wordpress-supabase-vector-content-scoring
📑 What Is a WordPress-to-Vector-Database Content Pipeline? Why Supabase and pgvector for Legal Marketing Content Building the WordPress Content Ingestion Pipeline Semantic Content Scoring: How It Works Using Semantic Scores to Build and Update Hub-and-Spoke Architecture Optimizing for SEO, GEO, AEO, and AIO Simultaneously Measurement Framework for Vector-Powered Content Systems Frequently Asked Questions 🔑 Key Takeaways A WordPress-to-Supabase vector pipeline converts static page content into searchable embeddings, enabling semantic analysis of your entire content library in a single PostgreSQL database. Supabase’s pgvector extension supports hybrid search—combining vector similarity with traditional SQL filters—which Supabase documentation describes as enabling semantic, full-text, and metadata queries in unified operations (Supabase AI & Vectors Docs, 2025). Semantic content scoring uses cosine similarity between page embeddings and topic-cluster vectors to identify coverage gaps, cannibalization, and linking opportunities across hub-and-spoke architectures. Research published in the Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data … Learn More