Text-to-SQL Chat Execution Using Schema Retrieval and AI Prompts
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Solution Overview
Problem
Interacting with SQL databases can be challenging due to complex database designs and the need for users to master SQL concepts, making it difficult for non-technical users to effectively access and modify data.
Innovation Solution
A computing system that uses generative artificial intelligence to process natural language commands, generating SQL code to interact with databases, allowing users to input plain language queries that are automatically converted into structured database queries, executed, and returned with results in a user-friendly format.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If users interact with SQL databases directly using traditional SQL queries, then data access and modification capabilities are maintained, but the ease of operation deteriorates due to the need to master complex SQL concepts and syntax
Solution Approach 1:
The patent introduces a text-to-SQL generation system as an intermediary between users and databases. This system translates natural language queries into executable SQL statements, eliminating the need for users to directly master SQL syntax while maintaining full database interaction capabilities. The intermediary handles the complexity of SQL generation, allowing users to interact with databases through intuitive natural language.
2Productivity
If users learn and master SQL concepts to effectively access and modify data, then the productivity and precision of data operations improve, but the time required for learning and setup increases
Solution Approach 1:
The system performs preliminary action by pre-compiling and storing database schemas, table structures, and relationships before user interaction. When a user submits a natural language query, the system already has the necessary structural information ready to generate accurate SQL statements immediately, eliminating the need for users to learn SQL concepts from scratch while maintaining high operational efficiency.
3Ease of operation
If the system generates SQL code from natural language using AI models, then the ease of operation improves for non-technical users, but the device complexity and computational resources required increase
Solution Approach 1:
The patent implements a universal text-to-SQL generation system that can handle diverse query types, database schemas, and user skill levels through a single integrated AI model. This multi-functional system serves both technical and non-technical users, providing consistent ease of operation across different scenarios while consolidating complexity into one unified architecture rather than requiring multiple specialized components.
Data Source
AI summary
Systems and methods are provided for processing a natural language command using structured query language (SQL). A computing system includes a retrieval system and a SQL language model. The computing system receives a natural language command and generates a prompt with a database schema using the retrieval system. The prompt identifies relevant tables and generates an augmented prompt with table identities. The SQL language model generates SQL code based on the augmented prompt, which is executed on the database. The computing system uses the result to initiate an executable action. The computing system receives feedback about the executable action, and transmits a result message responsive to the natural language command.