docs(store): Integrate supabase into the store package

- Adds documentation for the new store integration

Author: junaidbinfarooq

examples/.env

@@ -101,6 +101,46 @@ LMSTUDIO_HOST_URL=http://127.0.0.1:1234
 QDRANT_HOST=http://127.0.0.1:6333
 QDRANT_SERVICE_API_KEY=changeMe
 
+# SurrealDB (store)
+SURREALDB_HOST=http://127.0.0.1:8000
+SURREALDB_USER=symfony
+SURREALDB_PASS=symfony
+
+# Neo4J (store)
+NEO4J_HOST=http://127.0.0.1:7474
+NEO4J_DATABASE=neo4j
+NEO4J_USERNAME=neo4j
+NEO4J_PASSWORD=symfonyai
+
+# Typesense (store)
+TYPESENSE_HOST=http://127.0.0.1:8108
+TYPESENSE_API_KEY=changeMe
+
+# Milvus (store)
+MILVUS_HOST=http://127.0.0.1:19530
+MILVUS_API_KEY=root:Milvus
+MILVUS_DATABASE=symfony
+
+# Cloudflare (store)
+CLOUDFLARE_ACCOUNT_ID=
+CLOUDFLARE_API_KEY=
+
+# Cerebras
+CEREBRAS_API_KEY=
+
+CHROMADB_HOST=http://127.0.0.1
+CHROMADB_PORT=8001
+
+# For using Clickhouse (store)
+CLICKHOUSE_HOST=http://symfony:[email protected]:8123
+CLICKHOUSE_DATABASE=symfony
+CLICKHOUSE_TABLE=symfony
+
+# Weaviate (store)
+WEAVIATE_HOST=http://127.0.0.1:8080
+WEAVIATE_API_KEY=symfony
+
+# Supabase (store)
 SUPABASE_URL=
 SUPABASE_API_KEY=
 SUPABASE_TABLE=

examples/commands/stores.php

@@ -24,7 +24,6 @@
 use Symfony\AI\Store\Bridge\Neo4j\Store as Neo4jStore;
 use Symfony\AI\Store\Bridge\Postgres\Store as PostgresStore;
 use Symfony\AI\Store\Bridge\Qdrant\Store as QdrantStore;
-use Symfony\AI\Store\Bridge\Supabase\Store as SupabaseStore;
 use Symfony\AI\Store\Bridge\SurrealDb\Store as SurrealDbStore;
 use Symfony\AI\Store\Bridge\Typesense\Store as TypesenseStore;
 use Symfony\AI\Store\Bridge\Weaviate\Store as WeaviateStore;
@@ -89,15 +88,6 @@
         env('QDRANT_SERVICE_API_KEY'),
         'symfony',
     ),
-    'supabase' => static fn (): SupabaseStore => new SupabaseStore(
-        http_client(),
-        env('SUPABASE_URL'),
-        env('SUPABASE_API_KEY'),
-        env('SUPABASE_TABLE'),
-        env('SUPABASE_VECTOR_FIELD'),
-        env('SUPABASE_VECTOR_DIMENSION'),
-        env('SUPABASE_MATCH_FUNCTION'),
-    ),
     'surrealdb' => static fn (): SurrealDbStore => new SurrealDbStore(
         httpClient: http_client(),
         endpointUrl: env('SURREALDB_HOST'),

examples/rag/supabase.php

@@ -51,7 +51,7 @@ functionName: env('SUPABASE_MATCH_FUNCTION')
 }
 
 $platform = PlatformFactory::create(
-    env('OLLAMA_HOST_URL') ?? 'http://localhost:11434',
+    env('OLLAMA_HOST_URL'),
     http_client()
 );
 

src/ai-bundle/config/options.php

@@ -354,7 +354,7 @@
                             ->children()
                                 ->scalarNode('url')->isRequired()->cannotBeEmpty()->end()
                                 ->scalarNode('api_key')->isRequired()->cannotBeEmpty()->end()
-                                ->scalarNode('table')->isRequired()->cannotBeEmpty()->end()
+                                ->scalarNode('table')->end()
                                 ->scalarNode('vector_field')->end()
                                 ->integerNode('vector_dimension')->end()
                                 ->scalarNode('function_name')->end()

