refactor: 重构项目结构和文件组织

- 将文档文件移动到docs目录下分类存放
- 将测试文件移动到tests目录
- 将工具脚本移动到tools目录
- 更新README中的文件路径说明
- 删除重复和过时的文件
- 优化项目目录结构，提升可维护性

tools/memory_bank/detailed_memory_bank_info.py

@@ -0,0 +1,150 @@
+#!/usr/bin/env python3
+"""
+详细查看和测试Vertex AI Memory Bank功能
+"""
+
+import sys
+import os
+import asyncio
+import json
+from datetime import datetime
+sys.path.append('src')
+
+from jixia.memory.factory import get_memory_backend
+from config.doppler_config import get_google_genai_config
+
+async def test_memory_bank_functionality():
+    print("🧠 详细测试Memory Bank功能")
+    print("=" * 60)
+    
+    # 获取配置
+    config = get_google_genai_config()
+    project_id = config.get('project_id')
+    location = config.get('location', 'us-central1')
+    
+    print(f"📊 项目ID: {project_id}")
+    print(f"📍 位置: {location}")
+    print(f"🕐 测试时间: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}")
+    print()
+    
+    try:
+        # 获取Memory Bank后端
+        memory_backend = get_memory_backend()
+        print(f"✅ Memory Bank后端: {type(memory_backend).__name__}")
+        print()
+        
+        # 选择一个智能体进行详细测试
+        test_agent = "lvdongbin"
+        print(f"🧙‍♂️ 测试智能体: {test_agent} (吕洞宾)")
+        print("-" * 40)
+        
+        # 1. 创建/获取Memory Bank
+        print("1️⃣ 创建Memory Bank...")
+        memory_bank_id = await memory_backend.create_memory_bank(
+            agent_name=test_agent,
+            display_name=f"测试Memory Bank - {test_agent}"
+        )
+        print(f"   ✅ Memory Bank ID: {memory_bank_id}")
+        print()
+        
+        # 2. 添加不同类型的记忆
+        print("2️⃣ 添加测试记忆...")
+        
+        # 添加对话记忆
+        conversation_memory = await memory_backend.add_memory(
+            agent_name=test_agent,
+            content="在关于AI伦理的辩论中，我强调了技术发展应该以人为本，不能忽视道德考量。",
+            memory_type="conversation",
+            debate_topic="AI伦理与技术发展",
+            metadata={"opponent": "铁拐李", "stance": "支持伦理优先"}
+        )
+        print(f"   📝 对话记忆: {conversation_memory}")
+        
+        # 添加偏好记忆
+        preference_memory = await memory_backend.add_memory(
+            agent_name=test_agent,
+            content="我偏好使用古典哲学的智慧来论证现代问题，特别是道家思想。",
+            memory_type="preference",
+            metadata={"philosophy": "道家", "style": "古典智慧"}
+        )
+        print(f"   ⚙️ 偏好记忆: {preference_memory}")
+        
+        # 添加知识记忆
+        knowledge_memory = await memory_backend.add_memory(
+            agent_name=test_agent,
+            content="区块链技术的核心是去中心化和不可篡改性，这与道家'无为而治'的理念有相通之处。",
+            memory_type="knowledge",
+            debate_topic="区块链技术应用",
+            metadata={"domain": "技术", "connection": "哲学"}
+        )
+        print(f"   📚 知识记忆: {knowledge_memory}")
+        
+        # 添加策略记忆
+        strategy_memory = await memory_backend.add_memory(
+            agent_name=test_agent,
+            content="在辩论中，当对手使用激进论点时，我会用温和的反问来引导思考，而不是直接对抗。",
+            memory_type="strategy",
+            metadata={"tactic": "温和引导", "effectiveness": "高"}
+        )
+        print(f"   🎯 策略记忆: {strategy_memory}")
+        print()
+        
+        # 3. 测试记忆搜索
+        print("3️⃣ 测试记忆搜索...")
+        
+        # 搜索关于AI的记忆
+        ai_memories = await memory_backend.search_memories(
+            agent_name=test_agent,
+            query="AI 人工智能 伦理",
+            limit=5
+        )
+        print(f"   🔍 搜索'AI 人工智能 伦理': 找到 {len(ai_memories)} 条记忆")
+        for i, memory in enumerate(ai_memories, 1):
+            print(f"      {i}. {memory.get('content', '')[:50]}...")
