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fix: fix structured_output_enabled miss in second validate (#35747)
Co-authored-by: autofix-ci[bot] <114827586+autofix-ci[bot]@users.noreply.github.com> Co-authored-by: Yunlu Wen <yunlu.wen@dify.ai>
This commit is contained in:
wangxiaolei
fatelei
parent
9d96e6e520
commit
29388b2a89
@ -365,7 +365,8 @@ class DifyNodeFactory(NodeFactory):
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(including pydantic ValidationError, which subclasses ValueError),
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if node type is unknown, or if no implementation exists for the resolved version
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"""
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typed_node_config = NodeConfigDictAdapter.validate_python(adapt_node_config_for_graph(node_config))
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adapted_node_config = adapt_node_config_for_graph(node_config)
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typed_node_config = NodeConfigDictAdapter.validate_python(adapted_node_config)
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node_id = typed_node_config["id"]
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node_data = typed_node_config["data"]
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node_class = self._resolve_node_class(node_type=node_data.type, node_version=str(node_data.version))
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@ -373,6 +374,11 @@ class DifyNodeFactory(NodeFactory):
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# Re-validate using the resolved node class so workflow-local node schemas
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# stay explicit and constructors receive the concrete typed payload.
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resolved_node_data = self._validate_resolved_node_data(node_class, node_data)
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config_for_node_init: BaseNodeData | dict[str, Any]
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if isinstance(resolved_node_data, BaseNodeData):
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config_for_node_init = resolved_node_data.model_dump(mode="python", by_alias=True)
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else:
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config_for_node_init = resolved_node_data
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node_type = node_data.type
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node_init_kwargs_factories: Mapping[NodeType, Callable[[], dict[str, object]]] = {
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BuiltinNodeTypes.CODE: lambda: {
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@ -442,7 +448,7 @@ class DifyNodeFactory(NodeFactory):
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node_init_kwargs = node_init_kwargs_factories.get(node_type, lambda: {})()
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return node_class(
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node_id=node_id,
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config=resolved_node_data,
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config=config_for_node_init,
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graph_init_params=self.graph_init_params,
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graph_runtime_state=self.graph_runtime_state,
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**node_init_kwargs,
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@ -474,10 +480,7 @@ class DifyNodeFactory(NodeFactory):
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include_retriever_attachment_loader: bool,
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include_jinja2_template_renderer: bool,
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) -> dict[str, object]:
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validated_node_data = cast(
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LLMCompatibleNodeData,
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self._validate_resolved_node_data(node_class=node_class, node_data=node_data),
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)
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validated_node_data = cast(LLMCompatibleNodeData, node_data)
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model_instance = self._build_model_instance_for_llm_node(validated_node_data)
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node_init_kwargs: dict[str, object] = {
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"credentials_provider": self._llm_credentials_provider,
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@ -5,6 +5,7 @@ from graphon.graph_events import (
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NodeRunStreamChunkEvent,
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)
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from .test_mock_config import MockConfigBuilder
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from .test_table_runner import TableTestRunner
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@ -44,3 +45,51 @@ def test_tool_in_chatflow():
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assert stream_chunk_events[0].chunk == "hello, dify!", (
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f"Expected chunk to be 'hello, dify!', but got {stream_chunk_events[0].chunk}"
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)
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def test_answer_can_render_llm_structured_output_in_chatflow():
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runner = TableTestRunner()
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fixture_data = runner.workflow_runner.load_fixture("basic_chatflow")
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nodes = fixture_data["workflow"]["graph"]["nodes"]
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answer_node = next(node for node in nodes if node["id"] == "answer")
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answer_node["data"]["answer"] = "{{#llm.structured_output#}}"
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mock_config = (
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MockConfigBuilder()
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.with_node_output(
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"llm",
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{
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"text": "plain text",
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"structured_output": {"type": "greeting"},
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"usage": {
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"prompt_tokens": 10,
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"completion_tokens": 5,
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"total_tokens": 15,
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},
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"finish_reason": "stop",
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},
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)
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.build()
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)
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graph, graph_runtime_state = runner.workflow_runner.create_graph_from_fixture(
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fixture_data=fixture_data,
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query="hello",
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use_mock_factory=True,
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mock_config=mock_config,
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)
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engine = GraphEngine(
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workflow_id="test_workflow",
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graph=graph,
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graph_runtime_state=graph_runtime_state,
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command_channel=InMemoryChannel(),
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config=GraphEngineConfig(),
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)
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events = list(engine.run())
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success_events = [e for e in events if isinstance(e, GraphRunSucceededEvent)]
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assert success_events, "Workflow should complete successfully"
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assert success_events[-1].outputs["answer"] == '{\n "type": "greeting"\n}'
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@ -86,3 +86,80 @@ def test_execute_answer():
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assert result.status == WorkflowNodeExecutionStatus.SUCCEEDED
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assert result.outputs["answer"] == "Today's weather is sunny\nYou are a helpful AI.\n{{img}}\nFin."
