Text to SQL uses LLMs to translate natural language into executable SQL, but its reliability often falls short of expectations. We’ve moved this blip to Hold to discourage its use in unsupervised workflows — for example, dynamically converting user-generated queries where the output is hidden or automated. In these cases, LLMs frequently hallucinate due to limited schema or domain understanding, risking incorrect data retrieval or unintended data modification. The non-deterministic nature of LLM outputs also makes debugging and auditing errors challenging.
We advise treating Text to SQL with caution, human review is required for all generated queries. For agentic business intelligence, avoid direct database access and instead use a governed data abstraction semantic layer — such as Cube or dbt's semantic layer — or a semantically rich access layer like GraphQL or MCP.
Text to SQL 是一种用于将自然语言查询转换为可以由数据库执行的 SQL 查询的技术。尽管大语言模型能够理解和转换自然语言，但在你自己的 schema 中创建准确的 SQL 仍然存在很大的挑战。为此可以引入 Vanna，它是一个在 Python 中用于 SQL 生成的检索增强生成（RAG）开源框架。Vanna 分两步工作：首先你需要使用数据定义语言语句（DDLs）和示范 SQL 描述你的结构，并为它们创建嵌入向量，然后再用自然语言提出问题。尽管 Vanna 可以与任何大语言模型协作，我们还是推荐你评估 NSQL，它是一个用于 Text to SQL 任务的领域特定大语言模型。 检索增强生成
