企业级多模态文档理解框架，让自然语言直接查询数据库和文本
eSapiens: A Real-World NLP Framework for Multimodal Document Understanding and Enterprise Knowledge Processing
- 双模块架构：文本转SQL+混合检索生成，打通结构化与非结构化数据
- 在RAGTruth基准上超越FAISS基线，减少幻觉并提升答案完整性
- 支持严格溯源验证，适合金融、医疗等高要求企业场景
我们提出eSapiens，一个面向企业环境的统一问答系统，通过双模块架构连接结构化数据库与非结构化文本语料。系统结合文本转SQL规划器与混合检索增强生成（RAG）管道，实现对关系型数据和自由格式文档的自然语言访问。为提升答案可靠性，RAG模块集成稠密与稀疏检索、商业级重排序及引用验证环路，确保内容一致性。我们在RAGTruth基准上评估了eSapiens在五个主流大语言模型上的表现，分析完整性、幻觉率与上下文利用效率等维度。结果表明，eSapiens在上下文相关性与生成质量上优于FAISS基线，并可选启用严格接地控制，适用于高风险应用场景。本工作提供了一个可部署的、具备溯源能力的企业级问答框架。
原文摘要 · Abstract (English)
We introduce eSapiens, a unified question-answering system designed for enterprise settings, which bridges structured databases and unstructured textual corpora via a dual-module architecture. The system combines a Text-to-SQL planner with a hybrid Retrieval-Augmented Generation (RAG) pipeline, enabling natural language access to both relational data and free-form documents. To enhance answer faithfulness, the RAG module integrates dense and sparse retrieval, commercial reranking, and a citation verification loop that ensures grounding consistency. We evaluate eSapiens on the RAGTruth benchmark across five leading large language models (LLMs), analyzing performance across key dimensions such as completeness, hallucination, and context utilization. Results demonstrate that eSapiens outperforms a FAISS baseline in contextual relevance and generation quality, with optional strict-grounding controls for high-stakes scenarios. This work provides a deployable framework for robust, citation-aware question answering in real-world enterprise applications.
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