adjusting vector_db_id

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

docs/_static/llama-stack-spec.html

@@ -13593,16 +13593,11 @@
                     "provider_id": {
                         "type": "string",
                         "description": "The ID of the provider to use for this vector store."
-                    },
-                    "provider_vector_db_id": {
-                        "type": "string",
-                        "description": "The provider-specific vector database ID."
                     }
                 },
                 "additionalProperties": false,
                 "required": [
-                    "name",
+                    "name"
-                    "provider_vector_db_id"
                 ],
                 "title": "OpenaiCreateVectorStoreRequest"
             },

docs/_static/llama-stack-spec.yaml

@@ -9496,14 +9496,9 @@ components:
           type: string
           description: >-
             The ID of the provider to use for this vector store.
-        provider_vector_db_id:
-          type: string
-          description: >-
-            The provider-specific vector database ID.
       additionalProperties: false
       required:
         - name
-        - provider_vector_db_id
       title: OpenaiCreateVectorStoreRequest
     VectorStoreFileCounts:
       type: object

llama_stack/apis/vector_io/vector_io.py

@@ -346,7 +346,6 @@ class VectorIO(Protocol):
         embedding_model: str | None = None,
         embedding_dimension: int | None = 384,
         provider_id: str | None = None,
-        provider_vector_db_id: str = "",
     ) -> VectorStoreObject:
         """Creates a vector store.
 
@@ -358,7 +357,6 @@ class VectorIO(Protocol):
         :param embedding_model: The embedding model to use for this vector store.
         :param embedding_dimension: The dimension of the embedding vectors (default: 384).
         :param provider_id: The ID of the provider to use for this vector store.
-        :param provider_vector_db_id: The provider-specific vector database ID.
         :returns: A VectorStoreObject representing the created vector store.
         """
         ...

llama_stack/distribution/routers/vector_io.py

@@ -126,7 +126,6 @@ class VectorIORouter(VectorIO):
         embedding_model: str | None = None,
         embedding_dimension: int | None = None,
         provider_id: str | None = None,
-        provider_vector_db_id: str = "",
     ) -> VectorStoreObject:
         logger.debug(f"VectorIORouter.openai_create_vector_store: name={name}, provider_id={provider_id}")
 
@@ -144,7 +143,7 @@ class VectorIORouter(VectorIO):
             embedding_model,
             embedding_dimension,
             provider_id,
-            provider_vector_db_id,
+            vector_db_id,
             name,
         )
         return await self.routing_table.get_provider_impl(registered_vector_db.identifier).openai_create_vector_store(

llama_stack/providers/remote/vector_io/pgvector/pgvector.py

@@ -247,7 +247,6 @@ class PGVectorVectorIOAdapter(VectorIO, VectorDBsProtocolPrivate):
         embedding_model: str | None = None,
         embedding_dimension: int | None = 384,
         provider_id: str | None = None,
-        provider_vector_db_id: str = "",
     ) -> VectorStoreObject:
         raise NotImplementedError("OpenAI Vector Stores API is not supported in PGVector")
 

llama_stack/providers/remote/vector_io/qdrant/qdrant.py

@@ -214,7 +214,6 @@ class QdrantVectorIOAdapter(VectorIO, VectorDBsProtocolPrivate):
         embedding_model: str | None = None,
         embedding_dimension: int | None = 384,
         provider_id: str | None = None,
-        provider_vector_db_id: str | None = None,
     ) -> VectorStoreObject:
         raise NotImplementedError("OpenAI Vector Stores API is not supported in Qdrant")