+        print()
+        
+        # 搜索策略相关记忆
+        strategy_memories = await memory_backend.search_memories(
+            agent_name=test_agent,
+            query="辩论 策略",
+            memory_type="strategy",
+            limit=3
+        )
+        print(f"   🎯 搜索策略记忆: 找到 {len(strategy_memories)} 条记忆")
+        for i, memory in enumerate(strategy_memories, 1):
+            print(f"      {i}. {memory.get('content', '')[:50]}...")
+        print()
+        
+        # 4. 获取智能体上下文
+        print("4️⃣ 获取智能体上下文...")
+        context = await memory_backend.get_agent_context(
+            agent_name=test_agent,
+            debate_topic="AI伦理与技术发展"
+        )
+        print(f"   📋 上下文长度: {len(context)} 字符")
+        print(f"   📋 上下文预览: {context[:200]}...")
+        print()
+        
+        # 5. 显示所有记忆类型的统计
+        print("5️⃣ 记忆统计...")
+        memory_types = ["conversation", "preference", "knowledge", "strategy"]
+        for mem_type in memory_types:
+            memories = await memory_backend.search_memories(
+                agent_name=test_agent,
+                query="",
+                memory_type=mem_type,
+                limit=100
+            )
+            print(f"   📊 {mem_type}: {len(memories)} 条记忆")
+        
+        print()
+        print("🎉 Memory Bank功能测试完成!")
+        print("=" * 60)
+        
+    except Exception as e:
+        print(f"❌ 测试失败: {e}")
+        import traceback
+        traceback.print_exc()
+
+if __name__ == "__main__":
+    asyncio.run(test_memory_bank_functionality())

tools/memory_bank/list_memory_banks.py

@@ -0,0 +1,83 @@
+#!/usr/bin/env python3
+"""
+使用项目现有的Memory Bank代码来查看实例
+"""
+
+import sys
+import os
+import asyncio
+sys.path.append('src')
+
+from jixia.memory.factory import get_memory_backend
+from config.doppler_config import get_google_genai_config
+
+async def list_memory_banks():
+    """使用项目的Memory Bank工厂来查看实例"""
+    
+    print("🧠 使用项目Memory Bank工厂查看实例")
+    print("="*50)
+    
+    try:
+        # 获取配置
+        config = get_google_genai_config()
+        print(f"📊 项目ID: {config.get('project_id')}")
+        print(f"📍 位置: {config.get('location')}")
+        print(f"🔑 Memory Bank启用: {config.get('memory_bank_enabled')}")
+        
+        # 获取Memory Bank后端
+        print("\n🔍 正在获取Memory Bank后端...")
+        memory_backend = get_memory_backend()
+        print(f"✅ 成功获取Memory Bank后端: {type(memory_backend).__name__}")
+        
+        # 八仙列表
+        immortals = [
+            "tieguaili", "zhongliquan", "lvdongbin", "hehe_erxian", 
+            "lantsaihe", "hanxiangzi", "caoguo_jiu", "hexiangu"
+        ]
+        
+        print(f"\n🔍 正在检查八仙的Memory Bank实例...")
+        print("="*50)
+        
+        for immortal in immortals:
+            try:
+                print(f"\n🧙‍♂️ {immortal}:")
+                
+                # 尝试创建Memory Bank
+                memory_bank_id = await memory_backend.create_memory_bank(immortal)
+                print(f"   ✅ Memory Bank ID: {memory_bank_id}")
+                
+                # 尝试搜索一些记忆（如果有的话）
+                try:
+                    memories = await memory_backend.search_memories(immortal, "投资", limit=3)
+                    if memories:
+                        print(f"   📝 找到 {len(memories)} 条记忆")
+                        for i, memory in enumerate(memories[:2], 1):
+                            content = memory.get('content', '无内容')[:50]
+                            print(f"      {i}. {content}...")
+                    else:
+                        print(f"   📭 暂无记忆")
+                except Exception as e:
+                    print(f"   ⚠️  无法搜索记忆: {str(e)[:50]}...")
+                    
+            except Exception as e:
+                print(f"   ❌ 错误: {str(e)[:50]}...")
+        
+        print(f"\n🎉 Memory Bank检查完成!")
+        
+    except Exception as e:
+        print(f"\n❌ 主要错误: {str(e)}")
+        print(f"🔧 错误类型: {type(e).__name__}")
+        
+        # 显示一些调试信息
+        print("\n🔍 调试信息:")
+        print(f"   Python路径: {sys.path[:3]}...")
