Learn how to leverage Pydantic in FastAPI to handle optional fields, validate nested data structures, enforce complex business rules with model validators, forbid extra fields, work with polymorphic models, and validate query and path parameters, all illustrated with clear Python code examples.
This tutorial explains how to add request rate limiting to a FastAPI application using the SlowAPI library, covering both IP‑based limits and custom token‑based strategies, with installation steps, code examples, and best‑practice recommendations.
This article compares two primary FastAPI project structure strategies—file‑type based and module‑function based—explaining why a well‑organized architecture improves scalability, maintainability, and team collaboration, and provides concrete directory layouts and best‑practice guidelines for building robust backend applications.
This article explains how to apply SOLID principles and common design patterns such as DAO, service layer, and dependency inversion to FastAPI, demonstrating code examples that separate responsibilities, improve testability, and create a clean, maintainable backend architecture.
This guide walks you through setting up a local DeepSeek large language model, covering environment preparation, model acquisition, dependency installation, FastAPI service creation, Docker containerization, optional front‑end interface, performance tuning, and common troubleshooting steps.
This article demonstrates how to build a web‑based voice chatbot by integrating Whisper speech‑to‑text, llama.cpp LLM inference, and WebSocket communication, detailing both the frontend JavaScript implementation and the Python FastAPI backend, along with Docker deployment and example code.
This tutorial walks through creating a LangChain tool‑calling agent that combines a real‑time web search tool, a Qdrant vector store for local knowledge retrieval, and a custom Bazi fortune‑telling service, all wrapped in a FastAPI application for interactive use.
This article introduces LangServe, explains its key features for LLM deployment, walks through environment setup, shows how to build a FastAPI‑based REST API with code examples, demonstrates testing via Postman and remote client calls, and summarizes its benefits for AI model serving.
This article presents six hands‑on Python generative‑AI projects—ranging from a Llama 2 chatbot built with Streamlit and Replicate to natural‑language‑to‑SQL conversion using LlamaIndex and SQLAlchemy—complete with environment setup, required code snippets, deployment tips and resource links for further exploration.
This tutorial demonstrates building a FastAPI‑powered “Moyu Office” page that dynamically shows today’s date, weekday, and the remaining days until the weekend and major Chinese holidays, using Jinja2 templates, the zhdate library for lunar‑solar conversion, and deployment behind Nginx.
This article compares FastAPI and Flask, highlighting FastAPI's speed, developer experience, and standards support, while providing practical migration guidance, code examples, configuration tips, async handling, dependency injection, testing, deployment, and documentation differences for Python web developers.
This article explains how to craft system prompts that give an AI agent a distinct personality and emotional behavior, shows how to implement an emotion‑detection chain, compares ChatPromptTemplate.from_messages with from_template, and integrates the agent into a FastAPI service with full code examples.
This tutorial walks through installing FastAPI and related packages, creating a basic FastAPI app, adding chat, PDF, and text endpoints, integrating LangChain tools for AI responses, implementing a WebSocket echo service, and running the server with uvicorn, all illustrated with code snippets and screenshots.
This article compares Flask and FastAPI, explains their core differences in speed, developer experience, and standards, and provides step‑by‑step migration instructions, code examples, configuration tips, and deployment strategies for building modern Python web APIs.
This guide explains how to build a Retrieval‑Augmented Generation (RAG) system using JD Cloud documentation as a knowledge base, storing document embeddings in ClickHouse, leveraging LangChain for vector retrieval, and exposing query and answer services via FastAPI and a Gradio UI.
This guide walks through creating a Retrieval‑Augmented Generation pipeline that harvests JD Cloud documentation, stores vector embeddings in ClickHouse, and serves queries via FastAPI, LangChain, a Qwen LLM, and a Gradio front‑end.
This article introduces five essential Python libraries—MLflow, Streamlit, FastAPI, XGBoost, and ELI5—that every junior or intermediate machine‑learning engineer and data scientist should master to streamline experiment tracking, build interactive web apps, deploy models efficiently, achieve fast accurate predictions, and improve model interpretability.
This guide shows how to create a tiny gzip‑compressed file, serve it with FastAPI, and exploit automatic decompression in Python's requests library to force a crawler to consume massive memory, effectively turning compression into a denial‑of‑service weapon.
