Elasticsearch Vector

Elasticsearch Vector refers to the dense vector field type and k-NN search capabilities added to Elasticsearch starting in version 7.3 and substantially enhanced in versions 8.x with HNSW indexing and the dense_vector field supporting up to 4,096 dimensions. Elasticsearch's vector search lets enterprises add semantic capabilities on top of existing keyword search infrastructure without operating a separate vector database, leveraging features like sharding, replication, security, and audit logging that are already validated for AI compliance. Hybrid search combining BM25 keyword scoring with dense vector similarity is a core use case, supported by Reciprocal Rank Fusion. Elasticsearch also added the ELSER and E5 inference services for on-platform embedding generation, removing the need for separate inference infrastructure. Major enterprise users of Elasticsearch — including financial services, healthcare, and government — adopted vector search as an incremental upgrade rather than a full migration to a dedicated vector store. AI governance frameworks treat Elasticsearch vector indexes under existing search governance policies, simplifying responsible AI rollout.

Elasticsearch Vector + Centralpoint: Centralpoint integrates Elasticsearch vector search alongside dedicated vector databases under one model-agnostic platform, letting you keep using your existing Elasticsearch investment for hybrid retrieval. Tokens are metered across whichever LLM generates the final answer — Claude, OpenAI, Gemini, LLAMA — and chatbots deploy via one line of JavaScript.


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