在Elasticsearch中，索引模板（Index Templates）是用来预定义新创建索引的设置和映射的一种机制。当你创建了一个索引模板，它会包含一系列的默认设置和映射规则，这些规则会在满足一定条件的新索引被创建时自动应用。
索引模板通过index_patterns字段来指定模板适用的索引名称模式。当一个新的索引被创建，Elasticsearch会查找是否有任何模板的index_patterns与该索引名称匹配。如果有匹配的模板，那么该模板的设置和映射将被应用到新创建的索引上。
因此，如果你创建了一个名为content_erp_nlp_help_online的索引模板，并且在其中定义了index_patterns为["content_erp_nlp_help_online"]，那么当你尝试创建一个确切名称为content_erp_nlp_help_online的索引时，该模板将会被应用，从而自动配置索引的设置和映射。
但是，需要注意的是，如果在创建索引时显式指定了某些设置或映射，那么这些显式指定的值将优先于模板中的值。此外，一旦索引已经被创建，索引模板的更改将不会影响到已经存在的索引。
索引模板还可以通过通配符模式来匹配多个索引。例如，如果模板的index_patterns为["content_*"]，那么所有以content_开头的索引都会应用该模板。
总结来说，索引模板是一种策略，它允许你预设一组设置和映射，以便在创建符合特定命名模式的新索引时自动应用这些预设。这极大地简化了管理大量索引的过程，尤其是当这些索引具有相似的特性时。
ES 8.14 新的创建模板的方法：
PUT /_index_template/content_erp_nlp_help
{
"index_patterns": ["content_erp*"],
"priority": 100,
"template": {
"settings": {
"analysis": {
"analyzer": {
"my_ik_analyzer": {
"type": "ik_smart"
}
}
},
"number_of_shards": 1
},
"mappings": {
"properties": {
"id": {
"type": "long"
},
"content": {
"type": "text",
"analyzer": "ik_max_word",
"search_analyzer": "ik_smart"
},
"content_vector": {
"type": "dense_vector",
"similarity": "cosine",
"index": true,
"dims": 768,
"element_type": "float",
"index_options": {
"type": "hnsw",
"m": 16,
"ef_construction": 128
}
},
"content_answer": {
"type": "text",
"analyzer": "ik_max_word",
"search_analyzer": "ik_smart"
},
"title": {
"type": "text",
"analyzer": "ik_max_word",
"search_analyzer": "ik_smart"
},
"param": {
"type": "text",
"analyzer": "ik_max_word",
"search_analyzer": "ik_smart"
},
"type": {
"type": "text",
"analyzer": "ik_max_word",
"search_analyzer": "ik_smart"
},
"questionId": {
"type": "text",
"analyzer": "ik_max_word",
"search_analyzer": "ik_smart"
},
"createTime": {
"type": "text",
"analyzer": "ik_max_word",
"search_analyzer": "ik_smart"
},
"updateTime": {
"type": "text",
"analyzer": "ik_max_word",
"search_analyzer": "ik_smart"
},
"hitCount": {
"type": "text",
"analyzer": "ik_max_word",
"search_analyzer": "ik_smart"
},
"answerPattern": {
"type": "text",
"analyzer": "ik_max_word",
"search_analyzer": "ik_smart"
},
"nearQuestionVOList": {
"type": "text",
"analyzer": "ik_max_word",
"search_analyzer": "ik_smart"
},
"questionEnclosureVOList": {
"type": "text",
"analyzer": "ik_max_word",
"search_analyzer": "ik_smart"
},
"questionRelationVOList": {
"type": "text",
"analyzer": "ik_max_word",
"search_analyzer": "ik_smart"
},
"rmsRoutingAnswerVos": {
"type": "text",
"analyzer": "ik_max_word",
"search_analyzer": "ik_smart"
}
}
}
}
}查询模板：
GET /_index_template/*
GET /_index_template/content_erp_nlp_help
Java实现的代码：
public int createIndexTemp(String indexTempName) throws Exception {
// 创建RestClient实例
