Plugins/fix backend ci errors (#12615)

api/core/model_runtime/model_providers/__base/large_language_model.py

@@ -107,11 +107,46 @@ class LargeLanguageModel(AIModel):
                 content_list = []
                 usage = LLMUsage.empty_usage()
                 system_fingerprint = None
+                tools_calls: list[AssistantPromptMessage.ToolCall] = []
+
+                def increase_tool_call(new_tool_calls: list[AssistantPromptMessage.ToolCall]):
+                    def get_tool_call(tool_name: str):
+                        if not tool_name:
+                            return tools_calls[-1]
+
+                        tool_call = next(
+                            (tool_call for tool_call in tools_calls if tool_call.function.name == tool_name), None
+                        )
+                        if tool_call is None:
+                            tool_call = AssistantPromptMessage.ToolCall(
+                                id="",
+                                type="",
+                                function=AssistantPromptMessage.ToolCall.ToolCallFunction(name=tool_name, arguments=""),
+                            )
+                            tools_calls.append(tool_call)
+
+                        return tool_call
+
+                    for new_tool_call in new_tool_calls:
+                        # get tool call
+                        tool_call = get_tool_call(new_tool_call.function.name)
+                        # update tool call
+                        if new_tool_call.id:
+                            tool_call.id = new_tool_call.id
+                        if new_tool_call.type:
+                            tool_call.type = new_tool_call.type
+                        if new_tool_call.function.name:
+                            tool_call.function.name = new_tool_call.function.name
+                        if new_tool_call.function.arguments:
+                            tool_call.function.arguments += new_tool_call.function.arguments
+
                 for chunk in result:
                     if isinstance(chunk.delta.message.content, str):
                         content += chunk.delta.message.content
                     elif isinstance(chunk.delta.message.content, list):
                         content_list.extend(chunk.delta.message.content)
+                    if chunk.delta.message.tool_calls:
+                        increase_tool_call(chunk.delta.message.tool_calls)
 
                     usage = chunk.delta.usage or LLMUsage.empty_usage()
                     system_fingerprint = chunk.system_fingerprint
@@ -120,7 +155,10 @@ class LargeLanguageModel(AIModel):
                 result = LLMResult(
                     model=model,
                     prompt_messages=prompt_messages,
-                    message=AssistantPromptMessage(content=content or content_list),
+                    message=AssistantPromptMessage(
+                        content=content or content_list,
+                        tool_calls=tools_calls,
+                    ),
                     usage=usage,
                     system_fingerprint=system_fingerprint,
                 )

api/core/tools/builtin_tool/providers/time/tools/timezone_conversion.py

@@ -48,6 +48,6 @@ class TimezoneConversionTool(BuiltinTool):
             datetime_with_tz = input_timezone.localize(local_time)
             # timezone convert
             converted_datetime = datetime_with_tz.astimezone(output_timezone)
-            return converted_datetime.strftime(format=time_format)
+            return converted_datetime.strftime(format=time_format)  # type: ignore
         except Exception as e:
             raise ToolInvokeError(str(e))

api/core/tools/builtin_tool/providers/webscraper/webscraper.py

@@ -5,4 +5,7 @@ from core.tools.builtin_tool.provider import BuiltinToolProviderController
 
 class WebscraperProvider(BuiltinToolProviderController):
     def _validate_credentials(self, user_id: str, credentials: dict[str, Any]) -> None:
+        """
+        Validate credentials
+        """
         pass

api/tests/integration_tests/model_runtime/__mock/plugin_daemon.py

@@ -0,0 +1,44 @@
+import os
+from collections.abc import Callable
+
+import pytest
+
+# import monkeypatch
+from _pytest.monkeypatch import MonkeyPatch
+
+from core.plugin.manager.model import PluginModelManager
+from tests.integration_tests.model_runtime.__mock.plugin_model import MockModelClass
+
+
+def mock_plugin_daemon(
+    monkeypatch: MonkeyPatch,
+) -> Callable[[], None]:
+    """
+    mock openai module
+
+    :param monkeypatch: pytest monkeypatch fixture
+    :return: unpatch function
+    """
+
+    def unpatch() -> None:
+        monkeypatch.undo()
+
+    monkeypatch.setattr(PluginModelManager, "invoke_llm", MockModelClass.invoke_llm)
+    monkeypatch.setattr(PluginModelManager, "fetch_model_providers", MockModelClass.fetch_model_providers)
+    monkeypatch.setattr(PluginModelManager, "get_model_schema", MockModelClass.get_model_schema)
+
+    return unpatch
+
+
+MOCK = os.getenv("MOCK_SWITCH", "false").lower() == "true"
+
+
+@pytest.fixture
+def setup_model_mock(monkeypatch):
+    if MOCK:
+        unpatch = mock_plugin_daemon(monkeypatch)
+
+    yield
+
+    if MOCK:
+        unpatch()

