package search

import (
	"sort"
	"strings"

	"github.com/aisim/kb-cli/internal/index"
)

// SearchOptions 搜索选项
type SearchOptions struct {
	Expanded []string // 扩展词
	Symptom  []string // 症状词
	TopN     int      // 返回前 N 条
}

// SearchResult 搜索结果
type SearchResult struct {
	ID      int64
	Path    string
	Title   string
	Section string
	Score   int
}

// Search 执行搜索
func Search(store *index.Store, keywords []string, opts SearchOptions) ([]SearchResult, error) {
	// 合并所有关键词
	allKeywords := append(keywords, opts.Expanded...)
	allKeywords = append(allKeywords, opts.Symptom...)

	// FTS5 搜索
	ftsResults, err := store.FTSSearch(allKeywords, 100)
	if err != nil {
		return nil, err
	}

	// 评分
	scoreMap := make(map[int64]int)
	for _, r := range ftsResults {
		score := 0

		// 关键词匹配评分
		for _, kw := range keywords {
			score += scoreResult(r, kw, ScoreNormal)
		}
		for _, kw := range opts.Expanded {
			score += scoreResult(r, kw, ScoreExpanded)
		}
		for _, kw := range opts.Symptom {
			score += scoreResult(r, kw, ScoreSymptom)
		}

		scoreMap[r.ID] = score
	}

	// 转换为结果列表
	var results []SearchResult
	for _, r := range ftsResults {
		results = append(results, SearchResult{
			ID:      r.ID,
			Path:    r.Path,
			Title:   r.Title,
			Section: r.Section,
			Score:   scoreMap[r.ID],
		})
	}

	// 按分数排序
	sort.Slice(results, func(i, j int) bool {
		return results[i].Score > results[j].Score
	})

	// 限制返回数量
	if opts.TopN > 0 && len(results) > opts.TopN {
		results = results[:opts.TopN]
	}

	return results, nil
}

// scoreResult 计算单个结果的得分
func scoreResult(r index.FTSResult, keyword string, scoreType ScoreType) int {
	score := 0
	kw := strings.ToLower(keyword)

	// 路径匹配
	if strings.Contains(strings.ToLower(r.Path), kw) {
		score += CalcScore(keyword, "path", scoreType)
	}

	// 标题匹配
	if strings.Contains(strings.ToLower(r.Title), kw) {
		score += CalcScore(keyword, "title", scoreType)
	}

	// 板块匹配
	if strings.Contains(strings.ToLower(r.Section), kw) {
		score += CalcScore(keyword, "tag", scoreType)
	}

	// 内容匹配（FTS 已经匹配，给基础分）
	score += CalcScore(keyword, "content", scoreType)

	return score
}