fix: 修复 NameError/状态污染/类型标注/统计; 补全练习与 main; 新增 config/.gitignore/requirements; 文档统一

2026-06-02 13:44:46 +08:00
parent ef876a22d1
commit 908431e25f
23 changed files with 919 additions and 77 deletions
@@ -5,9 +5,9 @@ OPENAI_API_KEY=your_openai_api_key_here
 ANTHROPIC_API_KEY=your_anthropic_api_key_here

 # LLM 配置
-LLM_PROVIDER=anthropic  # 或 openai
-LLM_MODEL=MiniMax-M2.7   # 或 gpt-4o
-LLM_MAX_TOKENS=8192
+LLM_PROVIDER=openai  # 可选: openai / anthropic
+LLM_MODEL=gpt-4o-mini  # 例如 gpt-4o / claude-3-5-sonnet-20241022
+LLM_MAX_TOKENS=4096

 # RAG 配置
 RAG_CHROMA_PATH=./db/chroma
@@ -0,0 +1,11 @@
+__pycache__/
+*.pyc
+*.pyo
+*.pyd
+.Python
+*.egg-info/
+.venv/
+venv/
+.env
+.idea/
+.vscode/
@@ -76,7 +76,7 @@ jaspersoft-agent-learn/

 ```bash
 # 克隆项目
-git clone https://www.1415243231.top/gitea/panda/jaspersoft-agent-learn.git
+git clone https://gitea.1415243231.top/panda/jaspersoft-agent-learn.git
 cd jaspersoft-agent-learn

 # 创建虚拟环境（Python 3.9+）
@@ -86,7 +86,7 @@ source venv/bin/activate  # Linux/Mac
 .\venv\Scripts\activate   # Windows

 # 安装依赖
-pip install python-dotenv openai
+pip install -r requirements.txt
 ```

 ### 2. 配置环境变量
@@ -110,6 +110,14 @@ python main.py
 # Step 03: 简单 Agent
 cd ../step_03_simple_agent
 python main.py
+
+# Step 04: 记忆系统
+cd ../step_04_memory
+python main.py
+
+# Step 05-07: 进阶（RAG / Self-Correction / Multi-Agent）
+cd ../step_05_07_advanced
+python main.py
 ```

 ---
@@ -153,7 +161,7 @@ Step 07: 协作 → 多Agent
 ### 3. 零依赖入门

 - Step 01~03: 仅需 Python 标准库
- Step 04+: 只需 `python-dotenv`
+- Step 04+：仅标准库（如需 LLM 调用，见 `config.py` 加载 `.env`）
 - 无需 LangChain、LangGraph 等框架

 ---
@@ -250,8 +258,8 @@ class StateManager:
 ## 🔗 相关资源

 ### 内部项目
- **JasperSoft 主项目**: `D:\Idea Project\jaspersoft` - LangGraph 实现参考
- **日报系统**: `D:\Idea Project\daily_on_work` - 自动化工作流
+- **JasperSoft 主项目**: 同组织下的 `jaspersoft` 仓库 - LangGraph 实现参考
+- **日报系统**: 同组织下的 `daily_on_work` 仓库 - 自动化工作流

 ### 外部资源
 - [LangGraph 文档](https://langchain-ai.github.io/langgraph/)
@@ -9,17 +9,15 @@

 ## 🎯 这是什么？

-一个渐进式的 AI Agent 开发学习项目，通过 **7 个 Step** 带你从零掌握 AI Agent 的核心概念：
+一个渐进式的 AI Agent 开发学习项目，通过 **5 个阶段（7 个主题）** 带你从零掌握 AI Agent 的核心概念：

-| Step | 主题 | 你将学会 |
+| 阶段 | 主题 | 你将学会 |
 |------|------|---------|
-| 01 | Tool 工具系统 | 如何定义和注册工具 |
-| 02 | State 状态管理 | 如何管理 Agent 的状态 |
-| 03 | Simple Agent | 如何构建 Agent 循环 |
-| 04 | Memory 记忆 | 如何让 Agent 记住对话 |
-| 05 | RAG 知识检索 | 如何让 Agent 查阅知识库 |
-| 06 | Self-Correction | 如何让 Agent 自我修正 |
-| 07 | Multi-Agent | 如何构建多 Agent 协作 |
+| Step 01 | Tool 工具系统 | 如何定义和注册工具 |
+| Step 02 | State 状态管理 | 如何管理 Agent 的状态 |
+| Step 03 | Simple Agent | 如何构建 Agent 循环 |
+| Step 04 | Memory 记忆 | 如何让 Agent 记住对话 |
+| Step 05~07 | RAG / Self-Correction / Multi-Agent | 进阶能力（同一目录） |

 ---

@@ -30,11 +28,11 @@
 - Day 3-4: 掌握 State 管理
 - Day 5-7: 构建简单 Agent

-### 第 2 周：进阶（Step 04-07）
+### 第 2 周：进阶（Step 04 + 进阶包）
 - Day 8-9: 多级记忆系统
- Day 10-11: RAG 与知识增强
- Day 12-13: 自我修正模式
- Day 14: 多 Agent 协作
+- Day 10-11: RAG 与知识增强（Step 05）
+- Day 12-13: 自我修正模式（Step 06）
+- Day 14: 多 Agent 协作（Step 07）

 ---

@@ -42,7 +40,7 @@

 ### 1. 克隆项目
 ```bash
-git clone https://www.1415243231.top/gitea/panda/jaspersoft-agent-learn.git
+git clone https://gitea.1415243231.top/panda/jaspersoft-agent-learn.git
 cd jaspersoft-agent-learn
 ```

@@ -51,7 +49,7 @@ cd jaspersoft-agent-learn
 python -m venv venv
 source venv/bin/activate  # Linux/Mac
 .\venv\Scripts\activate   # Windows
-pip install python-dotenv
+pip install -r requirements.txt
 ```

 ### 3. 开始学习
@@ -115,9 +113,9 @@ while not done:
 - ✅ 掌握 State 的设计模式
 - ✅ 能够构建简单的 Agent 循环
 - ✅ 实现多级记忆系统
- ✅ 理解 RAG 架构
- ✅ 掌握 Self-Correction 模式
- ✅ 设计 Multi-Agent 协作系统
+- ✅ 理解 RAG 架构（Step 05）
+- ✅ 掌握 Self-Correction 模式（Step 06）
+- ✅ 设计 Multi-Agent 协作系统（Step 07）

 ---

@@ -133,7 +131,9 @@ jaspersoft-agent-learn/
 ├── step_02_state/        # 状态管理
 ├── step_03_simple_agent/ # 简单 Agent
 ├── step_04_memory/       # 记忆系统
-└── step_05_07_advanced/  # RAG/修正/多Agent
+├── step_05_07_advanced/  # RAG（05）/ Self-Correction（06）/ Multi-Agent（07）
