465 lines
18 KiB
Python
465 lines
18 KiB
Python
"""智能编剧系统工作流图定义
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该模块定义了智能编剧系统的完整工作流程图，包括各个节点和边的连接关系。
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"""
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from typing import TypedDict, Annotated, Dict, Any, List, TypedDict, Optional
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from langgraph.graph.state import RunnableConfig
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from agent.scheduler import SchedulerAgent
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from agent.build_bible import BuildBibleAgent
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from agent.episode_create import EpisodeCreateAgent
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from agent.script_analysis import ScriptAnalysisAgent
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from agent.strategic_planning import StrategicPlanningAgent
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from langchain_core.messages import AnyMessage,HumanMessage
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from langgraph.graph import StateGraph, START, END
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from utils.logger import get_logger
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import operator
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import json
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import config
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from tools.database.mongo import client # type: ignore
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from langgraph.checkpoint.mongodb import MongoDBSaver
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logger = get_logger(__name__)
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# 定义一个简单的替换函数
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def replace_value(old_val, new_val):
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"""一个简单的合并函数，用于替换旧值"""
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return new_val
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# 状态类型定义
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class InputState(TypedDict):
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"""工作流输入状态"""
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input_data: Annotated[list[AnyMessage], operator.add]
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from_type: Annotated[str, replace_value]
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session_id: Annotated[str, replace_value]
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class OutputState(TypedDict):
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"""工作流输出状态"""
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session_id: Annotated[str, replace_value]
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status: Annotated[str, replace_value]
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error: Annotated[str, replace_value]
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agent_message: Annotated[str, replace_value] # 智能体回复
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class NodeInfo(TypedDict):
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"""工作流信息"""
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step: Annotated[str, replace_value] # 阶段名称 [wait_for_input,script_analysis,strategic_planning,build_bible,episode_create_loop, finish]
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status: Annotated[str, replace_value] # 当前阶段的状态 [waiting,running,failed,completed]
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reason: Annotated[str, replace_value] # 失败原因
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retry_count: Annotated[int, replace_value] # 重试次数
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from_type: Annotated[str, replace_value] # 本次请求来着哪里 [user, agent]
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class ScriptwriterState(TypedDict, total=False):
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"""智能编剧工作流整体状态"""
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# 输入数据
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input_data: Annotated[list[HumanMessage], operator.add]
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session_id: Annotated[str, replace_value]
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from_type: Annotated[str, replace_value] # 本次请求来着哪里 [user, agent]
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# 节点间状态
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next_node: Annotated[str, replace_value] # 下一个节点
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workflow_step: Annotated[str, replace_value] # 阶段名称 [wait_for_input,script_analysis,strategic_planning,build_bible,episode_create_loop, finish]
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workflow_status: Annotated[str, replace_value] # 当前阶段的状态 [waiting,running,failed,completed]
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workflow_reason: Annotated[str, replace_value] # 失败原因
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workflow_retry_count: Annotated[int, replace_value] # 重试次数
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# 中间状态
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agent_script_id: Annotated[str, replace_value] # 剧本ID 包括原文
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agent_plan: Annotated[Dict[str, Any], replace_value] #剧本计划
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script_bible: Annotated[Dict[str, Any], replace_value] #剧本圣经
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episode_list: Annotated[List, replace_value] # 章节列表 完成状态、产出章节id
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# 输出数据
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agent_message: Annotated[str, replace_value] # 智能体回复
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status: Annotated[str, replace_value]
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error: Annotated[str, replace_value]
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class ScriptwriterGraph:
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"""智能编剧工作流图类
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管理智能编剧系统的完整工作流程，包括：
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- 剧本接收
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- 诊断分析
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- 策略制定
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- 剧本圣经构建
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- 剧本创作
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- 迭代调整
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"""
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def __init__(self):
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"""初始化工作流图"""
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self.graph = None
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self.memory = MongoDBSaver(client, db_name=config.MONGO_CHECKPOINT_DB_NAME)
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self._build_graph()
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def node_router(self, state: ScriptwriterState) -> str:
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"""节点路由函数"""
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print(f'node_router state {state}')
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next_node = state.get("next_node", 'pause_node')
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# 修复：当 next_node 为空字符串时，设置默认值
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if not next_node:
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next_node = 'pause_node' # 设置为暂停节点
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print(f'node_router next_node {next_node}')
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return next_node
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def _build_graph(self) -> None:
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"""构建工作流图"""
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try:
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# 创建智能体
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print("创建智能体")
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# 调度智能体
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self.schedulerAgent = SchedulerAgent(
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tools=[],
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SchedulerList=[
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{
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"name": "scheduler_node",
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"desc": "调度智能体节点",
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},
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{
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"name": "script_analysis_node",
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"desc": "原始剧本分析节点",
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},
