Summary

Semantic search enables you to find data based on meaning rather than exact keyword matches by leveraging embedding vectors and vector similarity. This module explored how to use the vector and azure_ai extensions in Azure Database for PostgreSQL - Flexible Server to generate, store, and query embeddings for rich semantic search capabilities.

In this module, you:

• Examined the concepts of semantic search and embedding vectors.
• Understood the difference between lexical search and semantic search.
• Evaluated the capabilities provided by the azure_ai extension for PostgreSQL.
• Installed and explored the vector and azure_ai extensions in an Azure Database for PostgreSQL - Flexible Server database.

References

• Tutorial: Create a semantic search with Azure Database for PostgreSQL and Azure OpenAI
• Azure AI extension in Azure Database for PostgreSQL
• How to use pgvector on Azure Database for PostgreSQL
• How to allow extensions in Azure Database for PostgreSQL