pengxiao
219232de74
以讲义内容为骨架迁移到标准目录格式： - officefile/ 主内容（12章 + 附录 + CC4SI补充） - dofile/ 代码示例（11个Python脚本） - data/ 图片资源 - output/ 生成输出（忽略） - Archive/ 归档旧目录（忽略） - .claude/skills/ 保留markdown-to-docx工具链 - .pandoc/ 保留CSL和本地化配置 Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
720 lines
22 KiB
Markdown
720 lines
22 KiB
Markdown
# 01.2 状态与状态机
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## 核心问题
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> 在AI系统中，"状态"到底是什么？
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> 为什么状态管理是Agent系统的核心？
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> LangGraph是如何用状态机设计工作流的？
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---
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## 概念讲解
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### 什么是状态
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**状态**是系统在某一时刻的快照，包含所有影响未来行为的信息：
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```
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系统状态 = 所有相关的变量值
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例如：生态分析工作流的状态
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{
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"input_data": {...}, # 输入数据
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"current_step": "buffer", # 当前步骤
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"intermediate_results": {...}, # 中间结果
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"user_preferences": {...}, # 用户偏好
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"error_count": 0, # 错误计数
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"checkpoint_reached": False # 检查点状态
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}
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```
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**状态的类型**：
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| 类型 | 说明 | 例子 |
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|-----|------|------|
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| 静态状态 | 初始输入，不变化 | 输入文件路径、参数 |
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| 动态状态 | 运行中变化 | 当前步骤、累积结果 |
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| 控制状态 | 影响流程走向 | 分支条件、错误标志 |
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| 会话状态 | 跨请求持久化 | 用户偏好、历史记录 |
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### 为什么状态管理很重要
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**1. 断点续传**
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```python
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# 没有状态管理：出错后必须从头开始
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def analysis_without_state():
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step1()
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step2() # 如果这里出错
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step3() # 这些都要重做
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# 有状态管理：可以从断点继续
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class AnalysisWithState:
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def __init__(self):
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self.state = {"current_step": 0}
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def run(self):
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if self.state["current_step"] < 1:
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step1()
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self.state["current_step"] = 1
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if self.state["current_step"] < 2:
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try:
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step2()
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self.state["current_step"] = 2
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except Exception:
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# 保存状态，下次可以从这里继续
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save_state(self.state)
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raise
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if self.state["current_step"] < 3:
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step3()
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```
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**2. 人机协同**
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```python
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# HITL需要状态来知道在哪里需要人类介入
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class HITLWorkflow:
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def __init__(self):
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self.state = {
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"step": "identify_sources",
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"pending_review": True,
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"sources": None,
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"human_feedback": None
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}
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def next_action(self):
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if self.state["pending_review"]:
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return "request_human_review"
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elif self.state["human_feedback"]:
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return "incorporate_feedback"
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else:
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return "proceed_to_next_step"
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```
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**3. 调试和可解释性**
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```python
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# 状态历史记录了整个决策过程
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class StatefulAgent:
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def __init__(self):
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self.state_history = []
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def decide(self, context):
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# 记录状态
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self.state_history.append({
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"timestamp": now(),
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"state": self.state.copy(),
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"context": context,
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"decision": None
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})
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# 做决策
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decision = self._make_decision(context)
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self.state_history[-1]["decision"] = decision
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return decision
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def explain(self):
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"""回溯决策过程"""
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return self.state_history
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```
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### 状态机
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**状态机**是描述系统状态转换的模型：
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```
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┌─────────┐
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│ 初始 │
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│ state │
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└────┬────┘
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│ event: start
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↓
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┌─────────┐
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│ 加载数据 │
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└────┬────┘
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│ success
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↓
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┌─────────┐ error ┌─────────┐
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│ 分析处理 │ ─────────────→│ 错误 │
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└────┬────┘ └─────────┘
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│ success │ retry
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↓ │
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┌─────────┐ │
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│ 人类 │ │
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│ 审查 │ │
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└────┬────┘ │
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│ approve │
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↓ │
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┌─────────┐ │
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│ 完成 │←───────────────────────┘
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└─────────┘
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```
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**状态机的要素**：
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1. **状态 (State)**：系统可能处于的情况
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2. **事件 (Event)**：触发状态转换的条件
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3. **转换 (Transition)**：从一个状态到另一个状态
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4. **动作 (Action)**：状态转换时执行的操作
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---
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## 设计原理
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### LangGraph的状态设计哲学
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LangGraph是构建有状态Agent的框架，其核心思想：
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```python
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from typing import TypedDict
