Building Generative AI Services with FastAPI: A Practical Approach to Developing Context-Rich Generative AI Applications (Final Release)КНИГИ » ПРОГРАММИНГ
Название: Building Generative AI Services with FastAPI: A Practical Approach to Developing Context-Rich Generative AI Applications (Final Release) Автор: Ali Parandeh Издательство: O’Reilly Media, Inc. Год: 2025 Страниц: 577 Язык: английский Формат: epub Размер: 25.2 MB
Ready to build applications using Generative AI? This practical book outlines the process necessary to design and build production grade AI services with a FastAPI web server that communicate seamlessly with databases, payment systems, and external APIs. You'll learn how to develop autonomous generative AI agents that stream outputs in real-time and interact with other models. Web developers, data scientists, and DevOps engineers will learn to implement end-to-end production-ready services that leverage Generative AI.
You'll learn design patterns to manage software complexity, implement FastAPI lifespan for AI model integration, handle long-running generative tasks, perform content filtering, cache outputs, implement retrieval augmented generation (RAG) with a vector database, implement usage/cost monitoring and tracking, protect services with your own authentication and authorization mechanisms, and effectively control stream outputs directly from GenAI models. You'll explore efficient testing methods for AI outputs, validation against databases, and deployment patterns using Docker for robust microservices in the cloud.
The objective of this book is to help you explore the challenges of developing, securing, testing, and deploying Generative AI as services integrated with your own external systems and applications. This book centers on constructing modular, type-safe generative AI services in FastAPI with seamless database schema handling support and model integration to power backends that can generate new data. The significance of these topics stems from the growing demand for building flexible services that can adapt to changing requirements, maintain high performance, and scale efficiently using the microservice pattern. You will also learn the process of enriching your services with contextual data from a variety of sources such as databases, the web, external systems, and files uploaded by users. A few generative models require heavy processing power and memory to function. You will explore how to handle these models in production and how to scale your services to handle the load. You will also explore how to handle long-running tasks such as model inference. Finally, we will discuss authentication concepts, security considerations, performance optimization, testing, and deployment of production-ready Generative AI services.
Prerequisites: This book assumes no prior knowledge of generative AI and won’t require you to fully understand how generative models work. I will be covering the intuition of how such models generate data but will not dive into their underlying mathematics. As this is a FastAPI book for generative AI applications, I do assume some familiarity with this web framework. Furthermore, the book does assume some experience with Python, with Docker for deployment, with how the web works, and with communicating through the HTTP protocol. Finally, the book won’t require knowledge of deep learning frameworks such as Tensorflow and Keras.
Build generative services that interact with databases, external APIs, and more Learn how to load AI models into a FastAPI lifecycle memory Monitor and log model requests and responses within services Use authentication and authorization patterns hooked with generative models Handle and cache long-running inference tasks Stream model outputs via streaming events and WebSockets into browsers or files Automate the retraining process of generative models by exposing event-driven endpoints
Ali Parandeh is a Chartered Engineer with the UK Engineering Council and a Microsoft and Google certified developer, data engineer, and data scientist.
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Building Generative AI Services with FastAPI: A Practical Approach to Developing Context-Rich Generative AI Applications (Final Release) 
