add MoviePilot AI agent implementation and workflow manager

app/agent/__init__.py

@@ -0,0 +1,354 @@
+"""MoviePilot AI智能体实现"""
+
+import asyncio
+import threading
+from typing import Dict, List, Any
+
+from langchain.agents import AgentExecutor, create_openai_tools_agent
+from langchain.prompts import ChatPromptTemplate, MessagesPlaceholder
+from langchain_community.callbacks import get_openai_callback
+from langchain_core.callbacks import AsyncCallbackHandler
+from langchain_core.chat_history import InMemoryChatMessageHistory
+from langchain_core.messages import HumanMessage, AIMessage, ToolCall
+from langchain_core.runnables.history import RunnableWithMessageHistory
+
+from app.agent.memory import ConversationMemoryManager
+from app.agent.prompt import PromptManager
+from app.agent.tools import MoviePilotToolFactory
+from app.core.config import settings
+from app.helper.message import MessageHelper
+from app.log import logger
+
+
+class StreamingCallbackHandler(AsyncCallbackHandler):
+    """流式输出回调处理器"""
+
+    def __init__(self, session_id: str):
+        self._lock = threading.Lock()
+        self.session_id = session_id
+        self.current_message = ""
+        self.message_helper = MessageHelper()
+
+    async def get_message(self):
+        """获取当前消息内容，获取后清空"""
+        with self._lock:
+            if not self.current_message:
+                return ""
+            msg = self.current_message
+            logger.info(f"Agent消息: {msg}")
+            self.current_message = ""
+            return msg
+
+    async def on_llm_new_token(self, token: str, **kwargs):
+        """处理新的token"""
+        if not token:
+            return
+        with self._lock:
+            # 缓存当前消息
+            self.current_message += token
+
+
+class MoviePilotAgent:
+    """MoviePilot AI智能体"""
+
+    def __init__(self, session_id: str, user_id: str = None):
+        self.session_id = session_id
+        self.user_id = user_id
+
+        # 消息助手
+        self.message_helper = MessageHelper()
+
+        # 记忆管理器
+        self.memory_manager = ConversationMemoryManager()
+
+        # 提示词管理器
+        self.prompt_manager = PromptManager()
+
+        # 回调处理器
+        self.callback_handler = StreamingCallbackHandler(
+            session_id=session_id
+        )
+
+        # LLM模型
+        self.llm = self._initialize_llm()
+
+        # 工具
+        self.tools = self._initialize_tools()
+
+        # 会话存储
+        self.session_store = self._initialize_session_store()
+
+        # 提示词模板
+        self.prompt = self._initialize_prompt()
+
+        # Agent执行器
+        self.agent_executor = self._create_agent_executor()
+
+    def _initialize_llm(self):
+        """初始化LLM模型"""
+        provider = settings.LLM_PROVIDER.lower()
+        api_key = settings.LLM_API_KEY
+        if not api_key:
+            raise ValueError("未配置 LLM_API_KEY")
+
+        if provider == "google":
+            from langchain_google_genai import ChatGoogleGenerativeAI
+            return ChatGoogleGenerativeAI(
+                model=settings.LLM_MODEL,
+                google_api_key=api_key,
+                max_retries=3,
+                temperature=settings.LLM_TEMPERATURE,
+                streaming=True,
+                callbacks=[self.callback_handler]
+            )
+        elif provider == "deepseek":
+            from langchain_deepseek import ChatDeepSeek
+            return ChatDeepSeek(
+                model=settings.LLM_MODEL,
+                api_key=api_key,
+                max_retries=3,
+                temperature=settings.LLM_TEMPERATURE,
+                streaming=True,
+                callbacks=[self.callback_handler],
+                stream_usage=True
+            )
+        else:
+            from langchain_openai import ChatOpenAI
+            return ChatOpenAI(
+                model=settings.LLM_MODEL,
+                api_key=api_key,
+                max_retries=3,
+                base_url=settings.LLM_BASE_URL,
+                temperature=settings.LLM_TEMPERATURE,
+                streaming=True,
+                callbacks=[self.callback_handler],
+                stream_usage=True
+            )
+
+    def _initialize_tools(self) -> List:
