ai-saas / saas-search

Commit 648cb4c26be522956d4d692fcd59c8a99be7da3f

Authored by tangwang
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ES docs

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config/env_config.py
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... ... @@ -22,6 +22,16 @@ ES_CONFIG = {
22 22 'password': os.getenv('ES_PASSWORD'),
23 23 }
24 24  
  25 +# Runtime environment & index namespace
  26 +# RUNTIME_ENV: 当前运行环境，建议使用 prod / uat / test / dev 等枚举值
  27 +RUNTIME_ENV = os.getenv('RUNTIME_ENV', 'prod')
  28 +# ES_INDEX_NAMESPACE: 用于按环境隔离索引的命名空间前缀，例如 "uat_" / "test_"
  29 +# 为空字符串时表示不加前缀（通常是 prod 环境）
  30 +ES_INDEX_NAMESPACE = os.getenv('ES_INDEX_NAMESPACE')
  31 +if ES_INDEX_NAMESPACE is None:
  32 + # 未显式配置时，非 prod 环境默认加 "<env>_" 前缀，prod 环境默认不加前缀
  33 + ES_INDEX_NAMESPACE = '' if RUNTIME_ENV == 'prod' else f'{RUNTIME_ENV}_'
  34 +
25 35 # Redis Configuration
26 36 REDIS_CONFIG = {
27 37 'host': os.getenv('REDIS_HOST', 'localhost'),
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docs/ES/1_ES安装.md 0 → 100644
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  1 +
  2 +
  3 +
  4 +## 一、安装
  5 +### 1. 安装
  6 +```shell
  7 +# 参考install_elasticsearch.sh。
  8 +
  9 +ELASTICSEARCH_VERSION="8.18.0"
  10 +
  11 +wget -qO - https://artifacts.elastic.co/GPG-KEY-elasticsearch | sudo rpm --import
  12 +yum install -y https://artifacts.elastic.co/downloads/elasticsearch/elasticsearch-$ELASTICSEARCH_VERSION-x86_64.rpm
  13 +
  14 +# 设置Elasticsearch的系统服务
  15 +systemctl daemon-reload
  16 +systemctl start elasticsearch
  17 +systemctl enable elasticsearch
  18 +```
  19 +安装、配置好后验证命令： curl -X GET "http://localhost:9200"
  20 +
  21 +
  22 +### 3. 工程&安全方面设置
  23 +
  24 +性能相关配置：
  25 +```shell
  26 +vi /etc/elasticsearch/jvm.options
  27 +堆内存大小：
  28 +-Xms32g
  29 +-Xmx32g
  30 +```
  31 +
  32 +/etc/elasticsearch/elasticsearch.yml ：
  33 +
  34 +Fielddata Cache（用于聚合和排序操作）：
  35 +indices.fielddata.cache.size: 40% # 将 fielddata 缓存大小设置为 40% 的物理内存
  36 +Query Cache（用于查询缓存）：
  37 +indices.queries.cache.size: 30% # 将查询缓存设置为 30% 的物理内存
  38 +
  39 +修改的系统配置：
  40 +vi /etc/sysctl.conf
  41 +```shell
  42 +# tangwang
  43 +# 调整系统句柄限制
  44 +fs.file-max = 1000000
  45 +# 优化文件描述符 可以分配的vma数量
  46 +vm.max_map_count = 655360
  47 +# 优化脏内存页，es写入削峰
  48 +vm.dirty_ratio=10
  49 +vm.dirty_background_ratio=5
  50 +vm.dirty_writeback_centisecs=200
  51 +# 优化系统回收inode cache权重
  52 +vm.vfs_cache_pressure=200
  53 +vm.dirty_expire_centisecs=6000
  54 +# 尽可能低的使用swap,内存很充裕可以设置为0，较低设置为1
  55 +vm.swappiness=0
  56 +```
  57 +vi /etc/security/limits.conf
  58 +```shell
  59 +# tangwang 修改 下面都加了10倍
  60 +# End of file
  61 +* soft nofile 655350
  62 +* hard nofile 655350
  63 +root soft nofile 655350
  64 +root hard nofile 655350
  65 +* soft nofile 655350
  66 +* hard nofile 655350
  67 +
  68 +
  69 +# tangwang 补充下面的
  70 +* soft nproc 102400
  71 +* hard nproc 409600
  72 +# 允许进程锁定内存
  73 +* soft memlock unlimited
  74 +* hard memlock unlimited
  75 +```
  76 +
  77 +
  78 +### TOOD :
  79 +配置为单节点模式，discovery.type: single-node 但是还需要其他配套改动。
  80 +
  81 +用户名 密码 安全方面
  82 +kibana用户名密码
  83 +
  84 +## 其他说明
  85 +ES 8.x版本特性
  86 +在 8.0版本 中，提供新的kNN 搜索 API。以前，Elasticsearch 仅支持使用script_score带有向量函数的查询进行精确的 kNN 搜索。虽然此方法可以保证准确的结果，但它通常会导致搜索速度缓慢，并且无法很好地适应大型数据集。作为索引速度较慢和准确性不完美的代价，新的 kNN 搜索 API 允许您以更快的速度对更大的数据集运行近似 kNN 搜索。
  87 +
  88 +在 8.4版本中，把knn搜索加入到search API中，以支持 ANN 搜索。它由与旧 _knn_search端点相同的 Lucene ANN 功能提供支持。该knn选项可以与其他搜索功能（例如查询和聚合）结合使用。
  89 +
  90 +在 8.7版本中，允许多个 KNN 搜索子句。某些向量搜索场景需要使用几个 kNN 子句进行相关性排名，例如，当基于多个字段进行排名时，每个字段都有自己的向量，或者当文档包含图像向量和文本向量时。用户可能希望获得基于所有这些 kNN 子句的组合的相关性排名。
  91 +
  92 +在 8.8版本 中，将 Introducing Elastic Learned Sparse Encoder: Elastic’s AI model for semantic search — Elastic Search Labs 模型引入到我们的机器学习模型库中，您可以开箱即用。ELSER 通过启用语义搜索来提高搜索结果的相关性。这种搜索方法考虑单词的含义，而不是仅仅依赖字面术语。ELSER 是一种预训练的域外稀疏向量模型，无需对特定源数据进行微调。它从一开始就为您提供相关的搜索结果。并且在此版本中，还提升了KNN检索语法，加入了similarity参数，允许过滤给定相似性之外的最近邻结果。
  93 +
  94 +在8.9版本中。text_embedding query_vector_builderkNN 搜索的扩展普遍可用。
  95 +
  96 +在8.10版本中，启用跨段并行 knn 搜索，使得 knn 查询对由多个段组成的分片更快，来优化knn搜索的性能。
  97 +
  98 +
  99 +
  100 +
  101 +
  102 +8.15 更为丰富的工具用于微调相关性、更高的模型灵活性、矢量搜索的改进
  103 +
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docs/ES/1_ES配置和使用.md 0 → 100644
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  1 +# Elasticsearch 文档
  2 +
  3 +## 相关链接
  4 +- 接口文档：http://rap.essa.top:88/workspace/myWorkspace.do?projectId=78#2187
  5 +- Kibana 控制台：http://120.76.41.98:5601/app/dev_tools#/console/shell
  6 +
  7 +## 分词方面
  8 +
  9 +Ansj 分词插件安装
  10 +ES可以用的中文分词，效果最好的是hanLP和ansj，其次是jieba。
  11 +
  12 +我们老的搜索 solr 已经在几年前替代掉了ik，使用的是mmseg。但是我没找到mmseg的ES插件。
  13 +
  14 +为了分词方面不至于比老版本差，这里先安装了ansj
  15 +
  16 +### 1. 下载插件
  17 +从 [elasticsearch-analysis-ansj releases](https://github.com/NLPchina/elasticsearch-analysis-ansj/releases) 选择对应版本下载：
  18 +
  19 +- ES 8.18 版本：
  20 +```bash
  21 +wget https://github.com/NLPchina/elasticsearch-analysis-ansj/archive/refs/tags/v8.18.0.zip
  22 +```
  23 +
  24 +- ES 8.17 版本：
  25 +```bash
  26 +wget https://github.com/NLPchina/elasticsearch-analysis-ansj/archive/refs/tags/v8.17.6.zip
  27 +```
  28 +
  29 +### 2. 编译
  30 +执行 `mvn package` 命令，编译成功后将在 `target/releases/` 目录生成插件压缩包：
  31 +`elasticsearch-analysis-ansj-<版本号>-release.zip`
  32 +
  33 +### 3. 安装步骤
  34 +1. 进入 ES 安装路径（默认：`/usr/share/elasticsearch/`）
  35 +2. 执行安装命令：
  36 +```bash
  37 +bin/elasticsearch-plugin install file:///xxx/绝对路径到/elasticsearch-analysis-ansj-8.18.0.0-release.zip
  38 +```
  39 +3. 重启服务：
  40 +```bash
  41 +systemctl restart elasticsearch
  42 +```
  43 +
  44 +其他分词插件安装方法：
  45 +《3.1_hanlp安装.md》
  46 +《3.2_jieba插件安装.md》
  47 +在ES8上面安装过，但是没试过具体的版本 8.17 8.18
  48 +
  49 +### 4. 配置说明
  50 +停用词、同义词配置位于 `<ES_HOME>/config/elasticsearch-analysis-ansj/ansj.cfg.yml`（暂未使用）
  51 +
  52 +## 字段说明
  53 +
  54 +```bash
  55 +需要的字段：
  56 +id 商品skuId
  57 +goods_id 商品spuId
  58 +buyer_id 所属专属采购商id
  59 +trader_buyer_ids 所属贸易商名下平台客户的专属采购商id
  60 +goods_certification_types 商品证书类型
  61 +supplier_code 供应商编码
  62 +supplier_name 供应商名称
  63 +supplier_certification_code 供应商企业证书编码（列表）
  64 +auth_buyer_level_list 商品可见采购商等级（集合）
  65 +show_price_level_list 价格可见采购商等级（集合）
  66 +goods_composition 成分列表（材质）
  67 +compositions_main_secondary 物料主副（主：1，副：2），格式：物料代码_主副类型
  68 +goods_key_word_zh 商品关键词中文
  69 +goods_key_word_en 商品关键词英文
  70 +goods_key_word_ru 商品关键词俄文
  71 +goods_copyright 版权（自有、第三方、无授权、A货）
  72 +goods_main_material 主材质（字典：材质）
  73 +is_in_new_protect 是否在新品保护期（0否，1是）
  74 +goods_new_protect_date_stamp 新品保护期日期时间戳
  75 +goods_attribute_name_zh spu属性中文（列表）
  76 +goods_attribute_name_en spu属性英文（列表）
  77 +goods_attribute_name_ru spu属性俄文（列表）
  78 +purchase_moq 采购MOQ
  79 +ts 触发索引的时间
  80 +deliver_day 货期
  81 +factory_no 工厂货号
  82 +factory_no_buyer 工厂货号(客户)
  83 +fir_on_sell_time 首次上架时间
  84 +fir_on_sell_time_stamp 首次上架时间timestamp
  85 +no 商品编码
  86 +hs_no 宏升编码
  87 +package_type 包装类型值（来自商品属性编码：PKG）
  88 +package_type_id 包装类型ID（来自商品属性编码：PKG）
  89 +labelId_by_skuId_essaone_* essaone商品标签，国家编码标识
  90 +sale_goods_certificate_* 商品证书ID，国家编码标识
  91 +labelId_by_skuId_essa_* essa商品标签，区域ID标识
  92 +```
  93 +
  94 +## Mapping 配置
  95 +参考文件 `create_index.sh`：
  96 +
  97 +## 快速入门
  98 +
  99 +### Shell
  100 +参考 [索引和查询测试](../docs/3.3_索引和查询测试.md) 包含了在ES服务器进行本地进行一些常用的查询操作。
  101 +
  102 +### Python
  103 +- [test_index_and_search.py](../tests/test_index_and_search.py) 是一个简单的例子，创建索引，导入数据，查询数据
  104 +- [batch_bulk_goods.py](../batch_bulk_goods.py) 功能是 通过sql 读取最近3年的所有数据，按batch（1000）通过bulk接口进行逐批入库，入库到goods索引。
  105 +
  106 +### Kibana
  107 +
  108 +#### 分词相关
  109 +```bash
  110 +# 索引分词
  111 +GET /_cat/ansj?text=14寸第4代真眼珠实身冰雪公仔带手动大推车,搪胶雪宝宝&type=index_ansj
  112 +
  113 +# 查询分词
  114 +GET /_cat/ansj?text=14寸第4代真眼珠实身冰雪公仔带手动大推车,搪胶雪宝宝&type=query_ansj
  115 +
  116 +# 查看配置
  117 +GET /_cat/ansj/config
  118 +```
  119 +#### 查询相关
  120 +GET /goods/_search
  121 +{
  122 + "query": {
  123 + "match_all": {}
  124 + },
  125 + "size": 5
  126 +}
  127 +
  128 +#### 1. 查看字段分词结果
  129 +```bash
  130 +# 查看中文名称分词结果
  131 +GET /_cat/ansj?text=14寸第4代真眼珠实身冰雪公仔带手动大推车&type=index_ansj
  132 +
  133 +# 查看英文名称分词结果
  134 +GET /_cat/ansj?text=14 inch 4th generation real eye snow doll with manual cart&type=standard
  135 +```
  136 +
  137 +#### 2. 查看索引随机10条内容
  138 +```bash
  139 +GET /goods/_search
  140 +{
  141 + "size": 10,
  142 + "query": {
  143 + "function_score": {
  144 + "query": { "match_all": {} },
  145 + "random_score": {}
  146 + }
  147 + }
  148 +}
  149 +```
  150 +
  151 +#### 3. 关键词查询
  152 +```bash
  153 +# 简单关键词匹配
  154 +GET /goods/_search
  155 +{
  156 + "query": {
  157 + "match": {
  158 + "name_zh": "冰雪公仔"
  159 + }
  160 + }
  161 +}
  162 +
  163 +# 多字段关键词匹配
  164 +GET /goods/_search
  165 +{
  166 + "query": {
  167 + "multi_match": {
  168 + "query": "冰雪公仔",
  169 + "fields": ["name_zh", "sub_name_zh", "category_name_zh"]
  170 + }
  171 + }
  172 +}
  173 +```
  174 +
  175 +#### 4. 向量查询
  176 +```bash
  177 +# 使用向量相似度查询
  178 +GET /goods/_search
  179 +{
  180 + "query": {
  181 + "script_score": {
  182 + "query": { "match_all": {} },
  183 + "script": {
  184 + "source": "cosineSimilarity(params.query_vector, 'name_prefix') + 1.0",
  185 + "params": {
  186 + "query_vector": [0.1, 0.2, ...] # 1024维向量
  187 + }
  188 + }
  189 + }
  190 + }
  191 +}
  192 +```
  193 +
  194 +#### 5. SKUID查询
  195 +```bash
  196 +# 精确匹配SKUID
  197 +GET /goods/_search
  198 +{
  199 + "query": {
  200 + "term": {
  201 + "goods_id": "2817667"
  202 + }
  203 + }
  204 +}
  205 +```
  206 +
  207 +#### 6. 名称查询测试
  208 +```bash
  209 +# 中文名称模糊匹配
  210 +GET /goods/_search
  211 +{
  212 + "query": {
  213 + "match": {
  214 + "name_zh": {
  215 + "query": "冰雪公仔",
  216 + "fuzziness": "AUTO"
  217 + }
  218 + }
  219 + }
  220 +}
  221 +
  222 +# 英文名称匹配
  223 +GET /goods/_search
  224 +{
  225 + "query": {
  226 + "match": {
  227 + "name_en": "snow doll"
  228 + }
  229 + }
  230 +}
  231 +
  232 +# 俄语名称匹配
  233 +GET /goods/_search
  234 +{
  235 + "query": {
  236 + "match": {
  237 + "name_ru": "снежная кукла"
  238 + }
  239 + }
  240 +}
  241 +
  242 +# 使用 match_phrase 进行短语匹配
  243 +GET /goods/_search
  244 +{
  245 + "query": {
  246 + "match_phrase": {
  247 + "name_zh": "冰雪公仔"
  248 + }
  249 + }
  250 +}
  251 +
  252 +# 使用 match_phrase 进行多语言短语匹配
  253 +GET /goods/_search
  254 +{
  255 + "query": {
  256 + "bool": {
  257 + "should": [
  258 + {
  259 + "match_phrase": {
  260 + "name_zh": "冰雪公仔"
  261 + }
  262 + },
  263 + {
  264 + "match_phrase": {
  265 + "name_en": "snow doll"
  266 + }
  267 + },
  268 + {
  269 + "match_phrase": {
  270 + "name_ru": "снежная кукла"
  271 + }
  272 + }
  273 + ],
  274 + "minimum_should_match": 1
  275 + }
  276 + }
  277 +}
  278 +
  279 +# 使用 match_phrase 配合 slop 参数进行模糊短语匹配
  280 +GET /goods/_search
  281 +{
  282 + "query": {
  283 + "match_phrase": {
  284 + "name_zh": {
  285 + "query": "冰雪公仔",
  286 + "slop": 2
  287 + }
  288 + }
  289 + }
  290 +}
  291 +
  292 +# 多语言 match_phrase 配合 slop 参数
  293 +GET /goods/_search
  294 +{
  295 + "query": {
  296 + "bool": {
  297 + "should": [
  298 + {
  299 + "match_phrase": {
  300 + "name_zh": {
  301 + "query": "冰雪公仔",
  302 + "slop": 2
  303 + }
  304 + }
  305 + },
  306 + {
  307 + "match_phrase": {
  308 + "name_en": {
  309 + "query": "snow doll",
  310 + "slop": 1
  311 + }
  312 + }
  313 + },
  314 + {
  315 + "match_phrase": {
  316 + "name_ru": {
  317 + "query": "снежная кукла",
  318 + "slop": 2
  319 + }
  320 + }
  321 + }
  322 + ],
  323 + "minimum_should_match": 1
  324 + }
  325 + }
  326 +}
  327 +```
  328 +
  329 +#### 7. 多语言查询测试
  330 +```bash
  331 +# 同时查询中英文名称
  332 +GET /goods/_search
  333 +{
  334 + "query": {
  335 + "bool": {
  336 + "should": [
  337 + {
  338 + "match": {
  339 + "name_zh": "冰雪公仔"
  340 + }
  341 + },
  342 + {
  343 + "match": {
  344 + "name_en": "snow doll"
  345 + }
  346 + }
  347 + ],
  348 + "minimum_should_match": 1
  349 + }
  350 + }
  351 +}
  352 +```
  353 +
  354 +#### 8. 向量索引查询测试
  355 +注意：向量查询中的向量维度必须与索引中定义的维度匹配（1024维）
  356 +```bash
  357 +# 使用向量相似度进行商品推荐
  358 +GET /goods/_search
  359 +{
  360 + "query": {
  361 + "script_score": {
  362 + "query": { "match_all": {} },
  363 + "script": {
  364 + "source": "cosineSimilarity(params.query_vector, 'ru_name') + 1.0",
  365 + "params": {
  366 + "query_vector": [0.1, 0.2, ...] # 1024维向量
  367 + }
  368 + }
  369 + }
  370 + },
  371 + "size": 10
  372 +}
  373 +```
  374 +
  375 +#### 9. 关键词+向量索引组合查询测试
  376 +```bash
  377 +# 关键词搜索+向量相似度提权
  378 +GET /goods/_search
  379 +{
  380 + "query": {
  381 + "function_score": {
  382 + "query": {
  383 + "match": {
  384 + "name_zh": "冰雪公仔",
  385 + "boost": 1.0
  386 + }
  387 + },
  388 + "functions": [
  389 + {
  390 + "script_score": {
  391 + "script": {
  392 + "source": "cosineSimilarity(params.query_vector, 'name_prefix') + 1.0",
  393 + "params": {
  394 + "query_vector": [0.1, 0.2, ...] # 1024维向量
  395 + }
  396 + }
  397 + }
  398 + }
  399 + ],
  400 + "boost_mode": "multiply"
  401 + }
  402 + }
  403 +}
  404 +
  405 +# source 可以支持embedding为空 ： "source": "doc['embedding'].isEmpty() ? 1.0 : dotProduct(params.query_vector, 'embedding') + 1.0",
  406 +
  407 +两者乘起来：
  408 +{
  409 + "query": {
  410 + "function_score": {
  411 + "score_mode": "sum",
  412 + "boost_mode": "multiply",
  413 + "query": {
  414 + "match": {
  415 + "content": {
  416 + "query": keywords,
  417 + "boost": 1.0
  418 + }
  419 + }
  420 + },
  421 + "functions": [
  422 + {
  423 + "script_score": {
  424 + "script": {
  425 + "source": "doc['embedding'].isEmpty() ? 1.0 : dotProduct(params.query_vector, 'embedding') + 1.0",
  426 + "params": {"query_vector": context.embeddings[0][1]}
  427 + }
  428 + }
  429 + }
  430 + ]
  431 + }
  432 + }
  433 + }
  434 +
  435 +#### 9. 向量搜索+关键词搜索
  436 +GET /goods/_search
  437 +{
  438 + "query": {
  439 + "match": {
  440 + "content": {
  441 + "query": "玩具",
  442 + "boost": 1.0
  443 + }
  444 + }
  445 + },
  446 + "knn": {
  447 + "field": "name_prefix",
  448 + "query_vector": [-0.05291186273097992, ...],
  449 + "k": 5,
  450 + "num_candidates": 10,
  451 + "boost": 1.0
  452 + }
  453 + }
  454 +
  455 +
  456 +
  457 +
  458 +参考代码：
  459 +```python
  460 + def execute_search(self, context, search_type="match_phrase", search_type_attachment=0, size=10):
  461 + query = context.query
  462 + normalized_query = context.normalized_query
  463 + core_term = context.core_term
  464 + keywords = context.keywords
  465 + knn_boost_keywords = core_term if core_term else keywords
  466 + expand = context.expand
  467 +
  468 + seen_queries = set()
  469 + unique_queries = []
  470 + for q, weight in [(query, 1.0), (normalized_query, 1.0), (keywords, 0.5)]:
  471 + if q and q not in seen_queries:
  472 + unique_queries.append((q, weight))
  473 + seen_queries.add(q)
  474 +
  475 + # 关于混合检索：
  476 + # knn和文本查询同时作用：
  477 + # 8.12之前query里面不能包含knn，kNN搜索作为查询已在 8.12 版本中引入: https://www.elastic.co/search-labs/blog/knn-query-elasticsearch
  478 + # {
  479 + # "size": 3,
  480 + # "query": {
  481 + # "bool": {
  482 + # "should": [
  483 + # {
  484 + # "knn": {
  485 + # "field": "embedding",
  486 + # "query_vector": [2,2,2,0],
  487 + # "num_candidates": 10,
  488 + # "_name": "knn_query"
  489 + # }
  490 + # },
  491 + # {
  492 + # "match": {
  493 + # "description": {
  494 + # "query": "luxury",
  495 + # "_name": "bm25query"
  496 + # }
  497 + # }
  498 + # }
  499 + # ]
  500 + # }
  501 + #
  502 + # knn里面不能包含query。
  503 + # knn和query并列（hybrid search 混合检索），是求或的关系。
  504 + # knn里面可以加filter，比如： "filter": {"match": {"my_label": "red"}}
  505 +
  506 + if search_type == "match_phrase":
  507 + body = {
  508 + "query": {
  509 + "bool": {
  510 + "should": [
  511 + {
  512 + "match_phrase": {
  513 + "content": {
  514 + "query": unique_query,
  515 + "boost": weight,
  516 + "slop": search_type_attachment
  517 + }
  518 + }
  519 + } for unique_query, weight in unique_queries
  520 + ],
  521 + "minimum_should_match": 1
  522 + }
  523 + }
  524 + }
  525 + # 纯关键词检索 2
  526 + elif search_type == "match_keywords":
  527 + body = {
  528 + "query": {
  529 + "bool": {
  530 + "must": [
  531 + {
  532 + "match": {
  533 + "content": {"query": core_term, "boost": 1.0}
  534 + }
  535 + }
  536 + ],
  537 + "should": [
  538 + {
  539 + "match": {
  540 + "content": {"query": q, "boost": boost}
  541 + }
  542 + } for (q, boost) in [(keywords, 1.0), (expand, 0.6), (normalized_query, 1.0)] if q
  543 + ],
  544 + "minimum_should_match": 1
  545 + }
  546 + }
  547 + }
  548 + # 关键词搜索+向量排序
  549 + elif search_type == "match&boost":
  550 + body = {
  551 + "query": {
  552 + "function_score": {
  553 + "score_mode": "sum",
  554 + "boost_mode": "multiply",
  555 + "query": {
  556 + "match": {
  557 + "content": {
  558 + "query": keywords,
  559 + "boost": 1.0
  560 + }
  561 + }
  562 + },
  563 + "functions": [
  564 + {
  565 + "script_score": {
  566 + "script": {
  567 + "source": "doc['embedding'].isEmpty() ? 1.0 : dotProduct(params.query_vector, 'embedding') + 1.0",
  568 + "params": {"query_vector": context.embeddings[0][1]}
  569 + }
  570 + }
  571 + }
  572 + ]
  573 + }
  574 + }
  575 + }
  576 + # 这个太慢
  577 + elif search_type == "match&boost2":
  578 + body = {
  579 + "query": {
  580 + "script_score": {
  581 + "query": {
  582 + "match": {
  583 + "content": {
  584 + "query": keywords,
  585 + "boost": 1.0
  586 + }
  587 + }
  588 + },
  589 + "script": {
  590 + "source": "doc['embedding'].isEmpty() ? 1.0 : dotProduct(params.query_vector, 'embedding') + 1.0",
  591 + "params": {"query_vector": context.embeddings[0][1]}
  592 + }
  593 + }
  594 + }
  595 + }
  596 + # 向量搜索+关键词搜索
  597 + elif search_type == "match&knn":
  598 + body = {
  599 + "query": {
  600 + "match": {
  601 + "content": {
  602 + "query": knn_boost_keywords,
  603 + "boost": 1.0
  604 + }
  605 + }
  606 + },
  607 + "knn": {
  608 + "field": "embedding",
  609 + "query_vector": context.embeddings[search_type_attachment][1],
  610 + "k": 5,
  611 + "num_candidates": 10,
  612 + "boost": 1.0
  613 + }
  614 + }
  615 + # 纯向量搜索
  616 + elif search_type == "knn":
  617 + body = {
  618 + "knn": {
  619 + "field": "embedding",
  620 + "query_vector": context.embeddings[search_type_attachment][1],
  621 + "k": 5,
  622 + "num_candidates": 10
  623 + }
  624 + }
  625 +
  626 + need_embedding = (search_type == "match&boost")
  627 + need_highlights = (search_type != "knn")
  628 +
  629 + body["_source"] = {"excludes": ["keywords", "quotes"]}
  630 + if not need_embedding:
  631 + body["_source"]["excludes"].append("embedding")
  632 + # 在填充highlight之前写入search_from
  633 + search_from = f'searchtype[{search_type}],param[{search_type_attachment}],body:{body}'
  634 +
  635 + if need_highlights:
  636 + body["_source"]["excludes"] = []
  637 + body["highlight"] = {
  638 + "pre_tags": [settings.HIGHTLIGHT_PRE_TAG],
  639 + "post_tags": [settings.HIGHTLIGHT_POST_TAG],
  640 + "fields": {"chapter_name": {}, "content": {}}
  641 + }
  642 +
  643 + body["size"] = size
  644 +
  645 + se_debug_info = ''
  646 + start_time = time.time()
  647 + try:
  648 + es_response = context.es.search(index=context.index_name, body=body)
  649 + except Exception as e:
  650 + se_debug_info = f'Error in executing search: {e}. request: {body}'
  651 + return None, se_debug_info
  652 + end_time = time.time()
  653 + elapsed_time = end_time - start_time
  654 + total_hits = es_response.get("hits", {}).get("total", {}).get("value", 0)
  655 + returned_hits = len(es_response.get("hits", {}).get("hits", []))
  656 +
  657 + if not '"' in search_from:
  658 + search_from = search_from.replace('\'', '"')
  659 + search_from = search_from if len(search_from) < 400 else search_from[:400] + '...'
