Vector Similarity Search Indexes
The lesson covers indexing the embedding column for fast similarity search using the IVFFlat and HNSW algorithms, explaining their performance and accuracy tradeoffs, the supported distance functions, the requirement that the index function match the query operator, and how to manage the index lifecycle and update stale embeddings.
Previous Module
Introducing Azure Database for PostgreSQL
This Module
AI Vector Search on PostgreSQL
Introduction to pgvector
Store and Query Embeddings with pgvector
Vector Similarity Search Indexes
Vector Similarity Search for Semantic Retrieval
Retrieval Patterns for RAG Pipelines
Optimize Vector Search in Azure Database for PostgreSQL
Tune PostgreSQL for pgvector
Choose and Configure Vector Indexes
Optimize Data Layout for Filtered Search
Scale PostgreSQL for High-Volume Workloads