Chinese developers are increasingly combining traditional Java backend frameworks with large language models to create natural language interfaces for databases. This post walks through building a Text-to-SQL system using SpringBoot, PostgreSQL, and SiliconFlow's open-source LLM offerings. The architecture follows a typical enterprise pattern: a RESTful backend that translates user questions into SQL queries, executes them against PostgreSQL, and returns results in natural language. Key considerations include prompt engineering for accurate SQL generation, handling complex joins, and ensuring query safety. This approach is particularly relevant for teams with existing Java infrastructure who want to add AI capabilities without rewriting their entire stack. The trend signals a shift toward pragmatic AI adoption in enterprise environments, where LLMs are treated as components within established architectures rather than standalone products.
A practical guide to creating a Text-to-SQL system using SpringBoot, PostgreSQL, and a Chinese open-source LLM provider, reflecting a growing trend in enterprise AI integration.