src/ai-bundle/doc/index.rst

@@ -100,7 +100,7 @@ Configuration
                     name: !php/const Symfony\AI\Platform\Bridge\ElevenLabs::TEXT_TO_SPEECH
                 tools: false
         store:
-            # also azure_search, meilisearch, memory, mongodb, pinecone, qdrant and surrealdb are supported as store type
+            # also azure_search, meilisearch, memory, mongodb, pinecone, qdrant, surrealdb, and supabase are supported as store type
             chroma_db:
                 # multiple collections possible per type
                 default:
@@ -131,7 +131,7 @@ Configuration
             default:
                 vectorizer: 'ai.vectorizer.openai_embeddings'
                 store: 'ai.store.chroma_db.default'
-                
+
             research:
                 vectorizer: 'ai.vectorizer.mistral_embeddings'
                 store: 'ai.store.memory.research'
@@ -355,13 +355,13 @@ Vectorizers are defined in the ``vectorizer`` section of your configuration:
                     name: !php/const Symfony\AI\Platform\Bridge\OpenAi\Embeddings::TEXT_EMBEDDING_3_SMALL
                     options:
                         dimensions: 512
-                        
+
             openai_large:
                 platform: 'ai.platform.openai'
                 model:
                     class: 'Symfony\AI\Platform\Bridge\OpenAi\Embeddings'
                     name: !php/const Symfony\AI\Platform\Bridge\OpenAi\Embeddings::TEXT_EMBEDDING_3_LARGE
-                    
+
             mistral_embed:
                 platform: 'ai.platform.mistral'
                 model:
@@ -379,19 +379,19 @@ Once configured, vectorizers can be referenced by name in indexer configurations
             documents:
                 vectorizer: 'ai.vectorizer.openai_small'
                 store: 'ai.store.chroma_db.documents'
-                
+
             research:
                 vectorizer: 'ai.vectorizer.openai_large'
                 store: 'ai.store.chroma_db.research'
-                
+
             knowledge_base:
                 vectorizer: 'ai.vectorizer.mistral_embed'
                 store: 'ai.store.memory.kb'
 
 **Benefits of Configured Vectorizers**
 
 * **Reusability**: Define once, use in multiple indexers
-* **Consistency**: Ensure all indexers using the same vectorizer have identical embedding configuration  
+* **Consistency**: Ensure all indexers using the same vectorizer have identical embedding configuration
 * **Maintainability**: Change vectorizer settings in one place
 
 Profiler

src/store/CHANGELOG.md

@@ -45,6 +45,7 @@ CHANGELOG
    - Pinecone
    - PostgreSQL with pgvector extension
    - Qdrant
+   - Supabase
    - SurrealDB
    - Typesense
    - Weaviate

src/store/composer.json

@@ -17,6 +17,7 @@
         "pinecone",
         "postgres",
         "qdrant",
+        "supabase",
         "surrealdb",
         "typesense",
         "weaviate"

