform**: OpenAI integration with API key from environment
 - **Store**: ChromaDB vector store for similarity search
-- **Indexer**: Text embedding model configuration
+- **Ingester**: Text embedding model configuration
 
 ### Chat Implementations
 Each chat type follows the pattern:
@@ -100,4 +100,4 @@ Chat history stored in Symfony sessions with component-specific keys (e.g., 'blo
 - ChromaDB runs on port 8080 (mapped from container port 8000)
 - Application follows Symfony best practices with dependency injection
 - LiveComponents provide real-time UI updates without custom JavaScript
-- MCP server enables tool integration for AI agents
+- MCP server enables tool integration for AI agents

demo/config/packages/ai.yaml

@@ -89,7 +89,7 @@ ai:
         openai:
             platform: 'ai.platform.openai'
             model: 'text-embedding-ada-002'
-    indexer:
+    ingester:
         blog:
             loader: 'Symfony\AI\Store\Document\Loader\RssFeedLoader'
             source: 'https://feeds.feedburner.com/symfony/blog'

docs/bundles/ai-bundle.rst

@@ -124,7 +124,7 @@ Advanced Example with Multiple Agents
             mistral_embeddings:
                 platform: 'ai.platform.mistral'
                 model: 'mistral-embed'
-        indexer:
+        ingester:
             default:
                 loader: 'Symfony\AI\Store\Document\Loader\InMemoryLoader'
                 vectorizer: 'ai.vectorizer.openai_embeddings'
@@ -721,26 +721,26 @@ The ``ai:store:drop`` command drops the infrastructure for a store (e.g., remove
     This command only works with stores that implement ``ManagedStoreInterface``.
     Not all store types support drop operations.
 
-``ai:store:index``
-~~~~~~~~~~~~~~~~~~
+``ai:store:ingest``
+~~~~~~~~~~~~~~~~~~~
 
-The ``ai:store:index`` command indexes documents into a store using a configured indexer.
+The ``ai:store:ingest`` command ingests documents into a store using a configured ingester.
 
 .. code-block:: terminal
 
-    $ php bin/console ai:store:index <indexer>
+    $ php bin/console ai:store:ingest <ingester>
 
-    # Index using the default indexer
-    $ php bin/console ai:store:index default
+    # Ingest using the default ingester
+    $ php bin/console ai:store:ingest default
 
     # Override the configured source with a single file
-    $ php bin/console ai:store:index blog --source=/path/to/file.txt
+    $ php bin/console ai:store:ingest blog --source=/path/to/file.txt
 
     # Override with multiple sources
-    $ php bin/console ai:store:index blog --source=/path/to/file1.txt --source=/path/to/file2.txt
+    $ php bin/console ai:store:ingest blog --source=/path/to/file1.txt --source=/path/to/file2.txt
 
-The ``--source`` (or ``-s``) option allows you to override the source(s) configured in your indexer.
-This is useful for ad-hoc indexing operations or testing different data sources.
+The ``--source`` (or ``-s``) option allows you to override the source(s) configured in your ingester.
+This is useful for ad-hoc ingesting operations or testing different data sources.
 
 Usage
 -----
@@ -935,7 +935,7 @@ Vectorizers
 -----------
 
 Vectorizers are components that convert text documents into vector embeddings for storage and retrieval.
-They can be configured once and reused across multiple indexers, providing better maintainability and consistency.
+They can be configured once and reused across multiple ingesters, providing better maintainability and consistency.
 
