THOR：面向按需检索的 Transformer 启发式模块
THOR: Transformer Heuristics for On-Demand Retrieval
AI总结：
本文提出 THOR 模块，通过解耦编排架构、模式感知、自校正循环和只读护栏，将自然语言转换为安全的企业级 SQL 查询，使非技术用户无需 SQL 即可进行可靠的即席数据分析。
摘要：
我们介绍由 eSapiens 设计与实现的 THOR（Transformer Heuristics for On-Demand Retrieval）模块，这是一个安全、可扩展的引擎，可将自然语言问题转换为面向企业数据库的、经过验证的只读 SQL 分析。该 Text-to-SQL 模块采用解耦的编排/执行架构：Supervisor Agent 负责路由查询，Schema Retrieval 动态注入表和列元数据，SQL Generation Agent 生成受只读护栏保护的单语句 SELECT 查询。集成的 Self-Correction & Rating 循环捕获空结果、执行错误或低质量输出，并触发最多五次 LLM 驱动的重新生成尝试。最后，Result Interpretation Agent 生成简洁、人类可读的洞察，并将原始数据行交给 Insight & Intelligence 引擎以进行可视化或预测。跨金融、销售和运营场景的冒烟测试证明了可靠的即席查询和自动化定期报告能力。通过嵌入模式感知、容错执行和合规护栏，THOR 模块使非技术用户能够以零 SQL 的简便性和企业级安全性访问实时数据。
英文摘要：
We introduce the THOR (Transformer Heuristics for On-Demand Retrieval) Module, designed and implemented by eSapiens, a secure, scalable engine that transforms natural-language questions into verified, read-only SQL analytics for enterprise databases. The Text-to-SQL module follows a decoupled orchestration/execution architecture: a Supervisor Agent routes queries, Schema Retrieval dynamically injects table and column metadata, and a SQL Generation Agent emits single-statement SELECT queries protected by a read-only guardrail. An integrated Self-Correction & Rating loop captures empty results, execution errors, or low-quality outputs and triggers up to five LLM-driven regeneration attempts. Finally, a Result Interpretation Agent produces concise, human-readable insights and hands raw rows to the Insight & Intelligence engine for visualization or forecasting. Smoke tests across finance, sales, and operations scenarios demonstrate reliable ad-hoc querying and automated periodic reporting. By embedding schema awareness, fault-tolerant execution, and compliance guardrails, the THOR Module empowers non-technical users to access live data with zero-SQL simplicity and enterprise-grade safety.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
