Although vector databases have been gaining popularity for retrieval-augmented generation (RAG) use cases, research and experience reports suggest combining traditional full-text search with vector search (into a hybrid search) can yield superior results. Through Elasticsearch Relevance Engine (ESRE), the well-established full-text search platform Elasticsearch supports built-in and custom embedding models, vector search and hybrid search with ranking mechanisms such as Reciprocal Rank Fusion. Even though this space is still maturing, in our experience, using these ESRE features along with the traditional filtering, sorting and ranking capabilities that come with Elasticsearch has yielded promising results, suggesting that established search platforms that support semantic search are not to be passed over.
