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## 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
150 lines
4.0 KiB
Python
150 lines
4.0 KiB
Python
#!/usr/bin/env python3
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"""测试 Tavily 搜索功能 - 直接调用 API"""
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import sys
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from pathlib import Path
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from dataclasses import dataclass
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from datetime import datetime
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from typing import List, Optional
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from dotenv import load_dotenv
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# 路径设置
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project_root = Path(__file__).resolve().parent.parent.parent
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sys.path.insert(0, str(project_root))
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load_dotenv(project_root / ".env")
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import os
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TAVILY_API_KEY = os.getenv("TAVILY_API_KEY")
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TAVILY_MAX_RESULTS = int(os.getenv("TAVILY_MAX_RESULTS") or "5")
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@dataclass
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class SearchResult:
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"""搜索结果数据类"""
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title: str
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url: str
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snippet: str
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source: str = "DuckDuckGo"
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timestamp: datetime = None
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def __post_init__(self):
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if self.timestamp is None:
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self.timestamp = datetime.now()
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def test_tavily_api_key():
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"""测试 API Key 配置"""
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print("=" * 60)
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print("测试 1: 检查 Tavily API Key")
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print("=" * 60)
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if TAVILY_API_KEY:
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print(f"✓ TAVILY_API_KEY 已配置: {TAVILY_API_KEY[:15]}...")
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else:
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print("✗ TAVILY_API_KEY 未配置")
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print()
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def test_tavily_search_direct():
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"""直接测试 Tavily API"""
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print("=" * 60)
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print("测试 2: 直接调用 Tavily API")
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print("=" * 60)
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if not TAVILY_API_KEY:
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print("✗ 未配置 API Key,跳过测试")
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return
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from tavily import TavilyClient
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client = TavilyClient(api_key=TAVILY_API_KEY)
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test_queries = [
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"Python 编程语言最新版本",
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"LangGraph AI 框架",
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]
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for query in test_queries:
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print(f"\n搜索: {query}")
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print("-" * 40)
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try:
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response = client.search(
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query=query,
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max_results=3,
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include_answer=True,
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include_raw_content=False
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)
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print(f"✓ 搜索成功")
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print(f" - 结果数量: {len(response.get('results', []))}")
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# 打印结果
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for i, item in enumerate(response.get("results", []), 1):
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print(f"\n [{i}] {item.get('title', '')}")
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print(f" URL: {item.get('url', '')}")
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print(f" 摘要: {item.get('content', '')[:100]}...")
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# 如果有 answer
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if response.get("answer"):
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print(f"\n 🤖 AI 摘要: {response['answer'][:200]}...")
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except Exception as e:
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print(f"✗ 搜索失败: {e}")
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print()
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def test_web_search_integration():
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"""测试 web_search 模块集成"""
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print("=" * 60)
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print("测试 3: 测试 web_search 模块集成")
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print("=" * 60)
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# 直接导入 web_search 模块(避免循环依赖)
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web_search_path = project_root / "backend" / "app" / "core" / "web_search.py"
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if not web_search_path.exists():
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print(f"✗ web_search.py 不存在于 {web_search_path}")
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return
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print(f"✓ 找到 web_search.py: {web_search_path}")
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# 使用 exec 动态加载模块
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import importlib.util
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spec = importlib.util.spec_from_file_location("web_search_module", web_search_path)
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web_search_module = importlib.util.module_from_spec(spec)
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try:
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spec.loader.exec_module(web_search_module)
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print("✓ web_search 模块加载成功")
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except Exception as e:
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print(f"✗ 模块加载失败: {e}")
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return
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# 测试搜索
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print("\n执行搜索测试:")
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try:
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result = web_search_module.web_search("今天天气怎么样", max_results=3)
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print(f"✓ 搜索成功,返回 {len(result)} 字符")
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print("-" * 40)
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print(result[:800] + "..." if len(result) > 800 else result)
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except Exception as e:
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print(f"✗ 搜索失败: {e}")
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print()
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def main():
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print("\n" + "=" * 60)
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print("🚀 Tavily 搜索功能测试")
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print("=" * 60 + "\n")
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test_tavily_api_key()
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test_tavily_search_direct()
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test_web_search_integration()
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print("=" * 60)
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print("✅ 测试完成")
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print("=" * 60)
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if __name__ == "__main__":
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main()
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