#!/usr/bin/env python3 """ RAG系统完整测试示例 集成文档处理、重排检索、智能问答等功能 """ import asyncio import sys import os import warnings from pathlib import Path import shutil # 过滤警告信息 warnings.filterwarnings("ignore", category=FutureWarning, module="torch") warnings.filterwarnings("ignore", category=UserWarning) # 添加项目路径 sys.path.append(os.path.join(os.path.dirname(__file__), '..', 'src')) from base_rag.core import BaseRAG, FileStatus class ComprehensiveRAG(BaseRAG): """综合RAG实现 - 支持多格式文档和重排检索""" async def ingest(self, file_paths): """批量导入文档""" if isinstance(file_paths, str): file_paths = [file_paths] results = [] for file_path in file_paths: result = await self.process_file_to_vector_store(file_path) results.append(result) return results async def query(self, question: str) -> str: """智能问答实现 - 集成重排和智能组合prompt""" print("🎯 使用重排检索相关文档...") # 1. 使用重排检索获取最相关的文档 docs = await self.similarity_search_with_rerank(question) if not docs: return "抱歉,没有找到相关信息。请尝试其他问题或添加更多文档。" if not self.llm: # 如果没有LLM,返回格式化的检索结果 sources = [] contexts = [] image_count = 0 for i, doc in enumerate(docs): source = doc.metadata.get('source_file', f'文档{i+1}') doc_type = doc.metadata.get('type', 'text') content = doc.page_content.strip() if source not in sources: sources.append(source) if doc_type == 'image': image_count += 1 contexts.append(f"🖼️ 图片{image_count}: {content}") else: contexts.append(f"📄 {content}") context = "\n\n".join(contexts) sources_str = "、".join(sources) stats = f"({len(docs)-image_count}文本" if image_count > 0: stats += f" + {image_count}图片" stats += ")" return f"基于文档({sources_str}){stats}的信息:\n\n{context}" # 2. 组合上下文和问题的智能prompt contexts = [] sources = [] image_count = 0 for i, doc in enumerate(docs): source = doc.metadata.get('source_file', f'文档{i+1}') doc_type = doc.metadata.get('type', 'text') content = doc.page_content.strip() if source not in sources: sources.append(source) if doc_type == 'image': image_count += 1 contexts.append(f"图片内容{image_count}: {content}") else: contexts.append(f"文档片段{i+1}: {content}") context = "\n\n".join(contexts) sources_str = "、".join(sources) # 3. 构建智能prompt prompt = f"""请基于以下上下文信息回答用户的问题。 上下文信息来源: {sources_str} 包含内容: {len(docs)-image_count}个文本片段{f'和{image_count}个图片内容' if image_count > 0 else ''} 上下文内容: {context} 用户问题: {question} 回答要求: 1. 基于上下文信息提供准确、详细的回答 2. 如果上下文中包含图片信息,请结合图片内容回答 3. 如果上下文信息不足以回答问题,请明确说明 4. 回答要条理清晰,重点突出 5. 用中文回答 回答:""" print("🤔 正在基于重排后的文档生成智能答案...") # 4. 调用LLM生成回答 try: if hasattr(self.llm, 'invoke'): response = self.llm.invoke(prompt) else: response = self.llm(prompt) # 添加来源信息 sources_info = f"\n\n📚 信息来源: {sources_str}" if image_count > 0: sources_info += f" (包含{image_count}个图片内容)" return response + sources_info except Exception as e: print(f"❌ LLM调用失败: {e}") # 备用方案:返回格式化的检索结果 return f"LLM暂时不可用,但找到了相关信息:\n\n{context}\n\n📚 来源: {sources_str}" async def clear_data(test_name: str): """清理测试数据""" paths_to_clear = [ f"./storage/chroma_db/{test_name}", f"./storage/status_db/{test_name}.db" ] for path in paths_to_clear: path_obj = Path(path) if path_obj.exists(): if