+        print(f"   当前目录: {os.getcwd()}")
+        print(f"   环境变量:")
+        for key in ['GOOGLE_API_KEY', 'GOOGLE_CLOUD_PROJECT_ID', 'VERTEX_MEMORY_BANK_ENABLED']:
+            value = os.getenv(key, '未设置')
+            if 'API_KEY' in key and value != '未设置':
+                value = value[:10] + '...' if len(value) > 10 else value
+            print(f"     {key}: {value}")
+
+if __name__ == "__main__":
+    asyncio.run(list_memory_banks())

tools/memory_bank/memory_bank_web_interface.py

@@ -0,0 +1,299 @@
+#!/usr/bin/env python3
+"""
+Vertex AI Memory Bank Web界面
+一个简单的Streamlit应用，用于通过Web界面访问和管理Memory Bank
+"""
+
+import streamlit as st
+import asyncio
+import sys
+import os
+from datetime import datetime
+
+# 添加项目路径
+sys.path.append('/Users/ben/liurenchaxin/src')
+
+try:
+    from jixia.memory.factory import get_memory_backend
+except ImportError as e:
+    st.error(f"无法导入jixia模块: {e}")
+    st.info("请确保已激活虚拟环境并安装了所需依赖")
+    st.stop()
+
+# 页面配置
+st.set_page_config(
+    page_title="Memory Bank 管理界面",
+    page_icon="🧠",
+    layout="wide"
+)
+
+# 标题
+st.title("🧠 Vertex AI Memory Bank 管理界面")
+st.markdown("---")
+
+# 侧边栏配置
+st.sidebar.header("配置")
+project_id = st.sidebar.text_input("项目ID", value="inner-radius-469712-e9")
+location = st.sidebar.text_input("区域", value="us-central1")
+
+# 八仙列表
+EIGHT_IMMORTALS = [
+    "lvdongbin", "tieguaili", "hanxiangzi", "lanzaihe",
+    "hesengu", "zhonghanli", "caogujiu", "hanzhongli"
+]
+
+# 缓存Memory Bank后端
+@st.cache_resource
+def get_memory_backend_cached():
+    """获取Memory Bank后端（缓存）"""
+    try:
+        loop = asyncio.new_event_loop()
+        asyncio.set_event_loop(loop)
+        backend = loop.run_until_complete(get_memory_backend("vertex"))
+        return backend, loop
+    except Exception as e:
+        st.error(f"初始化Memory Bank失败: {e}")
+        return None, None
+
+# 异步函数包装器
+def run_async(coro):
+    """运行异步函数"""
+    backend, loop = get_memory_backend_cached()
+    if backend is None:
+        return None
+    try:
+        return loop.run_until_complete(coro)
+    except Exception as e:
+        st.error(f"操作失败: {e}")
+        return None
+
+# 主界面
+tab1, tab2, tab3, tab4 = st.tabs(["📋 Memory Bank列表", "🔍 搜索记忆", "➕ 添加记忆", "📊 统计信息"])
+
+with tab1:
+    st.header("Memory Bank 实例列表")
+    
+    if st.button("🔄 刷新列表", key="refresh_list"):
+        st.rerun()
+    
+    # 显示八仙Memory Bank状态
+    cols = st.columns(4)
+    for i, immortal in enumerate(EIGHT_IMMORTALS):
+        with cols[i % 4]:
+            with st.container():
+                st.subheader(f"🧙‍♂️ {immortal}")
+                
+                # 检查Memory Bank状态
+                backend, _ = get_memory_backend_cached()
+                if backend:
+                    try:
+                        # 尝试获取agent context来验证Memory Bank存在
+                        context = run_async(backend.get_agent_context(immortal))
+                        if context is not None:
+                            st.success("✅ 活跃")
+                            
+                            # 显示记忆数量
+                            memories = run_async(backend.search_memories(immortal, "", limit=100))
+                            if memories:
+                                st.info(f"📝 记忆数量: {len(memories)}")
+                            else:
+                                st.info("📝 记忆数量: 0")
+                        else:
+                            st.warning("⚠️ 未初始化")
+                    except Exception as e:
+                        st.error(f"❌ 错误: {str(e)[:50]}...")
