Spring AI 2.0 adds self‑healing structured output with schema validation and provider‑side constraints, letting Java applications receive reliable JSON objects from large language models, eliminating brittle string‑cleaning code while still requiring business‑level validation.
This article walks through using Spring AI with Ollama to enforce JSON‑schema‑based structured output for agents, showing why structured responses matter, how Spring AI generates schemas from Java beans, and providing complete runnable code for both basic and advanced tool‑calling scenarios.
The article breaks down the reliability challenges of LLM Function Calling, categorizes five failure modes, and presents concrete engineering safeguards such as precise schema design, tool description, constraint enforcement, few‑shot calibration, structured output, validation‑feedback loops, monitoring, and risk‑aware trade‑offs.
In fast‑changing APIs, traditional assertions break when fields change, but a dual‑engine approach using JSON Schema for structural contracts and Pydantic for business rules provides a resilient, maintainable testing solution that adapts to evolution while keeping tests focused on critical data.
This guide shows how to eliminate manual POJO coding in Spring Boot 3 by using jsonschema2pojo to generate Java classes directly from JSON Schema or raw JSON, covering Maven dependencies, configuration, code examples, and plugin setup.
This article explains how using JSON Schema as a single source of truth can drive both front‑end rendering and back‑end validation in Java SpringBoot applications, eliminating duplicated validation logic, reducing maintenance costs, and enabling dynamic, versioned forms with real‑time user feedback.
Learn how to create flexible, low‑code dynamic forms by leveraging JSON Schema with SpringBoot on the backend and Vue3 on the frontend, covering schema definition, validation, storage, API design, and automatic UI rendering, plus advanced tips for versioning, low‑code builders, and multi‑platform support.
DripTable, a React‑based dynamic list framework from JD Retail, lets developers quickly generate configurable backend tables using JSON Schema, offering low‑code development, customizable rendering, and extensible components for efficient enterprise UI creation.
This tutorial demonstrates how to validate incoming JSON payloads in Spring Boot 3 by generating JSON Schemas, creating a custom filter that checks request bodies before they reach business logic, and registering the filter, complete with code examples and test screenshots.
DripTable is an open‑source, React‑based enterprise table solution that uses JSON Schema for low‑code configuration, offering fast list development, dynamic extensibility, theme flexibility, and visual schema generation for both configuration and application sides.
This article explains how to encapsulate data type processing in an API automation framework by creating a Python utility class that provides conversion, validation, and assertion methods, defines JSON Schemas, and demonstrates writing and executing Pytest test cases to ensure correct data types and values.
DripTable, an open‑source React and JSON‑Schema based table framework from JD.com, lets developers quickly generate configurable, extensible enterprise‑grade list pages through low‑code setup, with clear installation steps, code examples, and visual configuration tools for both the generator and runtime sides.
This article introduces DripTable, an open‑source React‑based low‑code table framework that uses JSON Schema for rapid backend‑admin list creation, outlines its advantages, explains when to adopt it, and provides step‑by‑step installation and usage instructions for both the configuration and application sides.
This article walks through the challenges of prompting small vision language models, demonstrates a conventional JSON‑based prompt, then shows how to define Pydantic models, embed their JSON schema into prompts, run inference with Qwen2.5‑VL, and visualize the structured results.
This article explains how to integrate the networknt JSON Schema validator into a Spring Boot application to enforce structured AI output, covering dependency setup, schema definition, service implementation, custom output validator, and exception handling, ensuring reliable, correctly formatted JSON responses from AI models.
This article provides concise Python code examples demonstrating how to read, write, and manipulate data using popular libraries such as PyYAML, toml, jsonschema, xmltodict, lxml, BeautifulSoup4, html5lib, and cssutils, covering scenarios from configuration files to XML/HTML parsing and CSS styling.
This article explains how to integrate JSON Schema validation into a Spring Boot application, covering dependency setup, schema definition, bean configuration, service implementation, controller exposure, and testing with curl to ensure JSON payloads conform to the defined rules.
Spring AI has added full support for OpenAI's structured output feature, enabling developers to define JSON schemas for AI responses, with example code showing how to configure prompts, response formats, and BeanOutputConverter for seamless JSON-to-Java bean conversion, plus Spring Boot starter configuration.
This guide explains how to integrate JSON Schema validation into a Spring Boot application, covering dependency setup, schema definition, bean configuration, service implementation, controller handling, and testing with curl examples to ensure JSON payloads conform to the defined rules.
The article provides ten code examples demonstrating how to validate JSON data with Python's jsonschema library, covering basic validation, required fields, nested schemas, arrays, uniqueness, enums, ranges, pattern matching, conditional logic, and custom error messages.
This guide explains what JSON Schema is, its core components, and how to use the PHP library justinrainbow/json-schema to install, validate data, coerce types, apply defaults, handle inline references, configure validation flags, and run tests for robust API development.
This article explains how Formily parses JSON Schema, builds a form tree, handles data binding, validation, and submission, and walks through the internal rendering flow—including createSchemaField, RecursionField, and ReactiveField—while providing practical examples and discussing complex linked‑field scenarios and performance considerations.
This article presents a self‑developed automated testing platform for backend services that achieves full test‑case coverage with low maintenance cost by unifying HTTP and RPC access, introducing a parameter‑pool, integrating JSON Schema and JSONPath validation, and leveraging traffic‑driven test‑case generation and scheduling.
DripTable is an open‑source, React‑based enterprise table visualization tool that uses JSON Schema for low‑code, configurable, and extensible dynamic list pages, offering fast development, theming flexibility, and a visual schema generator for backend CMS and low‑code table scenarios.