The guide walks through installing and quantizing the open‑source ChatGLM3‑6B model and the m3e‑base embedder on a MacBook, wrapping them with a FastAPI OpenAI‑compatible service, routing requests through a One‑API gateway, storing metadata in MongoDB and vectors in PostgreSQL pgvector, deploying FastGPT for RAG, ingesting data, and demonstrating 5‑7 second response times, while outlining future improvements.
The article explains how to enhance the open‑source AutoDev coding assistant by configuring your own large language model, customizing IDE behavior, defining custom prompts and actions, and adjusting request/response handling via FastAPI, offering step‑by‑step JSON examples and guidance for advanced developers.
This article presents a curated collection of Python web frameworks, explains what a web framework is, outlines their purposes, and provides a ranked overview of each framework's official site, advantages, and disadvantages to help developers choose the most suitable option for their projects.
This article explores the various Python frameworks and tools—Django, Django REST Framework, Flask, Requests, Swagger/OpenAPI, Marshmallow, Pydantic, Starlette, and Uvicorn—that inspired FastAPI, highlighting the features each contributed such as automatic documentation, micro‑framework flexibility, type‑hinted validation, and high‑performance async support.
This guide explains how to add, manage, and persist asynchronous scheduled jobs in a FastAPI application using APScheduler's AsyncIOScheduler with SQLite storage, covering task types, code examples, limitations, and alternative solutions such as arq, fastapi‑utils, and Celery.
This article presents a detailed survey of over thirty Python web frameworks, describing their official sites, main features, advantages, and drawbacks, and offers guidance on selecting popular options such as Django, Flask, FastAPI, and many others for various development needs.
This article explains how to employ Frida RPC to intercept and forward encrypted mobile app requests, demonstrates the required environment, provides hook scripts for encryption and decryption, and shows how to wrap them in a FastAPI service for automated crawling.
After two weeks of research, this guide presents a ranked list of 37 Python web frameworks—detailing their official sites, strengths, weaknesses, and visual overviews—to help developers choose the most supported and suitable framework for their projects.
FastAPI is a high‑performance, easy‑to‑learn Python web framework that lets you build robust APIs quickly, offering features like speed, concise code, automatic validation, and OpenAPI compatibility, with simple installation via pip and interactive documentation out of the box.
This article explains how Ctrip tackled the long, costly ML model development‑to‑deployment pipeline by adopting and extending MLflow for full lifecycle management, covering model persistence, tracking, serving, custom pyfunc models, Dockerized deployment, scaling, and performance monitoring.
This tutorial shows how to create a single‑page web app that displays the current date, weekday, and the number of days remaining until major holidays and the weekend, using FastAPI for the backend, Jinja2 for templating, and the zhdate library for lunar calendar conversion.
This article reviews eight Python frameworks—Django REST, Flask‑Restful, Falcon, Connexion, FastAPI, Hug, Eve, and Cornice—explaining their features, ideal project sizes, and how they simplify and accelerate the creation of robust APIs.
This article introduces eight Python frameworks—Django REST, Flask‑Restful, Falcon, Connexion, FastAPI, Hug, Eve, and Cornice—that help developers build APIs quickly and efficiently, highlighting each framework’s strengths, suitable project sizes, and key features.
This article introduces FakeAPI, a FastAPI‑based mock data service that lets front‑end developers quickly generate realistic JSON payloads, explains its endpoints, payload formats, repeat feature, and shows how to deploy it locally using Docker.
This article explains how to control request frequency in Python using the ratelimiter library for synchronous and asynchronous code, and introduces SlowAPI as a flexible solution for FastAPI and Flask, helping backend developers reduce server load and protect APIs from abuse.
This article walks through using FastAPI middleware for request timing, redirection, host whitelisting, and CORS, demonstrates background tasks and custom response codes, and explains various deployment options including Uvicorn, Gunicorn, Docker, and Nginx, providing complete code examples.
This article completes a FastAPI series by detailing middleware usage, request redirection, trusted host and CORS settings, background task handling, custom HTTP status responses, and multiple deployment options—including Uvicorn, Gunicorn, Docker, and Nginx—accompanied by full code examples.
This tutorial walks through deploying FastAPI in production, covering router inclusion as blueprints, handling multiple HTTP methods, integrating SQLAlchemy for database operations, applying Pydantic data validation, and implementing robust exception handling for a reliable backend service.
This article introduces FastAPI, a modern high‑performance Python web framework, walks through installation, basic routing, async handling, query and path parameters, data modeling with Pydantic, template rendering with Jinja2, and compares its speed to Flask, providing complete code examples and screenshots.
