haixunMaster/haixun-backend/internal/library/placement/dual_track.go

package placement

import (
	"context"
	"fmt"
	"strings"

	libbrave "haixun-backend/internal/library/brave"
	libkg "haixun-backend/internal/library/knowledge"
)

const (
	relevanceLimitPerTag = 12
	recencyLimitPerTag   = 8
)

type ScanCandidate struct {
	Permalink       string
	ExternalID      string
	Author          string
	Text            string
	SearchTag       string
	QueryDimension  QueryDimension
	GraphNodeID     string
	ProductFitScore int
	Source          DiscoverChannel
	HasRelevance    bool
	HasRecency      bool
	Priority        string
	LikeCount       int
	ReplyCount      int
	EngagementScore int
	PlacementScore  int
	SolvedByProduct bool
	Replies         []ReplyCandidate
}

type DualTrackInput struct {
	Nodes      []libkg.Node
	Exclusions []string
	Member     MemberContext
	Client     *libbrave.Client
	Crawler    CrawlerSearchFn
	Limit      int // max queries budget; 0 = default
}

type DualTrackProgress func(message string, pct int)

// CollectTagQueries builds crawl jobs from selected graph nodes.
func CollectTagQueries(nodes []libkg.Node) []TagQuery {
	out := make([]TagQuery, 0, len(nodes)*4)
	for _, node := range nodes {
		if !node.SelectedForScan {
			continue
		}
		fit := node.ProductFitScore
		for _, tag := range node.DerivedTags.Relevance {
			tag = strings.TrimSpace(tag)
			if tag == "" {
				continue
			}
			q := BuildRelevanceQuery(tag)
			if q == "" {
				continue
			}
			out = append(out, TagQuery{
				Tag:             tag,
				Query:           q,
				Dimension:       QueryRelevance,
				GraphNodeID:     node.ID,
				ProductFitScore: fit,
			})
		}
		for _, tag := range node.DerivedTags.Recency {
			tag = strings.TrimSpace(tag)
			if tag == "" {
				continue
			}
			q7 := BuildRecencyQuery(tag, IdealMaxPostAgeDays)
			if q7 != "" {
				out = append(out, TagQuery{
					Tag:             tag,
					Query:           q7,
					Dimension:       QueryRecency,
					GraphNodeID:     node.ID,
					ProductFitScore: fit,
					RecencyDays:     IdealMaxPostAgeDays,
				})
			}
			q30 := BuildRecencyQuery(tag, MaxPostAgeDays)
			if q30 != "" && q30 != q7 {
				out = append(out, TagQuery{
					Tag:             tag,
					Query:           q30,
					Dimension:       QueryRecency,
					GraphNodeID:     node.ID,
					ProductFitScore: fit,
					RecencyDays:     MaxPostAgeDays,
				})
			}
		}
	}
	return out
}

// RunDualTrackDiscover executes relevance + recency queries and merges by permalink.
func RunDualTrackDiscover(ctx context.Context, input DualTrackInput, onProgress DualTrackProgress) ([]ScanCandidate, error) {
	queries := CollectTagQueries(input.Nodes)
	if len(queries) == 0 {
		return nil, fmt.Errorf("沒有勾選的節點或可用 tag")
	}

	merged := map[string]*ScanCandidate{}
	order := make([]string, 0, 64)

	runQuery := func(tq TagQuery, limit int) error {
		posts, channel, err := discoverForQuery(ctx, input, tq, limit)
		if err != nil {
			return err
		}
		for _, post := range posts {
			if MatchesExclusion(post.Text, input.Exclusions) {
				continue
			}
			if !PassesPlacementFilter(post.Text) {
				continue
			}
			key := post.Permalink
			if key == "" {
				continue
			}
			existing, ok := merged[key]
			if !ok {
				priority := "relevant"
				if tq.Dimension == QueryRecency {
					priority = "recent"
				}
				extID := post.ExternalID
				if extID == "" {
					if parsed, ok := ParseThreadsPostFromWebResult(post.Text, "", post.Permalink); ok {
						extID = parsed.ExternalID
					}
				}
				merged[key] = &ScanCandidate{
					Permalink:       post.Permalink,
					ExternalID:      extID,
					Author:          post.Author,
					Text:            post.Text,
					SearchTag:       tq.Tag,
					QueryDimension:  tq.Dimension,
					GraphNodeID:     tq.GraphNodeID,
					ProductFitScore: tq.ProductFitScore,
					Source:          channel,
					HasRelevance:    tq.Dimension == QueryRelevance,
					HasRecency:      tq.Dimension == QueryRecency,
					Priority:        priority,
					PlacementScore:  computePlacementScore(post.Text, tq.ProductFitScore, tq.Dimension == QueryRecency),
					SolvedByProduct: tq.ProductFitScore >= 55,
				}
				order = append(order, key)
				continue
			}
			if tq.Dimension == QueryRelevance {
				existing.HasRelevance = true
			}
			if tq.Dimension == QueryRecency {
				existing.HasRecency = true
			}
			if tq.ProductFitScore > existing.ProductFitScore {
				existing.ProductFitScore = tq.ProductFitScore
				existing.SolvedByProduct = tq.ProductFitScore >= 55
			}
		}
		return nil
	}

