An inverted index maps terms to documents, allowing retrieval without scanning every document. Analyzers normalize text through tokenization, case normalization, stemming, synonyms, and language-specific rules. These choices change recall and precision.

Ranking may combine lexical relevance such as BM25 with freshness, field weights, popularity, personalization, and business constraints. Vector similarity can retrieve semantically related material but may miss exact terms or violate product expectations unless combined with lexical retrieval and filters.

Evaluate relevance with representative queries and labeled judgments, not just latency. Monitor zero-result searches, click-through, index freshness, and query failures. Search is a user-facing product feature, and a fast response can still be irrelevant or unauthorized.