+        """初始化工具列表"""
+        return MoviePilotToolFactory.create_tools(
+            session_id=self.session_id,
+            user_id=self.user_id,
+            message_helper=self.message_helper
+        )
+
+    @staticmethod
+    def _initialize_session_store() -> Dict[str, InMemoryChatMessageHistory]:
+        """初始化内存存储"""
+        return {}
+
+    def get_session_history(self, session_id: str) -> InMemoryChatMessageHistory:
+        """获取会话历史"""
+        if session_id not in self.session_store:
+            chat_history = InMemoryChatMessageHistory()
+            messages: List[dict] = self.memory_manager.get_recent_messages_for_agent(
+                session_id=session_id,
+                user_id=self.user_id
+            )
+            if messages:
+                for msg in messages:
+                    if msg.get("role") == "user":
+                        chat_history.add_user_message(HumanMessage(content=msg.get("content", "")))
+                    elif msg.get("role") == "agent":
+                        chat_history.add_ai_message(AIMessage(content=msg.get("content", "")))
+                    elif msg.get("role") == "tool_call":
+                        metadata = msg.get("metadata", {})
+                        chat_history.add_ai_message(AIMessage(
+                            content=msg.get("content", ""),
+                            tool_calls=[ToolCall(
+                                id=metadata.get("call_id"),
+                                name=metadata.get("tool_name"),
+                                args=metadata.get("parameters"),
+                            )]
+                        ))
+                    elif msg.get("role") == "tool_result":
+                        chat_history.add_ai_message(AIMessage(content=msg.get("content", "")))
+                    elif msg.get("role") == "system":
+                        chat_history.add_ai_message(AIMessage(content=msg.get("content", "")))
+            self.session_store[session_id] = chat_history
+        return self.session_store[session_id]
+
+    @staticmethod
+    def _initialize_prompt() -> ChatPromptTemplate:
+        """初始化提示词模板"""
+        try:
+            prompt = ChatPromptTemplate.from_messages([
+                ("system", "{system_prompt}"),
+                MessagesPlaceholder(variable_name="chat_history"),
+                ("user", "{input}"),
+                MessagesPlaceholder(variable_name="agent_scratchpad"),
+            ])
+            logger.info("LangChain提示词模板初始化成功")
+            return prompt
+        except Exception as e:
+            logger.error(f"初始化提示词失败: {e}")
+            raise e
+
+    def _create_agent_executor(self) -> RunnableWithMessageHistory:
+        """创建Agent执行器"""
+        try:
+            agent = create_openai_tools_agent(
+                llm=self.llm,
+                tools=self.tools,
+                prompt=self.prompt
+            )
+            executor = AgentExecutor(
+                agent=agent,
+                tools=self.tools,
+                verbose=settings.LLM_VERBOSE,
+                max_iterations=settings.LLM_MAX_ITERATIONS,
+                return_intermediate_steps=True,
+                handle_parsing_errors=True,
+                early_stopping_method="force"
+            )
+            return RunnableWithMessageHistory(
+                executor,
+                self.get_session_history,
+                input_messages_key="input",
+                history_messages_key="chat_history"
+            )
+        except Exception as e:
+            logger.error(f"创建Agent执行器失败: {e}")
+            raise e
+
+    async def process_message(self, message: str) -> str:
+        """处理用户消息"""
+        try:
+            # 添加用户消息到记忆
+            await self.memory_manager.add_memory(
+                self.session_id,
+                user_id=self.user_id,
+                role="user",
+                content=message
+            )
+
+            # 构建输入上下文
+            input_context = {
+                "system_prompt": self.prompt_manager.get_agent_prompt(),
+                "input": message
+            }
+
+            # 执行Agent
+            logger.info(f"Agent执行推理: session_id={self.session_id}, input={message}")