api/tests/integration_tests/model_runtime/__mock/plugin_model.py

@@ -0,0 +1,249 @@
+import datetime
+import uuid
+from collections.abc import Generator, Sequence
+from decimal import Decimal
+from json import dumps
+
+# import monkeypatch
+from typing import Optional
+
+from core.model_runtime.entities.common_entities import I18nObject
+from core.model_runtime.entities.llm_entities import LLMMode, LLMResult, LLMResultChunk, LLMResultChunkDelta, LLMUsage
+from core.model_runtime.entities.message_entities import AssistantPromptMessage, PromptMessage, PromptMessageTool
+from core.model_runtime.entities.model_entities import (
+    AIModelEntity,
+    FetchFrom,
+    ModelFeature,
+    ModelPropertyKey,
+    ModelType,
+)
+from core.model_runtime.entities.provider_entities import ConfigurateMethod, ProviderEntity
+from core.plugin.entities.plugin_daemon import PluginModelProviderEntity
+from core.plugin.manager.model import PluginModelManager
+
+
+class MockModelClass(PluginModelManager):
+    def fetch_model_providers(self, tenant_id: str) -> Sequence[PluginModelProviderEntity]:
+        """
+        Fetch model providers for the given tenant.
+        """
+        return [
+            PluginModelProviderEntity(
+                id=uuid.uuid4().hex,
+                created_at=datetime.datetime.now(),
+                updated_at=datetime.datetime.now(),
+                provider="openai",
+                tenant_id=tenant_id,
+                plugin_unique_identifier="langgenius/openai/openai",
+                plugin_id="langgenius/openai",
+                declaration=ProviderEntity(
+                    provider="openai",
+                    label=I18nObject(
+                        en_US="OpenAI",
+                        zh_Hans="OpenAI",
+                    ),
+                    description=I18nObject(
+                        en_US="OpenAI",
+                        zh_Hans="OpenAI",
+                    ),
+                    icon_small=I18nObject(
+                        en_US="https://example.com/icon_small.png",
+                        zh_Hans="https://example.com/icon_small.png",
+                    ),
+                    icon_large=I18nObject(
+                        en_US="https://example.com/icon_large.png",
+                        zh_Hans="https://example.com/icon_large.png",
+                    ),
+                    supported_model_types=[ModelType.LLM],
+                    configurate_methods=[ConfigurateMethod.PREDEFINED_MODEL],
+                    models=[
+                        AIModelEntity(
+                            model="gpt-3.5-turbo",
+                            label=I18nObject(
+                                en_US="gpt-3.5-turbo",
+                                zh_Hans="gpt-3.5-turbo",
+                            ),
+                            model_type=ModelType.LLM,
+                            fetch_from=FetchFrom.PREDEFINED_MODEL,
+                            model_properties={},
+                            features=[ModelFeature.TOOL_CALL, ModelFeature.MULTI_TOOL_CALL],
+                        ),
+                        AIModelEntity(
+                            model="gpt-3.5-turbo-instruct",
+                            label=I18nObject(
+                                en_US="gpt-3.5-turbo-instruct",
+                                zh_Hans="gpt-3.5-turbo-instruct",
+                            ),
+                            model_type=ModelType.LLM,
+                            fetch_from=FetchFrom.PREDEFINED_MODEL,
+                            model_properties={
+                                ModelPropertyKey.MODE: LLMMode.COMPLETION,
+                            },
+                            features=[],
+                        ),
+                    ],
+                ),
+            )
+        ]
+
+    def get_model_schema(
+        self,
+        tenant_id: str,
+        user_id: str,
+        plugin_id: str,
+        provider: str,
+        model_type: str,
+        model: str,
+        credentials: dict,
+    ) -> AIModelEntity | None:
+        """
+        Get model schema
+        """
+        return AIModelEntity(
+            model=model,
+            label=I18nObject(
+                en_US="OpenAI",
+                zh_Hans="OpenAI",
+            ),
+            model_type=ModelType(model_type),
+            fetch_from=FetchFrom.PREDEFINED_MODEL,
+            model_properties={},
+            features=[ModelFeature.TOOL_CALL, ModelFeature.MULTI_TOOL_CALL] if model == "gpt-3.5-turbo" else [],
+        )
+
+    @staticmethod
+    def generate_function_call(
+        tools: Optional[list[PromptMessageTool]],
+    ) -> Optional[AssistantPromptMessage.ToolCall]:
+        if not tools or len(tools) == 0:
+            return None
+        function: PromptMessageTool = tools[0]
+        function_name = function.name
+        function_parameters = function.parameters
+        function_parameters_type = function_parameters["type"]
+        if function_parameters_type != "object":
+            return None
