Traditional full-text search indexing in PostgreSQL relies on whitespace tokenization, which fails for logographical languages like Chinese, Japanese, and Korean where words are written continuously.

This causes entire sentences to become single lexemes, making keyword searches miss relevant matches.

Developers have resorted to third-party extensions with static dictionaries or external preprocessing pipelines, but these introduce operational friction: unsupported extensions in managed environments, network latency, data exposure risks, and inability to handle semantic ambiguity or modern jargon.