# Azure OpenAI

Microsoft Azure service for deploying and integrating OpenAI foundational and reasoning models in applications.

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## Start here: Azure SQL Foundations series

DevFeed: [Start here: Azure SQL Foundations series](<https://devfeed.tech/articles/start-here-azure-sql-foundations-series-23833.md>)

Original publisher: [Read original article](<https://devblogs.microsoft.com/blog/start-here-azure-sql-foundations-series/>)

Author: Anna Hoffman

Published: 2026-08-25T16:00:18Z

Content type: article

Language: en

Sources: [Developer Blogs](<https://devfeed.tech/sources/developer-blogs.md>)

Topics: [Azure SQL](<https://devfeed.tech/topics/azure-sql.md>), [Microsoft](<https://devfeed.tech/topics/microsoft.md>), [migration](<https://devfeed.tech/topics/migration.md>), [Artificial Intelligence](<https://devfeed.tech/topics/ai.md>), [Azure OpenAI](<https://devfeed.tech/topics/azure-openai.md>), [DevOps](<https://devfeed.tech/topics/devops.md>), [GitHub](<https://devfeed.tech/topics/github.md>), [Retrieval Augmented Generation (RAG)](<https://devfeed.tech/topics/retrieval-augmented-generation-rag.md>), [API](<https://devfeed.tech/topics/api.md>), [MCP Server](<https://devfeed.tech/topics/mcp-server.md>)

Tags: [ai](<https://devfeed.tech/tags/ai.md>), [azure](<https://devfeed.tech/tags/azure.md>), [azure-openai](<https://devfeed.tech/tags/azure-openai.md>), [azure-sql](<https://devfeed.tech/tags/azure-sql.md>), [devops](<https://devfeed.tech/tags/devops.md>), [github](<https://devfeed.tech/tags/github.md>), [guide](<https://devfeed.tech/tags/guide.md>), [how-to](<https://devfeed.tech/tags/how-to.md>), [json](<https://devfeed.tech/tags/json.md>), [langchain](<https://devfeed.tech/tags/langchain.md>), [mcp](<https://devfeed.tech/tags/mcp.md>), [microsoft-for-developers](<https://devfeed.tech/tags/microsoft-for-developers.md>), [migration](<https://devfeed.tech/tags/migration.md>), [powershell](<https://devfeed.tech/tags/powershell.md>), [rag](<https://devfeed.tech/tags/rag.md>), [terraform](<https://devfeed.tech/tags/terraform.md>)

### AI overview

Microsoft introduces the Azure SQL Database Foundations series, a set of four videos covering Hyperscale databases, modernization and migration, AI features, and operational data. Each episode includes a repository for hands-on follow-up.

### Source excerpt

Most developers I talk to aren't asking whether Azure SQL Database can handle their next app. They're asking where to start when it comes to modernization, migration, and AI in the database. If you're reading this, you're probably in a similar boat: you've got an existing schema or databases, a scaling question you haven't had [...] The post Start here: Azure SQL Foundations series appeared first on Microsoft for Developers.

## Browser automation with Pydantic AI + Playwright

DevFeed: [Browser automation with Pydantic AI + Playwright](<https://devfeed.tech/articles/browser-automation-with-pydantic-ai-playwright-21751.md>)

Original publisher: [Read original article](<http://blog.pamelafox.org/2026/08/browser-automation-with-pydantic-ai.html>)

Author: Pamela Fox (noreply@blogger.com)

Published: 2026-08-20T21:57:18Z

Content type: tutorial

Language: en

Sources: [Pamela Fox](<https://devfeed.tech/sources/pamela-fox.md>)

Topics: [Playwright](<https://devfeed.tech/topics/playwright.md>), [Pydantic](<https://devfeed.tech/topics/pydantic.md>), [Browser Automation](<https://devfeed.tech/topics/browser-automation.md>), [Artificial Intelligence](<https://devfeed.tech/topics/ai.md>), [Azure OpenAI](<https://devfeed.tech/topics/azure-openai.md>), [Entra ID](<https://devfeed.tech/topics/entra-id.md>), [Authentication](<https://devfeed.tech/topics/authentication.md>), [OAuth 2.0](<https://devfeed.tech/topics/oauth2.md>), [OpenTelemetry](<https://devfeed.tech/topics/opentelemetry.md>)

