PuppyChatter让开发者用简单接口调用多模型，避免厂商绑定
Accelerating AI-Powered Research: The PuppyChatter Framework for Usable and Flexible Tooling
- 统一接口抽象层，兼容多种大模型且无需改代码
- 保留SDK易用性，同时实现跨平台模型切换能力
- 适合希望快速迭代、避免依赖单一AI服务商的开发者
本研究针对基于大语言模型（LLMs）开发AI应用时面临的挑战展开。尽管主流厂商提供API和SDK以支持开发，但前者需手动构造复杂请求，后者则带来严重的厂商锁定问题。现有模型抽象框架虽缓解了厂商依赖，却引入额外复杂性和潜在安全风险。为此，本文提出PuppyChatter框架，旨在保持厂商专属SDK的直观易用性，同时遵循模型抽象的无厂商依赖原则，从而构建更简洁灵活的开发范式。
原文摘要 · Abstract (English)
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.
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