Semantic search — after an editor types one sentence

The editor types a sentence; the AI reads the intent, recalls candidates two ways, reranks, and returns explainable results. It works like a talent show:

Step	What happens	Tech
⓪ Read the need	The AI parses your sentence: pulls conditions (region/award/mood…), imagines what the best-fit film looks like	LLM query understanding: taxonomy tag signals + HyDE plot guess + keywords
① Recall	Two broad sweeps for candidates: semantic (close in meaning) + literal (word hits)	vector recall (bge-m3+Qdrant) + BM25 (FTS5+jieba)
② Wildcard	Films not recalled but matching strong conditions (region/award…) get an invite — guaranteed to appear	strong-condition tag injection
③ Judge rerank	The model scores each candidate against your query pairwise and reorders	cross-encoder (bce-reranker)
④ Condition boost	Films matching your conditions move up; wildcards only appear, rank stays	14-dim weighted boost (no hard filter, never empty)
⑤ Honest match	Three tiers by raw-sentence × library cosine: strong caps at 95% / partial at 68% / no-match at 42% + warning. #1's % reflects "is it really there," never a fake 100%	3 confidence tiers (cosine; CE only orders, see Honest match)

Every result card explains "why this one": matches [music][american] · semantic+guess+literal (which conditions, and how it was found — see Explainable results).

→ Want to see real output? Click a preset query on the Search replay.