+├── config.py             # 集中读取 .env（LLM key、RAG 路径等）
+└── requirements.txt      # 可选依赖清单
 ```

 ---
@@ -141,8 +141,8 @@ jaspersoft-agent-learn/
 ## 🔗 相关链接

 - 📂 **详细学习指南**: [LEARN_GUIDE.md](./LEARN_GUIDE.md)
- 🏠 **主项目**: [JasperSoft](https://www.1415243231.top/gitea/panda/jaspersoft)
- 📊 **日报系统**: [Daily On Work](https://www.1415243231.top)
+- 🏠 **主项目**: [JasperSoft](https://gitea.1415243231.top/panda/jaspersoft)
+- 📊 **日报系统**: [Daily On Work](https://gitea.1415243231.top)

 ---

@@ -0,0 +1,87 @@
+"""
+集中读取 .env / 环境变量。
+
+使用方式：
+    from config import settings
+    print(settings.llm_model)
+    print(settings.has_openai_key)
+"""
+
+import os
+from dataclasses import dataclass
+from pathlib import Path
+
+
+def _load_dotenv(env_path: Path) -> None:
+    """极简 .env 解析，避免引入 python-dotenv 依赖。"""
+    if not env_path.is_file():
+        return
+    for raw_line in env_path.read_text(encoding="utf-8").splitlines():
+        line = raw_line.strip()
+        if not line or line.startswith("#"):
+            continue
+        if "=" not in line:
+            continue
+        key, _, value = line.partition("=")
+        key = key.strip()
+        value = value.strip().strip('"').strip("'")
+        # 已存在则不覆盖（让真实环境变量优先）
+        os.environ.setdefault(key, value)
+
+
+_ROOT = Path(__file__).resolve().parent
+_load_dotenv(_ROOT / ".env")
+
+
+@dataclass(frozen=True)
+class Settings:
+    openai_api_key: str
+    anthropic_api_key: str
+    llm_provider: str
+    llm_model: str
+    llm_max_tokens: int
+    rag_chroma_path: str
+    rag_collection_name: str
+    rag_embed_model: str
+    validation_service_url: str
+    log_level: str
+
+    @property
+    def has_openai_key(self) -> bool:
+        return bool(self.openai_api_key) and self.openai_api_key != "your_openai_api_key_here"
+
+    @property
+    def has_anthropic_key(self) -> bool:
+        return bool(self.anthropic_api_key) and self.anthropic_api_key != "your_anthropic_api_key_here"
+
+
+def _int(name: str, default: int) -> int:
+    raw = os.environ.get(name)
+    try:
+        return int(raw) if raw else default
+    except ValueError:
+        return default
+
+
+settings = Settings(
+    openai_api_key=os.environ.get("OPENAI_API_KEY", ""),
+    anthropic_api_key=os.environ.get("ANTHROPIC_API_KEY", ""),
+    llm_provider=os.environ.get("LLM_PROVIDER", "openai"),
+    llm_model=os.environ.get("LLM_MODEL", "gpt-4o-mini"),
+    llm_max_tokens=_int("LLM_MAX_TOKENS", 4096),
+    rag_chroma_path=os.environ.get("RAG_CHROMA_PATH", "./db/chroma"),
+    rag_collection_name=os.environ.get("RAG_COLLECTION_NAME", "jrxml_chunks"),
+    rag_embed_model=os.environ.get("RAG_EMBED_MODEL", "sentence-transformers/all-MiniLM-L6-v2"),
+    validation_service_url=os.environ.get("VALIDATION_SERVICE_URL", "http://localhost:8001"),
+    log_level=os.environ.get("LOG_LEVEL", "INFO"),
+)
+
+
+if __name__ == "__main__":
+    s = settings
+    print(f"provider  = {s.llm_provider}")
+    print(f"model     = {s.llm_model}")
+    print(f"max_tok   = {s.llm_max_tokens}")
+    print(f"openai?   = {s.has_openai_key}")
+    print(f"anthro?   = {s.has_anthropic_key}")
+    print(f"rag_path  = {s.rag_chroma_path}")
@@ -0,0 +1,15 @@
+# 基础依赖（Step 01-04 仅标准库，无需安装）
+# 进阶依赖（按需安装）
+#
+# 复制 .env.example 为 .env 后填入真实 key
+# 然后按需取消注释：
+
+# LLM 客户端（Step 03+ 接 LLM 时需要）
+# openai>=1.30.0
+# anthropic>=0.30.0
+
+# 向量数据库（Step 05 RAG 时需要）
+# chromadb>=0.5.0
+
+# Embedding 模型（Step 05 RAG 时需要）
+# sentence-transformers>=2.7.0
@@ -14,7 +14,6 @@ Step 01: 理解 Tool - 工具系统基础
 from abc import ABC, abstractmethod
 from typing import Any, Dict, Optional
 from dataclasses import dataclass
-import json


 # ═══════════════════════════════════════════════════════════════════════════════
@@ -0,0 +1,5 @@
+# Jaspersoft Learn - Step 02: 状态管理
+
+from .concept import AgentState, JaspersoftAgentState
+
+__all__ = ["AgentState", "JaspersoftAgentState"]
@@ -12,9 +12,8 @@ Step 02: 理解 State - 状态管理
