Text2SQL Production Guide: Semantics, Safety, and Evaluation
This Juejin AI tutorial argues that Text2SQL failures usually stem from semantic misalignment rather than invalid SQL, including ambiguous metric definitions, time conventions, oversized schemas, and complex joins. It recommends a six-stage chain spanning schema pruning, semantic-layer injection, few-shot examples, SQL generation, validation, and result explanation, alongside four database safeguards. The article also advocates execution-based evaluation rather than exact SQL-string matching.
The guide provides a practical production framework for teams building Text2SQL systems, showing why governed metadata, explicit business definitions, restricted database permissions, and execution-result evaluation are central to reliable query generation.
假设你的数据库有几十张表。用户问："上个月华东区的退款率是多少？"
模型生成的 SQL 语法完全正确、能跑通、能返回一个数字。但数字和 BI 系统里对不上。
排查发现：
- 它把"退款率"算成了"退款订单数 ÷ 总订单数"，而业务定义是"退款金额 ÷ 成交金额"；
- "上个月"按自然月算，而业务口径是按结算周期。
这就是 Text2SQL 的真实困境：语法从来不是瓶颈，语义才是。
第一部分：五个真实难点
1.1 难点一：Schema 太大
几十张表、几百个字段，不可能全部塞进上下文。