patch example, default envs and docs after test

Author: chr-hertel

src/store/doc/bridges/supabase.rst renamed to docs/components/store/supabase.rst

@@ -1,24 +1,21 @@
 Supabase Bridge
 ===============
 
-The Supabase bridge provides vector storage capabilities using Supabase's pgvector extension through the REST API.
+The Supabase bridge provides vector storage capabilities using `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.
+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.
 
 Requirements
 ~~~~~~~~~~~~
 
-* Supabase project with pgvector extension enabled
-* Pre-configured table with vector column
-* Pre-configured RPC function for similarity search
+* Enable `pgvector extension`_ in the relevant schema of your Supabase project for using `vector`_ column types.
+* Add columns for embedding (type `vector`) and metadata (type `jsonb`) to your table
+* Pre-configured RPC `function`_ for similarity search
+
+See section below for detailed SQL commands.
 
 Database Setup
 --------------
@@ -39,7 +36,7 @@ Create the `documents` table
 
     CREATE TABLE IF NOT EXISTS documents (
         id UUID PRIMARY KEY DEFAULT gen_random_uuid(),
-        embedding vector(1536) NOT NULL,
+        embedding vector(768) NOT NULL,
         metadata JSONB
     );
 
@@ -49,7 +46,7 @@ Create the similarity search function
 .. code-block:: sql
 
     CREATE OR REPLACE FUNCTION match_documents(
-        query_embedding vector(1536),
+        query_embedding vector(768),
         match_count int DEFAULT 10,
         match_threshold float DEFAULT 0.0
     )
@@ -97,7 +94,7 @@ Basic Configuration
         'your-anon-key',
         'documents',        // table name
         'embedding',        // vector field name
-        1536,              // vector dimension
+        768,               // vector dimension (depending on your embedding model)
         'match_documents'   // function name
     );
 
@@ -115,7 +112,7 @@ Bundle Configuration
                     api_key: '%env(SUPABASE_API_KEY)%'
                     table: 'documents'
                     vector_field: 'embedding'
-                    vector_dimension: 1536
+                    vector_dimension: 768
                     function_name: 'match_documents'
 
 Environment Variables
@@ -142,7 +139,7 @@ Adding Documents
 
     $document = new VectorDocument(
         Uuid::v4(),
-        new Vector([0.1, 0.2, 0.3, /* ... 1536 dimensions */]),
+        new Vector([0.1, 0.2, 0.3, /* ... 768 dimensions */]),
         new Metadata(['title' => 'My Document', 'category' => 'example'])
     );
 
@@ -153,7 +150,7 @@ Querying Documents
 
 .. code-block:: php
 
-    $queryVector = new Vector([0.1, 0.2, 0.3, /* ... 1536 dimensions */]);
+    $queryVector = new Vector([0.1, 0.2, 0.3, /* ... 768 dimensions */]);
 
     $results = $store->query($queryVector, [
         'max_items' => 10,
@@ -184,7 +181,7 @@ Change ``embedding`` to your preferred field name in both the SQL setup and conf
 Vector Dimension
 ~~~~~~~~~~~~~~~~
 
-Change ``1536`` to match your embedding model's dimensions in both the SQL setup and configuration.
+Change ``768`` to match your embedding model's dimensions in both the SQL setup and configuration.
 
 Distance Metric
 ~~~~~~~~~~~~~~~
@@ -223,9 +220,7 @@ Security Considerations
 * 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)
+.. _`pgvector`: https://github.com/pgvector/pgvector
+.. _`pgvector extension`: https://supabase.com/docs/guides/database/extensions/pgvector
+.. _`vector`: https://supabase.com/docs/guides/ai/vector-columns
+.. _`function`: https://supabase.com/docs/guides/database/functions

examples/.env

@@ -160,7 +160,7 @@ DEEPSEEK_API_KEY=
 # Supabase (store)
 SUPABASE_URL=
 SUPABASE_API_KEY=
-SUPABASE_TABLE=
-SUPABASE_VECTOR_FIELD=
-SUPABASE_VECTOR_DIMENSION=
-SUPABASE_MATCH_FUNCTION=
+SUPABASE_TABLE=documents
+SUPABASE_VECTOR_FIELD=embedding
+SUPABASE_VECTOR_DIMENSION=768 # when using Ollama with nomic-embed-text
+SUPABASE_MATCH_FUNCTION=match_documents

examples/rag/supabase.php

@@ -14,7 +14,6 @@
 use Symfony\AI\Agent\Toolbox\Tool\SimilaritySearch;
 use Symfony\AI\Agent\Toolbox\Toolbox;
 use Symfony\AI\Fixtures\Movies;
-use Symfony\AI\Platform\Bridge\Ollama\Ollama;
 use Symfony\AI\Platform\Bridge\Ollama\PlatformFactory;
 use Symfony\AI\Platform\Message\Message;
 use Symfony\AI\Platform\Message\MessageBag;
@@ -32,6 +31,10 @@
     httpClient: http_client(),
     url: env('SUPABASE_URL'),
     apiKey: env('SUPABASE_API_KEY'),
+    table: env('SUPABASE_TABLE'),
+    vectorFieldName: env('SUPABASE_VECTOR_FIELD'),
+    vectorDimension: (int) env('SUPABASE_VECTOR_DIMENSION'),
+    functionName: env('SUPABASE_MATCH_FUNCTION'),
 );
 
 $documents = [];
@@ -44,20 +47,17 @@
     );
 }
 
-$platform = PlatformFactory::create(
-    env('OLLAMA_HOST_URL'),
-    http_client()
-);
+$platform = PlatformFactory::create(env('OLLAMA_HOST_URL'), http_client());
 
-$vectorizer = new Vectorizer($platform, new Ollama('mxbai-embed-large'));
+$vectorizer = new Vectorizer($platform, env('OLLAMA_EMBEDDINGS'));
 $loader = new InMemoryLoader($documents);
 $indexer = new Indexer($loader, $vectorizer, $store, logger: logger());
 $indexer->index();
 
 $similaritySearch = new SimilaritySearch($vectorizer, $store);
 $toolbox = new Toolbox([$similaritySearch], logger: logger());
 $processor = new AgentProcessor($toolbox);
-$agent = new Agent($platform, new Ollama('llama3.2:3b'), [$processor], [$processor], logger: logger());
+$agent = new Agent($platform, env('OLLAMA_LLM'), [$processor], [$processor]);
 
 $messages = new MessageBag(
     Message::forSystem('Please answer all user questions only using SimilaritySearch function.'),

src/ai-bundle/config/options.php

@@ -656,12 +656,12 @@
                                     ->defaultValue('http_client')
                                     ->info('Service ID of the HTTP client to use')
                                 ->end()
-                                ->scalarNode('url')->isRequired()->cannotBeEmpty()->end()
-                                ->scalarNode('api_key')->isRequired()->cannotBeEmpty()->end()
-                                ->scalarNode('table')->end()
-                                ->scalarNode('vector_field')->end()
+                                ->stringNode('url')->isRequired()->cannotBeEmpty()->end()
+                                ->stringNode('api_key')->isRequired()->cannotBeEmpty()->end()
+                                ->stringNode('table')->end()
+                                ->stringNode('vector_field')->end()
                                 ->integerNode('vector_dimension')->end()
-                                ->scalarNode('function_name')->end()
+                                ->stringNode('function_name')->end()
                             ->end()
                         ->end()
                     ->end()