hotai.news
← Back to AI WikiAI Wiki / Core concepts

What is full-text search? Indexing, term matching, and ranking

Understand full-text search through product-model queries, distinguishing indexed terms, literal lookup, and semantic retrieval, with analyzers, fields, and missed-result checks.

HotAI.news · Sources checked with CodexPublished on 2026-10-20Sources checked on 2026-10-20
Our take

Understand the concept

Full-text search indexes text to retrieve and rank matches for query terms. It often tokenizes or normalizes rather than scanning every original character, and does not guarantee every literal substring match. Fields and language analysis affect matching. Exact names suit lexical search, while paraphrases may need additional handling or semantic retrieval.

01

Indexes process text before query matching

An analyzer may split a hyphenated product code into terms, producing different behavior from literal matching. PostgreSQL and Azure describe analysis as central to retrieval. Inspect actual analyzed terms rather than assuming visible characters ensure a hit.

Case handling and stop words can change queries; Chinese needs suitable language analysis. Full-text does not mean every field is indexed. Title-only indexing can miss a model code in the body.

02

Distinguish relevance ranking from filtering

Ranking can consider term frequency and length; Azure uses BM25 for text relevance. Filters restrict records, such as current versions. High relevance is not truth, and unfiltered old documentation can outrank new material.

Exact identifiers need suitable field types and syntax rather than necessarily natural-language queries. Quotes and special characters are implementation-specific. This explanation includes no search-instance connection or universal query statement.

03

Diagnose misses and false matches with known documents

For a known document, check body indexing, analyzed terms, and filters. Ranking changes cannot recover unindexed content. Split identifiers call for analyzer or field adjustments; locate the failing stage first.

Semantic or hybrid retrieval may help missed paraphrases, evaluated on fixed questions. Choose lexical and vector approaches by task, not novelty. Preserve versions and source positions for later answer verification.

Checked sources

These sources support the concepts and methods described here. Examples and test scope are labeled separately; check the version of the tools you use.

Back to AI Wiki ↗