+        function_parameters_properties = function_parameters["properties"]
+        function_parameters_required = function_parameters["required"]
+        parameters = {}
+        for parameter_name, parameter in function_parameters_properties.items():
+            if parameter_name not in function_parameters_required:
+                continue
+            parameter_type = parameter["type"]
+            if parameter_type == "string":
+                if "enum" in parameter:
+                    if len(parameter["enum"]) == 0:
+                        continue
+                    parameters[parameter_name] = parameter["enum"][0]
+                else:
+                    parameters[parameter_name] = "kawaii"
+            elif parameter_type == "integer":
+                parameters[parameter_name] = 114514
+            elif parameter_type == "number":
+                parameters[parameter_name] = 1919810.0
+            elif parameter_type == "boolean":
+                parameters[parameter_name] = True
+
+        return AssistantPromptMessage.ToolCall(
+            id=str(uuid.uuid4()),
+            type="function",
+            function=AssistantPromptMessage.ToolCall.ToolCallFunction(
+                name=function_name,
+                arguments=dumps(parameters),
+            ),
+        )
+
+    @staticmethod
+    def mocked_chat_create_sync(
+        model: str,
+        prompt_messages: list[PromptMessage],
+        tools: Optional[list[PromptMessageTool]] = None,
+    ) -> LLMResult:
+        tool_call = MockModelClass.generate_function_call(tools=tools)
+
+        return LLMResult(
+            id=str(uuid.uuid4()),
+            model=model,
+            prompt_messages=prompt_messages,
+            message=AssistantPromptMessage(content="elaina", tool_calls=[tool_call] if tool_call else []),
+            usage=LLMUsage(
+                prompt_tokens=2,
+                completion_tokens=1,
+                total_tokens=3,
+                prompt_unit_price=Decimal(0.0001),
+                completion_unit_price=Decimal(0.0002),
+                prompt_price_unit=Decimal(1),
+                prompt_price=Decimal(0.0001),
+                completion_price_unit=Decimal(1),
+                completion_price=Decimal(0.0002),
+                total_price=Decimal(0.0003),
+                currency="USD",
+                latency=0.001,
+            ),
+        )
+
+    @staticmethod
+    def mocked_chat_create_stream(
+        model: str,
+        prompt_messages: list[PromptMessage],
+        tools: Optional[list[PromptMessageTool]] = None,
+    ) -> Generator[LLMResultChunk, None, None]:
+        tool_call = MockModelClass.generate_function_call(tools=tools)
+
+        full_text = "Hello, world!\n\n```python\nprint('Hello, world!')\n```"
+        for i in range(0, len(full_text) + 1):
+            if i == len(full_text):
+                yield LLMResultChunk(
+                    model=model,
+                    prompt_messages=prompt_messages,
+                    delta=LLMResultChunkDelta(
+                        index=0,
+                        message=AssistantPromptMessage(
+                            content="",
+                            tool_calls=[tool_call] if tool_call else [],
+                        ),
+                    ),
+                )
+            else:
+                yield LLMResultChunk(
+                    model=model,
+                    prompt_messages=prompt_messages,
+                    delta=LLMResultChunkDelta(
+                        index=0,
+                        message=AssistantPromptMessage(
+                            content=full_text[i],
+                            tool_calls=[tool_call] if tool_call else [],
+                        ),
+                        usage=LLMUsage(
+                            prompt_tokens=2,
+                            completion_tokens=17,
+                            total_tokens=19,
+                            prompt_unit_price=Decimal(0.0001),
+                            completion_unit_price=Decimal(0.0002),
+                            prompt_price_unit=Decimal(1),
+                            prompt_price=Decimal(0.0001),
+                            completion_price_unit=Decimal(1),
+                            completion_price=Decimal(0.0002),
+                            total_price=Decimal(0.0003),
+                            currency="USD",
+                            latency=0.001,
+                        ),
+                    ),
+                )
+
+    def invoke_llm(
+        self: PluginModelManager,
+        *,
+        tenant_id: str,
+        user_id: str,
+        plugin_id: str,
+        provider: str,
+        model: str,
+        credentials: dict,
+        prompt_messages: list[PromptMessage],
+        model_parameters: Optional[dict] = None,
+        tools: Optional[list[PromptMessageTool]] = None,
+        stop: Optional[list[str]] = None,
+        stream: bool = True,
+    ):
+        return MockModelClass.mocked_chat_create_stream(model=model, prompt_messages=prompt_messages, tools=tools)