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{
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"name": "strategic_planning_node",
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"desc": "确立改编目标节点",
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},
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{
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"name": "build_bible_node",
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"desc": "剧本圣经构建节点",
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},
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{
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"name": "episode_create_node",
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"desc": "单集创作节点",
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},
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{
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"name": "end_node",
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"desc": "结束节点，任务失败终止时使用，结束后整个工作流将停止"
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}
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]
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)
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self.scriptAnalysisAgent = ScriptAnalysisAgent(
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tools=[],
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SchedulerList=[
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{
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"name": "scheduler_node",
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"desc": "调度智能体节点",
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}
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]
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)
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self.strategicPlanningAgent = StrategicPlanningAgent(
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tools=[],
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SchedulerList=[
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{
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"name": "scheduler_node",
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"desc": "调度智能体节点",
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}
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]
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)
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self.buildBibleAgent = BuildBibleAgent(
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tools=[],
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SchedulerList=[
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{
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"name": "scheduler_node",
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"desc": "调度智能体节点",
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}
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]
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)
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self.episodeCreate = EpisodeCreateAgent(
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tools=[],
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SchedulerList=[
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{
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"name": "scheduler_node",
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"desc": "调度智能体节点",
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}
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]
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)
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# 创建状态图
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logger.info("创建状态图")
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workflow = StateGraph(ScriptwriterState, input_schema=InputState, output_schema=OutputState)
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# 添加节点
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logger.info("添加节点")
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workflow.add_node("scheduler_node", self.scheduler_node)
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workflow.add_node("script_analysis_node", self.script_analysis_node)
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workflow.add_node("strategic_planning_node", self.strategic_planning_node)
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workflow.add_node("build_bible_node", self.build_bible_node)
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workflow.add_node("episode_create_node", self.episode_create_node)
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workflow.add_node("end_node", self.end_node)
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workflow.add_node("pause_node", self.pause_node)
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# 添加边
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workflow.set_entry_point("scheduler_node")
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# 所有功能节点执行完成后，都返回给调度节点
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workflow.add_edge("script_analysis_node", "scheduler_node")
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workflow.add_edge("strategic_planning_node", "scheduler_node")
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workflow.add_edge("build_bible_node", "scheduler_node")
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workflow.add_edge("episode_create_node", "scheduler_node")
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# 添加条件边：由调度节点决定下一个路由
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workflow.add_conditional_edges(
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"scheduler_node",
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self.node_router,
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{
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"script_analysis_node": "script_analysis_node",
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"strategic_planning_node": "strategic_planning_node",
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"build_bible_node": "build_bible_node",
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"episode_create_node": "episode_create_node",
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# 用户确认和暂停逻辑在这里处理，不需要单独的边
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"end_node": "end_node",
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"pause_node": "pause_node",
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}
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)
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workflow.add_edge("end_node", END)
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# 编译图
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self.graph = workflow.compile(checkpointer=self.memory)
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logger.info("工作流图构建完成")
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except Exception as e:
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logger.error(f"构建工作流图失败: {e}")
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raise
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# --- 定义图中的节点 ---
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async def scheduler_node(self, state: ScriptwriterState)-> ScriptwriterState:
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"""调度节点"""
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try:
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session_id = state.get("session_id", "")
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from_type = state.get("from_type", "")
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input_data = state.get("input_data", [])
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logger.info(f"调度节点 {session_id} 输入参数: {input_data} from_type:{from_type}")
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reslut = await self.schedulerAgent.ainvoke(state)
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ai_message_str = reslut['messages'][-1].content
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ai_message = json.loads(ai_message_str)
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logger.info(f"调度节点结果: {ai_message}")
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step:str = ai_message.get('step', '')
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status:str = ai_message.get('status', '')
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next_agent:str = ai_message.get('agent', '')
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return_message:str = ai_message.get('message', '')
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retry_count:int = int(ai_message.get('retry_count', '0'))
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next_node:str = ai_message.get('node', 'pause_node')
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if next_node == 'scheduler_node':
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# 返回自身 代表暂停