Tags: [agents](<https://devfeed.tech/tags/agents.md>), [ai](<https://devfeed.tech/tags/ai.md>), [api-keys](<https://devfeed.tech/tags/api-keys.md>), [authentication](<https://devfeed.tech/tags/authentication.md>), [automation](<https://devfeed.tech/tags/automation.md>), [azure-openai](<https://devfeed.tech/tags/azure-openai.md>), [browser](<https://devfeed.tech/tags/browser.md>), [microsoft](<https://devfeed.tech/tags/microsoft.md>), [microsoft-foundry](<https://devfeed.tech/tags/microsoft-foundry.md>), [openai](<https://devfeed.tech/tags/openai.md>), [playwright](<https://devfeed.tech/tags/playwright.md>), [python](<https://devfeed.tech/tags/python.md>)

### AI overview

This tutorial explains how to combine Pydantic AI with Playwright to build agents that browse websites programmatically. It covers connecting Pydantic AI to Microsoft Foundry models through an OpenAI-compatible endpoint, using Entra token-based authentication, and applying Playwright for browsing, manual QA, and design iteration.

### Source excerpt

When we build agents, we often want to give them the ability to browse the web: open webpages, navigate from one page to the other, and read the content of a webpage. By combining Pydantic AI with the Playwright capability from Pydantic AI Harness, we can build agents that browse the web safely and programmatically. Using Pydantic AI with Microsoft Foundry models Pydantic AI is an open-source model-agnostic framework from Pydantic for building LLM-based applications and agents. It's type-safe and supports OpenTelemetry, making it a great choice for robust production applications. We can use Pydantic-AI with Microsoft Foundry models using either API keys or Entra token-based authentication. When possible, we always recommend the keyless route, so that's what we'll demonstrate here. We use the azure-identity package to authenticate with Entra, using either local or managed identity, and get back a token provider callback function for that credential: from azure.identity.aio import AzureDeveloperCliCredential, get_bearer_token_provider credential = AzureDeveloperCliCredential() token_provider = get_bearer_token_provider(credential, "https://cognitiveservices.azure.com/.default") Then we use the OpenAI package to configure the model connection: from openai import AsyncOpenAI client = AsyncOpenAI( base_url=os.environ["AZURE_OPENAI_ENDPOINT"] + "/openai/v1", api_key=token_provider, ) model = OpenAIChatModel( model_name=os.environ["AZURE_OPENAI_CHAT_DEPLOYMENT"], provider=OpenAIProvider(openai_client=client), ) Let's explain the options used above: base_url: We point this at the OpenAI-compatible endpoint for our Foundry model. This endpoint works for Azure OpenAI models (like gpt-5.4, which this project deploys), and for cross-provider Foundry models that support the OpenAI v1 API, like Kimi-K2.7-Code. The base URL looks like "https://AZURE_OPENAI_SERVICE_NAME.openai.azure.com/openai/v1". api_key: We pass in the token provider callback function that generates OAuth2 token

## Watch the recordings from my Python + AI series

DevFeed: [Watch the recordings from my Python + AI series](<https://devfeed.tech/articles/watch-the-recordings-from-my-python-ai-series-21743.md>)

Original publisher: [Read original article](<http://blog.pamelafox.org/2025/10/watch-recordings-from-my-python-ai.html>)

Author: Pamela Fox (noreply@blogger.com)

Published: 2025-10-31T14:22:00Z

Content type: article

Language: en

Sources: [Pamela Fox](<https://devfeed.tech/sources/pamela-fox.md>)

Topics: [Python](<https://devfeed.tech/topics/python.md>), [Large Language Model](<https://devfeed.tech/topics/llm.md>), [Embeddings](<https://devfeed.tech/topics/embeddings.md>), [Retrieval Augmented Generation (RAG)](<https://devfeed.tech/topics/retrieval-augmented-generation-rag.md>), [Azure OpenAI](<https://devfeed.tech/topics/azure-openai.md>), [Model Context Protocol](<https://devfeed.tech/topics/model-context-protocol.md>), [LangChain](<https://devfeed.tech/topics/langchain.md>), [SDK](<https://devfeed.tech/topics/sdk.md>), [OpenAI](<https://devfeed.tech/topics/openai.md>), [Ollama](<https://devfeed.tech/topics/ollama.md>)