+                else:
+                    st.error("❌ 连接失败")
+
+with tab2:
+    st.header("🔍 搜索记忆")
+    
+    col1, col2 = st.columns([1, 2])
+    
+    with col1:
+        selected_agent = st.selectbox("选择Agent", EIGHT_IMMORTALS, key="search_agent")
+        search_query = st.text_input("搜索关键词", placeholder="输入要搜索的内容...", key="search_query")
+        search_limit = st.slider("结果数量", 1, 50, 10, key="search_limit")
+        
+        if st.button("🔍 搜索", key="search_button"):
+            if search_query:
+                with st.spinner("搜索中..."):
+                    backend, _ = get_memory_backend_cached()
+                    if backend:
+                        memories = run_async(backend.search_memories(selected_agent, search_query, limit=search_limit))
+                        st.session_state['search_results'] = memories
+                        st.session_state['search_agent'] = selected_agent
+                        st.session_state['search_query'] = search_query
+            else:
+                st.warning("请输入搜索关键词")
+    
+    with col2:
+        st.subheader("搜索结果")
+        
+        if 'search_results' in st.session_state and st.session_state['search_results']:
+            st.success(f"找到 {len(st.session_state['search_results'])} 条记忆")
+            
+            for i, memory in enumerate(st.session_state['search_results']):
+                with st.expander(f"记忆 {i+1}: {memory.get('content', 'N/A')[:50]}..."):
+                    st.write(f"**内容**: {memory.get('content', 'N/A')}")
+                    st.write(f"**类型**: {memory.get('memory_type', 'N/A')}")
+                    st.write(f"**时间**: {memory.get('timestamp', 'N/A')}")
+                    if 'metadata' in memory:
+                        st.write(f"**元数据**: {memory['metadata']}")
+        elif 'search_results' in st.session_state:
+            st.info("未找到匹配的记忆")
+        else:
+            st.info("请执行搜索以查看结果")
+
+with tab3:
+    st.header("➕ 添加记忆")
+    
+    col1, col2 = st.columns([1, 1])
+    
+    with col1:
+        add_agent = st.selectbox("选择Agent", EIGHT_IMMORTALS, key="add_agent")
+        memory_type = st.selectbox("记忆类型", ["conversation", "preference", "knowledge", "strategy"], key="memory_type")
+        memory_content = st.text_area("记忆内容", placeholder="输入要添加的记忆内容...", height=150, key="memory_content")
+        
+        # 可选的元数据
+        st.subheader("元数据（可选）")
+        importance = st.slider("重要性", 1, 10, 5, key="importance")
+        tags = st.text_input("标签（用逗号分隔）", placeholder="标签1, 标签2, 标签3", key="tags")
+        
+        if st.button("➕ 添加记忆", key="add_memory_button"):
+            if memory_content:
+                with st.spinner("添加记忆中..."):
+                    backend, _ = get_memory_backend_cached()
+                    if backend:
+                        # 准备元数据
+                        metadata = {
+                            "importance": importance,
+                            "timestamp": datetime.now().isoformat(),
+                            "source": "web_interface"
+                        }
+                        if tags:
+                            metadata["tags"] = [tag.strip() for tag in tags.split(",")]
+                        
+                        # 添加记忆
+                        success = run_async(backend.add_memory(
+                            agent_id=add_agent,
+                            content=memory_content,
+                            memory_type=memory_type,
+                            metadata=metadata
+                        ))
+                        
+                        if success:
+                            st.success("✅ 记忆添加成功！")
+                            # 清空输入
+                            st.session_state['memory_content'] = ""
+                            st.session_state['tags'] = ""
+                        else:
+                            st.error("❌ 添加记忆失败")
+            else:
+                st.warning("请输入记忆内容")
+    
+    with col2:
+        st.subheader("添加记忆预览")
+        if memory_content:
+            st.info(f"**Agent**: {add_agent}")
+            st.info(f"**类型**: {memory_type}")
+            st.info(f"**内容**: {memory_content}")
+            st.info(f"**重要性**: {importance}/10")
+            if tags:
+                st.info(f"**标签**: {tags}")
+        else:
+            st.info("输入记忆内容以查看预览")
+
+with tab4:
+    st.header("📊 统计信息")
+    
+    if st.button("🔄 刷新统计", key="refresh_stats"):
+        st.rerun()
+    
+    # 获取统计信息
+    backend, _ = get_memory_backend_cached()
+    if backend:
+        stats_data = []
+        
+        for immortal in EIGHT_IMMORTALS:
+            try:
+                # 获取记忆数量
+                memories = run_async(backend.search_memories(immortal, "", limit=1000))
+                memory_count = len(memories) if memories else 0
+                
+                # 获取agent context
+                context = run_async(backend.get_agent_context(immortal))
+                status = "活跃" if context else "未初始化"
+                
+                stats_data.append({
+                    "Agent": immortal,
+                    "状态": status,
+                    "记忆数量": memory_count,
+                    "最后更新": datetime.now().strftime("%Y-%m-%d %H:%M:%S")
+                })
+            except Exception as e:
+                stats_data.append({
+                    "Agent": immortal,
+                    "状态": "错误",
+                    "记忆数量": 0,
+                    "最后更新": f"错误: {str(e)[:30]}..."