path_obj.is_dir(): shutil.rmtree(path_obj) else: path_obj.unlink() print(f"🧹 已清理 {test_name} 的历史数据") async def test_document_processing(): """测试文档处理功能""" print("📂 文档处理测试") print("-" * 40) # 创建RAG实例 rag = ComprehensiveRAG( vector_store_name="comprehensive_test", retriever_top_k=5, persist_directory="./storage/chroma_db/comprehensive_test", storage_directory="./storage/files", status_db_path="./storage/status_db/comprehensive_test.db", # 启用重排功能 rerank_config={ "enabled": True, "type": "local", "model": "BAAI/bge-reranker-base", "top_k": 5 }, # 启用图片处理 image_config={ "enabled": True, "type": "local", "model": "Salesforce/blip-image-captioning-base" }, embedding_config={ "type": "local", "model_name": "BAAI/bge-small-zh-v1.5" } ) # 查找测试文件 test_dir = Path("./test_files") test_files = [] # 支持的文件类型和优先级 file_priorities = { ".txt": 1, ".md": 1, # 基础文本 ".pdf": 2, ".docx": 2, # 文档类型 ".csv": 3, ".xlsx": 3, # 数据类型 ".png": 4, ".jpg": 4 # 图片类型(如果有的话) } if test_dir.exists(): for file_path in test_dir.iterdir(): if file_path.is_file() and file_path.suffix.lower() in file_priorities: priority = file_priorities[file_path.suffix.lower()] test_files.append((priority, str(file_path), file_path.suffix.upper())) # 按优先级排序 test_files.sort(key=lambda x: x[0]) if not test_files: print("⚠️ 未找到测试文件,请在 ./test_files 目录下放置测试文档") return rag, [] print(f"📁 发现 {len(test_files)} 个测试文件") processed_files = [] total_chunks = 0 for priority, file_path, file_type in test_files[:6]: # 限制处理6个文件 filename = Path(file_path).name print(f"\n📄 处理 {file_type}: {filename}") try: result = await rag.process_file_to_vector_store(file_path) if result.get('success'): chunks = result.get('chunks_count', 0) total_chunks += chunks processed_files.append(filename) status = "✅ 新处理" if "处理完成" in result['message'] else "♻️ 已存在" print(f" {status}: {chunks} 个文档片段") else: error_msg = result.get('message', '未知错误') print(f" ❌ 失败: {error_msg}") except Exception as e: print(f" ❌ 异常: {str(e)}") print(f"\n📊 处理结果: {len(processed_files)} 个文件, 共 {total_chunks} 个文档片段") return rag, processed_files async def test_retrieval_and_rerank(): """测试检索和重排功能""" print("\n🔍 检索和重排测试") print("-" * 40) # 复用文档处理的RAG实例 rag = ComprehensiveRAG( vector_store_name="comprehensive_test", retriever_top_k=5, persist_directory="./storage/chroma_db/comprehensive_test", rerank_config={ "enabled": True, "type": "local", "model": "BAAI/bge-reranker-base", "top_k": 3 } ) test_query = "Python编程语言的特点和优势" print(f"🔍 测试查询: {test_query}") # 1. 普通检索 print("\n📋 普通检索结果:") try: normal_docs = await rag.similarity_search(test_query, k=5) for i, doc in enumerate(normal_docs[:3], 1): source = doc.metadata.get('source_file', f'文档{i}') content = doc.page_content[:80] + "..." if len(doc.page_content) > 80 else doc.page_content print(f" {i}. [{source}] {content}") except Exception as e: print(f" ❌ 普通检索失败: {e}") # 2. 