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# 定义状态类型
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class AnalysisState(TypedDict):
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"""生态网络分析状态"""
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# 输入数据
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input_path: str
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parameters: dict
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# 处理过程
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current_step: str
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intermediate_results: dict
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# 人机交互
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review_requested: bool
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human_feedback: str
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# 输出
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final_result: dict
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errors: list
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# 状态图定义
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workflow = StateGraph(AnalysisState)
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# 添加节点（处理步骤）
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workflow.add_node("load_data", load_data_node)
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workflow.add_node("identify_sources", identify_sources_node)
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workflow.add_node("human_review", human_review_node)
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workflow.add_node("extract_corridors", extract_corridors_node)
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# 添加边（状态转换）
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workflow.add_edge("load_data", "identify_sources")
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workflow.add_conditional_edge(
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"identify_sources",
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should_review, # 条件函数
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{
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"review": "human_review",
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"continue": "extract_corridors"
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}
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)
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# 编译为可执行图
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app = workflow.compile()
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```
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**核心概念**：
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1. **状态即消息**：状态在节点间传递
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2. **图即流程**：有向图描述工作流
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3. **条件分支**：基于状态的动态路由
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### 工作流状态机实现
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```python
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from enum import Enum
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from typing import Dict, Any, Callable, Optional
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from dataclasses import dataclass, field
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class WorkflowState(Enum):
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"""工作流状态枚举"""
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IDLE = "idle"
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LOADING = "loading"
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PROCESSING = "processing"
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REVIEWING = "reviewing"
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COMPLETED = "completed"
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ERROR = "error"
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@dataclass
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class WorkflowContext:
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"""工作流上下文（状态数据）"""
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data: Dict[str, Any] = field(default_factory=dict)
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current_step: int = 0
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errors: list = field(default_factory=list)
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metadata: Dict[str, Any] = field(default_factory=dict)
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class StateMachine:
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"""通用状态机"""
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def __init__(self, initial_state: WorkflowState):
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self.state = initial_state
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self.context = WorkflowContext()
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self.transitions: Dict[WorkflowState, Dict[str, WorkflowState]] = {}
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self.actions: Dict[tuple[WorkflowState, WorkflowState], Callable] = {}
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def add_transition(self,
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from_state: WorkflowState,
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event: str,
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to_state: WorkflowState,
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action: Callable = None):
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"""添加状态转换"""
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if from_state not in self.transitions:
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self.transitions[from_state] = {}
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self.transitions[from_state][event] = to_state
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if action:
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self.actions[(from_state, to_state)] = action
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def trigger(self, event: str, **kwargs) -> bool:
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"""触发事件"""
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if self.state not in self.transitions:
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raise ValueError(f"没有从状态 {self.state} 的转换")
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if event not in self.transitions[self.state]:
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print(f"事件 {event} 在状态 {self.state} 下无效")
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return False
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# 获取目标状态
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new_state = self.transitions[self.state][event]
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old_state = self.state
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# 执行转换动作
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action = self.actions.get((old_state, new_state))
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if action:
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result = action(self.context, **kwargs)
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if result is False: # 动作失败，不转换
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return False
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# 更新状态
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self.state = new_state
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print(f"状态转换: {old_state} → {new_state}")
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return True
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# 生态分析工作流状态机
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class EcologicalAnalysisWorkflow:
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"""生态网络分析工作流"""
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def __init__(self):
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# 创建状态机
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self.sm = StateMachine(WorkflowState.IDLE)
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# 定义转换
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self.sm.add_transition(WorkflowState.IDLE, "start", WorkflowState.LOADING)
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self.sm.add_transition(WorkflowState.LOADING, "loaded", WorkflowState.PROCESSING)
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self.sm.add_transition(WorkflowState.LOADING, "error", WorkflowState.ERROR)
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self.sm.add_transition(WorkflowState.PROCESSING, "complete", WorkflowState.REVIEWING)
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self.sm.add_transition(WorkflowState.PROCESSING, "error", WorkflowState.ERROR)
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self.sm.add_transition(WorkflowState.REVIEWING, "approved", WorkflowState.COMPLETED)
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self.sm.add_transition(WorkflowState.REVIEWING, "rejected", WorkflowState.PROCESSING)
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self.sm.add_transition(WorkflowState.ERROR, "retry", WorkflowState.LOADING)
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def run(self, data_path: str):
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"""执行工作流"""
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# 启动
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self.sm.trigger("start", data_path=data_path)
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# 模拟加载
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print("加载数据...")
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self.sm.trigger("loaded")
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# 模拟处理
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print("处理数据...")
