Schema-Based API Integration for Natural Language Applications
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Solution Overview
Problem
Natural language models are limited by their access to training data, are expensive to train, and require significant resources and time for integrating with external applications, leading to inefficiencies and increased costs.
Innovation Solution
A method for integrating external APIs with natural language models by accessing a manifest file and specification hosted by the API publisher, allowing seamless integration and updates without user involvement, using a user interface to register and integrate APIs, and generating plugins for communication.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If natural language models are trained with more data and resources to improve accuracy, then model performance improves, but training costs and time increase significantly
Solution Approach 1:
The patent applies preliminary action by pre-training the natural language model on general knowledge before fine-tuning it for specific tasks. This allows the model to already possess substantial accuracy before deployment, reducing the need for extensive task-specific training and thereby reducing training time and costs while maintaining high performance.
Solution Approach 2:
The training process is segmented into multiple stages: pre-training on large corpora, domain-specific fine-tuning, and task-specific adaptation. This segmentation allows each stage to focus on specific objectives with appropriate resource allocation, improving overall efficiency and reducing total training time while maintaining model accuracy.
2Reliability
If external APIs are integrated manually with natural language models, then integration reliability improves, but integration complexity and resource requirements increase
Solution Approach 1:
The patent introduces an intermediary layer that standardizes the interface between natural language models and external APIs. This intermediary handles authentication, request formatting, and response parsing, ensuring reliable integration while shielding developers from complex implementation details, thereby reducing integration complexity.
Solution Approach 2:
The patent creates a universal integration framework that can work with multiple different APIs through a common interface. This multi-functional approach allows the same natural language model to interact with various external services without requiring custom integration code for each API, reducing both complexity and resource requirements while maintaining reliability through consistent handling.
3Stability of the object's composition
If natural language models access only training data, then data consistency is maintained, but adaptability to external applications is limited
Solution Approach 1:
The patent implements a dynamic data access architecture where the natural language model can switch between static training data and dynamic external API data based on user needs. This allows the model to maintain consistency when working with training data while gaining adaptability to access up-to-date external information, effectively resolving the contradiction between stability and versatility.
Data Source
AI summary
Disclosed herein are methods, systems, and computer-readable media for integrating an application programming interface (API) with a natural language model user interface. In one embodiment, a method includes receiving a registration of an external API via a user interface connected to a natural language model, the natural language model being configured to integrate a plurality of external APIs, accessing a manifest file hosted at a first online location by a publisher of the external API, the manifest file comprising parameters for interfacing with the external API and a second online location of a specification associated with the external API, the parameters and second online location being defined by the publisher of the external API, accessing the specification at the second online location, and integrating the external API with the natural language model based on data from at least one of the manifest file or the specification.