Technical implementation of retrieval enhancement generation
SQLBot adopts Retrieval Augmented Generation (RAG) technology to solve the "illusion" problem of large models in the professional field. When a user asks "Calculate the repurchase rate of customers in East China", the system will first extract the key semantics of "East China" and "repurchase rate", and then retrieve the relevant table schema information from the connected database in real time, including the customer_region field and the repurchase rate. Schema information, including customer_region field, order_history table relationship, etc., and add these structured metadata to LLM as prompt words.
Technical tests show that the RAG mechanism improves the accuracy of the first generation of complex queries from 58% to 89%. In particular, for the existence of multi-layer JOIN or complex WHERE conditions, the system can automatically identify: 1) foreign key relationships between tables; 2) formatting requirements for date fields; and 3) aggregation methods for numeric fields. The implementation data of an e-commerce platform shows that the average reduction of SQL debugging time after use is 62%.
The system also supports manual maintenance of data dictionaries, adding business comments such as "Customer Class = Class A" to fields to further optimize semantic matching accuracy.
This answer comes from the articleSQLBot: The Intelligent Bot That Converts Natural Language to SQL QueriesThe
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