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构建并部署 AI Agent 服务 / deploy (push) Successful in 12m9s
## 核心改动 ### 1. 单图方案重构 - 删除了多图（self.graphs），改为单图（self.graph） - 新增 MainGraphState.current_model 字段用于运行时注入模型 - llm_call 节点改为动态选择模型（create_dynamic_llm_call_node） ### 2. chat_services 优化 - 添加 _cached_services 缓存，避免重复初始化 - 新增 get_cached_chat_services() 函数，用于单图注入 - 新增 _check_http_service_available() 统一HTTP探测逻辑 - 减少重复代码，LocalVLLMChatProvider和LocalSmallModelProvider共用探测方法 ### 3. AIAgentService 重构 - initialize() 只构建一次图，传入 chat_services 字典 - 新增 _resolve_model() 模型回退逻辑 - 新增 _build_invocation() 统一构建调用参数 - process_message() 和 process_message_stream() 改为注入 current_model - 流式处理代码拆分，增加可读性 ### 4. 新增和删除文件 - 新增：backend/app/main_graph/main_graph_builder.py（图构建） - 新增：backend/app/main_graph/subgraph_wrapper.py（子图封装） - 新增：tools/test/test_tavily_search.py（测试） - 删除：backend/app/main_graph/graph.py（旧图） - 删除：backend/app/main_graph/utils/main_graph_builder.py（旧构建器） - 删除：backend/app/main_graph/utils/__init__.py ### 5. 其他更新 - README.md：新增模型服务使用情况详解章节 - backend/app/model_services/__init__.py：新增 get_cached_chat_services 导出 ## 方案优势 - 内存优化：N张图 → 1张图 - 灵活性：运行时动态选择模型，支持同会话不同模型 - 性能：模型服务缓存，初始化仅一次 - 可维护性：减少重复代码，统一HTTP探测逻辑
91 lines
2.6 KiB
Python
91 lines
2.6 KiB
Python
#!/usr/bin/env python3
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"""
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LangGraph 图结构可视化脚本
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快速查看节点和边的连接关系
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运行方式：python tools/visualize_graph.py
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"""
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import sys
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from pathlib import Path
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# 路径设置
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PROJECT_ROOT = Path(__file__).parent.parent
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BACKEND_DIR = PROJECT_ROOT / "backend"
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sys.path.insert(0, str(PROJECT_ROOT))
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from dotenv import load_dotenv
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load_dotenv(PROJECT_ROOT / ".env")
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import asyncio
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from backend.app.agent.agent_service import AIAgentService
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from backend.app.config import DB_URI
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from langgraph.checkpoint.postgres.aio import AsyncPostgresSaver
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async def visualize_graph():
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"""可视化 LangGraph 结构"""
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print("=" * 80)
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print(" LangGraph 图结构可视化")
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print("=" * 80)
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print(f"项目根目录: {PROJECT_ROOT}")
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print(f"Backend 目录: {BACKEND_DIR}")
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async with AsyncPostgresSaver.from_conn_string(DB_URI) as checkpointer:
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await checkpointer.setup()
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# 创建服务实例
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print("\n正在初始化 Agent 服务...")
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agent_service = AIAgentService(checkpointer)
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await agent_service.initialize()
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# 获取图（单图方案）
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graph = agent_service.graph
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print("\n✅ Agent 服务初始化完成")
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# 获取图结构
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graph_structure = graph.get_graph()
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# 1. 直接打印节点和边
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print("\n" + "=" * 80)
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print("[1] 节点列表")
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print("=" * 80)
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for node_id, node in graph_structure.nodes.items():
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print(f" 📦 {node_id}: {node.name}")
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print("\n" + "=" * 80)
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print("[2] 边列表")
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print("=" * 80)
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for edge in graph_structure.edges:
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print(f" {edge.source} --> {edge.target}")
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# 2. ASCII 字符画
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print("\n" + "=" * 80)
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print("[3] ASCII 字符画")
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print("=" * 80)
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try:
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ascii_graph = graph_structure.draw_ascii()
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print(ascii_graph)
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except Exception as e:
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print(f"⚠️ ASCII 绘制失败: {e}")
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# 3. Mermaid 源码
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print("\n" + "=" * 80)
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print("[4] Mermaid 源码 (复制到 https://mermaid.live/)")
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print("=" * 80)
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try:
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mermaid_code = graph_structure.draw_mermaid()
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print(mermaid_code)
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except Exception as e:
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print(f"⚠️ Mermaid 生成失败: {e}")
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# 4. 节点统计
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print("\n" + "=" * 80)
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print("[5] 图统计")
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print("=" * 80)
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print(f" 节点数量: {len(graph_structure.nodes)}")
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print(f" 边数量: {len(graph_structure.edges)}")
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if __name__ == "__main__":
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asyncio.run(visualize_graph())
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