feat(module13): update code and environment dependencies
This commit is contained in:
Annyfee
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import streamlit as st
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# === 输入框 === st.chat_input
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prompt = st.chat_input("请输入问题")
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if prompt:
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# 处理用户输入
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st.write(f"你输入了:{prompt}")
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# === 气泡 === st.chat_messages:自动生成对话气泡
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# 用户气泡
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with st.chat_message("user", avatar="👤"):
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st.write("你是人吗")
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# 助手气泡
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with st.chat_message("assistant", avatar="🤖"):
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st.write("似乎不太像是人")
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# === 折叠状态栏 === st.status:收纳中间步骤
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with st.status("Agent 正在思考...",expanded=True): # expanded:默认是否打开折叠栏
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st.write("正在查询数据库...")
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st.write("正在转接专员...")
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# 最终更新状态
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st.success("处理完成")
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# === 动态占位符 === st.empty():先占位,后续更新
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import time
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response = st.empty()
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for msg in ["处理中...", "完成！"]:
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response.markdown(msg)
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time.sleep(1)
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# === 记忆中枢 === st.session_state:状态持久化
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# 初始化
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if "messages" not in st.session_state:
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st.session_state.messages = []
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# 读写
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st.session_state.messages.append({"role":"user","content":prompt})
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# === 侧边栏 === st.sidebar
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with st.sidebar:
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st.write('我是侧边栏')
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st.json({'User':"张三"})
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# === 状态同步器 === st.rerun():让UI与最新状态同步
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st.session_state.cur_agent = "Refund"
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# 注意：在聊天机器人等交互场景中，通常这样写（安全）：
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# if some_condition: # 只有在用户提交新消息后
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# st.session_state.cur_agent = "RefundAgent"
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# st.rerun() # 手动刷新，显示最新状态
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# 如果像上面这样无条件调用 st.rerun()，会造成无限循环刷新，
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# 所以这里注释掉，仅用于静态演示。
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# st.rerun()
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@@ -0,0 +1,25 @@
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import streamlit as st
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st.set_page_config(page_title="智能客服驾驶舱",layout="wide") # 标签页的命名
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st.title("✈️ 智能航天客服 Swarm") # 标题
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# 侧边栏
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with st.sidebar:
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st.header("📦 驾驶舱监控") # 侧边栏标题
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st.info("当前坐席:前台 TriageAgent") # 侧边栏高亮信息
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st.subheader("用户画像") # 侧边栏副标题
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st.json({"name":"张三","vip":True})
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# 画聊天历史(模拟)
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with st.chat_message("user",avatar="👤"): # 用一个小表情代表发言人头像
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st.write("我要退票")
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with st.chat_message("assistant",avatar="🤖"):
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st.write("好的，为您转接退票专员...")
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# 画输入框
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prompt = st.chat_input("请输入您的问题")
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if prompt:
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# 当用户输入后，页面会刷新，显示下面的内容
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with st.chat_message("user",avatar="👤"):
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st.write(prompt)
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@@ -0,0 +1,75 @@
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import sys
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import os
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# 添加项目根目录到 Python 路径
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sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
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import streamlit as st
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from agents import Runner, set_tracing_disabled
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from agents.agent import Agent
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from agents.models.openai_chatcompletions import OpenAIChatCompletionsModel
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from openai import AsyncOpenAI
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from config import OPENAI_API_KEY
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set_tracing_disabled(True)
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# 初始化模型
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client = AsyncOpenAI(api_key=OPENAI_API_KEY,base_url="https://api.deepseek.com")
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model = OpenAIChatCompletionsModel(model="deepseek-chat",openai_client=client)
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# 定义一个通用Agent
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smart_agent = Agent(
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name="SmartAssistant",
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instructions="""
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你是航空公司智能客服助手，客户姓名：张三（白金会员），航班号：CA1234。
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你具备以下能力：
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- 用户说“退票”“退款”“取消” → 回复：✅ 退款申请已提交，预计3个工作日内原路返回。
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- 用户说“改签”“换航班” → 回复：✅ 明日同航线航班尚有余座，已为您预留，可随时确认改签。
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- 其他任何问题 → 礼貌、专业地直接回答
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回复要简洁、自然、带表情符号，让用户感到温暖。
