chore: improve type checking

api/core/app/entities/queue_entities.py

@@ -7,9 +7,8 @@ from pydantic import BaseModel, ConfigDict, Field
 
 from core.model_runtime.entities.llm_entities import LLMResult, LLMResultChunk
 from core.rag.entities.citation_metadata import RetrievalSourceMetadata
-from core.workflow.entities import AgentNodeStrategyInit
+from core.workflow.entities import AgentNodeStrategyInit, ToolCall, ToolResult
 from core.workflow.enums import WorkflowNodeExecutionMetadataKey
-from core.workflow.graph_events import ToolCall, ToolResult
 from core.workflow.nodes import NodeType
 
 

api/core/callback_handler/index_tool_callback_handler.py

@@ -5,7 +5,6 @@ from sqlalchemy import select
 
 from core.app.apps.base_app_queue_manager import AppQueueManager, PublishFrom
 from core.app.entities.app_invoke_entities import InvokeFrom
-from core.app.entities.queue_entities import QueueRetrieverResourcesEvent
 from core.rag.entities.citation_metadata import RetrievalSourceMetadata
 from core.rag.index_processor.constant.index_type import IndexStructureType
 from core.rag.models.document import Document
@@ -90,6 +89,8 @@ class DatasetIndexToolCallbackHandler:
     # TODO(-LAN-): Improve type check
     def return_retriever_resource_info(self, resource: Sequence[RetrievalSourceMetadata]):
         """Handle return_retriever_resource_info."""
+        from core.app.entities.queue_entities import QueueRetrieverResourcesEvent
+
         self._queue_manager.publish(
             QueueRetrieverResourcesEvent(retriever_resources=resource), PublishFrom.APPLICATION_MANAGER
         )

api/tests/unit_tests/core/workflow/nodes/test_llm_node_streaming.py

@@ -0,0 +1,148 @@
+import types
+from collections.abc import Generator
+from typing import Any
+
+import pytest
+
+from core.model_runtime.entities.llm_entities import LLMUsage
+from core.workflow.entities import ToolCallResult
+from core.workflow.entities.tool_entities import ToolResultStatus
+from core.workflow.node_events import ModelInvokeCompletedEvent, NodeEventBase
+from core.workflow.nodes.llm.node import LLMNode
+
+
+class _StubModelInstance:
+    """Minimal stub to satisfy _stream_llm_events signature."""
+
+    provider_model_bundle = None
+
+
+def _drain(generator: Generator[NodeEventBase, None, Any]):
+    events: list = []
+    try:
+        while True:
+            events.append(next(generator))
+    except StopIteration as exc:
+        return events, exc.value
+
+
+@pytest.fixture(autouse=True)
+def patch_deduct_llm_quota(monkeypatch):
+    # Avoid touching real quota logic during unit tests
+    monkeypatch.setattr("core.workflow.nodes.llm.node.llm_utils.deduct_llm_quota", lambda **_: None)
+
+
+def _make_llm_node(reasoning_format: str) -> LLMNode:
+    node = LLMNode.__new__(LLMNode)
+    object.__setattr__(node, "_node_data", types.SimpleNamespace(reasoning_format=reasoning_format, tools=[]))
+    object.__setattr__(node, "tenant_id", "tenant")
+    return node
+
+
+def test_stream_llm_events_extracts_reasoning_for_tagged():
+    node = _make_llm_node(reasoning_format="tagged")
+    tagged_text = "<think>Thought</think>Answer"
+    usage = LLMUsage.empty_usage()
+
+    def generator():
+        yield ModelInvokeCompletedEvent(
+            text=tagged_text,
+            usage=usage,
+            finish_reason="stop",
+            reasoning_content="",
+            structured_output=None,
+        )
+
+    events, returned = _drain(
+        node._stream_llm_events(generator(), model_instance=types.SimpleNamespace(provider_model_bundle=None))
+    )
+
+    assert events == []
+    clean_text, reasoning_content, gen_reasoning, gen_clean, ret_usage, finish_reason, structured, gen_data = returned
+    assert clean_text == tagged_text  # original preserved for output
+    assert reasoning_content == ""  # tagged mode keeps reasoning separate
+    assert gen_clean == "Answer"  # stripped content for generation
+    assert gen_reasoning == "Thought"  # reasoning extracted from <think> tag
+    assert ret_usage == usage
+    assert finish_reason == "stop"
+    assert structured is None
+    assert gen_data is None
+
+    # generation building should include reasoning and sequence
+    generation_content = gen_clean or clean_text
+    sequence = [
+        {"type": "reasoning", "index": 0},
+        {"type": "content", "start": 0, "end": len(generation_content)},
+    ]
+    assert sequence == [
+        {"type": "reasoning", "index": 0},
+        {"type": "content", "start": 0, "end": len("Answer")},
+    ]
+
+
+def test_stream_llm_events_no_reasoning_results_in_empty_sequence():
+    node = _make_llm_node(reasoning_format="tagged")
+    plain_text = "Hello world"
+    usage = LLMUsage.empty_usage()
+
+    def generator():
+        yield ModelInvokeCompletedEvent(
+            text=plain_text,
+            usage=usage,
+            finish_reason=None,
+            reasoning_content="",
+            structured_output=None,
+        )
+
+    events, returned = _drain(
+        node._stream_llm_events(generator(), model_instance=types.SimpleNamespace(provider_model_bundle=None))
+    )
+
+    assert events == []
+    _, _, gen_reasoning, gen_clean, *_ = returned
+    generation_content = gen_clean or plain_text
+    assert gen_reasoning == ""
+    assert generation_content == plain_text
+    # Empty reasoning should imply empty sequence in generation construction
+    sequence = []
+    assert sequence == []
+
+
+def test_serialize_tool_call_strips_files_to_ids():
+    file_cls = pytest.importorskip("core.file").File
+    file_type = pytest.importorskip("core.file.enums").FileType
+    transfer_method = pytest.importorskip("core.file.enums").FileTransferMethod
+
+    file_with_id = file_cls(
+        id="f1",
+        tenant_id="t",
+        type=file_type.IMAGE,
+        transfer_method=transfer_method.REMOTE_URL,
+        remote_url="http://example.com/f1",
+        storage_key="k1",
+    )
+    file_with_related = file_cls(
+        id=None,
+        tenant_id="t",
+        type=file_type.IMAGE,
+        transfer_method=transfer_method.REMOTE_URL,
+        related_id="rel2",
+        remote_url="http://example.com/f2",
+        storage_key="k2",
+    )
+    tool_call = ToolCallResult(
+        id="tc",
+        name="do",
+        arguments='{"a":1}',
+        output="ok",
+        files=[file_with_id, file_with_related],
+        status=ToolResultStatus.SUCCESS,
+    )
+
+    serialized = LLMNode._serialize_tool_call(tool_call)
+
+    assert serialized["files"] == ["f1", "rel2"]
+    assert serialized["id"] == "tc"
+    assert serialized["name"] == "do"
+    assert serialized["arguments"] == '{"a":1}'
+    assert serialized["output"] == "ok"