  660 +
  661 + str_body = str(body)
  662 + if not '"' in str_body:
  663 + str_body = str_body.replace('\'', '"')
  664 + se_debug_info = f'({elapsed_time:.2f} seconds. Total: {total_hits}. Returned: {returned_hits}) : {search_from[:400]}'
  665 +
  666 + if not 'hits' in es_response or not 'hits' in es_response['hits']:
  667 + se_debug_info += f' InvalidResponce in executing search: {e}. request: {body}'
  668 + return None, se_debug_info
  669 +
  670 + for hit in es_response['hits']['hits']:
  671 + hit['search_from'] = search_from
  672 +
  673 + return es_response, se_debug_info
  674 +
  675 +```
  676 +
  677 +
  678 +# 测试向量：
  679 +# [-0.05291186273097992, 0.0274342093616724, -0.016730275005102158, 0.010487289167940617, -0.022640341892838478, -0.048682719469070435, 0.04544096067547798, 0.023079438135027885, 0.007221410982310772, 0.023566091433167458, 0.026696473360061646, 0.08252757787704468, -0.042835772037506104, 0.0009668126585893333, -0.02860398218035698, -0.004426108207553625, -0.002644421299919486, -0.027699561789631844, 0.005749804899096489, -0.04468372091650963, -0.0296687763184309, -0.009487600065767765, 0.020041221752762794, 0.00778265530243516, 0.008522099815309048, 0.03497027978301048, -0.021573258563876152, -0.028293319046497345, -8.54598984005861e-05, -0.03164539486169815, -0.017121458426117897, -0.0006902766763232648, 0.04650883004069328, -0.030234992504119873, -0.010207684710621834, -0.035288386046886444, -0.0047269039787352085, -0.0006454040994867682, -0.056146346032619476, 0.008901881985366344, 0.010757357813417912, -0.013022932223975658, 0.04627145081758499, -0.020669423043727875, 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  680 +```
0 681 \ No newline at end of file
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  1 +
  2 +
  3 +
  4 +
  5 +
  6 +## 1. yum安装
  7 +添加yum仓库。
  8 +Kibana通常不是yum默认仓库的一部分，因此需要添加 Elastic 仓库：：
  9 +```shell
  10 +
  11 +导入 Elastic 签名密钥：
  12 +sudo rpm --import https://artifacts.elastic.co/GPG-KEY-elasticsearch
  13 +
  14 +添加 Elastic 仓库：
  15 +sudo tee /etc/yum.repos.d/elastic.repo <<EOF
  16 +[elastic-8.x]
  17 +name=Elastic repository for 8.x packages
  18 +baseurl=https://artifacts.elastic.co/packages/8.x/yum
  19 +gpgcheck=1
  20 +gpgkey=https://artifacts.elastic.co/GPG-KEY-elasticsearch
  21 +enabled=1
  22 +autorefresh=1
  23 +type=rpm-md
  24 +EOF
  25 +```
  26 +然后可以安装 （版本要匹配，否则后面会有错误）
  27 +```shell
  28 +sudo yum install -y kibana-8.18.0
  29 +```
  30 +
  31 +## 2. 修改配置文件
  32 +```shell
  33 +# 使用yum安装的kibana，默认安装的主目录在/usr/share/kibana中。kibana配置文件的位置为/etc/kibana/kibana.yml。
  34 +vim /etc/kibana/kibana.yml
  35 +# 补充内容：
  36 +server.host: "0.0.0.0"
  37 +elasticsearch.hosts: ["http://ip:9200"]
  38 +i18n.locale: "zh-CN"
  39 +```
  40 +## 3. 启动
  41 +```shell
  42 +# 启动kibana
  43 +systemctl start kibana
  44 +systemctl status kibana
  45 +# 设置开机自启动
  46 +systemctl enable kibana
  47 +```
  48 +
  49 +在阿里云上面配置允许访问5601端口后，可以浏览器打开：
  50 +http://120.76.41.98:5601/
  51 +
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  1 +
  2 +
  3 +
  4 +TODO：
  5 +错误也是access denied ("java.io.FilePermission" "/usr/share/elasticsearch/data/model/perceptron/large/cws.bin" "read")
  6 +
  7 +一、环境：
  8 +[root@192 elasticsearch]# bin/elasticsearch --version
  9 +Version: 7.10.2, Build: default/rpm/747e1cc71def077253878a59143c1f785afa92b9/2021-01-13T00:42:12.435326Z, JVM: 15.0.1
  10 +[root@192 ~]# javac -version
  11 +javac 1.8.0_362
  12 +
  13 +二、jdk java.policy补充配置：
  14 +[root@192 elasticsearch]# tail /usr/lib/jvm/java-1.8.0-openjdk-1.8.0.362.b08-3.el8.x86_64/jre/lib/security/java.policy
  15 + permission java.util.PropertyPermission "java.vm.specification.name", "read";
  16 + permission java.util.PropertyPermission "java.vm.version", "read";
  17 + permission java.util.PropertyPermission "java.vm.vendor", "read";
  18 + permission java.util.PropertyPermission "java.vm.name", "read";
  19 +
  20 + permission java.util.PropertyPermission "sun.security.pkcs11.disableKeyExtraction", "read";
  21 + permission java.net.SocketPermission "*", "connect,resolve";
  22 + permission java.io.FilePermission "-","read,write,delete";
  23 +};
  24 +
  25 +三、安装插件：
  26 +cd /usr/share/elasticsearch/
  27 +bin/elasticsearch-plugin install file:///ssd/samba_root1/projects/hanLP/elasticsearch-analysis-hanlp-7.10.2.zip
  28 +
  29 +chown -R elasticsearch:elasticsearch plugins/
  30 +
  31 +四、插件plugin-security.policy配置
  32 +因为本身已经配置了plugins/analysis-hanlp下的配置，而错误提示是用的elasticsearch/data/model 而不是 elasticsearch/plugins/analysis-hanlp/data/model，因此加上了上级目录的读写权限。
  33 +vi plugins/analysis-hanlp/plugin-security.policy
  34 +补充：
  35 + permission java.io.FilePermission "-", "read,write,delete";
  36 +
  37 +五、这时候提示model下的文件不存在，引文Model下是空的：
  38 +1）发现data/model用的不是插件下的data/model，这个data跟plugins同级，因此会把data拷贝一份到与plugins同级，使得两种路径都能访问到
  39 +2）models下只有一个readme，因此我会把hanLP官网下载下来的fulldata，把model下的内容拷贝过来
  40 +
  41 +ll plugins/analysis-hanlp/data/model/perceptron/large/cws.bin
  42 +
  43 +把data拷贝到上级
  44 +cd /usr/share/elasticsearch
  45 +cp -r /ssd/samba_root1/projects/hanLP/data/model/* plugins/analysis-hanlp/data/model/
  46 +chown -R elasticsearch:elasticsearch plugins/
  47 +cp -r /ssd/samba_root1/projects/hanLP/data .
  48 +chown -R elasticsearch:elasticsearch data/
  49 +
  50 +确保两个路径都能访问到：
  51 +ll /usr/share/elasticsearch/data/model/perceptron/large/cws.bin
  52 +ll /usr/share/elasticsearch/plugins/analysis-hanlp/data/model/perceptron/large/cws.bin
  53 +
  54 +六、还是提示同样的错误：
  55 +java.security.AccessControlException: access denied ("java.io.FilePermission" "/usr/share/elasticsearch/data/model/perceptron/large/cws.bin" "read")
  56 +但是这个路径是存在的：
  57 +[root@192 elasticsearch]# l /usr/share/elasticsearch/data/model/perceptron/large/cws.bin
  58 +-rw-r--r-- 1 elasticsearch elasticsearch 278038786 3月 9 19:44 /usr/share/elasticsearch/data/model/perceptron/large/cws.bin
  59 +
  60 +
  61 +
  62 +
  63 +
  64 +使用参考：
  65 +https://www.bilibili.com/read/cv22886449/
  66 +
  67 +一、python库安装
  68 +pip install hanlp -i https://pypi.tuna.tsinghua.edu.cn/simple
  69 +pip install --upgrade "hanlp[full]" -i https://pypi.tuna.tsinghua.edu.cn/simple
  70 +
  71 +# 环境准备：
  72 +# pip install hanlp -i https://pypi.tuna.tsinghua.edu.cn/simple
  73 +# # 安装完整版，注意zsh对于方括号[]要用引号包裹起来
  74 +# pip install --upgrade "hanlp[full]" -i https://pypi.tuna.tsinghua.edu.cn/simple
  75 +# 参考
  76 +# https://github.com/hankcs/HanLP/blob/doc-zh/plugins/hanlp_demo/hanlp_demo/zh/tok_stl.ipynb
  77 +# https://github.com/hankcs/HanLP/blob/doc-zh/plugins/hanlp_demo/hanlp_demo/zh/tok_mtl.ipynb
  78 +
  79 +(另外有一个 pyhanlp，看到官网上用的是hanlp，不是pyhanlp，先忽略)
  80 +
  81 +
  82 +
  83 +二、ES插件安装：
  84 +1. ES版本适配
  85 +hanlp插件只支持到7.10.2，因此将ES降到7.10.2
  86 +注意降级之前要把之前的数据都删掉（尝试把data数据下的路径删掉还不行），删掉后要yum remove elastics*，然后再安装。否则降级后ES不能适配新的数据格式，启动失败。
  87 +（删除之前把 /etc/elasticsearch/elasticsearch.yml 备份下来，否则又要重新改一次）
  88 +
  89 +es起来后，相应的pip的es版本也要更新：
  90 +pip install elasticsearch==7.10.1
  91 +
  92 +2. 安装插件
  93 +1）下载
  94 +hanlp：
  95 +https://github.com/KennFalcon/elasticsearch-analysis-hanlp
  96 +这里找一个最新的：
  97 +https://github.com/KennFalcon/elasticsearch-analysis-hanlp/releases
  98 +
  99 +2）安装插件：注意文件路径前面要加file://
  100 +cd /usr/share/elasticsearch
  101 +bin/elasticsearch-plugin install file:///ssd/samba_root1/projects/hanLP/elasticsearch-analysis-hanlp-7.10.2.zip
  102 +
  103 +如果要移出插件：
  104 +bin/elasticsearch-plugin remove analysis-hanlp
  105 +
  106 +3）安装数据包
  107 +https://github.com/hankcs/HanLP/releases?page=1
  108 +看到2020年5月以来、直至最新，都是这个数据包：数据包兼容data-for-1.7.5.zip md5=1d9e1be4378b2dbc635858d9c3517aaa
  109 +
  110 +unzip 后，把老的data备份，把新的拷贝过来：
  111 +
  112 +cd /usr/share/elasticsearch/plugins/analysis-hanlp
  113 +mv data data__bak_replace_by_full_data
  114 +cp /ssd/samba_root1/projects/hanLP/data . -rf
  115 +
  116 +
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  1 +
  2 +
  3 +
  4 +插件地址：
  5 +https://gitcode.com/sing1ee/elasticsearch-jieba-plugin/tree/8.4.1?init=initRepo
  6 +安装参考文档：
  7 +https://blog.csdn.net/weixin_42326851/article/details/124615116
  8 +
  9 +
  10 +简明步骤
  11 +git clone git@gitcode.com:sing1ee/elasticsearch-jieba-plugin.git
  12 +cd elasticsearch-jieba-plugin
  13 +git checkout 8.4.1
  14 +# 注意如果Elasticsearch不是8.4.1，需要按照readme修改配置文件，包括ES版本、target JAVA版本
  15 +# 查看ES版本
  16 +# bin/elasticsearch --version
  17 +git submodule update --init --recursive
  18 +gradle wrapper
  19 +./gradlew pz
  20 +
  21 +TODO 拷贝到插件目录
  22 +
  23 +
  24 +检查
  25 +bin/elasticsearch-plugin list
  26 +
  27 +
  28 +测试
  29 +新建POST请求: http://localhost:9200/_analyze
  30 +
  31 +请求body：
  32 +
  33 +{
  34 + "analyzer": "jieba_index",
  35 + "text": "美国伊拉克"
  36 +}
  37 +
  38 +
  39 +
  40 +
  41 +
  42 +
  43 +详细步骤
  44 +1. 安装gradle：
  45 +到这里找到下载地址：https://services.gradle.org/distributions/
  46 +注意要用7.6.4 版本（之前用8.6版本出错，改为7.6.4好了）
  47 +wget下来二进制包（zip），解压到某个目录（~/.env下），配置好PATH：
  48 +~/.bashrc添加：
  49 +export GRADLE_HOME="$HOME/.env/gradle-7.6.4"
  50 +export PATH=${GRADLE_HOME}/bin::${PATH}
  51 +
  52 +2. 拉插件（elasticsearch-jieba-plugin）拉下来、打包
  53 +注意要用ssh、不要用https clone（用https，clone下来没问题，init就出问题，没找到原因。重新输密码、上传公钥、清除knownhosts，都不行）：
  54 +git clone git@gitcode.com:sing1ee/elasticsearch-jieba-plugin.git
  55 +cd elasticsearch-jieba-plugin
  56 +git checkout 8.4.1
  57 +git submodule update --init --recursive
  58 +
  59 +/usr/share/elasticsearch/bin/elasticsearch --version
  60 +得到版本号：8.12.2
  61 +修改两个文件的ES版本：
  62 +src/main/resources/plugin-descriptor.properties
  63 +build.gradle （里面有两处修改）
  64 +
  65 +然后打包：
  66 +gradle wrapper
  67 +./gradlew pz
  68 +打包好的插件在目录： ./build/distributions
  69 +
  70 +gradlew pz又出错，提示找不到tools.jar：Could not find tools.jar. Please check that /usr/lib/jvm/java-1.8.0-openjdk-1.8.0.292.b10-1.el8_4.x86_64/jre contains a valid JDK installation.
  71 +有的人是因为没配置JAVA_HOME，我这里，根据错误提示，路径找对了，但是这个路径下确实没有tools.jar。
  72 +因此只能重装jdk。
  73 +yum install java-1.8.0-openjdk
  74 +重装了，原来的jdk目录不存在了，有了新的：
  75 +/usr/lib/jvm/java-1.8.0-openjdk-1.8.0.362.b08-3.el8.x86_64/
  76 +但是这个目录同样没有tools.jar 。后面找到资料，openjdk后面加个新，安装所有java1.8相关的东西，这个问题得到了修复：
  77 +yum -y install java-1.8.0-openjdk*
  78 +
  79 +又出了另外一个问题：
  80 +Execution failed for task ':compileJava'.
