chore: Auto-detect Provider ID when only 1 Vector Store Provider avai… (#3802)

# What does this PR do?
2 main changes:

1. Remove `provider_id` requirement in call to vector stores and
2. Removes "register first embedding model" logic 
   - Now forces embedding model id as required on Vector Store creation

Simplifies the UX for OpenAI to:

```python
vs = client.vector_stores.create(
    name="my_citations_db",
    extra_body={
        "embedding_model": "ollama/nomic-embed-text:latest",
    }
)
```


<!-- If resolving an issue, uncomment and update the line below -->
<!-- Closes #[issue-number] -->

## Test Plan
<!-- Describe the tests you ran to verify your changes with result
summaries. *Provide clear instructions so the plan can be easily
re-executed.* -->

---------

Signed-off-by: Francisco Javier Arceo <farceo@redhat.com>

llama_stack/core/routers/vector_io.py

@@ -55,30 +55,18 @@ class VectorIORouter(VectorIO):
         logger.debug("VectorIORouter.shutdown")
         pass
 
-    async def _get_first_embedding_model(self) -> tuple[str, int] | None:
-        """Get the first available embedding model identifier."""
-        try:
-            # Get all models from the routing table
-            all_models = await self.routing_table.get_all_with_type("model")
+    async def _get_embedding_model_dimension(self, embedding_model_id: str) -> int:
+        """Get the embedding dimension for a specific embedding model."""
+        all_models = await self.routing_table.get_all_with_type("model")
 
-            # Filter for embedding models
-            embedding_models = [
-                model
-                for model in all_models
-                if hasattr(model, "model_type") and model.model_type == ModelType.embedding
-            ]
-
-            if embedding_models:
-                dimension = embedding_models[0].metadata.get("embedding_dimension", None)
+        for model in all_models:
+            if model.identifier == embedding_model_id and model.model_type == ModelType.embedding:
+                dimension = model.metadata.get("embedding_dimension")
                 if dimension is None:
-                    raise ValueError(f"Embedding model {embedding_models[0].identifier} has no embedding dimension")
-                return embedding_models[0].identifier, dimension
-            else:
-                logger.warning("No embedding models found in the routing table")
-                return None
-        except Exception as e:
-            logger.error(f"Error getting embedding models: {e}")
-            return None
+                    raise ValueError(f"Embedding model '{embedding_model_id}' has no embedding_dimension in metadata")
+                return int(dimension)
+
+        raise ValueError(f"Embedding model '{embedding_model_id}' not found or not an embedding model")
 
     async def register_vector_db(
         self,
@@ -129,20 +117,30 @@ class VectorIORouter(VectorIO):
         # Extract llama-stack-specific parameters from extra_body
         extra = params.model_extra or {}
         embedding_model = extra.get("embedding_model")
-        embedding_dimension = extra.get("embedding_dimension", 384)
+        embedding_dimension = extra.get("embedding_dimension")
         provider_id = extra.get("provider_id")
 
         logger.debug(f"VectorIORouter.openai_create_vector_store: name={params.name}, provider_id={provider_id}")
 
-        # If no embedding model is provided, use the first available one
-        # TODO: this branch will soon be deleted so you _must_ provide the embedding_model when
-        # creating a vector store
+        # Require explicit embedding model specification
         if embedding_model is None:
-            embedding_model_info = await self._get_first_embedding_model()
-            if embedding_model_info is None:
-                raise ValueError("No embedding model provided and no embedding models available in the system")
-            embedding_model, embedding_dimension = embedding_model_info
-            logger.info(f"No embedding model specified, using first available: {embedding_model}")
+            raise ValueError("embedding_model is required in extra_body when creating a vector store")
+
+        if embedding_dimension is None:
+            embedding_dimension = await self._get_embedding_model_dimension(embedding_model)
+
+        # Auto-select provider if not specified
+        if provider_id is None:
+            num_providers = len(self.routing_table.impls_by_provider_id)
+            if num_providers == 0:
+                raise ValueError("No vector_io providers available")
+            if num_providers > 1:
+                available_providers = list(self.routing_table.impls_by_provider_id.keys())
+                raise ValueError(
+                    f"Multiple vector_io providers available. Please specify provider_id in extra_body. "
+                    f"Available providers: {available_providers}"
+                )
+            provider_id = list(self.routing_table.impls_by_provider_id.keys())[0]
 
         vector_db_id = f"vs_{uuid.uuid4()}"
         registered_vector_db = await self.routing_table.register_vector_db(

llama_stack/providers/utils/memory/openai_vector_store_mixin.py

@@ -353,14 +353,12 @@ class OpenAIVectorStoreMixin(ABC):
         provider_vector_db_id = extra.get("provider_vector_db_id")
         embedding_model = extra.get("embedding_model")
         embedding_dimension = extra.get("embedding_dimension", 384)
-        provider_id = extra.get("provider_id")
+        # use provider_id set by router; fallback to provider's own ID when used directly via --stack-config
+        provider_id = extra.get("provider_id") or getattr(self, "__provider_id__", None)
 
