标题： 增强检索增强生成用于结构化企业及内部数据 显示英文标题

标题： Advancing Retrieval-Augmented Generation for Structured Enterprise and Internal Data

摘要： 组织越来越多地依赖专有企业数据，包括人力资源记录、结构化报告和表格文档，用于关键决策。 虽然大型语言模型（LLMs）具有强大的生成能力，但它们受到静态预训练、短上下文窗口以及处理异构数据格式的挑战的限制。 传统检索增强生成（RAG）框架解决了一些这些差距，但通常在处理结构化和半结构化数据时遇到困难。 本工作提出了一种先进的RAG框架，结合使用密集嵌入（all-mpnet-base-v2）和BM25的混合检索策略，并通过SpaCy NER和交叉编码器重新排序进行元数据感知过滤。 该框架应用语义分块以保持文本连贯性，并保留表格数据结构以保持行列表达的完整性。 量化索引优化了检索效率，而人工介入反馈和对话记忆提高了适应性。 在企业数据集上的实验显示显著改进：Precision@5提高了15%（90对75），Recall@5提高了13%（87对74），平均倒数排名提高了16%（0.85对0.69）。 定性评估显示在5点李克特量表上，忠实度（4.6对3.0）、完整度（4.2对2.5）和相关度（4.5对3.2）得分更高。 这些结果证明了该框架在为企业任务提供准确、全面和上下文相关的响应方面的有效性。 未来的工作包括扩展到多模态数据并集成基于代理的检索。 源代码将发布在https://github.com/CheerlaChandana/Enterprise-Chatbot 显示英文摘要

摘要： Organizations increasingly rely on proprietary enterprise data, including HR records, structured reports, and tabular documents, for critical decision-making. While Large Language Models (LLMs) have strong generative capabilities, they are limited by static pretraining, short context windows, and challenges in processing heterogeneous data formats. Conventional Retrieval-Augmented Generation (RAG) frameworks address some of these gaps but often struggle with structured and semi-structured data. This work proposes an advanced RAG framework that combines hybrid retrieval strategies using dense embeddings (all-mpnet-base-v2) and BM25, enhanced by metadata-aware filtering with SpaCy NER and cross-encoder reranking. The framework applies semantic chunking to maintain textual coherence and retains tabular data structures to preserve row-column integrity. Quantized indexing optimizes retrieval efficiency, while human-in-the-loop feedback and conversation memory improve adaptability. Experiments on enterprise datasets show notable improvements: Precision@5 increased by 15 percent (90 versus 75), Recall@5 by 13 percent (87 versus 74), and Mean Reciprocal Rank by 16 percent (0.85 versus 0.69). Qualitative evaluations show higher scores in Faithfulness (4.6 versus 3.0), Completeness (4.2 versus 2.5), and Relevance (4.5 versus 3.2) on a 5-point Likert scale. These results demonstrate the framework's effectiveness in delivering accurate, comprehensive, and contextually relevant responses for enterprise tasks. Future work includes extending to multimodal data and integrating agent-based retrieval. The source code will be released at https://github.com/CheerlaChandana/Enterprise-Chatbot