""" 基于图RAG的智能烹饪助手 - 主程序 整合传统检索和图RAG检索，实现真正的图数据优势 """ import os import sys import time import logging from typing import List, Optional # 设置日志 logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(name)s - %(levelname)s - %(message)s') logger = logging.getLogger(__name__) # 添加当前目录到Python路径 sys.path.append(os.path.dirname(os.path.abspath(__file__))) from dotenv import load_dotenv from config import DEFAULT_CONFIG, GraphRAGConfig from rag_modules import ( GraphDataPreparationModule, MilvusIndexConstructionModule, GenerationIntegrationModule ) from rag_modules.hybrid_retrieval import HybridRetrievalModule from rag_modules.graph_rag_retrieval import GraphRAGRetrieval from rag_modules.intelligent_query_router import IntelligentQueryRouter, QueryAnalysis # 加载环境变量 load_dotenv() class AdvancedGraphRAGSystem: """ 图RAG系统 核心特性： 1. 智能路由：自动选择最适合的检索策略 2. 双引擎检索：传统混合检索 + 图RAG检索 3. 图结构推理：多跳遍历、子图提取、关系推理 4. 查询复杂度分析：深度理解用户意图 5. 自适应学习：基于反馈优化系统性能 """ def __init__(self, config: Optional[GraphRAGConfig] = None): self.config = config or DEFAULT_CONFIG # 核心模块 self.data_module = None self.index_module = None self.generation_module = None # 检索引擎 self.traditional_retrieval = None self.graph_rag_retrieval = None self.query_router = None # 系统状态 self.system_ready = False def initialize_system(self): """初始化高级图RAG系统""" logger.info("启动高级图RAG系统...") try: # 1. 数据准备模块 print("初始化数据准备模块...") self.data_module = GraphDataPreparationModule( uri=self.config.neo4j_uri, user=self.config.neo4j_user, password=self.config.neo4j_password, database=self.config.neo4j_database ) # 2. 向量索引模块 print("初始化Milvus向量索引...") self.index_module = MilvusIndexConstructionModule( host=self.config.milvus_host, port=self.config.milvus_port, collection_name=self.config.milvus_collection_name, dimension=self.config.milvus_dimension, model_name=self.config.embedding_model ) # 3. 生成模块 print("初始化生成模块...") self.generation_module = GenerationIntegrationModule( model_name=self.config.llm_model, temperature=self.config.temperature, max_tokens=self.config.max_tokens ) # 4. 传统混合检索模块 print("初始化传统混合检索...") self.traditional_retrieval = HybridRetrievalModule( config=self.config, milvus_module=self.index_module, data_module=self.data_module, llm_client=self.generation_module.client ) # 5. 图RAG检索模块 print("初始化图RAG检索引擎...") self.graph_rag_retrieval = GraphRAGRetrieval( config=self.config, llm_client=self.generation_module.client ) # 6. 智能查询路由器 print("初始化智能查询路由器...") self.query_router = IntelligentQueryRouter( traditional_retrieval=self.traditional_retrieval, graph_rag_retrieval=self.graph_rag_retrieval, llm_client=self.generation_module.client, config=self.config ) print("✅ 高级图RAG系统初始化完成！") except Exception as e: logger.error(f"系统初始化失败: {e}") raise def build_knowledge_base(self): """构建知识库（如果需要）""" print("\n检查知识库状态...") try: # 检查Milvus集合是否存在 if self.index_module.has_collection(): print("✅ 发现已存在的知识库，尝试加载...") if self.index_module.load_collection(): print("知识库加载成功！") # 重要：即使从已存在的知识库加载，也需要加载图数据以支持图索引 print("加载图数据以支持图检索...") self.data_module.load_graph_data() print("构建菜谱文档...") self.data_module.build_recipe_documents() print("进行文档分块...") chunks = self.data_module.chunk_documents( chunk_size=self.config.chunk_size, chunk_overlap=self.config.chunk_overlap ) self._initialize_retrievers(chunks) return