rogee
c72e359e48
- ArticleEmbeddingRepo (new): Upsert, GetByArticleID, DeleteByArticleID, SearchByEmbedding using pgvector cosine distance (vector_embedding column) - ArticleEmbedding model: add VectorEmbedding pgvector.Vector field alongside existing JSONB Embedding (backward compatible) - ArticleService: add SemanticSearch() — generates query embedding via LLM, searches articles by cosine similarity; add GenerateEmbedding() — creates and stores article embedding from title+description+content - ArticleHandler: add SemanticSearch endpoint GET /portals/:portal_id/articles/semantic_search?query=... - bootstrap.go: inject articleEmbeddingRepo + llmProvider into ArticleService - router.go: register /articles/semantic_search route - migration 000049: add vector(1536) column to article_embeddings table, create ivfflat index, migrate existing JSONB data to vector format Verified: go build + go vet + go test all pass
76 lines
2.5 KiB
Go
76 lines
2.5 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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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.Term = emb.Term
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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 articles []model.Article
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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.* 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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ORDER BY ae.vector_embedding <=> ?
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LIMIT ?
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`, portalID, embedding, limit).Scan(&articles).Error
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if err != nil {
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return nil, err
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}
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return articles, nil
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}
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