Demonstrating GPT-DB: Generating Query-Specific and Customizable Code for SQL Processing with GPT-4

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Abstract

GPT-DB generates code for SQL processing in general-purpose programming languages such as Python. Generated code can be freely customized using user-provided natural language instructions. This enables users, for instance, to try out specific libraries for SQL processing or to generate non-standard output while processing. GPT-DB is based on OpenAI’s GPT model series, neural networks capable of translating natural language instructions into code. By default, GPT-DB exploits the most recently released GPT-4 model whereas visitors may also select prior versions for comparison. GPTDB automatically generates query-specific prompts, instructing GPT on code generation. These prompts include a description of the target database, as well as logical query plans described as natural language text, and instructions for customization. GPTDB automatically verifies, and possibly re-generates, code using a reference database system for result comparisons. It enables users to select code samples for training, thereby increasing accuracy for future queries. The proposed demonstration showcases code generation for various queries and with varying instructions for code customization.