else: print("❌ 知识库加载失败，开始重建...") print("未找到已存在的集合，开始构建新的知识库...") # 从Neo4j加载图数据 print("从Neo4j加载图数据...") self.data_module.load_graph_data() # 构建菜谱文档 print("构建菜谱文档...") self.data_module.build_recipe_documents() # 进行文档分块 print("进行文档分块...") chunks = self.data_module.chunk_documents( chunk_size=self.config.chunk_size, chunk_overlap=self.config.chunk_overlap ) # 构建Milvus向量索引 print("构建Milvus向量索引...") if not self.index_module.build_vector_index(chunks): raise Exception("构建向量索引失败") # 初始化检索器 self._initialize_retrievers(chunks) # 显示统计信息 self._show_knowledge_base_stats() print("✅ 知识库构建完成！") except Exception as e: logger.error(f"知识库构建失败: {e}") raise def _initialize_retrievers(self, chunks: List = None): """初始化检索器""" print("初始化检索引擎...") # 如果没有chunks，从数据模块获取 if chunks is None: chunks = self.data_module.chunks or [] # 初始化传统检索器 self.traditional_retrieval.initialize(chunks) # 初始化图RAG检索器 self.graph_rag_retrieval.initialize() self.system_ready = True print("✅ 检索引擎初始化完成！") def _show_knowledge_base_stats(self): """显示知识库统计信息""" print(f"\n知识库统计:") # 数据统计 stats = self.data_module.get_statistics() print(f" 菜谱数量: {stats.get('total_recipes', 0)}") print(f" 食材数量: {stats.get('total_ingredients', 0)}") print(f" 烹饪步骤: {stats.get('total_cooking_steps', 0)}") print(f" 文档数量: {stats.get('total_documents', 0)}") print(f" 文本块数: {stats.get('total_chunks', 0)}") # Milvus统计 milvus_stats = self.index_module.get_collection_stats() print(f" 向量索引: {milvus_stats.get('row_count', 0)} 条记录") # 图RAG统计 route_stats = self.query_router.get_route_statistics() print(f" 路由统计: 总查询 {route_stats.get('total_queries', 0)} 次") if stats.get('categories'): categories = list(stats['categories'].keys())[:10] print(f" 🏷️ 主要分类: {', '.join(categories)}") def ask_question_with_routing(self, question: str, stream: bool = False, explain_routing: bool = False): """ 智能问答：自动选择最佳检索策略 """ if not self.system_ready: raise ValueError("系统未就绪，请先构建知识库") print(f"\n❓ 用户问题: {question}") # 显示路由决策解释（可选） if explain_routing: explanation = self.query_router.explain_routing_decision(question) print(explanation) start_time = time.time() try: # 1. 智能路由检索 print("执行智能查询路由...") relevant_docs, analysis = self.query_router.route_query(question, self.config.top_k) # 2. 显示路由信息 strategy_icons = { "hybrid_traditional": "🔍", "graph_rag": "🕸️", "combined": "🔄" } strategy_icon = strategy_icons.get(analysis.recommended_strategy.value, "❓") print(f"{strategy_icon} 使用策略: {analysis.recommended_strategy.value}") print(f"📊 复杂度: {analysis.query_complexity:.2f}, 关系密集度: {analysis.relationship_intensity:.2f}") # 3. 显示检索结果信息 if relevant_docs: doc_info = [] for doc in relevant_docs: recipe_name = doc.metadata.get('recipe_name', '未知内容') search_type = doc.metadata.get('search_type', doc.metadata.get('route_strategy', 'unknown')) score = doc.metadata.get('final_score', doc.metadata.get('relevance_score', 0)) doc_info.append(f"{recipe_name}({search_type}, {score:.3f})") print(f"📋 找到 {len(relevant_docs)} 个相关文档: {', '.join(doc_info[:3])}") if len(doc_info) > 3: print(f" 等 {len(relevant_docs)} 个结果...") else: # 保持返回值签名一致：始终返回 (result, analysis) return "抱歉，没有找到相关的烹饪信息。请尝试其他问题。", analysis # 4. 