api/tests/integration_tests/workflow/nodes/__mock/model.py

@@ -0,0 +1,50 @@
+from unittest.mock import MagicMock
+
+from core.app.entities.app_invoke_entities import ModelConfigWithCredentialsEntity
+from core.entities.provider_configuration import ProviderConfiguration, ProviderModelBundle
+from core.entities.provider_entities import CustomConfiguration, CustomProviderConfiguration, SystemConfiguration
+from core.model_manager import ModelInstance
+from core.model_runtime.entities.model_entities import ModelType
+from core.model_runtime.model_providers.model_provider_factory import ModelProviderFactory
+from models.provider import ProviderType
+
+
+def get_mocked_fetch_model_config(
+    provider: str,
+    model: str,
+    mode: str,
+    credentials: dict,
+):
+    model_provider_factory = ModelProviderFactory(tenant_id="test_tenant")
+    model_type_instance = model_provider_factory.get_model_type_instance(provider, ModelType.LLM)
+    provider_model_bundle = ProviderModelBundle(
+        configuration=ProviderConfiguration(
+            tenant_id="1",
+            provider=model_provider_factory.get_provider_schema(provider),
+            preferred_provider_type=ProviderType.CUSTOM,
+            using_provider_type=ProviderType.CUSTOM,
+            system_configuration=SystemConfiguration(enabled=False),
+            custom_configuration=CustomConfiguration(provider=CustomProviderConfiguration(credentials=credentials)),
+            model_settings=[],
+        ),
+        model_type_instance=model_type_instance,
+    )
+    model_instance = ModelInstance(provider_model_bundle=provider_model_bundle, model=model)
+    model_schema = model_provider_factory.get_model_schema(
+        provider=provider,
+        model_type=model_type_instance.model_type,
+        model=model,
+        credentials=credentials,
+    )
+    assert model_schema is not None
+    model_config = ModelConfigWithCredentialsEntity(
+        model=model,
+        provider=provider,
+        mode=mode,
+        credentials=credentials,
+        parameters={},
+        model_schema=model_schema,
+        provider_model_bundle=provider_model_bundle,
+    )
+
+    return MagicMock(return_value=(model_instance, model_config))

api/tests/integration_tests/workflow/nodes/test_llm.py

@@ -7,12 +7,7 @@ from unittest.mock import MagicMock
 
 import pytest
 
-from core.app.entities.app_invoke_entities import InvokeFrom, ModelConfigWithCredentialsEntity
+from core.app.entities.app_invoke_entities import InvokeFrom
-from core.entities.provider_configuration import ProviderConfiguration, ProviderModelBundle
-from core.entities.provider_entities import CustomConfiguration, CustomProviderConfiguration, SystemConfiguration
-from core.model_manager import ModelInstance
-from core.model_runtime.entities.model_entities import ModelType
-from core.model_runtime.model_providers import ModelProviderFactory
 from core.workflow.entities.variable_pool import VariablePool
 from core.workflow.enums import SystemVariableKey
 from core.workflow.graph_engine.entities.graph import Graph
@@ -22,11 +17,11 @@ 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.provider import ProviderType
 from models.workflow import WorkflowNodeExecutionStatus, WorkflowType
+from tests.integration_tests.workflow.nodes.__mock.model import get_mocked_fetch_model_config
 
 """FOR MOCK FIXTURES, DO NOT REMOVE"""
-from tests.integration_tests.model_runtime.__mock.openai import setup_openai_mock
+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
 
 
@@ -81,15 +76,19 @@ def init_llm_node(config: dict) -> LLMNode:
     return node
 
 
-@pytest.mark.parametrize("setup_openai_mock", [["chat"]], indirect=True)
+def test_execute_llm(setup_model_mock):
-def test_execute_llm(setup_openai_mock):
     node = init_llm_node(
         config={
             "id": "llm",
             "data": {
                 "title": "123",
                 "type": "llm",
-                "model": {"provider": "openai", "name": "gpt-3.5-turbo", "mode": "chat", "completion_params": {}},
+                "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#}}"},
@@ -103,37 +102,15 @@ def test_execute_llm(setup_openai_mock):
 
     credentials = {"openai_api_key": os.environ.get("OPENAI_API_KEY")}
 
-    provider_instance = ModelProviderFactory().get_provider_instance("openai")
-    model_type_instance = provider_instance.get_model_instance(ModelType.LLM)
-    provider_model_bundle = ProviderModelBundle(
-        configuration=ProviderConfiguration(
-            tenant_id="1",
-            provider=provider_instance.get_provider_schema(),
-            preferred_provider_type=ProviderType.CUSTOM,
-            using_provider_type=ProviderType.CUSTOM,
-            system_configuration=SystemConfiguration(enabled=False),
-            custom_configuration=CustomConfiguration(provider=CustomProviderConfiguration(credentials=credentials)),
-            model_settings=[],
-        ),
-        model_type_instance=model_type_instance,
-    )
-    model_instance = ModelInstance(provider_model_bundle=provider_model_bundle, model="gpt-3.5-turbo")
-    model_schema = model_type_instance.get_model_schema("gpt-3.5-turbo")
-    assert model_schema is not None
-    model_config = ModelConfigWithCredentialsEntity(
-        model="gpt-3.5-turbo",
-        provider="openai",
-        mode="chat",
-        credentials=credentials,
-        parameters={},
-        model_schema=model_schema,
-        provider_model_bundle=provider_model_bundle,
-    )
-
     # Mock db.session.close()
     db.session.close = MagicMock()
 