+            await self._execute_agent(input_context)
+
+            # 获取Agent回复
+            agent_message = await self.callback_handler.get_message()
+
+            # 发送Agent回复给用户
+            self.message_helper.put(
+                message=agent_message,
+                role="system"
+            )
+
+            # 添加Agent回复到记忆
+            await self.memory_manager.add_memory(
+                session_id=self.session_id,
+                user_id=self.user_id,
+                role="agent",
+                content=agent_message
+            )
+
+            return agent_message
+
+        except Exception as e:
+            error_message = f"处理消息时发生错误: {str(e)}"
+            logger.error(error_message)
+            # 发送错误消息给用户
+            self.message_helper.put(
+                message=error_message,
+                role="system",
+                title="MoviePilot助手错误"
+            )
+            return error_message
+
+    async def _execute_agent(self, input_context: Dict[str, Any]) -> Dict[str, Any]:
+        """执行LangChain Agent"""
+        try:
+            with get_openai_callback() as cb:
+                result = await self.agent_executor.ainvoke(
+                    input_context,
+                    config={"configurable": {"session_id": self.session_id}},
+                    callbacks=[self.callback_handler]
+                )
+                logger.info(f"LLM调用消耗: \n{cb}")
+
+                if cb.total_tokens > 0:
+                    result["token_usage"] = {
+                        "prompt_tokens": cb.prompt_tokens,
+                        "completion_tokens": cb.completion_tokens,
+                        "total_tokens": cb.total_tokens
+                    }
+            return result
+        except asyncio.CancelledError:
+            logger.info(f"Agent执行被取消: session_id={self.session_id}")
+            return {
+                "output": "任务已取消",
+                "intermediate_steps": [],
+                "token_usage": {}
+            }
+        except Exception as e:
+            logger.error(f"Agent执行失败: {e}")
+            return {
+                "output": f"执行过程中发生错误: {str(e)}",
+                "intermediate_steps": [],
+                "token_usage": {}
+            }
+
+    async def cleanup(self):
+        """清理智能体资源"""
+        if self.session_id in self.session_store:
+            del self.session_store[self.session_id]
+        logger.info(f"MoviePilot智能体已清理: session_id={self.session_id}")
+
+
+class AgentManager:
+    """AI智能体管理器"""
+
+    def __init__(self):
+        self.active_agents: Dict[str, MoviePilotAgent] = {}
+        self.memory_manager = ConversationMemoryManager()
+
+    async def initialize(self):
+        """初始化管理器"""
+        await self.memory_manager.initialize()
+
+    async def close(self):
+        """关闭管理器"""
+        await self.memory_manager.close()
+        # 清理所有活跃的智能体
+        for agent in self.active_agents.values():
+            await agent.cleanup()
+        self.active_agents.clear()
+
+    async def process_message(self, session_id: str, user_id: str, message: str) -> str:
+        """处理用户消息"""
+        # 获取或创建Agent实例
+        if session_id not in self.active_agents:
+            logger.info(f"创建新的AI智能体实例，session_id: {session_id}, user_id: {user_id}")
+            agent = MoviePilotAgent(
+                session_id=session_id,
+                user_id=user_id
+            )
+            agent.memory_manager = self.memory_manager
+            self.active_agents[session_id] = agent
+        else:
+            agent = self.active_agents[session_id]
+            agent.user_id = user_id  # 确保user_id是最新的
+
+        # 处理消息
+        return await agent.process_message(message)
+
+    async def clear_session(self, session_id: str, user_id: str):
+        """清空会话"""
+        if session_id in self.active_agents:
+            agent = self.active_agents[session_id]
+            await agent.cleanup()
+            del self.active_agents[session_id]
+            await self.memory_manager.clear_memory(session_id, user_id)
+            logger.info(f"会话 {session_id} 的记忆已清空")
+
+
+# 全局智能体管理器实例
+agent_manager = AgentManager()

