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构建并部署 AI Agent 服务 / deploy (push) Successful in 5m31s
- 重构 fast_paths.py，让 fast_chitchat 和 fast_rag 都进入 llm_call 而不是直接设置 final_result - 修改 check_fast_path_success 函数返回 'llm_call' 而不是 'success' - 更新 main_graph_builder.py 的条件边配置，支持路由到 llm_call - 在快速路径节点中添加清除 state.final_result 的逻辑，避免复用旧结果 - 重构 RAG 工具初始化方式，使用模块级变量管理 - 修改 finalize.py 让它返回 final_result - 更新 agent_service.py 的 RAG 工具注入方式 - 简化 hybrid_router.py 的代码结构 - 清理 rag_nodes.py 的全局变量相关代码 - 更新相关测试文件
73 lines
1.8 KiB
Python
73 lines
1.8 KiB
Python
# app/rag_initializer.py
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from app.rag.tools import create_rag_tool
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from app.rag.retriever import create_parent_hybrid_retriever
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from app.model_services import get_embedding_service
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from app.logger import info, warning
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import sys
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# 全局 RAG 工具
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_rag_tool = None
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_initialized = False
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def get_rag_tool() -> callable:
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"""获取全局 RAG 工具"""
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return _rag_tool
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def is_initialized() -> bool:
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"""检查是否已初始化"""
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return _initialized
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async def init_rag_tool(local_llm_creator, force: bool = False):
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"""
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初始化 RAG 工具（注册到模块级变量）
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Args:
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local_llm_creator: 返回 LLM 实例的函数
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force: 是否强制重新初始化
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Returns:
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RAG 工具（@tool 装饰函数）或 None
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"""
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global _rag_tool, _initialized
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# 防止重复初始化
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if _initialized and not force:
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info("[RAG] 已初始化，跳过")
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return _rag_tool
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try:
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info("🔄 正在初始化 RAG 检索系统...")
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embeddings = get_embedding_service()
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retriever = create_parent_hybrid_retriever(
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collection_name="rag_documents",
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search_k=5,
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embeddings=embeddings
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)
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rewrite_llm = local_llm_creator()
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rag_tool = create_rag_tool(
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retriever=retriever,
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llm=rewrite_llm,
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num_queries=3,
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rerank_top_n=5
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)
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_rag_tool = rag_tool
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_initialized = True
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info(f"✅ RAG 检索工具初始化成功 (id={id(rag_tool)})")
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return rag_tool
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except Exception as e:
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warning(f"⚠️ RAG 检索工具初始化失败: {e}")
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return None
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def reset():
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"""重置（用于测试）"""
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global _rag_tool, _initialized
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_rag_tool = None
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_initialized = False
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