Unverified50% confidenceFactExact time
Keyword matching splits a query into terms and relies on inverted indexes and statistical signals such as TF-IDF or BM25 to judge relevance; semantic matching encodes the entire context into a dense vector and measures semantic closeness with cosine similarity.
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50%
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Long-term
Relevance
10/21/2026
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Related Claims
UnverifiedA common approach to relevance ranking is a TF-IDF/BM25-like weighted model that also considers field weights; MiniSearch allows configuring weights for different fields and supports applying different score coefficients to prefix-match and fuzzy-match results.80% similarVerifiedHybrid Search combines dense vector semantic similarity with BM25 sparse keyword matching, with results from both paths re-ranked by a Re-Ranker model before being injected into the LLM context.77% similarVerified混合检索（Hybrid Search）将向量检索与BM25关键词检索的结果通过RRF（倒数排名融合）等算法合并，兼顾语义理解与精确匹配76% similar
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