Tags: [ai](<https://devfeed.tech/tags/ai.md>), [embeddings](<https://devfeed.tech/tags/embeddings.md>), [generative-ai](<https://devfeed.tech/tags/generative-ai.md>), [langchain](<https://devfeed.tech/tags/langchain.md>), [large-language-models-llms](<https://devfeed.tech/tags/large-language-models-llms.md>), [mcp](<https://devfeed.tech/tags/mcp.md>), [ollama](<https://devfeed.tech/tags/ollama.md>), [openai](<https://devfeed.tech/tags/openai.md>), [python](<https://devfeed.tech/tags/python.md>), [rag](<https://devfeed.tech/tags/rag.md>)

### AI overview

A blog article provides recordings and materials from a nine-part Python and generative AI series. It covers language models, embeddings, retrieval-augmented generation, evaluation and safety, AI agents, Model Context Protocol, and related Python examples using services including GitHub Models, Ollama, Azure OpenAI, and OpenAI models.

### Source excerpt

My colleague and I just wrapped up a live series on Python + AI, a nine-part journey diving deep into how to use generative AI models from Python. I gave the english streams while my colleague Gwen gave the spanish streams (and I hung out in her live chat, working on my technical spanish!). The series introduced multiple types of models, including LLMs, embedding models, and vision models. We dug into popular techniques like RAG, tool calling, and structured outputs. We assessed AI quality and safety using automated evaluations and red-teaming. Finally, we developed AI agents using popular Python agents frameworks and explored the new Model Context Protocol (MCP). To apply the concepts, we put together code examples that run for free thanks to GitHub Models, a service that provides free models to every GitHub account holder for experimentation and education. The examples are also compatible with local models (via Ollama), Azure OpenAI, or OpenAI.com models. Even if you missed the live series, you can still access all the material using the links below! If you're an instructor, feel free to use the slides and code examples in your own classes. Python + AI: Large Language Models 📺 Watch recording In this session, we explore Large Language Models (LLMs), the models that power ChatGPT and GitHub Copilot. We use Python to interact with LLMs using popular packages like the OpenAI SDK and LangChain. We experiment with prompt engineering and few-shot examples to improve outputs. We also demonstrate how to build a full-stack app powered by LLMs and explain the importance of concurrency and streaming for user-facing AI apps. Slides for this session Code repository with examples: python-openai-demos Python + AI: Vector embeddings 📺 Watch recording In our second session, we dive into a different type of model: the vector embedding model. A vector embedding is a way to encode text or images as an array of floating-point numbers. Vector embeddings enable similarity search across

## Agents Need Responsibility

DevFeed: [Agents Need Responsibility](<https://devfeed.tech/articles/agents-need-responsibility-33465.md>)

Original publisher: [Read original article](<https://timkellogg.me/blog/2025/05/25/ai-responsibility>)

Published: 2025-05-25T00:00:00Z

Content type: opinion

Language: en

Sources: [Tim Kellogg](<https://devfeed.tech/sources/tim-kellogg.md>)

Topics: [Artificial Intelligence](<https://devfeed.tech/topics/ai.md>), [GitHub Copilot](<https://devfeed.tech/topics/github-copilot.md>), [Azure OpenAI](<https://devfeed.tech/topics/azure-openai.md>), [ChatGPT](<https://devfeed.tech/topics/chatgpt.md>)

Tags: [agents](<https://devfeed.tech/tags/agents.md>), [ai](<https://devfeed.tech/tags/ai.md>), [ai-agents](<https://devfeed.tech/tags/ai-agents.md>), [chatgpt](<https://devfeed.tech/tags/chatgpt.md>), [github-copilot](<https://devfeed.tech/tags/github-copilot.md>)

### AI overview

This opinion argues that AI agents need clear accountability for their actions. It presents GitHub Copilot's identification of both the agent that performed work and the human who verified it as a model for responsibility, and argues that neither users nor AI systems should be allowed to escape accountability when problems occur.

### Source excerpt

Someone must take responsibility, always. AI agents are no different, yet we're seeing seeing agents hit the market where nothing is taking responsibility for their actions. These won't work, they're simply bubbles waiting to be popped. And there is a better way.

