Semantic search compares learned vector representations, so it can match related meanings across different wording. BM25 (Best Matching 25) uses exact terms, term frequency, document length, and word rarity to rank documents.
Embedding search is strong for natural-language questions, synonyms, and paraphrases. BM25 is strong for names, codes, error strings, numbers, and rare terms. Dense search may return a conceptually related but wrong item, while keyword search may miss a good paraphrase.
Many production systems use both. They retrieve candidates from each method, combine the rankings, and optionally rerank them with a stronger model. Evaluate all variants carefully on the same relevance dataset.
This answer doesn't lend itself to a diagram - it reads best . No credits were charged.
Why there's no diagram: “”
The interactive diagram is below the answer - jump to diagram ↓ · Below it, the related concept . Jump to it ↓
The diagram below the answer is the concept . Jump to it ↓