This article introduces DripTable, a JD‑open‑source, React‑based enterprise table framework that uses JSON Schema for low‑code configuration, outlines its features, advantages, usage scenarios, and provides step‑by‑step installation and code examples for both the visual configuration side and the runtime side.
DripTable is an open‑source, React‑based enterprise table solution that uses JSON Schema for low‑code configuration, offering fast, configurable, and extensible dynamic list rendering with visual generators and easy integration for backend and frontend developers.
This article presents a comprehensive backend automated testing framework that unifies HTTP and RPC access, introduces a parameter‑pool concept, leverages JSON Schema and JSONPath for validation, and outlines coverage metrics, test case generation, discovery, and continuous improvement to achieve near‑100% API test coverage.
This article details the design and implementation of a self‑built automated testing platform for backend services that achieves near‑full test‑case coverage by unifying HTTP/RPC access, introducing a parameter‑pool, leveraging JSON Schema and JSONPath, and automating case generation and promotion.
The article shows how to automate low‑code visual‑form creation by using AI to parse a one‑sentence requirement, split the task into summarizing components and few‑shot prompting, generate a JSON‑Schema DSL, and ensure smooth real‑time delivery via SSE and proper proxy configuration.
The article presents a language‑agnostic, low‑maintenance automated backend interface testing system that unifies HTTP and RPC calls, uses a parameter pool, JSON Schema and JSONPath for assertions, generates test cases from live traffic, measures coverage, and continuously updates suites to achieve near‑full coverage.
The article explains the difference between compile‑time and runtime type checking, why runtime checks are essential for data‑exchange scenarios in TypeScript projects, and compares several practical solutions—including JSON schemas, API‑based libraries like Zod, class‑validator, TypeScript‑JSON‑Schema, TypeScript‑is, and DeepKit—highlighting their advantages, drawbacks, and implementation details.
This article explains ByteDance's end‑to‑end data‑point (埋点) validation system, covering its technical challenges—usability, accuracy, real‑time visibility, stability, and extensibility—along with SDK integration, QR‑code workflow, JSON‑Schema verification, push‑service architecture, SLA metrics, and future automation plans.
This article explores the concept of property panels, illustrates their use across various domains such as hardware controls and design tools, and provides a detailed guide on building, rendering, and validating them with JSON Schema and extensible front‑end architectures.
This article explains how 58's Process Open Platform uses a low‑code approach with a visual form designer and a JSON‑schema‑driven renderer to dramatically improve the efficiency of creating, maintaining, and deploying business forms and approval workflows.
DripForm is an open‑source React form component from JD Retail iPaaS that offers configurable rendering, validation, and visual editing, enabling developers to efficiently build complex, dynamic forms with unified front‑end/back‑end validation, extensibility, and low‑code visual generation.
This article introduces Drip-Table, a low-code front‑end framework from JD Retail that visualizes web list creation, outlines its architecture, core capabilities, JSON Schema definition, real‑world case study, and future roadmap, highlighting how it cuts development effort by up to 75% while supporting complex list requirements.
This article explains how MySQL 8.0 introduces strict JSON schema validation, demonstrates the json_schema_valid and json_schema_validation_report functions with array and object examples, and shows how to enforce schema checks in tables using CHECK constraints.
This article explains how a Formily‑based drag‑and‑drop form designer works, covering its three‑pane UI layout, the JSON Schema output that drives form rendering, key TypeScript interfaces, and the conversion methods that let non‑developers configure forms visually.
The talk outlines low‑code’s evolution and booming market, explains how visual, schema‑driven platforms like Poseidon Builder let developers of any skill rapidly create and maintain middle‑office web pages with minimal code, dramatically cut development time, and aim toward future auto‑code generation.
The OpenAPI Specification 3.1.0 has been released, introducing a top‑level jsonSchemaDialect field, updated JSON Schema support, refined URI handling, enhanced file‑upload descriptions, and numerous clarifications that modernize API description and improve consistency across implementations.
This article outlines the design and implementation of a Vue‑powered visual editor, covering its three‑part architecture (editor, component library, backend), JSON schema hierarchy, component creation and configuration, state management with Vuex, drag‑and‑drop mechanics, and server‑side rendering.
NetEase Cloud Music’s frontend team created a JSON‑based form framework that extends Ant Design, offering async validation with error and warning states, reverse‑listener field linking, four exclusive form modes, flexible option loading, and performance‑optimized core‑render separation for complex B‑end applications.
This article explains how a frontend configuration engine can be designed to boost page development efficiency by using component‑based architecture, detailing attribute definitions, data requests, hierarchical relationships, and cross‑component interactions, and illustrates the approach with JSON schemas and React code examples.
This article introduces a frontend building system covering its motivation, business scenarios, core modules, architecture diagram, deployment strategy, JSON Schema usage, data configuration, and visualisation, while also sharing the speaker's background and recruitment information for the team.
This guide explains how to use the Open Application Model (OAM) to classify services, write OAM definitions with JSON Schema, implement an OAM operator, and uniformly expose and consume cloud resources across platforms, illustrated with Kubernetes examples and code snippets.
The article describes a scalable placement system for Xianyu that links front‑end resource slots with multiple backend data sources via a visual‑schema‑driven API, enabling personalized banner and feed content, batch request optimization, rapid campaign management, and reduced developer effort while serving millions of users daily.
This article introduces JSON Schema, explains its role in defining and validating JSON data structures, shows how to obtain implementations for various languages, and provides practical examples and configuration items for validating API responses in backend testing scenarios.
This article introduces JSON Schema, explains how to obtain implementations for various languages, demonstrates its usage for validating API responses, and details the most common configuration keywords and examples to help developers efficiently enforce JSON data formats.
