让浏览器内AI处理长文本，突破上下文长度限制
CAG: Chunked Augmented Generation for Google Chrome's Built-in Gemini Nano
- 将大输入分块处理，智能调度模型生成
- 在浏览器内高效处理长文档和数据集
- 无需外部接口，适合网页端AI应用开发者
我们提出分块增强生成（CAG），一种专为克服Google Chrome内置Gemini Nano模型上下文窗口限制而设计的架构。尽管Chrome集成Gemini Nano标志着将AI能力直接引入浏览器的重要进展，但其有限的上下文窗口对处理大输入构成挑战。CAG通过智能输入分块与处理策略，实现对海量内容的高效处理，同时在浏览器约束下保持模型性能。我们的实现特别适用于在Chrome中直接处理大型文档和数据集，使复杂AI功能无需依赖外部API即可通过浏览器访问。立即体验：https://github.com/vivekVells/cag-js。
原文摘要 · Abstract (English)
We present Chunked Augmented Generation (CAG), an architecture specifically designed to overcome the context window limitations of Google Chrome's built-in Gemini Nano model. While Chrome's integration of Gemini Nano represents a significant advancement in bringing AI capabilities directly to the browser, its restricted context window poses challenges for processing large inputs. CAG addresses this limitation through intelligent input chunking and processing strategies, enabling efficient handling of extensive content while maintaining the model's performance within browser constraints. Our implementation demonstrates particular efficacy in processing large documents and datasets directly within Chrome, making sophisticated AI capabilities accessible through the browser without external API dependencies. Get started now at https://github.com/vivekVells/cag-js.
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