import json import os 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.enums import SystemVariableKey from core.workflow.graph_engine.entities.graph import Graph from core.workflow.graph_engine.entities.graph_init_params import GraphInitParams from core.workflow.graph_engine.entities.graph_runtime_state import GraphRuntimeState from core.workflow.nodes.event import RunCompletedEvent from core.workflow.nodes.llm.node import LLMNode from extensions.ext_database import db 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: graph_config = { "edges": [ { "id": "start-source-next-target", "source": "start", "target": "llm", }, ], "nodes": [{"data": {"type": "start"}, "id": "start"}, config], } graph = Graph.init(graph_config=graph_config) # Use proper UUIDs for database compatibility tenant_id = "9d2074fc-6f86-45a9-b09d-6ecc63b9056b" app_id = "9d2074fc-6f86-45a9-b09d-6ecc63b9056c" workflow_id = "9d2074fc-6f86-45a9-b09d-6ecc63b9056d" user_id = "9d2074fc-6f86-45a9-b09d-6ecc63b9056e" init_params = GraphInitParams( tenant_id=tenant_id, app_id=app_id, workflow_type=WorkflowType.WORKFLOW, workflow_id=workflow_id, graph_config=graph_config, user_id=user_id, user_from=UserFrom.ACCOUNT, invoke_from=InvokeFrom.DEBUGGER, call_depth=0, ) # construct variable pool variable_pool = VariablePool( system_variables={ SystemVariableKey.QUERY: "what's the weather today?", SystemVariableKey.FILES: [], SystemVariableKey.CONVERSATION_ID: "abababa", SystemVariableKey.USER_ID: "aaa", }, user_inputs={}, environment_variables=[], conversation_variables=[], ) variable_pool.add(["abc", "output"], "sunny") node = LLMNode( id=str(uuid.uuid4()), graph_init_params=init_params, graph=graph, graph_runtime_state=GraphRuntimeState(variable_pool=variable_pool, start_at=time.perf_counter()), config=config, ) return node def test_execute_llm(flask_req_ctx): node = init_llm_node( config={ "id": "llm", "data": { "title": "123", "type": "llm", "model": { "provider": "langgenius/openai/openai", "name": "gpt-3.5-turbo", "mode": "chat", "completion_params": {}, }, "prompt_template": [ { "role": "system", "text": "you are a helpful assistant.\ntoday's weather is {{#abc.output#}}.", }, {"role": "user", "text": "{{#sys.query#}}"}, ], "memory": None, "context": {"enabled": False}, "vision": {"enabled": False}, }, }, ) credentials = {"openai_api_key": os.environ.get("OPENAI_API_KEY")} # 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="This is a test response from the mocked LLM.") 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 = "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" # 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 with ( patch.object(node, "_fetch_model_config", mock_fetch_model_config_func), patch("core.model_manager.ModelManager.get_model_instance", mock_get_model_instance), ): # execute node result = node._run() assert isinstance(result, Generator) for item in result: if isinstance(item, RunCompletedEvent): assert item.run_result.status == WorkflowNodeExecutionStatus.SUCCEEDED assert item.run_result.process_data is not None assert item.run_result.outputs is not None assert item.run_result.outputs.get("text") is not None 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): """ Test execute LLM node with jinja2 """ node = init_llm_node( config={ "id": "llm", "data": { "title": "123", "type": "llm", "model": {"provider": "openai", "name": "gpt-3.5-turbo", "mode": "chat", "completion_params": {}}, "prompt_config": { "jinja2_variables": [ {"variable": "sys_query", "value_selector": ["sys", "query"]}, {"variable": "output", "value_selector": ["abc", "output"]}, ] }, "prompt_template": [ { "role": "system", "text": "you are a helpful assistant.\ntoday's weather is {{#abc.output#}}", "jinja2_text": "you are a helpful assistant.\ntoday's weather is {{output}}.", "edition_type": "jinja2", }, { "role": "user", "text": "{{#sys.query#}}", "jinja2_text": "{{sys_query}}", "edition_type": "basic", }, ], "memory": None, "context": {"enabled": False}, "vision": {"enabled": False}, }, }, ) # 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): 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 with ( patch.object(node, "_fetch_model_config", mock_fetch_model_config_func), patch("core.model_manager.ModelManager.get_model_instance", mock_get_model_instance), ): # execute node result = node._run() for item in result: if isinstance(item, RunCompletedEvent): assert item.run_result.status == WorkflowNodeExecutionStatus.SUCCEEDED assert item.run_result.process_data is not None assert "sunny" in json.dumps(item.run_result.process_data) assert "what's the weather today?" in json.dumps(item.run_result.process_data) def test_extract_json(): llm_texts = [ '\n\n{"name": "test", "age": 123', # resoning model (deepseek-r1) '{"name":"test","age":123}', # json schema model (gpt-4o) '{\n "name": "test",\n "age": 123\n}', # small model (llama-3.2-1b) '```json\n{"name": "test", "age": 123}\n```', # json markdown (deepseek-chat) '{"name":"test",age:123}', # without quotes (qwen-2.5-0.5b) ] result = {"name": "test", "age": 123} assert all(_parse_structured_output(item) == result for item in llm_texts)