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- #!/usr/bin/env python3
- import os
- import sys
- from typing import Iterator
- import dotenv
- from agno.agent import Agent
- from agno.memory.v2.db.sqlite import SqliteMemoryDb
- from agno.memory.v2.memory import Memory
- from agno.models.openai import OpenAILike
- from agno.storage.sqlite import SqliteStorage
- from agno.tools.yfinance import YFinanceTools
- # 加载环境变量
- dotenv.load_dotenv()
- # 全局联系人数组
- CONTACTS = [
- {"name": "张三", "phone": "138-0000-1001"},
- {"name": "李四", "phone": "139-0000-1002"},
- {"name": "王五", "phone": "136-0000-1003"},
- {"name": "赵六", "phone": "137-0000-1004"},
- {"name": "孙七", "phone": "135-0000-1005"},
- {"name": "周八", "phone": "133-0000-1006"},
- {"name": "吴九", "phone": "188-0000-1007"},
- {"name": "郑十", "phone": "180-0000-1008"}
- ]
- # 转账工具
- def transfer_money(name: str, phone: str, amount: float):
- """转账工具"""
- return f"转账成功,转账金额为:{amount},转账给:{name},转账电话:{phone}"
- def find_contact_by_name(name: str):
- """根据姓名查找联系人信息"""
- for contact in CONTACTS:
- if name in contact["name"] or contact["name"] in name:
- return f"找到联系人:{contact['name']},电话:{contact['phone']}"
- return f"未找到姓名包含'{name}'的联系人"
- def get_account_balance():
- """随机生成账户余额"""
- import random
- # 生成1000到50000之间的随机余额
- balance = round(random.uniform(1000, 50000), 2)
- return f"您的当前账户余额为:¥{balance:,.2f}"
- def create_memory_agent():
- """创建具有Memory功能的Agent"""
-
- # 检查环境变量
- api_key = os.getenv("BAILIAN_API_KEY")
- base_url = os.getenv("BAILIAN_API_BASE_URL")
-
- if not api_key or not base_url:
- print("❌ 错误: 请确保设置了以下环境变量:")
- print(" - BAILIAN_API_KEY")
- print(" - BAILIAN_API_BASE_URL")
- print("\n💡 您可以创建 .env 文件来设置这些变量")
- return None
-
- try:
- print("🚀 正在创建具有Memory功能的Agent...")
-
- # 创建模型
- model = OpenAILike(
- id="qwen3-32b",
- api_key=api_key,
- base_url=base_url,
- request_params={"extra_body": {"enable_thinking": False}},
- )
-
- # UserId for the memories
- user_id = "user_001"
- # Database file for memory and storage
- db_file = "tmp/agent_memory.db"
-
- # 创建tmp目录(如果不存在)
- os.makedirs("tmp", exist_ok=True)
-
- # Initialize memory.v2
- memory = Memory(
- model=model, # 使用相同的模型进行记忆管理
- db=SqliteMemoryDb(table_name="user_memories", db_file=db_file),
- )
-
- # Initialize storage
- storage = SqliteStorage(table_name="agent_sessions", db_file=db_file)
-
- # 定义工具函数
- def get_current_time():
- """获取当前时间的工具函数"""
- import datetime
- current_time = datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S")
- return f"当前时间是: {current_time}"
-
- def remember_info(info: str):
- """主动记住信息的工具函数"""
- return f"我已经记住了这个信息: {info}"
-
- # Create Agent with Memory and Storage
- agent = Agent(
- model=model,
- # Store memories in a database
- memory=memory,
- # Give the Agent the ability to update memories
- enable_agentic_memory=True,
- # Run the MemoryManager after each response
- enable_user_memories=True,
- # Store the chat history in the database
- storage=storage,
- # Add the chat history to the messages
- add_history_to_messages=True,
- # Number of history runs to include
- num_history_runs=3,
- # Tools
- tools=[
- # YFinanceTools(stock_price=True, company_info=True),
- find_contact_by_name,
- transfer_money,
- # get_current_time,
- # remember_info,
- get_account_balance
- ],
- markdown=False, # 终端模式关闭markdown
- show_tool_calls=True,
- instructions="""
- 你是一个具有记忆功能的智能AI助手,检测到用户有转账意图的时候会收集转账所需的信息(转账需要收款人姓名、收款人电话、转账金额)如果获得了收款人的姓名,可以根据收款人的姓名调用{find_contact_by_name}去获取对应的收款人电话再调用{get_account_balance}获取账户余额,如果用户需要转账的余额大于账户余额,则提示用户账户余额不足,如果没有对应的收款人电话再询问用户,基于通义千问模型。你的特点:
- 💬 **对话原则**:
- - 回答简洁明了,避免冗长
- - 你只使用markdown格式化输出
- - 当你收集完成转账信息后,会用JSON格式输出,并询问用户是否确认转账
- - 如果用户确认转账,则调用转账工具{transfer_money}函数进行转账,并输出转账结果
- - 如果用户不确认转账,则不进行转账
- 让我们开始愉快的对话吧!我会记住我们的每次交流。
- """,
- )
-
- print("✅ Memory Agent 创建成功!")