  81 +> 无效的源发行版: 18
  82 +原因是我们指定的源发行版不匹配Java编译器的版本。
  83 +1. 确保这里配置对了
  84 +sourceCompatibility = JavaVersion.VERSION_18 // 不同版本的es，需要不同版本的java环境，注意修改这里
  85 +targetCompatibility = JavaVersion.VERSION_18 // 不同版本的es，需要不同版本的java环境，注意修改这里
  86 +2. elasticsearch --version 提示ES的java版本是21，因此将以上配置改为21。 为了简化问题，决定回退ES版本，改回8.4.1，这样插件的配置文件都不用改了，直接用8.4.1分支就行。
  87 +3. java版本是1.8，不是18（之前以为1.8就是18，卡在这里大半天）。重新安装jdk18就好了。
  88 +javac -version
  89 +javac 1.8.0_362
  90 +# 注意yum里面只到17，没有18，下载jdk18，以rpm方式安装：https://www.oracle.com/java/technologies/javase/jdk18-archive-downloads.html
  91 +# 注意如果之前自己手动配置了JAVA_HOME，要把他删掉。
  92 +yum remove java-1.8.0-* （之前yum remove java*，有可能有问题）
  93 +rpm -ivh jdk-18.0.2.1_linux-x64_bin.rpm
  94 +现在三个的版本就都对了：
  95 +javac -version
  96 +javac 18.0.2.1
  97 +
  98 +[root@192 ~]# elasticsearch --version
  99 +warning: ignoring JAVA_HOME=/usr/lib/jvm/java-1.8.0-openjdk; using bundled JDK
  100 +Version: 8.4.1, Build: rpm/2bd229c8e56650b42e40992322a76e7914258f0c/2022-08-26T12:11:43.232597118Z, JVM: 18.0.2
  101 +
  102 +gradle --version （gradle用的jdk是1.8 不用管，插件的readme上注明gradle版本7.6匹配ES版本8.4.1）
  103 +Gradle 7.6.4
  104 +JVM: 1.8.0_362
  105 +
  106 +
  107 +
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  1 +
  2 +
  3 +
  4 +
  5 +cd /usr/share/elasticsearch/plugins/
  6 +wget https://release.infinilabs.com/analysis-ik/stable/elasticsearch-analysis-ik-8.18.0.zip
  7 +unzip elasticsearch-analysis-ik-8.18.0.zip -d elasticsearch-analysis-ik-8.18.0
  8 +systemctl restart elasticsearch
  9 +
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  1 +
  2 +https://github.com/NLPchina/elasticsearch-analysis-ansj
  3 +
  4 +
  5 +git clone https://github.com/NLPchina/elasticsearch-analysis-ansj.git
  6 +cd elasticsearch-analysis-ansj
  7 +git checkout v8.3.3
  8 +mvn package
  9 +
  10 +会提示编译失败（v8.x版本都会失败，7.x就没问题），错误信息如下：
  11 +[ERROR] /ssd/samba_root1/projects/ansj/elasticsearch-analysis-ansj/src/main/java/org/ansj/elasticsearch/plugin/AnalysisAnsjPlugin.java:[14,34] 无法访问org.apache.lucene.analysis.Analyzer
  12 +[ERROR] 错误的类文件: /home/hello/.m2/repository/org/apache/lucene/lucene-core/9.2.0/lucene-core-9.2.0.jar(org/apache/lucene/analysis/Analyzer.class)
  13 +[ERROR] 类文件具有错误的版本 55.0, 应为 52.0
  14 +[ERROR] 请删除该文件或确保该文件位于正确的类路径子目录中。
  15 +
  16 +7.x版本都没问题，8.x就会出这个错误。尝试把jdk版本从1.8改成 11, 17, 18、升级maven版本到3.9.6都不行(又提示类文件具有错误的版本 61.0, 应为 55.0）。
  17 +有人说可能是依赖冲突，不知道是否可以修改下环境，使得不冲突。
  18 +
  19 +因此自己编译只能编译v7.17.16。8.x，release里面有现成的版本：
  20 +./bin/elasticsearch-plugin install https://github.com/NLPchina/elasticsearch-analysis-ansj/releases/download/v8.7.0/elasticsearch-analysis-ansj-8.7.0.0-release.zip
  21 +
  22 +测试：
  23 +kibana中 ： GET /_cat/ansj?text=中国&type=index_ansj
  24 +或者：
  25 +curl -X GET "http://localhost:9200/_cat/ansj?text=中国&type=index_ansj"
  26 +
  27 +
  28 +curl -X GET "http://localhost:9200/_cat/ansj?pretty" -H 'Content-Type: application/json' -d'
  29 +{
  30 + "type": "index_ansj",
  31 + "text": "中国"
  32 +}'
  33 +
  34 +# 注意事项
  35 +ansj分词如果没有设置停用词的话，空格也会被分出来，搜出来的结果空格也会被飘红（如果query带空格的话）。所以一定要设置停用词表。
  36 +
  37 +词性说明：
  38 + 附-词性表格
  39 + 符号 词性 解释
  40 + n 名词 取英语名词noun的第1个字母。
  41 + np 人名
  42 + nr 人名 名词代码n和“人(ren)”的声母并在一起。
  43 + nh 人名
  44 + ns 地名 名词代码n和处所词代码s并在一起
  45 + ni 机构名
  46 + nz 其它专名
  47 + nt 机构团体名 “团”的声母为t，名词代码n和t并在一起。
  48 + nl 名词性惯用语
  49 + nd 方位名词
  50 + ng 名词性语素
  51 + b 区别词
  52 + m 数词 取英语numeral的第3个字母，n，u已有他用。
  53 + q 量词 取英语quantity的第1个字母。
  54 + mq 数量词
  55 + t 时间词 取英语time的第1个字母。
  56 + f 方位词 取汉字“方” 的声母。
  57 + s 处所词 取英语space的第1个字母
  58 + v 动词 取英语动词verb的第一个字母。
  59 + vd 副动词
  60 + vn 名动词
  61 + vshi 动词“是”
  62 + vyou 动词“有”
  63 + vf 趋向动词
  64 + vx 形式动词
  65 + vi 不及物动词（内动词）
  66 + vl 动词性惯用语
  67 + vg 动词性语素
  68 + a 形容词 取英语形容词adjective的第1个字母。
  69 + an 名形词
  70 + ad 副形词
  71 + an 名形词
  72 + ag 形容词性语素
  73 + al 形容词性惯用语
  74 + d 副词 取adverb的第2个字母，因其第1个字母已用于形容词。
  75 + h 前接成分 取英语head的第1个字母。
  76 + k 后接成分
  77 + i 成语 取英语成语idiom的第1个字母。
  78 + j 简称 取汉字“简”的声母。
  79 + l 习用语 习用语尚未成为成语，有点“临时性”，取“临”的声母
  80 + r 代词 取英语代词pronoun的第2个字母,因p已用于介词。
  81 + c 连词 取英语连词conjunction的第1个字母。
  82 + p 介词 取英语介词prepositional的第1个字母。
  83 + u 助词 取英语助词auxiliary 的第2个字母,因a已用于形容词。
  84 + ud 结构助词 有
  85 + ug 时态助词 你
  86 + uj 结构助词的 迈向
  87 + ul 时态助词了 完成
  88 + uv 结构助词地 满怀信心
  89 + uz 时态助词着 眼看
  90 + y 语气助词 取汉字“语”的声母。
  91 + e 叹词 取英语叹词exclamation的第1个字母。
  92 + o 拟声词 取英语拟声词onomatopoeia的第1个字母。
  93 + g 语素 绝大多数语素都能作为合成词的“词根”，取汉字“根”的声母。
  94 + w 标点
  95 + x 其它 非语素字只是一个符号，字母x通常用于代表未知数、符号。
  96 + z 状态词 取汉字“状”的声母的前一个字母。
  97 + tg 时语素 时间词性语素。时间词代码为t,在语素的代码g前面置以T。
0 98 \ No newline at end of file
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  1 +# 查询官方文档：
  2 +https://link.zhihu.com/?target=https%3A//www.elastic.co/guide/cn/elasticsearch/guide/current/query-dsl-intro.html
  3 +
  4 +
  5 +# 查看配置
  6 +curl "localhost:9200/_nodes/settings?pretty=true"
  7 +
  8 +
  9 +# 测试多字段搜索（支持中文分词）：
  10 +curl -X GET "localhost:9200/goods/_search" -H 'Content-Type: application/json' -d'
  11 +{
  12 + "query": {
  13 + "multi_match": {
  14 + "query": "玩具",
  15 + "fields": ["name^2", "name_zh^2", "sub_name_zh^2", "brand_name_zh^2", "category_name_zh^2"],
  16 + "fuzziness": "AUTO"
  17 + }
  18 + },
  19 + "highlight": {
  20 + "fields": {
  21 + "name": {},
  22 + "name_zh": {},
  23 + "sub_name_zh": {},
  24 + "brand_name_zh": {},
  25 + "category_name_zh": {}
  26 + }
  27 + },
  28 + "from": 0,
  29 + "size": 10
  30 +}'
  31 +
  32 +# 测试精确匹配：
  33 +curl -X GET "localhost:9200/goods/_search" -H "Content-Type: application/json" -d'
  34 +{
  35 + "query": {
  36 + "term": {
  37 + "goods_id": "2683337"
  38 + }
  39 + }
  40 +}'
  41 +
  42 +
  43 +# 查看索引分词情况：
  44 +curl -X GET "http://localhost:9200/goods/_doc/1?pretty"
  45 +curl -X GET "http://localhost:9200/goods/_termvectors/1?fields=*&pretty"
  46 +
  47 +# 如果文档不存在，找出任意一个文档，得到ID：
  48 +curl -X GET "http://localhost:9200/goods/_search?size=1&pretty"
  49 +# 得到ID后查询termvectors：
  50 +curl -X GET "http://localhost:9200/goods/_termvectors/[DOC_ID]?fields=*&pretty"
  51 +
  52 +
  53 +# 测试analyzer：
  54 +curl -X GET "http://localhost:9200/goods/_analyze?pretty" -H 'Content-Type: application/json' -d'
  55 +{
  56 + "text": "玩具",
  57 + "analyzer": "index_ansj"
  58 +}'
  59 +
  60 +
  61 +
  62 +# 测试向量搜索：
  63 +curl -X GET "localhost:9200/goods/_search" -H "Content-Type: application/json" -d'
  64 +{
  65 + "query": {
  66 + "script_score": {
  67 + "query": {"match_all": {}},
  68 + "script": {
  69 + "source": "cosineSimilarity(params.query_vector, 'name_prefix') + 1.0",
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  71 + }
  72 + }
  73 + }
  74 +}'
... ...
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  1 +# Elasticsearch 查询相关
  2 +
  3 +## 根据商品ID查询
  4 +
  5 +```json
  6 +GET /uat_spu/_search
  7 +{
  8 + "query": {
  9 + "term": {
  10 + "_id": "3588136"
  11 + }
  12 + }
  13 +}
  14 +```
  15 +
  16 +## 查label_id
  17 +
  18 +```json
  19 +GET /uat_spu/_search
  20 +{"query":{"bool":{"must":[{"term":{ "platform_sku_essaone": true }},{"term":{"label_id_list":166}}]}}}
  21 +```
  22 +
  23 +## collapse折叠
  24 +
  25 +```json
  26 +GET /spu/_search
  27 +{
  28 + "_source": ["_id", "name_zh", "goods_id", "*"],
  29 + "size": 1000,
  30 + "query": {
  31 + "match": {
  32 + "name_zh": {
  33 + "query": "园艺"
  34 + }
  35 + }
  36 + },
  37 + "collapse": {
  38 + "field": "goods_id", // 按 goods_id 字段折叠分组
  39 + "inner_hits": {
  40 + "_source": ["_id"],
  41 + "name": "top_docs", // 自定义内部结果名称
  42 + "size": 3, // 每组取1个文档
  43 + "sort": [{"_score": "desc"}] // 按评分降序排序
  44 + }
  45 + },
  46 + "sort": ["_score"] // 确保外层排序兼容折叠功能
  47 +}
  48 +```
  49 +
  50 +## 在架、某平台商品数
  51 +
  52 +```json
  53 +GET /uat_spu/_search
  54 +{
  55 + "track_total_hits": 100000,
  56 + "query": {
  57 + "bool": {
  58 + "filter": [
  59 + {
  60 + "term": {
  61 + "platform_sku_essaone": true
  62 + }
  63 + },
  64 + {
  65 + "term": {
  66 + "platform_rule_essaone": true
  67 + }
  68 + },
  69 + {
  70 + "terms": {
  71 + "goods_copyright": [
  72 + "1",
  73 + "2",
  74 + "3"
  75 + ]
  76 + }
  77 + }
  78 + ]
  79 + }
  80 + }
  81 +}
  82 +```
  83 +
  84 +## ID查询
  85 +
  86 +```json
  87 +GET /uat_spu/_search
  88 +{
  89 + "query": {
  90 + "term": {
  91 + "_id": "3302275"
  92 + }
  93 + }
  94 +}
  95 +```
  96 +
  97 +## 商品名搜索
  98 +
  99 +```json
  100 +GET /spu/_search
  101 +{
  102 + "_source": {
  103 + },
  104 + "size": 1000,
  105 + "query": {
  106 + "match": {
  107 + "name_zh": {
  108 + "query": "园艺工具四件套",
  109 + "boost": 111.0
  110 + }
  111 + }
  112 + },
  113 + "aggs" :{
  114 + "goods_agg": {
  115 + "terms": {
  116 + "field": "goods_id",
  117 + "size": 100
  118 + },
  119 + "aggs": {
  120 + "top_hits": {
  121 + "top_hits": {
  122 + "size": 1,
  123 + "sort": [{"_score": "desc"}]
  124 + }
  125 + }
  126 + }
  127 + }
  128 + }
  129 +}
  130 +```
  131 +
  132 +## phrase搜索
  133 +
  134 +```json
  135 +GET /uat_spu/_search
  136 +{
  137 + "_source":["id","goods_id","name_zh"],
  138 + "query":{
  139 + "multi_match": {
  140 + "_name": "base_query",
  141 + "fields": [
  142 + "name_zh^2.0",
  143 + "goods_keyword_zh^1.0",
  144 + "category_name_zh^1.0",
  145 + "sale_category_keyword_zh^1.0",
  146 + "sub_name_zh^1.0"
  147 + ],
  148 + "query": "戏水玩具"
  149 + }
  150 + }
  151 +}
  152 +```
  153 +
  154 +## aggs聚合查询
  155 +
  156 +```json
  157 +GET /spu/_search
  158 +{
  159 + "_source": {
  160 + "excludes": [
  161 + "embedding_name_zh",
  162 + "embedding_name_ru", "*"
  163 + ],
  164 + "includes": ["_id", "name_zh"]
  165 + },
  166 + "size": 1000,
  167 + "query": {
  168 + "match": {
  169 + "name_zh": {
  170 + "query": "园艺"
  171 + }
  172 + }
  173 + },
  174 + "aggs" :{
  175 + "goods_agg": {
  176 + "terms": {
  177 + "field": "goods_id",
  178 + "size": 100
  179 + },
  180 + "aggs": {
  181 + "top_hits": {
  182 + "top_hits": {
  183 + "size": 1,
  184 + "sort": [{"_score": "desc"}]
  185 + }
  186 + }
  187 + }
  188 + }
  189 + }
  190 +}
  191 +```
  192 +
  193 +## 检查某个字段是否有值
  194 +```json
  195 +GET /uat_spu/_search
  196 +{
  197 + "track_total_hits": true,
  198 + "size": 0, // 不返回 hits，只返回总数
  199 + "query": {
  200 + "exists": {
  201 + "field": "goods_keyword_zh"
  202 + }
  203 + }
  204 +}
  205 +```
  206 +如果是nested字段，要这么查：
  207 +```json
  208 +GET /uat_spu/_search
  209 +{
  210 + "track_total_hits": true,
  211 + "size": 0, // 只看总数
  212 + "query": {
  213 + "nested": {
  214 + "path": "embedding_pic_h14",
  215 + "query": {
  216 + "exists": {
  217 + "field": "embedding_pic_h14.vector" // 或 url，只要子文档里任意字段有值
  218 + }
  219 + }
  220 + }
  221 + }
  222 +}
  223 +```
  224 +
  225 +```json
  226 +```
  227 +
  228 +## 分词测试
  229 +
  230 +### ik_smart分词器
  231 +
  232 +```json
  233 +POST spu/_analyze
  234 +{
  235 + "analyzer": "ik_smart",
  236 + "text": "14寸第4代-眼珠实身冰雪公仔带手动大推车,搪胶雪宝宝"
  237 +}
  238 +```
  239 +
  240 +### ik_max_word分词器
  241 +
  242 +```json
  243 +POST spu/_analyze
  244 +{
  245 + "analyzer": "ik_max_word",
  246 + "text": "14寸第4代-眼珠实身冰雪公仔带手动大推车,搪胶雪宝宝"
  247 +}
  248 +```
  249 +
  250 +### query_ansj分词器
  251 +
  252 +```json
  253 +POST spu/_analyze
  254 +{
  255 + "analyzer": "query_ansj",
  256 + "text": "14寸第4代-眼珠实身冰雪公仔带手动大推车,搪胶雪宝宝"
  257 +}
  258 +```
  259 +
  260 +### index_ansj分词器
  261 +
  262 +```json
  263 +POST spu_test/_analyze
  264 +{
  265 + "analyzer": "index_ansj",
  266 + "text": "14寸第4代-眼珠实身冰雪公仔带手动大推车,搪胶雪宝宝"
  267 +}
  268 +```
  269 +
  270 +## 查商品名
  271 +
  272 +```json
  273 +GET /spu/_search
  274 +{
  275 + "_source": {
  276 + "includes": [
  277 + "name_zh",
  278 + "category_name_zh",
  279 + "sub_name_zh",
  280 + "label_id_list",
  281 + "brand_id",
  282 + "category_id",
  283 + "price_range",
  284 + "on_sell_days_boost"
  285 + ]
  286 + },
  287 + "collapse": {
  288 + "field": "goods_id",
  289 + "inner_hits": {
  290 + "name": "top_hits",
  291 + "size": 1,
  292 + "_source": false // 避免返回完整_source
  293 + }
  294 + },
  295 + "from": 0,
  296 + "query": {
  297 + "function_score": {
  298 + "query": {
  299 + "bool": {
  300 + "minimum_should_match": 1,
  301 + "should": [
  302 + {
  303 + "multi_match": {
  304 + "_name": "base_query",
  305 + "fields": [
  306 + "name_zh^2.0",
  307 + "goods_keyword_zh^1.0",
  308 + "category_name_zh^1.0",
  309 + "sale_category_keyword_zh^1.0",
  310 + "sub_name_zh^1.0"
  311 + ],
  312 + "minimum_should_match": "66%",
  313 + "query": "网球",
  314 + "tie_breaker": 0.301
  315 + }
  316 + }
  317 + ]
  318 + }
  319 + }
  320 + }
  321 + },
  322 + "size": 1000,
  323 + "track_scores": true
  324 +}
  325 +```
  326 +
  327 +# 配置相关
  328 +
  329 +## 查看索引信息
  330 +
  331 +```bash
  332 +GET /_cat/indices/spu?v
  333 +```
  334 +
  335 +```bash
  336 +GET /spu/_mapping
  337 +```
  338 +
  339 +```bash
  340 +GET /spu/_settings
  341 +```
  342 +
  343 +## 设置刷新时间
  344 +
  345 +```json
  346 +PUT /spu/_settings
  347 +{
  348 + "refresh_interval": -1
  349 +}
  350 +```
  351 +
  352 +```json
  353 +PUT /uat_spu/_settings
  354 +{
  355 + "refresh_interval": "600s"
  356 +}
  357 +```
  358 +
  359 +## 设置preload
  360 +
  361 +```json
  362 +POST uat_spu/_close
  363 +PUT uat_spu/_settings
  364 +{
  365 + "index.store.preload": []
  366 +}
  367 +
  368 +PUT uat_spu/_settings
  369 +{
  370 + "index.store.preload": ["vex", "veq"]
  371 +}
  372 +# ["nvd", "dvd", "tim", "doc", "dim"]
  373 +
  374 +POST uat_spu/_open
  375 +
  376 +```
  377 +
  378 +## 修改分片数目
  379 +主分片的数目（number_of_shards）只能创建索引的时候修改
  380 +
  381 +
  382 +## 查看索引的分片数量、大小分布
  383 +```json
  384 +GET _cat/indices/uat_spu?v&s=store.size:desc
  385 +GET _cat/shards/uat_spu
  386 +```
  387 +
  388 +## 类别查询：索引中该类别总数
  389 +
  390 +```json
  391 +GET /spu/_search
  392 +{
  393 + "track_total_hits": 10000000,
  394 + "query": {"terms": {
  395 + "sale_category_all": [37619]
  396 + }}
  397 +}
  398 +```
  399 +
  400 +## 类别查询：模拟平台规则
  401 +
  402 +除了类别限定，包括：
  403 +1. essaone平台展示过滤
  404 +2. 非专属商品
  405 +3. status 2 4 5
  406 +4. goods_copyright 1,2,3
  407 +
  408 +```json
  409 +GET /spu/_search
  410 +{
  411 + "track_total_hits": 10000000,
  412 + "aggs": {
  413 + "unique_count": {
  414 + "cardinality": {
  415 + "field": "goods_id"
  416 + }
  417 + }
  418 + },
  419 + "collapse": {
  420 + "field": "goods_id"
  421 + },
  422 + "query": {
  423 + "bool": {
  424 + "filter": [
  425 + {
  426 + "bool": {
  427 + "minimum_should_match": 1,
  428 + "should": [
  429 + {
  430 + "term": {
  431 + "platform_sku_essaone": true
  432 + }
  433 + },
  434 + {
  435 + "bool": {
  436 + "must": [
  437 + {
  438 + "term": {
  439 + "platform_rule_essaone": true
  440 + }
  441 + },
  442 + {
  443 + "bool": {
  444 + "must_not": {
  445 + "exists": {
  446 + "field": "platform_sku_essaone"
  447 + }
  448 + }
  449 + }
  450 + }
  451 + ]
  452 + }
  453 + }
  454 + ]
  455 + }
  456 + },
  457 + {
  458 + "bool": {
  459 + "must_not": {
  460 + "exists": {
  461 + "field": "buyer_id"
  462 + }
  463 + }
  464 + }
  465 + },
  466 + {
  467 + "terms": {
  468 + "sale_category_all": [37619]
  469 + }
  470 + },
  471 + {
  472 + "terms": {
  473 + "status": [2,4,5]
  474 + }
  475 + },
  476 + {
  477 + "terms": {
  478 + "goods_copyright": [
  479 + "1",
  480 + "2",
  481 + "3"
  482 + ]
  483 + }
  484 + }
  485 + ]
  486 + }
  487 + }
  488 +}
  489 +```
  490 +
  491 +## 关键词查询
  492 +
  493 +```json
  494 +GET /spu/_search
  495 +{
  496 + "size": 1000,
  497 + "from": 0,
  498 + "_source": {
  499 + "includes": ["name_zh","goods_keyword_zh","sub_name_zh","category_name_zh","goods_keyword_zh","sale_category_keyword_zh"]
  500 + },
  501 + "query": {
  502 + "multi_match": {
  503 + "query": "遥控车",
  504 + "fields": ["name_zh^3.0", "goods_keyword_zh^1.0", "category_name_zh^1.0", "sale_category_keyword_zh^1.0", "sub_name_zh^1.0"],
  505 + "minimum_should_match": "64%",
  506 + "tie_breaker": 0.3,
  507 + "_name": "base_query"
  508 + }
  509 + },
  510 + "track_scores": true,
  511 + "collapse": {
  512 + "field": "goods_id"
  513 + }
  514 +}
  515 +```
  516 +
  517 +## 慢查询日志
  518 +
  519 +```json
  520 +PUT /spu,spu_test/_settings
  521 +{
  522 + "index.search.slowlog.threshold.query.warn": "1s",
  523 + "index.search.slowlog.threshold.query.info": "800ms",
  524 + "index.search.slowlog.threshold.query.debug": "600ms",
  525 + "index.search.slowlog.threshold.query.trace": "400ms",
  526 + "index.search.slowlog.threshold.fetch.warn": "500ms",
  527 + "index.search.slowlog.threshold.fetch.info": "400ms",
  528 + "index.search.slowlog.threshold.fetch.debug": "200ms",
  529 + "index.search.slowlog.threshold.fetch.trace": "100ms"
  530 +}
  531 +```
  532 +
  533 +```bash
  534 +GET /spu,spu_test/_settings?include_defaults=true&filter_path=**.slowlog
  535 +```
  536 +
  537 +---
  538 +
  539 +# 一些问题
  540 +
  541 +有一个商品，只命中了"玩具"、没有命中"遥控"，两个词命中一个词（注意是所有的字段 总共只命中一个词，玩具命中多次，但是没有任何一个字段有"遥控"），minimum_should_match应该没有达到76%吧，76%是按照什么算的？为什么命中了这个查询条件？
  542 +
  543 +```json
  544 +"multi_match": {
  545 + "_name": "base_query",
  546 + "fields": [
  547 + "name_zh^2.0",
  548 + "goods_keyword_zh^1.0",
  549 + "category_name_zh^1.0",
  550 + "sale_category_keyword_zh^1.0",
  551 + "sub_name_zh^1.0"
  552 + ],
  553 + "minimum_should_match": "76%",
  554 + "query": "遥控玩具",
  555 + "tie_breaker": 0.3
  556 +}
  557 +```
  558 +
  559 +tie_breaker 导致
  560 +
  561 +```json
  562 +GET /spu/_search
  563 +{
  564 + "_source": {
  565 + "excludes": [
  566 + "embedding_name_zh",
  567 + "embedding_name_ru", "*"
  568 + ],
  569 + "includes": ["_id", "name_zh"]
  570 + },
  571 + "size": 1000,
  572 + "query": {
  573 + "match": {
  574 + "name_zh": {
  575 + "query": "园艺"
  576 + }
  577 + }
  578 + },
  579 + "aggs" :{
  580 + "goods_agg": {
  581 + "terms": {
  582 + "field": "goods_id",
  583 + "size": 100
  584 + },
  585 + "aggs": {
  586 + "top_hits": {
  587 + "top_hits": {
  588 + "size": 1,
  589 + "sort": [{"_score": "desc"}]
  590 + }
  591 + }
  592 + }
  593 + }
  594 + }
  595 +}
  596 +
  597 +
  598 +商品搜索查询模板
  599 +```json
  600 +GET /spu/_search
  601 +{
  602 + "_source": {
  603 + "includes": [
  604 + "name_zh"
  605 + ]
  606 + },
  607 + "aggs": {
  608 + "category_stats": {
  609 + "terms": {
  610 + "field": "sale_category_one_all",
  611 + "size": 100
  612 + }
  613 + },
  614 + "spu_attr_stats": {
  615 + "terms": {
  616 + "field": "attribute_option_list",
  617 + "size": 50
  618 + }
  619 + },
  620 + "unique_count": {
  621 + "cardinality": {
  622 + "field": "goods_id"
  623 + }
  624 + }
  625 + },
  626 + "collapse": {
  627 + "field": "goods_id"
  628 + },
  629 + "from": 0,
  630 + "query": {
  631 + "function_score": {
  632 + "boost_mode": "multiply",
  633 + "functions": [
  634 + {
  635 + "filter": {
  636 + "term": {
  637 + "is_video": true
  638 + }
  639 + },
  640 + "weight": 1.05
  641 + }
  642 + ],
  643 + "query": {
  644 + "bool": {
  645 + "filter": [
  646 + {
  647 + "terms": {
  648 + "status": [2,4,5]
  649 + }
  650 + }
  651 + ],
  652 + "must": [
  653 + {
  654 + "bool": {
  655 + "minimum_should_match": 1,
  656 + "should": [
  657 + {
  658 + "multi_match": {
  659 + "_name": "base_query",
  660 + "fields": [
  661 + "name_en^3.0",
  662 + "goods_keyword_en^1.0",
  663 + "category_name_en^1.3",
  664 + "sale_category_keyword_en^1.0",
  665 + "sub_name_en^1.0"
  666 + ],
  667 + "minimum_should_match": "75%",
  668 + "operator": "AND",
  669 + "query": "changshen flashing toys",
  670 + "tie_breaker": 0.9
  671 + }
  672 + },
  673 + {
  674 + "knn": {
  675 + "_name": "knn_query",
  676 + "field": "embedding_name_zh",
  677 + "k": 40,
  678 + "num_candidates": 120,
  679 + "query_vector": [...]
