fix test

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

updating structure of default

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

fix model id creation

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

docs/docs/building_applications/rag.mdx

@@ -87,20 +87,19 @@ Llama Stack provides OpenAI-compatible RAG capabilities through:
 To enable automatic vector store creation without specifying embedding models, configure a default embedding model in your run.yaml like so:
 
 ```yaml
-models:
-  - model_id: nomic-ai/nomic-embed-text-v1.5
-    provider_id: inline::sentence-transformers
-    metadata:
-      embedding_dimension: 768
-
 vector_stores:
-  default_embedding_model_id: nomic-ai/nomic-embed-text-v1.5
+  default_provider_id: faiss
+  default_embedding_model:
+    provider_id: sentence-transformers
+    model_id: nomic-ai/nomic-embed-text-v1.5
 ```
 
 With this configuration:
-- `client.vector_stores.create()` works without requiring embedding model parameters
+- `client.vector_stores.create()` works without requiring embedding model or provider parameters
-- The system automatically uses the default model and its embedding dimension for any newly created vector store
+- The system automatically uses the default vector store provider (`faiss`) when multiple providers are available
-- The `vector_stores` section explicitly configures which embedding model to use as default
+- The system automatically uses the default embedding model (`sentence-transformers/nomic-ai/nomic-embed-text-v1.5`) for any newly created vector store
+- The `default_provider_id` specifies which vector storage backend to use
+- The `default_embedding_model` specifies both the inference provider and model for embeddings
 
 ## Vector Store Operations
 
@@ -109,14 +108,15 @@ With this configuration:
 You can create vector stores with automatic or explicit embedding model selection:
 
 ```python
-# Automatic - uses default configured embedding model
+# Automatic - uses default configured embedding model and vector store provider
 vs = client.vector_stores.create()
 
-# Explicit - specify embedding model when you need a specific one
+# Explicit - specify embedding model and/or provider when you need specific ones
 vs = client.vector_stores.create(
     extra_body={
-        "embedding_model": "nomic-ai/nomic-embed-text-v1.5",
+        "provider_id": "faiss",  # Optional: specify vector store provider
-        "embedding_dimension": 768
+        "embedding_model": "sentence-transformers/nomic-ai/nomic-embed-text-v1.5",
+        "embedding_dimension": 768  # Optional: will be auto-detected if not provided
     }
 )
 ```

llama_stack/core/datatypes.py

@@ -351,17 +351,30 @@ class AuthenticationRequiredError(Exception):
     pass
 
 
+class DefaultEmbeddingModel(BaseModel):
+    """Configuration for default embedding model."""
+
+    provider_id: str = Field(
+        ...,
+        description="ID of the inference provider that serves the embedding model (e.g., 'sentence-transformers').",
+    )
+    model_id: str = Field(
+        ...,
+        description="ID of the embedding model (e.g., 'nomic-ai/nomic-embed-text-v1.5').",
+    )
+
+
 class VectorStoresConfig(BaseModel):
     """Configuration for vector stores in the stack."""
 
-    embedding_model_id: str = Field(
+    default_provider_id: str | None = Field(
-        ...,
-        description="ID of the embedding model to use as default for vector stores when none is specified. Must reference a model defined in the 'models' section.",
-    )
-    provider_id: str | None = Field(
         default=None,
         description="ID of the vector_io provider to use as default when multiple providers are available and none is specified.",
     )
+    default_embedding_model: DefaultEmbeddingModel | None = Field(
+        default=None,
+        description="Default embedding model configuration for vector stores.",
+    )
 
 
 class QuotaPeriod(StrEnum):

llama_stack/core/routers/vector_io.py

@@ -126,7 +126,11 @@ class VectorIORouter(VectorIO):
 
         # Use default embedding model if not specified
         if embedding_model is None and self.vector_stores_config is not None:
-            embedding_model = self.vector_stores_config.embedding_model_id
+            if self.vector_stores_config.default_embedding_model is not None:
+                # Construct the full model ID with provider prefix
+                embedding_provider_id = self.vector_stores_config.default_embedding_model.provider_id
+                model_id = self.vector_stores_config.default_embedding_model.model_id
+                embedding_model = f"{embedding_provider_id}/{model_id}"
 
         if embedding_model is not None and embedding_dimension is None:
             embedding_dimension = await self._get_embedding_model_dimension(embedding_model)
@@ -139,8 +143,8 @@ class VectorIORouter(VectorIO):
             if num_providers > 1:
                 available_providers = list(self.routing_table.impls_by_provider_id.keys())
                 # Use default configured provider
-                if self.vector_stores_config and self.vector_stores_config.provider_id:
+                if self.vector_stores_config and self.vector_stores_config.default_provider_id:
-                    default_provider = self.vector_stores_config.provider_id
+                    default_provider = self.vector_stores_config.default_provider_id
                     if default_provider in available_providers:
                         provider_id = default_provider
                         logger.debug(f"Using configured default vector store provider: {provider_id}")

llama_stack/core/stack.py

@@ -135,41 +135,52 @@ async def validate_vector_stores_config(run_config: StackRunConfig, impls: dict[
         return
 
     vector_stores_config = run_config.vector_stores
-    default_model_id = vector_stores_config.embedding_model_id
 
