Graph databases offer unparalleled flexibility for managing interconnected data, yet the lack of strict schema enforcement often leads to runtime uncertainties and complex query development. This paper introduces Graphify, an end-to-end framework that enables developers to visually model graph data schemas and automatically synthesize a fully functional, type-safe backend. This paper proposes a formal mapping of GraphQL artifacts to the Gremlin traversal machine, supporting complete CRUD operations and arbitrarily nested queries. The system generates a transpiler capable of converting complex GraphQL requests into a single, optimized Gremlin query, including advanced features such as nested logical predicates, multi-key sorting, and pagination. At the core of the framework is a recursive state machine algorithm that processes GraphQL Abstract Syntax Trees (ASTs) with linear time complexity $O(S)$ relative to the number of selected fields. This paper demonstrates the practical efficiency and theoretical robustness of the approach through formal complexity analysis and empirical evaluation using the MovieLens 100k dataset. The result is a system that enables the generation of graph interfaces in minutes, bridging the gap between flexible graph storage and type-safe API consumption.

翻译：图数据库为管理互联数据提供了无与伦比的灵活性，但缺乏严格的模式约束常导致运行时不确定性及复杂的查询开发。本文介绍Graphify，一个端到端框架，允许开发者可视化建模图数据模式，并自动合成一个功能完备、类型安全的后端。本文提出了一种形式化映射方法，将GraphQL工件映射到Gremlin遍历机，支持完整的增删改查操作及任意嵌套的查询。该系统生成的转译器可将复杂GraphQL请求转换为单一、优化的Gremlin查询，包含嵌套逻辑谓词、多键排序及分页等高级特性。框架核心是一个递归状态机算法，其处理GraphQL抽象语法树的时间复杂度为$O(S)$，其中$S$为所选字段数量。本文通过形式化复杂度分析及使用MovieLens 100k数据集进行的实证评估，展示了该方法的实际效率与理论稳健性。最终成果是一个能在数分钟内生成图接口的系统，弥合了灵活图存储与类型安全API消费之间的鸿沟。