bvandeusen
5b79aa1047
Adds a 6th argument (currentVector SessionVector) to LoadCandidates, bulk-fetches the user's active contextual_likes in one query, and sets ContextualMatchScore on each candidate's ScoringInputs via max-similarity over that candidate's like vectors. Adds helper + 6 integration tests covering no-likes, single match, multi-like max, soft-delete filtering, seed-like filtering, and seed-current short-circuit. Adds contextual_likes to the TRUNCATE list in testPool to prevent cross-test leakage. internal/api/radio.go is intentionally left broken (Task 6 fixes it). Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
84 lines
2.2 KiB
Go
84 lines
2.2 KiB
Go
package recommendation
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import (
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"context"
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"encoding/json"
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"time"
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"github.com/jackc/pgx/v5/pgtype"
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"git.fabledsword.com/bvandeusen/minstrel/internal/db/dbq"
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)
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// LoadCandidates fetches the candidate pool for radio scoring. Combines
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// the existing track+stats query with a one-shot bulk fetch of the user's
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// active contextual_likes, mapping each candidate to its max similarity
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// against currentVector. Pass currentVector with Seed=true to short-circuit
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// the contextual term to 0 (cold-start path).
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func LoadCandidates(
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ctx context.Context,
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q *dbq.Queries,
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userID, seedID pgtype.UUID,
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recentlyPlayedHours int,
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currentVector SessionVector,
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) ([]Candidate, error) {
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rows, err := q.LoadRadioCandidates(ctx, dbq.LoadRadioCandidatesParams{
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UserID: userID,
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ID: seedID,
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Column3: float64(recentlyPlayedHours),
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})
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if err != nil {
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return nil, err
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}
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likes, err := loadContextualLikesByTrack(ctx, q, userID)
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if err != nil {
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return nil, err
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}
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out := make([]Candidate, 0, len(rows))
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for _, r := range rows {
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var lpt *time.Time
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if r.LastPlayedAt.Valid {
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t := r.LastPlayedAt.Time
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lpt = &t
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}
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ctxScore := ContextualMatchScore(currentVector, likes[r.Track.ID], DefaultSimilarityWeights)
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out = append(out, Candidate{
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Track: r.Track,
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Inputs: ScoringInputs{
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IsGeneralLiked: r.IsLiked,
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LastPlayedAt: lpt,
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PlayCount: int(r.PlayCount),
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SkipCount: int(r.SkipCount),
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ContextualMatchScore: ctxScore,
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},
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})
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}
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return out, nil
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}
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// loadContextualLikesByTrack fetches the user's active contextual_likes in
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// one query and groups them by track_id. Rows whose session_vector fails
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// to unmarshal are skipped with no error (don't poison scoring over one
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// bad row); the SQL query already filters NULL vectors.
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func loadContextualLikesByTrack(
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ctx context.Context,
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q *dbq.Queries,
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userID pgtype.UUID,
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) (map[pgtype.UUID][]SessionVector, error) {
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rows, err := q.ListActiveContextualLikesForUser(ctx, userID)
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if err != nil {
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return nil, err
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}
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out := make(map[pgtype.UUID][]SessionVector, len(rows))
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for _, r := range rows {
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var v SessionVector
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if err := json.Unmarshal(r.SessionVector, &v); err != nil {
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continue
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}
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out[r.TrackID] = append(out[r.TrackID], v)
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}
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return out, nil
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}
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