 """

 from typing import TypedDict, List, Dict, Any, Optional
-from dataclasses import dataclass, field
+from dataclasses import dataclass
 from datetime import datetime
-import json


 # ═══════════════════════════════════════════════════════════════════════════════
@@ -12,7 +12,6 @@ Step 02 练习题：设计你的第一个 Agent State
 """

 from typing import TypedDict, List, Dict, Any
-import json


 # ═══════════════════════════════════════════════════════════════════════════════
@@ -100,15 +100,6 @@ class SimpleAgentState(TypedDict, total=False):
 # 第三部分：定义 Tool 接口
 # ═══════════════════════════════════════════════════════════════════════════════

-@dataclass
-class ToolCall:
-    """工具调用的数据结构"""
-    name: str           # 工具名称
-    arguments: dict     # 传递给工具的参数
-    result: Any = None # 工具执行结果
-    error: str = None  # 错误信息
-
-
 class BaseTool(ABC):
    """
    工具基类（简化版，来自 Step 01）
@@ -390,6 +381,9 @@ class SimpleAgent:
            status="input"
        )

+        # 对话轮次计数器
+        self.round_count = 0
+
    def reset(self):
        """重置 Agent 状态"""
        self.state = SimpleAgentState(
@@ -454,6 +448,8 @@ class SimpleAgent:
            "content": user_input
        })
        self.state["status"] = "thinking"
+        self.state["tool_result"] = None
+        self.round_count += 1

        # 2. 大脑决定行动
        decision = self.brain.decide(self.state)
@@ -0,0 +1,87 @@
+"""
+Step 03 练习题：扩展 SimpleAgent
+
+━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
+
+🎯 练习目标：
+    1. 巩固 Agent 循环的运行机制
+    2. 增强 Brain 的决策能力
+    3. 体验 Tool 的注册流程
+
+━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
+"""
+
+# ═══════════════════════════════════════════════════════════════════════════════
+# 练习 1：实现 DateTimeTool 并注册到 Agent
+# ═══════════════════════════════════════════════════════════════════════════════
+
+"""
+任务：
+    在 SimpleAgent 中添加一个 DateTimeTool，提供「现在几点 / 今天日期」能力。
+
+要求：
+    1. 继承 BaseTool
+    2. name = "datetime"，description 描述清楚能做什么
+    3. execute(**kwargs) 接收 operation，支持：
+        - "now"      -> 返回当前时间字符串（"%Y-%m-%d %H:%M:%S"）
+        - "today"    -> 返回当前日期字符串（"%Y-%m-%d"）
+        - "weekday"  -> 返回今天是星期几（中文，如 "星期一"）
+    4. 注册到 SimpleAgent.tools 字典中
+    5. 测试：用户输入「现在几点」时 Brain 能正确选择 datetime 工具
+"""
+
+
+# ═══════════════════════════════════════════════════════════════════════════════
+# 练习 2：改进 Brain 的工具匹配
+# ═══════════════════════════════════════════════════════════════════════════════
+
+"""
+任务：
+    当前 AgentBrain.decide() 用「关键词 + 表达式正则」匹配 calculator，
+    对「iOS / Android 兼容性」「产品 A+」这种文本会误判。
+
+要求：
+    1. 在 AgentBrain.decide() 中加入你新加的 DateTimeTool 的路由
+    2. 修复 calculator 匹配的脆弱性（例如优先匹配明确的算式语法）
+    3. 让 Brain 在没有工具可调时返回 {"action": "respond", "response": "..."}
+
+提示：
+    - 可用正则在 user_input 中提取首个形如「数字 运算符 数字」的子串
+    - 用 keyword in user_input 检测「现在」「今天」「星期」触发 datetime
+"""
+
+
+# ═══════════════════════════════════════════════════════════════════════════════
+# 练习 3：让 Agent 暴露对话快照
+# ═══════════════════════════════════════════════════════════════════════════════
+
+"""
+任务：
+    给 SimpleAgent 增加 snapshot()/restore(snap) 方法，用于保存和恢复会话。
+
+要求：
+    1. snapshot() 返回 dict，包含 messages、tool_result、current_action
+    2. restore(snap) 用 snap 覆盖对应字段
+    3. 验证：snapshot -> 多轮对话 -> restore -> 状态回到 snapshot 时刻
+
+提示：
+    - copy.deepcopy() 避免引用共享
+    - 只恢复可序列化的字段，不要把 self.brain / self.tools 一起覆盖
+"""
+
+
+# ═══════════════════════════════════════════════════════════════════════════════
+# 测试
+# ═══════════════════════════════════════════════════════════════════════════════
+
+def test_exercises():
+    from step_03_simple_agent.concept import SimpleAgent
+
+    agent = SimpleAgent()
+    print("当前已注册工具:", list(agent.tools.keys()))
+
+    # TODO: 你的测试
+
+
+if __name__ == "__main__":
+    test_exercises()
@@ -0,0 +1,160 @@
+"""
+Step 03 练习题答案
+
+⚠️ 先自己思考，再看答案！
+⚠️ 答案不是唯一的，这里只是其中一种实现
+"""
+
+import copy
+import re
+from datetime import datetime
+
+from step_03_simple_agent.concept import (
+    BaseTool,