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""",
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model=model
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)
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# Streamlit UI组件
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st.set_page_config(page_title="智能客服驾驶舱",layout="wide") # 标签页命名
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st.title("✈️ 智能航天客服 Swarm") # 页面标题
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# 侧边栏:驾驶舱监控(先固定不变动)
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with st.sidebar:
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st.header("🖥️ 驾驶舱监控")
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st.success("当前坐席: 智能助理 SmartAssistant 🤖") # success用作高亮块显示
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st.subheader("用户画像")
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st.json({"user_name": "张三(白金会员)", "flight_no": "CA1234"})
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# 会话状态与历史消息
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if "messages" not in st.session_state: # 首次运行时，初始化空列表，用于存储聊天消息。
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st.session_state["messages"] = []
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for msg in st.session_state["messages"]: # 重新渲染历史消息，确保聊天上下文在页面刷新后依然可见
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avatar = "👤" if msg['role'] == 'user' else "🤖"
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with st.chat_message(msg['role'],avatar=avatar):
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st.write(msg["content"])
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# 用户输入与核心交互
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prompt = st.chat_input("请输入您的问题（试试：我要退票 / 想改签 / 你好）")
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if prompt:
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# 显示用户信息
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st.session_state.messages.append({"role":"user","content":prompt})
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with st.chat_message("user",avatar="👤"):
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st.write(prompt)
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# 调用Agents SDK
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with st.spinner("思考中..."): # 执行耗时操作时，显示旋转的加载动画
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result = Runner.run_sync( # 同步运行智能体
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smart_agent, # 参数:agent
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st.session_state.messages # 参数:对话历史
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)
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# 显示AI回复
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reply = result.final_output # 最终AI回复内容
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st.session_state.messages.append({"role":"assistant","content":reply})
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with st.chat_message("assistant",avatar="🤖"):
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st.write(reply)
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@@ -0,0 +1,167 @@
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import sys
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import os
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import asyncio
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import nest_asyncio
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import uuid
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from agents import Runner, set_tracing_disabled, SQLiteSession
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from openai.types.responses import ResponseTextDeltaEvent
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sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
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import streamlit as st
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from m12_agents_sdk_swarm.s01_tools import context_variables
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from m12_agents_sdk_swarm.s02_agent import triage_agent, amap_server
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nest_asyncio.apply()
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set_tracing_disabled(True)
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# 生成/获取动态 Session ID
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if "session_id" not in st.session_state:
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st.session_state.session_id = f"session_{uuid.uuid4().hex[:8]}"
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async def init_mcp():
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try:
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await amap_server[0].connect()
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return "✅ 高德地图（按需连接）"
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except Exception as e:
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return f"⚠️ MCP 连接失败"
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mcp_status = asyncio.run(init_mcp())
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st.set_page_config(page_title="智能客服驾驶舱", layout="wide")
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st.title("✈️ 智能航空客服 Multi-Agent 系统")
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st.caption(f"🚀 实战三：基于 Agents SDK 的多智能体协作 (ID: {st.session_state.session_id})") # 小字体显示文本
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# 初始化持久化 Session
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if "session" not in st.session_state:
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st.session_state.session = SQLiteSession( # 持久化内存，将AI对话历史搬到数据库中
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session_id=st.session_state.session_id,
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db_path="./m13_streamlit/conversations.db"
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)
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if "display_messages" not in st.session_state: # 存储对话记录
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st.session_state.display_messages = []
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if "tool_logs_history" not in st.session_state: # 存储工具调用记录
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st.session_state.tool_logs_history = []
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# 封装侧边栏渲染函数，以便在初次加载和转接发生时，都能向同一个占位符刷新内容
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def render_agent_status(placeholder, agent):
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name = agent.name
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if name == "TriageAgent":
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placeholder.info("当前坐席: 前台 TriageAgent")
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elif name == "RefundAgent":
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placeholder.success("当前坐席: 退票专员 RefundAgent 🚨")
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elif name == "ChangeAgent":
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placeholder.warning("当前坐席: 改签专员 ChangeAgent 🔄")
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with st.sidebar:
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st.header("🖥️ 驾驶舱监控")
