chore!: BREAKING CHANGE: vector_db_id -> vector_store_id (#3923)

# What does this PR do?


## Test Plan
CI
vector_io tests will fail until next client sync

passed with
https://github.com/llamastack/llama-stack-client-python/pull/286 checked
out locally

src/llama_stack/apis/agents/agents.py

@@ -149,13 +149,13 @@ class ShieldCallStep(StepCommon):
 class MemoryRetrievalStep(StepCommon):
     """A memory retrieval step in an agent turn.
 
-    :param vector_db_ids: The IDs of the vector databases to retrieve context from.
+    :param vector_store_ids: The IDs of the vector databases to retrieve context from.
     :param inserted_context: The context retrieved from the vector databases.
     """
 
     step_type: Literal[StepType.memory_retrieval] = StepType.memory_retrieval
     # TODO: should this be List[str]?
-    vector_db_ids: str
+    vector_store_ids: str
     inserted_context: InterleavedContent
 
 

src/llama_stack/apis/tools/rag_tool.py

@@ -190,13 +190,13 @@ class RAGToolRuntime(Protocol):
     async def insert(
         self,
         documents: list[RAGDocument],
-        vector_db_id: str,
+        vector_store_id: str,
         chunk_size_in_tokens: int = 512,
     ) -> None:
         """Index documents so they can be used by the RAG system.
 
         :param documents: List of documents to index in the RAG system
-        :param vector_db_id: ID of the vector database to store the document embeddings
+        :param vector_store_id: ID of the vector database to store the document embeddings
         :param chunk_size_in_tokens: (Optional) Size in tokens for document chunking during indexing
         """
         ...
@@ -205,13 +205,13 @@ class RAGToolRuntime(Protocol):
     async def query(
         self,
         content: InterleavedContent,
-        vector_db_ids: list[str],
+        vector_store_ids: list[str],
         query_config: RAGQueryConfig | None = None,
     ) -> RAGQueryResult:
         """Query the RAG system for context; typically invoked by the agent.
 
         :param content: The query content to search for in the indexed documents
-        :param vector_db_ids: List of vector database IDs to search within
+        :param vector_store_ids: List of vector database IDs to search within
         :param query_config: (Optional) Configuration parameters for the query operation
         :returns: RAGQueryResult containing the retrieved content and metadata
         """

src/llama_stack/apis/vector_io/vector_io.py

@@ -529,17 +529,17 @@ class VectorIO(Protocol):
 
     # this will just block now until chunks are inserted, but it should
     # probably return a Job instance which can be polled for completion
-    # TODO: rename vector_db_id to vector_store_id once Stainless is working
+    # TODO: rename vector_store_id to vector_store_id once Stainless is working
     @webmethod(route="/vector-io/insert", method="POST", level=LLAMA_STACK_API_V1)
     async def insert_chunks(
         self,
-        vector_db_id: str,
+        vector_store_id: str,
         chunks: list[Chunk],
         ttl_seconds: int | None = None,
     ) -> None:
         """Insert chunks into a vector database.
 
-        :param vector_db_id: The identifier of the vector database to insert the chunks into.
+        :param vector_store_id: The identifier of the vector database to insert the chunks into.
         :param chunks: The chunks to insert. Each `Chunk` should contain content which can be interleaved text, images, or other types.
             `metadata`: `dict[str, Any]` and `embedding`: `List[float]` are optional.
             If `metadata` is provided, you configure how Llama Stack formats the chunk during generation.
@@ -548,17 +548,17 @@ class VectorIO(Protocol):
         """
         ...
 
-    # TODO: rename vector_db_id to vector_store_id once Stainless is working
+    # TODO: rename vector_store_id to vector_store_id once Stainless is working
     @webmethod(route="/vector-io/query", method="POST", level=LLAMA_STACK_API_V1)
     async def query_chunks(
         self,
-        vector_db_id: str,
+        vector_store_id: str,
         query: InterleavedContent,
         params: dict[str, Any] | None = None,
     ) -> QueryChunksResponse:
         """Query chunks from a vector database.
 
-        :param vector_db_id: The identifier of the vector database to query.
+        :param vector_store_id: The identifier of the vector database to query.
         :param query: The query to search for.
         :param params: The parameters of the query.
         :returns: A QueryChunksResponse.