重排检索 print("\n🎯 重排后检索结果:") try: rerank_docs = await rag.similarity_search_with_rerank(test_query, k=3) for i, doc in enumerate(rerank_docs, 1): source = doc.metadata.get('source_file', f'文档{i}') content = doc.page_content[:80] + "..." if len(doc.page_content) > 80 else doc.page_content print(f" {i}. [{source}] {content}") except Exception as e: print(f" ❌ 重排检索失败: {e}") return rag async def test_intelligent_qa(rag): """测试智能问答功能""" print("\n💭 智能问答测试") print("-" * 40) # 尝试设置LLM try: from langchain_community.llms import Ollama rag.llm = Ollama(model="qwen3:4b", base_url="http://localhost:11434") print("🤖 已连接本地LLM (Ollama)") has_llm = True except Exception as e: print(f"⚠️ 未连接LLM,将使用检索模式: {e}") has_llm = False # 测试问题集 test_questions = [ "Python编程语言有什么特点?", # "数据科学的主要应用领域有哪些?", # "机器学习和深度学习的区别是什么?", # "文档中有哪些关于人工智能的内容?", # "图片中显示了什么信息?" # 测试图片内容 ] print(f"🔥 开始问答测试 ({'LLM模式' if has_llm else '检索模式'})") for i, question in enumerate(test_questions, 1): print(f"\n❓ 问题 {i}: {question}") print(" " + "-" * 35) try: answer = await rag.query(question) if has_llm and "📚 信息来源:" in answer: # LLM模式:分离答案和来源 parts = answer.split("\n\n📚 信息来源:") main_answer = parts[0] source_info = "📚 信息来源:" + parts[1] if len(parts) > 1 else "" print(f" 💡 {main_answer[:150]}...") if source_info: print(f" {source_info}") else: # 检索模式或简单回答 if len(answer) > 200: print(f" 💡 {answer[:200]}...") if "基于文档(" in answer: source_line = answer.split('\n')[0] print(f" 📚 {source_line}") else: print(f" 💡 {answer}") except Exception as e: print(f" ❌ 查询失败: {str(e)}") async def show_system_status(rag): """显示系统状态""" print("\n📊 系统状态总览") print("-" * 40) try: # 文件处理状态 file_statuses = await rag.get_file_processing_status() if file_statuses: print("📁 文档处理状态:") completed = sum(1 for s in file_statuses if s['status'] == FileStatus.COMPLETED.value) error = sum(1 for s in file_statuses if s['status'] == FileStatus.ERROR.value) print(f" ✅ 成功: {completed} 个文件") if error > 0: print(f" ❌ 失败: {error} 个文件") # 配置信息 print("\n⚙️ 配置信息:") print(f" 🎯 重排功能: {'✅ 启用' if rag.rerank_config.get('enabled') else '❌ 禁用'}") print(f" 🖼️ 图片处理: {'✅ 启用' if rag.image_config.get('enabled') else '❌ 禁用'}") print(f" 🤖 LLM模型: {'✅ 已连接' if rag.llm else '❌ 未连接'}") print(f" 📊 检索数量: Top {rag.retriever_top_k}") except Exception as e: print(f"❌ 状态获取失败: {e}") async def main(): """主测试流程""" print("🚀 RAG系统综合测试") print("=" * 50) # 清理历史数据 await clear_data("comprehensive_test") print() try: # 1. 文档处理测试 rag, processed_files = await test_document_processing() if not processed_files: print("❌ 没有成功处理的文档,测试终止") return # 2. 检索重排测试 rag = await test_retrieval_and_rerank() # 3. 智能问答测试 await test_intelligent_qa(rag) # 4. 系统状态 await show_system_status(rag) print("\n" + "=" * 50) print("🎉 RAG系统测试完成!") print() print("✅ 已验证功能:") print(" 📄 多格式文档处理 (TXT/MD/PDF/DOCX/CSV/XLSX)") print(" 🖼️ 图片内容提取和识别") print(" 🎯 智能重排检索") print(" 💭 上下文问答") print(" 📊 混合内容处理") print() print("💡 使用建议:") print(" 1. 确保 ./test_files 目录下有测试文档") print(" 2. 安装 Ollama 并启动本地LLM获得更好体验") print(" 3. 重排功能需要下载BGE模型,首次运行较慢") print(" 4. 图片处理需要BLIP模型,可提升多媒体文档效果") except Exception as e: print(f"\n❌ 测试过程中发生错误: {e}") import traceback traceback.print_exc() print("\n" + "=" * 50) if __name__ == "__main__": asyncio.run(main())