chat-python/data_cleanup_script.py

import sys
import os
import logging
from datetime import datetime
from typing import List, Dict, Set, Tuple, Optional
import mysql.connector
from mysql.connector import Error
from pymilvus import MilvusClient

# 添加项目路径到sys.path
sys.path.append(os.path.dirname(os.path.abspath(__file__)))

from config import milvus_uri, mysql_config
from utils.get_logger import setup_logger

# 设置日志
logger = setup_logger(__name__)

class VectorDataCleaner:
    """向量数据清理工具类"""
    
    def __init__(self):
        """初始化数据清理工具"""
        self.mysql_config = mysql_config
        self.milvus_uri = milvus_uri
        self.milvus_client = None
        self.mysql_conn = None
        self._connect_databases()
        
    def _connect_databases(self):
        """连接数据库"""
        # 连接MySQL
        try:
            self.mysql_conn = mysql.connector.connect(**self.mysql_config)
            logger.info("MySQL数据库连接成功")
        except Error as e:
            logger.error(f"MySQL数据库连接失败: {e}")
            raise
        
        # 连接Milvus
        try:
            self.milvus_client = MilvusClient(uri=self.milvus_uri)
            logger.info("Milvus向量数据库连接成功")
        except Exception as e:
            logger.error(f"Milvus向量数据库连接失败: {e}")
            raise
    
    def close_connections(self):
        """关闭数据库连接"""
        if self.mysql_conn:
            self.mysql_conn.close()
            logger.info("MySQL连接已关闭")
        # Milvus客户端会自动管理连接
    
    def get_mysql_slices(self, knowledge_id: str, document_id: str = None) -> Set[str]:
        """
        从MySQL获取指定知识库/文件的所有切片ID
        
        Args:
            knowledge_id: 知识库ID
            document_id: 文档ID，可选。如果不提供则获取整个知识库的切片
            
        Returns:
            Set[str]: 切片ID集合
        """
        if not self.mysql_conn:
            logger.error("MySQL连接未建立")
            return set()
        
        cursor = None
        try:
            cursor = self.mysql_conn.cursor()
            
            if document_id:
                # 查询指定文档的切片
                query = """
                SELECT slice_id FROM slice_info 
                WHERE knowledge_id = %s AND document_id = %s
                """
                cursor.execute(query, (knowledge_id, document_id))
                logger.info(f"查询知识库 {knowledge_id} 中文档 {document_id} 的切片")
            else:
                # 查询整个知识库的切片
                query = """
                SELECT slice_id FROM slice_info 
                WHERE knowledge_id = %s
                """
                cursor.execute(query, (knowledge_id,))
                logger.info(f"查询知识库 {knowledge_id} 的所有切片")
            
            results = cursor.fetchall()
            slice_ids = {row[0] for row in results}
            
            logger.info(f"MySQL中找到 {len(slice_ids)} 个切片")
            return slice_ids
            
        except Error as e:
            logger.error(f"查询MySQL切片数据失败: {e}")
            return set()
        finally:
            if cursor:
                cursor.close()
    
    def get_milvus_slices(self, knowledge_id: str, document_id: str = None) -> Set[str]:
        """
        从Milvus获取指定知识库/文件的所有切片ID
        
        Args:
            knowledge_id: 知识库ID（也是collection名称）
            document_id: 文档ID，可选。如果不提供则获取整个知识库的切片
            
        Returns:
            Set[str]: 切片ID集合
        """
        if not self.milvus_client:
            logger.error("Milvus连接未建立")
            return set()
        
        try:
            # 检查collection是否存在
            if not self.milvus_client.has_collection(collection_name=knowledge_id):
                logger.warning(f"Milvus中不存在知识库 {knowledge_id} 对应的collection")
                return set()
            
            # 构建查询条件
            if document_id:
                filter_expr = f"doc_id == '{document_id}'"
                logger.info(f"查询Milvus知识库 {knowledge_id} 中文档 {document_id} 的切片")
            else:
                filter_expr = None
                logger.info(f"查询Milvus知识库 {knowledge_id} 的所有切片")
            
            # 查询所有切片ID（分批查询以避免限制）
            all_results = []
            offset = 0
            limit = 10000  # 每次查询10000条
            
            while True:
                results = self.milvus_client.query(
                    collection_name=knowledge_id,
                    filter=filter_expr,
                    output_fields=["chunk_id"],
                    limit=limit,
                    offset=offset
                )
                
                if not results:
                    break
                    
                all_results.extend(results)
                
