AI-Generated Data Connectors With Knowledge Graph Defect Debugging
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Solution Overview
Problem
Collecting data from heterogeneous data sources for data warehouses is cumbersome and resource-intensive due to differences in Application Programming Interfaces (APIs), requiring significant developer resources.
Innovation Solution
Utilizing generative AI and large language models to automatically build and monitor data connectors, leveraging a knowledge graph to represent API information, and detecting and addressing defects in the connectors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual code development is used to access heterogeneous data sources, then data collection functionality is achieved, but developer resources and time consumption increase significantly
Solution Approach 1:
The system enables self-service by automatically generating data connector code through AI models. The system crawls API documentation, builds knowledge graphs, and generates functional connectors without requiring manual developer intervention for each data source integration task.
Solution Approach 2:
The system performs preliminary actions by pre-crawling and storing API documentation in knowledge graphs before actual data collection needs arise. This pre-processing of API information enables rapid connector generation when data sources need to be accessed.
2Adaptability or versatility
If custom code is developed for each data source, then specific data access requirements are met, but system complexity and maintenance burden increase
Solution Approach 1:
The system implements universality by creating a unified knowledge graph framework that handles multiple heterogeneous data sources through a common interface. The AI model generates standardized connector code that adapts to different data sources without requiring custom development for each one.
Solution Approach 2:
The knowledge graph serves as an intermediary layer between the data collection system and heterogeneous data sources. It stores standardized API information that mediates between diverse data source interfaces and the unified data collection requirements.
3Productivity
If AI-generated connectors are deployed, then development resources are reduced, but defect detection and validation become critical
Solution Approach 1:
The system implements feedback mechanisms by monitoring deployed connectors for defects and using this information to improve future connector generation. The system tracks connector performance and uses feedback loops to refine the AI models and reduce defect rates over time.
Data Source
AI summary
A device is disclosed that monitors connectors deployed in a data collection system. The device deploys a data connector generated from a connector knowledge representation, the connector knowledge representation generated from an API source knowledge graph using a large language model. The device receives information describing a defect reported in connection with the deployed data connector, and determines a portion of the connector knowledge graph associated with the defect. The device maps the portion of the connector knowledge graph to a portion of the API source knowledge graph and identifies a portion of an API documentation associated with the portion of the API source knowledge graph. The device reports information describing the defect, the information comprising at least a portion of the API documentation associated with the portion of the API source knowledge graph.