-    node._fetch_model_config = MagicMock(return_value=(model_instance, model_config))
+    node._fetch_model_config = get_mocked_fetch_model_config(
+        provider="langgenius/openai/openai",
+        model="gpt-3.5-turbo",
+        mode="chat",
+        credentials=credentials,
+    )
 
     # execute node
     result = node._run()
@@ -149,8 +126,7 @@ def test_execute_llm(setup_openai_mock):
 
 
 @pytest.mark.parametrize("setup_code_executor_mock", [["none"]], indirect=True)
-@pytest.mark.parametrize("setup_openai_mock", [["chat"]], indirect=True)
+def test_execute_llm_with_jinja2(setup_code_executor_mock, setup_model_mock):
-def test_execute_llm_with_jinja2(setup_code_executor_mock, setup_openai_mock):
     """
     Test execute LLM node with jinja2
     """
@@ -190,38 +166,15 @@ def test_execute_llm_with_jinja2(setup_code_executor_mock, setup_openai_mock):
 
     credentials = {"openai_api_key": os.environ.get("OPENAI_API_KEY")}
 
-    provider_instance = ModelProviderFactory().get_provider_instance("openai")
-    model_type_instance = provider_instance.get_model_instance(ModelType.LLM)
-    provider_model_bundle = ProviderModelBundle(
-        configuration=ProviderConfiguration(
-            tenant_id="1",
-            provider=provider_instance.get_provider_schema(),
-            preferred_provider_type=ProviderType.CUSTOM,
-            using_provider_type=ProviderType.CUSTOM,
-            system_configuration=SystemConfiguration(enabled=False),
-            custom_configuration=CustomConfiguration(provider=CustomProviderConfiguration(credentials=credentials)),
-            model_settings=[],
-        ),
-        model_type_instance=model_type_instance,
-    )
-
-    model_instance = ModelInstance(provider_model_bundle=provider_model_bundle, model="gpt-3.5-turbo")
-    model_schema = model_type_instance.get_model_schema("gpt-3.5-turbo")
-    assert model_schema is not None
-    model_config = ModelConfigWithCredentialsEntity(
-        model="gpt-3.5-turbo",
-        provider="openai",
-        mode="chat",
-        credentials=credentials,
-        parameters={},
-        model_schema=model_schema,
-        provider_model_bundle=provider_model_bundle,
-    )
-
     # Mock db.session.close()
     db.session.close = MagicMock()
 
-    node._fetch_model_config = MagicMock(return_value=(model_instance, model_config))
+    node._fetch_model_config = get_mocked_fetch_model_config(
+        provider="langgenius/openai/openai",
+        model="gpt-3.5-turbo",
+        mode="chat",
+        credentials=credentials,
+    )
 
     # execute node
     result = node._run()

api/tests/integration_tests/workflow/nodes/test_parameter_extractor.py

@@ -4,14 +4,7 @@ import uuid
 from typing import Optional
 from unittest.mock import MagicMock
 
-import pytest
+from core.app.entities.app_invoke_entities import InvokeFrom
-
-from core.app.entities.app_invoke_entities import InvokeFrom, ModelConfigWithCredentialsEntity
-from core.entities.provider_configuration import ProviderConfiguration, ProviderModelBundle
-from core.entities.provider_entities import CustomConfiguration, CustomProviderConfiguration, SystemConfiguration
-from core.model_manager import ModelInstance
-from core.model_runtime.entities.model_entities import ModelType
-from core.model_runtime.model_providers.model_provider_factory import ModelProviderFactory
 from core.workflow.entities.variable_pool import VariablePool
 from core.workflow.enums import SystemVariableKey
 from core.workflow.graph_engine.entities.graph import Graph
@@ -20,53 +13,11 @@ from core.workflow.graph_engine.entities.graph_runtime_state import GraphRuntime
 from core.workflow.nodes.parameter_extractor.parameter_extractor_node import ParameterExtractorNode
 from extensions.ext_database import db
 from models.enums import UserFrom
-from models.provider import ProviderType
+from tests.integration_tests.workflow.nodes.__mock.model import get_mocked_fetch_model_config
 
