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test(api): cover remaining workflow typing branches
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Yanli 盐粒
@ -0,0 +1,110 @@
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from collections.abc import Iterator
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from core.app.apps.base_app_generate_response_converter import AppGenerateResponseConverter
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from core.app.entities.app_invoke_entities import InvokeFrom
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from core.app.entities.task_entities import AppBlockingResponse
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from core.errors.error import QuotaExceededError
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class DummyResponseConverter(AppGenerateResponseConverter):
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_blocking_response_type = AppBlockingResponse
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@classmethod
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def convert_blocking_full_response(cls, blocking_response: AppBlockingResponse) -> dict[str, str]:
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return {"mode": "blocking-full", "task_id": blocking_response.task_id}
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@classmethod
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def convert_blocking_simple_response(cls, blocking_response: AppBlockingResponse) -> dict[str, str]:
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return {"mode": "blocking-simple", "task_id": blocking_response.task_id}
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@classmethod
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def convert_stream_full_response(cls, stream_response: Iterator[object]):
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for _ in stream_response:
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yield {"mode": "stream-full"}
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@classmethod
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def convert_stream_simple_response(cls, stream_response: Iterator[object]):
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for _ in stream_response:
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yield {"mode": "stream-simple"}
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def test_convert_routes_to_full_or_simple_modes() -> None:
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blocking = AppBlockingResponse(task_id="task-1")
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assert DummyResponseConverter.convert(blocking, InvokeFrom.DEBUGGER) == {
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"mode": "blocking-full",
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"task_id": "task-1",
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}
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assert DummyResponseConverter.convert(blocking, InvokeFrom.WEB_APP) == {
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"mode": "blocking-simple",
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"task_id": "task-1",
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}
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assert list(DummyResponseConverter.convert(iter([object()]), InvokeFrom.SERVICE_API)) == [{"mode": "stream-full"}]
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assert list(DummyResponseConverter.convert(iter([object()]), InvokeFrom.WEB_APP)) == [{"mode": "stream-simple"}]
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def test_get_simple_metadata_preserves_new_retriever_fields() -> None:
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metadata = {
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"retriever_resources": [
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{
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"dataset_id": "dataset-1",
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"dataset_name": "Dataset",
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"document_id": "document-1",
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"segment_id": "segment-1",
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"position": 1,
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"data_source_type": "upload_file",
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"document_name": "Document",
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"score": 0.9,
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"hit_count": 2,
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"word_count": 128,
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"segment_position": 3,
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"index_node_hash": "hash",
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"content": "content",
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"page": 5,
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"title": "Title",
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"files": [{"id": "file-1"}],
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"summary": "summary",
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}
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],
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"annotation_reply": "hidden",
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"usage": {"latency": 0.1},
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}
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result = DummyResponseConverter._get_simple_metadata(metadata)
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assert result == {
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"retriever_resources": [
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{
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"dataset_id": "dataset-1",
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"dataset_name": "Dataset",
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"document_id": "document-1",
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"segment_id": "segment-1",
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"position": 1,
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"data_source_type": "upload_file",
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"document_name": "Document",
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"score": 0.9,
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"hit_count": 2,
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"word_count": 128,
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"segment_position": 3,
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"index_node_hash": "hash",
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"content": "content",
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"page": 5,
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"title": "Title",
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"files": [{"id": "file-1"}],
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"summary": "summary",
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}
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]
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}
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def test_error_to_stream_response_uses_specific_and_fallback_mappings() -> None:
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quota_response = DummyResponseConverter._error_to_stream_response(QuotaExceededError())
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fallback_response = DummyResponseConverter._error_to_stream_response(RuntimeError("boom"))
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assert quota_response["code"] == "provider_quota_exceeded"
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assert quota_response["status"] == 400
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assert fallback_response == {
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"code": "internal_server_error",