生成回答 print("🎯 智能生成回答...") if stream: try: for chunk_text in self.generation_module.generate_adaptive_answer_stream(question, relevant_docs): print(chunk_text, end="", flush=True) print("\n") result = "流式输出完成" except Exception as stream_error: logger.error(f"流式输出过程中出现错误: {stream_error}") print(f"\n⚠️ 流式输出中断，切换到标准模式...") # 使用非流式作为后备 result = self.generation_module.generate_adaptive_answer(question, relevant_docs) else: result = self.generation_module.generate_adaptive_answer(question, relevant_docs) # 5. 性能统计 end_time = time.time() print(f"\n⏱️ 问答完成，耗时: {end_time - start_time:.2f}秒") return result, analysis except Exception as e: logger.error(f"问答处理失败: {e}") return f"抱歉，处理问题时出现错误：{str(e)}", None def run_interactive(self): """运行交互式问答""" if not self.system_ready: print("❌ 系统未就绪，请先构建知识库") return print("\n欢迎使用尝尝咸淡RAG烹饪助手！") print("可用功能：") print(" - 'stats' : 查看系统统计") print(" - 'rebuild' : 重建知识库") print(" - 'quit' : 退出系统") print("\n" + "="*50) while True: try: user_input = input("\n您的问题: ").strip() if not user_input: continue if user_input.lower() == 'quit': break elif user_input.lower() == 'stats': self._show_system_stats() continue elif user_input.lower() == 'rebuild': self._rebuild_knowledge_base() continue # 普通问答 - 使用默认设置 use_stream = True # 默认使用流式输出 explain_routing = False # 默认不显示路由决策 print("\n回答:") result, analysis = self.ask_question_with_routing( user_input, stream=use_stream, explain_routing=explain_routing ) if not use_stream and result: print(f"{result}\n") except KeyboardInterrupt: break except Exception as e: print(f"处理问题时出错: {e}") import traceback traceback.print_exc() print("\n👋 感谢使用尝尝咸淡RAG烹饪助手！") self._cleanup() def _show_system_stats(self): """显示系统统计信息""" print("\n系统运行统计") print("=" * 40) # 路由统计 route_stats = self.query_router.get_route_statistics() total_queries = route_stats.get('total_queries', 0) if total_queries > 0: print(f"总查询次数: {total_queries}") print(f"传统检索: {route_stats.get('traditional_count', 0)} ({route_stats.get('traditional_ratio', 0):.1%})") print(f"图RAG检索: {route_stats.get('graph_rag_count', 0)} ({route_stats.get('graph_rag_ratio', 0):.1%})") print(f"组合策略: {route_stats.get('combined_count', 0)} ({route_stats.get('combined_ratio', 0):.1%})") else: print("暂无查询记录") # 知识库统计 self._show_knowledge_base_stats() def _rebuild_knowledge_base(self): """重建知识库""" print("\n准备重建知识库...") # 确认操作 confirm = input("⚠️ 这将删除现有的向量数据并重新构建，是否继续？(y/N): ").strip().lower() if confirm != 'y': print("❌ 重建操作已取消") return try: print("删除现有的Milvus集合...") if self.index_module.delete_collection(): print("✅ 现有集合已删除") else: print("删除集合时出现问题，继续重建...") # 重新构建知识库 print("开始重建知识库...") self.build_knowledge_base() print("✅ 知识库重建完成！") except Exception as e: logger.error(f"重建知识库失败: {e}") print(f"❌ 重建失败: {e}") print("建议：请检查Milvus服务状态后重试") def _cleanup(self): """清理资源""" if self.data_module: self.data_module.close() if self.traditional_retrieval: self.traditional_retrieval.close() if self.graph_rag_retrieval: self.graph_rag_retrieval.close() if self.index_module: self.index_module.close() def main(): """主函数""" try: print("启动高级图RAG系统...") # 创建高级图RAG系统 rag_system = AdvancedGraphRAGSystem() # 初始化系统 rag_system.initialize_system() # 构建知识库 rag_system.build_knowledge_base() # 运行交互式问答 rag_system.run_interactive() except Exception as e: logger.error(f"系统运行失败: {e}") import traceback traceback.print_exc() print(f"\n❌ 系统错误: {e}") if __name__ == "__main__": main()