What is XiYan-SQL
XiYan-SQL is a natural language to SQL (NL2SQL) framework launched by Alibaba. Based on a multi-generator ensemble strategy, it combines prompt engineering and supervised fine-tuning to improve the quality of SQL query generation. XiYan-SQL introduces the M-Schema semi-structured schema representation to enhance understanding of database structures, including data types, primary keys, and example values. XiYan-SQL generates and optimizes SQL queries based on a three-stage process, including schema linking, example-based learning (ICL) and SFT generators, and error correction and selection models.
Main features of XiYan-SQL
- Natural language understanding: Understands users' natural language query requests and converts them into SQL query statements.
- Database structure understanding: Based on the M-Schema schema representation, understands the structure of the database, including tables, fields, data types, etc.
- SQL query generation: Generates corresponding SQL query statements based on users' natural language requests and the database structure.
- Query optimization: Optimizes generated SQL queries to improve query efficiency and accuracy.
- Error correction: Identifies and corrects potential errors during the process of generating SQL queries.
- Multi-database adaptability: Adapts to different types of databases, including relational and non-relational databases.
Technical principles of XiYan-SQL
- Multi-generator ensemble strategy: XiYan-SQL combines multiple generators, each responsible for generating different parts of SQL, improving generation quality.
- Prompt Engineering: Based on carefully designed prompts, guides the model to better understand users' query intent.
- Supervised fine-tuning (SFT): Based on a pretrained model, uses supervised learning to fine-tune the model, adapting it to specific database structures and query requirements.
- M-Schema schema representation: XiYan-SQL introduces M-Schema, a semi-structured database schema representation method that contains database metadata, such as table names, field names, data types, etc., enhancing the model's understanding of database structures.
- Three-stage process：
- Schema linking: Identifies and links relevant elements in the database schema.
- Generator: Based on the linked schema information and users' query intent, generates SQL query candidates.
- Optimization and selection: Based on the error correction model and selection model, optimizes and filters the generated SQL queries 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 simplification: Non-technical users can query databases directly in natural language without learning complex SQL syntax.
- Data analyst tool: Data analysts use natural language to describe data requirements, and XiYan-SQL automatically generates the corresponding SQL queries, improving work efficiency.
- Intelligent assistants and chatbots: Integrated into intelligent assistants or chatbots to understand users' query requests and retrieve information directly from databases.
- Education and training: In the field of education, help students and beginners more easily understand and learn database query languages.
- Business Intelligence (BI) tools: In BI tools, serve as a backend processing engine to convert users' natural language queries into query statements that the database can understand.