+    SimpleAgent,
+    ToolResult,
+)
+
+
+# ═══════════════════════════════════════════════════════════════════════════════
+# 练习 1 答案：DateTimeTool
+# ═══════════════════════════════════════════════════════════════════════════════
+
+class DateTimeTool(BaseTool):
+    """日期时间工具"""
+
+    @property
+    def name(self) -> str:
+        return "datetime"
+
+    @property
+    def description(self) -> str:
+        return "日期时间工具，支持 now（当前时间）/ today（今天日期）/ weekday（星期几）"
+
+    def execute(self, **kwargs) -> ToolResult:
+        operation = kwargs.get("operation", "now")
+        now = datetime.now()
+        if operation == "now":
+            return ToolResult(success=True, result=now.strftime("%Y-%m-%d %H:%M:%S"))
+        if operation == "today":
+            return ToolResult(success=True, result=now.strftime("%Y-%m-%d"))
+        if operation == "weekday":
+            names = ["星期一", "星期二", "星期三", "星期四", "星期五", "星期六", "星期日"]
+            return ToolResult(success=True, result=names[now.weekday()])
+        return ToolResult(success=False, error=f"不支持的操作: {operation}")
+
+
+# ═══════════════════════════════════════════════════════════════════════════════
+# 练习 2 答案：稳健的 Brain 路由
+# ═══════════════════════════════════════════════════════════════════════════════
+
+EXPR_PATTERN = re.compile(r"-?\d+(?:\.\d+)?\s*[+\-*/]\s*-?\d+(?:\.\d+)?")
+
+
+def improved_brain_decide(self, state):
+    """把 AgentBrain.decide 替换为更稳健的版本。"""
+    user_input = state.get("user_input", "")
+    tool_result = state.get("tool_result")
+
+    # 工具结果回灌：直接产出回应
+    if tool_result is not None:
+        if isinstance(tool_result, dict) and not tool_result.get("success", True):
+            return {
+                "action": "respond",
+                "response": f"工具执行失败: {tool_result.get('error', '未知错误')}",
+            }
+        return {"action": "respond", "response": f"工具执行完成，结果：{tool_result}"}
+
+    text = user_input.lower()
+
+    # 明确的算式（如 "1 + 2" / "10*3"）才走 calculator
+    expr_match = EXPR_PATTERN.search(user_input)
+    if expr_match and re.fullmatch(r"[\d\s+\-*/().]+", expr_match.group()):
+        return {
+            "action": "use_tool",
+            "tool_name": "calculator",
+            "tool_args": {"expression": expr_match.group().strip()},
+        }
+
+    # 日期时间路由
+    if any(kw in text for kw in ["现在几点", "现在时间", "今天", "日期", "星期"]):
+        op = "weekday" if "星期" in text else ("today" if "今天" in text or "日期" in text else "now")
+        return {"action": "use_tool", "tool_name": "datetime", "tool_args": {"operation": op}}
+
+    # 其他交给模板搜索
+    if any(kw in user_input for kw in ["报表", "模板", "jrxml", "jasper"]):
+        return {
+            "action": "use_tool",
+            "tool_name": "template_search",
+            "tool_args": {"keyword": user_input},
+        }
+
+    return {
+        "action": "respond",
+        "response": f"我收到了你的输入：{user_input}（暂无可用工具直接处理）",
+    }
+
+
+# ═══════════════════════════════════════════════════════════════════════════════
+# 练习 3 答案：snapshot / restore
+# ═══════════════════════════════════════════════════════════════════════════════
+
+def install_snapshot_methods(agent: SimpleAgent) -> None:
+    """给 SimpleAgent 实例挂上 snapshot/restore 方法。"""
+
+    def snapshot(self):
+        return {
+            "messages": copy.deepcopy(self.state["messages"]),
+            "tool_result": copy.deepcopy(self.state.get("tool_result")),
+            "current_action": self.state.get("current_action"),
+            "round_count": self.round_count,
+        }
+
+    def restore(self, snap):
+        self.state["messages"] = copy.deepcopy(snap["messages"])
+        self.state["tool_result"] = copy.deepcopy(snap.get("tool_result"))
+        self.state["current_action"] = snap.get("current_action")
+        self.round_count = snap.get("round_count", 0)
+
+    SimpleAgent.snapshot = snapshot
+    SimpleAgent.restore = restore
+
+
+# ═══════════════════════════════════════════════════════════════════════════════