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"message": "Internal Server Error, please contact support.",
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"status": 500,
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}
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@ -0,0 +1,46 @@
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import pytest
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from pydantic import ValidationError
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from core.workflow.nodes.agent.entities import AgentNodeData
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def test_agent_input_accepts_variable_selector_and_mixed_values() -> None:
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node_data = AgentNodeData.model_validate(
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{
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"title": "Agent",
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"agent_strategy_provider_name": "provider",
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"agent_strategy_name": "strategy",
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"agent_strategy_label": "Strategy",
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"agent_parameters": {
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"query": {"type": "variable", "value": ["start", "query"]},
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"tools": {"type": "mixed", "value": [{"provider": "builtin", "name": "search"}]},
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},
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}
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)
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assert node_data.agent_parameters["query"].value == ["start", "query"]
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assert node_data.agent_parameters["tools"].value == [{"provider": "builtin", "name": "search"}]
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def test_agent_input_rejects_invalid_variable_selector_and_unknown_type() -> None:
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with pytest.raises(ValidationError):
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AgentNodeData.model_validate(
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{
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"title": "Agent",
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"agent_strategy_provider_name": "provider",
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"agent_strategy_name": "strategy",
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"agent_strategy_label": "Strategy",
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"agent_parameters": {"query": {"type": "variable", "value": "start.query"}},
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}
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)
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with pytest.raises(ValidationError, match="Unknown agent input type"):
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AgentNodeData.model_validate(
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{
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"title": "Agent",
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"agent_strategy_provider_name": "provider",
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"agent_strategy_name": "strategy",
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"agent_strategy_label": "Strategy",
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"agent_parameters": {"query": {"type": "unsupported", "value": "hello"}},
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}
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)
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@ -15,6 +15,7 @@ from dify_graph.entities import GraphInitParams
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from dify_graph.file import File, FileTransferMethod, FileType
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from dify_graph.model_runtime.entities import LLMMode
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from dify_graph.model_runtime.entities.common_entities import I18nObject
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from dify_graph.model_runtime.entities.llm_entities import LLMResult, LLMResultWithStructuredOutput, LLMUsage
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from dify_graph.model_runtime.entities.message_entities import (
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AssistantPromptMessage,
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ImagePromptMessageContent,
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@ -120,6 +121,54 @@ def test_prompt_config_converts_none_jinja_variables() -> None:
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assert prompt_config.jinja2_variables == []
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def test_fetch_structured_output_schema_validates_required_object_shape() -> None:
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assert LLMNode.fetch_structured_output_schema(structured_output={"schema": {"type": "object", "a": 1}}) == {
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"type": "object",
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"a": 1,
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}
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with pytest.raises(Exception, match="valid structured output schema"):
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LLMNode.fetch_structured_output_schema(structured_output={"schema": None})
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def test_handle_blocking_result_separates_reasoning_and_structured_output() -> None:
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saver = mock.MagicMock(spec=LLMFileSaver)
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event = LLMNode.handle_blocking_result(
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invoke_result=LLMResultWithStructuredOutput(
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model="gpt",
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message=AssistantPromptMessage(content="<think>reasoning</think>answer"),
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usage=LLMUsage.empty_usage(),
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structured_output={"answer": "done"},
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),
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saver=saver,
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file_outputs=[],
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reasoning_format="separated",
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request_latency=1.2345,
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)
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assert event.text == "answer"
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assert event.reasoning_content == "reasoning"
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assert event.structured_output == {"answer": "done"}
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assert event.usage.latency == 1.234
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def test_handle_blocking_result_keeps_tagged_text_without_structured_output() -> None:
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saver = mock.MagicMock(spec=LLMFileSaver)
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event = LLMNode.handle_blocking_result(
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invoke_result=LLMResult(
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model="gpt",
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message=AssistantPromptMessage(content="plain text"),
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usage=LLMUsage.empty_usage(),
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),
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saver=saver,
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file_outputs=[],
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)
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assert event.text == "plain text"