         # Derive the canonical vector_db_id (allow override, else generate)
         vector_db_id = provider_vector_db_id or generate_object_id("vector_store", lambda: f"vs_{uuid.uuid4()}")
 
-        if provider_id is None:
-            raise ValueError("Provider ID is required")
-
         if embedding_model is None:
             raise ValueError("Embedding model is required")
 
@@ -369,6 +367,9 @@ class OpenAIVectorStoreMixin(ABC):
             raise ValueError("Embedding dimension is required")
 
         # Register the VectorDB backing this vector store
+        if provider_id is None:
+            raise ValueError("Provider ID is required but was not provided")
+
         vector_db = VectorDB(
             identifier=vector_db_id,
             embedding_dimension=embedding_dimension,

tests/integration/vector_io/test_openai_vector_stores.py

@@ -146,8 +146,6 @@ def test_openai_create_vector_store(
         metadata={"purpose": "testing", "environment": "integration"},
         extra_body={
             "embedding_model": embedding_model_id,
-            "embedding_dimension": embedding_dimension,
-            "provider_id": "my_provider",
         },
     )
 
@@ -175,8 +173,6 @@ def test_openai_list_vector_stores(
         metadata={"type": "test"},
         extra_body={
             "embedding_model": embedding_model_id,
-            "embedding_dimension": embedding_dimension,
-            "provider_id": "my_provider",
         },
     )
     store2 = client.vector_stores.create(
@@ -184,8 +180,6 @@ def test_openai_list_vector_stores(
         metadata={"type": "test"},
         extra_body={
             "embedding_model": embedding_model_id,
-            "embedding_dimension": embedding_dimension,
-            "provider_id": "my_provider",
         },
     )
 
@@ -220,8 +214,6 @@ def test_openai_retrieve_vector_store(
         metadata={"purpose": "retrieval_test"},
         extra_body={
             "embedding_model": embedding_model_id,
-            "embedding_dimension": embedding_dimension,
-            "provider_id": "my_provider",
         },
     )
 
@@ -249,8 +241,6 @@ def test_openai_update_vector_store(
         metadata={"version": "1.0"},
         extra_body={
             "embedding_model": embedding_model_id,
-            "embedding_dimension": embedding_dimension,
-            "provider_id": "my_provider",
         },
     )
     time.sleep(1)
@@ -282,8 +272,6 @@ def test_openai_delete_vector_store(
         metadata={"purpose": "deletion_test"},
         extra_body={
             "embedding_model": embedding_model_id,
-            "embedding_dimension": embedding_dimension,
-            "provider_id": "my_provider",
         },
     )
 
@@ -314,8 +302,6 @@ def test_openai_vector_store_search_empty(
         metadata={"purpose": "search_testing"},
         extra_body={
             "embedding_model": embedding_model_id,
-            "embedding_dimension": embedding_dimension,
-            "provider_id": "my_provider",
         },
     )
 
@@ -346,8 +332,6 @@ def test_openai_vector_store_with_chunks(
         metadata={"purpose": "chunks_testing"},
         extra_body={
             "embedding_model": embedding_model_id,
-            "embedding_dimension": embedding_dimension,
-            "provider_id": "my_provider",
         },
     )
 
@@ -412,8 +396,6 @@ def test_openai_vector_store_search_relevance(
         metadata={"purpose": "relevance_testing"},
         extra_body={
             "embedding_model": embedding_model_id,
-            "embedding_dimension": embedding_dimension,
-            "provider_id": "my_provider",
         },
     )
 
@@ -457,8 +439,6 @@ def test_openai_vector_store_search_with_ranking_options(
         metadata={"purpose": "ranking_testing"},
         extra_body={
             "embedding_model": embedding_model_id,
-            "embedding_dimension": embedding_dimension,
-            "provider_id": "my_provider",
         },
     )
 