app/agent/agent.py

@@ -1,354 +0,0 @@
-"""MoviePilot AI智能体实现"""
-
-import asyncio
-import threading
-from typing import Dict, List, Any
-
-from langchain.agents import AgentExecutor, create_openai_tools_agent
-from langchain.prompts import ChatPromptTemplate, MessagesPlaceholder
-from langchain_community.callbacks import get_openai_callback
-from langchain_core.callbacks import AsyncCallbackHandler
-from langchain_core.chat_history import InMemoryChatMessageHistory
-from langchain_core.messages import HumanMessage, AIMessage, ToolCall
-from langchain_core.runnables.history import RunnableWithMessageHistory
-
-from app.agent.memory import ConversationMemoryManager
-from app.agent.prompt import PromptManager
-from app.agent.tools import MoviePilotToolFactory
-from app.core.config import settings
-from app.helper.message import MessageHelper
-from app.log import logger
-
-
-class StreamingCallbackHandler(AsyncCallbackHandler):
-    """流式输出回调处理器"""
-
-    def __init__(self, session_id: str):
-        self._lock = threading.Lock()
-        self.session_id = session_id
-        self.current_message = ""
-        self.message_helper = MessageHelper()
-
-    async def get_message(self):
-        """获取当前消息内容，获取后清空"""
-        with self._lock:
-            if not self.current_message:
-                return ""
-            msg = self.current_message
-            logger.info(f"Agent消息: {msg}")
-            self.current_message = ""
-            return msg
-
-    async def on_llm_new_token(self, token: str, **kwargs):
-        """处理新的token"""
-        if not token:
-            return
-        with self._lock:
-            # 缓存当前消息
-            self.current_message += token
-
-
-class MoviePilotAgent:
-    """MoviePilot AI智能体"""
-
-    def __init__(self, session_id: str, user_id: str = None):
-        self.session_id = session_id
-        self.user_id = user_id
-
-        # 消息助手
-        self.message_helper = MessageHelper()
-
-        # 记忆管理器
-        self.memory_manager = ConversationMemoryManager()
-
-        # 提示词管理器
-        self.prompt_manager = PromptManager()
-
-        # 回调处理器
-        self.callback_handler = StreamingCallbackHandler(
-            session_id=session_id
-        )
-
-        # LLM模型
-        self.llm = self._initialize_llm()
-
-        # 工具
-        self.tools = self._initialize_tools()
-
-        # 会话存储
-        self.session_store = self._initialize_session_store()
-
-        # 提示词模板
-        self.prompt = self._initialize_prompt()
-
-        # Agent执行器
-        self.agent_executor = self._create_agent_executor()
-
-    def _initialize_llm(self):
-        """初始化LLM模型"""
-        provider = settings.LLM_PROVIDER.lower()
-        api_key = settings.LLM_API_KEY
-        if not api_key:
-            raise ValueError("未配置 LLM_API_KEY")
-
-        if provider == "google":
-            from langchain_google_genai import ChatGoogleGenerativeAI
-            return ChatGoogleGenerativeAI(
-                model=settings.LLM_MODEL,
-                google_api_key=api_key,
-                max_retries=3,
-                temperature=settings.LLM_TEMPERATURE,
-                streaming=True,
-                callbacks=[self.callback_handler]
-            )
-        elif provider == "deepseek":
-            from langchain_deepseek import ChatDeepSeek
-            return ChatDeepSeek(
-                model=settings.LLM_MODEL,
-                api_key=api_key,
-                max_retries=3,
-                temperature=settings.LLM_TEMPERATURE,
-                streaming=True,
-                callbacks=[self.callback_handler],
-                stream_usage=True
-            )
-        else:
-            from langchain_openai import ChatOpenAI
-            return ChatOpenAI(
-                model=settings.LLM_MODEL,
-                api_key=api_key,
-                max_retries=3,
-                base_url=settings.LLM_BASE_URL,
-                temperature=settings.LLM_TEMPERATURE,
-                streaming=True,
-                callbacks=[self.callback_handler],
-                stream_usage=True
-            )
-
-    def _initialize_tools(self) -> List:
-        """初始化工具列表"""
-        return MoviePilotToolFactory.create_tools(
-            session_id=self.session_id,
-            user_id=self.user_id,
-            message_helper=self.message_helper
-        )
-
-    @staticmethod
-    def _initialize_session_store() -> Dict[str, InMemoryChatMessageHistory]:
-        """初始化内存存储"""
-        return {}
-
-    def get_session_history(self, session_id: str) -> InMemoryChatMessageHistory:
-        """获取会话历史"""
-        if session_id not in self.session_store:
-            chat_history = InMemoryChatMessageHistory()
-            messages: List[dict] = self.memory_manager.get_recent_messages_for_agent(
-                session_id=session_id,
-                user_id=self.user_id
-            )
-            if messages:
-                for msg in messages:
-                    if msg.get("role") == "user":
-                        chat_history.add_user_message(HumanMessage(content=msg.get("content", "")))
-                    elif msg.get("role") == "agent":
-                        chat_history.add_ai_message(AIMessage(content=msg.get("content", "")))
-                    elif msg.get("role") == "tool_call":
-                        metadata = msg.get("metadata", {})
-                        chat_history.add_ai_message(AIMessage(
-                            content=msg.get("content", ""),
-                            tool_calls=[ToolCall(
-                                id=metadata.get("call_id"),
-                                name=metadata.get("tool_name"),
-                                args=metadata.get("parameters"),
-                            )]
-                        ))
-                    elif msg.get("role") == "tool_result":
-                        chat_history.add_ai_message(AIMessage(content=msg.get("content", "")))
-                    elif msg.get("role") == "system":
-                        chat_history.add_ai_message(AIMessage(content=msg.get("content", "")))
-            self.session_store[session_id] = chat_history
-        return self.session_store[session_id]
-
-    @staticmethod
-    def _initialize_prompt() -> ChatPromptTemplate:
