Technical troubleshooting in enterprise environments often involves navigating diverse, heterogeneous data sources to resolve complex issues effectively. This paper presents a novel agentic AI solution built on a Weighted Retrieval-Augmented Generation (RAG) Framework tailored for enterprise technical troubleshooting. By dynamically weighting retrieval sources such as product manuals, internal knowledge bases, FAQs, and troubleshooting guides based on query context, the framework prioritizes the most relevant data. For instance, it gives precedence to product manuals for SKU-specific queries while incorporating general FAQs for broader issues. The system employs FAISS for efficient dense vector search, coupled with a dynamic aggregation mechanism to seamlessly integrate results from multiple sources. A Llama-based self-evaluator ensures the contextual accuracy and confidence of the generated responses before delivering them. This iterative cycle of retrieval and validation enhances precision, diversity, and reliability in response generation. Preliminary evaluations on large enterprise datasets demonstrate the framework's efficacy in improving troubleshooting accuracy, reducing resolution times, and adapting to varied technical challenges. Future research aims to enhance the framework by integrating advanced conversational AI capabilities, enabling more interactive and intuitive troubleshooting experiences. Efforts will also focus on refining the dynamic weighting mechanism through reinforcement learning to further optimize the relevance and precision of retrieved information. By incorporating these advancements, the proposed framework is poised to evolve into a comprehensive, autonomous AI solution, redefining technical service workflows across enterprise settings.

翻译：企业环境中的技术故障排除通常需要有效利用多样化的异构数据源来解决复杂问题。本文提出了一种基于加权检索增强生成（RAG）框架的新型智能AI解决方案，专为企业技术故障排除设计。该框架根据查询上下文动态加权检索来源（如产品手册、内部知识库、常见问题解答和故障排除指南），优先选择最相关的数据。例如，针对特定SKU的查询会优先参考产品手册，而对更广泛的问题则纳入通用常见问题解答。系统采用FAISS进行高效的稠密向量搜索，并结合动态聚合机制无缝整合多源检索结果。基于Llama的自评估器在交付前确保生成响应的上下文准确性和置信度。这种检索与验证的迭代循环提升了响应生成的精确性、多样性和可靠性。在大型企业数据集上的初步评估表明，该框架能有效提高故障排除准确性、缩短解决时间并适应多样化的技术挑战。未来研究旨在通过集成先进的对话式AI能力来增强框架，实现更具交互性和直观性的故障排除体验。同时将重点通过强化学习优化动态加权机制，进一步提升检索信息的相关性和精确度。通过这些改进，所提出的框架有望发展为一个全面的自主AI解决方案，重塑企业环境中的技术服务流程。