Azure AI Application Templates: Accelerating AI Development

AI application templates provide developers with well-supported and easily deployable reference implementations to kickstart their AI projects. These templates are categorized into standard blocks and comprehensive solutions, each addressing specific developer needs and use cases. This article explores these templates, highlighting their key features and benefits for building robust AI applications on Azure.

Standard blocks are smaller, focused examples targeting specific scenarios and tasks. Many of these blocks demonstrate functionalities used in a comprehensive chat application leveraging custom data. Examples include: * **Load Balancing with Azure Container Apps:** Extends chat application capabilities beyond Azure OpenAI token and model quotas. * **Document Security Configuration:** Ensures user access to chat application responses is based on their permissions. * **Chat Application Response Evaluation:** Evaluates chat application responses against a set of correct answers to compare changes. * **Load Testing with Locust:** Performs load testing on Python chat applications to ensure they don't exceed Azure OpenAI TPM quotas. * **Securing AI Applications with Keyless Authentication:** Protects Azure OpenAI Python chat applications using passwordless authentication.

Comprehensive solutions are end-to-end reference examples that include documentation, source code, and deployment instructions. These solutions are designed to be adopted and extended for custom purposes. Examples include: * **Chat with Data using Azure OpenAI and AI Search (.NET & Python & Java & JavaScript):** Demonstrates the Retrieval-Augmented Generation (RAG) pattern, utilizing Azure AI Search and Azure OpenAI for ChatGPT-like interfaces. * **Contoso Chat Retail Copilot (.NET):** Enhances customer interaction with an intelligent chat agent for a conceptual outdoor retail store. * **Process Automation with Speech-to-Text and Summarization (.NET):** Automates the processing of issues reported by field workers, converting speech to text and summarizing the problem. * **Multi-Modal Creative Writing Copilot (Python):** A multi-agent solution for assisting users in writing articles, leveraging Bing Search and Azure AI Search. * **Contoso Chat Retail Copilot with Azure AI Foundry:** A retail copilot solution using the RAG pattern for responding to retail and customer data queries. * **Process Automation with Speech-to-Text and Summarization (Python):** Creates a web application for employees to report issues via text or speech, summarizing the information for relevant departments. * **API Analytics Copilot (Python):** An API assistant for chatting with tabular data and performing natural language analysis. * **Banking Assistant with Multi-Agent Architecture (Java):** A banking personal assistant designed to transform user interactions with bank account information and payment features.

The Chat with Data template is a comprehensive solution demonstrating the Retrieval-Augmented Generation (RAG) pattern. It leverages Azure AI Search for information retrieval and Azure OpenAI's large language models to power a ChatGPT-like Q&A interface. This template is available in multiple languages including .NET, Python, Java, and JavaScript, making it accessible to a wide range of developers. It showcases the integration of Azure services to create a powerful and intelligent chat application.

The Contoso Chat Retail Copilot template implements a virtual store that enhances customer interactions and sales support through an intelligent chat agent. This agent uses the Retrieval-Augmented Generation (RAG) pattern within the Microsoft Azure AI Stack, enriched with semantic kernel and query support. It provides a conversational interface for customers to ask questions and receive relevant answers based on their purchase history, ensuring responsible AI practices for quality and safety.

This template automates the processing of issues reported by field workers in a manufacturing company. It converts audio inputs from speech to text and then uses LLMs to summarize the problem, returning the results in a structured format. This solution streamlines the reporting process, making it easier for employees to communicate issues and for the company to address them efficiently. It leverages Azure's speech-to-text capabilities and summarization techniques to provide a comprehensive automation solution.

The Multi-Modal Creative Writing Copilot is a creative solution for building multiple agents that assist users in writing articles. It demonstrates how to create and work with AI agents managed by Azure OpenAI. The solution includes a Flask application, a research agent using the Bing Search API, a product agent using Azure AI Search, a writer agent for combining research and product information, and an editor agent for refining the article. This template showcases the power of AI agents in enhancing the writing process.

This project is designed as a proof of concept (PoC) to explore the innovative field of AI generation in the context of multi-agent architectures. Using the Java and Microsoft Semantic Kernel AI orchestration platform, the goal is to create a chat web application to demonstrate the effectiveness and reliability of using AI-generated agents to transform user interactions with web clicks into natural language conversations, while maximizing the use of existing workload data and APIs. The main use case revolves around a banking personal assistant designed to change the way users interact with bank account information, transaction history, and payment features.

This template is a serverless AI chatbot using LangChain.js and Azure, utilizing a set of enterprise documents to generate responses to user queries. It uses a fictitious company, Contoso Real Estate, and the experience allows its customers to ask questions about support for using their products. The sample data contains a set of documents describing the terms of service, privacy policy, and support guide. This template showcases the integration of LangChain.js and Azure services to create a powerful and intelligent chat application.

AI application templates on Azure provide a valuable starting point for developers looking to build intelligent applications. By offering pre-built solutions and standard blocks, these templates accelerate the development process and ensure high-quality implementations. Whether you're building a chat application, automating processes, or creating a multi-agent system, Azure's AI application templates offer the tools and resources you need to succeed.