-    if Api.models not in impls:
+    # Validate default embedding model if configured
-        raise ValueError(f"Models API is not available but vector_stores config requires model '{default_model_id}'")
+    if vector_stores_config.default_embedding_model:
+        default_embedding_model = vector_stores_config.default_embedding_model
+        provider_id = default_embedding_model.provider_id
+        model_id = default_embedding_model.model_id
+        # Construct the full model identifier
+        default_model_id = f"{provider_id}/{model_id}"
 
-    models_impl = impls[Api.models]
+        if Api.models not in impls:
-    response = await models_impl.list_models()
+            raise ValueError(
-    models_list = response.data if hasattr(response, "data") else response
+                f"Models API is not available but vector_stores config requires model '{default_model_id}'"
+            )
 
-    # find default embedding model
+        models_impl = impls[Api.models]
-    default_model = None
+        response = await models_impl.list_models()
-    for model in models_list:
+        models_list = response.data if hasattr(response, "data") else response
-        if model.identifier == default_model_id:
-            default_model = model
-            break
 
-    if not default_model:
+        # find default embedding model
-        available_models = [m.identifier for m in models_list if m.model_type == "embedding"]
+        default_model = None
-        raise ValueError(
+        for model in models_list:
-            f"Embedding model '{default_model_id}' not found. Available embedding models: {available_models}"
+            if model.identifier == default_model_id:
-        )
+                default_model = model
+                break
 
-    if default_model.model_type != "embedding":
+        if not default_model:
-        raise ValueError(f"Model '{default_model_id}' is type '{default_model.model_type}', not 'embedding'")
+            available_models = [m.identifier for m in models_list if m.model_type == "embedding"]
+            raise ValueError(
+                f"Embedding model '{default_model_id}' not found. Available embedding models: {available_models}"
+            )
 
-    embedding_dimension = default_model.metadata.get("embedding_dimension")
+        if default_model.model_type != "embedding":
-    if embedding_dimension is None:
+            raise ValueError(f"Model '{default_model_id}' is type '{default_model.model_type}', not 'embedding'")
-        raise ValueError(f"Embedding model '{default_model_id}' is missing 'embedding_dimension' in metadata")
 
-    try:
+        embedding_dimension = default_model.metadata.get("embedding_dimension")
-        int(embedding_dimension)
+        if embedding_dimension is None:
-    except ValueError as err:
+            raise ValueError(f"Embedding model '{default_model_id}' is missing 'embedding_dimension' in metadata")
-        raise ValueError(f"Embedding dimension '{embedding_dimension}' cannot be converted to an integer") from err
 
-    logger.debug(f"Validated default embedding model: {default_model_id} (dimension: {embedding_dimension})")
+        try:
+            int(embedding_dimension)
+        except ValueError as err:
+            raise ValueError(f"Embedding dimension '{embedding_dimension}' cannot be converted to an integer") from err
+
+        logger.debug(f"Validated default embedding model: {default_model_id} (dimension: {embedding_dimension})")
+
+    # If no default embedding model is configured, that's fine - validation passes
 
 
 class EnvVarError(Exception):

llama_stack/distributions/ci-tests/run.yaml

@@ -255,4 +255,7 @@ server:
 telemetry:
   enabled: true
 vector_stores:
-  embedding_model_id: sentence-transformers/nomic-ai/nomic-embed-text-v1.5
+  default_provider_id: faiss
+  default_embedding_model:
+    provider_id: sentence-transformers
+    model_id: nomic-ai/nomic-embed-text-v1.5

llama_stack/distributions/starter-gpu/run.yaml

@@ -258,4 +258,7 @@ server:
 telemetry:
   enabled: true
 vector_stores:
-  embedding_model_id: sentence-transformers/nomic-ai/nomic-embed-text-v1.5
+  default_provider_id: faiss
+  default_embedding_model:
+    provider_id: sentence-transformers
+    model_id: nomic-ai/nomic-embed-text-v1.5

llama_stack/distributions/starter/run.yaml

@@ -255,4 +255,7 @@ server:
 telemetry:
   enabled: true
 vector_stores:
-  embedding_model_id: sentence-transformers/nomic-ai/nomic-embed-text-v1.5
+  default_provider_id: faiss
+  default_embedding_model:
+    provider_id: sentence-transformers
+    model_id: nomic-ai/nomic-embed-text-v1.5

