Accelerating AI-Powered Research: The PuppyChatter Framework for Usable and Flexible Tooling

TL;DR

The paper introduces PuppyChatter, a framework that simplifies AI application development by combining the ease of vendor SDKs with vendor-neutral model abstraction, enhancing flexibility and security.

Contribution

PuppyChatter uniquely integrates SDK-like usability with vendor-neutral abstraction, addressing vendor lock-in and security issues in AI development frameworks.

Findings

PuppyChatter reduces development complexity for AI applications.

The framework maintains vendor neutrality while offering SDK-like simplicity.

It improves security by avoiding additional abstraction layers.

Abstract

This research addresses the challenges inherent in developing Artificial Intelligence (AI) applications, particularly those leveraging Large Language Models (LLMs). While AI vendors provide Application Programming Interfaces (APIs) and Software Development Kits (SDKs) to facilitate developer interaction, the former often requires intricate manual request construction, and the latter can lead to significant vendor lock-in. Furthermore, existing model abstraction frameworks, though mitigating vendor dependency, introduce an additional layer of complexity and potential security concerns. To reconcile these conflicting factors, the study introduces PuppyChatter, a novel software framework designed to preserve the intuitive simplicity of vendor-specific SDKs while simultaneously adhering to the vendor-neutrality principles characteristic of model abstraction, thereby offering a more streamlined and flexible development paradigm.