SQL has traditionally served as the fundamental language for the majority of data analytics tasks. We often come up with questions that need data to get answers. SQL is essential for converting business requirements into executable code for data retrieval. However, with AI, we can develop an AI agent capable of addressing business inquiries without necessitating SQL expertise.

This post will demonstrate the construction of an AI agent from inception using LangChain and DuckDB. After building your own AI agent for SQL, you can accomplish data analytics tasks fairly quickly. Even more intriguingly, we can use a random dataset from Kaggle to test the AI’s ability to analyze data in SQL.

Tools & Data Used Today

• DuckDB: DuckDB is an in-process SQL OLAP database management system; it is easy to install & write analytics SQL queries from a CSV/JSON file in seconds.
• LangChain: LangChain is a composable framework to build with LLMs. Given the rapid growth of AI agents, a key component of developing our agents is using a framework like LangChian to manage the LLM process and carry out specific…