--- title: "Hybrid vs vector vs semantic search" canonical: "https://xerj.org/science/hybrid-vs-vector-vs-semantic-search" updated: "2026-11-14" source: "content/science/hybrid-vs-vector-vs-semantic-search.md" --- # Hybrid vs vector vs semantic search **Short answer** (updated 2026-11-14): What is the difference between hybrid, vector, and semantic search? Vector search ranks passages by closeness in a vector space. Semantic search is the usual name for ranking by meaning, and it almost always uses vectors. Hybrid search also runs a full-text arm, such as BM25, and fuses the two lists. **TL;DR** Vector search ranks by closeness in a vector space. Semantic search is the usual name for ranking by meaning, and it almost always uses vectors. Hybrid search also runs a full-text arm, such as BM25, and fuses the two lists. ## Agent prompt ```text Act as a coding agent. Read https://xerj.org/llms.txt, install XERJ, start a local node, index one folder I point you to, and run one search so I can see a cited passage. ``` *Figure: This comparison is a teaching account, not a new benchmark. Measured hybrid scores stay on the hybrid-search how-to and the published quality answers.* ## How the three differ A full-text ranker such as [BM25](/science/bm25) scores a document by the query terms it contains, how rare those terms are, and document length. It is strong on exact tokens: names, error strings, identifiers. [Vector search](/science/semantic-search) turns the query and each passage into vectors and ranks by closeness. It can match a paraphrase. It is weaker where two similar identifiers sit close together. Semantic search is the usual name for ranking by meaning. In practice that almost always means a vector query. The names are close, and product pages sometimes treat them as the same thing. This page keeps both phrases because people search for both. [Hybrid search](/science/hybrid-search) runs both a full-text arm and a vector arm on the same question, then merges the ranks. Reciprocal rank fusion is a common way to merge them without mixing raw scores. The formula is on the [reciprocal rank fusion](/science/reciprocal-rank-fusion) page. ## How XERJ uses it XERJ answers a full-text query with BM25 by default. Neural or hybrid mode adds a vector arm. The default embedder is lexical and runs offline. The how-to, including the request shape, is [how to run hybrid search in XERJ](/answers/what-is-hybrid-search). A side-by-side of the two arms is [BM25 vs vector search](/answers/bm25-vs-vector-search). This page does not add a new quality number. ## History and research Dense vector retrieval is described by Karpukhin and colleagues in [Dense Passage Retrieval for Open-Domain Question Answering](https://arxiv.org/abs/2004.04906) (EMNLP 2020). BM25 is surveyed by Robertson and Zaragoza in [The Probabilistic Relevance Framework: BM25 and Beyond](https://doi.org/10.1561/1500000019) (2009). Fusion is described by Cormack, Clarke and Buettcher in [Reciprocal Rank Fusion outperforms Condorcet and individual Rank Learning Methods](https://cormack.uwaterloo.ca/cormacksigir09-rrf.pdf) (SIGIR 2009). Those papers are the research sources. They are not XERJ measurements. ## Guides For a hands-on view, read [vector database vs full-text search](/answers/vector-database-vs-full-text-search), [BM25 vs vector search](/answers/bm25-vs-vector-search) and the [vectors reference](/docs/vectors). ## Related terms [Hybrid search](/science/hybrid-search), [BM25](/science/bm25), [semantic search](/science/semantic-search), and [reciprocal rank fusion](/science/reciprocal-rank-fusion). ## FAQ ### What is vector search? It ranks passages by closeness in a vector space. A paraphrase can match even when it shares few words with the query. ### Is semantic search the same as vector search? In practice, almost always. Semantic search means ranking by meaning, and the usual method is a vector query. Some writers use the names as if they were identical. ### What does hybrid search add? It also runs a full-text arm, such as BM25, and fuses the two ranked lists. Exact names and close meanings can both appear in the results. ### Where should I start in XERJ? The concept pages are hybrid search, BM25, and semantic search. The how-to is the hybrid search answer. This page is only the comparison. ## Related - [What is hybrid search?](/science/hybrid-search) - [BM25 vs vector search](/science/bm25) - [What is semantic search?](/science/semantic-search) - [Reciprocal rank fusion formula](/science/reciprocal-rank-fusion) - [How to run hybrid search in XERJ](/answers/what-is-hybrid-search) - [BM25 vs vector search: which one should you use?](/answers/bm25-vs-vector-search) - [Neurosymbolic AI vs LLMs](/science/neurosymbolic-ai) (same intent: Science) - [What is syntax-aware search?](/science/syntax-aware-search) (same intent: Science) - [What is agentic memory?](/science/agentic-memory) (same intent: Science)