mirror of
https://github.com/Ed1s0nZ/CyberStrikeAI.git
synced 2026-03-31 16:20:28 +02:00
352 lines
9.5 KiB
Go
352 lines
9.5 KiB
Go
package knowledge
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import (
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"context"
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"database/sql"
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"encoding/json"
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"fmt"
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"regexp"
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"strings"
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"github.com/google/uuid"
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"go.uber.org/zap"
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)
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// Indexer 索引器，负责将知识项分块并向量化
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type Indexer struct {
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db *sql.DB
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embedder *Embedder
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logger *zap.Logger
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chunkSize int // 每个块的最大token数（估算）
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overlap int // 块之间的重叠token数
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}
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// NewIndexer 创建新的索引器
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func NewIndexer(db *sql.DB, embedder *Embedder, logger *zap.Logger) *Indexer {
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return &Indexer{
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db: db,
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embedder: embedder,
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logger: logger,
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chunkSize: 512, // 默认512 tokens
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overlap: 50, // 默认50 tokens重叠
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}
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}
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// ChunkText 将文本分块（支持重叠）
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func (idx *Indexer) ChunkText(text string) []string {
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// 按Markdown标题分割
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chunks := idx.splitByMarkdownHeaders(text)
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// 如果块太大，进一步分割
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result := make([]string, 0)
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for _, chunk := range chunks {
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if idx.estimateTokens(chunk) <= idx.chunkSize {
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result = append(result, chunk)
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} else {
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// 按段落分割
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subChunks := idx.splitByParagraphs(chunk)
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for _, subChunk := range subChunks {
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if idx.estimateTokens(subChunk) <= idx.chunkSize {
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result = append(result, subChunk)
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} else {
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// 按句子分割（支持重叠）
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chunksWithOverlap := idx.splitBySentencesWithOverlap(subChunk)
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result = append(result, chunksWithOverlap...)
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}
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}
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}
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}
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return result
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}
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// splitByMarkdownHeaders 按Markdown标题分割
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func (idx *Indexer) splitByMarkdownHeaders(text string) []string {
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// 匹配Markdown标题 (# ## ### 等)
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headerRegex := regexp.MustCompile(`(?m)^#{1,6}\s+.+$`)
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// 找到所有标题位置
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matches := headerRegex.FindAllStringIndex(text, -1)
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if len(matches) == 0 {
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return []string{text}
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}
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chunks := make([]string, 0)
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lastPos := 0
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for _, match := range matches {
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start := match[0]
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if start > lastPos {
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chunks = append(chunks, strings.TrimSpace(text[lastPos:start]))
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}
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lastPos = start
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}
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// 添加最后一部分
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if lastPos < len(text) {
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chunks = append(chunks, strings.TrimSpace(text[lastPos:]))
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}
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// 过滤空块
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result := make([]string, 0)
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for _, chunk := range chunks {
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if strings.TrimSpace(chunk) != "" {
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result = append(result, chunk)
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}
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}
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if len(result) == 0 {
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return []string{text}
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}
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return result
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}
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// splitByParagraphs 按段落分割
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func (idx *Indexer) splitByParagraphs(text string) []string {
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paragraphs := strings.Split(text, "\n\n")
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result := make([]string, 0)
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for _, p := range paragraphs {
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if strings.TrimSpace(p) != "" {
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result = append(result, strings.TrimSpace(p))
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}
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}
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return result
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}
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// splitBySentences 按句子分割（用于内部，不包含重叠逻辑）
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func (idx *Indexer) splitBySentences(text string) []string {
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// 简单的句子分割（按句号、问号、感叹号）
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sentenceRegex := regexp.MustCompile(`[.!?]+\s+`)
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sentences := sentenceRegex.Split(text, -1)
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result := make([]string, 0)
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for _, s := range sentences {
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if strings.TrimSpace(s) != "" {
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result = append(result, strings.TrimSpace(s))
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}
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}
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return result
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}
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// splitBySentencesWithOverlap 按句子分割并应用重叠策略
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func (idx *Indexer) splitBySentencesWithOverlap(text string) []string {
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if idx.overlap <= 0 {
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// 如果没有重叠，使用简单分割
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return idx.splitBySentencesSimple(text)
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}
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sentences := idx.splitBySentences(text)
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if len(sentences) == 0 {
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return []string{}
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}
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result := make([]string, 0)
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currentChunk := ""
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for _, sentence := range sentences {
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testChunk := currentChunk
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if testChunk != "" {
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testChunk += "\n"
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}
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testChunk += sentence
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testTokens := idx.estimateTokens(testChunk)
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if testTokens > idx.chunkSize && currentChunk != "" {
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// 当前块已达到大小限制，保存它
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result = append(result, currentChunk)
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// 从当前块的末尾提取重叠部分
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overlapText := idx.extractLastTokens(currentChunk, idx.overlap)
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if overlapText != "" {
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// 如果有重叠内容，作为下一个块的起始
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currentChunk = overlapText + "\n" + sentence
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} else {
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// 如果无法提取足够的重叠内容，直接使用当前句子
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currentChunk = sentence
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}
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} else {
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currentChunk = testChunk
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}
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}
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// 添加最后一个块
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if strings.TrimSpace(currentChunk) != "" {
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result = append(result, currentChunk)
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}
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// 过滤空块
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filtered := make([]string, 0)
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for _, chunk := range result {
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if strings.TrimSpace(chunk) != "" {
