{"title":"演示GPT-DB:使用GPT-4为SQL处理生成特定于查询和可定制的代码","authors":"Immanuel Trummer","doi":"10.14778/3611540.3611630","DOIUrl":null,"url":null,"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. GPT-DB 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. GPT-DB 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.","PeriodicalId":54220,"journal":{"name":"Proceedings of the Vldb Endowment","volume":"17 1","pages":"0"},"PeriodicalIF":2.6000,"publicationDate":"2023-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Demonstrating GPT-DB: Generating Query-Specific and Customizable Code for SQL Processing with GPT-4\",\"authors\":\"Immanuel Trummer\",\"doi\":\"10.14778/3611540.3611630\",\"DOIUrl\":null,\"url\":null,\"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. GPT-DB 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. GPT-DB 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.\",\"PeriodicalId\":54220,\"journal\":{\"name\":\"Proceedings of the Vldb Endowment\",\"volume\":\"17 1\",\"pages\":\"0\"},\"PeriodicalIF\":2.6000,\"publicationDate\":\"2023-08-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the Vldb Endowment\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.14778/3611540.3611630\",\"RegionNum\":3,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"COMPUTER SCIENCE, INFORMATION SYSTEMS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the Vldb Endowment","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.14778/3611540.3611630","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, INFORMATION SYSTEMS","Score":null,"Total":0}
Demonstrating GPT-DB: Generating Query-Specific and Customizable Code for SQL Processing with GPT-4
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. GPT-DB 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. GPT-DB 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.
期刊介绍:
The Proceedings of the VLDB (PVLDB) welcomes original research papers on a broad range of research topics related to all aspects of data management, where systems issues play a significant role, such as data management system technology and information management infrastructures, including their very large scale of experimentation, novel architectures, and demanding applications as well as their underpinning theory. The scope of a submission for PVLDB is also described by the subject areas given below. Moreover, the scope of PVLDB is restricted to scientific areas that are covered by the combined expertise on the submission’s topic of the journal’s editorial board. Finally, the submission’s contributions should build on work already published in data management outlets, e.g., PVLDB, VLDBJ, ACM SIGMOD, IEEE ICDE, EDBT, ACM TODS, IEEE TKDE, and go beyond a syntactic citation.