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print(f"调度节点 暂停等待")
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return {
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"agent_message": return_message,
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}
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else:
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return {
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"next_node":next_node,
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"workflow_step":step,
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"workflow_status":status,
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# "workflow_reason":return_message,
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"workflow_retry_count":retry_count,
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"agent_message":return_message,
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}
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except Exception as e:
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return {
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"next_node":'end_node',
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"agent_message": "执行失败",
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"error": str(e) or '系统错误，工作流已终止',
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'status':'failed',
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}
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async def script_analysis_node(self, state: ScriptwriterState)-> ScriptwriterState:
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"""第二步：诊断分析与资产评估"""
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print("\n--- 正在进行诊断分析 ---")
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session_id = state.get("session_id", "")
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print(f"报告已生成: TEST")
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return {}
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async def confirm_analysis_node(self, state: ScriptwriterState)-> ScriptwriterState:
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"""用户确认分析报告节点"""
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print("\n等待用户确认分析报告...")
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return {}
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async def strategic_planning_node(self, state: ScriptwriterState)-> ScriptwriterState:
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"""第三步：确立改编目标与战略蓝图"""
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print("\n--- 正在制定战略蓝图 ---")
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print(f"战略蓝图已生成: TEST")
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return {}
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async def build_bible_node(self, state: ScriptwriterState)-> ScriptwriterState:
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"""第四步：确立改编目标与战略蓝图"""
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print("\n--- 正在制定战略蓝图 ---")
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print(f"战略蓝图已生成: TEST")
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return {}
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async def episode_create_node(self, state: ScriptwriterState)-> ScriptwriterState:
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"""第五步：动态创作与闭环校验（循环主体）"""
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num_episodes = 3 # 假设每次创作3集
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episode_list = []
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return {"episode_list": episode_list}
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async def pause_node(self, state: ScriptwriterState)-> ScriptwriterState:
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""" 暂停节点 处理并完成所有数据状态 """
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print(f"langgraph 暂停等待")
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return {
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"session_id": state.get("session_id", ""),
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"status": state.get('status', ''),
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"error": state.get('error', ''),
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"agent_message": state.get('agent_message', '')
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}
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async def end_node(self, state: ScriptwriterState)-> OutputState:
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""" 结束节点 处理并完成所有数据状态 """
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print(f"langgraph 所有任务完成")
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return {
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"session_id": state.get("session_id", ""),
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"status": state.get('status', ''),
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"error": state.get('error', ''),
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"agent_message": state.get('agent_message', ''),
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}
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async def run(self, session_id: str, input_data: list[AnyMessage], thread_id: str|None = None) -> OutputState:
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"""运行工作流
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Args:
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session_id: 会话ID
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input_data: 输入数据
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thread_id: 线程ID
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Returns:
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工作流执行结果
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"""
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try:
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logger.info("开始运行智能编剧工作流")
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# 配置包含线程 ID
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config:RunnableConfig = {"configurable": {"thread_id": thread_id}}
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# 初始化状态
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initial_state: InputState = {
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'input_data': input_data,
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'session_id': session_id,
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'from_type': 'user',
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}
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# 运行工作流
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if self.graph is None:
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raise RuntimeError("工作流图未正确初始化")
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result = await self.graph.ainvoke(
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initial_state,
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config,
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# stream_mode='values'
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)
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# logger.info(f"工作流执行结果: {result}")
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if not result:
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raise ValueError("工作流执行结果为空")
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# 构造输出状态
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output_result: OutputState = {
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'session_id': result.get('session_id', ''),
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'status': result.get('status', ''),
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'error': result.get('error', ''),
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'agent_message': result.get('agent_message', ''),
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}
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logger.info("智能编剧工作流运行完成")
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return output_result
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except Exception as e:
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logger.error(f"运行工作流失败: {e}")
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import traceback
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traceback.print_exc()
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raise
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async def get_checkpoint_history(self, thread_id: str):
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"""获取检查点历史"""
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config:RunnableConfig = {"configurable": {"thread_id": thread_id}}
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try:
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history_generator = self.memory.list(config, limit=10)
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print("正在获取检查点历史...")