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# 确保有大脑（session_state）记住当前 Agent
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if "current_agent" not in st.session_state:
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st.session_state.current_agent = triage_agent
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# 当代码执行到对应位置时，自动更新上方侧边栏内容
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agent_status_placeholder = st.empty()
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# 初始渲染：基于当前state里的Agent
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render_agent_status(agent_status_placeholder, st.session_state.current_agent)
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st.subheader("👤 用户画像")
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st.json(context_variables)
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st.subheader("🔌 MCP 状态")
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st.caption(mcp_status)
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st.subheader("📊 会话统计")
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st.metric("消息数", len(st.session_state.display_messages))
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if st.button("🗑️ 清空对话"):
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asyncio.run(st.session_state.session.clear_session()) # 调用SQLiteSession对象的clear_session的方法，删除所有session_id等于当前ID的记录
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st.session_state.session_id = f"session_{uuid.uuid4().hex[:8]}" # 生成新的session_id
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st.session_state.display_messages = [] # 重置UI缓存
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st.session_state.tool_logs_history = []
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st.session_state.current_agent = triage_agent
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st.rerun()
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# 渲染历史消息
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for i, msg in enumerate(st.session_state.display_messages):
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avatar = "👤" if msg["role"] == "user" else "🤖"
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with st.chat_message(msg["role"], avatar=avatar):
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st.write(msg["content"])
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if i < len(st.session_state.tool_logs_history) and st.session_state.tool_logs_history[i]: # 确保列表索引存在且实际工具调用有记录
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with st.expander("🔧 查看工具调用", expanded=False): # 默认折叠
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for log in st.session_state.tool_logs_history[i]:
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st.caption(log)
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prompt = st.chat_input("请输入您的问题")
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if prompt:
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st.session_state.display_messages.append({"role": "user", "content": prompt})
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st.session_state.tool_logs_history.append([]) # 提前占座，保证索引顺序
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with st.chat_message("user", avatar="👤"): # 直接写入气泡
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st.write(prompt)
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with st.chat_message("assistant", avatar="🤖"):
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message_placeholder = st.empty() # 先占位，AI思考后再填入
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with st.status("Agent 正在思考...", expanded=True) as status:
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async def process_stream():
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stream = Runner.run_streamed(
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st.session_state.current_agent,
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input=prompt, # 问题
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context=context_variables, # 全局上下文
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session=st.session_state.session # 读:从数据库提取聊天记录/写:回复结束，自动将新一轮对话存入数据库
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)
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# 收集局部变量，再一起叠加到全局变量中
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reply = "" # 回复内容累加器
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tool_logs = [] # 工具日志收集
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current_agent_name = st.session_state.current_agent.name
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async for event in stream.stream_events(): # 流式事件
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if event.type == "raw_response_event":
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if isinstance(event.data, ResponseTextDeltaEvent):
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delta = event.data.delta or "" # 提取新字符
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reply += delta
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message_placeholder.write(reply) # 实时刷新UI
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# 当agent转换时
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elif event.type == "agent_updated_stream_event":
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new_agent = event.new_agent
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if current_agent_name != new_agent.name:
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log_msg = f"🔀 转接: {current_agent_name} → {new_agent.name}"
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status.write(log_msg) # 写入折叠栏
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tool_logs.append(log_msg)
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# 1. 更新“大脑”（状态持久化）
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st.session_state.current_agent = new_agent
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# 2. 实时更新“脸面”（让侧边栏占位符立刻变色/变字）
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render_agent_status(agent_status_placeholder, new_agent)
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current_agent_name = new_agent.name
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elif event.type == "run_item_stream_event":
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if event.name == "tool_called":
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tool_name = event.item.raw_item.name # 提取被调用工具的名字
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log_msg = f"🔧 调用: {tool_name}"
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status.write(log_msg) # 写入折叠栏
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tool_logs.append(log_msg)
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return reply, tool_logs # 返回完整回复的字符串与收集到的动作日志
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reply, tool_logs = asyncio.run(process_stream())
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status.update(label="✅ 处理完成", state="complete", expanded=False) # 标识处理成功
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# 将(本轮)对话记录/工具调用，分别存入(总)对话记录/工具调用
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st.session_state.display_messages.append({"role": "assistant", "content": reply})
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st.session_state.tool_logs_history.append(tool_logs)
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