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Teams using lexical-only search should add semantic retrieval alongside keyword matching before considering any replacement. In OpenSearch, a hybrid query can retrieve results from both lexical and vector clauses, while a search pipeline combines their rankings. Whether that improves relevance—or meets your latency, resource, and filtering requirements—depends on your data and query mix, so evaluate it against your current search before rollout.
Why migrate to hybrid search first?
Keyword search remains useful when a query contains an exact product name, identifier, phrase, or other term that must match. OpenSearch uses BM25 as its default keyword-scoring algorithm, but lexical matching can miss a relevant document when it expresses the same idea with different words. Dense vector retrieval uses embeddings to find semantically related content; it can broaden retrieval, but it does not make exact-term matching obsolete.
A staged migration keeps both behaviors available while you measure the effect of semantic retrieval. It also gives you a direct comparison point: the existing lexical ranking is the baseline, and the hybrid ranking is a candidate configuration rather than an assumed improvement.
Plan the migration in six steps
1. Record a lexical baseline
Before changing the index or ranking, collect representative queries and document what good results look like for each. Include both intent-based queries and exact-term queries, such as names or identifiers. Record current relevance judgments or accepted results, latency, and how filters affect the returned documents. Keep this set fixed while comparing candidate configurations so ranking changes are visible.
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2. Choose how to create embeddings
OpenSearch supports ingesting vectors generated elsewhere or generating embeddings through an ingest pipeline. With pipeline-generated embeddings, retain the source text field and map it to the embedding output field. This preserves the text needed for keyword search and gives you a traceable input for the vector representation.
Choose a compatible embedding model and plan how its vectors will be produced and maintained. The appropriate model and operating arrangement depend on your data and deployment; the OpenSearch documentation does not establish a universally best model for every workload.
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3. Create a vector-capable index
Configure the index for k-NN and declare a knn_vector field. Its dimension setting must match the vectors produced by the selected model. Choose the vector data type, distance space, and indexing method for the workload, then validate the resulting index with representative data rather than treating an example configuration as a production recommendation.
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Build a hybrid query with a lexical clause and a semantic clause. Attach a search pipeline that combines their results. OpenSearch documents two broad combination approaches: score normalization, which normalizes clause scores before combining them, and reciprocal rank fusion (RRF), which combines results according to their positions in each ranking rather than the raw scores.
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Neither approach should be assumed to win in advance. Compare normalization and RRF on the same query set and relevance judgments. With normalization, the normalization and combination techniques—and any weights you choose—are part of the experiment. With RRF, rank positions drive fusion, so compare its ordering against the results your users need.
5. Evaluate relevance and operational cost
Measure both search quality and system behavior. Include exact-term and intent-based queries, as well as the filters users actually apply. Track p95 and p99 latency, indexing throughput, memory and CPU use, vector index size, recall, and returned-result counts. Also account for the effort of operating the embedding process and the ease of returning to the previous ranking.
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OpenSearch’s tuning guidance emphasizes that performance depends on the dataset and available node resources. Its discussion names Faiss as a possible choice when indexing throughput matters and Lucene as a candidate for relatively smaller datasets; these are workload-dependent starting points to benchmark, not universal engine recommendations.
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Test filter placement with the exact constraints and selectivity found in production. OpenSearch documents efficient filtering during k-NN search for supported engines and methods. By contrast, post-filtering can return fewer than k results when the filter is selective, because filtering occurs after approximate retrieval. Exact scoring-script filtering can also become slow when it must score a large filtered subset.
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Some faceted-aggregation cases may have separate reasons to post-filter, so do not treat one filter strategy as correct for every query. Verify whether returned documents satisfy the required constraints and whether the result count is sufficient for each important filter pattern.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Choose a retrieval approach for the job
| Approach | How it retrieves | What to weigh |
|---|---|---|
| Lexical search | Matches terms using keyword scoring; BM25 is OpenSearch’s default keyword-scoring algorithm. | Retains exact-term behavior, but can miss semantically related content that uses different wording. |
| Dense semantic search | Embeddings stored in a vector field are searched with k-NN. | Can retrieve by meaning; dense methods can consume substantial memory and CPU. The vector mapping must use dimensions compatible with the model. |
| Neural sparse search | Sparse token-weight representations are searched through an inverted index. | OpenSearch describes its efficiency as similar to BM25. Neural sparse ANN support is identified as introduced in OpenSearch 3.3; check the deployed version before depending on that mode. |
| Hybrid search | Combines lexical and semantic query results through a search pipeline. | Preserves access to lexical matching while adding semantic retrieval. Compare score normalization and RRF using your own relevance judgments and workload measurements. |
Dense and neural sparse retrieval are not interchangeable implementation details: the former uses dense vectors and k-NN, while the latter uses sparse token weights and an inverted index. Neural sparse retrieval can also be combined with dense semantic search. Which path to use depends on the deployed OpenSearch version, workload, and measured results.
Make filtering behavior part of the relevance test
Filtering changes more than query syntax: its placement can affect which documents qualify and how many results are returned. For each important filter, record its selectivity, whether all returned results must satisfy it, and the minimum useful result count. Then test filtering during k-NN search where supported, and compare that behavior with any post-filtering or exact-scoring approach you are considering.
- Confirm that every returned document meets the intended constraints.
- Measure result counts for selective filters, not just broad filters.
- Check latency and resource use for the actual filtered subsets.
- Use a post-filter only where its behavior fits the use case, including any separate needs around faceted aggregations.
Promote a candidate only after a controlled comparison
Run the lexical baseline and each candidate hybrid setup on the same representative queries and data. Keep exact-term and intent-based judgments visible as separate cases: an overall score can conceal a regression on a query type users rely on. Compare relevance alongside p95/p99 latency, recall, indexing throughput, memory and CPU consumption, index size, filter behavior, and embedding operations.
There is no workload-independent answer for the best embedding model, engine, vector settings, fusion method, or hybrid weights. Set acceptance thresholds from your service requirements, test with representative workload data, and retain the lexical configuration as a rollback option until the candidate meets those thresholds. OpenSearch’s documentation provides configuration patterns and tuning considerations, not a guarantee of production performance for a particular deployment.
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