llama_stack/distributions/starter/starter.py

@@ -9,6 +9,7 @@ from typing import Any
 
 from llama_stack.core.datatypes import (
     BuildProvider,
+    DefaultEmbeddingModel,
     Provider,
     ProviderSpec,
     ShieldInput,
@@ -249,7 +250,11 @@ def get_distribution_template(name: str = "starter") -> DistributionTemplate:
                 default_tool_groups=default_tool_groups,
                 default_shields=default_shields,
                 vector_stores_config=VectorStoresConfig(
-                    embedding_model_id="sentence-transformers/nomic-ai/nomic-embed-text-v1.5"
+                    default_provider_id="faiss",
+                    default_embedding_model=DefaultEmbeddingModel(
+                        provider_id="sentence-transformers",
+                        model_id="nomic-ai/nomic-embed-text-v1.5",
+                    ),
                 ),
             ),
         },

tests/integration/vector_io/test_openai_vector_stores.py

@@ -8,9 +8,8 @@ import time
 from io import BytesIO
 
 import pytest
-from llama_stack_client import BadRequestError, NotFoundError
+from llama_stack_client import BadRequestError
 from openai import BadRequestError as OpenAIBadRequestError
-from openai import NotFoundError as OpenAINotFoundError
 
 from llama_stack.apis.vector_io import Chunk
 from llama_stack.core.library_client import LlamaStackAsLibraryClient
@@ -839,7 +838,7 @@ def test_openai_vector_store_list_files_invalid_vector_store(
     if isinstance(compat_client, LlamaStackAsLibraryClient):
         errors = ValueError
     else:
-        errors = (NotFoundError, OpenAINotFoundError)
+        errors = (BadRequestError, OpenAIBadRequestError)
 
     with pytest.raises(errors):
         compat_client.vector_stores.files.list(vector_store_id="abc123")
@@ -1528,11 +1527,11 @@ def test_openai_vector_store_file_batch_error_handling(
             batch_id="non_existent_batch_id",
         )
 
-    # Test operations on non-existent vector store (returns NotFoundError)
+    # Test operations on non-existent vector store (returns BadRequestError)
     if isinstance(compat_client, LlamaStackAsLibraryClient):
         vector_store_errors = ValueError
     else:
-        vector_store_errors = (NotFoundError, OpenAINotFoundError)
+        vector_store_errors = (BadRequestError, OpenAIBadRequestError)
 
     with pytest.raises(vector_store_errors):  # Should raise an error for non-existent vector store
         compat_client.vector_stores.file_batches.create(

tests/unit/core/test_stack_validation.py

@@ -11,7 +11,7 @@ from unittest.mock import AsyncMock
 import pytest
 
 from llama_stack.apis.models import Model, ModelType
-from llama_stack.core.datatypes import StackRunConfig, VectorStoresConfig
+from llama_stack.core.datatypes import DefaultEmbeddingModel, StackRunConfig, VectorStoresConfig
 from llama_stack.core.stack import validate_vector_stores_config
 from llama_stack.providers.datatypes import Api
 
@@ -20,7 +20,15 @@ class TestVectorStoresValidation:
     async def test_validate_missing_model(self):
         """Test validation fails when model not found."""
         run_config = StackRunConfig(
-            image_name="test", providers={}, vector_stores=VectorStoresConfig(embedding_model_id="missing")
+            image_name="test",
+            providers={},
+            vector_stores=VectorStoresConfig(
+                default_provider_id="faiss",
+                default_embedding_model=DefaultEmbeddingModel(
+                    provider_id="p",
+                    model_id="missing",
+                ),
+            ),
         )
         mock_models = AsyncMock()
         mock_models.list_models.return_value = []
@@ -31,12 +39,20 @@ class TestVectorStoresValidation:
     async def test_validate_success(self):
         """Test validation passes with valid model."""
         run_config = StackRunConfig(
-            image_name="test", providers={}, vector_stores=VectorStoresConfig(embedding_model_id="valid")
+            image_name="test",
+            providers={},
+            vector_stores=VectorStoresConfig(
+                default_provider_id="faiss",
+                default_embedding_model=DefaultEmbeddingModel(
+                    provider_id="p",
+                    model_id="valid",
+                ),
+            ),
         )
         mock_models = AsyncMock()
         mock_models.list_models.return_value = [
             Model(
-                identifier="valid",
+                identifier="p/valid",  # Must match provider_id/model_id format
                 model_type=ModelType.embedding,
                 metadata={"embedding_dimension": 768},
                 provider_id="p",