Introduction
AI applications require efficient storage and retrieval of vector embeddings to power semantic search, recommendations, and retrieval-augmented generation. This module guides you through implementing vector search capabilities in Azure Database for PostgreSQL using the pgvector extension, enabling you to build AI solutions that find semantically similar content from your data.
Imagine you're a developer building a knowledge base search system for a legal services firm. The system needs to help attorneys find relevant case documents, contracts, and legal precedents based on the meaning of their queries rather than exact keyword matches. When an attorney searches for "breach of fiduciary duty in corporate merger," the system must return documents that discuss similar concepts even if they use different terminology.
Your team has chosen Azure Database for PostgreSQL because the firm already stores document metadata and client information there. Rather than introducing a separate vector database and managing data synchronization, you want to add vector search capabilities directly to the existing PostgreSQL instance. The system must handle hundreds of thousands of legal documents, support real-time queries with sub-second response times, and update embeddings as new documents arrive daily.
You need to enable the pgvector extension, design a schema that stores embeddings alongside document metadata, create indexes that balance search speed against accuracy, and build queries that retrieve the most relevant documents for RAG-powered legal research assistants.
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