 Configuring Vectorizers
 ~~~~~~~~~~~~~~~~~~~~~~~
@@ -961,15 +961,15 @@ Vectorizers are defined in the ``vectorizer`` section of your configuration:
                 platform: 'ai.platform.mistral'
                 model: 'mistral-embed'
 
-Using Vectorizers in Indexers
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+Using Vectorizers in Ingesters
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
-Once configured, vectorizers can be referenced by name in indexer configurations:
+Once configured, vectorizers can be referenced by name in ingester configurations:
 
 .. code-block:: yaml
 
     ai:
-        indexer:
+        ingester:
             documents:
                 loader: 'Symfony\AI\Store\Document\Loader\TextFileLoader'
                 vectorizer: 'ai.vectorizer.openai_small'
@@ -988,14 +988,14 @@ Once configured, vectorizers can be referenced by name in indexer configurations
 Benefits of Configured Vectorizers
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
-* **Reusability**: Define once, use in multiple indexers
-* **Consistency**: Ensure all indexers using the same vectorizer have identical embedding configuration
+* **Reusability**: Define once, use in multiple ingesters
+* **Consistency**: Ensure all ingesters using the same vectorizer have identical embedding configuration
 * **Maintainability**: Change vectorizer settings in one place
 
 Retrievers
 ----------
 
-Retrievers are the opposite of indexers. While indexers populate a vector store with documents,
+Retrievers are the opposite of ingesters. While ingesters populate a vector store with documents,
 retrievers allow you to search for documents in a store based on a query string.
 They vectorize the query and retrieve similar documents from the store.
 

docs/components/store.rst

@@ -19,19 +19,21 @@ implemented by different concrete and vendor-specific implementations, so called
 On top of those bridges, the Store component provides higher level features to populate and query those stores with and
 for documents.
 
-Indexing
---------
+Ingesting
+---------
 
-One higher level feature is the :class:`Symfony\\AI\\Store\\Indexer`. The purpose of this service is to populate a store with documents.
+One higher level feature is the :class:`Symfony\\AI\\Store\\Ingester`. The purpose of this service is to populate a store with documents.
 Therefore it accepts one or multiple :class:`Symfony\\AI\\Store\\Document\\TextDocument` objects, converts them into embeddings and stores them in the
 used vector store::
 
     use Symfony\AI\Store\Document\TextDocument;
     use Symfony\AI\Store\Indexer;
+    use Symfony\AI\Store\Ingester;
 
-    $indexer = new Indexer($platform, $model, $store);
-    $document = new TextDocument('This is a sample document.');
-    $indexer->index($document);
+    $document = [new TextDocument('This is a sample document.')];
+    $loader = new InMemoryLoader($documents)
+    $indexer = new Ingester($loader, new Indexer($vectorizer, $store));
+    $indexer->index();
 
 You can find more advanced usage in combination with an Agent using the store for RAG in the examples folder.
 

docs/cookbook/rag-implementation.rst

@@ -89,17 +89,20 @@ Use a vectorizer to convert documents into embeddings and store them::
     use Symfony\AI\Store\Document\Loader\InMemoryLoader;
     use Symfony\AI\Store\Document\Vectorizer;
     use Symfony\AI\Store\Indexer;
+    use Symfony\AI\Store\Ingester;
 
     $platform = PlatformFactory::create(env('OPENAI_API_KEY'));
     $vectorizer = new Vectorizer($platform, 'text-embedding-3-small');
-    $indexer = new Indexer(
+    $ingester = new Ingester(
         new InMemoryLoader($documents),
-        $vectorizer,
-        $store
+        new Indexer(
+            $vectorizer,
+            $store
+        ),
     );
-    $indexer->index($documents);
+    $ingester->ingest();
 
-The indexer handles:
+The ingester handles:
 
 * Loading documents from the source
 * Generating vector embeddings
@@ -324,7 +327,7 @@ Index documents in batches for better performance::
 
     $batchSize = 100;
     foreach (array_chunk($documents, $batchSize) as $batch) {
-        $indexer->index($batch);
+        $ingester->ingest(options: $batch);
     }
 
 Caching Embeddings

examples/indexer/index-file-loader.php → examples/ingester/ingest-file-loader.php

@@ -15,29 +15,31 @@
 use Symfony\AI\Store\Document\Transformer\TextSplitTransformer;
 use Symfony\AI\Store\Document\Vectorizer;
 use Symfony\AI\Store\Indexer;
+use Symfony\AI\Store\Ingester;
 use Symfony\AI\Store\InMemory\Store as InMemoryStore;
 
 require_once dirname(__DIR__).'/bootstrap.php';
 
 $platform = PlatformFactory::create(env('OPENAI_API_KEY'), http_client());
 $store = new InMemoryStore();
 $vectorizer = new Vectorizer($platform, 'text-embedding-3-small');
-$indexer = new Indexer(
+$ingester = new Ingester(
     loader: new TextFileLoader(),
-    vectorizer: $vectorizer,
-    store: $store,
-    source: [
-        dirname(__DIR__, 2).'/fixtures/movies/gladiator.md',
-        dirname(__DIR__, 2).'/fixtures/movies/inception.md',
-        dirname(__DIR__, 2).'/fixtures/movies/jurassic-park.md',
-    ],
-    transformers: [