-        """初始化提示词模板"""
-        try:
-            prompt = ChatPromptTemplate.from_messages([
-                ("system", "{system_prompt}"),
-                MessagesPlaceholder(variable_name="chat_history"),
-                ("user", "{input}"),
-                MessagesPlaceholder(variable_name="agent_scratchpad"),
-            ])
-            logger.info("LangChain提示词模板初始化成功")
-            return prompt
-        except Exception as e:
-            logger.error(f"初始化提示词失败: {e}")
-            raise e
-
-    def _create_agent_executor(self) -> RunnableWithMessageHistory:
-        """创建Agent执行器"""
-        try:
-            agent = create_openai_tools_agent(
-                llm=self.llm,
-                tools=self.tools,
-                prompt=self.prompt
-            )
-            executor = AgentExecutor(
-                agent=agent,
-                tools=self.tools,
-                verbose=settings.LLM_VERBOSE,
-                max_iterations=settings.LLM_MAX_ITERATIONS,
-                return_intermediate_steps=True,
-                handle_parsing_errors=True,
-                early_stopping_method="force"
-            )
-            return RunnableWithMessageHistory(
-                executor,
-                self.get_session_history,
-                input_messages_key="input",
-                history_messages_key="chat_history"
-            )
-        except Exception as e:
-            logger.error(f"创建Agent执行器失败: {e}")
-            raise e
-
-    async def process_message(self, message: str) -> str:
-        """处理用户消息"""
-        try:
-            # 添加用户消息到记忆
-            await self.memory_manager.add_memory(
-                self.session_id,
-                user_id=self.user_id,
-                role="user",
-                content=message
-            )
-
-            # 构建输入上下文
-            input_context = {
-                "system_prompt": self.prompt_manager.get_agent_prompt(),
-                "input": message
-            }
-
-            # 执行Agent
-            logger.info(f"Agent执行推理: session_id={self.session_id}, input={message}")
-            await self._execute_agent(input_context)
-
-            # 获取Agent回复
-            agent_message = await self.callback_handler.get_message()
-
-            # 发送Agent回复给用户
-            self.message_helper.put(
-                message=agent_message,
-                role="system"
-            )
-
-            # 添加Agent回复到记忆
-            await self.memory_manager.add_memory(
-                session_id=self.session_id,
-                user_id=self.user_id,
-                role="agent",
-                content=agent_message
-            )
-
-            return agent_message
-
-        except Exception as e:
-            error_message = f"处理消息时发生错误: {str(e)}"
-            logger.error(error_message)
-            # 发送错误消息给用户
-            self.message_helper.put(
-                message=error_message,
-                role="system",
-                title="MoviePilot助手错误"
-            )
-            return error_message
-
-    async def _execute_agent(self, input_context: Dict[str, Any]) -> Dict[str, Any]:
-        """执行LangChain Agent"""
-        try:
-            with get_openai_callback() as cb:
-                result = await self.agent_executor.ainvoke(
-                    input_context,
-                    config={"configurable": {"session_id": self.session_id}},
-                    callbacks=[self.callback_handler]
-                )
-                logger.info(f"LLM调用消耗: \n{cb}")
-
-                if cb.total_tokens > 0:
-                    result["token_usage"] = {
-                        "prompt_tokens": cb.prompt_tokens,
-                        "completion_tokens": cb.completion_tokens,
-                        "total_tokens": cb.total_tokens
-                    }
-            return result
-        except asyncio.CancelledError:
-            logger.info(f"Agent执行被取消: session_id={self.session_id}")
-            return {
-                "output": "任务已取消",
-                "intermediate_steps": [],
-                "token_usage": {}
-            }
-        except Exception as e:
-            logger.error(f"Agent执行失败: {e}")
-            return {
-                "output": f"执行过程中发生错误: {str(e)}",
-                "intermediate_steps": [],
-                "token_usage": {}
-            }
-
-    async def cleanup(self):
-        """清理智能体资源"""
-        if self.session_id in self.session_store:
-            del self.session_store[self.session_id]
-        logger.info(f"MoviePilot智能体已清理: session_id={self.session_id}")
-
-
-class AgentManager:
-    """AI智能体管理器"""
-
-    def __init__(self):
-        self.active_agents: Dict[str, MoviePilotAgent] = {}
-        self.memory_manager = ConversationMemoryManager()
-
-    async def initialize(self):
-        """初始化管理器"""
-        await self.memory_manager.initialize()
-
-    async def close(self):
-        """关闭管理器"""
-        await self.memory_manager.close()
-        # 清理所有活跃的智能体
-        for agent in self.active_agents.values():
-            await agent.cleanup()
-        self.active_agents.clear()
-
-    async def process_message(self, session_id: str, user_id: str, message: str) -> str:
-        """处理用户消息"""
-        # 获取或创建Agent实例
-        if session_id not in self.active_agents:
-            logger.info(f"创建新的AI智能体实例，session_id: {session_id}, user_id: {user_id}")
-            agent = MoviePilotAgent(
-                session_id=session_id,
-                user_id=user_id
-            )
-            agent.memory_manager = self.memory_manager
-            self.active_agents[session_id] = agent
-        else:
-            agent = self.active_agents[session_id]
-            agent.user_id = user_id  # 确保user_id是最新的
-
-        # 处理消息
-        return await agent.process_message(message)
-
-    async def clear_session(self, session_id: str, user_id: str):
-        """清空会话"""
-        if session_id in self.active_agents:
-            agent = self.active_agents[session_id]
-            await agent.cleanup()
-            del self.active_agents[session_id]
-            await self.memory_manager.clear_memory(session_id, user_id)
-            logger.info(f"会话 {session_id} 的记忆已清空")
-
-
-# 全局智能体管理器实例
-agent_manager = AgentManager()

