Background In the application scenarios of Elasticsearch, the storage of large amounts of data may significantly impact the read and write performance of Elasticsearch. Therefore, it is necessary to split indexes according to certain data types. This article explains through relevant technical research whether splitting data on Elasticsearch will affect query results in AI search scenarios. It also compares the implementation principles of other vector databases currently available in the industry with those currently using Elasticsearch. Goals Elasticsearch vs. Milvus: Comparison in AIC use cases Investigate the data storage mechanisms and query processes of mainstream vector databases in the current industry (Qdrant, Milvus). Conduct an in-depth analysis of how they handle data updates (...