feat!: Migrate Vector DB IDs to Vector Store IDs (breaking change) (#3253)

# What does this PR do?
This change migrates the VectorDB id generation to Vector Stores.

This is a breaking change for **_some users_** that may have application
code using the `vector_db_id` parameter in the request of the VectorDB
protocol instead of the `VectorDB.identifier` in the response.

By default we will now create a Vector Store every time we register a
VectorDB. The caveat with this approach is that this maps the
`vector_db_id` → `vector_store.name`. This is a reasonable tradeoff to
transition users towards OpenAI Vector Stores.

As an added benefit, registering VectorDBs will result in them appearing
in the VectorStores admin UI.

### Why?
This PR makes the `POST` API call to `/v1/vector-dbs` swap the
`vector_db_id` parameter in the **request body** into the VectorStore's
name field and sets the `vector_db_id` to the generated vector store id
(e.g., `vs_038247dd-4bbb-4dbb-a6be-d5ecfd46cfdb`).

That means that users would have to do something like follows in their
application code:

```python
res = client.vector_dbs.register(
    vector_db_id='my-vector-db-id', 
    embedding_model='ollama/all-minilm:l6-v2', 
    embedding_dimension=384,
)
vector_db_id = res.identifier
```

And then the rest of their code would behave, including `VectorIO`'s
insert protocol using `vector_db_id` in the request.

An alternative implementation would be to just delete the `vector_db_id`
parameter in `VectorDB` but the end result would still require users
having to write `vector_db_id = res.identifier` since
`VectorStores.create()` generates the ID for you.

So this approach felt the easiest way to migrate users towards
VectorStores (subsequent PRs will be added to trigger `files.create()`
and `vector_stores.files.create()`).

## Test Plan
Unit tests and integration tests have been added.

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

llama_stack/core/routing_tables/vector_dbs.py

@@ -52,7 +52,6 @@ class VectorDBsRoutingTable(CommonRoutingTableImpl, VectorDBs):
         provider_vector_db_id: str | None = None,
         vector_db_name: str | None = None,
     ) -> VectorDB:
-        provider_vector_db_id = provider_vector_db_id or vector_db_id
         if provider_id is None:
             if len(self.impls_by_provider_id) > 0:
                 provider_id = list(self.impls_by_provider_id.keys())[0]
@@ -69,14 +68,33 @@ class VectorDBsRoutingTable(CommonRoutingTableImpl, VectorDBs):
             raise ModelTypeError(embedding_model, model.model_type, ModelType.embedding)
         if "embedding_dimension" not in model.metadata:
             raise ValueError(f"Model {embedding_model} does not have an embedding dimension")
+
+        provider = self.impls_by_provider_id[provider_id]
+        logger.warning(
+            "VectorDB is being deprecated in future releases in favor of VectorStore. Please migrate your usage accordingly."
+        )
+        vector_store = await provider.openai_create_vector_store(
+            name=vector_db_name or vector_db_id,
+            embedding_model=embedding_model,
+            embedding_dimension=model.metadata["embedding_dimension"],
+            provider_id=provider_id,
+            provider_vector_db_id=provider_vector_db_id,
+        )
+
+        vector_store_id = vector_store.id
+        actual_provider_vector_db_id = provider_vector_db_id or vector_store_id
+        logger.warning(
+            f"Ignoring vector_db_id {vector_db_id} and using vector_store_id {vector_store_id} instead. Setting VectorDB {vector_db_id} to VectorDB.vector_db_name"
+        )
+
         vector_db_data = {
-            "identifier": vector_db_id,
+            "identifier": vector_store_id,
             "type": ResourceType.vector_db.value,
             "provider_id": provider_id,
-            "provider_resource_id": provider_vector_db_id,
+            "provider_resource_id": actual_provider_vector_db_id,
             "embedding_model": embedding_model,
             "embedding_dimension": model.metadata["embedding_dimension"],
-            "vector_db_name": vector_db_name,
+            "vector_db_name": vector_store.name,
         }
         vector_db = TypeAdapter(VectorDBWithOwner).validate_python(vector_db_data)
         await self.register_object(vector_db)