                # 如果返回的结果少于limit，说明已经查询完毕
                if len(results) < limit:
                    break
                    
                offset += limit
            
            results = all_results
            
            slice_ids = {result["chunk_id"] for result in results if "chunk_id" in result}
            
            logger.info(f"Milvus中找到 {len(slice_ids)} 个切片")
            return slice_ids
            
        except Exception as e:
            logger.error(f"查询Milvus切片数据失败: {e}")
            return set()
    
    def get_milvus_slice_details(self, knowledge_id: str, chunk_ids: List[str]) -> List[Dict]:
        """
        获取Milvus中指定切片的详细信息
        
        Args:
            knowledge_id: 知识库ID
            chunk_ids: 切片ID列表
            
        Returns:
            List[Dict]: 切片详细信息列表
        """
        if not self.milvus_client or not chunk_ids:
            return []
        
        try:
            # 构建查询条件
            chunk_ids_str = "', '".join(chunk_ids)
            filter_expr = f"chunk_id in ['{chunk_ids_str}']"
            
            results = self.milvus_client.query(
                collection_name=knowledge_id,
                filter=filter_expr,
                output_fields=["pk", "chunk_id", "doc_id", "content"],
                limit=len(chunk_ids)
            )
            
            return results
            
        except Exception as e:
            logger.error(f"获取Milvus切片详细信息失败: {e}")
            return []
    
    def delete_milvus_slices(self, knowledge_id: str, chunk_ids: List[str]) -> Tuple[bool, str]:
        """
        删除Milvus中的指定切片
        
        Args:
            knowledge_id: 知识库ID
            chunk_ids: 要删除的切片ID列表
            
        Returns:
            Tuple[bool, str]: (是否成功, 消息)
        """
        if not self.milvus_client or not chunk_ids:
            return True, "没有需要删除的切片"
        
        try:
            # 分批删除，避免一次删除太多数据
            batch_size = 100
            total_deleted = 0
            
            for i in range(0, len(chunk_ids), batch_size):
                batch_chunk_ids = chunk_ids[i:i + batch_size]
                
                # 先查询获取主键
                chunk_ids_str = "', '".join(batch_chunk_ids)
                filter_expr = f"chunk_id in ['{chunk_ids_str}']"
                
                results = self.milvus_client.query(
                    collection_name=knowledge_id,
                    filter=filter_expr,
                    output_fields=["pk"],
                    limit=len(batch_chunk_ids)
                )
                
                if not results:
                    continue
                
                # 提取主键
                primary_keys = [result["pk"] for result in results]
                
                # 执行删除
                delete_result = self.milvus_client.delete(
                    collection_name=knowledge_id,
                    ids=primary_keys
                )
                
                total_deleted += len(primary_keys)
                logger.info(f"删除了 {len(primary_keys)} 个切片，累计删除 {total_deleted} 个")
            
            # 执行flush和compact操作
            self.milvus_client.flush(collection_name=knowledge_id)
            logger.info(f"成功删除 {total_deleted} 个脏数据切片")
            
            return True, f"成功删除 {total_deleted} 个脏数据切片"
            
        except Exception as e:
            logger.error(f"删除Milvus切片失败: {e}")
            return False, f"删除失败: {str(e)}"
    
    def cleanup_data(self, knowledge_id: str, document_id: str = None, dry_run: bool = True) -> Dict:
        """
        执行数据清理
        
        Args:
            knowledge_id: 知识库ID
            document_id: 文档ID，可选
            dry_run: 是否为试运行模式（不实际删除数据）
            
        Returns:
            Dict: 清理结果统计
        """
        logger.info(f"开始数据清理 - 知识库: {knowledge_id}, 文档: {document_id or '全部'}, 试运行: {dry_run}")
        
        # 检查数据库连接
        if not self.mysql_conn:
            return {"error": "MySQL连接失败"}
        
        if not self.milvus_client:
            return {"error": "Milvus连接失败"}
        
        try:
            # 获取MySQL中的切片ID
            mysql_slice_ids = self.get_mysql_slices(knowledge_id, document_id)
            
            # 获取Milvus中的切片ID
            milvus_slice_ids = self.get_milvus_slices(knowledge_id, document_id)
            
            # 找出需要删除的切片（在Milvus中但不在MySQL中）
            dirty_slice_ids = milvus_slice_ids - mysql_slice_ids
            
            # 找出缺失的切片（在MySQL中但不在Milvus中）
            missing_slice_ids = mysql_slice_ids - milvus_slice_ids
            
            result = {
                "knowledge_id": knowledge_id,
                "document_id": document_id,
                "mysql_slice_count": len(mysql_slice_ids),
                "milvus_slice_count": len(milvus_slice_ids),
                "dirty_slice_count": len(dirty_slice_ids),
                "missing_slice_count": len(missing_slice_ids),
                "dirty_slice_ids": list(dirty_slice_ids),
                "missing_slice_ids": list(missing_slice_ids),
                "dry_run": dry_run
            }
            
