Implement vector search with Azure Database for PostgreSQL
Intermediate
Developer
Azure Database for PostgreSQL
Learn how to implement vector search using the pgvector extension in Azure Database for PostgreSQL. Store embeddings, create vector indexes, and build semantic retrieval patterns for AI applications.
Learning objectives
After completing this module, you'll be able to:
- Store and query vector embeddings using the pgvector extension in Azure Database for PostgreSQL
- Execute vector similarity searches using different distance metrics and operators
- Create and manage vector indexes to optimize search performance
- Implement embedding update and refresh strategies for evolving datasets
- Build retrieval patterns that integrate PostgreSQL vector search with RAG pipelines
Prerequisites
Before beginning this module, you should have:
- Programming experience with Python.
- Basic understanding of Azure services and cloud computing concepts.
- Familiarity with relational databases and SQL fundamentals.
- Understanding of machine learning concepts including embeddings and similarity search.