# Hybrid search

> Hybrid search runs keyword search and semantic search on the same query and merges the results, so it catches exact terms and paraphrased meaning together.

- Page: https://methodcrew.com/glossary/hybrid-search
- Last updated: 2026-10-28
- Publisher: [The Method Crew](https://methodcrew.com/)
- Also known as: hybrid retrieval

## How it works

Keyword search, usually scored with the BM25 algorithm, is precise on names, codes and rare terms. [Semantic search](https://methodcrew.com/glossary/semantic-search) is good at paraphrase. Hybrid search sends the query to both, then combines the 2 ranked lists. A common way to combine them is [reciprocal rank fusion](https://www.elastic.co/docs/reference/elasticsearch/rest-apis/reciprocal-rank-fusion) (Elastic's documentation shows it merging a BM25 query with a vector query), which rewards documents that rank well in either list and does not need the 2 scoring systems to share a scale. Some systems weight one side more, for example favoring keywords when a query looks like an order number.

A reranker often comes next: a model that reads the query with each of the top candidates and puts them in final order.

Elasticsearch, OpenSearch, Weaviate, Qdrant and Postgres (full-text search plus pgvector) can all run hybrid search, so it rarely needs an extra product.

## How businesses use it

Hybrid search is our default retrieval setup for [RAG knowledge systems](https://methodcrew.com/services/rag-knowledge-systems), because business documents mix plain language with exact identifiers:

- Support content full of product names, error codes and version numbers
- Contracts where a clause number matters as much as the topic
- Parts catalogs, with SKUs next to free-text descriptions
- Policies that use internal acronyms nobody outside the company would recognize

## Where it goes wrong

Hybrid search adds tuning work. The keyword side needs sensible text processing for your language and field, and the weighting between the 2 sides has to be tested on real queries from your logs. Rerankers add latency and a per-query cost, so use them where answer quality matters more than speed.

The bigger risk is skipping measurement. Without a test set of real questions and the documents that should answer them, nobody can tell whether a change helped or hurt. We build that test set before tuning anything, and it stays with you after handover so your team can rerun it.

## Example (hypothetical)

Hypothetical: a 150-person industrial supplier's assistant answers questions about part 7731-C with details from part 7731-A. Adding keyword search alongside the vector index and fusing the 2 result lists puts the exact part's datasheet first.

## Related terms

- [Semantic search](https://methodcrew.com/glossary/semantic-search): Semantic search finds results by meaning instead of exact words, so a query can match relevant content even when they share no vocabulary.
- [Embeddings](https://methodcrew.com/glossary/embeddings): Embeddings are lists of numbers that capture the meaning of text or images, so software can measure how closely related one item is to another.
- [Vector database](https://methodcrew.com/glossary/vector-database): A vector database stores embeddings, the numeric fingerprints of text or images, and quickly finds the stored items whose meaning is closest to a query.
- [Retrieval-augmented generation (RAG)](https://methodcrew.com/glossary/retrieval-augmented-generation): Retrieval-augmented generation is a method where a system first searches your own documents for relevant passages, then gives them to a language model to answer from.
- [Chunking](https://methodcrew.com/glossary/chunking): Chunking is splitting documents into smaller passages before they are embedded and indexed, so a retrieval system can find and return the exact part that answers a question.

## Services where this matters

- [RAG and knowledge systems](https://methodcrew.com/services/rag-knowledge-systems): Your documents turned into an assistant that cites its sources. $15,000 to $75,000 per project.

## Articles that use this term

- [How to build a company knowledge assistant that cites its sources](https://methodcrew.com/insights/build-rag-knowledge-assistant)

Full glossary: https://methodcrew.com/glossary