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def test_execute_answer_renders_structured_output_object_as_json() -> None:
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init_params = build_test_graph_init_params(
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workflow_id="1",
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graph_config={"nodes": [], "edges": []},
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tenant_id="1",
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app_id="1",
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user_id="1",
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user_from=UserFrom.ACCOUNT,
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invoke_from=InvokeFrom.DEBUGGER,
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call_depth=0,
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)
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variable_pool = VariablePool(
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system_variables=build_system_variables(user_id="aaa", files=[]),
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user_inputs={},
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environment_variables=[],
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conversation_variables=[],
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)
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variable_pool.add(["1777539038857", "structured_output"], {"type": "greeting"})
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graph_runtime_state = GraphRuntimeState(variable_pool=variable_pool, start_at=time.perf_counter())
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node = AnswerNode(
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node_id=str(uuid.uuid4()),
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graph_init_params=init_params,
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graph_runtime_state=graph_runtime_state,
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config=AnswerNodeData(
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title="123",
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type="answer",
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answer="{{#1777539038857.structured_output#}}",
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),
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)
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result = node._run()
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assert result.status == WorkflowNodeExecutionStatus.SUCCEEDED
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assert result.outputs["answer"] == '{\n "type": "greeting"\n}'
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def test_execute_answer_falls_back_to_plain_selector_text_when_structured_output_missing() -> None:
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init_params = build_test_graph_init_params(
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workflow_id="1",
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graph_config={"nodes": [], "edges": []},
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tenant_id="1",
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app_id="1",
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user_id="1",
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user_from=UserFrom.ACCOUNT,
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invoke_from=InvokeFrom.DEBUGGER,
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call_depth=0,
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)
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variable_pool = VariablePool(
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system_variables=build_system_variables(user_id="aaa", files=[]),
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user_inputs={},
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environment_variables=[],
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conversation_variables=[],
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)
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graph_runtime_state = GraphRuntimeState(variable_pool=variable_pool, start_at=time.perf_counter())
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node = AnswerNode(
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node_id=str(uuid.uuid4()),
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graph_init_params=init_params,
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graph_runtime_state=graph_runtime_state,
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config=AnswerNodeData(
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title="123",
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type="answer",
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answer="{{#1777539038857.structured_output#}}",
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),
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)
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result = node._run()
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assert result.status == WorkflowNodeExecutionStatus.SUCCEEDED
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assert result.outputs["answer"] == "1777539038857.structured_output"
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@ -10,14 +10,20 @@ from core.workflow.nodes.knowledge_index import KNOWLEDGE_INDEX_NODE_TYPE
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from graphon.entities.base_node_data import BaseNodeData
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from graphon.enums import BuiltinNodeTypes, NodeType
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from graphon.nodes.code.entities import CodeLanguage
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from graphon.nodes.llm.entities import LLMNodeData
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from graphon.variables.segments import StringSegment
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def _assert_typed_node_config(config, *, node_id: str, node_type: NodeType, version: str = "1") -> None:
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_ = node_id
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assert isinstance(config, BaseNodeData)
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assert config.type == node_type
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assert config.version == version
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if isinstance(config, BaseNodeData):
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assert config.type == node_type
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assert config.version == version
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return
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assert isinstance(config, dict)
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assert config["type"] == node_type
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assert config["version"] == version
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def _node_constructor(*, return_value):