  680 + }
  681 + }
  682 + ]
  683 + }
  684 + }
  685 + ]
  686 + }
  687 + },
  688 + "score_mode": "sum"
  689 + }
  690 + },
  691 + "size": 1000,
  692 + "sort": [
  693 + {
  694 + "_score": {
  695 + "order": "desc"
  696 + }
  697 + }
  698 + ],
  699 + "track_scores": true
  700 +}
  701 +```
0 702 \ No newline at end of file
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  1 +
  2 +
  3 +工厂搜索 依赖于关键词召回、没有做语义召回和精排，加上语义召回和精排则成本过高，所以对分词效果仍然很依赖。badcase： q=锦鸿 无法召回 锦鸿达
  4 +1. 虽然商品搜索已经不强依赖于分词效果，但是工厂搜索仍然对分词效果有很大依赖，所以进行了优化。
  5 +2. 优化内容：
  6 + 1) index_ansj + 自定义词典（需要配置自定义词典。 不配置自定义词典的时候，index_ansj分词结果和query_ansj完全一致，配置自定义词典时，index_ansj 将 ）
  7 + 2) ansj自定义词典 补充属性词
  8 +
  9 +
  10 +
  11 +
  12 +
  13 +| Query | IK_Smart 分词结果 | IK_Max_Word 分词结果 |
  14 +|---------|---------------------------------------------|--------------------------------------------------|
  15 +| 锦鸿达 | 锦, 鸿达 | 锦, 鸿达 |
  16 +| 锦鸿 | 锦, 鸿 | 锦, 鸿 |
  17 +| 14寸 4代 | 14寸, 4代 | 14, 寸, 4, 代 |
  18 +| q=14寸第4代-眼珠实身冰雪公仔带手动大推车,搪胶雪宝宝 | 14寸, 第, 4代, 眼珠, 实, 身, 冰雪, 公, 仔, 带, 手动, 大, 推车, 搪, 胶, 雪, 宝宝 | 14, 寸, 第, 4, 代, 眼珠, 实, 身, 冰雪, 公仔, 带, 手动, 大, 推车, 搪, 胶, 雪, 宝宝 |
  19 +| 金毛犬-狗狗系列积木套 | 金毛犬, 狗狗, 系列, 积木, 套 | 金毛犬, 狗狗, 系列, 积木, 套 |
  20 +
  21 +
  22 +
  23 +| Query | query_ansj 分词结果 | index_ansj 分词结果 |
  24 +|---------|------------------------------------------|--------------------------------------------------|
  25 +| 锦鸿达 | 锦, 鸿达 | 锦, 鸿达, 鸿, 达 |
  26 +| 锦鸿 | 锦鸿 | 锦鸿, 锦, 鸿 |
  27 +| q=14寸第4代-眼珠实身冰雪公仔带手动大推车,搪胶雪宝宝 | 14寸, 第, 4代, 眼珠, 实, 身, 冰雪, 公仔, 带, 手动, 大, 推车, 搪, 胶, 雪, 宝宝 | 14寸, 寸, 第, 4代, 代, 眼珠, 眼, 珠, 实, 身, 冰雪, 冰, 雪, 公仔, 公, 仔, 带, 手动, 手, 动, 大, 推车, 推, 车, 搪, 胶, 雪, 宝宝, 宝 |
  28 +| 金毛犬-狗狗系列积木套 | 金毛犬, 狗狗, 系列, 积木, 套 | 金毛犬, 金毛, 金, 毛, 犬, 狗狗, 狗, 系列, 系, 列, 积木, 积, 木, 套 |
  29 +
  30 +
  31 +ansj 插件自定义词典补充属性词后：
  32 +| Query | query_ansj 分词结果 | index_ansj 分词结果 |
  33 +|---------|------------------------------------------|--------------------------------------------------|
  34 +| 锦鸿达 | 锦, 鸿达 | 锦, 鸿达, 鸿, 达 |
  35 +| 锦鸿 | 锦鸿 | 锦鸿, 锦, 鸿 |
  36 +| q=14寸第4代-眼珠实身冰雪公仔带手动大推车,搪胶雪宝宝 | 14寸, 第, 4代, 眼珠, 实身, 冰雪, 公仔, 带, 手动, 大, 推车, 搪胶, 雪, 宝宝 | 14寸, 寸, 第, 4代, 代, 眼珠, 眼, 珠, 实身, 实, 身, 冰雪, 冰, 雪, 公仔, 公, 仔, 带, 手动, 手, 动, 大, 推车, 推, 车, 搪胶, 搪, 胶, 雪, 宝宝, 宝 |
  37 +| 金毛犬-狗狗系列积木套 | 金毛犬, 狗狗, 系列, 积木, 套 | 金毛犬, 金毛, 金, 毛, 犬, 狗狗, 狗, 系列, 系, 列, 积木, 积, 木, 套 |
  38 +
  39 +
  40 +
  41 +
  42 +## ik分词器的问题：
  43 +
  44 +```bash
  45 +POST spu/_analyze
  46 +{
  47 + "analyzer": "ik_smart",
  48 + "text": "14寸第4代-眼珠实身冰雪公仔带手动大推车,搪胶雪宝宝"
  49 +}
  50 +
  51 +POST spu/_analyze
  52 +{
  53 + "analyzer": "ik_max_word",
  54 + "text": "14寸第4代-眼珠实身冰雪公仔带手动大推车,搪胶雪宝宝"
  55 +}
  56 +
  57 +# ik分词器的问题：
  58 +# ik_max_word 结果是： 14 寸 第 4 代
  59 +# 期望是能分出多种形式： 14 寸 14寸 第 4 代 4代
  60 +# ik_smart
  61 +# 结果是： 14寸 第 4代
  62 +# 因此 14寸 4代 都无法召回原始文档。
  63 +```
  64 +
  65 +
  66 +## ansj分词器的问题：
  67 +
  68 +### 1. index_ansj不符合预期
  69 +index模式（type=index_ansj）不符合预期 ：type=index_ansj type=query_ansj 结果永远都是一样的。
  70 +https://github.com/NLPchina/elasticsearch-analysis-ansj/issues/235
  71 +
  72 +已解决：需要配置自定义词典
  73 +
  74 +### 2. 特殊符号分词粘连问题
  75 +特殊符号（减号）会跟后面的词粘连到一起，导致搜索减号后面的词，无法匹配：
  76 +
  77 +```bash
  78 +GET /_cat/ansj?text=狗狗系列-柴犬积木&type=index_ansj
  79 +{
  80 +"name": "-柴",
  81 +"nature": "nrf",
  82 +"offe": 4,
  83 +"realName": "-柴",
  84 +"synonyms": null
  85 +},
  86 +
  87 +GET /_cat/ansj?text=狗狗系列-哈士奇犬&type=index_ansj
  88 +{
  89 +"name": "-哈士奇",
  90 +"nature": "nrf",
  91 +"offe": 4,
  92 +"realName": "-哈士奇",
  93 +"synonyms": null
  94 +},
  95 +```
  96 +
  97 +这样的话，如果搜索“柴犬”、“哈士奇”，就不能命中上面的句子。
  98 +
  99 +暂时解决办法是：
  100 +```json
  101 + "analyzer": {
  102 + "index_ansj": {
  103 + "type": "custom",
  104 + "char_filter": ["hyphen_to_space"],
  105 + "tokenizer": "index_ansj"
  106 + },
  107 + "query_ansj": {
  108 + "type": "custom",
  109 + "char_filter": ["hyphen_to_space"],
  110 + "tokenizer": "query_ansj"
  111 + }
  112 + }
  113 +```
  114 +
  115 +
  116 +
  117 +
  118 +
  119 +
  120 +
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  1 +
  2 +
  3 +
  4 +# 性能优化TODO
  5 +
  6 +
  7 +1. 主分片个数设置为12 / 16（暂定 16），replaca保持为0
  8 +2. 缩减索引&预加载内存（3年前的不做向量索引，在uat上 水哥已经做了，索引大小已经从120G减少到61G）。 之后设置预加载（UAT上面已经做了）：
  9 +```json
  10 +POST uat_spu/_close
  11 +PUT uat_spu/_settings
  12 +{
  13 + "index.store.preload": []
  14 +}
  15 +
  16 +PUT uat_spu/_settings
  17 +{
  18 + "index.store.preload": ["vex", "veq"]
  19 +}
  20 +PUT uat_spu/_settings
  21 +{
  22 + "index.store.preload": ["nvd", "dvd", "tim", "doc", "dim"]
  23 +}
  24 +# ["nvd", "dvd", "tim", "doc", "dim"]
  25 +
  26 +POST uat_spu/_open
  27 +```
  28 +3. 段优化 (在索引稳定后)：
  29 +POST /spu/_forcemerge?max_num_segments=1 。这将每个分片内的 Lucene 段合并到最少（理想是1个）
  30 +4. UAT索引迁出。
  31 +
  32 +
  33 +# old
  34 +
  35 +## 当前主要问题
  36 +
  37 +### 1. 按goods_id聚合/去重问题（collapse相关）
  38 +
  39 +当前采用collapse实现，但存在以下问题：
  40 +
  41 +#### 1.1 加inner_hits的性能问题
  42 +
  43 +我暂时放弃了 inner_hits，但是其实对inner_hits功能有较强的需求，是否有替代办法：能满足 按 goods_id 聚合、也能得到聚合下的原始文档ID、同时能满足分页需求。
  44 +
  45 +**对应案例：**
  46 +
  47 +将下面的"商品搜索实际案例"中的：
  48 +
  49 +```json
  50 +"collapse": {
  51 + "field": "goods_id"
  52 +}
  53 +```
  54 +
  55 +替换为：
  56 +
  57 +```json
  58 +"collapse": {
  59 + "field": "goods_id",
  60 + "inner_hits": {
  61 + "_source": false,
  62 + "name": "top_docs",
  63 + "size": 4
  64 + }
  65 +}
  66 +```
  67 +
  68 +可以看到耗时明显增加，耗时 330ms 增加到 3565ms。
  69 +
  70 +#### 1.2 分页不准的问题
  71 +
  72 +**背景：** ES的分页是基于索引文档的顺序，而不是折collapse叠后的顺序，这可能导致深度分页时的性能问题或结果不一致。
  73 +
  74 +这样会带来两个问题：
  75 +
  76 +##### a) total不准（total是collapse之前的个数）
  77 +我现在的方案是，aggs 再补充一项，计算一个按照 goods_id 的去重数，以此作为 total，从而得到准确的分页。
  78 +（ 也可以使用 cardinality 。 cardinality 提供的事近似计数。 希望快速估算唯一值数量，应该使用 cardinality ）
  79 +
  80 +见下面案例中的 aggs 中的 unique_count
  81 +
  82 +##### b) from不准
  83 +相关资料提示只是“深度分页时”不准，比如结果跟前面的页内内容重复。但是我试了一些请求，还没找到过重复的情况。
  84 +
  85 +并且，商品搜索因为有精排，只要请求的内容位于top1000，则会从ES获得top1000的所有结果，所以top1000内的内容不会有分页不准的问题。
  86 +
  87 +而对于工厂搜索，我测试了一些请求，也还没有发现分页内内容跟前面的页的内容重复的情况。
  88 +
  89 +所以ES内部实现的from已经是collapse之后的
  90 +
  91 +### 2. ES配置优化
  92 +
  93 +还有哪些要注意的。
  94 +
  95 +## 实际案例
  96 +
  97 +### 商品搜索实际案例（简化后）
  98 +
  99 +```json
  100 +GET /spu/_search
  101 +{
  102 + "_source": {
  103 + "includes": [
  104 + "name_zh"
  105 + ]
  106 + },
  107 + "aggs": {
  108 + "unique_count": {
  109 + "cardinality": {
  110 + "field": "goods_id"
  111 + }
  112 + },
  113 + "category_stats": {
  114 + "terms": {
  115 + "field": "sale_category_one_all",
  116 + "size": 10
  117 + }
  118 + },
  119 + "certification_stats": {
  120 + "terms": {
  121 + "field": "goods_certification_type_list",
  122 + "size": 10
  123 + }
  124 + },
  125 + "deliver_day_stats": {
  126 + "terms": {
  127 + "field": "deliver_day",
  128 + "size": 10
  129 + }
  130 + },
  131 + "label_stats": {
  132 + "terms": {
  133 + "field": "label_id_list",
  134 + "size": 10
  135 + }
  136 + },
  137 + "material_stats": {
  138 + "terms": {
  139 + "field": "goods_main_material",
  140 + "size": 10
  141 + }
  142 + },
  143 + "moq_stats": {
  144 + "terms": {
  145 + "field": "purchase_moq",
  146 + "size": 10
  147 + }
  148 + },
  149 + "package_lang_stats": {
  150 + "terms": {
  151 + "field": "goods_package_lang",
  152 + "size": 10
  153 + }
  154 + },
  155 + "package_type_stats": {
  156 + "terms": {
  157 + "field": "package_type",
  158 + "size": 10
  159 + }
  160 + },
  161 + "spu_attr_stats": {
  162 + "terms": {
  163 + "field": "attribute_option_list",
  164 + "size": 10
  165 + }
  166 + }
  167 + },
  168 + "collapse": {
  169 + "field": "goods_id"
  170 + },
  171 + "from": 0,
  172 + "query": {
  173 + "function_score": {
  174 + "boost_mode": "multiply",
  175 + "functions": [
  176 + {
  177 + "filter": {
  178 + "term": {
  179 + "label_id_list": 156
  180 + }
  181 + },
  182 + "weight": 1.1
  183 + },
  184 + {
  185 + "filter": {
  186 + "term": {
  187 + "is_video": true
  188 + }
  189 + },
  190 + "weight": 1.2
  191 + }
  192 + ],
  193 + "query": {
  194 + "bool": {
  195 + "filter": [
  196 + {
  197 + "term": {
  198 + "platform_sku_essaone": true
  199 + }
  200 + },
  201 + {
  202 + "term": {
  203 + "platform_rule_essaone": true
  204 + }
  205 + },
  206 + {
  207 + "bool": {
  208 + "should": [
  209 + {
  210 + "bool": {
  211 + "must_not": {
  212 + "exists": {
  213 + "field": "auth_level_list"
  214 + }
  215 + }
  216 + }
  217 + },
  218 + {
  219 + "term": {
  220 + "auth_level_list": "1"
  221 + }
  222 + }
  223 + ]
  224 + }
  225 + },
  226 + {
  227 + "terms": {
  228 + "status": [2,4,5]
  229 + }
  230 + },
  231 + {
  232 + "terms": {
  233 + "goods_copyright": ["1","2","3"]
  234 + }
  235 + }
  236 + ],
  237 + "must": [
  238 + {
  239 + "bool": {
  240 + "minimum_should_match": 1,
  241 + "should": [
  242 + {
  243 + "multi_match": {
  244 + "_name": "base_query",
  245 + "fields": [
  246 + "name_zh^2.0",
  247 + "goods_keyword_zh^1.0",
  248 + "category_name_zh^1.0",
  249 + "sale_category_keyword_zh^1.0",
  250 + "sub_name_zh^1.0"
  251 + ],
  252 + "minimum_should_match": "66%",
  253 + "query": "合金车",
  254 + "tie_breaker": 0.3
  255 + }
  256 + }
  257 + ]
  258 + }
  259 + }
  260 + ]
  261 + }
  262 + },
  263 + "score_mode": "max"
  264 + }
  265 + },
  266 + "size": 1000,
  267 + "track_scores": true
  268 +}
  269 +```
  270 +
  271 +### 工厂搜索实际案例（简化后）
  272 +
  273 +```json
  274 +GET /spu/_search
  275 +{
  276 + "_source": [
  277 + "id",
  278 + "goods_id",
  279 + "brand_id",
  280 + "name_zh"
  281 + ],
  282 + "aggs": {
  283 + "unique_count": {
  284 + "cardinality": {
  285 + "field": "brand_id"
  286 + }
  287 + },
  288 + "category_stats": {
  289 + "terms": {
  290 + "field": "sale_category_one_all",
  291 + "size": 10
  292 + }
  293 + },
  294 + "sale_market_stats": {
  295 + "terms": {
  296 + "field": "supplier_main_market_list",
  297 + "size": 10
  298 + }
  299 + }
  300 + },
  301 + "collapse": {
  302 + "field": "brand_id"
  303 + },
  304 + "from": 222,
  305 + "query": {
  306 + "bool": {
  307 + "filter": [
  308 + {
  309 + "term": {
  310 + "platform_sku_essaone": true
  311 + }
  312 + },
  313 + {
  314 + "term": {
  315 + "platform_rule_essaone": true
  316 + }
  317 + },
  318 + {
  319 + "bool": {
  320 + "should": [
  321 + {
  322 + "bool": {
  323 + "must_not": {
  324 + "exists": {
  325 + "field": "auth_level_list"
  326 + }
  327 + }
  328 + }
  329 + },
  330 + {
  331 + "term": {
  332 + "auth_level_list": "1"
  333 + }
  334 + }
  335 + ]
  336 + }
  337 + },
  338 + {
  339 + "terms": {
  340 + "status": [2,4,5]
  341 + }
  342 + },
  343 + {
  344 + "terms": {
  345 + "goods_copyright": [
  346 + "1",
  347 + "2",
  348 + "3"
  349 + ]
  350 + }
  351 + }
  352 + ],
  353 + "must": [
  354 + {
  355 + "bool": {
  356 + "minimum_should_match": 1,
  357 + "should": [
  358 + {
  359 + "multi_match": {
  360 + "fields": [
  361 + "brand_name_zh^10.0",
  362 + "name_zh^2.0",
  363 + "supplier_name^8.0"
  364 + ],
  365 + "minimum_should_match": "66%",
  366 + "query": "义乌市童吉拉",
  367 + "tie_breaker": 0.3
  368 + }
  369 + } ]
  370 + }
  371 + }
  372 + ]
  373 + }
  374 + },
  375 + "size": 20
  376 +}
  377 +```
  378 +
  379 +
  380 +### ES 索引配置方面，为提升查询性能，是否需要将副本数设置为1。
  381 +但是为了不影响写入性能，在每次全量前，先关闭副本，全量写入完成后再打开副本。
  382 +```json
  383 +PUT /spu_test/_settings
  384 +{
  385 + "number_of_replicas": 1
  386 +}
  387 +```
  388 +
  389 +
  390 +### 预热
  391 +2025/6/26 今天早上7点多一次查询(q=木质手工艺品玩具) ES查询阶段耗时20多秒，但是第二次查询就只要几百ms，改变query耗时也正常。
  392 +
  393 +
  394 +目前已经设置慢查询日志：
  395 +
  396 +PUT /spu,spu_test/_settings
  397 +{
  398 + "index.search.slowlog.threshold.query.warn": "10s",
  399 + "index.search.slowlog.threshold.query.info": "5s",
  400 + "index.search.slowlog.threshold.query.debug": "2s",
  401 + "index.search.slowlog.threshold.query.trace": "500ms",
  402 + "index.search.slowlog.threshold.fetch.warn": "1s",
  403 + "index.search.slowlog.threshold.fetch.info": "800ms",
  404 + "index.search.slowlog.threshold.fetch.debug": "500ms",
  405 + "index.search.slowlog.threshold.fetch.trace": "200ms"
  406 +}
  407 +
  408 +
  409 +PUT /spu,spu_test/_settings
  410 +{
  411 + "index.search.slowlog.threshold.query.warn": "1s",
  412 + "index.search.slowlog.threshold.query.info": "800ms",
  413 + "index.search.slowlog.threshold.query.debug": "600ms",
  414 + "index.search.slowlog.threshold.query.trace": "400ms",
  415 + "index.search.slowlog.threshold.fetch.warn": "500ms",
  416 + "index.search.slowlog.threshold.fetch.info": "400ms",
  417 + "index.search.slowlog.threshold.fetch.debug": "200ms",
  418 + "index.search.slowlog.threshold.fetch.trace": "100ms"
  419 +}
  420 +
  421 +动态调整日志级别 (无需重启)
  422 +通过集群设置 API 临时调整：
  423 +PUT /_cluster/settings
  424 +{
  425 + "transient": {
  426 + "logger.index.search.slowlog": "info"
  427 + }
  428 +}
  429 +
  430 +验证配置是否生效
  431 +GET /spu,spu_test/_settings?include_defaults=true&filter_path=**.slowlog
  432 +
  433 +日志文件
  434 +logs/<cluster_name>.log
  435 +
  436 +
  437 +
  438 +
  439 +## 慢查询记录
  440 +
  441 +
  442 +### 07/02 早上 7:45~7:47，100个里面又有8个超过1.5S的， 4个超过5S ， 2个超过14S
  443 +
  444 +#### 8888888946992436
  445 +2025-07-02 07:45:32,092 - INFO - [REQ: 8888888946992436 UID: 10639] REQUEST POST structure: {"keyword":"плащ","kt":"kwd","page":1,"size":100,"request_id":8888888946992436,"extra":{"userId":10639,"businessPlatform":"essa"}}
  446 +
  447 +2025-07-02 07:45:46,981 - INFO - [REQ: 8888888946992436 UID: 10639] qt:kwd total:14.889394521713257ms TIMING: QA=46ms, queryEncoding=0ms, userProfile=1ms, ES=14824ms, BgeReranking=1ms, BizReranking=1ms, Reranking=2ms, total=14889ms, sku_id_list_filling=12ms
  448 +
