Everything you need to make pgvector fast in production: HNSW vs IVFFlat, distance operators, m and ef_construction, ef_search at query time, partial indexes for multi-tenant data, and benchmarking your real workload.
A complete, end-to-end RAG backend built on PostgreSQL + pgvector and FastAPI. Real chunking, real embeddings, hybrid (vector + BM25) retrieval, prompt assembly, citations, and production gotchas.
Postgres ships with surprisingly capable full-text search built-in. Here’s how to use it for ranked text search without adding Elasticsearch to your stack.
Learn to read EXPLAIN ANALYZE output, pick the right index type for the query, and turn slow Postgres queries into fast ones — without guessing.
Build a production-ready FastAPI + SQLAlchemy 2.0 + PostgreSQL stack with async sessions, dependency-injected DB access, and Alembic migrations.
Everything I wish I’d known about the Django ORM earlier — how querysets really work, the N+1 problem, select_related vs prefetch_related, and the patterns that keep production code fast.
A primer on the parts of PostgreSQL that backend developers use every day — proper data types, indexing strategies, transactions, JSONB, CTEs, and more.
Step-by-step guide to creating a PostgreSQL database, a dedicated user with sane defaults, and wiring it into a Django project via environment variables.