 """FOR MOCK FIXTURES, DO NOT REMOVE"""
 from models.workflow import WorkflowNodeExecutionStatus, WorkflowType
-from tests.integration_tests.model_runtime.__mock.anthropic import setup_anthropic_mock
+from tests.integration_tests.model_runtime.__mock.plugin_daemon import setup_model_mock
-from tests.integration_tests.model_runtime.__mock.openai import setup_openai_mock
-
-
-def get_mocked_fetch_model_config(
-    provider: str,
-    model: str,
-    mode: str,
-    credentials: dict,
-):
-    model_provider_factory = ModelProviderFactory(tenant_id="test_tenant")
-    model_type_instance = model_provider_factory.get_model_type_instance(provider, ModelType.LLM)
-    provider_model_bundle = ProviderModelBundle(
-        configuration=ProviderConfiguration(
-            tenant_id="1",
-            provider=model_provider_factory.get_provider_schema(provider),
-            preferred_provider_type=ProviderType.CUSTOM,
-            using_provider_type=ProviderType.CUSTOM,
-            system_configuration=SystemConfiguration(enabled=False),
-            custom_configuration=CustomConfiguration(provider=CustomProviderConfiguration(credentials=credentials)),
-            model_settings=[],
-        ),
-        model_type_instance=model_type_instance,
-    )
-    model_instance = ModelInstance(provider_model_bundle=provider_model_bundle, model=model)
-    model_schema = model_provider_factory.get_model_schema(
-        provider=provider,
-        model_type=model_type_instance.model_type,
-        model=model,
-        credentials=credentials,
-    )
-    assert model_schema is not None
-    model_config = ModelConfigWithCredentialsEntity(
-        model=model,
-        provider=provider,
-        mode=mode,
-        credentials=credentials,
-        parameters={},
-        model_schema=model_schema,
-        provider_model_bundle=provider_model_bundle,
-    )
-
-    return MagicMock(return_value=(model_instance, model_config))
 
 
 def get_mocked_fetch_memory(memory_text: str):
@@ -133,8 +84,7 @@ def init_parameter_extractor_node(config: dict):
     )
 
 
-@pytest.mark.parametrize("setup_openai_mock", [["chat"]], indirect=True)
+def test_function_calling_parameter_extractor(setup_model_mock):
-def test_function_calling_parameter_extractor(setup_openai_mock):
     """
     Test function calling for parameter extractor.
     """
@@ -144,7 +94,12 @@ def test_function_calling_parameter_extractor(setup_openai_mock):
             "data": {
                 "title": "123",
                 "type": "parameter-extractor",
-                "model": {"provider": "openai", "name": "gpt-3.5-turbo", "mode": "chat", "completion_params": {}},
+                "model": {
+                    "provider": "langgenius/openai/openai",
+                    "name": "gpt-3.5-turbo",
+                    "mode": "chat",
+                    "completion_params": {},
+                },
                 "query": ["sys", "query"],
                 "parameters": [{"name": "location", "type": "string", "description": "location", "required": True}],
                 "instruction": "",
@@ -155,25 +110,13 @@ def test_function_calling_parameter_extractor(setup_openai_mock):
     )
 
     node._fetch_model_config = get_mocked_fetch_model_config(
-        provider="openai",
+        provider="langgenius/openai/openai",
         model="gpt-3.5-turbo",
         mode="chat",
         credentials={"openai_api_key": os.environ.get("OPENAI_API_KEY")},
     )
     db.session.close = MagicMock()
 
-    # construct variable pool
-    pool = VariablePool(
-        system_variables={
-            SystemVariableKey.QUERY: "what's the weather in SF",
-            SystemVariableKey.FILES: [],
-            SystemVariableKey.CONVERSATION_ID: "abababa",
-            SystemVariableKey.USER_ID: "aaa",
-        },
-        user_inputs={},
-        environment_variables=[],
-    )
-
     result = node._run()
 
     assert result.status == WorkflowNodeExecutionStatus.SUCCEEDED
@@ -182,8 +125,7 @@ def test_function_calling_parameter_extractor(setup_openai_mock):
     assert result.outputs.get("__reason") == None
 
