Describe the Azure AI extension

  • 4 minutes

The azure_ai extension is a powerful tool created by the Microsoft Postgres team that provides seamless integration between your Azure Database for PostgreSQL flexible server instance and various Azure AI Services. This integration streamlines the development process and enables you to incorporate AI features into your applications effortlessly.

You can access Azure AI Services such as natural language processing, text analytics, and generative AI language models from your SQL queries using a simple function call. This extension allows you to use the power of Azure AI and Machine Learning to add advanced analytics and insights to your applications without leaving your PostgreSQL database.

The extension makes it easy to integrate Azure OpenAI, Azure AI Services, and Azure Machine Learning into your database. Overall, the azure_ai extension is a game-changer for developers looking to add generative AI features to their applications. Its rich capabilities and seamless integration with Azure AI and Machine Learning services simplify the development process and enable you to build advanced AI applications directly within your PostgreSQL database.

What is the azure_ai extension?

The azure_ai extension provides a comprehensive suite of tools that can enhance the functionality of a PostgreSQL database by integrating Azure AI and Machine Learning Services. With the azure_ai extension, you can seamlessly incorporate generative AI capabilities into your database. This extension enables you to create new content based on existing information. These tools include a collection of schemas, user-defined functions (UDFs), and composite types that can be seamlessly integrated into any PostgreSQL database. When you use the power of Azure AI Services, you can benefit from advanced analytics, machine learning, and other AI-driven capabilities to gain deeper insights and make better decisions. With the azure_ai extension, PostgreSQL, you can unlock a whole new level of functionality and performance. The extension provides three primary service integrations:

Azure AI Services

The extension allows your database to call into various Azure AI Services to extract insights from data, such as text summarization, translation, and entity extraction. These services simplify the development process, making integrating AI features into your applications more accessible.

Azure OpenAI

The Azure OpenAI Service integration allows you to invoke the Azure OpenAI API directly from your database to generate vector embeddings. Combined with the vector extension, you can store the generated embeddings directly in your PostgreSQL database. These embeddings enable powerful capabilities like semantic search, recommendations, and anomaly detection.

Azure Machine Learning

The extension allows you to connect to Azure Machine Learning to perform inference. You can use a trained machine learning model to make predictions or generate outputs based on new, unseen data.

Enabling the azure_ai extension

Before you can use the azure_ai extension, it must be allowed and installed with your database:

  1. Allowlist configuration: Add the extension to your allowlist by running SHOW azure.extensions;.
  2. Installation: Connect to your target database and execute the following command to install the extension:
CREATE EXTENSION IF NOT EXISTS azure_ai;

Schema creation

Once installed, the extension creates four schemas within the database:

Schema Description
azure_ai Contains the configuration table and functions for interacting with the extension.
azure_cognitive Contains functions and composite types related to Azure Cognitive Services.
azure_ml Contains functions related to Azure Machine Learning inference.
azure_openai Contains the functions related to Azure OpenAI.

Configuring the extension

The extension creates the azure_ai.settings table, which is crucial in configuring and managing the extension settings. The table securely hosts endpoint and key settings related to the Azure AI Services you integrate into your database. To configure the extension, provide the necessary endpoints and API keys for authentication.

  1. Setting values:

    • The azure_ai.set_setting() function allows you to set various configuration values for Azure AI services. You can assign values to specific keys using this function.
    • For example, you can set the Azure OpenAI endpoint and provide the corresponding subscription key using the following commands:
    SELECT azure_ai.set_setting('azure_openai.endpoint', '{endpoint}');
SELECT azure_ai.set_setting('azure_openai.subscription_key', '{api-key}');

  2. Retrieving settings:

    • The azure_ai.get_setting() function lets you retrieve the values you previously set using set_setting().
    • Specify the key of the setting you want to view, and the function returns the associated value.
    • For instance, to verify the settings written in the configuration table, use:
    SELECT azure_ai.get_setting('azure_openai.endpoint');
SELECT azure_ai.get_setting('azure_openai.subscription_key');

Access keys and security

Your Azure AI access keys are similar to your account's root passwords. It's crucial to handle them with care. The best approach is to use Azure Key Vault to manage and rotate your keys.

Users who need to manage service keys used by the extension require the azure_ai_settings_manager role in the database. Functions that require this role include azure_ai.set_setting() and azure_ai.get_setting().

How does the azure_ai extension work?

Here's a quick example of how easy it is to use the azure_ai extension within an Azure Database for PostgreSQL flexible server:

  • Generating embeddings: You can create embeddings by calling a user-defined function (UDF) inline from SQL. For example:

    SELECT azure_openai.create_embeddings('text-embedding-ada-002', 'Learn about building intelligent applications with azure_ai extension and vector');

  • Language services: Need sentiment analysis? It's as simple as a UDF call from SQL:

    SELECT a.* FROM azure_cognitive.analyze_sentiment('The GenAI session was awesome', 'en') a;

  • Additional features: You can add vector columns to tables, create HNSW (Hierarchical Navigable Small World) indexes, and perform semantic searches - all powered by the azure_ai extension.