	total := len(queries)
	for i, tq := range queries {
		if onProgress != nil {
			pct := 10 + ((i + 1) * 75 / max(total, 1))
			onProgress(fmt.Sprintf("雙軌海巡 %d/%d：%s", i+1, total, tq.Tag), pct)
		}
		limit := relevanceLimitPerTag
		if tq.Dimension == QueryRecency {
			limit = recencyLimitPerTag
		}
		if err := runQuery(tq, limit); err != nil {
			return nil, err
		}
	}

	out := make([]ScanCandidate, 0, len(order))
	for _, key := range order {
		item := merged[key]
		if item.HasRelevance && item.HasRecency && item.ProductFitScore >= 45 {
			item.Priority = "gold"
		} else if item.HasRecency {
			item.Priority = "recent"
		} else {
			item.Priority = "relevant"
		}
		if item.ProductFitScore < 30 && item.Priority != "gold" {
			continue
		}
		item.PlacementScore = computePlacementScore(item.Text, item.ProductFitScore, item.HasRecency)
		item.SolvedByProduct = item.ProductFitScore >= 55
		out = append(out, *item)
	}
	if onProgress != nil {
		onProgress(fmt.Sprintf("合併完成，共 %d 篇候選貼文", len(out)), 90)
	}
	return out, nil
}

func discoverForQuery(ctx context.Context, input DualTrackInput, tq TagQuery, limit int) ([]DiscoverPost, DiscoverChannel, error) {
	req := DiscoverRequest{
		Query:   tq.Query,
		Keyword: tq.Tag,
		Recency: tq.Dimension == QueryRecency,
		Limit:   limit,
		Member:  input.Member,
		Crawler: input.Crawler,
	}
	posts, channel, err := Discover(ctx, req)
	if err == nil && len(posts) > 0 {
		return posts, channel, nil
	}
	if input.Client == nil || !input.Client.Enabled() {
		if err != nil {
			return nil, "", err
		}
		return nil, "", fmt.Errorf("Brave 未設定且 Threads API 無結果")
	}
	bravePosts, berr := discoverViaBrave(ctx, input.Client, input.Member, tq.Query, limit)
	if berr != nil {
		if err != nil {
			return nil, "", err
		}
		return nil, "", berr
	}
	return bravePosts, DiscoverBrave, nil
}

func discoverViaBrave(ctx context.Context, client *libbrave.Client, member MemberContext, query string, limit int) ([]DiscoverPost, error) {
	res, err := client.Search(ctx, libbrave.SearchOptions{
		Query:      query,
		Limit:      limit,
		Mode:       libbrave.ModeThreadsDiscover,
		Country:    member.BraveCountry,
		SearchLang: member.BraveSearchLang,
	})
	if err != nil {
		return nil, err
	}
	if res.Status != "success" || len(res.Results) == 0 {
		return nil, nil
	}
	out := make([]DiscoverPost, 0, len(res.Results))
	for _, item := range res.Results {
		parsed, ok := ParseThreadsPostFromWebResult(item.Title, item.Snippet, item.URL)
		if !ok {
			continue
		}
		out = append(out, DiscoverPost{
			Text:       parsed.Text,
			Permalink:  parsed.Permalink,
			ExternalID: parsed.ExternalID,
			Author:     parsed.Author,
			Source:     DiscoverBrave,
		})
	}
	return out, nil
}

func computePlacementScore(text string, productFit int, recent bool) int {
	score := 30 + productFit/4
	if HasPlacementIntent(text) {
		score += 20
	}
	if LooksLikeRecommendationPost(text) {
		score += 12
	}
	if recent {
		score += 15
	}
	if productFit >= 60 {
		score += 8
	}
	if score > 100 {
		return 100
	}
	return score
}

func max(a, b int) int {
	if a > b {
		return a
	}
	return b
}