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refactor: decouple Node and NodeData (#22581)

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Signed-off-by: -LAN- <laipz8200@outlook.com>
Co-authored-by: QuantumGhost <obelisk.reg+git@gmail.com>
This commit is contained in:
-LAN-
2025-07-18 10:08:51 +08:00
committed by GitHub
parent 54c56f2d05
commit 460a825ef1
65 changed files with 2305 additions and 1146 deletions
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197
api/tests/integration_tests/workflow/nodes/test_llm.py
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@@ -2,15 +2,10 @@ import json
import time
import uuid
from collections.abc import Generator
from decimal import Decimal
from unittest.mock import MagicMock, patch
import pytest
from core.app.entities.app_invoke_entities import InvokeFrom
from core.llm_generator.output_parser.structured_output import _parse_structured_output
from core.model_runtime.entities.llm_entities import LLMResult, LLMUsage
from core.model_runtime.entities.message_entities import AssistantPromptMessage
from core.workflow.entities.variable_pool import VariablePool
from core.workflow.entities.workflow_node_execution import WorkflowNodeExecutionStatus
from core.workflow.graph_engine.entities.graph import Graph
@@ -24,8 +19,6 @@ from models.enums import UserFrom
from models.workflow import WorkflowType
"""FOR MOCK FIXTURES, DO NOT REMOVE"""
from tests.integration_tests.model_runtime.__mock.plugin_daemon import setup_model_mock
from tests.integration_tests.workflow.nodes.__mock.code_executor import setup_code_executor_mock
def init_llm_node(config: dict) -> LLMNode:
@@ -84,10 +77,14 @@ def init_llm_node(config: dict) -> LLMNode:
config=config,
)
# Initialize node data
if "data" in config:
node.init_node_data(config["data"])
return node
def test_execute_llm(flask_req_ctx):
def test_execute_llm():
node = init_llm_node(
config={
"id": "llm",
@@ -95,7 +92,7 @@ def test_execute_llm(flask_req_ctx):
"title": "123",
"type": "llm",
"model": {
"provider": "langgenius/openai/openai",
"provider": "openai",
"name": "gpt-3.5-turbo",
"mode": "chat",
"completion_params": {},
@@ -114,53 +111,62 @@ def test_execute_llm(flask_req_ctx):
},
)
# Create a proper LLM result with real entities
mock_usage = LLMUsage(
prompt_tokens=30,
prompt_unit_price=Decimal("0.001"),
prompt_price_unit=Decimal(1000),
prompt_price=Decimal("0.00003"),
completion_tokens=20,
completion_unit_price=Decimal("0.002"),
completion_price_unit=Decimal(1000),
completion_price=Decimal("0.00004"),
total_tokens=50,
total_price=Decimal("0.00007"),
currency="USD",
latency=0.5,
)
db.session.close = MagicMock()
mock_message = AssistantPromptMessage(content="This is a test response from the mocked LLM.")
# Mock the _fetch_model_config to avoid database calls
def mock_fetch_model_config(**_kwargs):
from decimal import Decimal
from unittest.mock import MagicMock
mock_llm_result = LLMResult(
model="gpt-3.5-turbo",
prompt_messages=[],
message=mock_message,
usage=mock_usage,
)
from core.model_runtime.entities.llm_entities import LLMResult, LLMUsage
from core.model_runtime.entities.message_entities import AssistantPromptMessage
# Create a simple mock model instance that doesn't call real providers
mock_model_instance = MagicMock()
mock_model_instance.invoke_llm.return_value = mock_llm_result
# Create mock model instance
mock_model_instance = MagicMock()
mock_usage = LLMUsage(
prompt_tokens=30,
prompt_unit_price=Decimal("0.001"),
prompt_price_unit=Decimal(1000),
prompt_price=Decimal("0.00003"),
completion_tokens=20,
completion_unit_price=Decimal("0.002"),
completion_price_unit=Decimal(1000),
completion_price=Decimal("0.00004"),
total_tokens=50,
total_price=Decimal("0.00007"),
currency="USD",
latency=0.5,
)
mock_message = AssistantPromptMessage(content="Test response from mock")
mock_llm_result = LLMResult(
model="gpt-3.5-turbo",
prompt_messages=[],
message=mock_message,
usage=mock_usage,
)
mock_model_instance.invoke_llm.return_value = mock_llm_result
# Create a simple mock model config with required attributes
mock_model_config = MagicMock()
mock_model_config.mode = "chat"
mock_model_config.provider = "langgenius/openai/openai"
mock_model_config.model = "gpt-3.5-turbo"
mock_model_config.provider_model_bundle.configuration.tenant_id = "9d2074fc-6f86-45a9-b09d-6ecc63b9056b"
# Create mock model config
mock_model_config = MagicMock()
mock_model_config.mode = "chat"
mock_model_config.provider = "openai"
mock_model_config.model = "gpt-3.5-turbo"
mock_model_config.parameters = {}
# Mock the _fetch_model_config method
def mock_fetch_model_config_func(_node_data_model):
return mock_model_instance, mock_model_config
# Also mock ModelManager.get_model_instance to avoid database calls
def mock_get_model_instance(_self, **kwargs):
return mock_model_instance
