feat: Persist Variables for Enhanced Debugging Workflow (#20699)
This pull request introduces a feature aimed at improving the debugging experience during workflow editing. With the addition of variable persistence, the system will automatically retain the output variables from previously executed nodes. These persisted variables can then be reused when debugging subsequent nodes, eliminating the need for repetitive manual input. By streamlining this aspect of the workflow, the feature minimizes user errors and significantly reduces debugging effort, offering a smoother and more efficient experience. Key highlights of this change: - Automatic persistence of output variables for executed nodes. - Reuse of persisted variables to simplify input steps for nodes requiring them (e.g., `code`, `template`, `variable_assigner`). - Enhanced debugging experience with reduced friction. Closes #19735.
This commit is contained in:
QuantumGhost
@@ -8,8 +8,6 @@ from unittest.mock import MagicMock, patch
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import pytest
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from app_factory import create_app
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from configs import dify_config
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from core.app.entities.app_invoke_entities import InvokeFrom
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from core.model_runtime.entities.llm_entities import LLMResult, LLMUsage
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from core.model_runtime.entities.message_entities import AssistantPromptMessage
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@@ -30,21 +28,6 @@ from tests.integration_tests.model_runtime.__mock.plugin_daemon import setup_mod
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from tests.integration_tests.workflow.nodes.__mock.code_executor import setup_code_executor_mock
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@pytest.fixture(scope="session")
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def app():
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# Set up storage configuration
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os.environ["STORAGE_TYPE"] = "opendal"
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os.environ["OPENDAL_SCHEME"] = "fs"
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os.environ["OPENDAL_FS_ROOT"] = "storage"
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# Ensure storage directory exists
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os.makedirs("storage", exist_ok=True)
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app = create_app()
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dify_config.LOGIN_DISABLED = True
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return app
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def init_llm_node(config: dict) -> LLMNode:
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graph_config = {
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"edges": [
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@@ -102,197 +85,195 @@ def init_llm_node(config: dict) -> LLMNode:
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return node
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def test_execute_llm(app):
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with app.app_context():
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node = init_llm_node(
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config={
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"id": "llm",
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"data": {
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"title": "123",
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"type": "llm",
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"model": {
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"provider": "langgenius/openai/openai",
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"name": "gpt-3.5-turbo",
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"mode": "chat",
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"completion_params": {},
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},
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"prompt_template": [
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{
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"role": "system",
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"text": "you are a helpful assistant.\ntoday's weather is {{#abc.output#}}.",
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},
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{"role": "user", "text": "{{#sys.query#}}"},
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],
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"memory": None,
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"context": {"enabled": False},
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"vision": {"enabled": False},
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def test_execute_llm(flask_req_ctx):
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node = init_llm_node(
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config={
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"id": "llm",
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"data": {
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"title": "123",
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"type": "llm",
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"model": {
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"provider": "langgenius/openai/openai",
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"name": "gpt-3.5-turbo",
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"mode": "chat",
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"completion_params": {},
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},
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"prompt_template": [
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{
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"role": "system",
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"text": "you are a helpful assistant.\ntoday's weather is {{#abc.output#}}.",
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},
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{"role": "user", "text": "{{#sys.query#}}"},
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],
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"memory": None,
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"context": {"enabled": False},
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"vision": {"enabled": False},
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},
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)
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},
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)
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credentials = {"openai_api_key": os.environ.get("OPENAI_API_KEY")}
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credentials = {"openai_api_key": os.environ.get("OPENAI_API_KEY")}
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# Create a proper LLM result with real entities
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mock_usage = LLMUsage(
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prompt_tokens=30,
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prompt_unit_price=Decimal("0.001"),
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prompt_price_unit=Decimal("1000"),
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prompt_price=Decimal("0.00003"),
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completion_tokens=20,
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completion_unit_price=Decimal("0.002"),
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completion_price_unit=Decimal("1000"),
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completion_price=Decimal("0.00004"),
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total_tokens=50,
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total_price=Decimal("0.00007"),
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currency="USD",
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latency=0.5,
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)
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# Create a proper LLM result with real entities
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mock_usage = LLMUsage(
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prompt_tokens=30,
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prompt_unit_price=Decimal("0.001"),
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prompt_price_unit=Decimal("1000"),
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prompt_price=Decimal("0.00003"),
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completion_tokens=20,
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completion_unit_price=Decimal("0.002"),
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completion_price_unit=Decimal("1000"),
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completion_price=Decimal("0.00004"),
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total_tokens=50,
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total_price=Decimal("0.00007"),
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currency="USD",
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latency=0.5,
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)
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mock_message = AssistantPromptMessage(content="This is a test response from the mocked LLM.")
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mock_message = AssistantPromptMessage(content="This is a test response from the mocked LLM.")