## Agent-Facing Analytics, Data Lake Support, and More: A Year of ClickHouse Cloud on Azure

DevFeed: [Agent-Facing Analytics, Data Lake Support, and More: A Year of ClickHouse Cloud on Azure](<https://devfeed.tech/articles/agent-facing-analytics-data-lake-support-and-more-a-year-of-clickhouse-cloud-on-azure-4907.md>)

Original publisher: [Read original article](<https://clickhouse.com/blog/a-year-of-clickhouse-cloud-on-azure>)

Author: ClickHouse

Published: 2025-05-15T00:00:00Z

Content type: release

Language: en

Sources: [ClickHouse Blog](<https://devfeed.tech/sources/clickhouse-blog.md>)

Topics: [clickhouse](<https://devfeed.tech/topics/clickhouse.md>), [Azure](<https://devfeed.tech/topics/azure.md>), [Azure OpenAI](<https://devfeed.tech/topics/azure-openai.md>), [Model Context Protocol](<https://devfeed.tech/topics/model-context-protocol.md>), [Model Context Protocol (MCP)](<https://devfeed.tech/topics/model-context-protocol-mcp.md>), [LLM observability](<https://devfeed.tech/topics/llm-observability.md>), [real-time](<https://devfeed.tech/topics/real-time.md>), [Low Latency](<https://devfeed.tech/topics/low-latency.md>), [Cloud](<https://devfeed.tech/topics/cloud.md>)

Tags: [azure](<https://devfeed.tech/tags/azure.md>), [azure-openai](<https://devfeed.tech/tags/azure-openai.md>), [clickhouse](<https://devfeed.tech/tags/clickhouse.md>), [generative-ai](<https://devfeed.tech/tags/generative-ai.md>), [llm-observability](<https://devfeed.tech/tags/llm-observability.md>), [low-latency](<https://devfeed.tech/tags/low-latency.md>), [model-context-protocol](<https://devfeed.tech/tags/model-context-protocol.md>), [real-time](<https://devfeed.tech/tags/real-time.md>)

### AI overview

The article reviews a year of ClickHouse Cloud on Microsoft Azure and announces expanded support for AI ecosystems, data lakes, onboarding, programming languages, cloud architecture, enterprise security, and compliance. It highlights Azure OpenAI and Model Context Protocol integrations for agent-facing real-time analytics, as well as LLM observability and Airflow pipeline insights.

### Source excerpt

Read on to learn more about our latest product announcements for ClickHouse Cloud on Azure, from AI ecosystems and data lake support, to data onboarding, and more.

## Giving our AI superpowers with OpenAI Tools

DevFeed: [Giving our AI superpowers with OpenAI Tools](<https://devfeed.tech/articles/giving-our-ai-superpowers-with-openai-tools-40844.md>)

Original publisher: [Read original article](<https://mutto.fyi/posts/2024/06/ai-superpowers-tools/>)

Published: 2024-06-03T00:00:00Z

Content type: tutorial

Language: en

Sources: [Mutt0-ds Notes](<https://devfeed.tech/sources/mutt0-ds-notes.md>)

Topics: [OpenAI](<https://devfeed.tech/topics/openai.md>), [Artificial Intelligence](<https://devfeed.tech/topics/ai.md>), [function calling](<https://devfeed.tech/topics/function-calling.md>), [Azure OpenAI](<https://devfeed.tech/topics/azure-openai.md>), [Databases](<https://devfeed.tech/topics/databases.md>), [API](<https://devfeed.tech/topics/api.md>), [SQL](<https://devfeed.tech/topics/sql.md>), [LangChain](<https://devfeed.tech/topics/langchain.md>), [Retrieval Augmented Generation (RAG)](<https://devfeed.tech/topics/retrieval-augmented-generation-rag.md>)

Tags: [ai](<https://devfeed.tech/tags/ai.md>), [ai-tools](<https://devfeed.tech/tags/ai-tools.md>), [api](<https://devfeed.tech/tags/api.md>), [azure-openai](<https://devfeed.tech/tags/azure-openai.md>), [function-calling](<https://devfeed.tech/tags/function-calling.md>), [langchain](<https://devfeed.tech/tags/langchain.md>), [openai](<https://devfeed.tech/tags/openai.md>), [retrieval-augmented-generation-rag](<https://devfeed.tech/tags/retrieval-augmented-generation-rag.md>)

### AI overview

This article explains how the author used OpenAI Tools, also called Function Calling, with Azure OpenAI to build an AI Analyst connected to a complex order database. It contrasts this approach with earlier GPT-3.5, LangChain, SQL, and DAX-based methods, and distinguishes tool calling from Retrieval Augmented Generation.

### Source excerpt

In recent months, I have been experimenting with AI tools to leverage new, powerful technologies and create value. Like you, I am inundated...