+                })
+        
+        # 显示统计表格
+        st.dataframe(stats_data, use_container_width=True)
+        
+        # 显示汇总信息
+        col1, col2, col3, col4 = st.columns(4)
+        
+        total_agents = len(EIGHT_IMMORTALS)
+        active_agents = sum(1 for item in stats_data if item["状态"] == "活跃")
+        total_memories = sum(item["记忆数量"] for item in stats_data)
+        avg_memories = total_memories / total_agents if total_agents > 0 else 0
+        
+        with col1:
+            st.metric("总Agent数", total_agents)
+        
+        with col2:
+            st.metric("活跃Agent数", active_agents)
+        
+        with col3:
+            st.metric("总记忆数", total_memories)
+        
+        with col4:
+            st.metric("平均记忆数", f"{avg_memories:.1f}")
+
+# 页脚
+st.markdown("---")
+st.markdown(
+    """
+    <div style='text-align: center; color: #666;'>
+        🧠 Vertex AI Memory Bank Web界面 | 
+        <a href='https://cloud.google.com/vertex-ai/generative-ai/docs/agent-engine/memory-bank/overview' target='_blank'>官方文档</a>
+    </div>
+    """, 
+    unsafe_allow_html=True
+)
+
+# 使用说明
+with st.expander("📖 使用说明"):
+    st.markdown("""
+    ### 功能说明
+    
+    1. **Memory Bank列表**: 查看所有八仙角色的Memory Bank状态和记忆数量
+    2. **搜索记忆**: 在指定Agent的记忆中搜索特定内容
+    3. **添加记忆**: 为Agent添加新的记忆，支持不同类型和元数据
+    4. **统计信息**: 查看所有Agent的统计数据和汇总信息
+    
+    ### 使用前准备
+    
+    1. 确保已激活虚拟环境: `source venv/bin/activate`
+    2. 确保已设置Google Cloud认证: `gcloud auth application-default login`
+    3. 运行此界面: `streamlit run memory_bank_web_interface.py`
+    
+    ### 注意事项
+    
+    - Memory Bank目前仅在us-central1区域可用
+    - 搜索功能支持模糊匹配
+    - 添加的记忆会立即生效
+    - 统计信息实时更新
+    """)

tools/memory_bank/view_memory_banks_gcp.py

@@ -0,0 +1,154 @@
+#!/usr/bin/env python3
+"""
+通过Google Cloud Console查看Memory Bank资源
+"""
+
+import sys
+import os
+import asyncio
+import json
+import subprocess
+from datetime import datetime
+sys.path.append('src')
+
+from config.doppler_config import get_google_genai_config
+
+def get_access_token():
+    """获取Google Cloud访问令牌"""
+    try:
+        result = subprocess.run(
+            ['gcloud', 'auth', 'print-access-token'],
+            capture_output=True,
+            text=True,
+            check=True
+        )
+        return result.stdout.strip()
+    except subprocess.CalledProcessError as e:
+        print(f"❌ 获取访问令牌失败: {e}")
+        return None
+
+def make_api_request(url, token):
+    """发起API请求"""
+    import requests
+    
+    headers = {
+        'Authorization': f'Bearer {token}',
+        'Content-Type': 'application/json'
+    }
+    
+    try:
+        response = requests.get(url, headers=headers)
+        return response.status_code, response.json() if response.content else {}
+    except Exception as e:
+        return None, str(e)
+
+def main():
+    print("🔍 通过GCP API查看Memory Bank资源")
+    print("=" * 60)
+    
+    # 获取配置
+    config = get_google_genai_config()
+    project_id = config.get('project_id')
+    location = config.get('location', 'us-central1')
+    
+    print(f"📊 项目ID: {project_id}")
+    print(f"📍 位置: {location}")
+    print(f"🕐 查询时间: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}")
+    print()
+    
+    # 获取访问令牌
+    print("🔑 获取访问令牌...")
