XiYan-SQL - Alibaba launches a multi-generator integration framework for text-to-SQL conversion.
XiYan-SQL is a Natural Language to SQL (NL2SQL) framework launched by Alibaba. Based on a multi-generator integration strategy, it combines suggestive engineering and supervised fine-tuning to improve the quality of generated SQL queries. XiYan-SQL introduces M-Schema semi-structuring...
What is XiYan-SQL?
XiYan-SQL is a Natural Language to SQL (NL2SQL) framework launched by Alibaba. Based on a multi-generator integration strategy, it combines suggestive engineering and supervised fine-tuning to improve the quality of generated SQL queries. XiYan-SQL introduces M-Schema semi-structured architecture representation to enhance the understanding of database structures, including data types, primary keys, and instance values. XiYan-SQL generates and optimizes SQL queries based on a three-stage process, including schema linking, instance-based learning (ICL) and SFT-based generators, and error correction and selection models.
Main functions of XiYan-SQL
- Natural Language UnderstandingIt understands the user's natural language query request and converts it into an SQL query statement.
- Database structure understandingBased on the M-Schema architecture, we can understand the structure of the database, including tables, fields, data types, etc.
- SQL query generationGenerate corresponding SQL query statements based on the user's natural language request and the database structure.
- Query optimization: Optimize the generated SQL queries to improve query efficiency and accuracy.
- Error correctionDuring the process of generating SQL queries, identify and correct potential errors.
- Multi-database adaptabilityIt adapts to different types of databases, including relational and non-relational databases.
Technical Principles of XiYan-SQL
- Multi-generator integration strategyXiYan-SQL combines multiple generators, each responsible for generating different parts of the SQL, thus improving the quality of the generated SQL.
- Prompt EngineeringBased on carefully designed prompts, the model is guided to better understand the user's query intent.
- Supervisory fine-tuning (SFT)Based on the pre-trained model, supervised learning fine-tunes the model to adapt to specific database structures and query requirements.
- M-Schema architecture representationXiYan-SQL introduces M-Schema, a semi-structured database architecture representation method that includes database metadata such as table names, field names, and data types, enhancing the model's understanding of the database structure.
- Three-stage process:
- Architecture LinkIdentify and link related elements in the database schema.
- generatorBased on the architecture information of the links and the user's query intent, generate SQL query candidates.
- Optimization and SelectionThe generated SQL queries are optimized and filtered based on error correction and selection models to ensure that the generated query statements are accurate and efficient.
XiYan-SQL project address
- GitHub repository:https://github.com/XGenerationLab/XiYan-SQL
- arXiv technical paper:https://arxiv.org/pdf/2411.08599
Application scenarios of XiYan-SQL
- Database query simplificationNon-technical users can query the database directly using natural language without having to learn complex SQL syntax.
- Data Analyst ToolsData analysts describe their data needs in natural language, and XiYan-SQL automatically generates corresponding SQL queries, improving work efficiency.
- Smart assistants and chatbotsIt can be integrated into intelligent assistants or chatbots to understand user queries and retrieve information directly from the database.
- Education and trainingIn the field of education, it helps students and beginners to more easily understand and learn database query language.
- Business Intelligence (BI) ToolsIn BI tools, it acts as a backend processing engine, translating users' natural language queries into query statements that the database can understand.