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common/rag/gse/utils.go

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package gse
import (
"context"
"sort"
"github.com/go-ego/gse"
"github.com/go-ego/gse/hmm/extracker"
"github.com/go-ego/gse/hmm/segment"
"github.com/gogf/gf/v2/os/glog"
)
var GseTool *gseTool
// 初始化函数:程序启动时执行一次
func init() {
var err error
GseTool, err = newGseTool()
if err != nil {
glog.Error(context.Background(), err)
}
}
// gseTool 关键词提取工具gse v1.0.2 标准)
type gseTool struct {
seg gse.Segmenter
tfidf *extracker.TagExtracter
tr *extracker.TextRanker
}
// newGseTool 初始化工具(内置词典 + 停用词)
func newGseTool() (tool *gseTool, err error) {
// 1. 初始化分词器
var seg gse.Segmenter
// 内置词典(无外部文件)
err = seg.LoadDictEmbed()
if err != nil {
return
}
// 内置停用词v1.0.2 标准)
err = seg.LoadStopEmbed()
if err != nil {
return
}
// 2. 初始化 TF-IDF 提取器
tfidf := &extracker.TagExtracter{}
tfidf.WithGse(seg)
err = tfidf.LoadIdf()
if err != nil {
return
}
// 3. 初始化 TextRank 提取器
tr := &extracker.TextRanker{}
tr.WithGse(seg)
tool = &gseTool{
seg: seg,
tfidf: tfidf,
tr: tr,
}
return
}
// Cut 分词(关键词提取唯一正确模式:精确模式 + HMM
func (k *gseTool) Cut(text string) []string {
return k.seg.Cut(text, true)
}
// Keyword 最终输出:关键词 + 权重
type Keyword struct {
Word string `json:"word"`
Score float64 `json:"score"`
}
func (k *gseTool) Extract(text string, topN int) []Keyword {
// 1. 提取 TF-IDF
tfTags := k.extractTFIDF(text, topN)
// 2. 提取 TextRank
trTags := k.extractTextRank(text, topN)
// 3. 合并成最终关键词(业务最常用)
scoreMap := make(map[string]float64)
for _, tag := range tfTags {
scoreMap[tag.Text] = tag.Weight
}
for _, tag := range trTags {
scoreMap[tag.Text] = tag.Weight
}
// 转成切片并排序(高分在前)
res := make([]Keyword, 0, len(scoreMap))
for word, score := range scoreMap {
res = append(res, Keyword{Word: word, Score: score})
}
sort.Slice(res, func(i, j int) bool {
return res[i].Score > res[j].Score
})
return res
}
// ExtractTFIDF TF-IDF 关键词带权重90% 业务:文章标签、搜索、关键词
func (k *gseTool) extractTFIDF(text string, topN int) segment.Segments {
return k.tfidf.ExtractTags(text, topN)
}
// ExtractTextRank TextRank 关键词(带权重)长文本、摘要、语义理解
func (k *gseTool) extractTextRank(text string, topN int) segment.Segments {
return k.tr.TextRank(text, topN)
}