Search approaches

To deliver relevant search results, you need to choose the right search approach for your data and use case.

The following table provides an overview of the fundamental search techniques available in Elasticsearch:

Name	Description	Notes
Full-text search	Traditional lexical search with analyzers and relevance tuning	Essential foundation for keyword matching, works out of the box
Vector search	Similarity search using numerical vectors	Requires extra setup/resources, ideal for finding similar documents
Semantic search	Meaning-based search using natural language understanding	Requires ML models and vector infrastructure
Hybrid search	Combines lexical and vector/semantic approaches	Best balance for both keyword precision and semantic relevance
Re-ranking	Post-processing results to improve relevance	Optional ML-based enhancement for fine-tuned relevance
Geospatial search	Location-based search and spatial relationships	For maps, distance calculations, and shape queries

Tip

Full-text search is a very powerful tool in itself. One of the key strengths of Elasticsearch is its flexibility, allowing you to start with full-text search and gradually incorporate more complex or resource-intensive approaches over time.

Next step

Once you've chosen your search approach(es), you'll need to select a query language to implement them. Refer to query languages for search use cases to learn about the available options.