# Mock fetch_prompt_messages to avoid database calls
def mock_fetch_prompt_messages_1(**_kwargs):
from core.model_runtime.entities.message_entities import SystemPromptMessage, UserPromptMessage
return [
SystemPromptMessage(content="you are a helpful assistant. today's weather is sunny."),
UserPromptMessage(content="what's the weather today?"),
], []
with (
patch.object(node, "_fetch_model_config", mock_fetch_model_config_func),
patch("core.model_manager.ModelManager.get_model_instance", mock_get_model_instance),
patch.object(LLMNode, "_fetch_model_config", mock_fetch_model_config),
patch.object(LLMNode, "fetch_prompt_messages", mock_fetch_prompt_messages_1),
):
# execute node
result = node._run()
@@ -168,6 +174,9 @@ def test_execute_llm(flask_req_ctx):
for item in result:
if isinstance(item, RunCompletedEvent):
if item.run_result.status != WorkflowNodeExecutionStatus.SUCCEEDED:
print(f"Error: {item.run_result.error}")
print(f"Error type: {item.run_result.error_type}")
assert item.run_result.status == WorkflowNodeExecutionStatus.SUCCEEDED
assert item.run_result.process_data is not None
assert item.run_result.outputs is not None
@@ -175,8 +184,7 @@ def test_execute_llm(flask_req_ctx):
assert item.run_result.outputs.get("usage", {})["total_tokens"] > 0
@pytest.mark.parametrize("setup_code_executor_mock", [["none"]], indirect=True)
def test_execute_llm_with_jinja2(flask_req_ctx, setup_code_executor_mock):
def test_execute_llm_with_jinja2():
"""
Test execute LLM node with jinja2
"""
@@ -217,53 +225,60 @@ def test_execute_llm_with_jinja2(flask_req_ctx, setup_code_executor_mock):
# Mock db.session.close()
db.session.close = MagicMock()
# Create a proper LLM result with real entities
mock_usage = LLMUsage(
prompt_tokens=30,
prompt_unit_price=Decimal("0.001"),
prompt_price_unit=Decimal(1000),
prompt_price=Decimal("0.00003"),
completion_tokens=20,
completion_unit_price=Decimal("0.002"),
completion_price_unit=Decimal(1000),
completion_price=Decimal("0.00004"),
total_tokens=50,
total_price=Decimal("0.00007"),
currency="USD",
latency=0.5,
)
mock_message = AssistantPromptMessage(content="Test response: sunny weather and what's the weather today?")
mock_llm_result = LLMResult(
model="gpt-3.5-turbo",
prompt_messages=[],
message=mock_message,
usage=mock_usage,
)
# Create a simple mock model instance that doesn't call real providers
mock_model_instance = MagicMock()
mock_model_instance.invoke_llm.return_value = mock_llm_result
# Create a simple mock model config with required attributes
mock_model_config = MagicMock()
mock_model_config.mode = "chat"
mock_model_config.provider = "openai"
mock_model_config.model = "gpt-3.5-turbo"
mock_model_config.provider_model_bundle.configuration.tenant_id = "9d2074fc-6f86-45a9-b09d-6ecc63b9056b"
# Mock the _fetch_model_config method
def mock_fetch_model_config_func(_node_data_model):
def mock_fetch_model_config(**_kwargs):
from decimal import Decimal
from unittest.mock import MagicMock
from core.model_runtime.entities.llm_entities import LLMResult, LLMUsage
from core.model_runtime.entities.message_entities import AssistantPromptMessage
# Create mock model instance
mock_model_instance = MagicMock()
mock_usage = LLMUsage(
prompt_tokens=30,
prompt_unit_price=Decimal("0.001"),
prompt_price_unit=Decimal(1000),
prompt_price=Decimal("0.00003"),
completion_tokens=20,
completion_unit_price=Decimal("0.002"),
completion_price_unit=Decimal(1000),
completion_price=Decimal("0.00004"),
total_tokens=50,
total_price=Decimal("0.00007"),
currency="USD",
latency=0.5,
)
mock_message = AssistantPromptMessage(content="Test response: sunny weather and what's the weather today?")
mock_llm_result = LLMResult(
model="gpt-3.5-turbo",
prompt_messages=[],
message=mock_message,
usage=mock_usage,
)
mock_model_instance.invoke_llm.return_value = mock_llm_result
# Create mock model config
mock_model_config = MagicMock()
mock_model_config.mode = "chat"
mock_model_config.provider = "openai"
mock_model_config.model = "gpt-3.5-turbo"
mock_model_config.parameters = {}
return mock_model_instance, mock_model_config
# Also mock ModelManager.get_model_instance to avoid database calls
def mock_get_model_instance(_self, **kwargs):
return mock_model_instance
# Mock fetch_prompt_messages to avoid database calls
def mock_fetch_prompt_messages_2(**_kwargs):
from core.model_runtime.entities.message_entities import SystemPromptMessage, UserPromptMessage
return [
SystemPromptMessage(content="you are a helpful assistant. today's weather is sunny."),
UserPromptMessage(content="what's the weather today?"),
], []
with (
patch.object(node, "_fetch_model_config", mock_fetch_model_config_func),
patch("core.model_manager.ModelManager.get_model_instance", mock_get_model_instance),
patch.object(LLMNode, "_fetch_model_config", mock_fetch_model_config),
patch.object(LLMNode, "fetch_prompt_messages", mock_fetch_prompt_messages_2),
):
# execute node
result = node._run()