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RestClientrestClient.close();return statusCode;}