fix(vector-io): handle missing document_id in insert_chunks (#3521)

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.
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.

 # What does this PR do?

Fixes a KeyError crash in `/v1/vector-io/insert` when chunks are missing
`document_id` fields. The API
was failing even though `document_id` is optional according to the
schema.

  Closes #3494

  ## Test Plan

  **Before fix:**
  - POST to `/v1/vector-io/insert` with chunks → 500 KeyError
  - Happened regardless of where `document_id` was placed

  **After fix:**
  - Same request works fine → 200 OK
  - Tested with Postman using FAISS backend
  - Added unit test covering missing `document_id` scenarios

llama_stack/apis/vector_io/vector_io.py

@@ -93,6 +93,22 @@ class Chunk(BaseModel):
 
         return generate_chunk_id(str(uuid.uuid4()), str(self.content))
 
+    @property
+    def document_id(self) -> str | None:
+        """Returns the document_id from either metadata or chunk_metadata, with metadata taking precedence."""
+        # Check metadata first (takes precedence)
+        doc_id = self.metadata.get("document_id")
+        if doc_id is not None:
+            if not isinstance(doc_id, str):
+                raise TypeError(f"metadata['document_id'] must be a string, got {type(doc_id).__name__}: {doc_id!r}")
+            return doc_id
+
+        # Fall back to chunk_metadata if available (Pydantic ensures type safety)
+        if self.chunk_metadata is not None:
+            return self.chunk_metadata.document_id
+
+        return None
+
 
 @json_schema_type
 class QueryChunksResponse(BaseModel):

llama_stack/core/routers/vector_io.py

@@ -93,8 +93,10 @@ class VectorIORouter(VectorIO):
         chunks: list[Chunk],
         ttl_seconds: int | None = None,
     ) -> None:
+        doc_ids = [chunk.document_id 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)

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

@@ -272,7 +272,7 @@ 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.document_id 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

@@ -128,6 +128,37 @@ 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_document_id_with_invalid_type_raises_error():
+    """Ensure TypeError is raised when document_id is not a string."""
+    from llama_stack.apis.vector_io import Chunk
+
+    # Integer document_id should raise TypeError
+    chunk = Chunk(content="test", metadata={"document_id": 12345})
+    with pytest.raises(TypeError) as exc_info:
+        _ = chunk.document_id
+    assert "metadata['document_id'] must be a string" in str(exc_info.value)
+    assert "got int" in str(exc_info.value)
+
+
 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))