fix(vector-io): handle missing document_id in insert_chunks

Fixed KeyError when chunks don't have document_id in metadata or chunk_metadata.
Updated logging to safely extract document_id using getattr and RAG memory
to handle different document_id locations. Added test for missing document_id scenarios.

Fixes issue #3494 where /v1/vector-io/insert would crash with KeyError.

llama_stack/core/routers/vector_io.py

@@ -101,11 +101,15 @@ class VectorIORouter(VectorIO):
         chunks: list[Chunk],
         ttl_seconds: int | None = None,
     ) -> None:
+        doc_ids = [
+            getattr(chunk.chunk_metadata, "document_id", None) if chunk.chunk_metadata else None for chunk in chunks[:3]
+        ]
         logger.debug(
-            f"VectorIORouter.insert_chunks: {vector_db_id}, {len(chunks)} chunks, ttl_seconds={ttl_seconds}, chunk_ids={[chunk.metadata['document_id'] for chunk in chunks[:3]]}{' and more...' if len(chunks) > 3 else ''}",
+            f"VectorIORouter.insert_chunks: {vector_db_id}, {len(chunks)} chunks, "
+            f"ttl_seconds={ttl_seconds}, chunk_ids={doc_ids}{' and more...' if len(chunks) > 3 else ''}"
         )
         provider = await self.routing_table.get_provider_impl(vector_db_id)
-        return await provider.insert_chunks(vector_db_id, chunks, ttl_seconds)
+        await provider.insert_chunks(vector_db_id, chunks, ttl_seconds)
 
     async def query_chunks(
         self,

llama_stack/providers/inline/tool_runtime/rag/memory.py

@@ -279,7 +279,10 @@ class MemoryToolRuntimeImpl(ToolGroupsProtocolPrivate, ToolRuntime, RAGToolRunti
         return RAGQueryResult(
             content=picked,
             metadata={
-                "document_ids": [c.metadata["document_id"] for c in chunks[: len(picked)]],
+                "document_ids": [
+                    c.metadata.get("document_id") or (c.chunk_metadata.document_id if c.chunk_metadata else None)
+                    for c in chunks[: len(picked)]
+                ],
                 "chunks": [c.content for c in chunks[: len(picked)]],
                 "scores": scores[: len(picked)],
                 "vector_db_ids": [c.metadata["vector_db_id"] for c in chunks[: len(picked)]],

tests/unit/providers/vector_io/test_vector_io_openai_vector_stores.py

@@ -113,6 +113,25 @@ async def test_insert_chunks_missing_db_raises(vector_io_adapter):
         await vector_io_adapter.insert_chunks("db_not_exist", [])
 
 
+async def test_insert_chunks_with_missing_document_id(vector_io_adapter):
+    """Ensure no KeyError when document_id is missing or in different places."""
+    from llama_stack.apis.vector_io import Chunk, ChunkMetadata
+
+    fake_index = AsyncMock()
+    vector_io_adapter.cache["db1"] = fake_index
+
+    # Various document_id scenarios that shouldn't crash
+    chunks = [
+        Chunk(content="has doc_id in metadata", metadata={"document_id": "doc-1"}),
+        Chunk(content="no doc_id anywhere", metadata={"source": "test"}),
+        Chunk(content="doc_id in chunk_metadata", chunk_metadata=ChunkMetadata(document_id="doc-3")),
+    ]
+
+    # Should work without KeyError
+    await vector_io_adapter.insert_chunks("db1", chunks)
+    fake_index.insert_chunks.assert_awaited_once()
+
+
 async def test_query_chunks_calls_underlying_index_and_returns(vector_io_adapter):
     expected = QueryChunksResponse(chunks=[Chunk(content="c1")], scores=[0.1])
     fake_index = AsyncMock(query_chunks=AsyncMock(return_value=expected))