Merge 0fb6fecedb into sapling-pr-archive-ehhuang

src/llama_stack/apis/vector_io/vector_io.py

@@ -260,7 +260,7 @@ class VectorStoreSearchResponsePage(BaseModel):
     """
 
     object: str = "vector_store.search_results.page"
-    search_query: str
+    search_query: list[str]
     data: list[VectorStoreSearchResponse]
     has_more: bool = False
     next_page: str | None = None
@@ -478,7 +478,7 @@ class OpenAICreateVectorStoreRequestWithExtraBody(BaseModel, extra="allow"):
     name: str | None = None
     file_ids: list[str] | None = None
     expires_after: dict[str, Any] | None = None
-    chunking_strategy: dict[str, Any] | None = None
+    chunking_strategy: VectorStoreChunkingStrategy | None = None
     metadata: dict[str, Any] | None = None
 
 

src/llama_stack/core/routers/vector_io.py

@@ -20,6 +20,8 @@ from llama_stack.apis.vector_io import (
     SearchRankingOptions,
     VectorIO,
     VectorStoreChunkingStrategy,
+    VectorStoreChunkingStrategyStatic,
+    VectorStoreChunkingStrategyStaticConfig,
     VectorStoreDeleteResponse,
     VectorStoreFileBatchObject,
     VectorStoreFileContentsResponse,
@@ -167,6 +169,13 @@ class VectorIORouter(VectorIO):
         if embedding_dimension is not None:
             params.model_extra["embedding_dimension"] = embedding_dimension
 
+        # Set chunking strategy explicitly if not provided
+        if params.chunking_strategy is None or params.chunking_strategy.type == "auto":
+            # actualize the chunking strategy to static
+            params.chunking_strategy = VectorStoreChunkingStrategyStatic(
+                static=VectorStoreChunkingStrategyStaticConfig()
+            )
+
         return await provider.openai_create_vector_store(params)
 
     async def openai_list_vector_stores(