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# 使用列表推导式或 for 循环来收集所有检查点
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history = list(history_generator)
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print(f"找到 {len(history)} 个检查点:")
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for i, checkpoint_tuple in enumerate(history):
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# checkpoint_tuple 包含 config, checkpoint, metadata 等属性
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# print(f" - ID: {checkpoint_tuple}")
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checkpoint_data = checkpoint_tuple.checkpoint
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metadata = checkpoint_tuple.metadata
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print(f"检查点 {i+1}:")
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print(f" - ID: {checkpoint_data.get('id', 'N/A')}")
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print(f" - 状态: {checkpoint_data.get('channel_values', {})}")
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print(f" - 元数据: {metadata}")
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print("-" * 50)
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except Exception as e:
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print(f"获取历史记录时出错: {e}")
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def resume_from_checkpoint(self, thread_id: str, checkpoint_id: str):
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"""从检查点恢复执行"""
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config:RunnableConfig = {"configurable": {"thread_id": thread_id}}
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if checkpoint_id:
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config["configurable"]["checkpoint_id"] = checkpoint_id
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try:
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# 获取 CheckpointTuple 对象
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checkpoint_tuple = self.memory.get_tuple(config)
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if checkpoint_tuple:
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# 直接通过属性访问，而不是解包
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checkpoint_data = checkpoint_tuple.checkpoint
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metadata = checkpoint_tuple.metadata
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print(f"从检查点恢复:")
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print(f" - 检查点 ID: {checkpoint_data.get('id', 'N/A')}")
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print(f" - 状态: {checkpoint_data.get('channel_values', {})}")
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print(f" - 元数据: {metadata}")
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return checkpoint_data.get('channel_values', {})
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else:
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print(f"未找到线程 {thread_id} 的检查点")
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return None
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except Exception as e:
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print(f"恢复检查点时出错: {e}")
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return None
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def get_graph_visualization(self) -> str:
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"""获取工作流图的可视化表示
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Returns:
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图的文本表示
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"""
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try:
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if self.graph:
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with open('graph_visualization.png', 'wb') as f:
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f.write(self.graph.get_graph().draw_mermaid_png())
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print("图片已保存为 graph_visualization.png")
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return "工作流图未初始化"
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except Exception as e:
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logger.error(f"获取图可视化失败: {e}")
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return f"获取图可视化失败: {e}"
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if __name__ == "__main__":
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import asyncio
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async def main():
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print("测试")
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graph = ScriptwriterGraph()
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print("创建完成")
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|
# graph.get_graph_visualization()
|
|
# print("可视化完成")
|
|
# 运行工作流
|
|
session_id = "68c2c2915e5746343301ef71"
|
|
result = await graph.run(
|
|
session_id,
|
|
[HumanMessage(content="你好编剧,我想写小说!")],
|
|
session_id
|
|
)
|
|
print(f"最终结果: {result}")
|
|
|
|
asyncio.run(main())
|