From 5a2be87f658d01bc5b3b7ecdba92ac69bf09dcf7 Mon Sep 17 00:00:00 2001
From: ai_xiaopei <xiaopei@aisim.cn>
Date: Thu, 03 Sep 2026 15:15:37 +0800
Subject: [PATCH] fix(search): 搜索准确性四连修——FTS5 MATCH 引号包裹(连字符不再被解析成 MINUS);KeywordSearch 截断按命中关键词数排序(高频 bigram 不再挤出强相关文档);原词奖励档(完整短语压过泛化 bigram);tags 字段真实参与评分(此前误用 Section)
---
internal/search/engine.go | 60 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++----
1 files changed, 56 insertions(+), 4 deletions(-)
diff --git a/internal/search/engine.go b/internal/search/engine.go
index a1dd52a..b21224d 100644
--- a/internal/search/engine.go
+++ b/internal/search/engine.go
@@ -36,8 +36,24 @@
// 合并所有关键词
allKeywords := append(append(keywords, opts.Expanded...), opts.Symptom...)
+ // 长 CJK 词 bigram 展开(统一下沉):FTS5 unicode61 把连续中文当整串单 token,
+ // 长复合词整串 LIKE 匹配不到;ExpandCJKKeywords 拆成 bigram 滑动窗口
+ // (原词保留、去重保序)。bigram 参与检索,textScore 按 ScoreExpanded 档计权
+ // (与现有 Expanded 机制一致),原词仍按原词档。
+ searchKeywords := ExpandCJKKeywords(allKeywords)
+ originalSet := make(map[string]bool, len(allKeywords))
+ for _, kw := range allKeywords {
+ originalSet[kw] = true
+ }
+ var bigrams []string
+ for _, kw := range searchKeywords {
+ if !originalSet[kw] {
+ bigrams = append(bigrams, kw)
+ }
+ }
+
// 1. 双通道检索(ASCII 走 FTS,CJK 走 LIKE)
- candidates, err := store.KeywordSearch(allKeywords, 100)
+ candidates, err := store.KeywordSearch(searchKeywords, 100)
if err != nil {
return nil, err
}
@@ -55,12 +71,43 @@
for _, kw := range opts.Expanded {
score += scoreResult(r, kw, ScoreExpanded)
}
+ // bigram 展开词按 Expanded 档计权(与用户显式扩展词同档)
+ for _, kw := range bigrams {
+ score += scoreResult(r, kw, ScoreExpanded)
+ }
for _, kw := range opts.Symptom {
score += scoreResult(r, kw, ScoreSymptom)
}
textScore[r.ID] = score
}
+ // 原词奖励:候选由 bigram 召回时可能并不含用户原词("无法"×10 压过
+ // 含"无法开机"整词的文档)。原词真实出现在正文/标题/标签中时给强信号加分,
+ // 使"标题=原词"或"tags 含原词"的文档稳定压过只含泛化 bigram 的文档。
+ for _, r := range candidates {
+ content, _, _, err := store.GetNodeContent(r.ID)
+ if err != nil {
+ continue
+ }
+ lc := strings.ToLower(content)
+ titleL := strings.ToLower(r.Title)
+ for _, kw := range keywords {
+ kwL := strings.ToLower(kw)
+ hit := strings.Contains(lc, kwL) || strings.Contains(titleL, kwL)
+ if !hit {
+ for _, t := range r.Tags {
+ if strings.Contains(strings.ToLower(t), kwL) {
+ hit = true
+ break
+ }
+ }
+ }
+ if hit {
+ textScore[r.ID] += CalcScore(kw, "content", ScoreOriginal)
+ }
+ }
+ }
+
// 3. RWR 图质量(种子 = 候选前 20,全量加载邻接后按种子收敛)
seedIDs := make([]int64, 0, len(candidates))
for _, r := range candidates {
@@ -183,9 +230,14 @@
score += CalcScore(keyword, "title", scoreType)
}
- // 板块匹配
- if strings.Contains(strings.ToLower(r.Section), kw) {
- score += CalcScore(keyword, "tag", scoreType)
+ // 标签匹配(tags 字段真实参与评分;此前误用 Section,而 Section 恒为"其他")
+ if len(r.Tags) > 0 {
+ for _, t := range r.Tags {
+ if strings.Contains(strings.ToLower(t), kw) {
+ score += CalcScore(keyword, "tag", scoreType)
+ break
+ }
+ }
}
// 别名匹配(命中按 title 档计权,一次命中即计)
--
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