AI Graph API Cross-Linking via NLP Documentation Analysis
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Solution Overview
Problem
The complexity of different application programming interfaces (APIs) with varying requirements, such as programming languages, conventions, and documentation, hinders the efficient cooperation and cross-linking of functions to assist users, especially in responding to natural language queries, making it impractical for human programmers to develop systems that can effectively combine APIs for task assistance.
Innovation Solution
An artificial intelligence (AI) graph structure is maintained with API-agnostic semantic entities and function nodes, where natural language processing (NLP) is used to analyze API documentation to recognize new functions and update the graph structure, enabling automatic cross-linking and configuration of APIs to execute tasks without extensive coding.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If human programmers manually develop systems to combine APIs, then the system can effectively execute functions, but the complexity of varying API requirements (programming languages, conventions, documentation) makes the development process impractical and time-consuming
Solution Approach 1:
The system performs self-service by automatically analyzing API documentation, extracting function definitions, and generating cross-linking configurations without human intervention. The computer system autonomously processes API specifications, identifies functional relationships, and updates the graph structure, eliminating the need for manual programming effort while handling the complexity internally.
Solution Approach 2:
The patent replaces the mechanical process of manual API integration with an automated computational system. Instead of human programmers manually analyzing documentation and coding integrations, the system uses computer-based analysis of API documentation to automatically generate and update the graph structure, substituting human cognitive and manual labor with automated processing.
2Adaptability or versatility
If the system maintains a comprehensive AI graph structure with all APIs cross-linked, then task execution capability is enhanced, but the time and resources required to manually update and maintain the graph increase significantly
Solution Approach 1:
The system performs preliminary action by proactively analyzing new API documentation as soon as it becomes available, automatically extracting function information, and updating the graph structure in advance. This prevents accumulation of maintenance delays and ensures the graph remains current without requiring dedicated maintenance time windows or manual intervention.
Solution Approach 2:
The patent replaces manual graph maintenance activities with automated computational processes. The system automatically parses API documentation, extracts functional relationships, and updates the graph structure without human involvement, eliminating the time-consuming manual processes of analyzing documentation, identifying cross-links, and updating the graph data structure.
3Ease of operation
If the system uses automated NLP analysis to recognize new functions from API documentation, then the ease of integrating new APIs is improved, but the precision of function recognition may be compromised compared to manual analysis
Solution Approach 1:
The system creates a structured representation (copy) of the API documentation in the graph data structure, capturing the essential functional relationships. By maintaining this copied representation with proper data structures and relationships, the system preserves the precision of function recognition while enabling automated processing and integration.
Data Source
AI summary
A method for automatically cross-linking a plurality of APIs in an artificial intelligence (AI) graph structure comprises maintaining an AI graph structure defining a plurality of API-agnostic semantic entities, a plurality of function nodes, a plurality of input-adapter edges, and a plurality of output adapter edges. The method further comprises cross-linking a new function from a new API by computer-analyzing documentation of the new API with a natural language processing (NLP) machine in order to recognize the new function, and updating the AI graph data structure to include a new function node based on the new function.