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mock_llm_result = LLMResult(
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model="gpt-3.5-turbo",
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prompt_messages=[],
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message=mock_message,
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usage=mock_usage,
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)
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mock_llm_result = LLMResult(
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model="gpt-3.5-turbo",
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prompt_messages=[],
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message=mock_message,
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usage=mock_usage,
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)
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# Create a simple mock model instance that doesn't call real providers
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mock_model_instance = MagicMock()
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mock_model_instance.invoke_llm.return_value = mock_llm_result
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# Create a simple mock model instance that doesn't call real providers
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mock_model_instance = MagicMock()
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mock_model_instance.invoke_llm.return_value = mock_llm_result
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# Create a simple mock model config with required attributes
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mock_model_config = MagicMock()
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mock_model_config.mode = "chat"
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mock_model_config.provider = "langgenius/openai/openai"
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mock_model_config.model = "gpt-3.5-turbo"
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mock_model_config.provider_model_bundle.configuration.tenant_id = "9d2074fc-6f86-45a9-b09d-6ecc63b9056b"
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# Create a simple mock model config with required attributes
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mock_model_config = MagicMock()
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mock_model_config.mode = "chat"
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mock_model_config.provider = "langgenius/openai/openai"
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mock_model_config.model = "gpt-3.5-turbo"
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mock_model_config.provider_model_bundle.configuration.tenant_id = "9d2074fc-6f86-45a9-b09d-6ecc63b9056b"
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# Mock the _fetch_model_config method
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def mock_fetch_model_config_func(_node_data_model):
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return mock_model_instance, mock_model_config
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# Mock the _fetch_model_config method
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def mock_fetch_model_config_func(_node_data_model):
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return mock_model_instance, mock_model_config
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# Also mock ModelManager.get_model_instance to avoid database calls
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def mock_get_model_instance(_self, **kwargs):
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return mock_model_instance
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# Also mock ModelManager.get_model_instance to avoid database calls
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def mock_get_model_instance(_self, **kwargs):
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return mock_model_instance
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with (
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patch.object(node, "_fetch_model_config", mock_fetch_model_config_func),
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patch("core.model_manager.ModelManager.get_model_instance", mock_get_model_instance),
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):
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# execute node
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result = node._run()
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assert isinstance(result, Generator)
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with (
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patch.object(node, "_fetch_model_config", mock_fetch_model_config_func),
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patch("core.model_manager.ModelManager.get_model_instance", mock_get_model_instance),
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):
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# execute node
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result = node._run()
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assert isinstance(result, Generator)
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for item in result:
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if isinstance(item, RunCompletedEvent):
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assert item.run_result.status == WorkflowNodeExecutionStatus.SUCCEEDED
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assert item.run_result.process_data is not None
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assert item.run_result.outputs is not None
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assert item.run_result.outputs.get("text") is not None
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assert item.run_result.outputs.get("usage", {})["total_tokens"] > 0
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for item in result:
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if isinstance(item, RunCompletedEvent):
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assert item.run_result.status == WorkflowNodeExecutionStatus.SUCCEEDED
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assert item.run_result.process_data is not None
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assert item.run_result.outputs is not None
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assert item.run_result.outputs.get("text") is not None
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assert item.run_result.outputs.get("usage", {})["total_tokens"] > 0
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@pytest.mark.parametrize("setup_code_executor_mock", [["none"]], indirect=True)
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def test_execute_llm_with_jinja2(app, setup_code_executor_mock):
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def test_execute_llm_with_jinja2(flask_req_ctx, setup_code_executor_mock):
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"""
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Test execute LLM node with jinja2
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"""
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with app.app_context():
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node = init_llm_node(
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config={
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"id": "llm",
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"data": {
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"title": "123",
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"type": "llm",
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"model": {"provider": "openai", "name": "gpt-3.5-turbo", "mode": "chat", "completion_params": {}},
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"prompt_config": {
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"jinja2_variables": [
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{"variable": "sys_query", "value_selector": ["sys", "query"]},
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{"variable": "output", "value_selector": ["abc", "output"]},
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]
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},
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"prompt_template": [
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{
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"role": "system",
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"text": "you are a helpful assistant.\ntoday's weather is {{#abc.output#}}",
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"jinja2_text": "you are a helpful assistant.\ntoday's weather is {{output}}.",
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"edition_type": "jinja2",
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},
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{
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"role": "user",
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"text": "{{#sys.query#}}",
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"jinja2_text": "{{sys_query}}",
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"edition_type": "basic",
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},
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],
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"memory": None,
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"context": {"enabled": False},
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"vision": {"enabled": False},
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node = init_llm_node(
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config={
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"id": "llm",
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"data": {
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"title": "123",
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"type": "llm",
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"model": {"provider": "openai", "name": "gpt-3.5-turbo", "mode": "chat", "completion_params": {}},
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"prompt_config": {
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"jinja2_variables": [
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{"variable": "sys_query", "value_selector": ["sys", "query"]},
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{"variable": "output", "value_selector": ["abc", "output"]},
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]
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},
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"prompt_template": [
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{
