针对大语言模型在专业领域应用中存在的知识准确性不足、实时性欠缺和专业性局限等问题,提出了一种基于LangChain框架的张衡一号卫星电场数据RAG问答系统。通过融合RAG(Retrieval-Augmented Generation)技术与LLMs(Large Language Mode...针对大语言模型在专业领域应用中存在的知识准确性不足、实时性欠缺和专业性局限等问题,提出了一种基于LangChain框架的张衡一号卫星电场数据RAG问答系统。通过融合RAG(Retrieval-Augmented Generation)技术与LLMs(Large Language Models)的推理能力,利用LangChain的模块化组件(包括LLMs接入、提示词模板和任务链编排)和Milvus向量数据库,实现了专业知识的动态检索与生成优化。实验数据来源于41篇张衡一号卫星电场领域的核心文献,涵盖电场异常检测、数据处理方法等研究方向。实验结果表明,相较于普通Qwen-Plus模型,RAG增强版本在科学参数描述和数据分析方法适用性方面展现出更优的专业性、实时性和准确性。这证实了RAG技术可有效解决LLMs在专业领域的知识局限性,为构建高可靠性的专业知识问答系统提供了可行的技术方案,具有重要的实践价值和理论意义。展开更多
随着人工智能技术的迅猛发展,医疗问答系统已成为医疗信息检索和知识获取的重要工具。医疗领域涉及大量医学术语、复杂的疾病症状和治疗方案,传统查询方式难以高效、准确地满足医护人员和患者的信息需求。相比传统国内搜索引擎和原生开...随着人工智能技术的迅猛发展,医疗问答系统已成为医疗信息检索和知识获取的重要工具。医疗领域涉及大量医学术语、复杂的疾病症状和治疗方案,传统查询方式难以高效、准确地满足医护人员和患者的信息需求。相比传统国内搜索引擎和原生开源大语言模型(LLMs),基于LangChain的大模型医疗问答系统能够提供更高质量的答案,显著提升医疗知识检索的效率和精准度。因此,本研究提出了一种基于LangChain与大模型的医疗智能问答系统,结合命名实体识别(NER)、图谱查询和对话分析等技术,构建了一个专注于医疗领域的知识图谱及其查询与生成模块。通过设计和优化Prompt提示词,Agent Tool提升了大模型生成更精准、高质量医疗问答的能力。研究结果表明,该系统在医疗问答任务中的表现优异,准确度、方案可行性和上下文相关性等指标显著优于传统LLMs和国内知名大模型。该系统通过与大规模医疗知识图谱的结合,能够深入理解复杂的医疗问题,并提供精准的回答,呈现可视化图谱展示图,更直观地给用户反馈,同时具备较高的数据安全性和可迁移性。Nowadays, with the rapid development of artificial intelligence technology, medical question answering system has become an important tool for medical information retrieval and knowledge acquisition. The medical field involves a large number of medical terms, complicated disease symptoms and treatment plans, and traditional inquiry methods are difficult to meet the information needs of medical staff and patients efficiently and accurately. Compared with traditional domestic search engines and native open source large language model (LLMs), LangChain-based large model medical question answering system can provide higher quality answers, significantly improving the efficiency and accuracy of medical knowledge retrieval. Therefore, this study proposed a medical intelligent question and answer system based on LangChain and large model, combined with named entity recognition (NER), graph query and dialogue analysis and other technologies, to build a knowledge graph and query and generation module focusing on the medical field. By designing and optimizing Prompt words, Agent Tool improves the ability of large models to generate more accurate and high-quality medical questions and answers. The results show that the system performs well in medical question answering tasks, with significant improvements in accuracy, feasibility, and context relevance are significantly better than traditional LLMs and well-known domestic large models. Through the combination of large-scale medical knowledge graph, the system can deeply understand complex medical questions, provide accurate answers, present a visual map display graph, and give users more intuitive feedback, while having high data security and portability.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
文摘针对大语言模型在专业领域应用中存在的知识准确性不足、实时性欠缺和专业性局限等问题,提出了一种基于LangChain框架的张衡一号卫星电场数据RAG问答系统。通过融合RAG(Retrieval-Augmented Generation)技术与LLMs(Large Language Models)的推理能力,利用LangChain的模块化组件(包括LLMs接入、提示词模板和任务链编排)和Milvus向量数据库,实现了专业知识的动态检索与生成优化。实验数据来源于41篇张衡一号卫星电场领域的核心文献,涵盖电场异常检测、数据处理方法等研究方向。实验结果表明,相较于普通Qwen-Plus模型,RAG增强版本在科学参数描述和数据分析方法适用性方面展现出更优的专业性、实时性和准确性。这证实了RAG技术可有效解决LLMs在专业领域的知识局限性,为构建高可靠性的专业知识问答系统提供了可行的技术方案,具有重要的实践价值和理论意义。
文摘随着人工智能技术的迅猛发展,医疗问答系统已成为医疗信息检索和知识获取的重要工具。医疗领域涉及大量医学术语、复杂的疾病症状和治疗方案,传统查询方式难以高效、准确地满足医护人员和患者的信息需求。相比传统国内搜索引擎和原生开源大语言模型(LLMs),基于LangChain的大模型医疗问答系统能够提供更高质量的答案,显著提升医疗知识检索的效率和精准度。因此,本研究提出了一种基于LangChain与大模型的医疗智能问答系统,结合命名实体识别(NER)、图谱查询和对话分析等技术,构建了一个专注于医疗领域的知识图谱及其查询与生成模块。通过设计和优化Prompt提示词,Agent Tool提升了大模型生成更精准、高质量医疗问答的能力。研究结果表明,该系统在医疗问答任务中的表现优异,准确度、方案可行性和上下文相关性等指标显著优于传统LLMs和国内知名大模型。该系统通过与大规模医疗知识图谱的结合,能够深入理解复杂的医疗问题,并提供精准的回答,呈现可视化图谱展示图,更直观地给用户反馈,同时具备较高的数据安全性和可迁移性。Nowadays, with the rapid development of artificial intelligence technology, medical question answering system has become an important tool for medical information retrieval and knowledge acquisition. The medical field involves a large number of medical terms, complicated disease symptoms and treatment plans, and traditional inquiry methods are difficult to meet the information needs of medical staff and patients efficiently and accurately. Compared with traditional domestic search engines and native open source large language model (LLMs), LangChain-based large model medical question answering system can provide higher quality answers, significantly improving the efficiency and accuracy of medical knowledge retrieval. Therefore, this study proposed a medical intelligent question and answer system based on LangChain and large model, combined with named entity recognition (NER), graph query and dialogue analysis and other technologies, to build a knowledge graph and query and generation module focusing on the medical field. By designing and optimizing Prompt words, Agent Tool improves the ability of large models to generate more accurate and high-quality medical questions and answers. The results show that the system performs well in medical question answering tasks, with significant improvements in accuracy, feasibility, and context relevance are significantly better than traditional LLMs and well-known domestic large models. Through the combination of large-scale medical knowledge graph, the system can deeply understand complex medical questions, provide accurate answers, present a visual map display graph, and give users more intuitive feedback, while having high data security and portability.
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.
