Self Improving Text2Sql Agent with Dynamic Context and Continuous Learning (www.ashpreetbedi.com)

A new self-improving Text-to-SQL agent has been introduced that leverages dynamic context and a method dubbed "poor man's continuous learning." The agent enhances its ability to respond to queries by retrieving relevant schema and query patterns from a knowledge base at runtime, thereby avoiding the repetitive mistakes that often plague traditional Text-to-SQL systems. This method mirrors the approach taken by experienced data engineers who utilize past queries and insights to streamline their processes. As the agent identifies successful query executions, it stores these successes in its knowledge base, creating a feedback loop that allows for continuous improvement without altering the model's weights. This development is significant for the AI/ML community as it addresses common failures in Text-to-SQL applications, largely stemming from a lack of context. By integrating dynamic context into the querying process, the agent can more effectively generate SQL grounded in established patterns, significantly improving accuracy. Moreover, the architecture includes a robust production harness built on FastAPI and PostgreSQL, making it easy for developers to implement and adapt this solution for various applications. The approach not only optimizes SQL generation but also enriches the knowledge base with new learnings, enhancing performance with each interaction.