Phase 3.1: Help Center semantic search with pgvector
- 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
rogee
c72e359e48