Unlocking Semantic Search: Elasticsearch Vector Search & RAG Applications

Traditional keyword search cannot meet modern demands, especially in the era of intelligent applications. Enterprises now require semantic search that understands human intent and can handle various data types such as images, audio, and video.

At the Shenzhen DA Digital Intelligence Technology Conference (July 25‑26), Elastic chief evangelist Liu Xiaoguo presented the session "Using Elasticsearch for Vector Search and Building RAG Applications". Since version 8.0, Elasticsearch offers vector search (both dense and sparse vectors), which effectively addresses semantic text search and multimedia search, though it may lack interpretability.

Elasticsearch supports hybrid search—combining keyword and vector queries—to perform multi‑path recall and rank final results, significantly improving precision and recall.

With continuous performance improvements, Elasticsearch now provides production‑grade vector search for billions of records at reduced cost. Coupled with large models and GenAI, it can deliver accurate inference results.

Because enterprise data is constantly generated, large models cannot be updated in real time, leading to hallucinations when lacking context. By using Elasticsearch vector search to retrieve relevant enterprise data as context for large language models, these hallucinations are mitigated; this approach is known as Retrieval‑Augmented Generation (RAG).

The talk will detail Elasticsearch's vector search technology, recent updates, how to develop RAG applications, and demonstrate methods that go beyond traditional RAG.