Vector search in Elasticsearch
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Looking for a minimal configuration approach? The semantic_text field type provides an abstraction over vector search implementations with sensible defaults and automatic model management. It's the recommended way to start with Elasticsearch vector search. Learn more about semantic_text.
Vector search in Elasticsearch uses vector embeddings to power modern, AI-driven search experiences. With vectorized content, Elasticsearch retrieves results based on meaning and similarity, not just keywords or exact term matches.
Vector search is a core component of most semantic search workflows, but it can also be used independently for similarity matching use cases. Learn more about the broader benefits in the AI-powered search overview.
This guide focuses on the more manual technical implementations of vector search, outside of the higher-level semantic_text workflow.
The right approach depends on your requirements, data type, and use case.
Here’s a quick reference for the main vector field types and query types you can use:
| Vector type | Field type | Query type | Primary use case |
|---|---|---|---|
| Dense vectors | dense_vector |
knn |
Semantic similarity with your own embeddings model |
| Sparse vectors | sparse_vector |
sparse_vector |
Semantic term expansion using the ELSER model |
| Sparse or dense | semantic_text |
semantic |
Managed semantic search, agnostic to implementation details |
Dense vector search uses neural embeddings to capture semantic meaning. It translates content into fixed-length numeric vectors, where similar items cluster close together in vector space. This makes dense vectors ideal for:
- Finding semantically similar documents
- Matching user questions with answers
- Image and multimedia similarity search
- Content-based recommendations
Learn more about dense vector search in Elasticsearch.
Sparse vector search relies on the ELSER model to expand content with semantically related terms. This approach combines semantic understanding with explainability, making it a strong fit for:
- Enhanced keyword search
- Use cases requiring explainable results
- Domain-specific search
- Large-scale deployments