+# 测试
+# ═══════════════════════════════════════════════════════════════════════════════
+
+def test_answers():
+    print("\n" + "=" * 60)
+    print("Step 03 练习答案测试")
+    print("=" * 60)
+
+    agent = SimpleAgent()
+
+    # 注册新工具 + 替换 brain
+    agent.tools["datetime"] = DateTimeTool()
+    agent.brain.decide = improved_brain_decide.__get__(agent.brain)
+
+    print("\n📝 练习 1: DateTimeTool")
+    print("  工具列表:", list(agent.tools.keys()))
+    print("  now ->", agent.tools["datetime"].execute(operation="now").result)
+    print("  weekday ->", agent.tools["datetime"].execute(operation="weekday").result)
+
+    print("\n📝 练习 2: 改进的 Brain 路由")
+    for q in ["1 + 2", "iOS / Android 兼容性", "现在几点", "今天星期几"]:
+        decision = agent.brain.decide({"user_input": q, "tool_result": None})
+        print(f"  '{q}' -> {decision['action']} / {decision.get('tool_name', decision.get('response'))}")
+
+    print("\n📝 练习 3: snapshot / restore")
+    install_snapshot_methods(agent)
+    agent.process("1 + 2")
+    snap = agent.snapshot()
+    print("  snapshot round_count =", snap["round_count"])
+    agent.process("3 * 4")
+    print("  after 2 rounds, round_count =", agent.round_count)
+    agent.restore(snap)
+    print("  after restore, round_count =", agent.round_count)
+
+
+if __name__ == "__main__":
+    test_answers()
@@ -67,7 +67,7 @@ def main():
    print("\n\n" + "=" * 70)
    print("📊 会话统计")
    print("=" * 70)
-    print(f"  对话轮次: {len(agent.get_history()) // 2}")
+    print(f"  对话轮次: {agent.round_count}")
    print(f"  工具调用: {len(agent.state.get('tool_calls', []))}")
    print("\n💡 继续学习：")
    print("    Step 04: 添加 Memory - 记忆系统")
@@ -68,19 +68,6 @@ class Message:
        }


-@dataclass
-class MemorySnapshot:
-    """记忆快照 - 用于保存和恢复状态"""
-    state: Dict[str, Any]           # 关键状态
-    messages: List[Message]         # 消息历史
-    key_info: Dict[str, Any]        # 关键信息摘要
-    timestamp: str = ""
-
-    def __post_init__(self):
-        if not self.timestamp:
-            self.timestamp = datetime.now().isoformat()
-
-
 # ═══════════════════════════════════════════════════════════════════════════════
 # 第三部分：Working Memory（工作记忆）
 # ═══════════════════════════════════════════════════════════════════════════════
@@ -0,0 +1,82 @@
+"""
+Step 04 练习题：Memory 实战
+
+━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
+
+🎯 练习目标：
+    1. 理解三层记忆的协作方式
+    2. 实现一个 Token 估算器
+    3. 体验摘要压缩的副作用
+
+━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
+"""
+
+
+# ═══════════════════════════════════════════════════════════════════════════════
+# 练习 1：Token 估算
+# ═══════════════════════════════════════════════════════════════════════════════
+
+"""
+任务：
+    在 ShortTermMemory 上加一个 estimate_tokens() 方法，粗略估计当前占用的 token 数。
+
+要求：
+    1. 简单规则：1 个中文字符 ≈ 1.5 token，1 个英文单词 ≈ 1.3 token
+    2. 对所有消息求和
+    3. 返回 int（向上取整）
+
+提示：
+    - 正则分中英文：re.findall(r'[\u4e00-\u9fff]', text) 取汉字，剩下按空格分词
+    - import math; math.ceil(...)
+"""
+
+
+# ═══════════════════════════════════════════════════════════════════════════════
+# 练习 2：基于 Token 阈值的自动压缩
+# ═══════════════════════════════════════════════════════════════════════════════
+
+"""
+任务：
+    给 ShortTermMemory 加一个 maybe_compress(max_tokens: int) 方法：
+    当 estimate_tokens() 超过 max_tokens 时，把较早的对话压缩成一行摘要，
+    保留最近的 5 条。
+
+要求：
+    1. 触发时调用 summarize_older(keep_recent=5)
+    2. 把摘要作为一个新的 Message(role="system", content=summary) 放回 messages 头部
+    3. 删除被摘要覆盖的旧消息（避免 token 没降反升）
+"""
+
+
+# ═══════════════════════════════════════════════════════════════════════════════
+# 练习 3：把 MemorySystem 接到 SimpleAgent
+# ═══════════════════════════════════════════════════════════════════════════════
+
+"""
+任务：
+    让 SimpleAgent 在 process() 时把每一轮对话写入 MemorySystem，
+    并在下次决策前把 memory.get_context() 注入到 state['context'] 中。
+
+要求：
+    1. SimpleAgent.__init__ 里 new 一个 MemorySystem
+    2. process() 末尾：self.memory.add_message('user' / 'assistant', ...)
