92 lines
3.1 KiB
Go
92 lines
3.1 KiB
Go
package repository
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import (
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"context"
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"github.com/gochat/gochat/internal/model"
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"github.com/pgvector/pgvector-go"
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"gorm.io/gorm"
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)
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// ArticleEmbeddingRepo provides data access for ArticleEmbedding (semantic search).
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type ArticleEmbeddingRepo struct {
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db *gorm.DB
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}
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func NewArticleEmbeddingRepo(db *gorm.DB) *ArticleEmbeddingRepo {
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return &ArticleEmbeddingRepo{db: db}
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}
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func (r *ArticleEmbeddingRepo) DB() *gorm.DB { return r.db }
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// Upsert creates or updates an article embedding.
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func (r *ArticleEmbeddingRepo) Upsert(ctx context.Context, emb *model.ArticleEmbedding) error {
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// Try to find existing by article_id
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var existing model.ArticleEmbedding
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err := r.db.WithContext(ctx).Where("article_id = ?", emb.ArticleID).First(&existing).Error
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if err == gorm.ErrRecordNotFound {
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if r.db.Dialector != nil && r.db.Dialector.Name() == "sqlite" {
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return r.db.WithContext(ctx).Omit("VectorEmbedding").Create(emb).Error
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}
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return r.db.WithContext(ctx).Create(emb).Error
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}
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if err != nil {
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return err
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}
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// Update existing
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existing.Embedding = emb.Embedding
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existing.VectorEmbedding = emb.VectorEmbedding
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existing.Term = emb.Term
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if r.db.Dialector != nil && r.db.Dialector.Name() == "sqlite" {
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return r.db.WithContext(ctx).Omit("VectorEmbedding").Save(&existing).Error
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}
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return r.db.WithContext(ctx).Save(&existing).Error
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}
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// GetByArticleID retrieves the embedding for a specific article.
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func (r *ArticleEmbeddingRepo) GetByArticleID(ctx context.Context, articleID uint) (*model.ArticleEmbedding, error) {
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var emb model.ArticleEmbedding
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if err := r.db.WithContext(ctx).Where("article_id = ?", articleID).First(&emb).Error; err != nil {
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return nil, err
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}
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return &emb, nil
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}
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// DeleteByArticleID removes the embedding for an article.
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func (r *ArticleEmbeddingRepo) DeleteByArticleID(ctx context.Context, articleID uint) error {
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return r.db.WithContext(ctx).Where("article_id = ?", articleID).Delete(&model.ArticleEmbedding{}).Error
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}
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// SearchByEmbedding performs semantic search using pgvector cosine distance.
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// Returns articles sorted by similarity (closest first).
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// Note: This method is PG-only (requires pgvector extension).
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func (r *ArticleEmbeddingRepo) SearchByEmbedding(ctx context.Context, portalID uint, embedding pgvector.Vector, limit int) ([]model.Article, error) {
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if limit <= 0 {
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limit = 10
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}
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var rows []struct {
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model.Article
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Distance *float64 `gorm:"column:semantic_distance"`
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}
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// Join articles with article_embeddings, compute cosine distance on vector_embedding column.
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// pgvector cosine distance operator: <=> (for vector type)
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err := r.db.WithContext(ctx).Raw(`
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SELECT a.*, ae.vector_embedding <=> ? AS semantic_distance FROM articles a
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INNER JOIN article_embeddings ae ON ae.article_id = a.id
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WHERE a.portal_id = ? AND a.status = 'published'
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AND a.deleted_at IS NULL AND ae.deleted_at IS NULL
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ORDER BY semantic_distance
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LIMIT ?
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`, embedding, portalID, limit).Scan(&rows).Error
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if err != nil {
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return nil, err
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}
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articles := make([]model.Article, len(rows))
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for i := range rows {
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articles[i] = rows[i].Article
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articles[i].SemanticDistance = rows[i].Distance
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}
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return articles, nil
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}
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