A practical solution to improve the accuracy of complex queries
Multi-table join queries are a typical difficulty in text-to-SQL conversion, and OmniSQL copes with it in the following ways:
- Data structure preparation::
- CREATE statements for all relevant tables are provided in full in the prompt
- Explicitly label primary and foreign key relationships (e.g.
-- 外键: orders.user_id → users.id) - Processing ultra-complex schema using 32B large model (requires 24GB+ video memory)
- Cue word optimization::
- A step-by-step thinking template (Cot prompt) is used, which requires the model to analyze the associative relationships before generating SQL
- Example:
首先确定需要关联users和orders表，通过id字段... - Explicitly specify the table association method in the question (e.g., "Associate the order and user tables by customer ID").
- Post-processing calibration::
- utilization
EXPLAIN QUERY PLANVerify the execution path - Standard SQL for comparing similar problems within a dataset
- utilization
Special case handling: when there is a field ambiguity, use the fully qualified name in the question (e.g.users.addressspeciousaddress).
This answer comes from the articleOmniSQL: A Model for Transforming Natural Language into High-Quality SQL QueriesThe
May not be reproduced without permission:AI productivity tools " How to improve the accuracy of generated SQL in complex multi-table query scenarios?
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