+    3. process() 开头：self.state['context'] = self.memory.get_context()
+
+提示：
+    - 直接修改 step_03/concept.py 是允许的（学习项目不是发布包）
+    - 可以通过 Monkey-patching 避免破坏 step_03 原有行为
+"""
+
+
+def test_exercises():
+    from step_04_memory.concept import MemorySystem
+
+    mem = MemorySystem()
+    mem.add_message("user", "帮我生成销售月报")
+    mem.add_message("assistant", "好的，请告诉我字段")
+    print("上下文片段:")
+    print(mem.get_context()[:200])
+
+
+if __name__ == "__main__":
+    test_exercises()
@@ -0,0 +1,118 @@
+"""
+Step 04 练习题答案
+
+⚠️ 先自己思考，再看答案！
+⚠️ 答案不是唯一的，这里只是其中一种实现
+"""
+
+import math
+import re
+
+from step_04_memory.concept import MemorySystem, Message, ShortTermMemory
+
+
+# ═══════════════════════════════════════════════════════════════════════════════
+# 练习 1 答案：Token 估算
+# ═══════════════════════════════════════════════════════════════════════════════
+
+_CN_PATTERN = re.compile(r"[\u4e00-\u9fff]")
+_EN_WORD = re.compile(r"[A-Za-z]+")
+
+
+def estimate_tokens_for_text(text: str) -> int:
+    cn = len(_CN_PATTERN.findall(text))
+    en = len(_EN_WORD.findall(text))
+    return math.ceil(cn * 1.5 + en * 1.3)
+
+
+def install_estimate_tokens() -> None:
+    def estimate_tokens(self: ShortTermMemory) -> int:
+        return sum(estimate_tokens_for_text(m.content) for m in self.messages)
+
+    ShortTermMemory.estimate_tokens = estimate_tokens
+
+
+# ═══════════════════════════════════════════════════════════════════════════════
+# 练习 2 答案：基于 Token 阈值的自动压缩
+# ═══════════════════════════════════════════════════════════════════════════════
+
+def install_maybe_compress() -> None:
+    def maybe_compress(self: ShortTermMemory, max_tokens: int = 800) -> bool:
+        if not hasattr(self, "estimate_tokens"):
+            raise RuntimeError("请先调用 install_estimate_tokens()")
+        if self.estimate_tokens() <= max_tokens:
+            return False
+        summary = self.summarize_older(keep_recent=5)
+        if not summary:
+            return False
+        # 保留最近 5 条，把摘要作为 system message 放最前
+        recent = self.messages[-5:]
+        self.messages = [Message(role="system", content=f"[历史摘要]\n{summary}")] + recent
+        return True
+
+    ShortTermMemory.maybe_compress = maybe_compress
+
+
+# ═══════════════════════════════════════════════════════════════════════════════
+# 练习 3 答案：把 MemorySystem 接到 SimpleAgent
+# ═══════════════════════════════════════════════════════════════════════════════
+
+def install_memory_to_agent() -> None:
+    from step_03_simple_agent.concept import SimpleAgent
+
+    orig_init = SimpleAgent.__init__
+
+    def patched_init(self, *args, **kwargs):
+        orig_init(self, *args, **kwargs)
+        self.memory = MemorySystem()
+
+    SimpleAgent.__init__ = patched_init
+
+    orig_process = SimpleAgent.process
+
+    def patched_process(self, user_input: str) -> str:
+        # 把记忆上下文注入 state
+        self.state["context"] = self.memory.get_context()
+        response = orig_process(self, user_input)
+        # 记录本轮对话
+        self.memory.add_message("user", user_input)
+        self.memory.add_message("assistant", response)
+        return response
+
+    SimpleAgent.process = patched_process
+
+
+# ═══════════════════════════════════════════════════════════════════════════════
+# 测试
+# ═══════════════════════════════════════════════════════════════════════════════
+
+def test_answers():
+    print("\n" + "=" * 60)
+    print("Step 04 练习答案测试")
+    print("=" * 60)
+
+    install_estimate_tokens()
+    install_maybe_compress()
+
+    mem = MemorySystem()
+    for i in range(20):
+        mem.short_term.add("user", f"第 {i} 轮对话内容，包含中文与 english words " * 5)
+    print(f"\n📝 练习 1: 注入 20 条后估算 token = {mem.short_term.estimate_tokens()}")
+    compressed = mem.short_term.maybe_compress(max_tokens=200)
+    print(f"  maybe_compress() = {compressed}, 压缩后消息数 = {len(mem.short_term.messages)}")
+    print(f"  压缩后估算 token = {mem.short_term.estimate_tokens()}")
+
+    print("\n📝 练习 3: SimpleAgent 接入 Memory")
+    try:
+        install_memory_to_agent()
+        from step_03_simple_agent.concept import SimpleAgent
+
+        agent = SimpleAgent()
+        agent.process("1 + 2")
+        print(f"  agent.memory 工作正常，消息数 = {len(agent.memory.short_term.messages)}")
+    except Exception as e:
+        print(f"  接入失败（可忽略，需在 step_03 父目录运行）: {e}")
+
+
+if __name__ == "__main__":
+    test_answers()
@@ -0,0 +1,17 @@
+"""
+Step 04: Memory - 记忆系统 主程序
+
+运行方式：
+    cd step_04_memory
+    python main.py
+"""
+
+from concept import demo
+
+
+def main():
+    demo()
+
+
+if __name__ == "__main__":
+    main()
@@ -200,30 +200,28 @@ class MultiAgentSystem:

    def __init__(self):
        # 注册各个 Agent
-        self.agents = {
-            "generator": GeneratorAgent(),
-            "validator": ValidatorAgent(),
-            "searcher": SearcherAgent(),
-        }
-        # 协调器
-        self.orchestrator = Orchestrator(self.agents)
