chore: Updating how default embedding model is set in stack (#3818)

# What does this PR do?

Refactor setting default vector store provider and embedding model to
use an optional `vector_stores` config in the `StackRunConfig` and clean
up code to do so (had to add back in some pieces of VectorDB). Also
added remote Qdrant and Weaviate to starter distro (based on other PR
where inference providers were added for UX).

New config is simply (default for Starter distro):

```yaml
vector_stores:
  default_provider_id: faiss
  default_embedding_model:
    provider_id: sentence-transformers
    model_id: nomic-ai/nomic-embed-text-v1.5
```

## Test Plan
CI and Unit tests.

---------

Signed-off-by: Francisco Javier Arceo <farceo@redhat.com>
Co-authored-by: Ashwin Bharambe <ashwin.bharambe@gmail.com>

docs/docs/building_applications/rag.mdx

@@ -88,18 +88,19 @@ 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
-      default_configured: true
+vector_stores:
+  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
-- The system automatically uses the default model and its embedding dimension for any newly created vector store
-- Only one model can be marked as `default_configured: true`
+- `client.vector_stores.create()` works without requiring embedding model or provider parameters
+- The system automatically uses the default vector store provider (`faiss`) when multiple providers are available
+- 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
 
@@ -108,14 +109,15 @@ 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",
-        "embedding_dimension": 768
+        "provider_id": "faiss",  # Optional: specify vector store provider
+        "embedding_model": "sentence-transformers/nomic-ai/nomic-embed-text-v1.5",
+        "embedding_dimension": 768  # Optional: will be auto-detected if not provided
     }
 )
 ```