Training: Build AI Apps with Azure Database for PostgreSQL (AI-3019)

Azure PostgreSQL AI Training: Why Combine PostgreSQL and Generative AI

PostgreSQL is one of the most widely used databases in the world, and the pgvector extension makes it a compelling choice for storing embedding vectors. Rather than adding a dedicated vector database to your stack, you can centralize your relational and vector data in a single managed environment on Azure. This approach simplifies the architecture, reduces operational costs, and eases maintenance.

The AI-3019 training shows you how to take advantage of this combination to build robust generative AI applications. You learn to transform your existing data into embeddings, store them in PostgreSQL via pgvector, and use them in a RAG pipeline connected to Azure OpenAI. This approach lets you generate relevant, contextualized responses grounded in your own business data.

RAG Architecture with Azure Database for PostgreSQL

The RAG (Retrieval-Augmented Generation) pattern combines the power of language models with the precision of your proprietary data. Rather than relying solely on the model’s general knowledge, RAG first retrieves relevant information from your database before generating a response. This significantly reduces hallucinations and ensures reliable answers.

During the training, you implement each step of the RAG pipeline. You start by generating embeddings via Azure OpenAI models, then configure pgvector to store these vectors and perform cosine similarity searches. You then build the orchestration layer that connects the user query, vector search, and response generation. The Microsoft-provisioned cloud labs allow you to practice on real Azure infrastructure.

Pgvector and Vector Search in PostgreSQL

The pgvector extension transforms PostgreSQL into a high-performance vector database. It supports storing high-dimensional vectors, IVFFlat and HNSW indexes for faster searches, and distance operators (cosine, Euclidean, dot product). This extension integrates naturally into the PostgreSQL ecosystem, meaning you can combine vector search and classic SQL queries within the same transaction.

The training guides you through the optimal configuration of pgvector on Azure Database for PostgreSQL. You learn to choose the right vector dimensions, create appropriate indexes based on your data volume, and write efficient similarity search queries. You also discover how to handle incremental embedding updates when your source data changes.

Use Cases and Practical Applications

Generative AI applications based on PostgreSQL and RAG cover many enterprise use cases. You can build conversational assistants that answer questions about your internal documentation, intelligent recommendation systems, semantic search tools for your product catalogs, or agents capable of analyzing contracts and technical reports.

During the hands-on exercises, you develop a complete application that ingests documents, generates their embeddings, stores them in PostgreSQL, and allows users to ask questions in natural language. This hands-on experience gives you the foundation to adapt the architecture to your own projects when you return to the office.

Training Conditions at ITTA

ITTA, a Microsoft Learning Partner in French-speaking Switzerland, offers this training at its Geneva and Lausanne centers as well as in a virtual classroom. MCT-certified instructors guide you through the practical exercises in Azure cloud labs. The official Microsoft course materials (MOC) are included. Basic knowledge of PostgreSQL and development is recommended to get the most out of this one-day course.

Frequently Asked Questions

Do I need to know PostgreSQL to attend this training?

Basic SQL and PostgreSQL knowledge is recommended. You should be comfortable with common SQL queries and understand the fundamentals of relational databases.

What programming languages are used during the training?

The practical exercises primarily use Python for interacting with Azure OpenAI and PostgreSQL. Familiarity with Python or a similar language is advised.

What is the difference between pgvector and a dedicated vector database?

Pgvector lets you manage relational and vector data in a single system, simplifying your architecture. Dedicated vector databases offer higher performance at very large scale, but pgvector is perfectly suited to the majority of enterprise use cases.

Does this training cover Azure OpenAI in detail?

The training focuses on integrating Azure OpenAI into the RAG pipeline with PostgreSQL. You learn to call the embeddings and completion APIs, but the course does not cover full administration of the Azure OpenAI service.

Are the hands-on labs conducted on a real Azure environment?

Yes, you work on Azure cloud environments provisioned for the training. You configure and test each component on real infrastructure, which directly prepares you for production implementation.

Can I apply these skills to an on-premises PostgreSQL?

The pgvector extension works just as well on Azure Database for PostgreSQL as on an on-premises instance. The concepts and techniques learned during the training are transferable, although some management features are specific to Azure.