The Tool Desk
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How hybrid search works in OpenSearch
OpenSearch’s default document scoring uses Okapi BM25, a keyword-based method that can work well when queries and useful documents share terms. Semantic search adds a route that considers meaning, making it useful when people describe an idea differently from the way it appears in a document. Hybrid search runs lexical and semantic query clauses independently, then combines their results through a search pipeline.
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The hybrid query is a top-level query containing an array of clauses. Each clause calculates scores independently at shard level; a document can be returned if it matches at least one clause. The current query reference permits up to five clauses. The pipeline processes results between query execution and the final response. An ordinary Boolean query with should clauses is not an equivalent substitute: it does not invoke the hybrid pipeline’s normalization and combination processors.
OpenSearch documents hybrid search from version 2.11, rescoring support from 2.18, and RRF from 2.19. Check the documentation for the release you deploy, since available features can vary by version.
Choose how to combine the result lists
OpenSearch documents two broad approaches. Score-based methods normalize and combine scores; reciprocal rank fusion (RRF) combines positions in ranked lists rather than raw score magnitudes. Neither is universally best.
| Approach | What it combines | Useful starting point | What to tune or watch |
|---|---|---|---|
| Score normalization and combination | Normalizes clause scores, then combines them using a selected technique and optional weights. Documented normalization options include min-max, L2, and z-score; combination options include arithmetic, geometric, and harmonic techniques. | When differences in score strength should influence the final ranking or you need finer score controls. | Evaluate normalization, combination method, and weights on judgments from the target application. A normalization can behave poorly with a particular score distribution. |
| Reciprocal rank fusion (RRF) | Uses each document’s rank in each clause’s results, not the raw score values. A document near the top of multiple lists can outrank one near the top of only one. | When clause scores have different scales or you want a rank-based starting point before score calibration. | Evaluate rank constant and weights. RRF scores are rank signals, not calibrated probabilities; do not treat them as comparable quality scores across queries or apply a generic min_score. |
OpenSearch’s current RRF documentation gives a default rank_constant of 60. That setting compresses absolute RRF values, so interpret the ranks contributing to the result rather than treating the final _score as a universal measure of relevance. For the documented methods and configuration details, see Hybrid search and Reciprocal rank fusion.
Build the data and query paths
A usable semantic route depends on compatible embeddings for documents and queries, a vector field, and the ingestion and search configuration that connects those pieces. OpenSearch’s tutorial demonstrates generating document embeddings during ingestion and using a neural query at search time.
- Choose an embedding model. Confirm its configuration and vector dimensions; do not assume the dimensions in an example fit your model.
- Prepare the index and ingestion path. Map source text into a vector field through an ingest pipeline, and index records with both the searchable text and vector fields.
- Configure a search pipeline. Select the documented score normalization and combination flow or RRF to combine clause results.
- Submit a top-level hybrid query. Include lexical and semantic clauses, then send the search request through the pipeline.
- Evaluate and adjust. Compare results against relevance judgments representative of your application before settling on weights or fusion settings.
OpenSearch provides automated workflows for a more provisioned setup and manual configuration when you want component-level control. Its Getting started with semantic and hybrid search tutorial illustrates the setup path.
Rank #3
Keep the query shape and result depth in mind
The hybrid query is intended to sit at the top level. The query reference warns that wrapping it in constructs such as function_score, constant_score, script_score, or boosting can fail or bypass the expected normalization pipeline. If a score-boosting function is required, OpenSearch documents a Boolean-query alternative, but the hybrid normalization pipeline will not run in that arrangement.
Pagination depth also matters: it affects how many documents each clause contributes to normalization and combination, and can therefore change both the depth and order of final results. Test the depths your application will actually request.
Rank #4
Evaluate on the production-shaped system
There is no universally best fusion method, weight, or normalization choice. OpenSearch’s optimization guidance says relevance depends on the corpus, user behavior, and application domain. Use a judged query set that reflects real searches and assess outcomes with measures appropriate to the application. Compare configurations on the same shard layout planned for production.
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For further tuning considerations, see Daniel Wrigley’s OpenSearch article, Optimizing hybrid search in OpenSearch (dated December 30, 2024; the page also displays June 18, 2025).
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