This article examines the implementation of a virtual health assistant powered by Retrieval-Augmented Generation (RAG) and GPT-4, aimed at enhancing clinical support through personalized, real-time interactions with p...This article examines the implementation of a virtual health assistant powered by Retrieval-Augmented Generation (RAG) and GPT-4, aimed at enhancing clinical support through personalized, real-time interactions with patients. The system is hypothesized to improve healthcare accessibility, operational efficiency, and patient outcomes by automating routine tasks and delivering accurate health information. The assistant leverages natural language processing and real-time data retrieval models to respond to patient inquiries, schedule appointments, provide medication reminders, assist with symptom triage, and answer insurance-related questions. By integrating RAG-based virtual care, the system reduces the burden on healthcare specialists and helps mitigate healthcare disparities, particularly in rural areas where traditional care is limited. Although the initial scope of testing did not validate all potential benefits, the results demonstrated high patient satisfaction and strong response accuracy, both critical for systems of this nature. These findings underscore the transformative potential of AI-driven virtual health assistants in enhancing patient engagement, streamlining operational workflows, and improving healthcare accessibility, ultimately contributing to better outcomes and more cost-effective care delivery.展开更多
自然语言转换结构化查询语言(NL2SQL)能降低非专业人员操作数据库的技术门槛,从而提升用户体验和工作效率。此外,检索增强生成(RAG)技术可以通过引入外部知识库提升NL2SQL的性能。针对目前RAG在NL2SQL应用中存在的检索策略漏检率高和召...自然语言转换结构化查询语言(NL2SQL)能降低非专业人员操作数据库的技术门槛,从而提升用户体验和工作效率。此外,检索增强生成(RAG)技术可以通过引入外部知识库提升NL2SQL的性能。针对目前RAG在NL2SQL应用中存在的检索策略漏检率高和召回上下文的相关性不强等问题,提出一种分序检索重排序RAG(RAG-SRR)方法优化知识库构建、检索召回策略和提示词设计等环节。首先,从问答对、专业名词和数据库结构这3个方面进行领域知识库的构建:问答对根据文物艺术品拍卖监管的高频处理和查询的问题构建,专业名词根据拍卖行业标准构建,而数据库结构根据雅昌艺术拍卖网的数据构建;其次,在检索阶段采取分序检索的策略,并对3类知识库设置不同的优先级,且在召回阶段重排序检索的信息;最后,在提示词设计中给出提示词优化设计的原则及提示词模板。实验结果表明:在领域数据集、Spider数据集上,RAG-SRR方法与基于BERT(Bidirectional Encoder Representations from Transformers)模型和RESDSQL(Ranking-enhanced Encoding plus a Skeleton-aware Decoding framework for text-to-SQL)模型的方法的执行准确率分别至少提高了19.50、24.20和12.17、8.90个百分点。而在相同大语言模型下,RAG-SRR方法比未优化的RAG方法的执行准确率分别至少提高了12.83和15.60个百分点,与C3SQL方法相比,执行准确率分别至少提高了1.50和3.10个百分点。在使用Llama3.1-8B时,与DIN-SQL方法相比,执行准确率在中文语料数据集中提升0.30个百分点,在英文语料数据集中最多相差3.90个百分点;但在使用Qwen2.5-7B时,执行准确率分别提高1.60和4.10个百分点。可见,RAG-SRR方法具备较强的实用性和可移植性。展开更多
为解决财务人员数字技术应用能力不足、传统财务流程中数据采集质量差导致重复返工、人工数据处理效率低等问题,设计开发了财务共享辅助系统。采用机器人流程自动化(RPA,Robotic Process Automation)和图检索增强生成(Graph RAG,Graph-b...为解决财务人员数字技术应用能力不足、传统财务流程中数据采集质量差导致重复返工、人工数据处理效率低等问题,设计开发了财务共享辅助系统。采用机器人流程自动化(RPA,Robotic Process Automation)和图检索增强生成(Graph RAG,Graph-based Retrieval-Augmented Generation)技术,实现数据填报收集、RPA自动化处理、智能问答等功能,显著提升财务报账效率,为铁路局集团公司财务共享中心的建设提供支撑。展开更多
随着铁路旅客服务需求的持续增长,传统信息查询系统因理解能力有限、上下文感知不足等问题,难以满足乘客与调度人员对高效、准确信息服务的需求。针对这一问题,提出一种融合大语言模型(Large Language Models,LLMs)与检索增强生成(Retri...随着铁路旅客服务需求的持续增长,传统信息查询系统因理解能力有限、上下文感知不足等问题,难以满足乘客与调度人员对高效、准确信息服务的需求。针对这一问题,提出一种融合大语言模型(Large Language Models,LLMs)与检索增强生成(Retrieval-Augmented Generation,RAG)技术的智能问答系统,专注于铁路旅客服务场景下的信息问答任务。该系统通过RAG机制融合语义检索与语言生成,提升对铁路专业知识的响应能力和答案准确性,同时构建了覆盖行业规则和乘客服务内容的知识库。基于500条真实问题设计实验,对比关键词匹配与无检索生成方法。结果表明,本系统在500条真实问题测试中取得了91.2%的回答准确率,较关键词检索方法提升近19%,较ChatGLM提升约10%;平均响应时间为3.2 s,用户满意度评分达到4.6/5.0,各项指标均显著优于对比系统,验证了本方法在铁路旅客服务场景中的实用性与推广价值。展开更多
