Can Elasticsearch be used as a vector database?

Yes. Elasticsearch can be used as a vector database. The official documentation states that Elasticsearch stores embeddings and retrieves the most similar vectors, functioning as a vector database when you index embeddings in dense_vector or sparse_vector fields and query them with kNN or related methods (Elastic vector search docs).

What “vector database” means in Elasticsearch

Elasticsearch stores numeric embeddings and finds nearest neighbors to a query embedding. It supports dense vectors from neural models and sparse vectors from learned encoders such as ELSER, and can combine vector search with full-text search, filters and aggregations in one engine (Elastic vector search docs).

• Field types: dense_vector and sparse_vector (plus managed workflows via semantic_text) (Elastic vector search docs).
• Queries: k-nearest neighbor (knn) for dense vectors and a dedicated sparse vector query for sparse fields; usable via Query DSL or ES|QL (Elastic vector search docs).
• Hybrid retrieval: Combine keyword, vector and reranking in one workflow, including retrievers and ES|QL FORK/FUSE with RRF or linear fusion (Elastic vector search docs).

End-to-end workflow: ingestion, embeddings, indexing and query

1) Ingest and embed your content

• Choose an embedding model. Elastic offers built-in options: ELSER for sparse vectors, E5 and Jina models for dense or multimodal use, or connect third-party services through the Inference API (Cohere, OpenAI, Hugging Face, Bedrock, Azure OpenAI, Vertex AI). Use the same model for stored vectors and query vectors so similarity is meaningful (Elastic vector search docs).
• Chunk long documents. Splitting text before embedding improves matching to specific passages. Elastic provides chunking in its managed semantic_text flow; if you bring your own embeddings, chunk before indexing (Elastic vector search docs).

2) Index design and storage

• Pick a field type. Use dense_vector for continuous embeddings and sparse_vector for learned, term-like sparse representations. Vector dimensionality is fixed by your model and must match between stored and query vectors (Elastic vector search docs).
• Apply vector index mode. Vector-focused defaults can be enabled with index mode for vector search workloads (Elastic vector search docs):

{
  "settings": {
    "index": {
      "mode": "vectordb_document"
    }
  }
}

• Control memory and speed with quantization. Elasticsearch supports quantization for dense_vector fields (including BBQ, int8, and int4) to reduce memory and latency with accuracy and performance trade-offs that depend on the data and configuration (Elastic vector search docs).

3) Indexing

• Managed embeddings: The semantic_text field can handle embedding generation and chunking.
• Bring your own vectors: Generate embeddings externally and store them directly in a dense_vector or sparse_vector field (Elastic vector search docs).

4) Querying and retrieval

• Vector similarity: Use the knn query for dense vectors or the sparse vector query for sparse fields. You can also use ES|QL functions for nearest-neighbor and vector comparisons like cosine similarity (Elastic vector search docs).
• Filters and aggregations: Combine vector search with structured filters and analytics in the same request (Elastic vector search docs).
• Hybrid and reranking: Use retrievers or ES|QL FORK/FUSE to blend keyword and vector branches, using rank fusion to combine lists and, where supported, a separate model-based reranking step for stronger relevance (Elastic vector search docs). For background, see What is BM25 keyword search? and Semantic vs hybrid search.

Exact vs approximate kNN

The docs note that vector search typically uses approximate or exact k-nearest neighbor. Elasticsearch exposes kNN search and supporting tooling; your choice balances recall and latency for your workload (Elastic vector search docs).

Optimizations that matter in production

• Quantization: Enable BBQ, int8 or int4 quantization on dense_vector fields to cut memory and speed up similarity computation (Elastic vector search docs).
• Vector index mode: Start indexes in vectordb_document mode to get vector-friendly IO and merge settings without manual tuning (Elastic vector search docs).
• Chunking: Map queries to the most relevant passages rather than entire documents (Elastic vector search docs).
• Hybrid retrieval: Blend lexical and semantic signals and optionally rerank. For broader application patterns like RAG, see RAG in AI applications.

Actionable checklist

• Pick an embedding model (ELSER for sparse; E5 or Jina for dense/multimodal), and keep the same model for queries and indexed vectors (Elastic vector search docs).
• Define chunking rules for long documents before embedding.
• Create an index with index.mode: vectordb_document and choose dense_vector or sparse_vector fields.
• Record vector dimensionality from your model and validate it at ingest and query time.
• Enable quantization on dense vectors if memory/latency is a concern.
• Query with knn (dense) or sparse vector queries; add filters as needed.
• Combine lexical and vector retrieval with retrievers or ES|QL FORK/FUSE; consider RRF or reranking.
• Measure recall vs latency; tune chunk size, k, quantization and fusion weights accordingly.

FAQs

Do I need the same model for queries and indexed vectors?
Yes. Similarity only makes sense when vectors come from the same embedding model (Elastic vector search docs).

Can I bring my own embeddings?
Yes. You can store precomputed vectors in dense_vector or sparse_vector fields and search them directly (Elastic vector search docs).

Does Elasticsearch support hybrid keyword and vector retrieval?
Yes. You can combine keyword queries, vector kNN and sparse retrieval in one workflow and merge with retrievers or ES|QL FORK/FUSE using RRF or linear fusion (Elastic vector search docs).

What about optimizing storage and speed?
Use quantization for dense vectors and consider vector index mode for better out-of-the-box performance (Elastic vector search docs).