开源聊天机器人工具Talk2X，让网站轻松接入高效智能对话
Talk2X -- An Open-Source Toolkit Facilitating Deployment of LLM-Powered Chatbots on the Web
- 基于自动生成向量库的检索增强生成，提升响应效率
- 用户任务完成时间更短、准确率更高，体验显著改善
- 适合希望快速集成智能客服的开发者与开放科学平台
将大模型驱动的聊天机器人嵌入网站，正改变用户获取网络信息的方式。然而，主流方案多为闭源，限制了普及，且在实现细节和能耗透明度上不足。本文提出开源代理Talk2X，采用改进的检索增强生成（RAG）方法，并结合自动构建的向量数据库，兼顾能效与可扩展性。其架构适用于任意网站，为开发者提供即用型集成工具。通过混合方法评估，用户在开放科学仓库中完成特定资产获取任务时，使用Talk2X显著缩短任务完成时间，提高正确率与用户体验，能快速定位所需信息。研究推动了网络信息访问模式的持续演进。
原文摘要 · Abstract (English)
Integrated into websites, LLM-powered chatbots offer alternative means of navigation and information retrieval, leading to a shift in how users access information on the web. Yet, predominantly closed-sourced solutions limit proliferation among web hosts and suffer from a lack of transparency with regard to implementation details and energy efficiency. In this work, we propose our openly available agent Talk2X leveraging an adapted retrieval-augmented generation approach (RAG) combined with an automatically generated vector database, benefiting energy efficiency. Talk2X's architecture is generalizable to arbitrary websites offering developers a ready to use tool for integration. Using a mixed-methods approach, we evaluated Talk2X's usability by tasking users to acquire specific assets from an open science repository. Talk2X significantly improved task completion time, correctness, and user experience supporting users in quickly pinpointing specific information as compared to standard user-website interaction. Our findings contribute technical advancements to an ongoing paradigm shift of how we access information on the web.
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