@@ -500,8 +480,6 @@ def test_openai_vector_store_search_with_high_score_filter(
         metadata={"purpose": "high_score_filtering"},
         extra_body={
             "embedding_model": embedding_model_id,
-            "embedding_dimension": embedding_dimension,
-            "provider_id": "my_provider",
         },
     )
 
@@ -561,8 +539,6 @@ def test_openai_vector_store_search_with_max_num_results(
         metadata={"purpose": "max_num_results_testing"},
         extra_body={
             "embedding_model": embedding_model_id,
-            "embedding_dimension": embedding_dimension,
-            "provider_id": "my_provider",
         },
     )
 
@@ -596,8 +572,6 @@ def test_openai_vector_store_attach_file(
         name="test_store",
         extra_body={
             "embedding_model": embedding_model_id,
-            "embedding_dimension": embedding_dimension,
-            "provider_id": "my_provider",
         },
     )
 
@@ -666,8 +640,6 @@ def test_openai_vector_store_attach_files_on_creation(
         file_ids=file_ids,
         extra_body={
             "embedding_model": embedding_model_id,
-            "embedding_dimension": embedding_dimension,
-            "provider_id": "my_provider",
         },
     )
 
@@ -713,8 +685,6 @@ def test_openai_vector_store_list_files(
         name="test_store",
         extra_body={
             "embedding_model": embedding_model_id,
-            "embedding_dimension": embedding_dimension,
-            "provider_id": "my_provider",
         },
     )
 
@@ -799,8 +769,6 @@ def test_openai_vector_store_retrieve_file_contents(
         name="test_store",
         extra_body={
             "embedding_model": embedding_model_id,
-            "embedding_dimension": embedding_dimension,
-            "provider_id": "my_provider",
         },
     )
 
@@ -819,8 +787,6 @@ def test_openai_vector_store_retrieve_file_contents(
         attributes=attributes,
         extra_body={
             "embedding_model": embedding_model_id,
-            "embedding_dimension": embedding_dimension,
-            "provider_id": "my_provider",
         },
     )
 
@@ -857,8 +823,6 @@ def test_openai_vector_store_delete_file(
         name="test_store",
         extra_body={
             "embedding_model": embedding_model_id,
-            "embedding_dimension": embedding_dimension,
-            "provider_id": "my_provider",
         },
     )
 
@@ -918,8 +882,6 @@ def test_openai_vector_store_delete_file_removes_from_vector_store(
         name="test_store",
         extra_body={
             "embedding_model": embedding_model_id,
-            "embedding_dimension": embedding_dimension,
-            "provider_id": "my_provider",
         },
     )
 
@@ -965,8 +927,6 @@ def test_openai_vector_store_update_file(
         name="test_store",
         extra_body={
             "embedding_model": embedding_model_id,
-            "embedding_dimension": embedding_dimension,
-            "provider_id": "my_provider",
         },
     )
 
@@ -1026,8 +986,6 @@ def test_create_vector_store_files_duplicate_vector_store_name(
         name="test_store_with_files",
         extra_body={
             "embedding_model": embedding_model_id,
-            "embedding_dimension": embedding_dimension,
-            "provider_id": "my_provider",
         },
     )
     assert vector_store.file_counts.completed == 0
@@ -1040,8 +998,6 @@ def test_create_vector_store_files_duplicate_vector_store_name(
         name="test_store_with_files",
         extra_body={
             "embedding_model": embedding_model_id,
-            "embedding_dimension": embedding_dimension,
-            "provider_id": "my_provider",
         },
     )
 
@@ -1053,8 +1009,6 @@ def test_create_vector_store_files_duplicate_vector_store_name(
         file_id=file_ids[0],
         extra_body={
             "embedding_model": embedding_model_id,
-            "embedding_dimension": embedding_dimension,
-            "provider_id": "my_provider",
         },
     )
     assert created_file.status == "completed"
@@ -1065,8 +1019,6 @@ def test_create_vector_store_files_duplicate_vector_store_name(
         file_id=file_ids[1],
         extra_body={
             "embedding_model": embedding_model_id,
-            "embedding_dimension": embedding_dimension,
-            "provider_id": "my_provider",
         },
     )
     assert created_file_from_non_deleted_vector_store.status == "completed"
@@ -1087,8 +1039,6 @@ def test_openai_vector_store_search_modes(
         metadata={"purpose": "search_mode_testing"},
         extra_body={
             "embedding_model": embedding_model_id,
-            "embedding_dimension": embedding_dimension,
-            "provider_id": "my_provider",
         },
     )
 