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filtered = append(filtered, chunk)
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}
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}
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return filtered
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}
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// splitBySentencesSimple 按句子分割（简单版本，无重叠）
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func (idx *Indexer) splitBySentencesSimple(text string) []string {
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sentences := idx.splitBySentences(text)
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result := make([]string, 0)
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currentChunk := ""
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for _, sentence := range sentences {
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testChunk := currentChunk
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if testChunk != "" {
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testChunk += "\n"
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}
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testChunk += sentence
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if idx.estimateTokens(testChunk) > idx.chunkSize && currentChunk != "" {
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result = append(result, currentChunk)
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currentChunk = sentence
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} else {
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currentChunk = testChunk
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}
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}
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if currentChunk != "" {
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result = append(result, currentChunk)
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}
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return result
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}
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// extractLastTokens 从文本末尾提取指定token数量的内容
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func (idx *Indexer) extractLastTokens(text string, tokenCount int) string {
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if tokenCount <= 0 || text == "" {
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return ""
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}
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// 估算字符数（1 token ≈ 4字符）
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charCount := tokenCount * 4
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runes := []rune(text)
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if len(runes) <= charCount {
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return text
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}
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// 从末尾提取指定数量的字符
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// 尝试在句子边界处截断，避免截断句子中间
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startPos := len(runes) - charCount
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extracted := string(runes[startPos:])
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// 尝试找到第一个句子边界（句号、问号、感叹号后的空格）
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sentenceBoundary := regexp.MustCompile(`[.!?]+\s+`)
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matches := sentenceBoundary.FindStringIndex(extracted)
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if len(matches) > 0 && matches[0] > 0 {
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// 在句子边界处截断，保留完整句子
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extracted = extracted[matches[0]:]
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}
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return strings.TrimSpace(extracted)
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}
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// estimateTokens 估算token数（简单估算：1 token ≈ 4字符）
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func (idx *Indexer) estimateTokens(text string) int {
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return len([]rune(text)) / 4
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}
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// IndexItem 索引知识项（分块并向量化）
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func (idx *Indexer) IndexItem(ctx context.Context, itemID string) error {
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// 获取知识项（包含category和title，用于向量化）
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var content, category, title string
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err := idx.db.QueryRow("SELECT content, category, title FROM knowledge_base_items WHERE id = ?", itemID).Scan(&content, &category, &title)
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if err != nil {
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return fmt.Errorf("获取知识项失败: %w", err)
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}
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// 删除旧的向量
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_, err = idx.db.Exec("DELETE FROM knowledge_embeddings WHERE item_id = ?", itemID)
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if err != nil {
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return fmt.Errorf("删除旧向量失败: %w", err)
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}
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// 分块
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chunks := idx.ChunkText(content)
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idx.logger.Info("知识项分块完成", zap.String("itemId", itemID), zap.Int("chunks", len(chunks)))
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// 向量化每个块（包含category和title信息，以便向量检索时能匹配到风险类型）
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for i, chunk := range chunks {
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chunkPreview := chunk
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if len(chunkPreview) > 200 {
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chunkPreview = chunkPreview[:200] + "..."
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}
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// 将category和title信息包含到向量化的文本中
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// 格式："[风险类型: {category}] [标题: {title}]\n{chunk内容}"
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// 这样向量嵌入就会包含风险类型信息，即使SQL过滤失败，向量相似度也能帮助匹配
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textForEmbedding := fmt.Sprintf("[风险类型: %s] [标题: %s]\n%s", category, title, chunk)
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embedding, err := idx.embedder.EmbedText(ctx, textForEmbedding)
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if err != nil {
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idx.logger.Warn("向量化失败",
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zap.String("itemId", itemID),
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zap.Int("chunkIndex", i),
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zap.Int("chunkLength", len(chunk)),
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zap.String("chunkPreview", chunkPreview),
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zap.Error(err),
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)
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continue
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}
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// 保存向量
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chunkID := uuid.New().String()
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embeddingJSON, _ := json.Marshal(embedding)
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_, err = idx.db.Exec(
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"INSERT INTO knowledge_embeddings (id, item_id, chunk_index, chunk_text, embedding, created_at) VALUES (?, ?, ?, ?, ?, datetime('now'))",
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chunkID, itemID, i, chunk, string(embeddingJSON),
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)
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if err != nil {
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idx.logger.Warn("保存向量失败", zap.String("itemId", itemID), zap.Int("chunkIndex", i), zap.Error(err))
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continue
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}
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}
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idx.logger.Info("知识项索引完成", zap.String("itemId", itemID), zap.Int("chunks", len(chunks)))
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return nil
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}
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// HasIndex 检查是否存在索引
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func (idx *Indexer) HasIndex() (bool, error) {
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var count int
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err := idx.db.QueryRow("SELECT COUNT(*) FROM knowledge_embeddings").Scan(&count)
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if err != nil {
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return false, fmt.Errorf("检查索引失败: %w", err)
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}
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return count > 0, nil
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}
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// RebuildIndex 重建所有索引
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func (idx *Indexer) RebuildIndex(ctx context.Context) error {
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rows, err := idx.db.Query("SELECT id FROM knowledge_base_items")
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if err != nil {
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return fmt.Errorf("查询知识项失败: %w", err)
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}
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defer rows.Close()
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var itemIDs []string
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for rows.Next() {
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var id string
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if err := rows.Scan(&id); err != nil {
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return fmt.Errorf("扫描知识项ID失败: %w", err)
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}
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itemIDs = append(itemIDs, id)
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}
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idx.logger.Info("开始重建索引", zap.Int("totalItems", len(itemIDs)))
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for i, itemID := range itemIDs {
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if err := idx.IndexItem(ctx, itemID); err != nil {
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idx.logger.Warn("索引知识项失败", zap.String("itemId", itemID), zap.Error(err))
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continue
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}
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idx.logger.Debug("索引进度", zap.Int("current", i+1), zap.Int("total", len(itemIDs)))
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}
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idx.logger.Info("索引重建完成", zap.Int("totalItems", len(itemIDs)))
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return nil
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}
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