Build a robust text-to-SQL solution generating complex queries, self-correcting, and querying diverse data sources

This article discusses a solution to generate SQL queries from natural language using large language models and Amazon Bedrock. The key highlights are:

Specifically, the article covers:

• Challenges with text-to-SQL conversion like ambiguity in natural language, handling diverse data sources, and metadata complexity
• A solution architecture using Retrieval Augmented Generation (RAG) with AWS Glue Data Catalog metadata, a self-correction loop with Amazon Athena, and Athena as the SQL engine
• Implementation details with code snippets for building the knowledge base, generating prompts, invoking LLM (Anthropic Claude v2.1), and running SQL on Athena
• Test scenarios demonstrating the solution's capability to handle complex SQL queries and self-correction
• Extending the solution to other data sources supported by Athena connectors