@@ -1120,8 +1070,6 @@ def test_openai_vector_store_file_batch_create_and_retrieve(
         name="batch_test_store",
         extra_body={
             "embedding_model": embedding_model_id,
-            "embedding_dimension": embedding_dimension,
-            "provider_id": "my_provider",
         },
     )
 
@@ -1139,8 +1087,6 @@ def test_openai_vector_store_file_batch_create_and_retrieve(
         file_ids=file_ids,
         extra_body={
             "embedding_model": embedding_model_id,
-            "embedding_dimension": embedding_dimension,
-            "provider_id": "my_provider",
         },
     )
 
@@ -1187,8 +1133,6 @@ def test_openai_vector_store_file_batch_list_files(
         name="batch_list_test_store",
         extra_body={
             "embedding_model": embedding_model_id,
-            "embedding_dimension": embedding_dimension,
-            "provider_id": "my_provider",
         },
     )
 
@@ -1206,8 +1150,6 @@ def test_openai_vector_store_file_batch_list_files(
         file_ids=file_ids,
         extra_body={
             "embedding_model": embedding_model_id,
-            "embedding_dimension": embedding_dimension,
-            "provider_id": "my_provider",
         },
     )
 
@@ -1284,8 +1226,6 @@ def test_openai_vector_store_file_batch_cancel(
         name="batch_cancel_test_store",
         extra_body={
             "embedding_model": embedding_model_id,
-            "embedding_dimension": embedding_dimension,
-            "provider_id": "my_provider",
         },
     )
 
@@ -1303,8 +1243,6 @@ def test_openai_vector_store_file_batch_cancel(
         file_ids=file_ids,
         extra_body={
             "embedding_model": embedding_model_id,
-            "embedding_dimension": embedding_dimension,
-            "provider_id": "my_provider",
         },
     )
 
@@ -1343,8 +1281,6 @@ def test_openai_vector_store_file_batch_retrieve_contents(
         name="batch_contents_test_store",
         extra_body={
             "embedding_model": embedding_model_id,
-            "embedding_dimension": embedding_dimension,
-            "provider_id": "my_provider",
         },
     )
 
@@ -1367,8 +1303,6 @@ def test_openai_vector_store_file_batch_retrieve_contents(
         file_ids=file_ids,
         extra_body={
             "embedding_model": embedding_model_id,
-            "embedding_dimension": embedding_dimension,
-            "provider_id": "my_provider",
         },
     )
 
@@ -1420,8 +1354,6 @@ def test_openai_vector_store_file_batch_error_handling(
         name="batch_error_test_store",
         extra_body={
             "embedding_model": embedding_model_id,
-            "embedding_dimension": embedding_dimension,
-            "provider_id": "my_provider",
         },
     )
 
@@ -1433,8 +1365,6 @@ def test_openai_vector_store_file_batch_error_handling(
         file_ids=file_ids,
         extra_body={
             "embedding_model": embedding_model_id,
-            "embedding_dimension": embedding_dimension,
-            "provider_id": "my_provider",
         },
     )
 

tests/integration/vector_io/test_vector_io.py

@@ -52,8 +52,6 @@ def test_vector_db_retrieve(client_with_empty_registry, embedding_model_id, embe
         name=vector_db_name,
         extra_body={
             "embedding_model": embedding_model_id,
-            "embedding_dimension": embedding_dimension,
-            "provider_id": "my_provider",
         },
     )
 
@@ -73,8 +71,6 @@ def test_vector_db_register(client_with_empty_registry, embedding_model_id, embe
         name=vector_db_name,
         extra_body={
             "embedding_model": embedding_model_id,
-            "embedding_dimension": embedding_dimension,
-            "provider_id": "my_provider",
         },
     )
 
@@ -110,8 +106,6 @@ def test_insert_chunks(client_with_empty_registry, embedding_model_id, embedding
         name=vector_db_name,
         extra_body={
             "embedding_model": embedding_model_id,
-            "embedding_dimension": embedding_dimension,
-            "provider_id": "my_provider",
         },
     )
 
@@ -152,8 +146,6 @@ def test_insert_chunks_with_precomputed_embeddings(client_with_empty_registry, e
         name=vector_db_name,
         extra_body={
             "embedding_model": embedding_model_id,
-            "embedding_dimension": embedding_dimension,
-            "provider_id": "my_provider",
         },
     )
 