+    token = get_access_token()
+    if not token:
+        print("❌ 无法获取访问令牌")
+        return
+    print(f"✅ 访问令牌: {token[:20]}...")
+    print()
+    
+    # 尝试不同的API端点
+    api_endpoints = [
+        # Vertex AI API
+        f"https://aiplatform.googleapis.com/v1/projects/{project_id}/locations/{location}/operations",
+        f"https://aiplatform.googleapis.com/v1beta1/projects/{project_id}/locations/{location}/operations",
+        
+        # Generative Language API
+        f"https://generativelanguage.googleapis.com/v1beta/projects/{project_id}/locations/{location}/operations",
+        
+        # 尝试Memory Bank相关端点
+        f"https://aiplatform.googleapis.com/v1/projects/{project_id}/locations/{location}/memoryBanks",
+        f"https://aiplatform.googleapis.com/v1beta1/projects/{project_id}/locations/{location}/memoryBanks",
+        
+        # 尝试其他可能的端点
+        f"https://generativelanguage.googleapis.com/v1beta/projects/{project_id}/locations/{location}/memoryBanks",
+        f"https://generativelanguage.googleapis.com/v1/projects/{project_id}/locations/{location}/memoryBanks",
+    ]
+    
+    print("🌐 测试API端点...")
+    print("-" * 40)
+    
+    for i, endpoint in enumerate(api_endpoints, 1):
+        print(f"{i}. 测试: {endpoint.split('/')[-2]}/{endpoint.split('/')[-1]}")
+        
+        status_code, response = make_api_request(endpoint, token)
+        
+        if status_code == 200:
+            print(f"   ✅ 成功 (200): 找到 {len(response.get('operations', response.get('memoryBanks', [])))} 个资源")
+            if response:
+                print(f"   📄 响应预览: {str(response)[:100]}...")
+        elif status_code == 404:
+            print(f"   ⚠️ 未找到 (404): 端点不存在")
+        elif status_code == 403:
+            print(f"   🚫 权限不足 (403): 需要更多权限")
+        elif status_code:
+            print(f"   ❌ 错误 ({status_code}): {str(response)[:50]}...")
+        else:
+            print(f"   💥 请求失败: {response}")
+        print()
+    
+    # 查看项目信息
+    print("📋 项目信息...")
+    project_url = f"https://cloudresourcemanager.googleapis.com/v1/projects/{project_id}"
+    status_code, response = make_api_request(project_url, token)
+    
+    if status_code == 200:
+        print(f"   ✅ 项目名称: {response.get('name', 'N/A')}")
+        print(f"   📊 项目编号: {response.get('projectNumber', 'N/A')}")
+        print(f"   🏷️ 项目ID: {response.get('projectId', 'N/A')}")
+        print(f"   📅 创建时间: {response.get('createTime', 'N/A')}")
+        print(f"   🔄 生命周期: {response.get('lifecycleState', 'N/A')}")
+    else:
+        print(f"   ❌ 无法获取项目信息: {status_code}")
+    print()
+    
+    # 查看启用的服务
+    print("🔧 查看启用的AI相关服务...")
+    services_url = f"https://serviceusage.googleapis.com/v1/projects/{project_id}/services"
+    status_code, response = make_api_request(services_url, token)
+    
+    if status_code == 200:
+        services = response.get('services', [])
+        ai_services = [s for s in services if 'ai' in s.get('config', {}).get('name', '').lower() or 'generative' in s.get('config', {}).get('name', '').lower()]
+        
+        print(f"   📊 总服务数: {len(services)}")
+        print(f"   🤖 AI相关服务: {len(ai_services)}")
+        
+        for service in ai_services[:10]:  # 显示前10个
+            name = service.get('config', {}).get('name', 'Unknown')
+            state = service.get('state', 'Unknown')
+            print(f"      • {name}: {state}")
+    else:
+        print(f"   ❌ 无法获取服务信息: {status_code}")
+    
+    print()
+    print("🎯 Memory Bank访问建议:")
+    print("   1. 在Google Cloud Console中访问:")
+    print(f"      https://console.cloud.google.com/vertex-ai/generative/memory-banks?project={project_id}")
+    print("   2. 或者访问Vertex AI主页:")
+    print(f"      https://console.cloud.google.com/vertex-ai?project={project_id}")
+    print("   3. Memory Bank功能可能在'生成式AI'或'实验性功能'部分")
+    print()
+    print("🎉 GCP API查询完成!")
+    print("=" * 60)
+
+if __name__ == "__main__":
+    main()