  449 +2025-07-02 07:45:46,968 - INFO - [REQ: 8888888946992436 UID: 10639] ES_query_timing # ProductKeywordSearch # 14824.28 # {"size":1000,"from":0,"_source":{"includes":["name_zh","is_star","category_name_zh","sub_name_zh","label_id_list","is_video","brand_id","category_id","sale_category_all","price_range","on_sell_days_boost","ru_name"]},"query":{"function_score":{"query":{"bool":{"must":[{"bool":{"should":[{"multi_match":{"query":"плащ","fields":["name_ru^2.0","goods_keyword_ru^1.0","category_name_ru^1.0","sale_category_keyword_ru^1.0","sub_name_ru^1.0"],"minimum_should_match":"66%","tie_breaker":0.3,"_name":"base_query"}},{"knn":{"field":"embedding_name_ru","query_vector":[-0.035206761211156845,0.020660383626818657,-0.04898383840918541,0.01842888817191124,-0.059921182692050934,-0.013516250066459179,0.003757030935958028,0.014033733867108822,-0.027964092791080475,0.01029364112764597,-0.00787576287984848,0.02625346928834915,-0.01542199682444334,0.026301803067326546,0.052108507603406906,-0.050406284630298615,0.00483988830819726,0.04060962051153183,0.009472930803894997,-0.0005112694925628603,0.004651694558560848,0.0034800730645656586,0.018641093745827675,0.029394179582595825,0.020360751077532768,0.001675301929935813,-0.005129559896886349,-0.000619593309238553,-0.04827551171183586,-0.00630217557772994,0.020039061084389687,0.018405847251415253,0.034706272184848785,-0.08860606700181961,-0.04726770892739296,-0.032688140869140625,-0.018302561715245247,0.011481991037726402,-0.046269889920949936,0.014546985737979412,0.003089814679697156,0.010171250440180302,-0.011071375571191311,-0.01992928236722946,0.021175755187869072,0.01124000083655119,0.0372454971075058,-0.034685004502534866,-0.01343931071460247,0.02121714875102043,-0.023612814024090767,0.03798554837703705,0.06504618376493454,-0.009450038895010948,0.005906036589294672,0.030142072588205338,-0.03246299922466278,-0.030096540227532387,-0.0849074274301529,-0.020739320665597916,-0.03408554196357727,-0.0029811616986989975,-0.023305756971240044,0.027784742414951324,0.04067830741405487,0.109839528799057,-0.0005762992077507079,0.018221285194158554,-0.007281845901161432,-0.04129931703209877,-0.04749192297458649,-0.0005632156971842051,0.0058455271646380424,-0.004661038983613253,-0.05732613801956177,0.007009096443653107,0.05625062435865402,-0.025481490418314934,0.013432115316390991,0.005354376044124365,0.048216819763183594,-0.010861121118068695,0.0279295165091753,0.001105348696000874,-0.015214173123240471,-0.013066167943179607,-0.05451373755931854,0.004907973110675812,-0.041388705372810364,0.011942931450903416,0.025908607989549637,-0.004652232397347689,0.011163195595145226,-0.056879933923482895,-0.029385222122073174,0.005944914650171995,0.010291633196175098,0.009789329022169113,0.04474876821041107,0.009474224410951138,0.019974328577518463,-0.012655979953706264,-0.020499292761087418,-0.015026221051812172,0.031605109572410583,-0.026804130524396896,0.023546280339360237,0.055264607071876526,0.04840846732258797,-0.018858103081583977,0.004723322577774525,-0.01294822245836258,0.0012377514503896236,0.01827247440814972,0.025621123611927032,-0.023270349949598312,-0.014992653392255306,0.01914968341588974,0.015052393078804016,-0.013205647468566895,0.0169048048555851,0.004617445170879364,0.016027752310037613,-0.01378362812101841,-0.012757807970046997,-0.0346602164208889,-0.015302776359021664,-0.028003660961985588,0.0269561018794775,0.0047284201718866825,0.02838684618473053,0.03655833378434181,-0.01723506674170494,-0.005107777193188667,0.00955332349985838,-0.028216000646352768,0.022160036489367485,-0.011552958749234676,0.0027140516322106123,-0.06404043734073639,0.03287180885672569,0.06553228199481964,-0.03408421576023102,-0.01947455108165741,0.01903623901307583,-0.030194343999028206,0.08181223273277283,-0.001817873097024858,-0.013434878550469875,0.01553320698440075,0.008037151768803596,-0.0016663833521306515,0.061162009835243225,0.002955813892185688,0.031158601865172386,-0.015486419200897217,-0.023676788434386253,0.01596381515264511,0.013413977809250355,0.003498759353533387,-0.014206847175955772,0.06292533874511719,-0.005962688941508532,-0.010250173509120941,0.010891546495258808,-0.06812363117933273,0.019118888303637505,0.0033251780550926924,-0.011573049239814281,-0.019931120797991753,0.025516562163829803,-0.030447019264101982,0.02210240252315998,0.0037979227490723133,0.01469342689961195,0.010654212906956673,0.05843355879187584,0.010645532049238682,0.04514821618795395,-0.05915722995996475,-0.03231963887810707,-0.0037186243571341038,-0.015574388206005096,-0.053289689123630524,-0.062142301350831985,0.032532062381505966,-0.004566668067127466,-0.0005688689998351038,0.012460852973163128,-0.0002137328847311437,-0.03864103928208351,-0.053204890340566635,-0.003222765401005745,-0.0375785157084465,0.013661065138876438,0.036554817110300064,-0.005308432504534721,-0.021835779771208763,0.01610834337770939,-0.024434847757220268,0.019568176940083504,0.021495573222637177,0.027674177661538124,0.012535114772617817,-0.030019603669643402,-0.0057942504063248634,-0.028912071138620377,-0.017957061529159546,0.061209600418806076,-0.02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  450 +
  451 +
  452 +
  453 +#### 8888886761383165
  454 +
  455 +2025-07-02 07:46:00,929 - INFO - [REQ: 8888886761383165 UID: 15852] qt:kwd_factory total:5.89747166633606ms TIMING: QA=73ms, queryEncoding=28ms, userProfile=1ms, ES=190ms, BgeReranking=0ms, BizReranking=0ms, Reranking=0ms, total=5897ms, brand_statistics_query=5631ms
  456 +
  457 +2025-07-02 07:45:55,032 - INFO - [REQ: 8888886761383165 UID: 15852] REQUEST POST structure: {"keyword":"Double E","kt":"kwd_factory","page":1,"size":100,"request_id":8888886761383165,"extra":{"userId":15852,"businessPlatform":"essaone"}}
  458 +
  459 +2025-07-02 07:45:55,297 - INFO - [REQ: 8888886761383165 UID: 15852] ES_query_timing # FactoryKeywordSearch # 190.35 # {"query":{"bool":{"must":[{"bool":{"should":[{"multi_match":{"query":"Double E","fields":["brand_name_en^10.0","name_en^2.0","supplier_name^8.0"],"minimum_should_match":"66%","tie_breaker":0.3}},{"term":{"no":{"value":"Double E","boost":15.0}}},{"term":{"hs_no":{"value":"Double E","boost":15.0}}},{"term":{"factory_no":{"value":"Double E","boost":15.0}}},{"term":{"factory_no_buyer":{"value":"Double E","boost":15.0}}},{"term":{"supplier_code":{"value":"Double E","boost":15.0}}},{"match":{"factory_no_fuzzy":{"query":"Double E","boost":5.0}}},{"match":{"factory_no_buyer_fuzzy":{"query":"Double E","boost":5.0}}}],"minimum_should_match":1}}],"filter":[{"term":{"platform_sku_essaone":true}},{"term":{"platform_rule_essaone":true}},{"terms":{"status":[2,4,5]}}]}},"_source":["id","goods_id","brand_id","name_zh","category_name_zh","sub_name_zh","label_id_list","category_id","price_range","on_sell_days_boost","brand_name_zh","supplier_name","is_video"],"from":0,"size":100,"collapse":{"field":"brand_id"},"aggs":{"category_stats":{"terms":{"field":"sale_category_one_all","size":10}},"factory_certification_stats":{"terms":{"field":"supplier_certification_type_list","size":10}},"sale_market_stats":{"terms":{"field":"supplier_main_market_list","size":10}},"factory_category_stats":{"terms":{"field":"supplier_main_category_list","size":10}},"unique_count":{"cardinality":{"field":"brand_id"}}}}
  460 +
  461 +#### 8888886160313555
  462 +2025-07-02 07:45:52,314 - INFO - [REQ: 8888886160313555 UID: 16341] qt:kwd total:5.246635437011719ms TIMING: QA=54ms, queryEncoding=0ms, userProfile=1ms, ES=5082ms, BgeReranking=3ms, BizReranking=5ms, Reranking=11ms, total=5247ms, sku_id_list_filling=91ms
  463 +
  464 +2025-07-02 07:45:47,067 - INFO - [REQ: 8888886160313555 UID: 16341] REQUEST POST structure: {"keyword":"домик для игр","kt":"kwd","page":1,"size":100,"request_id":8888886160313555,"extra":{"userId":16341,"businessPlatform":"essa"}}
  465 +
  466 +2025-07-02 07:45:52,222 - INFO - [REQ: 8888886160313555 UID: 16341] ES_query_timing # ProductKeywordSearch # 5082.04 # {"size":1000,"from":0,"_source":{"includes":["name_zh","is_star","category_name_zh","sub_name_zh","label_id_list","is_video","brand_id","category_id","sale_category_all","price_range","on_sell_days_boost","ru_name"]},"query":{"function_score":{"query":{"bool":{"must":[{"bool":{"should":[{"multi_match":{"query":"домик для 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  467 +
  468 +#### 8888883840386376
  469 +2025-07-02 07:45:29,565 - INFO - [REQ: 8888883840386376 UID: 9987] qt:kwd_factory total:4.385730504989624ms TIMING: QA=80ms, queryEncoding=34ms, userProfile=1ms, ES=593ms, BgeReranking=0ms, BizReranking=0ms, Reranking=0ms, total=4386ms, brand_statistics_query=3710ms
  470 +
  471 +2025-07-02 07:45:25,179 - INFO - [REQ: 8888883840386376 UID: 9987] REQUEST POST structure: {"keyword":"澳高","kt":"kwd_factory","page":1,"size":100,"request_id":8888883840386376,"extra":{"userId":9987,"businessPlatform":"trader"}}
  472 +
  473 +2025-07-02 07:45:25,854 - INFO - [REQ: 8888883840386376 UID: 9987] ES_query_timing # FactoryKeywordSearch # 592.97 # {"query":{"bool":{"must":[{"bool":{"should":[{"multi_match":{"query":"澳高","fields":["brand_name_zh^10.0","name_zh^2.0","supplier_name^8.0"],"minimum_should_match":"66%","tie_breaker":0.3}},{"term":{"no":{"value":"澳高","boost":15.0}}},{"term":{"hs_no":{"value":"澳高","boost":15.0}}},{"term":{"factory_no":{"value":"澳高","boost":15.0}}},{"term":{"factory_no_buyer":{"value":"澳高","boost":15.0}}},{"term":{"supplier_code":{"value":"澳高","boost":15.0}}},{"match":{"factory_no_fuzzy":{"query":"澳高","boost":5.0}}},{"match":{"factory_no_buyer_fuzzy":{"query":"澳高","boost":5.0}}}],"minimum_should_match":1}}],"filter":[{"term":{"platform_sku_trader":true}},{"term":{"platform_rule_trader":true}},{"bool":{"should":[{"bool":{"must_not":{"exists":{"field":"trader_buyer_ids"}}}},{"term":{"trader_buyer_ids":9987}}]}},{"terms":{"status":[2,4,5]}}]}},"_source":["id","goods_id","brand_id","name_zh","category_name_zh","sub_name_zh","label_id_list","category_id","price_range","on_sell_days_boost","brand_name_zh","supplier_name","is_video"],"from":0,"size":100,"collapse":{"field":"brand_id"},"aggs":{"category_stats":{"terms":{"field":"sale_category_one_all","size":10}},"factory_certification_stats":{"terms":{"field":"supplier_certification_type_list","size":10}},"sale_market_stats":{"terms":{"field":"supplier_main_market_list","size":10}},"factory_category_stats":{"terms":{"field":"supplier_main_category_list","size":10}},"unique_count":{"cardinality":{"field":"brand_id"}}}}
  474 +
  475 +
  476 +### 0701 启动后 第一次查询
  477 +
  478 +2025-07-01 15:10:24,560 - INFO - [REQ: 99996874 UID: 11010] qt:kwd total:15.652546644210815ms TIMING: QA=248ms, queryEncoding=0ms, userProfile=2ms, ES=14733ms, BgeReranking=503ms, BizReranking=7ms, Reranking=514ms, total=15653ms, sku_id_list_filling=144ms
  479 +
  480 +2025-07-01 15:10:08,908 - INFO - [REQ: 99996874 UID: 11010] REQUEST POST structure: {"query":"积木车","kt":"kwd","top_k":5000,"recall_merge_output_size":5000,"rerank_size":1000,"rerank_enabled":true,"page":1,"size":100,"debug":"0","from_search_debug":1,"extra":{"userId":11010,"businessPlatform":"trader"}}
  481 +
  482 +2025-07-01 15:10:24,415 - INFO - [REQ: 99996874 UID: 11010] ES_query_timing # ProductKeywordSearch # 14732.69 # 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  486 +2025-06-29 21:58:26,099 - INFO - [REQ: 7621648571687939 UID: 12883] TIMING: QA=89ms, queryEncoding=38ms, userProfile=0ms, ES=12407ms, BgeReranking=43ms, BizReranking=1ms, Reranking=44ms, total=12594ms, sku_id_list_filling=50ms
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  489 +
  490 +
  491 +2025-06-28 15:07:36,526 - INFO - [REQ: 0 UID: -1] TIMING: QA=113ms, queryEncoding=0ms, userProfile=0ms, ES=20945ms, BgeReranking=1974ms, BizReranking=9ms, Reranking=1987ms, total=23079ms, sku_id_list_filling=23ms
  492 +```bash
  493 +{"size": 1000, "from": 0, "_source": {"includes": ["name_zh", "is_star", "category_name_zh", "sub_name_zh", "label_id_list", "is_video", "brand_id", "category_id", "sale_category_all", "price_range", "on_sell_days_boost", "brand_name_zh", "supplier_name"]}, "query": {"function_score": {"query": {"bool": {"must": [{"bool": {"should": [{"multi_match": {"query": "新款拉布布6寸搪胶PVC卡头袋6款混装", "fields": ["name_zh^2.0", "goods_keyword_zh^1.0", "category_name_zh^1.0", "sale_category_keyword_zh^1.0", "sub_name_zh^1.0"], "minimum_should_match": "66%", "tie_breaker": 0.3, "_name": "base_query"}}, {"knn": {"field": "embedding_name_zh", "query_vector": [0.04777948185801506, -0.009930620901286602, -0.03340917453169823, -0.010704109445214272, -0.0495409220457077, 0.014086474664509296, 0.0036832967307418585, -0.013905951753258705, -0.00912229623645544, -0.02091393806040287, 0.005698441993445158, 0.01829547993838787, -0.017833806574344635, 0.04031107947230339, 0.031932197511196136, 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{"filter": {"term": {"label_id_list": 158}}, "weight": 1.1}, {"filter": {"term": {"label_id_list": 159}}, "weight": 1.1}, {"filter": {"term": {"label_id_list": 162}}, "weight": 1.05}, {"filter": {"term": {"label_id_list": 163}}, "weight": 1.1}, {"filter": {"term": {"label_id_list": 164}}, "weight": 1.13}, {"filter": {"term": {"label_id_list": 165}}, "weight": 1.15}, {"field_value_factor": {"field": "on_sell_days_boost", "missing": 1.0}}, {"filter": {"term": {"is_video": true}}, "weight": 1.2}], "score_mode": "max", "boost_mode": "multiply"}}, "track_scores": true, "collapse": {"field": "goods_id"}, "aggs": {"unique_count": {"cardinality": {"field": "goods_id"}}}}
  494 +```
  495 +
  496 +### 1k线上搜索词测试
  497 +自己批量测试的1000条 只有一条超过1S、但是这一条耗时16S
  498 +但是这一千条请求，只填充 搜索词、搜索类型、platform几个参数，所以和线上的请求是有些差异的（没有携带各种业务过滤参数和聚合参数）
  499 +
  500 +2025-06-29 22:25:33,402 - INFO - [REQ: 7277635141262495 UID: 10031] TIMING: QA=81ms, queryEncoding=26ms, userProfile=0ms, ES=15532ms, BgeReranking=101ms, BizReranking=1ms, Reranking=103ms, total=15857ms, sku_id_list_filling=135ms
  501 +
  502 +{"keyword":"мяч баскетбольный","kt":"kwd","page":1,"size":100,"request_id":7277635141262495,"extra":{"userId":10031,"businessPlatform":"essaone"}}
  503 +
  504 +{"size":1000,"from":0,"_source":{"includes":["name_zh","is_star","category_name_zh","sub_name_zh","label_id_list","is_video","brand_id","category_id","sale_category_all","price_range","on_sell_days_boost","ru_name"]},"query":{"function_score":{"query":{"bool":{"must":[{"bool":{"should":[{"multi_match":{"query":"мяч 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  505 +
  506 +
  507 +