@@ -202,8 +194,6 @@ def test_query_returns_valid_object_when_identical_to_embedding_in_vdb(
         name=vector_db_name,
         extra_body={
             "embedding_model": embedding_model_id,
-            "embedding_dimension": embedding_dimension,
-            "provider_id": "my_provider",
         },
     )
 
@@ -234,3 +224,35 @@ def test_query_returns_valid_object_when_identical_to_embedding_in_vdb(
     assert len(response.chunks) > 0
     assert response.chunks[0].metadata["document_id"] == "doc1"
     assert response.chunks[0].metadata["source"] == "precomputed"
+
+
+def test_auto_extract_embedding_dimension(client_with_empty_registry, embedding_model_id):
+    vs = client_with_empty_registry.vector_stores.create(
+        name="test_auto_extract", extra_body={"embedding_model": embedding_model_id}
+    )
+    assert vs.id is not None
+
+
+def test_provider_auto_selection_single_provider(client_with_empty_registry, embedding_model_id):
+    providers = [p for p in client_with_empty_registry.providers.list() if p.api == "vector_io"]
+    if len(providers) != 1:
+        pytest.skip(f"Test requires exactly one vector_io provider, found {len(providers)}")
+
+    vs = client_with_empty_registry.vector_stores.create(
+        name="test_auto_provider", extra_body={"embedding_model": embedding_model_id}
+    )
+    assert vs.id is not None
+
+
+def test_provider_id_override(client_with_empty_registry, embedding_model_id):
+    providers = [p for p in client_with_empty_registry.providers.list() if p.api == "vector_io"]
+    if len(providers) != 1:
+        pytest.skip(f"Test requires exactly one vector_io provider, found {len(providers)}")
+
+    provider_id = providers[0].provider_id
+
+    vs = client_with_empty_registry.vector_stores.create(
+        name="test_provider_override", extra_body={"embedding_model": embedding_model_id, "provider_id": provider_id}
+    )
+    assert vs.id is not None
+    assert vs.metadata.get("provider_id") == provider_id

tests/unit/core/routers/test_vector_io.py

@@ -0,0 +1,57 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the terms described in the LICENSE file in
+# the root directory of this source tree.
+
+from unittest.mock import AsyncMock, Mock
+
+import pytest
+
+from llama_stack.apis.vector_io import OpenAICreateVectorStoreRequestWithExtraBody
+from llama_stack.core.routers.vector_io import VectorIORouter
+
+
+async def test_single_provider_auto_selection():
+    # provider_id automatically selected during vector store create() when only one provider available
+    mock_routing_table = Mock()
+    mock_routing_table.impls_by_provider_id = {"inline::faiss": "mock_provider"}
+    mock_routing_table.get_all_with_type = AsyncMock(
+        return_value=[
+            Mock(identifier="all-MiniLM-L6-v2", model_type="embedding", metadata={"embedding_dimension": 384})
+        ]
+    )
+    mock_routing_table.register_vector_db = AsyncMock(
+        return_value=Mock(identifier="vs_123", provider_id="inline::faiss", provider_resource_id="vs_123")
+    )
+    mock_routing_table.get_provider_impl = AsyncMock(
+        return_value=Mock(openai_create_vector_store=AsyncMock(return_value=Mock(id="vs_123")))
+    )
+    router = VectorIORouter(mock_routing_table)
+    request = OpenAICreateVectorStoreRequestWithExtraBody.model_validate(
+        {"name": "test_store", "embedding_model": "all-MiniLM-L6-v2"}
+    )
+
+    result = await router.openai_create_vector_store(request)
+    assert result.id == "vs_123"
+
+
+async def test_create_vector_stores_multiple_providers_missing_provider_id_error():
+    # if multiple providers are available, vector store create will error without provider_id
+    mock_routing_table = Mock()
+    mock_routing_table.impls_by_provider_id = {
+        "inline::faiss": "mock_provider_1",
+        "inline::sqlite-vec": "mock_provider_2",
+    }
+    mock_routing_table.get_all_with_type = AsyncMock(
+        return_value=[
+            Mock(identifier="all-MiniLM-L6-v2", model_type="embedding", metadata={"embedding_dimension": 384})
+        ]
+    )
+    router = VectorIORouter(mock_routing_table)
+    request = OpenAICreateVectorStoreRequestWithExtraBody.model_validate(
+        {"name": "test_store", "embedding_model": "all-MiniLM-L6-v2"}
+    )
+
+    with pytest.raises(ValueError, match="Multiple vector_io providers available"):
+        await router.openai_create_vector_store(request)