- print(f"📱 模型: qwen3-32b")
- print(f"🧠 记忆: SQLite数据库 ({db_file})")
- print(f"💾 存储: 会话历史记录")
- print(f"👤 用户ID: {user_id}")
-
- return agent, user_id, memory
-
- except Exception as e:
- print(f"❌ 创建Agent失败: {e}")
- print("💡 可能的原因:")
- print(" - API连接问题")
- print(" - 模型配置错误")
- print(" - 依赖包未正确安装")
- return None
- def print_chat_banner():
- """打印对话横幅"""
- banner = """
- 🧠 具有Memory功能的智能AI助手
- ===============================
- 💡 使用说明:
- - 直接输入问题开始对话
- - 输入 'quit' 或 'exit' 退出
- - 输入 'help' 查看更多帮助
- - 输入 'clear' 清屏
- - 输入 'memory' 查看记忆状态
- 🔥 功能特色:
- - 🧠 持久化记忆 - 跨会话记住您的信息
- - 🤖 智能对话 - 通义千问强大推理
- - 📊 实时数据 - 股票信息查询
- - 🎯 个性化 - 基于记忆提供定制建议
- - 💾 历史保存 - 自动保存对话历史
- """
- print(banner)
- def handle_special_commands(user_input: str) -> str:
- """处理特殊命令"""
- cmd = user_input.lower().strip()
-
- if cmd in ['quit', 'exit', 'bye', 'q']:
- return 'quit'
- elif cmd == 'help':
- help_text = """
- 🆘 命令帮助
- ===========
- 📝 基本命令:
- - quit/exit/bye/q : 退出对话
- - help : 显示此帮助
- - clear : 清屏
- - memory : 查看记忆状态
- """
- print(help_text)
- return 'help'
- elif cmd == 'clear':
- os.system('clear' if os.name == 'posix' else 'cls')
- print_chat_banner()
- return 'clear'
- elif cmd == 'memory':
- return 'memory'
-
- return 'continue'
- def print_memory_status(memory, user_id):
- """显示记忆状态"""
- try:
- print("\n🧠 Memory状态:")
- print(f" 用户ID: {user_id}")
- print(f" 数据库: tmp/agent_memory.db")
-
- # 尝试获取记忆信息
- try:
- memories = memory.get_user_memories(user_id=user_id)
- print(f" 记忆数量: {len(memories) if memories else 0}")
-
- if memories:
- print("\n📝 最近的记忆:")
- for i, mem in enumerate(memories[-3:], 1): # 显示最近3条记忆
- content = mem.get('content', '')[:100] # 限制显示长度
- print(f" {i}. {content}...")
- else:
- print(" 📭 暂无记忆内容")
-
- except Exception as e:
- print(f" 记忆系统: 已启用 (详情获取失败: {e})")
-
- print("\n💭 记忆功能可以帮助我:")
- print(" - 记住您的姓名、偏好和兴趣")
- print(" - 保持跨会话的对话连贯性")
- print(" - 提供基于历史的个性化建议")
- print(" - 回忆之前讨论过的话题")
-
- except Exception as e:
- print(f"❌ 无法获取记忆状态: {e}")
- def chat_with_memory_agent(agent, user_id, memory):
- """开始与Memory Agent对话"""
- if not agent:
- print("❌ Agent未创建成功,无法开始对话")
- return
-
- print_chat_banner()
- print("🎉 Memory Agent已就绪,开始对话吧!")
-
- conversation_count = 0
-
- try:
- while True:
- # 获取用户输入
- try:
- user_input = input(f"🙋 您 ({conversation_count+1}): ").strip()
- except (EOFError, KeyboardInterrupt):
- print("\n👋 对话结束,再见!")
- break
-
- # 跳过空输入
- if not user_input:
- continue
-
- # 处理特殊命令
- cmd_result = handle_special_commands(user_input)
- if cmd_result == 'quit':
- print("👋 感谢使用Memory Agent,我会记住我们的对话!")
- break
- elif cmd_result in ['help', 'clear']:
- continue
- elif cmd_result == 'memory':
- print_memory_status(memory, user_id)
- continue
-
- # Agent处理并回复
- conversation_count += 1
- print(f"\n🤖 AI助手 ({conversation_count}):")
- print("=" * 60)
-
- try:
- # 调用Agent处理用户问题,使用user_id来关联记忆
- response = agent.print_response(
- user_input,
- user_id=user_id, # 关联用户ID
- stream=True
- )
- print("=" * 60)
- print() # 空行分隔
-
- # 每3轮对话提示一次记忆功能
- if conversation_count % 3 == 0:
- print("🧠 提示:我已经记住了我们的对话内容,下次聊天时我仍会记得!")
-
- except KeyboardInterrupt:
- print("\n⚠️ 回复被中断,继续下一轮对话...")
- continue
- except Exception as e:
- print(f"❌ 处理消息出错: {e}")
- print("💡 请检查网络连接或API配置")
- continue
-
- except Exception as e:
- print(f"❌ 对话过程中出现错误: {e}")
-
- print("🔚 对话会话结束,记忆已保存")
- def main():
- """主函数"""
- try:
- print("🚀 启动具有Memory功能的AI对话系统...")
-
- # 创建Memory Agent
- result = create_memory_agent()
- if not result:
- sys.exit(1)
-
- agent, user_id, memory = result
-
- print("\n" + "="*50)
- print("🚀 启动对话模式...")
-
- # 开始对话
- chat_with_memory_agent(agent, user_id, memory)
-
- except Exception as e:
- print(f"❌ 启动失败: {e}")
- sys.exit(1)
- if __name__ == "__main__":
- main()
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