 
-@pytest.mark.parametrize("setup_openai_mock", [["chat"]], indirect=True)
+def test_instructions(setup_model_mock):
-def test_instructions(setup_openai_mock):
     """
     Test chat parameter extractor.
     """
@@ -193,7 +135,12 @@ def test_instructions(setup_openai_mock):
             "data": {
                 "title": "123",
                 "type": "parameter-extractor",
-                "model": {"provider": "openai", "name": "gpt-3.5-turbo", "mode": "chat", "completion_params": {}},
+                "model": {
+                    "provider": "langgenius/openai/openai",
+                    "name": "gpt-3.5-turbo",
+                    "mode": "chat",
+                    "completion_params": {},
+                },
                 "query": ["sys", "query"],
                 "parameters": [{"name": "location", "type": "string", "description": "location", "required": True}],
                 "reasoning_mode": "function_call",
@@ -204,7 +151,7 @@ def test_instructions(setup_openai_mock):
     )
 
     node._fetch_model_config = get_mocked_fetch_model_config(
-        provider="openai",
+        provider="langgenius/openai/openai",
         model="gpt-3.5-turbo",
         mode="chat",
         credentials={"openai_api_key": os.environ.get("OPENAI_API_KEY")},
@@ -228,8 +175,7 @@ def test_instructions(setup_openai_mock):
             assert "what's the weather in SF" in prompt.get("text")
 
 
-@pytest.mark.parametrize("setup_anthropic_mock", [["none"]], indirect=True)
+def test_chat_parameter_extractor(setup_model_mock):
-def test_chat_parameter_extractor(setup_anthropic_mock):
     """
     Test chat parameter extractor.
     """
@@ -239,7 +185,12 @@ def test_chat_parameter_extractor(setup_anthropic_mock):
             "data": {
                 "title": "123",
                 "type": "parameter-extractor",
-                "model": {"provider": "anthropic", "name": "claude-2", "mode": "chat", "completion_params": {}},
+                "model": {
+                    "provider": "langgenius/openai/openai",
+                    "name": "gpt-3.5-turbo",
+                    "mode": "chat",
+                    "completion_params": {},
+                },
                 "query": ["sys", "query"],
                 "parameters": [{"name": "location", "type": "string", "description": "location", "required": True}],
                 "reasoning_mode": "prompt",
@@ -250,10 +201,10 @@ def test_chat_parameter_extractor(setup_anthropic_mock):
     )
 
     node._fetch_model_config = get_mocked_fetch_model_config(
-        provider="anthropic",
+        provider="langgenius/openai/openai",
-        model="claude-2",
+        model="gpt-3.5-turbo",
         mode="chat",
-        credentials={"anthropic_api_key": os.environ.get("ANTHROPIC_API_KEY")},
+        credentials={"openai_api_key": os.environ.get("OPENAI_API_KEY")},
     )
     db.session.close = MagicMock()
 
@@ -275,8 +226,7 @@ def test_chat_parameter_extractor(setup_anthropic_mock):
                 assert '<structure>\n{"type": "object"' in prompt.get("text")
 
 
-@pytest.mark.parametrize("setup_openai_mock", [["completion"]], indirect=True)
+def test_completion_parameter_extractor(setup_model_mock):
-def test_completion_parameter_extractor(setup_openai_mock):
     """
     Test completion parameter extractor.
     """
@@ -287,7 +237,7 @@ def test_completion_parameter_extractor(setup_openai_mock):
                 "title": "123",
                 "type": "parameter-extractor",
                 "model": {
-                    "provider": "openai",
+                    "provider": "langgenius/openai/openai",
                     "name": "gpt-3.5-turbo-instruct",
                     "mode": "completion",
                     "completion_params": {},
@@ -302,7 +252,7 @@ def test_completion_parameter_extractor(setup_openai_mock):
     )
 
     node._fetch_model_config = get_mocked_fetch_model_config(
-        provider="openai",
+        provider="langgenius/openai/openai",
         model="gpt-3.5-turbo-instruct",
         mode="completion",
         credentials={"openai_api_key": os.environ.get("OPENAI_API_KEY")},
@@ -335,7 +285,7 @@ def test_extract_json_response():
                 "title": "123",
                 "type": "parameter-extractor",
                 "model": {
-                    "provider": "openai",
+                    "provider": "langgenius/openai/openai",
                     "name": "gpt-3.5-turbo-instruct",
                     "mode": "completion",
                     "completion_params": {},
@@ -361,8 +311,7 @@ def test_extract_json_response():
     assert result["location"] == "kawaii"
 