RestClientBuilder builder = RestClient.builder(new HttpHost("127.0.0.1", 9200, "http"));
RestClient restClient = builder.build();
// 定义请求体
String requestBody = "{\n" +
" \"index_patterns\": [\"content_erp*\"],\n" +
" \"priority\": 100,\n" +
" \"template\": {\n" +
" \"settings\": {\n" +
" \"analysis\": {\n" +
" \"analyzer\": {\n" +
" \"my_ik_analyzer\": {\n" +
" \"type\": \"ik_smart\"\n" +
" }\n" +
" }\n" +
" },\n" +
" \"number_of_shards\": 1\n" +
" },\n" +
" \"mappings\": {\n" +
" \"properties\": {\n" +
" \"id\": {\"type\": \"long\"},\n" +
" \"content\": {\"type\": \"text\",\"analyzer\": \"ik_max_word\",\"search_analyzer\": \"ik_smart\"},\n" +
" \"content_vector\": {\"type\": \"dense_vector\",\"similarity\": \"cosine\",\"index\": true,\"dims\": 768,\"element_type\": \"float\",\"index_options\": {\"type\": \"hnsw\",\"m\": 16,\"ef_construction\": 128}},\n" +
" \"content_answer\": {\"type\": \"text\",\"analyzer\": \"ik_max_word\",\"search_analyzer\": \"ik_smart\"},\n" +
" \"title\": {\"type\": \"text\",\"analyzer\": \"ik_max_word\",\"search_analyzer\": \"ik_smart\"},\n" +
" \"param\": {\"type\": \"text\",\"analyzer\": \"ik_max_word\",\"search_analyzer\": \"ik_smart\"},\n" +
" \"type\": {\"type\": \"text\",\"analyzer\": \"ik_max_word\",\"search_analyzer\": \"ik_smart\"},\n" +
" \"questionId\": {\"type\": \"text\",\"analyzer\": \"ik_max_word\",\"search_analyzer\": \"ik_smart\"},\n" +
" \"createTime\": {\"type\": \"text\",\"analyzer\": \"ik_max_word\",\"search_analyzer\": \"ik_smart\"},\n" +
" \"updateTime\": {\"type\": \"text\",\"analyzer\": \"ik_max_word\",\"search_analyzer\": \"ik_smart\"},\n" +
" \"hitCount\": {\"type\": \"text\",\"analyzer\": \"ik_max_word\",\"search_analyzer\": \"ik_smart\"},\n" +
" \"answerPattern\": {\"type\": \"text\",\"analyzer\": \"ik_max_word\",\"search_analyzer\": \"ik_smart\"},\n" +
" \"nearQuestionVOList\": {\"type\": \"text\",\"analyzer\": \"ik_max_word\",\"search_analyzer\": \"ik_smart\"},\n" +
" \"questionEnclosureVOList\": {\"type\": \"text\",\"analyzer\": \"ik_max_word\",\"search_analyzer\": \"ik_smart\"},\n" +
" \"questionRelationVOList\": {\"type\": \"text\",\"analyzer\": \"ik_max_word\",\"search_analyzer\": \"ik_smart\"},\n" +
" \"rmsRoutingAnswerVos\": {\"type\": \"text\",\"analyzer\": \"ik_max_word\",\"search_analyzer\": \"ik_smart\"}\n" +
" }\n" +
" }\n" +
" }\n" +
"}";
// 构建请求
Request request = new Request("PUT", "/_index_template/" + indexTempName);
request.setJsonEntity(requestBody);
// 发送请求并获取响应
Response response = restClient.performRequest(request);
// 处理响应
int statusCode = response.getStatusLine().getStatusCode();
System.out.println("Response status: " + statusCode);
// 关闭RestClient
restClient.close();
return statusCode;
}