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"role": "system",
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"text": "you are a helpful assistant.\ntoday's weather is {{#abc.output#}}",
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"jinja2_text": "you are a helpful assistant.\ntoday's weather is {{output}}.",
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"edition_type": "jinja2",
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},
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{
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"role": "user",
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"text": "{{#sys.query#}}",
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"jinja2_text": "{{sys_query}}",
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"edition_type": "basic",
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},
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],
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"memory": None,
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"context": {"enabled": False},
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"vision": {"enabled": False},
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},
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)
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},
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)
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# Mock db.session.close()
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db.session.close = MagicMock()
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# Mock db.session.close()
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db.session.close = MagicMock()
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# Create a proper LLM result with real entities
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mock_usage = LLMUsage(
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prompt_tokens=30,
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prompt_unit_price=Decimal("0.001"),
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prompt_price_unit=Decimal("1000"),
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prompt_price=Decimal("0.00003"),
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completion_tokens=20,
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completion_unit_price=Decimal("0.002"),
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completion_price_unit=Decimal("1000"),
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completion_price=Decimal("0.00004"),
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total_tokens=50,
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total_price=Decimal("0.00007"),
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currency="USD",
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latency=0.5,
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)
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# Create a proper LLM result with real entities
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mock_usage = LLMUsage(
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prompt_tokens=30,
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prompt_unit_price=Decimal("0.001"),
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prompt_price_unit=Decimal("1000"),
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prompt_price=Decimal("0.00003"),
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completion_tokens=20,
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completion_unit_price=Decimal("0.002"),
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completion_price_unit=Decimal("1000"),
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completion_price=Decimal("0.00004"),
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total_tokens=50,
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total_price=Decimal("0.00007"),
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currency="USD",
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latency=0.5,
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)
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mock_message = AssistantPromptMessage(content="Test response: sunny weather and what's the weather today?")
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mock_message = AssistantPromptMessage(content="Test response: sunny weather and what's the weather today?")
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mock_llm_result = LLMResult(
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model="gpt-3.5-turbo",
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prompt_messages=[],
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message=mock_message,
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usage=mock_usage,
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)
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mock_llm_result = LLMResult(
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model="gpt-3.5-turbo",
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prompt_messages=[],
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message=mock_message,
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usage=mock_usage,
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)
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# Create a simple mock model instance that doesn't call real providers
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mock_model_instance = MagicMock()
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mock_model_instance.invoke_llm.return_value = mock_llm_result
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# Create a simple mock model instance that doesn't call real providers
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mock_model_instance = MagicMock()
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mock_model_instance.invoke_llm.return_value = mock_llm_result
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# Create a simple mock model config with required attributes
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mock_model_config = MagicMock()
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mock_model_config.mode = "chat"
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mock_model_config.provider = "openai"
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mock_model_config.model = "gpt-3.5-turbo"
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mock_model_config.provider_model_bundle.configuration.tenant_id = "9d2074fc-6f86-45a9-b09d-6ecc63b9056b"
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# Create a simple mock model config with required attributes
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mock_model_config = MagicMock()
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mock_model_config.mode = "chat"
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mock_model_config.provider = "openai"
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mock_model_config.model = "gpt-3.5-turbo"
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mock_model_config.provider_model_bundle.configuration.tenant_id = "9d2074fc-6f86-45a9-b09d-6ecc63b9056b"
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# Mock the _fetch_model_config method
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def mock_fetch_model_config_func(_node_data_model):
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return mock_model_instance, mock_model_config
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# Mock the _fetch_model_config method
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def mock_fetch_model_config_func(_node_data_model):
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return mock_model_instance, mock_model_config
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# Also mock ModelManager.get_model_instance to avoid database calls
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def mock_get_model_instance(_self, **kwargs):
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return mock_model_instance
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# Also mock ModelManager.get_model_instance to avoid database calls
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def mock_get_model_instance(_self, **kwargs):
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return mock_model_instance
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with (
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patch.object(node, "_fetch_model_config", mock_fetch_model_config_func),
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patch("core.model_manager.ModelManager.get_model_instance", mock_get_model_instance),
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):
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# execute node
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result = node._run()
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with (
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patch.object(node, "_fetch_model_config", mock_fetch_model_config_func),
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patch("core.model_manager.ModelManager.get_model_instance", mock_get_model_instance),
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):
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# execute node
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result = node._run()
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for item in result:
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if isinstance(item, RunCompletedEvent):
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assert item.run_result.status == WorkflowNodeExecutionStatus.SUCCEEDED
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assert item.run_result.process_data is not None
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assert "sunny" in json.dumps(item.run_result.process_data)
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assert "what's the weather today?" in json.dumps(item.run_result.process_data)
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for item in result:
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if isinstance(item, RunCompletedEvent):
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assert item.run_result.status == WorkflowNodeExecutionStatus.SUCCEEDED
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assert item.run_result.process_data is not None
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assert "sunny" in json.dumps(item.run_result.process_data)
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assert "what's the weather today?" in json.dumps(item.run_result.process_data)
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def test_extract_json():
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