AI Data Source Discovery With Template-Matched API Code
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Solution Overview
Problem
Existing systems face challenges in accurately generating source code for data retrieval APIs, leading to bottlenecks in application development when suitable APIs are absent.
Innovation Solution
A system combining an API requirement template database with a code creation database, using generative AI to generate API source code by matching user-defined requirements with existing templates and source code, and deploying the generated code through a user interface.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If generative AI is used to automatically generate API source code from natural language, then productivity is improved, but manufacturing precision deteriorates due to low accuracy of generated code
Solution Approach 1:
The system implements feedback by comparing the generated API source code against a database of existing APIs and requirements. The similarity matching mechanism provides feedback on whether the generated code meets the specified requirements, allowing for iterative refinement and validation of the generated code quality.
Solution Approach 2:
The system performs preliminary action by storing and organizing existing API source code and requirements in a database before generation. This pre-prepared knowledge base enables the system to quickly retrieve and compare against similar existing APIs, improving both the speed and accuracy of code generation.
2Manufacturing precision
If a comprehensive API database is created to improve code generation accuracy, then device complexity increases due to multiple databases and comparison mechanisms
Solution Approach 1:
The system applies universality by creating a unified API database that serves multiple functions: storing API source code, storing requirements, enabling similarity comparison, and supporting code generation. This single database structure performs multiple roles that would otherwise require separate systems, reducing overall complexity while maintaining high accuracy.
3Productivity
If existing APIs are reused through template matching, then productivity is improved, but adaptability deteriorates when user requirements differ from existing templates
Solution Approach 1:
The system implements dynamics by making the code generation process adaptive rather than static. The similarity matching mechanism dynamically selects the most appropriate existing API template based on the user's specific requirements, allowing the system to flexibly adapt to varying needs while maintaining efficiency through template reuse.
Data Source
AI summary
Systems and methods described herein can involve, responsive to receiving a user request associated with an API type, retrieving a template from a template database; providing the template to a user to obtain a user-defined requirement; using the user-defined requirement to obtain from an API code creation database a historic requirement that is associated with the user-defined requirement and is associated with a source code; in response to the historic requirement being identical to the user-defined requirement, communicating an API endpoint to the user; in response to the historic requirement not being identical to the user-defined requirement, providing the user-defined requirement and the source code to a generative AI model to request an API source code; and in response to receiving the API source code and an endpoint name, communicating the endpoint name to the user.