            logger.info(f"数据统计 - MySQL切片: {len(mysql_slice_ids)}, "
                       f"Milvus切片: {len(milvus_slice_ids)}, "
                       f"脏数据: {len(dirty_slice_ids)}, "
                       f"缺失数据: {len(missing_slice_ids)}")
            
            if dirty_slice_ids:
                if dry_run:
                    logger.info(f"试运行模式 - 发现 {len(dirty_slice_ids)} 个脏数据切片，但不会实际删除")
                    # 获取脏数据的详细信息用于日志记录
                    dirty_details = self.get_milvus_slice_details(knowledge_id, list(dirty_slice_ids)[:10])  # 只获取前10个的详情
                    result["dirty_slice_samples"] = dirty_details
                else:
                    logger.info(f"开始删除 {len(dirty_slice_ids)} 个脏数据切片")
                    success, message = self.delete_milvus_slices(knowledge_id, list(dirty_slice_ids))
                    result["delete_success"] = success
                    result["delete_message"] = message
            else:
                logger.info("没有发现脏数据，数据一致性良好")
                result["delete_success"] = True
                result["delete_message"] = "没有脏数据需要删除"
            
            if missing_slice_ids:
                logger.warning(f"发现 {len(missing_slice_ids)} 个切片在MySQL中存在但在Milvus中缺失")
            
            return result
            
        except Exception as e:
            logger.error(f"数据清理过程中发生错误: {e}")
            return {"error": str(e)}
        finally:
            # 注意：不在这里关闭连接，因为可能还需要使用
            pass
    
    def generate_report(self, result: Dict) -> str:
        """
        生成清理报告
        
        Args:
            result: 清理结果
            
        Returns:
            str: 格式化的报告
        """
        if "error" in result:
            return f"清理失败: {result['error']}"
        
        report = f"""
数据清理报告
{'='*50}
知识库ID: {result['knowledge_id']}
文档ID: {result.get('document_id', '全部文档')}
执行时间: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}
试运行模式: {'是' if result['dry_run'] else '否'}

数据统计:
- MySQL中的切片数量: {result['mysql_slice_count']}
- Milvus中的切片数量: {result['milvus_slice_count']}
- 脏数据切片数量: {result['dirty_slice_count']}
- 缺失切片数量: {result['missing_slice_count']}

清理结果:
- 删除状态: {'成功' if result.get('delete_success', False) else '失败'}
- 删除消息: {result.get('delete_message', 'N/A')}

建议:
"""
        
        if result['dirty_slice_count'] > 0:
            report += f"- 发现 {result['dirty_slice_count']} 个脏数据切片"
            if result['dry_run']:
                report += "，建议在非试运行模式下执行清理\n"
            else:
                report += "，已执行清理\n"
        else:
            report += "- 数据一致性良好，无需清理\n"
        
        if result['missing_slice_count'] > 0:
            report += f"- 发现 {result['missing_slice_count']} 个切片在Milvus中缺失，建议检查数据同步\n"
        
        return report


def main():
    """主函数"""
    import argparse
    
    parser = argparse.ArgumentParser(description='数据清理脚本 - 清理向量库中的脏数据')
    parser.add_argument('knowledge_id', help='知识库ID')
    parser.add_argument('--document_id', help='文档ID（可选，不提供则清理整个知识库）')
    parser.add_argument('--dry-run', action='store_true', default=True, 
                       help='试运行模式（默认开启，不实际删除数据）')
    parser.add_argument('--execute', action='store_true', 
                       help='实际执行删除操作（关闭运行模式）')
    
    args = parser.parse_args()
    
    # 如果指定了--execute，则关闭运行模式
    dry_run = args.execute
    
    # 创建清理工具
    cleanup_tool = VectorDataCleaner()
    
    try:
        # 执行清理
        result = cleanup_tool.cleanup_data(
            knowledge_id=args.knowledge_id,
            document_id=args.document_id,
            dry_run=dry_run
        )
        
        # 生成并保存报告
        report = cleanup_tool.generate_report(result)
        print(report)
    finally:
        # 关闭连接
        cleanup_tool.close_connections()
    
    # 保存报告到文件
    timestamp = datetime.now().strftime('%Y%m%d_%H%M%S')
    os.makedirs("./data_clean", exist_ok=True)
    report_filename = f"./data_clean/data_cleanup_report_{args.knowledge_id}_{timestamp}.txt"
    
    try:
        with open(report_filename, 'w', encoding='utf-8') as f:
            f.write(report)
        print(f"\n报告已保存到: {report_filename}")
    except Exception as e:
        logger.error(f"保存报告失败: {e}")


if __name__ == "__main__":
    main()