کتاب ساخت سرویس‌های هوش مصنوعی مولد با FastAPI

مقدمه

What Is Generative AI? • Why Generative AI Services Will Power Future Applications • Facilitating the Creative Process • Suggesting Contextually Relevant Solutions • Personalizing the User Experience • Minimizing Delay in Resolving Customer Queries • Acting as an Interface to Complex Systems • Automating Manual Administrative Tasks • Scaling and Democratizing Content Generation • How to Build a Generative AI Service • Why Build Generative AI Services with FastAPI? • What Prevents the Adoption of Generative AI Services • Overview of the Capstone Project • Summary

شروع کار با FastAPI

Introduction to FastAPI • Setting Up Your Development Environment • Installing Python, FastAPI, and Required Packages • Creating a Simple FastAPI Web Server • FastAPI Features and Advantages • Inspired by Flask Routing Pattern • Handling Asynchronous and Synchronous Operations • Built-In Support for Background Tasks • Custom Middleware and CORS Support • Freedom to Customize Any Service Layer • Data Validation and Serialization • Rich Ecosystem of Plug-Ins • Automatic Documentation • Dependency Injection System • Lifespan Events • Security and Authentication Components • Bidirectional Web Socket, GraphQL, and Custom Response Support • Modern Python and IDE Integration with Sensible Defaults • FastAPI Project Structures • Flat Structure • Nested Structure • Modular Structure • Progressive Reorganization of Your FastAPI Project • Onion/Layered Application Design Pattern • Comparing FastAPI to Other Python Web Frameworks • FastAPI Limitations • Inefficient Model Memory Management • Limited Number of Threads • Restricted to Global Interpreter Lock • Lack of Support for Micro-Batch Processing Inference Requests • Cannot Efficiently Split AI Workloads Between CPU and GPU • Dependency Conflicts • Lack of Support for Resource-Intensive AI Workloads • Setting Up a Managed Python Environment and Tooling • Summary

یکپارچه‌سازی هوش مصنوعی و سرویس‌دهی مدل

Serving Generative Models • Language Models • Audio Models • Vision Models • Video Models • 3D Models • Strategies for Serving Generative AI Models • Be Model Agnostic: Swap Models on Every Request • Be Compute Efficient: Preload Models with the FastAPI Lifespan • Be Lean: Serve Models Externally • The Role of Middleware in Service Monitoring • Summary • Additional References

پیاده‌سازی سرویس‌های هوش مصنوعی با ایمنی نوع

Introduction to Type Safety • Implementing Type Safety • Type Annotations • Using Annotated • Dataclasses • Pydantic Models • How to Use Pydantic • Compound Pydantic Models • Field Constraints and Validators • Custom Field and Model Validators • Computed Fields • Model Export and Serialization • Parsing Environment Variables with Pydantic • Dataclasses or Pydantic Models in FastAPI • Summary

دستیابی به همزمانی در بارهای کاری هوش مصنوعی

Optimizing GenAI Services for Multiple Users • Optimizing for I/O Tasks with Asynchronous Programming • Synchronous Versus Asynchronous (Async) Execution • Async Programming with Model Provider APIs • Event Loop and Thread Pool in FastAPI • Blocking the Main Server • Project: Talk to the Web (Web Scraper) • Project: Talk to Documents (RAG) • Optimizing Model Serving for Memory- and Compute-Bound AI Inference Tasks • Compute-Bound Operations • Externalizing Model Serving • Managing Long-Running AI Inference Tasks • Summary • Additional References

ارتباط بلادرنگ با مدل‌های مولد

Web Communication Mechanisms • Regular/Short Polling • Long Polling • Server-Sent Events • WebSocket • Comparing Communication Mechanisms • Implementing SSE Endpoints • SSE with GET Request • SSE with POST Request • Implementing WS Endpoints • Streaming LLM Outputs with WebSocket • Handling WebSocket Exceptions • Designing APIs for Streaming • Summary

یکپارچه‌سازی پایگاه‌های داده در سرویس‌های هوش مصنوعی

The Role of a Database • Database Systems • Project: Storing User Conversations with an LLM in a Relational Database • Defining ORM Models • Creating a Database Engine and Session Management • Implementing CRUD Endpoints • Repository and Services Design Pattern • Managing Database Schemas Changes • Storing Data When Working with Real-Time Streams • Summary

احراز هویت و مجوزدهی

Authentication and Authorization • Authentication Methods • Basic Authentication • JSON Web Tokens (JWT) Authentication • Implementing OAuth Authentication • OAuth Authentication with GitHub • OAuth2 Flow Types • Authorization • Authorization Models • Role-Based Access Control • Relationship-Based Access Control • Attribute-Based Access Control • Hybrid Authorization Models • Summary

ایمن‌سازی سرویس‌های هوش مصنوعی

Usage Moderation and Abuse Protection • Guardrails • Input Guardrails • Output Guardrails • Guardrail Thresholds • Implementing a Moderation Guardrail • API Rate Limiting and Throttling • Implementing Rate Limits in FastAPI • Throttling Real-Time Streams • Summary

بهینه‌سازی سرویس‌های هوش مصنوعی

Optimization Techniques • Batch Processing • Caching • Model Quantization • Structured Outputs • Prompt Engineering • Fine-Tuning • Summary

تست سرویس‌های هوش مصنوعی

The Importance of Testing • Software Testing • Types of Tests • The Biggest Challenge in Testing Software • Planning Tests • Test Dimensions • Test Data • Test Phases • Test Environments • Testing Strategies • Challenges of Testing GenAI Services • Variability of Outputs (Flakiness) • Performance and Resource Constraints (Slow and Expensive) • Regression • Bias • Adversarial Attacks • Unbound Testing Coverage • Project: Implementing Tests for a RAG System • Unit Tests • Integration Testing • End-to-End Testing • Summary

استقرار سرویس‌های هوش مصنوعی

Deployment Options • Deploying to Virtual Machines • Deploying to Serverless Functions • Deploying to Managed App Platforms • Deploying with Containers • Containerization with Docker • Docker Architecture • Building Docker Images • Container Registries • Container Filesystem and Docker Layers • Docker Storage • Docker Networking • Enabling GPU Driver • Docker Compose • Enabling GPU Access in Docker Compose • Optimizing Docker Images • docker init • Summary