  508 +2025-06-28 12:00:09,683 - INFO - [REQ: 175108319225300 UID: -1] TIMING: QA=0ms, queryEncoding=0ms, userProfile=0ms, ES=13905ms, BgeReranking=0ms, BizReranking=5ms, Reranking=6ms, total=14649ms, sku_id_list_filling=732ms
  509 +
  510 +2025-06-28 12:00:34,322 - INFO - [REQ: 175108315581700 UID: -1] TIMING: QA=47ms, queryEncoding=0ms, userProfile=0ms, ES=14587ms, BgeReranking=787ms, BizReranking=1ms, Reranking=789ms, total=16736ms, sku_id_list_filling=1309ms
  511 +
  512 +2025-06-28 12:23:45,140 - INFO - [REQ: 175108461841000 UID: -1] TIMING: QA=0ms, queryEncoding=0ms, userProfile=0ms, ES=5829ms, BgeReranking=0ms, BizReranking=0ms, Reranking=0ms, total=5832ms
  513 +
  514 +2025-06-28 15:07:36,848 - INFO - [REQ: 0 UID: -1] TIMING: QA=139ms, queryEncoding=82ms, userProfile=0ms, ES=13312ms, BgeReranking=2098ms, BizReranking=10ms, Reranking=2111ms, total=15793ms, sku_id_list_filling=199ms
  515 +
  516 +2025-06-28 15:42:52,404 - INFO - [REQ: 17510963864686712 UID: 6712] TIMING: QA=0ms, queryEncoding=0ms, userProfile=1ms, ES=1990ms, BgeReranking=0ms, BizReranking=5ms, Reranking=6ms, total=2090ms, sku_id_list_filling=86ms
  517 +
  518 +
  519 +
  520 +-----------------------------------------
  521 +
  522 +## 参考资料
  523 +
  524 +### 1. 商品搜索、工厂搜索的聚合
  525 +
  526 +#### 1.1 聚合字段优化
  527 +- 商品搜索的 goodsId聚合、和工厂搜索的 brandId聚合，现在的 collapse 是否是最优方案，是否还能同时得到 inner_hits
  528 +
  529 +### 2. aggs 方面
  530 +
  531 +现在的方案是：
  532 +
  533 +```json
  534 +"aggs": {
  535 + "category_stats": {
  536 + "terms": {
  537 + "field": "sale_category_all",
  538 + "size": 100
  539 + }
  540 + }
  541 +}
  542 +```
  543 +
  544 +#### 2.1 top_hits聚合
  545 +预期是通过减少聚合的候选数量（只对topN进行聚合）来提升性能，但是测试好像更差。测试样本量也不够，写法也有好多种，因此看有没有专业建议。
  546 +
  547 +这种方式试了几个好像差不多：
  548 +
  549 +```json
  550 +"aggs": {
  551 + "top_hits_agg": {
  552 + "sampler": {
  553 + "shard_size": 5000 // 关键优化1：仅对前5000文档聚合
  554 + },
  555 + "aggs": {
  556 + // 所有的聚合字段
  557 + }
  558 + }
  559 +}
  560 +```
  561 +
  562 +或者对每个聚合字段加 top_hits 配置，不知道为什么很慢：
  563 +
  564 +```json
  565 +"aggs": {
  566 + "category_stats": {
  567 + "top_hits": {
  568 + "size": 2
  569 + }
  570 + }
  571 +}
  572 +```
  573 +
  574 +#### 2.2 execution_hint优化
  575 +加 "execution_hint": "map" 属性（全内存操作，适用聚合结果数量很小、内存够用的情况）
  576 +
  577 +```json
  578 +"aggs": {
  579 + "tags": {
  580 + "terms": {
  581 + "field": "status",
  582 + "execution_hint": "map"
  583 + }
  584 + }
  585 +}
  586 +```
  587 +
  588 +### 3. 检索表达式
  589 +
  590 +通过function_score设定提权。
  591 +
  592 +```json
  593 +GET /spu/_search
  594 +{
  595 + "aggs": {
  596 + "top_hits_agg": {
  597 + "sampler": {
  598 + "shard_size": 5000 // 关键优化1：仅对前5000文档聚合
  599 + }
  600 + }
  601 + },
  602 + "collapse": {
  603 + "field": "goods_id"
  604 + },
  605 + "from": 0,
  606 + "size": 1000,
  607 + "track_scores": true,
  608 + "query": {
  609 + "function_score": {
  610 + "query": {
  611 + "bool": {
  612 + "filter": [
  613 + {
  614 + "terms": {
  615 + "status": ["2", "4", "5"]
  616 + }
  617 + }
  618 + ],
  619 + "must": [
  620 + {
  621 + "bool": {
  622 + "minimum_should_match": 1,
  623 + "should": [
  624 + {
  625 + "multi_match": {
  626 + "fields": ["name_zh^2.0", "goods_keyword_zh", "category_name_zh", "sale_category_keyword_zh", "sub_name_zh"],
  627 + "query": "合金滑行 喷漆",
  628 + "minimum_should_match": 2,
  629 + "tie_breaker": 0.301
  630 + }
  631 + },
  632 + {
  633 + "knn": {
  634 + "field": "embedding_name_zh",
  635 + "k": 1000,
  636 + "num_candidates": 1000,
  637 + "query_vector": [0.1,1.1,0.6]
  638 + }
  639 + }
  640 + ]
  641 + }
  642 + }
  643 + ]
  644 + }
  645 + },
  646 + "functions": [
  647 + {
  648 + "filter": { "term": { "label_id_list": 156 } },
  649 + "weight": 1.1
  650 + },
  651 + {
  652 + "field_value_factor": {
  653 + "field": "on_sell_days_boost",
  654 + "missing": 1
  655 + }
  656 + }
  657 + ],
  658 + "score_mode": "max",
  659 + "boost_mode": "multiply"
  660 + }
  661 + },
  662 + "rescore": { // 关键优化2：仅对前N结果应用函数评分
  663 + "window_size": 2000,
  664 + "query": {
  665 + "rescore_query": {
  666 + "function_score": {
  667 + "query": {"match_all": {}},
  668 + "functions": [
  669 + {
  670 + "filter": {"term": {"label_id_list": 156}},
  671 + "weight": 1.1
  672 + },
  673 + {
  674 + "field_value_factor": {
  675 + "field": "on_sell_days_boost",
  676 + "missing": 1
  677 + }
  678 + }
  679 + ],
  680 + "score_mode": "max",
  681 + "boost_mode": "multiply"
  682 + }
  683 + }
  684 + }
  685 + }
  686 +}
  687 +```
  688 +
  689 +#### 可以考虑的优化：
  690 +1. 提权只对主检索表达式topN window_size
  691 +
  692 +### 4. ES索引优化
  693 +
  694 +所有的term字段，如果是int类型，改为int，现在是字符串。
  695 +
  696 +不过好像请求用int也能匹配到，所以ES本身检测如果是数值，那么也会为int类型建立一个倒排？这样的话，如果指定为int，只是节省了一些内存（不再需要为字符串类型建立倒排）
  697 +
  698 +### 5. ES配置优化建议
  699 +
  700 +针对单机、低QPS、5百万级别doc，性能优化建议。ES配置方面。
  701 +
... ...
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  1 +
  2 +wget https://artifacts.elastic.co/downloads/elasticsearch/elasticsearch-8.18.0-x86_64.rpm
  3 +# wget https://artifacts.elastic.co/downloads/elasticsearch/elasticsearch-9.0.0-x86_64.rpm
  4 +
  5 +sudo rpm -ivh elasticsearch-8.18.0-x86_64.rpm
  6 +
  7 +sudo systemctl daemon-reload
  8 +sudo systemctl enable elasticsearch.service
  9 +sudo systemctl start elasticsearch.service
  10 +
  11 +
  12 +
  13 +好的，下面是你安装 Elasticsearch 8.18.0 时的详细信息和解释：
  14 +警告：NOKEY
  15 +
  16 +这个警告表示你安装的 RPM 包是用一个密钥（d88e42b4）签名的，但是你的系统没有这个密钥。这通常是无害的，前提是你信任这个包的来源。如果想避免这个警告，你可以导入 Elasticsearch 的 GPG 密钥：
  17 +bash
  18 +sudo rpm --import https://artifacts.elastic.co/GPG-KEY-elasticsearch
  19 +安全自动配置
  20 +
  21 +Elasticsearch 已经自动启用了传输层和 HTTP 层的 TLS 加密，并且为内置的超级用户 elastic 生成了一个密码（6T_Oq+1Gd=E_MZt=zn6X）。你需要保管好这个密码，初次登录时会用到。
  22 +节点配置
  23 +
  24 +如果这个节点需要加入现有的 Elasticsearch 集群，你可以使用集群中生成的 enrollment token 来配置。运行提供的命令来加入集群：
  25 +bash
  26 +/usr/share/elasticsearch/bin/elasticsearch-reconfigure-node --enrollment-token <token-here>
  27 +服务配置
  28 +
  29 +Elasticsearch 没有在安装后自动启动，你需要运行以下命令来使服务自启动并启动它：
  30 +bash
  31 +sudo systemctl daemon-reload
  32 +sudo systemctl enable elasticsearch.service
  33 +sudo systemctl start elasticsearch.service
  34 +tmpfiles.d 的警告
  35 +
  36 +这个警告是由于某个配置文件中引用了 /var/run/elasticsearch，但是这个目录已经不再推荐使用，应该更新为 /run/elasticsearch。这只是一个配置上的小问题，不会影响 Elasticsearch 的正常工作。
  37 +
  38 +下一步：
  39 +
  40 +如果你希望消除 NOKEY 警告，可以导入 GPG 密钥（可选）。
  41 +使用以下命令启动 Elasticsearch 服务：
  42 +bash
  43 +sudo systemctl daemon-reload
  44 +sudo systemctl enable elasticsearch.service
  45 +sudo systemctl start elasticsearch.service
  46 +使用生成的 elastic 用户密码进行初次登录。
  47 +如果你有特殊需求，可以根据提示更新 tmpfiles 配置。
... ...
docs/ES/报警-企业微信消息推送配置说明.md 0 → 100644
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  1 +原始文档地址：
  2 +https://developer.work.weixin.qq.com/document/path/99110
  3 +
  4 +## 目录
  5 +- [如何使用消息推送](#如何使用消息推送)
  6 +- [消息类型及数据格式](#消息类型及数据格式)
  7 + - [文本类型](#文本类型)
  8 + - [markdown类型](#markdown类型)
  9 + - [markdown_v2类型](#markdown_v2类型)
  10 + - [图片类型](#图片类型)
  11 + - [图文类型](#图文类型)
  12 + - [文件类型](#文件类型)
  13 + - [语音类型](#语音类型)
  14 + - [模版卡片类型](#模版卡片类型)
  15 + - [文本通知模版卡片](#文本通知模版卡片)
  16 + - [图文展示模版卡片](#图文展示模版卡片)
  17 +- [消息发送频率限制](#消息发送频率限制)
  18 +- [文件上传接口](#文件上传接口)
  19 +
  20 +## 如何使用消息推送
  21 +
  22 +- 创建者可以在创建消息推送页面、创建完成页面、消息推送详情页面，看到该消息推送特有的 webhookurl。开发者可以按以下说明向这个地址发起 HTTP POST 请求，即可实现给该群组发送消息。下面举个简单的例子。
  23 +
  24 +假设 webhook 是：`https://qyapi.weixin.qq.com/cgi-bin/webhook/send?key=693a91f6-7xxx-4bc4-97a0-0ec2sifa5aaa`
  25 +
  26 +> 特别特别要注意：一定要**保护好消息推送的 webhook 地址**，避免泄漏！不要分享到 GitHub、博客等可被公开查阅的地方，否则坏人就可以用你的消息推送来发垃圾消息了。
  27 +
  28 +以下是用 curl 工具往群组推送文本消息的示例（注意要将 url 替换成你的消息推送 webhook 地址，content 必须是 utf8 编码）：
  29 +
  30 +```bash
  31 +curl 'https://qyapi.weixin.qq.com/cgi-bin/webhook/send?key=693axxx6-7aoc-4bc4-97a0-0ec2sifa5aaa' \
  32 + -H 'Content-Type: application/json' \
  33 + -d '
  34 + {
  35 + "msgtype": "text",
  36 + "text": {
  37 + "content": "hello world"
  38 + }
  39 + }'
  40 +```
  41 +
  42 +- 当前自定义消息推送支持文本（text）、markdown（markdown、markdown_v2）、图片（image）、图文（news）、文件（file）、语音（voice）、模板卡片（template_card）八种消息类型。
  43 +- 消息推送的 text/markdown 类型消息支持在 content 中使用 `@userid` 扩展语法来 @群成员（markdown_v2 类型消息不支持该扩展语法）
  44 +
  45 +## 消息类型及数据格式
  46 +
  47 +### 文本类型
  48 +
  49 +```json
  50 +{
  51 + "msgtype": "text",
  52 + "text": {
  53 + "content": "广州今日天气：29度，大部分多云，降雨概率：60%",
  54 + "mentioned_list":["wangqing","@all"],
  55 + "mentioned_mobile_list":["13800001111","@all"]
  56 + }
  57 +}
  58 +```
  59 +
  60 +| 参数 | 是否必填 | 说明 |
  61 +| --- | --- | --- |
  62 +| msgtype | 是 | 消息类型，此时固定为 text |
  63 +| content | 是 | 文本内容，最长不超过 2048 个字节，必须是 utf8 编码 |
  64 +| mentioned_list | 否 | userid 的列表，提醒群中的指定成员(@某个成员)，@all 表示提醒所有人，如果开发者获取不到 userid，可以使用 mentioned_mobile_list |
  65 +| mentioned_mobile_list | 否 | 手机号列表，提醒手机号对应的群成员(@某个成员)，@all 表示提醒所有人 |
  66 +
  67 +### markdown 类型
  68 +
  69 +```json
  70 +{
  71 + "msgtype": "markdown",
  72 + "markdown": {
  73 + "content": "实时新增用户反馈<font color=\"warning\">132例</font>，请相关同事注意。\n>类型:<font color=\"comment\">用户反馈</font>\n>普通用户反馈:<font color=\"comment\">117例</font>\n>VIP用户反馈:<font color=\"comment\">15例</font>"
  74 + }
  75 +}
  76 +```
  77 +
  78 +| 参数 | 是否必填 | 说明 |
  79 +| --- | --- | --- |
  80 +| msgtype | 是 | 消息类型，此时固定为 markdown |
  81 +| content | 是 | markdown 内容，最长不超过 4096 个字节，必须是 utf8 编码 |
  82 +
  83 +目前支持的 markdown 语法是如下的子集：
  84 +
  85 +1. 标题（支持 1 至 6 级标题，注意 # 与文字中间要有空格）
  86 +
  87 +```markdown
  88 +# 标题一
  89 +## 标题二
  90 +### 标题三
  91 +#### 标题四
  92 +##### 标题五
  93 +###### 标题六
  94 +```
  95 +
  96 +2. 加粗
  97 +3. 链接
  98 +
  99 +```markdown
  100 +[这是一个链接](https://work.weixin.qq.com/api/doc)
  101 +```
  102 +
  103 +4. 行内代码段（暂不支持跨行）
  104 +5. 引用
  105 +6. 字体颜色（只支持 3 种内置颜色）
  106 +
  107 +```markdown
  108 +<font color="info">绿色</font>
  109 +<font color="comment">灰色</font>
  110 +<font color="warning">橙红色</font>
  111 +```
  112 +
  113 +### markdown_v2 类型
  114 +
  115 +```json
  116 +{
  117 + "msgtype": "markdown_v2",
  118 + "markdown_v2": {
  119 + "content": "# 一、标题\n## 二级标题\n### 三级标题\n# 二、字体\n*斜体*\n\n**加粗**\n# 三、列表 \n- 无序列表 1 \n- 无序列表 2\n - 无序列表 2.1\n - 无序列表 2.2\n1. 有序列表 1\n2. 有序列表 2\n# 四、引用\n> 一级引用\n>>二级引用\n>>>三级引用\n# 五、链接\n[这是一个链接](https:work.weixin.qq.com\\/api\\/doc)\n![](https://res.mail.qq.com/node/ww/wwopenmng/images/independent/doc/test_pic_msg1.png)\n# 六、分割线\n\n---\n# 七、代码\n`这是行内代码`\n```\n这是独立代码块\n```\n\n# 八、表格\n| 姓名 | 文化衫尺寸 | 收货地址 |\n| :----- | :----: | -------: |\n| 张三 | S | 广州 |\n| 李四 | L | 深圳 |\n"
  120 + }
  121 +}
  122 +```
  123 +
  124 +| 参数 | 是否必填 | 说明 |
  125 +| --- | --- | --- |
  126 +| msgtype | 是 | 消息类型，此时固定为 markdown_v2。 |
  127 +| content | 是 | markdown_v2 内容，最长不超过 4096 个字节，必须是 utf8 编码。<br>特殊的，<br>1. markdown_v2 **不支持字体颜色、@群成员** 的语法，具体支持的语法可参考下面说明<br>2. 消息内容在 **客户端 4.1.36 版本以下（安卓端为 4.1.38 以下）** 消息表现为 **纯文本**，建议使用最新客户端版本体验 |
  128 +
  129 +目前支持的 markdown_v2 语法是如下的子集：
  130 +
  131 +1. 标题（支持 1 至 6 级标题，注意 # 与文字中间要有空格）
  132 +
  133 +```markdown
  134 +# 标题一
  135 +## 标题二
  136 +### 标题三
  137 +#### 标题四
  138 +##### 标题五
  139 +###### 标题六
  140 +```
  141 +
  142 +2. 字体
  143 +3. 列表
  144 +
  145 +```markdown
  146 +- 无序列表 1
  147 +- 无序列表 2
  148 + - 无序列表 2.1
  149 + - 无序列表 2.2
  150 +1. 有序列表 1
  151 +2. 有序列表 2
  152 +```
  153 +
  154 +4. 引用
  155 +5. 链接
  156 +
  157 +```markdown
  158 +[这是一个链接](https://work.weixin.qq.com/api/doc)
  159 +![这是一个图片](https://res.mail.qq.com/node/ww/wwopenmng/images/independent/doc/test_pic_msg1.png)
  160 +```
  161 +
  162 +6. 分割线
  163 +7. 代码
  164 +
  165 +```markdown
  166 +`这是行内代码`
  167 +
  168 +```
  169 +这是独立代码块
  170 +```
  171 +```
  172 +
  173 +8. 表格
  174 +
  175 +```markdown
  176 +| 姓名 | 文化衫尺寸 | 收货地址 |
  177 +| :----- | :----: | -------: |
  178 +| 张三 | S | 广州 |
  179 +| 李四 | L | 深圳 |
  180 +```
  181 +
  182 +### 图片类型
  183 +
  184 +```json
  185 +{
  186 + "msgtype": "image",
  187 + "image": {
  188 + "base64": "DATA",
  189 + "md5": "MD5"
  190 + }
  191 +}
  192 +```
  193 +
  194 +| 参数 | 是否必填 | 说明 |
  195 +| --- | --- | --- |
  196 +| msgtype | 是 | 消息类型，此时固定为 image |
  197 +| base64 | 是 | 图片内容的 base64 编码 |
  198 +| md5 | 是 | 图片内容（base64 编码前）的 md5 值 |
  199 +
  200 +> 注：图片（base64 编码前）最大不能超过 2M，支持 JPG、PNG 格式
  201 +
  202 +### 图文类型
  203 +
  204 +```json
  205 +{
  206 + "msgtype": "news",
  207 + "news": {
  208 + "articles" : [
  209 + {
  210 + "title" : "中秋节礼品领取",
  211 + "description" : "今年中秋节公司有豪礼相送",
  212 + "url" : "www.qq.com",
  213 + "picurl" : "https://res.mail.qq.com/node/ww/wwopenmng/images/independent/doc/test_pic_msg1.png"
  214 + }
  215 + ]
  216 + }
  217 +}
  218 +```
  219 +
  220 +| 参数 | 是否必填 | 说明 |
  221 +| --- | --- | --- |
  222 +| msgtype | 是 | 消息类型，此时固定为 news |
  223 +| articles | 是 | 图文消息，一个图文消息支持 1 到 8 条图文 |
  224 +| title | 是 | 标题，不超过 128 个字节，超过会自动截断 |
  225 +| description | 否 | 描述，不超过 512 个字节，超过会自动截断 |
  226 +| url | 是 | 点击后跳转的链接 |
  227 +| picurl | 否 | 图文消息的图片链接，支持 JPG、PNG 格式，较好的效果为大图 1068*455，小图 150*150 |
  228 +
  229 +### 文件类型
  230 +
  231 +```json
  232 +{
  233 + "msgtype": "file",
  234 + "file": {
  235 + "media_id": "3a8asd892asd8asd"
  236 + }
  237 +}
  238 +```
  239 +
  240 +| 参数 | 是否必填 | 说明 |
  241 +| --- | --- | --- |
  242 +| msgtype | 是 | 消息类型，此时固定为 file |
  243 +| media_id | 是 | 文件 id，通过下文的文件上传接口获取 |
  244 +
  245 +### 语音类型
  246 +
  247 +```json
  248 +{
  249 + "msgtype": "voice",
  250 + "voice": {
  251 + "media_id": "MEDIA_ID"
  252 + }
  253 +}
  254 +```
  255 +
  256 +| 参数 | 是否必填 | 说明 |
  257 +| --- | --- | --- |
  258 +| msgtype | 是 | 语音类型，此时固定为 voice |
  259 +| media_id | 是 | 语音文件 id，通过下文的文件上传接口获取 |
  260 +
  261 +### 模版卡片类型
  262 +
  263 +#### 文本通知模版卡片
  264 +
  265 +```json
  266 +{
  267 + "msgtype":"template_card",
  268 + "template_card":{
  269 + "card_type":"text_notice",
  270 + "source":{
  271 + "icon_url":"https://wework.qpic.cn/wwpic/252813_jOfDHtcISzuodLa_1629280209/0",
  272 + "desc":"企业微信",
  273 + "desc_color":0
  274 + },
  275 + "main_title":{
  276 + "title":"欢迎使用企业微信",
  277 + "desc":"您的好友正在邀请您加入企业微信"
  278 + },
  279 + "emphasis_content":{
  280 + "title":"100",
  281 + "desc":"数据含义"
  282 + },
  283 + "quote_area":{
  284 + "type":1,
  285 + "url":"https://work.weixin.qq.com/?from=openApi",