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assert event.reasoning_content == ""
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assert event.structured_output is None
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@pytest.fixture
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def llm_node_data() -> LLMNodeData:
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return LLMNodeData(
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@ -8,7 +8,7 @@ from unittest.mock import MagicMock, patch
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import pytest
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from core.tools.entities.tool_entities import ToolInvokeMessage
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from core.tools.entities.tool_entities import ToolInvokeMessage, ToolParameter
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from core.tools.utils.message_transformer import ToolFileMessageTransformer
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from dify_graph.file import File, FileTransferMethod, FileType
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from dify_graph.model_runtime.entities.llm_entities import LLMUsage
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@ -221,3 +221,67 @@ def test_tool_node_data_filters_missing_tool_parameter_values() -> None:
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)
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assert set(node_data.tool_parameters.keys()) == {"query"}
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def test_generate_parameters_reads_variables_and_optional_missing_inputs(tool_node: ToolNode) -> None:
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variable_pool = MagicMock()
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variable_pool.get.side_effect = [MagicMock(value="from-variable"), None]
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node_data = ToolNodeData.model_validate(
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{
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"title": "Tool",
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"provider_id": "provider",
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"provider_type": "builtin",
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"provider_name": "provider",
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"tool_name": "tool",
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"tool_label": "tool",
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"tool_configurations": {},
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"tool_parameters": {
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"query": {"type": "variable", "value": ["start", "query"]},
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"optional": {"type": "variable", "value": ["start", "optional"]},
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},
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}
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)
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tool_parameters = [
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ToolParameter.get_simple_instance("query", "query", ToolParameter.ToolParameterType.STRING, True),
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ToolParameter.get_simple_instance("optional", "optional", ToolParameter.ToolParameterType.STRING, False),
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]
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result = tool_node._generate_parameters(
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tool_parameters=tool_parameters,
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variable_pool=variable_pool,
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node_data=node_data,
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)
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assert result == {"query": "from-variable"}
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def test_generate_parameters_formats_logs_and_unknown_parameters(tool_node: ToolNode) -> None:
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variable_pool = MagicMock()
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variable_pool.convert_template.return_value = MagicMock(text="rendered", log="masked")
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node_data = ToolNodeData.model_validate(
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{
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"title": "Tool",
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"provider_id": "provider",
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"provider_type": "builtin",
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"provider_name": "provider",
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"tool_name": "tool",
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"tool_label": "tool",
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"tool_configurations": {},
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"tool_parameters": {
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"query": {"type": "mixed", "value": "{{ question }}"},
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"missing": {"type": "constant", "value": "literal"},
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},
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}
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)
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tool_parameters = [
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ToolParameter.get_simple_instance("query", "query", ToolParameter.ToolParameterType.STRING, True),
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]
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result = tool_node._generate_parameters(
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tool_parameters=tool_parameters,
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variable_pool=variable_pool,
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node_data=node_data,
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for_log=True,
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)
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assert result == {"query": "masked", "missing": None}
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@ -97,6 +97,22 @@ class TestWorkflowChildEngineBuilder:
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((sentinel.layer_two,), {}),
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]
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def test_build_child_engine_tolerates_invalid_graph_shape_until_graph_init(self):
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builder = workflow_entry._WorkflowChildEngineBuilder()
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with (
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patch.object(workflow_entry, "DifyNodeFactory", return_value=sentinel.factory),
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patch.object(workflow_entry.Graph, "init", side_effect=ValueError("invalid graph")),
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):
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with pytest.raises(ValueError, match="invalid graph"):
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builder.build_child_engine(
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workflow_id="workflow-id",
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graph_init_params=sentinel.graph_init_params,
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graph_runtime_state=sentinel.graph_runtime_state,
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graph_config={"nodes": "invalid"},
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root_node_id="root",
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)
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class TestWorkflowEntryInit:
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def test_rejects_call_depth_above_limit(self):
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