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self.sm.trigger("complete")
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# 审查
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print("等待审查...")
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# 这里会等待人类输入
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# 假设批准
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self.sm.trigger("approved")
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print(f"工作流完成，最终状态: {self.sm.state}")
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```
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---
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## 代码示例
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### 完整的状态机工作流
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```python
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"""
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完整的状态机工作流示例
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"""
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import json
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from typing import Dict, Any, List, Optional
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from dataclasses import dataclass, field, asdict
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from enum import Enum
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import time
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class State(Enum):
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"""状态枚举"""
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IDLE = "idle"
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LOAD_DATA = "load_data"
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IDENTIFY_SOURCES = "identify_sources"
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BUILD_RESISTANCE = "build_resistance"
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REVIEW_SOURCES = "review_sources"
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REVIEW_RESISTANCE = "review_resistance"
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EXTRACT_CORRIDORS = "extract_corridors"
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COMPLETED = "completed"
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ERROR = "error"
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@dataclass
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class WorkflowState:
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"""工作流状态数据"""
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current: State = State.IDLE
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step_number: int = 0
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data_path: Optional[str] = None
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sources: Optional[List[Dict]] = None
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resistance_weights: Optional[Dict] = None
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corridors: Optional[List[Dict]] = None
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errors: List[str] = field(default_factory=list)
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history: List[Dict] = field(default_factory=list)
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def transition_to(self, new_state: State, action: str = ""):
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"""状态转换"""
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old_state = self.current
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self.current = new_state
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self.step_number += 1
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# 记录历史
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self.history.append({
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"step": self.step_number,
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"from": old_state.value,
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"to": new_state.value,
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"action": action,
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"timestamp": time.time()
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})
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def to_dict(self) -> Dict:
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"""序列化"""
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return {
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"current": self.current.value,
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"step_number": self.step_number,
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"data_path": self.data_path,
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"sources": self.sources,
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"resistance_weights": self.resistance_weights,
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"corridors": self.corridors,
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"errors": self.errors,
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"history": self.history
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}
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def save(self, path: str):
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"""保存状态到文件"""
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with open(path, 'w') as f:
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json.dump(self.to_dict(), f, indent=2)
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@classmethod
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def load(cls, path: str) -> 'WorkflowState':
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"""从文件加载状态"""
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with open(path, 'r') as f:
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data = json.load(f)
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# 转换State枚举
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data["current"] = State(data["current"])
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return cls(**{k: v for k, v in data.items() if k != "history"})
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class EcologicalAnalysisAgent:
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"""生态分析智能体（有状态）"""
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def __init__(self):
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self.state = WorkflowState()
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self.review_callbacks = {
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State.REVIEW_SOURCES: self._review_sources,
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State.REVIEW_RESISTANCE: self._review_resistance
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}
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def start(self, data_path: str):
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"""启动分析"""
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self.state.data_path = data_path
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self.state.transition_to(State.LOAD_DATA, "开始加载数据")
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self._execute_current_step()
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def _execute_current_step(self):
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"""执行当前状态对应的操作"""
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handlers = {
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State.LOAD_DATA: self._handle_load_data,
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State.IDENTIFY_SOURCES: self._handle_identify_sources,
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State.BUILD_RESISTANCE: self._handle_build_resistance,
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State.REVIEW_SOURCES: self._handle_review,
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State.REVIEW_RESISTANCE: self._handle_review,
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State.EXTRACT_CORRIDORS: self._handle_extract_corridors,
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State.COMPLETED: self._handle_completed,
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State.ERROR: self._handle_error
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}
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handler = handlers.get(self.state.current)
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if handler:
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handler()
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def _handle_load_data(self):
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"""处理数据加载"""
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print(f"\n[状态: {self.state.current.value}] 加载数据: {self.state.data_path}")
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# 模拟加载
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try:
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# 这里实际会读取文件
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time.sleep(0.5)
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print("数据加载成功")
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self.state.transition_to(State.IDENTIFY_SOURCES, "数据加载完成")
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self._execute_current_step()
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except Exception as e:
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self.state.errors.append(str(e))
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self.state.transition_to(State.ERROR, f"加载失败: {e}")
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self._execute_current_step()
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def _handle_identify_sources(self):
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"""处理源地识别"""
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print(f"\n[状态: {self.state.current.value}] 识别生态源地...")
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# 模拟识别
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self.state.sources = [
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{"id": 1, "area": 1500, "type": "forest"},
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{"id": 2, "area": 800, "type": "wetland"}
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]
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print(f"识别到 {len(self.state.sources)} 个源地")
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self.state.transition_to(State.REVIEW_SOURCES, "源地识别完成，等待审查")
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self._execute_current_step()
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def _handle_build_resistance(self):
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"""处理阻力面构建"""
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print(f"\n[状态: {self.state.current.value}] 构建阻力面...")
