Vector Search in Postgres: Preparing Your Data for AI
You don't need a dedicated vector database to build AI features. I use pgvector inside PostgreSQL to store embeddings right next to relational data. It simplifies architecture, speeds up development, and keeps all your data in one source of truth.
The Overcomplicated AI Stack
Separate vector DBs add unnecessary complexity, cost, and data movement overhead.
Understanding Vector Embeddings
Plain-English conversion of text to vectors for semantic search. No math degree required.
The pgvector Advantage
Install extension, generate embeddings via API, SQL cosine similarity with JOINs. Full SQL examples and performance tips.
Use Cases
Programmatic SEO, lead-to-service matching, content recommendation. Real implementation walkthroughs.
Keep AI inside Postgres to future-proof the application and simplify ops forever.
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