-        new TextReplaceTransformer(search: '## Plot', replace: '## Synopsis'),
-        new TextSplitTransformer(chunkSize: 500, overlap: 100),
-    ],
+    indexer: new Indexer(
+        vectorizer: $vectorizer,
+        store: $store,
+        transformers: [
+            new TextReplaceTransformer(search: '## Plot', replace: '## Synopsis'),
+            new TextSplitTransformer(chunkSize: 500, overlap: 100),
+        ],
+    ),
 );
 
-$indexer->index();
+$ingester->ingest([
+    dirname(__DIR__, 2).'/fixtures/movies/gladiator.md',
+    dirname(__DIR__, 2).'/fixtures/movies/inception.md',
+    dirname(__DIR__, 2).'/fixtures/movies/jurassic-park.md',
+]);
 
 $vector = $vectorizer->vectorize('Roman gladiator revenge');
 $results = $store->query($vector);

examples/indexer/index-inmemory-loader.php → examples/ingester/ingest-inmemory-loader.php

@@ -16,6 +16,7 @@
 use Symfony\AI\Store\Document\Transformer\TextSplitTransformer;
 use Symfony\AI\Store\Document\Vectorizer;
 use Symfony\AI\Store\Indexer;
+use Symfony\AI\Store\Ingester;
 use Symfony\AI\Store\InMemory\Store as InMemoryStore;
 use Symfony\Component\Uid\Uuid;
 
@@ -38,17 +39,18 @@
     ),
 ];
 
-$indexer = new Indexer(
+$ingester = new Ingester(
     loader: new InMemoryLoader($documents),
-    vectorizer: $vectorizer,
-    store: $store,
-    source: null,
-    transformers: [
-        new TextSplitTransformer(chunkSize: 100, overlap: 20),
-    ],
+    indexer: new Indexer(
+        vectorizer: $vectorizer,
+        store: $store,
+        transformers: [
+            new TextSplitTransformer(chunkSize: 100, overlap: 20),
+        ],
+    ),
 );
 
-$indexer->index();
+$ingester->ingest();
 
 $vector = $vectorizer->vectorize('machine learning artificial intelligence');
 $results = $store->query($vector);

examples/indexer/index-rss-loader.php → examples/ingester/ingest-rss-loader.php

@@ -14,6 +14,7 @@
 use Symfony\AI\Store\Document\Transformer\TextSplitTransformer;
 use Symfony\AI\Store\Document\Vectorizer;
 use Symfony\AI\Store\Indexer;
+use Symfony\AI\Store\Ingester;
 use Symfony\AI\Store\InMemory\Store as InMemoryStore;
 use Symfony\Component\HttpClient\HttpClient;
 
@@ -22,20 +23,21 @@
 $platform = PlatformFactory::create(env('OPENAI_API_KEY'), http_client());
 $store = new InMemoryStore();
 $vectorizer = new Vectorizer($platform, 'text-embedding-3-small');
-$indexer = new Indexer(
+$ingester = new Ingester(
     loader: new RssFeedLoader(HttpClient::create()),
-    vectorizer: $vectorizer,
-    store: $store,
-    source: [
-        'https://feeds.feedburner.com/symfony/blog',
-        'https://www.tagesschau.de/index~rss2.xml',
-    ],
-    transformers: [
-        new TextSplitTransformer(chunkSize: 500, overlap: 100),
-    ],
+    indexer: new Indexer(
+        vectorizer: $vectorizer,
+        store: $store,
+        transformers: [
+            new TextSplitTransformer(chunkSize: 500, overlap: 100),
+        ],
+    )
 );
 
-$indexer->index();
+$ingester->ingest([
+    'https://feeds.feedburner.com/symfony/blog',
+    'https://www.tagesschau.de/index~rss2.xml',
+]);
 
 $vector = $vectorizer->vectorize('Week of Symfony');
 $results = $store->query($vector);

examples/indexer/index-with-filters.php → examples/ingester/ingest-with-filters.php

@@ -17,6 +17,7 @@
 use Symfony\AI\Store\Document\Transformer\TextTrimTransformer;
 use Symfony\AI\Store\Document\Vectorizer;
 use Symfony\AI\Store\Indexer;
+use Symfony\AI\Store\Ingester;
 use Symfony\AI\Store\InMemory\Store as InMemoryStore;
 use Symfony\Component\Uid\Uuid;
 
@@ -56,18 +57,19 @@
     new TextContainsFilter('SPAM:', caseSensitive: true),
 ];
 
-$indexer = new Indexer(
+$ingester = new Ingester(
     loader: new InMemoryLoader($documents),
-    vectorizer: $vectorizer,
-    store: $store,
-    source: null,
-    filters: $filters,
-    transformers: [
-        new TextTrimTransformer(),
-    ],
+    indexer: new Indexer(
+        vectorizer: $vectorizer,
+        store: $store,
+        filters: $filters,
+        transformers: [
+            new TextTrimTransformer(),
+        ],
+    ),
 );
 
-$indexer->index();
+$ingester->ingest();
 
 $vector = $vectorizer->vectorize('technology artificial intelligence');
 $results = $store->query($vector);

examples/memory/mariadb.php

@@ -23,6 +23,7 @@
 use Symfony\AI\Store\Document\TextDocument;
 use Symfony\AI\Store\Document\Vectorizer;
 use Symfony\AI\Store\Indexer;
+use Symfony\AI\Store\Ingester;
 use Symfony\Component\Uid\Uuid;
 
 require_once dirname(__DIR__).'/bootstrap.php';
@@ -57,8 +58,8 @@
 // create embeddings for documents as preparation of the chain memory
 $platform = PlatformFactory::create(env('OPENAI_API_KEY'), http_client());
 $vectorizer = new Vectorizer($platform, $embeddings = 'text-embedding-3-small');
-$indexer = new Indexer(new InMemoryLoader($documents), $vectorizer, $store, logger: logger());
-$indexer->index($documents);
+$ingester = new Ingester(new InMemoryLoader($documents), new Indexer($vectorizer, $store, logger: logger()), logger: logger());
+$ingester->ingest();
 
 // Execute a chat call that is utilizing the memory
 $embeddingsModel = $platform->getModelCatalog()->getModel($embeddings);