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# 模拟构建
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self.state.resistance_weights = {
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"forest": 1,
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"grassland": 10,
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"urban": 100,
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"water": 50
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}
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print("阻力面构建完成")
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self.state.transition_to(State.REVIEW_RESISTANCE, "阻力面构建完成，等待审查")
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self._execute_current_step()
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def _handle_review(self):
|
||
"""处理审查状态"""
|
||
print(f"\n[状态: {self.state.current.value}] 等待人类审查...")
|
||
|
||
callback = self.review_callbacks.get(self.state.current)
|
||
if callback:
|
||
result = callback()
|
||
|
||
if result == "approve":
|
||
if self.state.current == State.REVIEW_SOURCES:
|
||
self.state.transition_to(State.BUILD_RESISTANCE, "审查通过")
|
||
elif self.state.current == State.REVIEW_RESISTANCE:
|
||
self.state.transition_to(State.EXTRACT_CORRIDORS, "审查通过")
|
||
self._execute_current_step()
|
||
else:
|
||
# 拒绝，返回上一状态
|
||
print("审查未通过，重新执行...")
|
||
# 简化处理：直接继续
|
||
|
||
def _review_sources(self) -> str:
|
||
"""审查源地"""
|
||
print("\n=== 源地审查 ===")
|
||
print(f"识别到 {len(self.state.sources)} 个源地:")
|
||
for s in self.state.sources:
|
||
print(f" - ID {s['id']}: {s['type']}, 面积 {s['area']}")
|
||
|
||
# 实际实现中这里会等待人类输入
|
||
# 这里模拟自动批准
|
||
print("\n[模拟] 审查: 批准")
|
||
return "approve"
|
||
|
||
def _review_resistance(self) -> str:
|
||
"""审查阻力面"""
|
||
print("\n=== 阻力面审查 ===")
|
||
print("阻力权重:")
|
||
for land_type, weight in self.state.resistance_weights.items():
|
||
print(f" - {land_type}: {weight}")
|
||
|
||
print("\n[模拟] 审查: 批准")
|
||
return "approve"
|
||
|
||
def _handle_extract_corridors(self):
|
||
"""处理廊道提取"""
|
||
print(f"\n[状态: {self.state.current.value}] 提取生态廊道...")
|
||
|
||
# 模拟提取
|
||
self.state.corridors = [
|
||
{"from": 1, "to": 2, "length": 3500}
|
||
]
|
||
print(f"提取到 {len(self.state.corridors)} 条廊道")
|
||
|
||
self.state.transition_to(State.COMPLETED, "分析完成")
|
||
self._execute_current_step()
|
||
|
||
def _handle_completed(self):
|
||
"""处理完成状态"""
|
||
print(f"\n[状态: {self.state.current.value}] 工作流完成!")
|
||
print(f"\n=== 结果摘要 ===")
|
||
print(f"源地数量: {len(self.state.sources) if self.state.sources else 0}")
|
||
print(f"廊道数量: {len(self.state.corridors) if self.state.corridors else 0}")
|
||
print(f"执行步骤: {self.state.step_number}")
|
||
|
||
def _handle_error(self):
|
||
"""处理错误状态"""
|
||
print(f"\n[状态: {self.state.current.value}] 发生错误")
|
||
for error in self.state.errors:
|
||
print(f" - {error}")
|
||
|
||
def save_state(self, path: str):
|
||
"""保存当前状态"""
|
||
self.state.save(path)
|
||
print(f"状态已保存到: {path}")
|
||
|
||
def resume_from(self, path: str):
|
||
"""从保存的状态恢复"""
|
||
self.state = WorkflowState.load(path)
|
||
print(f"从状态恢复: {self.state.current.value}")
|
||
print(f"历史步骤: {self.state.step_number}")
|
||
self._execute_current_step()
|
||
|
||
# 使用示例
|
||
if __name__ == "__main__":
|
||
print("=== 生态分析状态机工作流 ===\n")
