Build AI Apps with Azure Database for PostgreSQL

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Use generative AI in Azure Database for PostgreSQL.
- Examine the concepts of generative AI and large language models and explore how they can be used to build rich AI applications.
- Evaluate the capabilities of azure_ai extension for PostgreSQL.
- Install and explore the azure_ai extension in an Azure Database for PostgreSQL - Flexible Server database.
Learn to enable semantic search in Azure Database for PostgreSQL.
- Examine the concepts of semantic search & embedding vectors.
- Understand the difference between lexical search & semantic search.
- Evaluate the capabilities provided by the azure_ai extension for PostgreSQL.
- Install and explore the vector and azure_ai extensions in an Azure Database for PostgreSQL - Flexible Server database.
Learn to integrate Azure AI Services with Azure Database for PostgreSQL to add intelligent features like summarization and sentiment analysis.
By the end of this module, you'll be able to:
- Summarize text data using Azure AI Language services in Azure Database for PostgreSQL.
- Perform sentiment analysis and opinion mining on text stored in your database.
- Extract key phrases, recognize named entities, and detect PII using Azure AI Language.
- Translate text into multiple languages using Azure Translator.
- Run inference using Azure Machine Learning endpoints directly from PostgreSQL.
Learn how to implement Retrieval Augmented Generation (RAG) on Azure Database for PostgreSQL using Azure AI and vector (pgvector) extensions. Build a Python-based application and explore advanced options like GraphRAG.
By the end of this module, you'll be able to:
- Explain the RAG pattern and its benefits for grounding large language models.
- Map RAG pipeline stages to Azure Database for PostgreSQL features and extensions.
- Build a basic RAG application using PostgreSQL and Python.
- Enhance performance and accuracy with indexing strategies and semantic re-ranking.
- Explore advanced RAG patterns, including GraphRAG for multi-hop reasoning.
Learn to build, integrate, and evaluate generative AI agents using Azure Database for PostgreSQL.
By the end of this module, you'll be able to:
- Understand agent architectures and frameworks
- Apply information retrieval for agents
- Implement and integrate agents with Azure services
- Build and test an AI agent solution
- Assess your knowledge of generative AI agents
Learn how GitHub Copilot and the PostgreSQL extension improve SQL development workflows in Visual Studio Code.
Learn how GitHub Copilot and the PostgreSQL extension support writing, refining, and troubleshooting SQL in Visual Studio Code.

Syllabus

Get started with generative AI in Azure Database for PostgreSQL
- Introduction
- Understand generative AI language models
- Describe the Azure AI extension
- Explore the Azure OpenAI schema
- Review the Azure Cognitive schema
- Explore semantic operators
- Examine the Azure Machine Learning schema
- Exercise-Explore the Azure AI extension
- Module assessment
- Summary
Enable semantic search in Azure Database for PostgreSQL
- Introduction
- Understand semantic search
- Store vectors in Azure Database for PostgreSQL
- Create embeddings with the Azure AI extension
- Exercise - Generate vector embeddings with Azure OpenAI
- Explore semantic search use cases
- Exercise - Create a search function for a recommendation system
- Module assessment
- Summary
Integrate AI Services to enrich your applications with intelligent features in Azure Database for PostgreSQL
- Introduction
- Summarize data with Azure AI Services and Azure Database for PostgreSQL
- Perform sentiment analysis and opinion mining in Azure Database for PostgreSQL
- Extract insights using Azure Language and Azure Database for PostgreSQL
- Translate text with Azure Translator and Azure Database for PostgreSQL
- Exercise - Use Azure AI services with Azure Database for PostgreSQL
- Use Azure Machine Learning for inference from Azure Database for PostgreSQL
- Exercise - Perform inference with Azure Machine Learning and Azure Database for PostgreSQL
- Module assessment
- Summary
Build RAG applications with Azure Database for PostgreSQL
- Introduction
- Understand RAG pattern with Azure Database for PostgreSQL
- Explore information retrieval challenges - scale and accuracy
- Enhance scale with vector indexes
- Build RAG Applications with Azure Database for PostgreSQL and Python
- Exercise: Build RAG applications with Azure Database for PostgreSQL and Python
- Improve accuracy with advanced RAG architectures
- Explore GraphRAG with Azure Database for PostgreSQL
- Exercise: Implement GraphRAG with Azure Database for PostgreSQL
- Module assessment
- Summary
Implement generative AI agents with Azure Database for PostgreSQL
- Introduction
- Understand AI agents with Azure Database for PostgreSQL
- Apply information retrieval for agents
- Evaluate agentic frameworks for integration with PostgreSQL
- Implement AI agents with Foundry Agent Service
- Exercise - Build an AI agent with Foundry Agent Service and Azure Database for PostgreSQL
- Integrate AI agents with MCP and PostgreSQL
- Module assessment
- Summary
Develop PostgreSQL solutions in Visual Studio Code with the PostgreSQL extension and GitHub Copilot
- Introduction
- Understand how the PostgreSQL extension supports SQL development
- Understand how GitHub Copilot integrates with the PostgreSQL extension
- Use GitHub Copilot to generate, refine, and troubleshoot SQL queries
- Exercise - Enhance PostgreSQL development with GitHub Copilot
- Knowledge check
- Summary