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junaidbinfarooqjunaidbinfarooq
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feat(store): Integrate supabase into the store package
- Adds support for supabase
1 parent d8dfb30 commit 67085ed

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‎examples/.env‎

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@@ -105,41 +105,9 @@ LMSTUDIO_HOST_URL=http://127.0.0.1:1234
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QDRANT_HOST=http://127.0.0.1:6333
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QDRANT_SERVICE_API_KEY=changeMe
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# SurrealDB (store)
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SURREALDB_HOST=http://127.0.0.1:8000
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SURREALDB_USER=symfony
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SURREALDB_PASS=symfony
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# Neo4J (store)
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NEO4J_HOST=http://127.0.0.1:7474
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NEO4J_DATABASE=neo4j
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NEO4J_USERNAME=neo4j
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NEO4J_PASSWORD=symfonyai
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# Typesense (store)
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TYPESENSE_HOST=http://127.0.0.1:8108
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TYPESENSE_API_KEY=changeMe
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# Milvus (store)
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MILVUS_HOST=http://127.0.0.1:19530
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MILVUS_API_KEY=root:Milvus
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MILVUS_DATABASE=symfony
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# Cloudflare (store)
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CLOUDFLARE_ACCOUNT_ID=
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CLOUDFLARE_API_KEY=
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# Cerebras
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CEREBRAS_API_KEY=
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CHROMADB_HOST=http://127.0.0.1
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CHROMADB_PORT=8001
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# For using Clickhouse (store)
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CLICKHOUSE_HOST=http://symfony:[email protected]:8123
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CLICKHOUSE_DATABASE=symfony
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CLICKHOUSE_TABLE=symfony
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# Weaviate (store)
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WEAVIATE_HOST=http://127.0.0.1:8080
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WEAVIATE_API_KEY=symfony
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SUPABASE_URL=
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SUPABASE_API_KEY=
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SUPABASE_TABLE=
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SUPABASE_VECTOR_FIELD=
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SUPABASE_VECTOR_DIMENSION=
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SUPABASE_MATCH_FUNCTION=

‎examples/rag/supabase.php‎

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<?php
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/*
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* This file is part of the Symfony package.
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*
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* (c) Fabien Potencier <[email protected]>
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*
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* For the full copyright and license information, please view the LICENSE
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* file that was distributed with this source code.
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*/
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use Symfony\AI\Agent\Agent;
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use Symfony\AI\Agent\Toolbox\AgentProcessor;
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use Symfony\AI\Agent\Toolbox\Tool\SimilaritySearch;
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use Symfony\AI\Agent\Toolbox\Toolbox;
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use Symfony\AI\Fixtures\Movies;
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use Symfony\AI\Platform\Bridge\Ollama\Ollama;
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use Symfony\AI\Platform\Bridge\Ollama\PlatformFactory;
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use Symfony\AI\Platform\Message\Message;
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use Symfony\AI\Platform\Message\MessageBag;
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use Symfony\AI\Store\Bridge\Supabase\Store;
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use Symfony\AI\Store\Document\Loader\InMemoryLoader;
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use Symfony\AI\Store\Document\Metadata;
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use Symfony\AI\Store\Document\TextDocument;
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use Symfony\AI\Store\Document\Vectorizer;
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use Symfony\AI\Store\Indexer;
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use Symfony\Component\Uid\Uuid;
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require_once dirname(__DIR__).'/bootstrap.php';
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echo "Make sure you've run the SQL setup from SUPABASE_SETUP.md first!\n\n";
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$store = new Store(
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http: http_client(),
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url: env('SUPABASE_URL'),
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apiKey: env('SUPABASE_API_KEY'),
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table: env('SUPABASE_TABLE'),
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vectorFieldName: env('SUPABASE_VECTOR_FIELD'),
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vectorDimension: (int) env('SUPABASE_VECTOR_DIMENSION'),
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functionName: env('SUPABASE_MATCH_FUNCTION')
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);
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$documents = [];
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foreach (Movies::all() as $movie) {
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$documents[] = new TextDocument(
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id: Uuid::v4(),
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content: 'Title: '.$movie['title'].\PHP_EOL.'Director: '.$movie['director'].\PHP_EOL.'Description: '.$movie['description'],
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metadata: new Metadata($movie),
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);
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}
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$platform = PlatformFactory::create(
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env('OLLAMA_HOST_URL') ?? 'http://localhost:11434',
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http_client()
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);
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$embeddingModel = new Ollama('mxbai-embed-large');
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$vectorizer = new Vectorizer($platform, $embeddingModel);
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$loader = new InMemoryLoader($documents);
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$indexer = new Indexer($loader, $vectorizer, $store, logger: logger());
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$indexer->index();
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$chatModel = new Ollama('llama3.2:3b');
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$similaritySearch = new SimilaritySearch($vectorizer, $store);
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$toolbox = new Toolbox([$similaritySearch], logger: logger());
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$processor = new AgentProcessor($toolbox);
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$agent = new Agent($platform, $chatModel, [$processor], [$processor], logger: logger());
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$messages = new MessageBag(
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Message::forSystem('Please answer all user questions only using SimilaritySearch function.'),
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Message::ofUser('Which movie fits the theme of technology?')
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);
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echo "Query: Which movie fits the theme of technology?\n";
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echo "Processing...\n";
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try {
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$result = $agent->call($messages);
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echo '✅ Response: '.$result->getContent()."\n\n";
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} catch (Exception $e) {
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echo '❌ Error: '.$e->getMessage()."\n\n";
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}
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$messages2 = new MessageBag(
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Message::forSystem('Please answer all user questions only using SimilaritySearch function.'),
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Message::ofUser('What are some good action movies?')
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);
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echo "Query: What are some good action movies?\n";
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echo "Processing...\n";
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try {
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$result2 = $agent->call($messages2);
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echo '✅ Response: '.$result2->getContent()."\n\n";
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} catch (Exception $e) {
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echo '❌ Error: '.$e->getMessage()."\n\n";
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}

