Store and Query Embeddings with pgvector
The lesson demonstrates how to store embeddings in PostgreSQL using the vector data type, design schema tables with vector(n) columns matching the embedding model dimensions, and compare embeddings using the Euclidean, cosine, and negative inner product distance operators in SQL.
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