Elasticsearch Vector DB

What is an Elasticsearch?

Elasticsearch is a free open-source search engine based on Apache Lucene with many goodies such as aggregation, analytics, and ETL using Logstash and Kibana dashboard which make it today the best search engine in the world with many capabilities to become a centralized database for many products and companies.

Elasticsearch can store Petabytes of data on many servers (nodes) and be searched and analyzed at a very high speed.

Elasticsearch is a great big data platform for unstructured data, as data is being stored in JSON format enabling auto schema detection plus adding fields on the fly with no schema modification.

What is Elasticsearch’s Vector DB?

Elasticsearch’s vector database version 8.11 offers an efficient way to create, store, and search vector embeddings at scale.

Combine text search and vector search for hybrid retrieval, resulting in the best of both capabilities for greater relevance and accuracy.

Elasticsearch includes a full vector database, multiple types of retrieval (text, sparse and dense vector, hybrid), and your choice of machine learning model architectures.

Build your search experience with aggregations, filtering and faceting, and auto-complete. Run your search in the cloud, on-prem, or air gapped.

Elastic k-Nearest Neighbor (kNN) Search

The k-nearest neighbor (kNN) algorithm performs a similarity search on fields of dense_vector type. This type of search, which is more appropriately called “approximate kNN”, accepts a vector or embedding as a search term, and finds entries in the index that are close.

Managed Cloud Deployment

Elastic Vector Search - Self-Managed (Basic) VS Managed Cloud:


Store embeddings


Generate embeddings


Custom embeddings



Search embeddings


Search BM25


Hybrid search (BM25 + Vectors)


Reciprocal Rank Fusion – RRF


Filtering, faceting, aggregations


Search autocomplete


Optimized for multiple data types (text, vector, geo)


Machine Learning

Support for several embedding models


Built-in semantic search model


Data inference pipelines


Search Experience Tools

Ingest tools (web crawler*, connectors*, API framework, beats, fleet, agent)


Document and field level security


Elastic Tools

Observability tools (Kibana)


Search UI components


Piped queries – ES|QL (coming soon)


The elastic vector dimensions have had limitations until version 8.9.2, with a maximum of 1024 dimensions. However, from version 8.10.0 onward, this limit has been increased to 2048 dimensions and further expanded to 4096 dimensions starting from version 8.11.

Semantic Search Example

We have performed a vector search over a Wikipedia dataset, using ‘multilingual-22-12’ as an embedding model for the queries.

First, we have created a vector search index, with a vector size of 768 and Cosine as similarity.

    PUT vector-search-index/_doc
    "my_vector": doc["emb"],
    "id": doc["id"],
    "title": doc["title"],
    "description": doc["description"],
    "url": doc["url"],
    "wiki_id": doc["wiki_id"],
    "views": doc["views"],
    "paragraph_id": doc["paragraph_id"],
    "langs": doc["langs"]

Now we can perform semantic vector search and hybrid search.

Here we perform a semantic search, with the query: “Food in the Chinese cuisine”

					POST vector-search-index/_search
    "knn": {
        "field": "my_vector",
        "query_vector": [0.5355, 0.081, ...], "HERE YOU PUT EMBEDDING"
        "k": 5,
        "num_candidates": 100



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