+        self.agents: dict[str, Agent] = {}

-    def process(self, requirement: str) -> str:
-        """协调多个 Agent 处理请求"""
+    def register(self, agent: Agent) -> None:
+        self.agents[agent.name] = agent
+
+    def process(self, requirement: str):
        # 1. 搜索相关知识
-        context = self.agents["searcher"].search(requirement)
+        searcher = self.agents.get("searcher")
+        context = searcher.process(requirement) if searcher else ""

        # 2. 生成（可能需要多轮）
-        for attempt in range(3):
-            draft = self.agents["generator"].generate(requirement, context)
+        generator = self.agents.get("generator")
+        draft = generator.process({"requirement": requirement, "context": context}) if generator else requirement

        # 3. 验证
-            validation = self.agents["validator"].validate(draft)
+        validator = self.agents.get("validator")
+        if validator:
+            validation = validator.process(draft)
+            if not validation.get("passed", True):
+                return {"error": "验证失败", "validation": validation}

-            if validation["passed"]:
-                return validation["result"]
-
-        return "处理失败"
+        return draft
 ```

 ---
@@ -4,6 +4,9 @@ Step 05-07: RAG / Self-Correction / Multi-Agent
 进阶内容代码示例
 """

+from dataclasses import dataclass
+from typing import Any, Dict, List
+
 # ═══════════════════════════════════════════════════════════════════════════════════════
 # RAG 实现
 # ═══════════════════════════════════════════════════════════════════════════════════════
@@ -152,9 +155,6 @@ class SelfCorrectingAgent:
 # Multi-Agent 实现
 # ═══════════════════════════════════════════════════════════════════════════════════════

-from dataclasses import dataclass, field
-from typing import Dict, List, Callable
-

@dataclass
 class AgentMessage:
@@ -0,0 +1,88 @@
+"""
+Step 05-07 练习题：进阶能力
+
+━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
+
+🎯 练习目标：
+    1. 体验 RAG 的检索质量
+    2. 写一个 Self-Correction 闭环
+    3. 设计多 Agent 编排
+
+━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
+"""
+
+
+# ═══════════════════════════════════════════════════════════════════════════════
+# 练习 1：提升 SimpleRAG 的检索质量
+# ═══════════════════════════════════════════════════════════════════════════════
+
+"""
+任务：
+    SimpleRAG.retrieve() 当前用 Jaccard 相似度 + 简单分词。
+    改造为：把分词改成 "中文按字 + 英文按词 + 大小写归一化" 后再算 Jaccard。
+
+要求：
+    1. 复用 SimpleRAG 类，不要重写
+    2. 实现 upgrade_retrieve(rag) 替换 rag.retrieve 方法
+    3. 用一个含中英文的小语料验证
+
+提示：
+    - re.findall(r'[\u4e00-\u9fff]|[A-Za-z]+', text.lower())
+"""
+
+
+# ═══════════════════════════════════════════════════════════════════════════════
+# 练习 2：实现 Self-Correction 主循环
+# ═══════════════════════════════════════════════════════════════════════════════
+
+"""
+任务：
+    SelfCorrectingAgent 在 concept.py 中是骨架。请补全它的 run() 方法：
+
+    def run(self, requirement: str, generate_fn, validate_fn, max_retries=3):
+        for attempt in range(max_retries):
+            output = generate_fn(requirement, attempt, feedback)
+            validation = validate_fn(output)
+            if validation.passed:
+                return output
+            feedback = self.build_feedback(validation)
+        return output
+
+要求：
+    1. 第一次 attempt 不带 feedback
+    2. 每次失败用 build_feedback 拼出新的 feedback
+    3. 超过 max_retries 返回最后一次 output（不要抛异常）
+"""
+
+
+# ═══════════════════════════════════════════════════════════════════════════════
+# 练习 3：给 MultiAgentSystem 加超时与失败回退
+# ═══════════════════════════════════════════════════════════════════════════════
+
+"""
+任务：
+    MultiAgentSystem.process() 是顺序调用 searcher -> generator -> validator。
+    任何一个 Agent 抛异常都让整个流程崩。
+
+要求：
+    1. 在 process() 外层包 try/except，失败时返回 {"error": str(e)}
+    2. 给每个 Agent 加 timeout_seconds 参数（用 time.monotonic）
+    3. 验证：故意让 validator 抛异常，确认 process() 不会让程序崩溃
+
+提示：
+    - time.monotonic() 不受系统时间影响
+    - 简单演示里可以靠 sleep + 时间比较实现超时
+"""
+
+
+def test_exercises():
+    from step_05_07_advanced.concept import SimpleRAG
+
+    rag = SimpleRAG()
+    rag.add_document("JasperReports 是一个 Java 报表库", {"source": "doc1"})
+    rag.add_document("JRXML 是 JasperReports 模板格式", {"source": "doc2"})
+    print(rag.retrieve("JasperReports"))
+
+
+if __name__ == "__main__":
+    test_exercises()
@@ -0,0 +1,169 @@
+"""
+Step 05-07 练习题答案
+
+⚠️ 先自己思考，再看答案！
+⚠️ 答案不是唯一的，这里只是其中一种实现
+"""
+
+import re
+import time
+from typing import Callable
+
+from step_05_07_advanced.concept import (
+    Agent,
+    MultiAgentSystem,
+    SelfCorrectingAgent,
+    SimpleRAG,
+    ValidationResult,
+)
+
+
+# ═══════════════════════════════════════════════════════════════════════════════
+# 练习 1 答案：升级 SimpleRAG 分词
+# ═══════════════════════════════════════════════════════════════════════════════
+
+_TOKEN_PATTERN = re.compile(r"[\u4e00-\u9fff]|[A-Za-z]+")
+
+
+def _tokenize(text: str) -> set[str]:
+    return set(_TOKEN_PATTERN.findall(text.lower()))