  286 + "appid":"APPID",
  287 + "pagepath":"PAGEPATH",
  288 + "title":"引用文本标题",
  289 + "quote_text":"Jack：企业微信真的很好用~\nBalian：超级好的一款软件！"
  290 + },
  291 + "sub_title_text":"下载企业微信还能抢红包！",
  292 + "horizontal_content_list":[
  293 + {
  294 + "keyname":"邀请人",
  295 + "value":"张三"
  296 + },
  297 + {
  298 + "keyname":"企微官网",
  299 + "value":"点击访问",
  300 + "type":1,
  301 + "url":"https://work.weixin.qq.com/?from=openApi"
  302 + },
  303 + {
  304 + "keyname":"企微下载",
  305 + "value":"企业微信.apk",
  306 + "type":2,
  307 + "media_id":"MEDIAID"
  308 + }
  309 + ],
  310 + "jump_list":[
  311 + {
  312 + "type":1,
  313 + "url":"https://work.weixin.qq.com/?from=openApi",
  314 + "title":"企业微信官网"
  315 + },
  316 + {
  317 + "type":2,
  318 + "appid":"APPID",
  319 + "pagepath":"PAGEPATH",
  320 + "title":"跳转小程序"
  321 + }
  322 + ],
  323 + "card_action":{
  324 + "type":1,
  325 + "url":"https://work.weixin.qq.com/?from=openApi",
  326 + "appid":"APPID",
  327 + "pagepath":"PAGEPATH"
  328 + }
  329 + }
  330 +}
  331 +```
  332 +
  333 +请求参数
  334 +
  335 +| 参数 | 类型 | 必须 | 说明 |
  336 +| --- | --- | --- | --- |
  337 +| msgtype | String | 是 | 消息类型，此时的消息类型固定为 `template_card` |
  338 +| template_card | Object | 是 | 具体的模版卡片参数 |
  339 +
  340 +template_card 的参数说明
  341 +
  342 +| 参数 | 类型 | 必须 | 说明 |
  343 +| --- | --- | --- | --- |
  344 +| card_type | String | 是 | 模版卡片的模版类型，文本通知模版卡片的类型为 `text_notice` |
  345 +| source | Object | 否 | 卡片来源样式信息，不需要来源样式可不填写 |
  346 +| source.icon_url | String | 否 | 来源图片的 url |
  347 +| source.desc | String | 否 | 来源图片的描述，建议不超过 13 个字 |
  348 +| source.desc_color | Int | 否 | 来源文字的颜色，目前支持：0(默认) 灰色，1 黑色，2 红色，3 绿色 |
  349 +| main_title | Object | 是 | 模版卡片的主要内容，包括一级标题和标题辅助信息 |
  350 +| main_title.title | String | 否 | 一级标题，建议不超过 26 个字。**模版卡片主要内容的一级标题 main_title.title 和二级普通文本 sub_title_text 必须有一项填写** |
  351 +| main_title.desc | String | 否 | 标题辅助信息，建议不超过 30 个字 |
  352 +| emphasis_content | Object | 否 | 关键数据样式 |
  353 +| emphasis_content.title | String | 否 | 关键数据样式的数据内容，建议不超过 10 个字 |
  354 +| emphasis_content.desc | String | 否 | 关键数据样式的数据描述内容，建议不超过 15 个字 |
  355 +| quote_area | Object | 否 | 引用文献样式，建议不与关键数据共用 |
  356 +| quote_area.type | Int | 否 | 引用文献样式区域点击事件，0 或不填代表没有点击事件，1 代表跳转 url，2 代表跳转小程序 |
  357 +| quote_area.url | String | 否 | 点击跳转的 url，quote_area.type 是 1 时必填 |
  358 +| quote_area.appid | String | 否 | 点击跳转的小程序的 appid，quote_area.type 是 2 时必填 |
  359 +| quote_area.pagepath | String | 否 | 点击跳转的小程序的 pagepath，quote_area.type 是 2 时选填 |
  360 +| quote_area.title | String | 否 | 引用文献样式的标题 |
  361 +| quote_area.quote_text | String | 否 | 引用文献样式的引用文案 |
  362 +| sub_title_text | String | 否 | 二级普通文本，建议不超过 112 个字。**模版卡片主要内容的一级标题 main_title.title 和二级普通文本 sub_title_text 必须有一项填写** |
  363 +| horizontal_content_list | Object[] | 否 | 二级标题+文本列表，该字段可为空数组，但有数据的话需确认对应字段是否必填，列表长度不超过 6 |
  364 +| horizontal_content_list.type | Int | 否 | 模版卡片的二级标题信息内容支持的类型，1 是 url，2 是文件附件，3 代表点击跳转成员详情 |
  365 +| horizontal_content_list.keyname | String | 是 | 二级标题，建议不超过 5 个字 |
  366 +| horizontal_content_list.value | String | 否 | 二级文本，如果 horizontal_content_list.type 是 2，该字段代表文件名称（要包含文件类型），建议不超过 26 个字 |
  367 +| horizontal_content_list.url | String | 否 | 链接跳转的 url，horizontal_content_list.type 是 1 时必填 |
  368 +| horizontal_content_list.media_id | String | 否 | 附件的 [media_id](#文件上传接口)，horizontal_content_list.type 是 2 时必填 |
  369 +| horizontal_content_list.userid | String | 否 | 成员详情的 userid，horizontal_content_list.type 是 3 时必填 |
  370 +| jump_list | Object[] | 否 | 跳转指引样式的列表，该字段可为空数组，但有数据的话需确认对应字段是否必填，列表长度不超过 3 |
  371 +| jump_list.type | Int | 否 | 跳转链接类型，0 或不填代表不是链接，1 代表跳转 url，2 代表跳转小程序 |
  372 +| jump_list.title | String | 是 | 跳转链接样式的文案内容，建议不超过 13 个字 |
  373 +| jump_list.url | String | 否 | 跳转链接的 url，jump_list.type 是 1 时必填 |
  374 +| jump_list.appid | String | 否 | 跳转链接的小程序的 appid，jump_list.type 是 2 时必填 |
  375 +| jump_list.pagepath | String | 否 | 跳转链接的小程序的 pagepath，jump_list.type 是 2 时选填 |
  376 +| card_action | Object | 是 | 整体卡片的点击跳转事件，text_notice 模版卡片中该字段为必填项 |
  377 +| card_action.type | Int | 是 | 卡片跳转类型，1 代表跳转 url，2 代表打开小程序。text_notice 模版卡片中该字段取值范围为 [1,2] |
  378 +| card_action.url | String | 否 | 跳转事件的 url，card_action.type 是 1 时必填 |
  379 +| card_action.appid | String | 否 | 跳转事件的小程序的 appid，card_action.type 是 2 时必填 |
  380 +| card_action.pagepath | String | 否 | 跳转事件的小程序的 pagepath，card_action.type 是 2 时选填 |
  381 +
  382 +#### 图文展示模版卡片
  383 +
  384 +```json
  385 +{
  386 + "msgtype":"template_card",
  387 + "template_card":{
  388 + "card_type":"news_notice",
  389 + "source":{
  390 + "icon_url":"https://wework.qpic.cn/wwpic/252813_jOfDHtcISzuodLa_1629280209/0",
  391 + "desc":"企业微信",
  392 + "desc_color":0
  393 + },
  394 + "main_title":{
  395 + "title":"欢迎使用企业微信",
  396 + "desc":"您的好友正在邀请您加入企业微信"
  397 + },
  398 + "card_image":{
  399 + "url":"https://wework.qpic.cn/wwpic/354393_4zpkKXd7SrGMvfg_1629280616/0",
  400 + "aspect_ratio":2.25
  401 + },
  402 + "image_text_area":{
  403 + "type":1,
  404 + "url":"https://work.weixin.qq.com",
  405 + "title":"欢迎使用企业微信",
  406 + "desc":"您的好友正在邀请您加入企业微信",
  407 + "image_url":"https://wework.qpic.cn/wwpic/354393_4zpkKXd7SrGMvfg_1629280616/0"
  408 + },
  409 + "quote_area":{
  410 + "type":1,
  411 + "url":"https://work.weixin.qq.com/?from=openApi",
  412 + "appid":"APPID",
  413 + "pagepath":"PAGEPATH",
  414 + "title":"引用文本标题",
  415 + "quote_text":"Jack：企业微信真的很好用~\nBalian：超级好的一款软件！"
  416 + },
  417 + "vertical_content_list":[
  418 + {
  419 + "title":"惊喜红包等你来拿",
  420 + "desc":"下载企业微信还能抢红包！"
  421 + }
  422 + ],
  423 + "horizontal_content_list":[
  424 + {
  425 + "keyname":"邀请人",
  426 + "value":"张三"
  427 + },
  428 + {
  429 + "keyname":"企微官网",
  430 + "value":"点击访问",
  431 + "type":1,
  432 + "url":"https://work.weixin.qq.com/?from=openApi"
  433 + },
  434 + {
  435 + "keyname":"企微下载",
  436 + "value":"企业微信.apk",
  437 + "type":2,
  438 + "media_id":"MEDIAID"
  439 + }
  440 + ],
  441 + "jump_list":[
  442 + {
  443 + "type":1,
  444 + "url":"https://work.weixin.qq.com/?from=openApi",
  445 + "title":"企业微信官网"
  446 + },
  447 + {
  448 + "type":2,
  449 + "appid":"APPID",
  450 + "pagepath":"PAGEPATH",
  451 + "title":"跳转小程序"
  452 + }
  453 + ],
  454 + "card_action":{
  455 + "type":1,
  456 + "url":"https://work.weixin.qq.com/?from=openApi",
  457 + "appid":"APPID",
  458 + "pagepath":"PAGEPATH"
  459 + }
  460 + }
  461 +}
  462 +```
  463 +
  464 +请求参数
  465 +
  466 +| 参数 | 类型 | 必须 | 说明 |
  467 +| --- | --- | --- | --- |
  468 +| msgtype | String | 是 | 模版卡片的消息类型为 `template_card` |
  469 +| template_card | Object | 是 | 具体的模版卡片参数 |
  470 +
  471 +template_card 的参数说明
  472 +
  473 +| 参数 | 类型 | 必须 | 说明 |
  474 +| --- | --- | --- | --- |
  475 +| card_type | String | 是 | 模版卡片的模版类型，图文展示模版卡片的类型为 `news_notice` |
  476 +| source | Object | 否 | 卡片来源样式信息，不需要来源样式可不填写 |
  477 +| source.icon_url | String | 否 | 来源图片的 url |
  478 +| source.desc | String | 否 | 来源图片的描述，建议不超过 13 个字 |
  479 +| source.desc_color | Int | 否 | 来源文字的颜色，目前支持：0(默认) 灰色，1 黑色，2 红色，3 绿色 |
  480 +| main_title | Object | 是 | 模版卡片的主要内容，包括一级标题和标题辅助信息 |
  481 +| main_title.title | String | 是 | 一级标题，建议不超过 26 个字 |
  482 +| main_title.desc | String | 否 | 标题辅助信息，建议不超过 30 个字 |
  483 +| card_image | Object | 是 | 图片样式 |
  484 +| card_image.url | String | 是 | 图片的 url |
  485 +| card_image.aspect_ratio | Float | 否 | 图片的宽高比，宽高比要小于 2.25，大于 1.3，不填该参数默认 1.3 |
  486 +| image_text_area | Object | 否 | 左图右文样式 |
  487 +| image_text_area.type | Int | 否 | 左图右文样式区域点击事件，0 或不填代表没有点击事件，1 代表跳转 url，2 代表跳转小程序 |
  488 +| image_text_area.url | String | 否 | 点击跳转的 url，image_text_area.type 是 1 时必填 |
  489 +| image_text_area.appid | String | 否 | 点击跳转的小程序的 appid，必须是与当前应用关联的小程序，image_text_area.type 是 2 时必填 |
  490 +| image_text_area.pagepath | String | 否 | 点击跳转的小程序的 pagepath，image_text_area.type 是 2 时选填 |
  491 +| image_text_area.title | String | 否 | 左图右文样式的标题 |
  492 +| image_text_area.desc | String | 否 | 左图右文样式的描述 |
  493 +| image_text_area.image_url | String | 是 | 左图右文样式的图片 url |
  494 +| quote_area | Object | 否 | 引用文献样式，建议不与关键数据共用 |
  495 +| quote_area.type | Int | 否 | 引用文献样式区域点击事件，0 或不填代表没有点击事件，1 代表跳转 url，2 代表跳转小程序 |
  496 +| quote_area.url | String | 否 | 点击跳转的 url，quote_area.type 是 1 时必填 |
  497 +| quote_area.appid | String | 否 | 点击跳转的小程序的 appid，quote_area.type 是 2 时必填 |
  498 +| quote_area.pagepath | String | 否 | 点击跳转的小程序的 pagepath，quote_area.type 是 2 时选填 |
  499 +| quote_area.title | String | 否 | 引用文献样式的标题 |
  500 +| quote_area.quote_text | String | 否 | 引用文献样式的引用文案 |
  501 +| vertical_content_list | Object[] | 否 | 卡片二级垂直内容，该字段可为空数组，但有数据的话需确认对应字段是否必填，列表长度不超过 4 |
  502 +| vertical_content_list.title | String | 是 | 卡片二级标题，建议不超过 26 个字 |
  503 +| vertical_content_list.desc | String | 否 | 二级普通文本，建议不超过 112 个字 |
  504 +| horizontal_content_list | Object[] | 否 | 二级标题+文本列表，该字段可为空数组，但有数据的话需确认对应字段是否必填，列表长度不超过 6 |
  505 +| horizontal_content_list.type | Int | 否 | 模版卡片的二级标题信息内容支持的类型，1 是 url，2 是文件附件，3 代表点击跳转成员详情 |
  506 +| horizontal_content_list.keyname | String | 是 | 二级标题，建议不超过 5 个字 |
  507 +| horizontal_content_list.value | String | 否 | 二级文本，如果 horizontal_content_list.type 是 2，该字段代表文件名称（要包含文件类型），建议不超过 26 个字 |
  508 +| horizontal_content_list.url | String | 否 | 链接跳转的 url，horizontal_content_list.type 是 1 时必填 |
  509 +| horizontal_content_list.media_id | String | 否 | 附件的 [media_id](#文件上传接口)，horizontal_content_list.type 是 2 时必填 |
  510 +| horizontal_content_list.userid | String | 否 | 成员详情的 userid，horizontal_content_list.type 是 3 时必填 |
  511 +| jump_list | Object[] | 否 | 跳转指引样式的列表，该字段可为空数组，但有数据的话需确认对应字段是否必填，列表长度不超过 3 |
  512 +| jump_list.type | Int | 否 | 跳转链接类型，0 或不填代表不是链接，1 代表跳转 url，2 代表跳转小程序 |
  513 +| jump_list.title | String | 是 | 跳转链接样式的文案内容，建议不超过 13 个字 |
  514 +| jump_list.url | String | 否 | 跳转链接的 url，jump_list.type 是 1 时必填 |
  515 +| jump_list.appid | String | 否 | 跳转链接的小程序的 appid，jump_list.type 是 2 时必填 |
  516 +| jump_list.pagepath | String | 否 | 跳转链接的小程序的 pagepath，jump_list.type 是 2 时选填 |
  517 +| card_action | Object | 是 | 整体卡片的点击跳转事件，news_notice 模版卡片中该字段为必填项 |
  518 +| card_action.type | Int | 是 | 卡片跳转类型，1 代表跳转 url，2 代表打开小程序。news_notice 模版卡片中该字段取值范围为 [1,2] |
  519 +| card_action.url | String | 否 | 跳转事件的 url，card_action.type 是 1 时必填 |
  520 +| card_action.appid | String | 否 | 跳转事件的小程序的 appid，card_action.type 是 2 时必填 |
  521 +| card_action.pagepath | String | 否 | 跳转事件的小程序的 pagepath，card_action.type 是 2 时选填 |
  522 +
  523 +## 消息发送频率限制
  524 +
  525 +每个消息推送发送的消息不能超过 20 条/分钟。
  526 +
  527 +## 文件上传接口
  528 +
  529 +> 素材上传得到 media_id，该 media_id 仅三天内有效
  530 +>
  531 +> media_id 只能是对应上传文件的消息推送可以使用
  532 +
  533 +**请求方式：** POST（**HTTPS**）
  534 +
  535 +**请求地址：** `https://qyapi.weixin.qq.com/cgi-bin/webhook/upload_media?key=KEY&type=TYPE`
  536 +
  537 +使用 multipart/form-data POST 上传文件或语音，文件标识名为"media"
  538 +
  539 +**参数说明：**
  540 +
  541 +| 参数 | 必须 | 说明 |
  542 +| --- | --- | --- |
  543 +| key | 是 | 调用接口凭证，消息推送 webhookurl 中的 key 参数 |
  544 +| type | 是 | 文件类型，分别有语音(voice)和普通文件(file) |
  545 +
  546 +POST 的请求包中，form-data 中媒体文件标识，应包含有 **filename**、filelength、content-type 等信息
  547 +
  548 +> filename 标识文件展示的名称。比如，使用该 media_id 发消息时，展示的文件名由该字段控制
  549 +
  550 +**请求示例：**
  551 +
  552 +```
  553 +POST https://qyapi.weixin.qq.com/cgi-bin/webhook/upload_media?key=693a91f6-7xxx-4bc4-97a0-0ec2sifa5aaa&type=file HTTP/1.1
  554 +Content-Type: multipart/form-data; boundary=-------------------------acebdf13572468
  555 +Content-Length: 220
  556 +
  557 +---------------------------acebdf13572468
  558 +Content-Disposition: form-data; name="media";filename="wework.txt"; filelength=6
  559 +Content-Type: application/octet-stream
  560 +
  561 +mytext
  562 +---------------------------acebdf13572468--
  563 +```
  564 +
  565 +**返回数据：**
  566 +
  567 +```json
  568 +{
  569 +"errcode": 0,
  570 +"errmsg": "ok",
  571 +"type": "file",
  572 +"media_id": "1G6nrLmr5EC3MMb_-zK1dDdzmd0p7cNliYu9V5w7o8K0",
  573 +"created_at": "1380000000"
  574 +}
  575 +```
  576 +
  577 +**参数说明：**
  578 +
  579 +| 参数 | 说明 |
  580 +| --- | --- |
  581 +| type | 文件类型，分别有语音(voice)和普通文件(file) |
  582 +| media_id | 媒体文件上传后获取的唯一标识，3 天内有效 |
  583 +| created_at | 媒体文件上传时间戳 |
  584 +
  585 +**上传的文件限制：**
  586 +
  587 +所有类型的文件大小均要求大于 5 个字节
  588 +
  589 +- 普通文件(file)：文件大小不超过 20M
  590 +- 语音(voice)：文件大小不超过 2M，播放长度不超过 60s，**仅支持** AMR 格式
0 591 \ No newline at end of file
... ...
docs/Usage-Guide.md
  Show/Hide comments   View file @648cb4c
... ... @@ -49,9 +49,9 @@ docker run -d \
49 49  
50 50 参考 [Elasticsearch官方文档](https://www.elastic.co/guide/en/elasticsearch/reference/8.11/install-elasticsearch.html)
51 51  
52   -#### 3. 配置环境变量
  52 +#### 3. 配置环境变量（支持多环境）
53 53  
54   -创建 `.env` 文件：
  54 +创建 `.env` 文件（以本地 / prod 环境为例）：
55 55  
56 56 ```bash
57 57 # MySQL配置
... ... @@ -74,6 +74,11 @@ REDIS_PASSWORD=BMfv5aI31kgHWtlx
74 74 # DeepL翻译API（可选）
75 75 DEEPL_AUTH_KEY=c9293ab4-ad25-479b-919f-ab4e63b429ed
76 76  
  77 +# 运行环境（用于区分 prod / uat / test / dev）
  78 +RUNTIME_ENV=prod
  79 +# ES 索引命名空间前缀（用于按环境隔离索引，prod 通常留空）
  80 +ES_INDEX_NAMESPACE=
  81 +
77 82 # API服务配置
78 83 API_HOST=0.0.0.0
79 84 API_PORT=6002
... ... @@ -101,7 +106,7 @@ cd /home/tw/SearchEngine
101 106 - **后端API**: http://localhost:6002
102 107 - **API文档**: http://localhost:6002/docs
103 108  
104   -### 方式2: 分步启动
  109 +### 方式2: 分步启动（单环境）
105 110  
106 111 #### 启动后端服务
107 112  
... ... @@ -109,7 +114,7 @@ cd /home/tw/SearchEngine
109 114 ./scripts/start_backend.sh
110 115 ```
111 116  
112   -后端API会在 http://localhost:6002 启动
  117 +后端API会在 http://localhost:6002 启动（使用当前 `.env` 中的 ES_HOST / DB_HOST / RUNTIME_ENV / ES_INDEX_NAMESPACE）
113 118  
114 119 #### 启动前端服务
115 120  
... ... @@ -119,7 +124,50 @@ cd /home/tw/SearchEngine
119 124  
120 125 前端界面会在 http://localhost:6003 启动
121 126  
122   -### 方式3: 手动启动
  127 +### 方式3: 多环境示例（prod / uat）
  128 +
  129 +假设有两套环境：
  130 +
  131 +- **prod**：正式环境，RUNTIME_ENV=prod，ES_INDEX_NAMESPACE 为空
  132 +- **uat**：联调环境，RUNTIME_ENV=uat，ES_INDEX_NAMESPACE=uat_
  133 +
  134 +可以在项目根目录准备两个配置文件：
  135 +
  136 +- `.env.prod`：
  137 +
  138 +```bash
  139 +RUNTIME_ENV=prod
  140 +ES_INDEX_NAMESPACE=
  141 +ES_HOST=http://prod-es:9200
  142 +DB_HOST=prod-mysql
  143 +...