介绍检索增强生成(Retrieval-Augmented Generation,RAG)技术在广电运维智能助手构建中的应用。阐述利用大语言模型(Large Language Model,LLM)和RAG技术搭建广电运维智能助手架构的过程,包含文档预处理、RAG引擎及LLM共3部分内容,最后...介绍检索增强生成(Retrieval-Augmented Generation,RAG)技术在广电运维智能助手构建中的应用。阐述利用大语言模型(Large Language Model,LLM)和RAG技术搭建广电运维智能助手架构的过程,包含文档预处理、RAG引擎及LLM共3部分内容,最后通过本地部署和系统微调将广电运维智能助手应用落地。展开更多
This exploration acquaints a momentous methodology with custom chatbot improvement that focuses on pro-ficiency close by viability.We accomplish this by joining three key innovations:LangChain,Retrieval Augmented Gene...This exploration acquaints a momentous methodology with custom chatbot improvement that focuses on pro-ficiency close by viability.We accomplish this by joining three key innovations:LangChain,Retrieval Augmented Generation(RAG),and enormous language models(LLMs)tweaked with execution proficient strategies like LoRA and QLoRA.LangChain takes into consideration fastidious fitting of chatbots to explicit purposes,guaranteeing engaged and important collaborations with clients.RAG’s web scratching capacities engage these chatbots to get to a tremendous store of data,empowering them to give exhaustive and enlightening reactions to requests.This recovered data is then decisively woven into reaction age utilizing LLMs that have been calibrated with an emphasis on execution productivity.This combination approach offers a triple advantage:further developed viability,upgraded client experience,and extended admittance to data.Chatbots become proficient at taking care of client questions precisely and productively,while instructive and logically pertinent reactions make a more regular and drawing in cooperation for clients.At last,web scratching enables chatbots to address a more extensive assortment of requests by conceding them admittance to a more extensive information base.By digging into the complexities of execution proficient LLM calibrating and underlining the basic job of web-scratched information,this examination offers a critical commitment to propelling custom chatbot plan and execution.The subsequent chatbots feature the monstrous capability of these advancements in making enlightening,easy to understand,and effective conversational specialists,eventually changing the manner in which clients cooperate with chatbots.展开更多
文摘This article examines the implementation of a virtual health assistant powered by Retrieval-Augmented Generation (RAG) and GPT-4, aimed at enhancing clinical support through personalized, real-time interactions with patients. The system is hypothesized to improve healthcare accessibility, operational efficiency, and patient outcomes by automating routine tasks and delivering accurate health information. The assistant leverages natural language processing and real-time data retrieval models to respond to patient inquiries, schedule appointments, provide medication reminders, assist with symptom triage, and answer insurance-related questions. By integrating RAG-based virtual care, the system reduces the burden on healthcare specialists and helps mitigate healthcare disparities, particularly in rural areas where traditional care is limited. Although the initial scope of testing did not validate all potential benefits, the results demonstrated high patient satisfaction and strong response accuracy, both critical for systems of this nature. These findings underscore the transformative potential of AI-driven virtual health assistants in enhancing patient engagement, streamlining operational workflows, and improving healthcare accessibility, ultimately contributing to better outcomes and more cost-effective care delivery.