‎src/store/src/Bridge/Supabase/Store.php‎

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<?php
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/*
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* This file is part of the Symfony package.
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*
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* (c) Fabien Potencier <[email protected]>
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*
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* For the full copyright and license information, please view the LICENSE
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* file that was distributed with this source code.
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*/
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namespace Symfony\AI\Store\Bridge\Supabase;
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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\AI\Store\Exception\InvalidArgumentException;
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use Symfony\AI\Store\Exception\RuntimeException;
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use Symfony\AI\Store\StoreInterface;
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use Symfony\Component\Uid\Uuid;
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use Symfony\Contracts\HttpClient\HttpClientInterface;
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/**
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* @author Junaid Farooq <[email protected]>
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*
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* Requires pgvector extension to be enabled and a pre-configured table/function.
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*
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* @see https://github.com/pgvector/pgvector
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* @see https://supabase.com/docs/guides/ai/vector-columns
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*
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* Supabase store using REST & RPC.
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*
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* Note: Unlike Postgres Store, this store requires manual setup of:
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* 1. pgvector extension
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* 2. Table with vector column
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* 3. RPC function for similarity search
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*
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* This is because Supabase doesn't allow arbitrary SQL execution via REST API.
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*/
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final readonly class Store implements StoreInterface
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{
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public function __construct(
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private HttpClientInterface $http,
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private string $url,
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private string $apiKey,
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private string $table = 'documents',
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private string $vectorFieldName = 'embedding',
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private int $vectorDimension = 1536,
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private string $functionName = 'match_documents',
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) {
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}
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public function add(VectorDocument ...$documents): void
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{
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if (0 === \count($documents)) {
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return;
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}
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$rows = [];
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foreach ($documents as $document) {
62+
if (\count($document->vector->getData()) !== $this->vectorDimension) {
63+
continue;
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}
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$rows[] = [
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'id' => $document->id->toRfc4122(),
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$this->vectorFieldName => $document->vector->getData(),
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'metadata' => $document->metadata->getArrayCopy(),
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];
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}
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$chunkSize = 200;
74+
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foreach (array_chunk($rows, $chunkSize) as $chunk) {
76+
$response = $this->http->request(
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'POST',
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\sprintf('%s/rest/v1/%s', $this->url, $this->table),
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[
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'headers' => [
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'apikey' => $this->apiKey,
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'Authorization' => 'Bearer '.$this->apiKey,
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'Content-Type' => 'application/json',
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'Prefer' => 'resolution=merge-duplicates',
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],
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'json' => $chunk,
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]
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);
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if ($response->getStatusCode() >= 400) {
91+
throw new RuntimeException('Supabase insert failed: '.$response->getContent(false));
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}
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}
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}
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/**
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* @param array{
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* max_items?: int,
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* limit?: int,
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* min_score?: float
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* } $options
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*/
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public function query(Vector $vector, array $options = []): array
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{
105+
if (\count($vector->getData()) !== $this->vectorDimension) {
106+
throw new InvalidArgumentException("Vector dimension mismatch: expected {$this->vectorDimension}");
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}
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$matchCount = $options['max_items'] ?? ($options['limit'] ?? 10);
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$threshold = $options['min_score'] ?? 0.0;
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$response = $this->http->request(
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'POST',
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\sprintf('%s/rest/v1/rpc/%s', $this->url, $this->functionName),
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[
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'headers' => [
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'apikey' => $this->apiKey,
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'Authorization' => 'Bearer '.$this->apiKey,
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'Content-Type' => 'application/json',
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],
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'json' => [
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'query_embedding' => $vector->getData(),
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'match_count' => $matchCount,
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'match_threshold' => $threshold,
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],
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]
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);
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if ($response->getStatusCode() >= 400) {
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throw new RuntimeException('Supabase query failed: '.$response->getContent(false));
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}
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$records = json_decode($response->getContent(), true, 512, \JSON_THROW_ON_ERROR);
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$documents = [];
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foreach ($records as $record) {
137+
if (
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!isset($record['id'], $record[$this->vectorFieldName], $record['metadata'], $record['score'])
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|| !\is_string($record['id'])
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) {
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continue;
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}
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$embedding = \is_array($record[$this->vectorFieldName]) ? $record[$this->vectorFieldName] : json_decode($record[$this->vectorFieldName] ?? '{}', true, 512, \JSON_THROW_ON_ERROR);
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$metadata = \is_array($record['metadata']) ? $record['metadata'] : json_decode($record['metadata'] ?? '{}', true, 512, \JSON_THROW_ON_ERROR);
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$documents[] = new VectorDocument(
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id: Uuid::fromString($record['id']),
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vector: new Vector($embedding),
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metadata: new Metadata($metadata),
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score: (float) $record['score'],
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);
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}
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return $documents;
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}
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}

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