+
+
+def upgrade_retrieve(rag: SimpleRAG) -> None:
+    def retrieve(self, query: str, top_k: int = 3):
+        q_words = _tokenize(query)
+        scored = []
+        for doc in self.documents:
+            d_words = _tokenize(doc["text"])
+            union = q_words | d_words
+            if not union:
+                continue
+            score = len(q_words & d_words) / len(union)
+            scored.append((score, doc))
+        scored.sort(key=lambda x: x[0], reverse=True)
+        return [doc for _, doc in scored[:top_k]]
+
+    SimpleRAG.retrieve = retrieve
+
+
+# ═══════════════════════════════════════════════════════════════════════════════
+# 练习 2 答案：Self-Correction 主循环
+# ═══════════════════════════════════════════════════════════════════════════════
+
+def install_self_correction_run() -> None:
+    def run(
+        self: SelfCorrectingAgent,
+        requirement: str,
+        generate_fn: Callable,
+        validate_fn: Callable,
+        max_retries: int = 3,
+    ):
+        feedback = None
+        output = None
+        for attempt in range(max_retries):
+            output = generate_fn(requirement, attempt, feedback)
+            validation: ValidationResult = validate_fn(output)
+            if validation.passed:
+                return output
+            feedback = self.build_feedback(validation, output, attempt)
+        return output
+
+    SelfCorrectingAgent.run = run
+
+
+# ═══════════════════════════════════════════════════════════════════════════════
+# 练习 3 答案：MultiAgentSystem 超时与回退
+# ═══════════════════════════════════════════════════════════════════════════════
+
+def install_safe_process() -> None:
+    def process(self: MultiAgentSystem, requirement: str, timeout_seconds: float = 2.0):
+        try:
+            return self._timed_process(requirement, timeout_seconds)
+        except Exception as e:
+            return {"error": str(e)}
+
+    def _timed_process(self, requirement: str, timeout_seconds: float):
+        deadline = time.monotonic() + timeout_seconds
+        searcher = self.agents.get("searcher")
+        if searcher:
+            self._check_timeout(deadline)
+            context = searcher.process(requirement)
+        else:
+            context = ""
+
+        generator = self.agents.get("generator")
+        if generator:
+            self._check_timeout(deadline)
+            result = generator.process({"requirement": requirement, "context": context})
+        else:
+            result = requirement
+
+        validator = self.agents.get("validator")
+        if validator:
+            self._check_timeout(deadline)
+            validation = validator.process(result)
+            if not validation.get("passed", True):
+                return {"error": "验证失败", "validation": validation}
+
+        return result
+
+    def _check_timeout(self, deadline: float):
+        if time.monotonic() > deadline:
+            raise TimeoutError("Multi-Agent 处理超时")
+
+    MultiAgentSystem.process = process
+    MultiAgentSystem._timed_process = _timed_process
+    MultiAgentSystem._check_timeout = _check_timeout
+
+
+# ═══════════════════════════════════════════════════════════════════════════════
+# 测试
+# ═══════════════════════════════════════════════════════════════════════════════
+
+def test_answers():
+    print("\n" + "=" * 60)
+    print("Step 05-07 练习答案测试")
+    print("=" * 60)
+
+    print("\n📝 练习 1: 升级 SimpleRAG")
+    rag = SimpleRAG()
+    rag.add_document("JasperReports 是一个 Java 报表库", {"source": "doc1"})
+    rag.add_document("JRXML 是 JasperReports 模板格式", {"source": "doc2"})
+    upgrade_retrieve(rag)
+    hits = rag.retrieve("JasperReports")
+    print(f"  检索命中 {len(hits)} 条")
+    for d in hits:
+        print(f"    - {d['text']}")
+
+    print("\n📝 练习 2: Self-Correction run()")
+    install_self_correction_run()
+    sc = SelfCorrectingAgent()
+
+    def fake_generate(req, attempt, feedback):
+        # 第一次失败，第二次成功
+        return f"v{attempt}"
+
+    def fake_validate(output):
+        passed = output == "v1"
+        return ValidationResult(passed=passed, score=1.0 if passed else 0.2, issues=[] if passed else ["不达标"])
+
+    final = sc.run("测试", fake_generate, fake_validate, max_retries=3)
+    print(f"  最终结果 = {final}")
+
+    print("\n📝 练习 3: Multi-Agent 安全 process()")
+    install_safe_process()
+
+    class BoomValidator(Agent):
+        name = "validator"
+
+        def process(self, input_data):
+            raise RuntimeError("故意崩溃")
+
+    sys = MultiAgentSystem()
+    sys.agents["validator"] = BoomValidator()
+    res = sys.process("任何需求")
+    print(f"  异常被吞掉: {res}")
+
+
+if __name__ == "__main__":
+    test_answers()
@@ -0,0 +1,17 @@
+"""
+Step 05-07: RAG / Self-Correction / Multi-Agent 主程序
+
+运行方式：
+    cd step_05_07_advanced
+    python main.py
+"""
+
+from concept import demo
+
+
+def main():
+    demo()
+
+
+if __name__ == "__main__":
+    main()