app/api/endpoints/workflow.py

@@ -11,7 +11,7 @@ from app.chain.workflow import WorkflowChain
 from app.core.config import global_vars
 from app.core.plugin import PluginManager
 from app.core.security import verify_token
-from app.core.workflow import WorkFlowManager
+from app.workflow import WorkFlowManager
 from app.db import get_async_db, get_db
 from app.db.models import Workflow
 from app.db.systemconfig_oper import SystemConfigOper

app/chain/message.py

@@ -847,7 +847,7 @@ class MessageChain(ChainBase):
 
             # 异步处理AI智能体请求
             import asyncio
-            from app.agent.agent import agent_manager
+            from app.agent import agent_manager
             
             # 生成会话ID
             session_id = f"user_{userid}_{hash(user_message) % 10000}"

app/chain/workflow.py

@@ -11,7 +11,7 @@ from pydantic.fields import Callable
 from app.chain import ChainBase
 from app.core.config import global_vars
 from app.core.event import Event, eventmanager
-from app.core.workflow import WorkFlowManager
+from app.workflow import WorkFlowManager
 from app.db.models import Workflow
 from app.db.workflow_oper import WorkflowOper
 from app.log import logger

app/startup/agent_initializer.py

@@ -26,7 +26,7 @@ class AgentInitializer:
                 logger.info("AI智能体功能未启用")
                 return True
             
-            from app.agent.agent import agent_manager
+            from app.agent import agent_manager
             self.agent_manager = agent_manager
             
             await agent_manager.initialize()

app/startup/workflow_initializer.py

@@ -1,5 +1,4 @@
-from app.core.workflow import WorkFlowManager
+from app.workflow import WorkFlowManager
-from app.chain.workflow import WorkflowChain
 
 
 def init_workflow():

app/workflow/__init__.py

@@ -46,7 +46,7 @@ class WorkFlowManager(metaclass=Singleton):
         # 加载所有动作
         self._actions = {}
         actions = ModuleHelper.load(
-            "app.actions",
+            "app.workflow.actions",
             filter_func=lambda _, obj: filter_func(obj)
         )
         for action in actions:

app/workflow/actions/add_download.py

@@ -2,7 +2,7 @@ from typing import Optional
 
 from pydantic import Field
 
-from app.actions import BaseAction
+from app.workflow.actions import BaseAction
 from app.chain.download import DownloadChain
 from app.chain.media import MediaChain
 from app.core.config import global_vars