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@ -546,6 +552,84 @@ class TestDifyNodeFactoryCreateNode:
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assert kwargs["unstructured_api_config"] is sentinel.unstructured_api_config
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assert kwargs["http_client"] is sentinel.http_client
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def test_build_llm_compatible_node_init_kwargs_preserves_structured_output_switch(self, factory):
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node_data = LLMNodeData.model_validate(
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{
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"type": BuiltinNodeTypes.LLM,
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"title": "LLM",
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"model": {"provider": "provider", "name": "model", "mode": "chat", "completion_params": {}},
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"prompt_template": [{"role": "system", "text": "x"}],
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"context": {"enabled": False, "variable_selector": []},
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"vision": {"enabled": False},
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"structured_output_enabled": True,
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"structured_output": {
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"schema": {
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"type": "object",
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"properties": {"type": {"type": "string"}},
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"required": ["type"],
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}
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},
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}
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)
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wrapped_model_instance = sentinel.wrapped_model_instance
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memory = sentinel.memory
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factory._build_model_instance_for_llm_node = MagicMock(return_value=sentinel.model_instance)
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factory._build_memory_for_llm_node = MagicMock(return_value=memory)
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with patch.object(node_factory, "DifyPreparedLLM", return_value=wrapped_model_instance) as prepared_llm:
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kwargs = factory._build_llm_compatible_node_init_kwargs(
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node_class=sentinel.node_class,
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node_data=node_data,
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wrap_model_instance=True,
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include_http_client=True,
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include_llm_file_saver=True,
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include_prompt_message_serializer=True,
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include_retriever_attachment_loader=True,
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include_jinja2_template_renderer=True,
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)
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assert node_data.structured_output_switch_on is True
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assert node_data.structured_output_enabled is True
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factory._build_model_instance_for_llm_node.assert_called_once_with(node_data)
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factory._build_memory_for_llm_node.assert_called_once_with(
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node_data=node_data,
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model_instance=sentinel.model_instance,
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)
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prepared_llm.assert_called_once_with(sentinel.model_instance)
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assert kwargs["model_instance"] is wrapped_model_instance
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def test_create_node_passes_alias_preserving_llm_config_to_constructor(self, monkeypatch, factory):
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created_node = object()
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constructor = _node_constructor(return_value=created_node)
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monkeypatch.setattr(factory, "_resolve_node_class", MagicMock(return_value=constructor))
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monkeypatch.setattr(factory, "_build_llm_compatible_node_init_kwargs", MagicMock(return_value={}))
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node_config = {
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"id": "llm-node-id",
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"data": {
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"type": BuiltinNodeTypes.LLM,
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"title": "LLM",
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"model": {"provider": "provider", "name": "model", "mode": "chat", "completion_params": {}},
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"prompt_template": [{"role": "system", "text": "x"}],
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"context": {"enabled": False, "variable_selector": []},
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"vision": {"enabled": False},
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"structured_output_enabled": True,
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"structured_output": {
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"schema": {
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"type": "object",
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"properties": {"type": {"type": "string"}},
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"required": ["type"],
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}
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},
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},
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}
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factory.create_node(node_config)
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config = constructor.call_args.kwargs["config"]
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assert isinstance(config, dict)
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assert config["structured_output_enabled"] is True
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assert "structured_output_switch_on" not in config
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@pytest.mark.parametrize(
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("node_type", "constructor_name", "expected_extra_kwargs"),
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[
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