Plugins/fix backend ci errors

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

@@ -107,11 +107,46 @@ def invoke(
                 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 @@ def invoke(
                 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 @@ def timezone_convert(current_time: str, source_timezone: str, target_timezone: s
             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 @@
 
 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)