 
-@pytest.mark.parametrize("setup_anthropic_mock", [["none"]], indirect=True)
+def test_chat_parameter_extractor_with_memory(setup_model_mock):
-def test_chat_parameter_extractor_with_memory(setup_anthropic_mock):
     """
     Test chat parameter extractor with memory.
     """
@@ -372,7 +321,12 @@ def test_chat_parameter_extractor_with_memory(setup_anthropic_mock):
             "data": {
                 "title": "123",
                 "type": "parameter-extractor",
-                "model": {"provider": "anthropic", "name": "claude-2", "mode": "chat", "completion_params": {}},
+                "model": {
+                    "provider": "langgenius/openai/openai",
+                    "name": "gpt-3.5-turbo",
+                    "mode": "chat",
+                    "completion_params": {},
+                },
                 "query": ["sys", "query"],
                 "parameters": [{"name": "location", "type": "string", "description": "location", "required": True}],
                 "reasoning_mode": "prompt",
@@ -383,10 +337,10 @@ def test_chat_parameter_extractor_with_memory(setup_anthropic_mock):
     )
 
     node._fetch_model_config = get_mocked_fetch_model_config(
-        provider="anthropic",
+        provider="langgenius/openai/openai",
-        model="claude-2",
+        model="gpt-3.5-turbo",
         mode="chat",
-        credentials={"anthropic_api_key": os.environ.get("ANTHROPIC_API_KEY")},
+        credentials={"openai_api_key": os.environ.get("OPENAI_API_KEY")},
     )
     node._fetch_memory = get_mocked_fetch_memory("customized memory")
     db.session.close = MagicMock()

api/tests/integration_tests/workflow/nodes/test_tool.py

@@ -1,13 +1,15 @@
 import time
 import uuid
+from unittest.mock import MagicMock
 
 from core.app.entities.app_invoke_entities import InvokeFrom
-from core.workflow.entities.node_entities import NodeRunResult
+from core.tools.utils.configuration import ToolParameterConfigurationManager
 from core.workflow.entities.variable_pool import VariablePool
 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.event import RunCompletedEvent
 from core.workflow.nodes.tool.tool_node import ToolNode
 from models.enums import UserFrom
 from models.workflow import WorkflowNodeExecutionStatus, WorkflowType
@@ -63,31 +65,28 @@ def test_tool_variable_invoke():
             "data": {
                 "title": "a",
                 "desc": "a",
-                "provider_id": "maths",
+                "provider_id": "time",
                 "provider_type": "builtin",
-                "provider_name": "maths",
+                "provider_name": "time",
-                "tool_name": "eval_expression",
+                "tool_name": "current_time",
-                "tool_label": "eval_expression",
+                "tool_label": "current_time",
                 "tool_configurations": {},
-                "tool_parameters": {
+                "tool_parameters": {},
-                    "expression": {
-                        "type": "variable",
-                        "value": ["1", "123", "args1"],
-                    }
-                },
             },
         }
     )
 
+    ToolParameterConfigurationManager.decrypt_tool_parameters = MagicMock(return_value={"format": "%Y-%m-%d %H:%M:%S"})
+
     node.graph_runtime_state.variable_pool.add(["1", "123", "args1"], "1+1")
 
     # execute node
     result = node._run()
-    assert isinstance(result, NodeRunResult)
+    for item in result:
-    assert result.status == WorkflowNodeExecutionStatus.SUCCEEDED
+        if isinstance(item, RunCompletedEvent):
-    assert result.outputs is not None
+            assert item.run_result.status == WorkflowNodeExecutionStatus.SUCCEEDED
-    assert "2" in result.outputs["text"]
+            assert item.run_result.outputs is not None
-    assert result.outputs["files"] == []
+            assert item.run_result.outputs.get("text") is not None
 
 
 def test_tool_mixed_invoke():
@@ -97,28 +96,25 @@ def test_tool_mixed_invoke():
             "data": {
                 "title": "a",
                 "desc": "a",
-                "provider_id": "maths",
+                "provider_id": "time",
                 "provider_type": "builtin",
-                "provider_name": "maths",
+                "provider_name": "time",
-                "tool_name": "eval_expression",
+                "tool_name": "current_time",
-                "tool_label": "eval_expression",
+                "tool_label": "current_time",
-                "tool_configurations": {},
+                "tool_configurations": {
-                "tool_parameters": {
+                    "format": "%Y-%m-%d %H:%M:%S",
-                    "expression": {
-                        "type": "mixed",
-                        "value": "{{#1.args1#}}",
-                    }
                 },
+                "tool_parameters": {},
             },
         }
     )
 
-    node.graph_runtime_state.variable_pool.add(["1", "args1"], "1+1")
+    ToolParameterConfigurationManager.decrypt_tool_parameters = MagicMock(return_value={"format": "%Y-%m-%d %H:%M:%S"})
 
     # execute node
     result = node._run()
-    assert isinstance(result, NodeRunResult)
+    for item in result:
-    assert result.status == WorkflowNodeExecutionStatus.SUCCEEDED
+        if isinstance(item, RunCompletedEvent):
-    assert result.outputs is not None
+            assert item.run_result.status == WorkflowNodeExecutionStatus.SUCCEEDED
-    assert "2" in result.outputs["text"]
+            assert item.run_result.outputs is not None
-    assert result.outputs["files"] == []
+            assert item.run_result.outputs.get("text") is not None