  144 +```
  145 +
  146 +- `.env.uat`：
  147 +
  148 +```bash
  149 +RUNTIME_ENV=uat
  150 +ES_INDEX_NAMESPACE=uat_
  151 +ES_HOST=http://uat-es:9200
  152 +DB_HOST=uat-mysql
  153 +...
  154 +```
  155 +
  156 +启动不同环境时：
  157 +
  158 +```bash
  159 +# 启动 UAT 后端 + 索引服务
  160 +cp .env.uat .env
  161 +./scripts/start_backend.sh
  162 +./scripts/start_indexer.sh
  163 +
  164 +# 启动 PROD 后端 + 索引服务
  165 +cp .env.prod .env
  166 +./scripts/start_backend.sh
  167 +./scripts/start_indexer.sh
  168 +```
  169 +
  170 +### 方式4: 手动启动
123 171  
124 172 #### 启动后端API服务
125 173  
... ... @@ -345,19 +393,25 @@ curl -X POST http://localhost:6002/search/image \
345 393  
346 394 ## 8. Suggestion 索引与接口使用
347 395  
348   -### 8.1 构建 Suggestion 索引（全量）
  396 +### 8.1 构建 Suggestion 索引（全量，多环境）
349 397  
350 398 Suggestion 索引会从：
351 399  
352 400 - ES 商品索引：`title.{lang}`, `qanchors.{lang}`
353 401 - MySQL 日志表：`shoplazza_search_log.query`（含 `language`、`request_params`）
354 402  
355   -聚合生成 `search_suggestions_tenant_{tenant_id}`。
  403 +聚合生成 `{ES_INDEX_NAMESPACE}search_suggestions_tenant_{tenant_id}`。
356 404  
357   -在项目根目录执行：
  405 +在项目根目录执行（以 UAT 环境、tenant_id=162 为例）：
358 406  
359 407 ```bash
360   -# 为指定租户全量重建 suggestion 索引（会删除旧索引）
  408 +# 1. 切换到 UAT 配置（包含 ES_INDEX_NAMESPACE=uat_）
  409 +cp .env.uat .env
  410 +
  411 +# 2. 启动索引服务（如尚未启动）
  412 +./scripts/start_indexer.sh
  413 +
  414 +# 3. 为指定租户全量重建 suggestion 索引（会删除旧索引）
361 415 python main.py build-suggestions \
362 416 --tenant-id 162 \
363 417 --es-host http://localhost:9200 \
... ... @@ -365,6 +419,9 @@ python main.py build-suggestions \
365 419 --recreate
366 420 ```
367 421  
  422 +UAT 环境下，索引名为：`uat_search_suggestions_tenant_162`；
  423 +prod 环境下（ES_INDEX_NAMESPACE 为空），索引名为：`search_suggestions_tenant_162`。
  424 +
368 425 可选参数：
369 426  
370 427 - `--days`：回溯日志天数（默认 30）
... ... @@ -378,10 +435,73 @@ python main.py build-suggestions \
378 435 全量构建完成后，可直接通过 `/search/suggestions` 获取自动补全结果：
379 436  
380 437 ```bash
  438 +# UAT 环境（本地或 UAT 集群）
381 439 curl "http://localhost:6002/search/suggestions?q=iph&size=5&language=en&with_results=true" \
382 440 -H "X-Tenant-ID: 162"
  441 +
  442 +# PROD 环境（域名 / 端口按实际部署调整）
  443 +curl "https://api.yourdomain.com/search/suggestions?q=iph&size=5&language=en&with_results=true" \
  444 + -H "X-Tenant-ID: 162"
  445 +```
  446 +
  447 +### 8.3 商品索引构建（search_products，多环境例子）
  448 +
  449 +以 tenant_id=162 为例，分别在 UAT / PROD 构建商品索引。
  450 +
  451 +#### UAT 环境（索引前缀 uat_）
  452 +
  453 +```bash
  454 +cd /home/tw/SearchEngine
  455 +
  456 +# 1. 切换到 UAT 环境配置
  457 +cp .env.uat .env
  458 +
  459 +# 2. 启动 indexer 服务（如尚未启动）
  460 +./scripts/start_indexer.sh
  461 +
  462 +# 3. 可选：重建索引结构（会删除旧索引）
  463 +./scripts/create_tenant_index.sh 162
  464 +# 实际创建的索引名为：uat_search_products_tenant_162
  465 +
  466 +# 4. 导入商品数据（全量索引）
  467 +curl -X POST "http://localhost:6004/indexer/reindex" \
  468 + -H "Content-Type: application/json" \
  469 + -d '{
  470 + "tenant_id": "162",
  471 + "batch_size": 500
  472 + }'
383 473 ```
384 474  
  475 +#### PROD 环境（无前缀）
  476 +
  477 +```bash
  478 +cd /home/tw/SearchEngine
  479 +
  480 +# 1. 切换到 PROD 环境配置
  481 +cp .env.prod .env
  482 +
  483 +# 2. 启动 indexer 服务
  484 +./scripts/start_indexer.sh
  485 +
  486 +# 3. 可选：重建索引结构（search_products_tenant_162）
  487 +./scripts/create_tenant_index.sh 162
  488 +
  489 +# 4. 导入商品数据
  490 +curl -X POST "http://localhost:6004/indexer/reindex" \
  491 + -H "Content-Type: application/json" \
  492 + -d '{
  493 + "tenant_id": "162",
  494 + "batch_size": 500
  495 + }'
  496 +```
  497 +
  498 +完成后：
  499 +
  500 +- UAT 环境商品索引：`uat_search_products_tenant_162`
  501 +- PROD 环境商品索引：`search_products_tenant_162`
  502 +
  503 +两套环境的搜索 / suggestion API 调用完全一致，只是连接到各自的后端 / ES。
  504 +
385 505 接口返回结构详见 `docs/搜索API对接指南.md` 的“3.7 搜索建议接口”章节。
386 506  
387 507 ---
... ...
docs/搜索API对接指南.md
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... ... @@ -867,20 +867,30 @@ curl &quot;http://localhost:6002/search/12345&quot; -H &quot;X-Tenant-ID: 162&quot;
867 867  
868 868 为租户创建索引需要两个步骤：
869 869  
870   -1. **创建索引结构**（可选，仅在需要更新 mapping 时执行）
  870 +1. **创建索引结构**（可选，仅在需要更新 mapping 或在新环境首次创建时执行）
871 871 - 使用脚本创建 ES 索引结构（基于 `mappings/search_products.json`）
872 872 - 如果索引已存在，会提示用户确认（会删除现有数据）
873 873  
874 874 2. **导入数据**（必需）
875 875 - 使用全量索引接口 `/indexer/reindex` 导入数据
876 876  
877   -**创建索引结构**：
  877 +**创建索引结构（支持多环境 namespace）**：
878 878  
879 879 ```bash
  880 +# 以 UAT 环境为例：
  881 +# 1. 准备 UAT 环境的 .env（包含 UAT 的 ES_HOST/DB_HOST 等）
  882 +# 2. 设置环境前缀（也可以直接在 .env 中配置）：
  883 +export RUNTIME_ENV=uat
  884 +export ES_INDEX_NAMESPACE=uat_
  885 +
  886 +# 3. 为 tenant_id=170 创建索引结构
880 887 ./scripts/create_tenant_index.sh 170
881 888 ```
882 889  
883   -脚本会自动从项目根目录的 `.env` 文件加载 ES 配置。
  890 +脚本会自动从项目根目录的 `.env` 文件加载 ES 配置，并根据 `ES_INDEX_NAMESPACE` 创建：
  891 +
  892 +- prod 环境（ES_INDEX_NAMESPACE 为空）：`search_products_tenant_170`
  893 +- UAT 环境（ES_INDEX_NAMESPACE=uat_）：`uat_search_products_tenant_170`
884 894  
885 895 **注意事项**：
886 896 - ⚠️ 如果索引已存在，脚本会提示确认，确认后会删除现有数据
... ... @@ -910,7 +920,7 @@ curl &quot;http://localhost:6002/search/12345&quot; -H &quot;X-Tenant-ID: 162&quot;
910 920  
911 921 #### 响应格式
912 922  
913   -**成功响应（200 OK）**:
  923 +**成功响应（200 OK）**（示例，实际 `index_name` 会带上 tenant 和环境前缀）:
914 924 ```json
915 925 {
916 926 "success": true,
... ... @@ -918,7 +928,7 @@ curl &quot;http://localhost:6002/search/12345&quot; -H &quot;X-Tenant-ID: 162&quot;
918 928 "indexed": 1000,
919 929 "failed": 0,
920 930 "elapsed_time": 12.34,
921   - "index_name": "search_products",
  931 + "index_name": "search_products_tenant_162",
922 932 "tenant_id": "162"
923 933 }
924 934 ```
... ...
full_bulk.sh deleted
View file @501066e
... ... @@ -1,2 +0,0 @@
1   -#curl -X POST "http://localhost:6004/indexer/reindex" -H "Content-Type: application/json" -d '{"tenant_id":"162","batch_size":500}'
2   -curl -X POST "http://localhost:6004/indexer/reindex" -H "Content-Type: application/json" -d '{"tenant_id":"170","batch_size":500}'
indexer/README.md
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... ... @@ -52,8 +52,10 @@
52 52 4. **索引结构调整为 per-tenant**
53 53 - 在 Java 中已统一使用：
54 54 - `indexName = elasticsearchProperties.buildIndexName(tenantId);`
55   - - 索引命名形如：`search_products_tenant_{tenant_id}`。
56   - - Python 侧对应 `get_tenant_index_name(tenant_id)`。
  55 + - 索引命名形如：`{namespace}search_products_tenant_{tenant_id}`，其中 `namespace` 用于区分 prod/uat/test 等环境。
  56 + - Python 侧通过 `indexer/mapping_generator.get_tenant_index_name(tenant_id)` 统一生成索引名：
  57 + - 索引命名规则为：`{ES_INDEX_NAMESPACE}search_products_tenant_{tenant_id}`；
  58 + - `ES_INDEX_NAMESPACE` 来源于 `config.env_config`，默认 prod 环境为空字符串，其它环境可设置为 `uat_`、`test_` 等。
57 59  
58 60 ### 2.2 Java 侧不再深入关心的部分
59 61  
... ... @@ -512,9 +514,9 @@ if spu.tags:
512 514 - **单文档查看**：`POST /indexer/documents`
513 515 - **健康检查**：`GET /indexer/health`
514 516  
515   -### 10.3 典型测试流程（以 tenant 170, spu_id 223167 为例）
  517 +### 10.3 典型测试流程（以 tenant 170, spu_id 223167 为例，支持多环境）
516 518  
517   -1. 启动 indexer 服务：
  519 +1. 启动 indexer 服务（以本地或 prod 环境为例，.env 中 RUNTIME_ENV=prod, ES_INDEX_NAMESPACE=''）：
518 520  
519 521 ```bash
520 522 ./scripts/stop.sh
... ... @@ -562,9 +564,10 @@ curl -X POST &quot;http://127.0.0.1:6004/indexer/build-docs-from-db&quot; \
562 564 }
563 565 ```
564 566  
565   -4. 使用 `docs/常用查询 - ES.md` 中的查询，对应验证 ES 索引中的文档：
  567 +4. 使用 `docs/常用查询 - ES.md` 中的查询，对应验证 ES 索引中的文档（注意索引名前缀）：
566 568  
567 569 ```bash
  570 +# prod / 本地环境：ES_INDEX_NAMESPACE 为空，索引名为 search_products_tenant_170
568 571 curl -u 'essa:***' \
569 572 -X GET 'http://localhost:9200/search_products_tenant_170/_search?pretty' \
570 573 -H 'Content-Type: application/json' \
... ... @@ -579,6 +582,23 @@ curl -u &#39;essa:***&#39; \
579 582 }
580 583 }
581 584 }'
  585 +
  586 +# UAT 环境：假设 .env 中配置 ES_INDEX_NAMESPACE=uat_，
  587 +# 则索引名为 uat_search_products_tenant_170
  588 +curl -u 'essa:***' \
  589 + -X GET 'http://localhost:9200/uat_search_products_tenant_170/_search?pretty' \
  590 + -H 'Content-Type: application/json' \
  591 + -d '{
  592 + "size": 5,
  593 + "_source": ["title", "tags"],
  594 + "query": {
  595 + "bool": {
  596 + "filter": [
  597 + { "term": { "spu_id": "223167" } }
  598 + ]
  599 + }
  600 + }
  601 + }'
582 602 ```
583 603  
584 604 通过这套流程可以完整验证：MySQL → Python 富化 → ES doc → ES 查询 的全链路行为是否符合预期。*** End Patch"""} ***!
... ...
indexer/mapping_generator.py
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... ... @@ -9,6 +9,8 @@ import json
9 9 import logging
10 10 from pathlib import Path
11 11  
  12 +from config.env_config import ES_INDEX_NAMESPACE
  13 +
12 14 logger = logging.getLogger(__name__)
13 15  
14 16 # Default index name (deprecated, use get_tenant_index_name instead)
... ... @@ -21,14 +23,15 @@ DEFAULT_MAPPING_FILE = Path(__file__).parent.parent / &quot;mappings&quot; / &quot;search_produ
21 23 def get_tenant_index_name(tenant_id: str) -> str:
22 24 """
23 25 Generate index name for a tenant.
24   -
25   - Args:
26   - tenant_id: Tenant ID
27   -
28   - Returns:
29   - Index name in format: search_products_tenant_{tenant_id}
  26 +
  27 + 索引命名规则统一为：
  28 + {ES_INDEX_NAMESPACE}search_products_tenant_{tenant_id}
  29 +
  30 + 其中 ES_INDEX_NAMESPACE 由 config.env_config.ES_INDEX_NAMESPACE 控制，
  31 + 用于区分 prod/uat/test 等不同运行环境。
30 32 """
31   - return f"search_products_tenant_{tenant_id}"
  33 + prefix = ES_INDEX_NAMESPACE or ""
  34 + return f"{prefix}search_products_tenant_{tenant_id}"
32 35  
33 36  
34 37 def load_mapping(mapping_file: str = None) -> Dict[str, Any]:
... ...
scripts/create_tenant_index.sh
  Show/Hide comments   View file @648cb4c
... ... @@ -16,6 +16,7 @@ fi
16 16 ES_HOST="${ES_HOST:-http://localhost:9200}"
17 17 ES_USERNAME="${ES_USERNAME:-}"
18 18 ES_PASSWORD="${ES_PASSWORD:-}"
  19 +ES_INDEX_NAMESPACE="${ES_INDEX_NAMESPACE:-}"
19 20  
20 21 # 检查命令行参数
21 22 if [ $# -eq 0 ]; then
... ... @@ -25,7 +26,8 @@ if [ $# -eq 0 ]; then
25 26 fi
26 27  
27 28 TENANT_ID="$1"
28   -ES_INDEX="search_products_tenant_${TENANT_ID}"
  29 +BASE_INDEX_NAME="search_products_tenant_${TENANT_ID}"
  30 +ES_INDEX="${ES_INDEX_NAMESPACE}${BASE_INDEX_NAME}"
29 31 MAPPING_FILE="mappings/search_products.json"
30 32  
31 33 # 检查 mapping 文件是否存在
... ... @@ -37,6 +39,7 @@ fi
37 39 # 手动确认
38 40 echo "创建索引前，将删除已有的同名索引。"
39 41 echo "索引名称: $ES_INDEX"
  42 +echo "（注意：包含环境前缀 ES_INDEX_NAMESPACE='${ES_INDEX_NAMESPACE}'）"
40 43 echo "请输入索引名称 '$ES_INDEX' 来确认："
41 44 read -r user_input
42 45  
... ...
suggestion/README.md
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... ... @@ -26,15 +26,15 @@
26 26  
27 27 ## 2. 总体架构
28 28  
29   -采用双索引架构：
  29 +采用双索引架构（支持多环境 namespace 前缀）：
30 30  
31   -- 商品索引：`search_products_tenant_{tenant_id}`
32   -- 建议词索引：`search_suggestions_tenant_{tenant_id}`
  31 +- 商品索引：`{ES_INDEX_NAMESPACE}search_products_tenant_{tenant_id}`
  32 +- 建议词索引：`{ES_INDEX_NAMESPACE}search_suggestions_tenant_{tenant_id}`
33 33  
34 34 在线查询主路径：
35 35  
36   -1. 仅查询 `search_suggestions_tenant_{tenant_id}` 得到 suggestion 列表。
37   -2. 对每条 suggestion 进行“结果直达”的二次查询（`msearch`）到 `search_products_tenant_{tenant_id}`：
  36 +1. 仅查询 `{ES_INDEX_NAMESPACE}search_suggestions_tenant_{tenant_id}` 得到 suggestion 列表。
  37 +2. 对每条 suggestion 进行“结果直达”的二次查询（`msearch`）到 `{ES_INDEX_NAMESPACE}search_products_tenant_{tenant_id}`：
38 38 - 使用 suggestion 文本对 `title.{lang}` / `qanchors.{lang}` 执行 `term` / `match_phrase_prefix` 组合查询。
39 39 3. 回填每条 suggestion 的商品卡片列表（例如每条 3~5 个）。
40 40  
... ... @@ -164,17 +164,17 @@ Header：
164 164  
165 165 输入：
166 166  
167   -- `search_products_tenant_{tenant_id}` 文档
  167 +- `{ES_INDEX_NAMESPACE}search_products_tenant_{tenant_id}` 文档
168 168 - MySQL 表：`shoplazza_search_log`
169 169  
170 170 输出：
171 171  
172   -- `search_suggestions_tenant_{tenant_id}` 全量文档
  172 +- `{ES_INDEX_NAMESPACE}search_suggestions_tenant_{tenant_id}` 全量文档
173 173  
174 174 ### 5.1 流程
175 175  
176   -1. 创建/重建 `search_suggestions_tenant_{tenant_id}`。
177   -2. 遍历 `search_products_tenant_{tenant_id}`（`scroll` 或 `search_after`）：
  176 +1. 创建/重建 `{ES_INDEX_NAMESPACE}search_suggestions_tenant_{tenant_id}`。
  177 +2. 遍历 `{ES_INDEX_NAMESPACE}search_products_tenant_{tenant_id}`（`scroll` 或 `search_after`）：
178 178 - 提取每个商品的 `title.{lang}`、`qanchors.{lang}`。
179 179 - 归一化文本（NFKC、trim、lower、空白折叠）。
180 180 - 产出候选词并累加：
... ... @@ -223,7 +223,7 @@ for tenant_id in tenants:
223 223 item.rank_score = compute_rank_score(item)
224 224 docs.append(to_suggestion_doc(tenant_id, lang, item))
225 225  
226   - bulk_upsert(index=f"search_suggestions_tenant_{tenant_id}", docs=docs)
  226 + bulk_upsert(index=f"{ES_INDEX_NAMESPACE}search_suggestions_tenant_{tenant_id}", docs=docs)
227 227 ```
228 228  
229 229 ## 6. 日志语言解析策略（已新增 language 字段）
... ... @@ -407,7 +407,7 @@ M3（优化）：
407 407  
408 408 ## 13. 实验与验证建议
409 409  
410   -以租户 `tenant_id=171` 为例，推荐如下验证流程（其他租户同理）：
  410 +以租户 `tenant_id=171` 为例，推荐如下验证流程（其它租户 / 环境同理，可通过 ES_INDEX_NAMESPACE 区分 prod / uat / test）：
411 411  
412 412 ### 13.1 构建索引
413 413  
... ... @@ -415,7 +415,7 @@ M3（优化）：
415 415 ./scripts/build_suggestions.sh 171 --days 30 --recreate
416 416 ```
417 417  
418   -期望 CLI 输出类似：
  418 +期望 CLI 输出类似（prod 环境，ES_INDEX_NAMESPACE 为空）：
419 419  
420 420 ```json
421 421 {
... ... @@ -440,11 +440,19 @@ M3（优化）：
440 440 ### 13.2 检查索引结构
441 441  
442 442 ```bash
  443 +# prod / 本地环境：ES_INDEX_NAMESPACE 为空
443 444 curl "http://localhost:9200/search_suggestions_tenant_171/_mapping?pretty"
444 445 curl "http://localhost:9200/search_suggestions_tenant_171/_count?pretty"
445 446 curl "http://localhost:9200/search_suggestions_tenant_171/_search?size=5&pretty" -d '{
446 447 "query": { "match_all": {} }
447 448 }'
  449 +
  450 +# UAT 环境：假设 ES_INDEX_NAMESPACE=uat_
  451 +curl "http://localhost:9200/uat_search_suggestions_tenant_171/_mapping?pretty"
  452 +curl "http://localhost:9200/uat_search_suggestions_tenant_171/_count?pretty"
  453 +curl "http://localhost:9200/uat_search_suggestions_tenant_171/_search?size=5&pretty" -d '{
  454 + "query": { "match_all": {} }
  455 +}'
448 456 ```
449 457  
450 458 重点确认：
... ...
suggestion/builder.py
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... ... @@ -19,12 +19,22 @@ from sqlalchemy import text
19 19 from config.tenant_config_loader import get_tenant_config_loader
20 20 from utils.es_client import ESClient
21 21 from suggestion.mapping import build_suggestion_mapping
  22 +from config.env_config import ES_INDEX_NAMESPACE
22 23  
23 24 logger = logging.getLogger(__name__)
24 25  
25 26  
26 27 def get_suggestion_index_name(tenant_id: str) -> str:
27   - return f"search_suggestions_tenant_{tenant_id}"
  28 + """
  29 + 生成 suggestion 索引名称。
  30 +
  31 + 命名规则：
  32 + {ES_INDEX_NAMESPACE}search_suggestions_tenant_{tenant_id}
  33 +
  34 + 通过 ES_INDEX_NAMESPACE 统一区分 prod/uat/test 等环境。
  35 + """
  36 + prefix = ES_INDEX_NAMESPACE or ""
  37 + return f"{prefix}search_suggestions_tenant_{tenant_id}"
28 38  
29 39  
30 40 @dataclass
... ...