|
||
|
||
agent = EcologicalAnalysisAgent()
|
||
|
||
# 执行工作流
|
||
agent.start("data.geojson")
|
||
|
||
# 可以保存状态
|
||
# agent.save_state("workflow_state.json")
|
||
|
||
# 可以从状态恢复
|
||
# new_agent = EcologicalAnalysisAgent()
|
||
# new_agent.resume_from("workflow_state.json")
|
||
```
|
||
|
||
---
|
||
|
||
## 案例分析
|
||
|
||
### LangGraph在空间分析中的应用
|
||
|
||
```python
|
||
"""
|
||
LangGraph风格的生态网络分析工作流
|
||
"""
|
||
from typing import TypedDict, Annotated, Literal
|
||
from operator import add
|
||
|
||
class EcologicalState(TypedDict):
|
||
"""生态分析状态类型"""
|
||
messages: Annotated[list, add] # 消息历史
|
||
input_data: dict
|
||
sources: list
|
||
resistance: dict
|
||
corridors: list
|
||
next_step: str
|
||
human_feedback: str
|
||
|
||
# 节点函数
|
||
def load_data_node(state: EcologicalState) -> EcologicalState:
|
||
"""加载数据节点"""
|
||
print("执行: load_data")
|
||
state["sources"] = [{"id": 1, "area": 1000}]
|
||
state["next_step"] = "identify"
|
||
return state
|
||
|
||
def identify_sources_node(state: EcologicalState) -> EcologicalState:
|
||
"""识别源地节点"""
|
||
print("执行: identify_sources")
|
||
state["sources"] = [{"id": i, "area": i * 100} for i in range(1, 6)]
|
||
state["next_step"] = "review"
|
||
return state
|
||
|
||
def human_review_node(state: EcologicalState) -> EcologicalState:
|
||
"""人类审查节点"""
|
||
print("执行: human_review")
|
||
print(f"待审查: {state['sources']}")
|
||
|
||
# 在实际实现中，这里会等待人类输入
|
||
state["human_feedback"] = "approved"
|
||
state["next_step"] = "build_resistance"
|
||
return state
|
||
|
||
def build_resistance_node(state: EcologicalState) -> EcologicalState:
|
||
"""构建阻力面节点"""
|
||
print("执行: build_resistance")
|
||
state["resistance"] = {"forest": 1, "urban": 100}
|
||
state["next_step"] = "complete"
|
||
return state
|
||
|
||
# 路由函数
|
||
def should_review(state: EcologicalState) -> Literal["review", "skip"]:
|
||
"""决定是否需要审查"""
|
||
if len(state.get("sources", [])) > 3:
|
||
return "review"
|
||
return "skip"
|
||
|
||
# 条件边
|
||
def route_after_identify(state: EcologicalState) -> str:
|
||
"""识别源地后的路由"""
|
||
if state.get("human_feedback") == "approved":
|
||
return "build_resistance"
|
||
return "identify" # 重新识别
|
||
|
||
print("""
|
||
┌──────────────┐
|
||
│ load_data │
|
||
└──────┬───────┘
|
||
│
|
||
↓
|
||
┌──────────────┐
|
||
│identify_sources│
|
||
└──────┬───────┘
|
||
│
|
||
├────→ [review?] ──→ human_review ──┐
|
||
│ No │
|
||
↓ ↓
|
||
┌──────────────┐ ┌──────────────┐
|
||
│build_resistance│◀──────────────────│ approved │
|
||
└──────────────┘ └──────────────┘
|
||
""")
|
||
```
|
||
|
||
---
|
||
|
||
## 反思与延伸
|
||
|
||
### 思考问题
|
||
|
||
1. **状态粒度**：状态应该有多细？太细会怎样，太粗会怎样？
|
||
|
||
2. **持久化策略**：哪些状态需要持久化？什么时候保存状态？
|
||
|
||
3. **并发处理**：如果多个Agent协同工作，如何管理共享状态？
|
||
|
||
4. **调试**：当状态机出错时，如何调试？
|
||
|
||
### 实践练习
|
||
|
||
1. **状态审计**：添加状态转换日志，分析工作流执行路径
|
||
|
||
2. **状态压缩**：实现状态序列化/反序列化，支持断点续传
|
||
|
||
3. **条件路由**：实现一个带多个分支的状态机
|
||
|
||
### 延伸阅读
|
||
|
||
- **"Designing Data-Intensive Applications"** (Kleppmann) - 状态管理理论
|
||
- **LangGraph文档** - 实际框架使用
|
||
- **"State Machine Design Patterns"** - 状态机设计模式
|
||
|
||
---
|
||
|
||
## 关键要点
|
||
|
||
1. **状态是系统在某一时刻的完整快照**
|
||
2. **状态机描述系统如何随事件转换状态**
|
||
3. **LangGraph用图结构表达有状态的工作流**
|
||
4. **良好的状态管理支持断点续传和HITL**
|
||
5. **状态历史是调试和可解释性的关键**
|