Elasticsearch
used in
0
stacks
Distributed search engine whose dense_vector kNN and hybrid retrieval support vector and RAG workloads.
Elasticsearch is a distributed search and analytics engine that added approximate nearest neighbor vector search via the dense_vector field type and kNN queries. It uses HNSW graphs for fast approximate retrieval and can combine BM25 lexical scoring with kNN for hybrid search. Through its Inference API it can generate or call text embedding models and combine vector search, reranking, and RAG in a single workflow. It can be self-managed or run on Elastic Cloud.
Strengths
- Mature, scalable engine with strong operational and ecosystem tooling
- Native hybrid search combining BM25 lexical and kNN vector retrieval
- Inference API integrates embedding and reranking models for RAG
- Available self-managed or as a managed Elastic Cloud service
Tradeoffs
- Heavier to operate than purpose-built embedded vector stores
- Vector features are newer than its long-established lexical search
- Cluster tuning and resource sizing add operational overhead