文摘自然语言转换结构化查询语言(NL2SQL)能降低非专业人员操作数据库的技术门槛,从而提升用户体验和工作效率。此外,检索增强生成(RAG)技术可以通过引入外部知识库提升NL2SQL的性能。针对目前RAG在NL2SQL应用中存在的检索策略漏检率高和召回上下文的相关性不强等问题,提出一种分序检索重排序RAG(RAG-SRR)方法优化知识库构建、检索召回策略和提示词设计等环节。首先,从问答对、专业名词和数据库结构这3个方面进行领域知识库的构建:问答对根据文物艺术品拍卖监管的高频处理和查询的问题构建,专业名词根据拍卖行业标准构建,而数据库结构根据雅昌艺术拍卖网的数据构建;其次,在检索阶段采取分序检索的策略,并对3类知识库设置不同的优先级,且在召回阶段重排序检索的信息;最后,在提示词设计中给出提示词优化设计的原则及提示词模板。实验结果表明:在领域数据集、Spider数据集上,RAG-SRR方法与基于BERT(Bidirectional Encoder Representations from Transformers)模型和RESDSQL(Ranking-enhanced Encoding plus a Skeleton-aware Decoding framework for text-to-SQL)模型的方法的执行准确率分别至少提高了19.50、24.20和12.17、8.90个百分点。而在相同大语言模型下,RAG-SRR方法比未优化的RAG方法的执行准确率分别至少提高了12.83和15.60个百分点,与C3SQL方法相比,执行准确率分别至少提高了1.50和3.10个百分点。在使用Llama3.1-8B时,与DIN-SQL方法相比,执行准确率在中文语料数据集中提升0.30个百分点,在英文语料数据集中最多相差3.90个百分点;但在使用Qwen2.5-7B时,执行准确率分别提高1.60和4.10个百分点。可见,RAG-SRR方法具备较强的实用性和可移植性。
文摘随着铁路旅客服务需求的持续增长,传统信息查询系统因理解能力有限、上下文感知不足等问题,难以满足乘客与调度人员对高效、准确信息服务的需求。针对这一问题,提出一种融合大语言模型(Large Language Models,LLMs)与检索增强生成(Retrieval-Augmented Generation,RAG)技术的智能问答系统,专注于铁路旅客服务场景下的信息问答任务。该系统通过RAG机制融合语义检索与语言生成,提升对铁路专业知识的响应能力和答案准确性,同时构建了覆盖行业规则和乘客服务内容的知识库。基于500条真实问题设计实验,对比关键词匹配与无检索生成方法。结果表明,本系统在500条真实问题测试中取得了91.2%的回答准确率,较关键词检索方法提升近19%,较ChatGLM提升约10%;平均响应时间为3.2 s,用户满意度评分达到4.6/5.0,各项指标均显著优于对比系统,验证了本方法在铁路旅客服务场景中的实用性与推广价值。
文摘介绍检索增强生成(Retrieval-Augmented Generation,RAG)技术在广电运维智能助手构建中的应用。阐述利用大语言模型(Large Language Model,LLM)和RAG技术搭建广电运维智能助手架构的过程,包含文档预处理、RAG引擎及LLM共3部分内容,最后通过本地部署和系统微调将广电运维智能助手应用落地。
文摘This exploration acquaints a momentous methodology with custom chatbot improvement that focuses on pro-ficiency close by viability.We accomplish this by joining three key innovations:LangChain,Retrieval Augmented Generation(RAG),and enormous language models(LLMs)tweaked with execution proficient strategies like LoRA and QLoRA.LangChain takes into consideration fastidious fitting of chatbots to explicit purposes,guaranteeing engaged and important collaborations with clients.RAG’s web scratching capacities engage these chatbots to get to a tremendous store of data,empowering them to give exhaustive and enlightening reactions to requests.This recovered data is then decisively woven into reaction age utilizing LLMs that have been calibrated with an emphasis on execution productivity.This combination approach offers a triple advantage:further developed viability,upgraded client experience,and extended admittance to data.Chatbots become proficient at taking care of client questions precisely and productively,while instructive and logically pertinent reactions make a more regular and drawing in cooperation for clients.At last,web scratching enables chatbots to address a more extensive assortment of requests by conceding them admittance to a more extensive information base.By digging into the complexities of execution proficient LLM calibrating and underlining the basic job of web-scratched information,this examination offers a critical commitment to propelling custom chatbot plan and execution.The subsequent chatbots feature the monstrous capability of these advancements in making enlightening,easy to understand,and effective conversational specialists,eventually changing the manner in which clients cooperate with chatbots.
