Text to SQL: Transform human natural language to structured query using Large Language Models
The episode discusses the task of converting natural language text into SQL queries, known as Text-to-SQL. It explains the challenges involved in this task and the historical approaches that were used. The episode then introduces the use of Language Model-based Models (LLMs) for Text-to-SQL, which have improved the accuracy and efficiency of the process. The episode describes the architectural patterns for implementing LLMs in SQL query generation, focusing on intent detection, entity recognition, mapping intent to database tables, schema filtering, SQL statement construction, SQL execution, and human-friendly output.