A field guide for engineers and operators running vector search on PostgreSQL. Move past tutorial-grade demos and into production: indexes that hold their recall under load, ingestion pipelines that survive bad batches, and queries that stay fast.
Whether you are an AI/ML engineer, a search-platform developer, a Python data-pipeline builder, or on a DevOps team, the goal here is the same — sub-50 ms p95 latency at scale without sacrificing accuracy. We cover the architectural fundamentals of vector storage, the calibration knobs for HNSW and IVFFlat indexes, and the engineering patterns that keep embedding ingestion resilient and idempotent.
Every page is hands-on: copy-ready SQL and Python, decision matrices, parameter heuristics, and operational checklists you can lift straight into a runbook.
Three pillars, each drilling from architecture down to concrete, production-tested procedures.
Vector storage mechanics, data-type selection, distance metrics, storage overhead, and security boundaries for production pgvector deployments.
Algorithm selection, m / ef_construction calibration, asynchronous index builds, validation, and error categorization for HNSW and IVFFlat.
Resilient Python ingestion: batching and chunking, async processing, type casting and normalization, and metadata schema design.