对话式健康代理：一个个性化的LLM驱动的代理框架
Conversational Health Agents: A Personalized LLM-Powered Agent Framework
AI总结：
针对当前对话式健康代理缺乏多步问题解决、个性化对话和多模态分析能力的问题，提出了开源LLM框架openCHA，通过编排器集成外部资源，实现个性化医疗查询响应，并验证了其在复杂任务中的有效性。
摘要：
对话式健康代理（CHAs）是提供医疗保健服务（如辅助和诊断）的交互式系统。当前的CHAs，特别是那些利用大型语言模型（LLMs）的系统，主要关注对话方面。然而，它们提供的代理能力有限，特别是缺乏多步问题解决、个性化对话和多模态数据分析。我们的目标是克服这些限制。我们提出了openCHA，一个开源的LLM驱动的框架，以使对话代理能够为用户的医疗保健查询生成个性化响应。该框架使开发者能够将外部来源（包括数据源、知识库和分析模型）集成到他们基于LLM的解决方案中。openCHA包含一个编排器，用于规划和执行动作以从外部来源收集信息，这对于形成对用户询问的响应是必不可少的。它促进知识获取、问题解决能力、多语言和多模态对话，并促进与各种AI平台的交互。我们通过两个演示和四个用例说明了该框架在处理复杂医疗保健任务方面的熟练程度。此外，我们通过GitHub向社区发布openCHA作为开源软件。
英文摘要：
Conversational Health Agents (CHAs) are interactive systems that provide healthcare services, such as assistance and diagnosis. Current CHAs, especially those utilizing Large Language Models (LLMs), primarily focus on conversation aspects. However, they offer limited agent capabilities, specifically lacking multi-step problem-solving, personalized conversations, and multimodal data analysis. Our aim is to overcome these limitations. We propose openCHA, an open-source LLM-powered framework, to empower conversational agents to generate a personalized response for users' healthcare queries. This framework enables developers to integrate external sources including data sources, knowledge bases, and analysis models, into their LLM-based solutions. openCHA includes an orchestrator to plan and execute actions for gathering information from external sources, essential for formulating responses to user inquiries. It facilitates knowledge acquisition, problem-solving capabilities, multilingual and multimodal conversations, and fosters interaction with various AI platforms. We illustrate the framework's proficiency in handling complex healthcare tasks via two demonstrations and four use cases. Moreover, we release openCHA as open source available to the community via GitHub.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