src/store/doc/bridges/supabase.rst

@@ -0,0 +1,231 @@
+Supabase Bridge
+===============
+
+The Supabase bridge provides vector storage capabilities using Supabase's pgvector extension through the REST API.
+
+.. note::
+
+* Unlike the Postgres Store, the Supabase Store requires manual setup of the database schema
+* because Supabase doesn't allow arbitrary SQL execution via REST API.
+
+Installation
+------------
+
+The Supabase bridge requires the pgvector extension and pre-configured database objects.
+
+Requirements
+~~~~~~~~~~~~
+
+* Supabase project with pgvector extension enabled
+* Pre-configured table with vector column
+* Pre-configured RPC function for similarity search
+
+Database Setup
+--------------
+
+Execute the following SQL commands in your Supabase SQL Editor:
+
+Enable ``pgvector`` extension
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. code-block:: sql
+
+    CREATE EXTENSION IF NOT EXISTS vector;
+
+Create the `documents` table
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. code-block:: sql
+
+    CREATE TABLE IF NOT EXISTS documents (
+        id UUID PRIMARY KEY DEFAULT gen_random_uuid(),
+        embedding vector(1536) NOT NULL,
+        metadata JSONB
+    );
+
+Create the similarity search function
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. code-block:: sql
+
+    CREATE OR REPLACE FUNCTION match_documents(
+        query_embedding vector(1536),
+        match_count int DEFAULT 10,
+        match_threshold float DEFAULT 0.0
+    )
+    RETURNS TABLE (
+        id UUID,
+        embedding vector,
+        metadata JSONB,
+        score float
+    )
+    LANGUAGE sql
+    AS $$
+        SELECT
+            documents.id,
+            documents.embedding,
+            documents.metadata,
+            1- (documents.embedding <=> query_embedding) AS score
+        FROM documents
+        WHERE 1- (documents.embedding <=> query_embedding) >= match_threshold
+        ORDER BY documents.embedding <=> query_embedding ASC
+        LIMIT match_count;
+    $$;
+
+Create an index for better performance
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. code-block:: sql
+
+    CREATE INDEX IF NOT EXISTS documents_embedding_idx
+    ON documents USING ivfflat (embedding vector_cosine_ops);
+
+Configuration
+-------------
+
+Basic Configuration
+~~~~~~~~~~~~~~~~~~~
+
+.. code-block:: php
+
+    use Symfony\AI\Store\Bridge\Supabase\Store;
+    use Symfony\Component\HttpClient\HttpClient;
+
+    $store = new Store(
+        HttpClient::create(),
+        'https://your-project.supabase.co',
+        'your-anon-key',
+        'documents',        // table name
+        'embedding',        // vector field name
+        1536,              // vector dimension
+        'match_documents'   // function name
+    );
+
+Bundle Configuration
+~~~~~~~~~~~~~~~~~~~~
+
+.. code-block:: yaml
+
+    # config/packages/ai.yaml
+    ai:
+        store:
+            supabase:
+                my_supabase_store:
+                    url: 'https://your-project.supabase.co'
+                    api_key: '%env(SUPABASE_API_KEY)%'
+                    table: 'documents'
+                    vector_field: 'embedding'
+                    vector_dimension: 1536
+                    function_name: 'match_documents'
+
+Environment Variables
+~~~~~~~~~~~~~~~~~~~~~
+
+.. code-block:: bash
+
+    # .env.local
+    SUPABASE_URL=https://your-project.supabase.co
+    SUPABASE_API_KEY=your-supabase-anon-key
+
+Usage
+-----
+
+Adding Documents
+~~~~~~~~~~~~~~~~
+
+.. code-block:: php
+
+    use Symfony\AI\Platform\Vector\Vector;
+    use Symfony\AI\Store\Document\Metadata;
+    use Symfony\AI\Store\Document\VectorDocument;
+    use Symfony\Component\Uid\Uuid;
+
+    $document = new VectorDocument(
+        Uuid::v4(),
+        new Vector([0.1, 0.2, 0.3, /* ... 1536 dimensions */]),
+        new Metadata(['title' => 'My Document', 'category' => 'example'])
+    );
+
+    $store->add($document);
+
+Querying Documents
+~~~~~~~~~~~~~~~~~~
+
+.. code-block:: php
+
+    $queryVector = new Vector([0.1, 0.2, 0.3, /* ... 1536 dimensions */]);
+
+    $results = $store->query($queryVector, [
+        'max_items' => 10,
+        'min_score' => 0.7
+    ]);
+
+    foreach ($results as $document) {
+        echo "ID: " . $document->id . "\n";
+        echo "Score: " . $document->score . "\n";
+        echo "Metadata: " . json_encode($document->metadata->getArrayCopy()) . "\n";
+    }
+
+Customization
+-------------
+
+You can customize the Supabase setup for different requirements:
+
+Table Name
+~~~~~~~~~~
+
+Change ``documents`` to your preferred table name in both the SQL setup and configuration.
+
+Vector Field Name
+~~~~~~~~~~~~~~~~~
+
+Change ``embedding`` to your preferred field name in both the SQL setup and configuration.
+
+Vector Dimension
+~~~~~~~~~~~~~~~~
+
+Change ``1536`` to match your embedding model's dimensions in both the SQL setup and configuration.
+
+Distance Metric
+~~~~~~~~~~~~~~~
+
+* Cosine: ``<=>`` (default, recommended for most embeddings)
+* Euclidean: ``<->``
+* Inner Product: ``<#>``
+
+Index Type
+~~~~~~~~~~
+
+* ``ivfflat``: Good balance of speed and accuracy
+* ``hnsw``: Better for high-dimensional vectors (requires PostgreSQL 14+)
+
+Limitations
+-----------
+
+* Manual schema setup required (no automatic table creation)
+* Limited to Supabase's REST API capabilities
+* Requires pre-configured RPC functions for complex queries
+* Vector dimension must be consistent across all documents
+
+Performance Considerations
+--------------------------
+
+* Use appropriate index types based on your vector dimensions
+* Consider using ``hnsw`` indexes for high-dimensional vectors
+* Batch document insertions when possible (up to 200 documents per request)
+* Monitor your Supabase usage limits and quotas
+
+Security Considerations
+-----------------------
+
+* Use row-level security (RLS) policies if needed
+* Consider using service role keys for server-side operations
+* Validate vector dimensions in your application code
+* Implement proper error handling for API failures
+
+Additional Resources
+--------------------
+
+* [Supabase Vector Documentation](https://supabase.com/docs/guides/ai/vector-columns)
+* [pgvector Documentation](https://github.com/pgvector/pgvector)
+* [Symfony AI Store Documentation](../../../README.md)