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- Fix class names: CachedPlatform→CachePlatform, OllamaApiCatalog→ModelCatalog, Indexer→DocumentIndexer/SourceIndexer, Store\Vector→Platform\Vector\Vector - Fix namespaces: TokenUsage, MemoryInputProcessor, ConfiguredSourceIndexer, SourceIndexer, DocumentIndexer, StructuredOutputSerializer - Fix method calls: asText()→getContent(), private property access→getters - Fix API signatures: StoreInterface::query() now requires QueryInterface, ChromaDb\Store constructor updated, Ollama PlatformFactory simplified - Remove unused imports: Gpt, Embeddings, VertexAi Model - Fix incorrect file reference: _model-listing.php→_model.php - Fix incorrect link target: Crawler Tool→firecrawl-crawl.php - Fix MessageBagInterface→MessageBag (interface doesn't exist)
140 lines
3.1 KiB
ReStructuredText
140 lines
3.1 KiB
ReStructuredText
AWS S3 Vectors Bridge
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=====================
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The AWS S3 Vectors bridge provides vector storage and similarity search integration using `AWS S3 Vectors`_.
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Requirements
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~~~~~~~~~~~~
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* An AWS account with access to S3 Vectors
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* The AsyncAws S3Vectors client
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Installation
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~~~~~~~~~~~~
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Install the bridge package:
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.. code-block:: terminal
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$ composer require symfony/ai-s3vectors-store
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Configuration
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-------------
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Basic Configuration
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~~~~~~~~~~~~~~~~~~~
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::
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use AsyncAws\S3Vectors\S3VectorsClient;
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use Symfony\AI\Store\Bridge\S3Vectors\Store;
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$client = new S3VectorsClient([
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'region' => 'us-east-1',
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]);
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$store = new Store(
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client: $client,
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vectorBucketName: 'my-vector-bucket',
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indexName: 'my-index',
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filter: [], // Optional: default filter for queries
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topK: 3, // Optional: default number of results
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);
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Setup the Vector Store
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~~~~~~~~~~~~~~~~~~~~~~
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Before using the store, you need to initialize it with the appropriate configuration::
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// Setup the vector bucket and index
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$store->setup([
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'dimension' => 1536,
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'distanceMetric' => \AsyncAws\S3Vectors\Enum\DistanceMetric::COSINE, // Optional
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'dataType' => \AsyncAws\S3Vectors\Enum\DataType::FLOAT32, // Optional
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'encryption' => ['kmsKeyId' => 'your-kms-key-id'], // Optional
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'tags' => ['env' => 'production'], // Optional
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]);
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Usage
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-----
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Add Documents
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~~~~~~~~~~~~~
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::
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use Symfony\AI\Platform\Vector\Vector;
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use Symfony\AI\Store\Document\Metadata;
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use Symfony\AI\Store\Document\VectorDocument;
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use Symfony\Component\Uid\Uuid;
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$document = new VectorDocument(
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id: Uuid::v4(),
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vector: new Vector([0.1, 0.2, 0.3, ...]),
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metadata: new Metadata(['title' => 'My Document'])
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);
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$store->add($document);
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Query Similar Vectors
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~~~~~~~~~~~~~~~~~~~~~
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::
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use Symfony\AI\Platform\Vector\Vector;
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use Symfony\AI\Store\Query\VectorQuery;
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$results = $store->query(
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query: new VectorQuery(new Vector([0.1, 0.2, 0.3, ...])),
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options: [
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'topK' => 5,
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'filter' => ['category' => 'documentation'],
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]
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);
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foreach ($results as $result) {
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echo $result->getMetadata()['title'] . ' (score: ' . $result->getScore() . ')' . PHP_EOL;
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}
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Remove Documents
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~~~~~~~~~~~~~~~~
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::
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$store->remove(['id1', 'id2']);
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Drop the Store
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~~~~~~~~~~~~~~
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::
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// Drop the index and bucket
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$store->drop();
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Features
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--------
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* Full CRUD operations for vector documents
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* Similarity search with configurable distance metrics (cosine, euclidean)
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* Metadata filtering support
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* KMS encryption support
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* Tag management
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* Batch operations
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Distance Metrics
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~~~~~~~~~~~~~~~~
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The bridge supports the following distance metrics:
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* ``COSINE`` - Cosine distance (default)
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* ``EUCLIDEAN`` - Euclidean distance
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* ``DOT_PRODUCT`` - Dot product distance
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Data Types
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~~~~~~~~~~
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The bridge supports the following data types for vectors:
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* ``FLOAT32`` - 32-bit floating point (default)
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.. _`AWS S3 Vectors`: https://docs.aws.amazon.com/AmazonS3/latest/userguide/s3-vectors.html
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