app/workflow/actions/add_subscribe.py

@@ -1,4 +1,4 @@
-from app.actions import BaseAction
+from app.workflow.actions import BaseAction
 from app.chain.subscribe import SubscribeChain
 from app.core.config import settings, global_vars
 from app.core.context import MediaInfo

app/workflow/actions/fetch_downloads.py

@@ -1,4 +1,4 @@
-from app.actions import BaseAction, ActionChain
+from app.workflow.actions import BaseAction, ActionChain
 from app.core.config import global_vars
 from app.schemas import ActionParams, ActionContext
 from app.log import logger

app/workflow/actions/fetch_medias.py

@@ -2,7 +2,7 @@ from typing import List, Optional
 
 from pydantic import Field
 
-from app.actions import BaseAction
+from app.workflow.actions import BaseAction
 from app.chain.recommend import RecommendChain
 from app.schemas import ActionParams, ActionContext
 from app.core.config import settings, global_vars

app/workflow/actions/fetch_rss.py

@@ -2,7 +2,7 @@ from typing import Optional
 
 from pydantic import Field
 
-from app.actions import BaseAction, ActionChain
+from app.workflow.actions import BaseAction, ActionChain
 from app.core.config import settings, global_vars
 from app.core.context import Context
 from app.core.metainfo import MetaInfo

app/workflow/actions/fetch_torrents.py

@@ -4,7 +4,7 @@ from typing import Optional, List
 
 from pydantic import Field
 
-from app.actions import BaseAction
+from app.workflow.actions import BaseAction
 from app.chain.search import SearchChain
 from app.core.config import global_vars
 from app.log import logger

app/workflow/actions/filter_medias.py

@@ -2,7 +2,7 @@ from typing import Optional
 
 from pydantic import Field
 
-from app.actions import BaseAction
+from app.workflow.actions import BaseAction
 from app.core.config import global_vars
 from app.log import logger
 from app.schemas import ActionParams, ActionContext

app/workflow/actions/filter_torrents.py

@@ -2,7 +2,7 @@ from typing import Optional, List
 
 from pydantic import Field
 
-from app.actions import BaseAction, ActionChain
+from app.workflow.actions import BaseAction, ActionChain
 from app.core.config import global_vars
 from app.helper.torrent import TorrentHelper
 from app.log import logger

app/workflow/actions/invoke_plugin.py

@@ -1,6 +1,6 @@
 from pydantic import Field
 
-from app.actions import BaseAction
+from app.workflow.actions import BaseAction
 from app.core.plugin import PluginManager
 from app.log import logger
 from app.schemas import ActionParams, ActionContext

app/workflow/actions/note.py

@@ -1,4 +1,4 @@
-from app.actions import BaseAction
+from app.workflow.actions import BaseAction
 from app.schemas import ActionContext
 
 

app/workflow/actions/scan_file.py

@@ -3,7 +3,7 @@ from typing import Optional
 
 from pydantic import Field
 
-from app.actions import BaseAction
+from app.workflow.actions import BaseAction
 from app.chain.storage import StorageChain
 from app.core.config import global_vars, settings
 from app.log import logger

app/workflow/actions/scrape_file.py

@@ -1,6 +1,6 @@
 from pathlib import Path
 
-from app.actions import BaseAction
+from app.workflow.actions import BaseAction
 from app.core.config import global_vars
 from app.schemas import ActionParams, ActionContext
 from app.chain.media import MediaChain

app/workflow/actions/send_event.py

@@ -1,4 +1,4 @@
-from app.actions import BaseAction
+from app.workflow.actions import BaseAction
 from app.core.event import eventmanager
 from app.schemas import ActionParams, ActionContext
 from app.schemas.types import ChainEventType

app/workflow/actions/send_message.py

@@ -2,7 +2,7 @@ from typing import List, Optional, Union
 
 from pydantic import Field
 
-from app.actions import BaseAction, ActionChain
+from app.workflow.actions import BaseAction, ActionChain
 from app.schemas import ActionParams, ActionContext, Notification
 from app.core.config import settings
 

app/workflow/actions/transfer_file.py

@@ -4,7 +4,7 @@ from typing import Optional
 
 from pydantic import Field
 
-from app.actions import BaseAction
+from app.workflow.actions import BaseAction
 from app.core.config import global_vars
 from app.db.transferhistory_oper import TransferHistoryOper
 from app.schemas import ActionParams, ActionContext