undo vector_store_id in params

llama_stack/apis/vector_io/vector_io.py

@@ -491,13 +491,11 @@ class OpenAICreateVectorStoreRequestWithExtraBody(BaseModel, extra="allow"):
 class OpenAICreateVectorStoreFileBatchRequestWithExtraBody(BaseModel, extra="allow"):
     """Request to create a vector store file batch with extra_body support.
 
-    :param vector_store_id: The ID of the vector store to create the file batch for
     :param file_ids: A list of File IDs that the vector store should use
     :param attributes: (Optional) Key-value attributes to store with the files
     :param chunking_strategy: (Optional) The chunking strategy used to chunk the file(s). Defaults to auto
     """
 
-    vector_store_id: str
     file_ids: list[str]
     attributes: dict[str, Any] | None = None
     chunking_strategy: VectorStoreChunkingStrategy | None = None
@@ -849,11 +847,13 @@ class VectorIO(Protocol):
     )
     async def openai_create_vector_store_file_batch(
         self,
+        vector_store_id: str,
         params: Annotated[OpenAICreateVectorStoreFileBatchRequestWithExtraBody, Body(...)],
     ) -> VectorStoreFileBatchObject:
         """Create a vector store file batch.
 
         Generate an OpenAI-compatible vector store file batch for the given vector store.
+        :param vector_store_id: The ID of the vector store to create the file batch for.
         :returns: A VectorStoreFileBatchObject representing the created file batch.
         """
         ...

llama_stack/core/library_client.py

@@ -513,6 +513,14 @@ class AsyncLlamaStackAsLibraryClient(AsyncLlamaStackClient):
         # Strip NOT_GIVENs to use the defaults in signature
         body = {k: v for k, v in body.items() if v is not NOT_GIVEN}
 
+        # Check if there's an unwrapped body parameter among multiple parameters
+        # (e.g., path param + body param like: vector_store_id: str, params: Annotated[Model, Body(...)])
+        unwrapped_body_param = None
+        for param in params_list:
+            if is_unwrapped_body_param(param.annotation):
+                unwrapped_body_param = param
+                break
+
         # Convert parameters to Pydantic models where needed
         converted_body = {}
         for param_name, param in sig.parameters.items():
@@ -522,5 +530,11 @@ class AsyncLlamaStackAsLibraryClient(AsyncLlamaStackClient):
                     converted_body[param_name] = value
                 else:
                     converted_body[param_name] = convert_to_pydantic(param.annotation, value)
+            elif unwrapped_body_param and param.name == unwrapped_body_param.name:
+                # This is the unwrapped body param - construct it from remaining body keys
+                base_type = get_args(param.annotation)[0]
+                # Extract only the keys that aren't already used by other params
+                remaining_keys = {k: v for k, v in body.items() if k not in converted_body}
+                converted_body[param.name] = base_type(**remaining_keys)
 
         return converted_body

llama_stack/core/routers/vector_io.py

@@ -383,13 +383,14 @@ class VectorIORouter(VectorIO):
 
     async def openai_create_vector_store_file_batch(
         self,
+        vector_store_id: str,
         params: Annotated[OpenAICreateVectorStoreFileBatchRequestWithExtraBody, Body(...)],
     ) -> VectorStoreFileBatchObject:
         logger.debug(
-            f"VectorIORouter.openai_create_vector_store_file_batch: {params.vector_store_id}, {len(params.file_ids)} files"
+            f"VectorIORouter.openai_create_vector_store_file_batch: {vector_store_id}, {len(params.file_ids)} files"
         )
-        provider = await self.routing_table.get_provider_impl(params.vector_store_id)
-        return await provider.openai_create_vector_store_file_batch(params)
+        provider = await self.routing_table.get_provider_impl(vector_store_id)
+        return await provider.openai_create_vector_store_file_batch(vector_store_id, params)
 
     async def openai_retrieve_vector_store_file_batch(
         self,

llama_stack/providers/utils/memory/openai_vector_store_mixin.py

@@ -978,10 +978,10 @@ class OpenAIVectorStoreMixin(ABC):
 
     async def openai_create_vector_store_file_batch(
         self,
+        vector_store_id: str,
         params: Annotated[OpenAICreateVectorStoreFileBatchRequestWithExtraBody, Body(...)],
     ) -> VectorStoreFileBatchObject:
         """Create a vector store file batch."""
-        vector_store_id = params.vector_store_id
         if vector_store_id not in self.openai_vector_stores:
             raise VectorStoreNotFoundError(vector_store_id)