26 Nov, 2025
2 commits
14 Nov, 2025
3 commits
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2. 向量服务不用本地预估,改用网络服务
13 Nov, 2025
2 commits
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主要变更: 1. 去掉数据源应用结构配置化,我们只针对店匠的spu sku表设计索引,数据灌入流程是写死的(只是满足测试需求,后面外层应用负责数据全量+增量灌入)。搜索系统主要关注如何适配外部搜索需求 目前有两个数据灌入脚本,一种是之前的,一种是现在的从两个店匠的表sku表+spu表读取并且以spu为单位组织doc。 - 配置只关注ES搜索相关配置,提高可维护性 - 创建base配置(店匠通用配置) 2. 索引结构重构(SPU维度) - 所有客户共享search_products索引,通过tenant_id隔离 - 支持嵌套variants字段(SKU变体数组) - 创建SPUTransformer用于SPU数据转换 3. API响应格式优化 - 约定一套搜索结果的格式,而不是直接暴露ES doc的结构(_id _score _source内的字段) - 添加ProductResult和VariantResult模型 - 添加suggestions和related_searches字段 (预留接口,逻辑暂未实现) 4. 数据导入流程 - 创建店匠数据导入脚本(ingest_shoplazza.py) - Pipeline层决定数据源,配置不包含数据源信息 - 创建测试数据生成和导入脚本 5. 文档更新 - 更新设计文档,反映新架构 - 创建BASE_CONFIG_GUIDE.md使用指南
12 Nov, 2025
4 commits
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核心改动: 1. 修复facets类型问题 - 统一使用Pydantic模型(FacetResult, FacetValue) - SearchResult.facets改为List[FacetResult] - _standardize_facets直接构建Pydantic对象 2. 修复RangeFilter支持日期时间 - RangeFilter字段改为Union[float, str] - 支持数值范围和ISO日期时间字符串 - 修复前端listing time筛选422错误 3. 重构ES查询结构(核心) - 使用function_score包裹整个查询 - 文本和KNN放入内层bool.should(minimum_should_match=1) - Filter在外层bool,同时作用于文本和KNN查询 - 添加时效性加权函数(days_since_last_update<=30 weight:1.1) 4. RankingEngine重构 - 重命名为RerankEngine(语义更准确) - 默认禁用(enabled=False) - 优先使用ES的function_score打分 5. 统一约定原则 - 移除所有字典兼容代码 - 全系统统一使用Pydantic模型 - build_facets只接受str或FacetConfig - _build_filters直接接受RangeFilter模型 修改文件: - search/multilang_query_builder.py: 重构查询构建逻辑 - search/es_query_builder.py: 统一Pydantic模型支持 - search/searcher.py: 使用RerankEngine,更新导入 - search/rerank_engine.py: 新建(从ranking_engine.py重命名) - search/ranking_engine.py: 删除 - search/__init__.py: 更新导出 - api/models.py: RangeFilter支持Union[float, str] 测试验证: ✓ Facets正常返回 ✓ Filter同时作用于文本和KNN ✓ 日期时间范围过滤正常 ✓ Function score时效性加权正常 ✓ 所有测试通过 架构原则:统一约定,不做兼容,保持简单
11 Nov, 2025
4 commits
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## 🎯 Major Features - Request context management system for complete request visibility - Structured JSON logging with automatic daily rotation - Performance monitoring with detailed stage timing breakdowns - Query analysis result storage and intermediate result tracking - Error and warning collection with context correlation ## 🔧 Technical Improvements - **Context Management**: Request-level context with reqid/uid correlation - **Performance Monitoring**: Automatic timing for all search pipeline stages - **Structured Logging**: JSON format logs with request context injection - **Query Enhancement**: Complete query analysis tracking and storage - **Error Handling**: Enhanced error tracking with context information ## 🐛 Bug Fixes - Fixed DeepL API endpoint (paid vs free API confusion) - Fixed vector generation (GPU memory cleanup) - Fixed logger parameter passing format (reqid/uid handling) - Fixed translation and embedding functionality ## 🌟 API Improvements - Simplified API interface (8→5 parameters, 37.5% reduction) - Made internal functionality transparent to users - Added performance info to API responses - Enhanced request correlation and tracking ## 📁 New Infrastructure - Comprehensive test suite (unit, integration, API tests) - CI/CD pipeline with automated quality checks - Performance monitoring and testing tools - Documentation and example usage guides ## 🔒 Security & Reliability - Thread-safe context management for concurrent requests - Automatic log rotation and structured output - Error isolation with detailed context information - Complete request lifecycle tracking 🤖 Generated with Claude Code Co-Authored-By: Claude <noreply@anthropic.com>
10 Nov, 2025
1 commit
08 Nov, 2025
2 commits