Query SQL with LangChain and Granite3 using Natural language
Master SQL querying with LangChain and Granite3 using natural language. Learn how to interact with databases more intuitively and execute SQL queries with ease using AI-driven tools.
At a Glance
Use LangChain and the IBM’s WatsonX Granite 3 large language model (LLM) to enable intuitive querying of MySQL databases using natural language. By translating natural language queries into SQL commands and executing them with LLMs, you simplify complex data manipulation and analysis. This approach makes data querying more accessible and efficient for professionals across various industries, streamlining workflows and enhancing decision-making processes.
Overview
In the rapidly evolving data management landscape, the efficiency of your database interactions can have a big influence on your business outcomes and strategic insights. In this guided project, you’ll create an intelligent agent-to-database interaction through natural language. By leveraging the capabilities of LangChain and IBM’s WatsonX Granite 3 LLM, you’ll learn how to bridge the gap between complex SQL queries and conversational language. Transform your approach to database management by making data queries more intuitive, accessible, and engaging for users across different professional backgrounds.
Objectives
In the project, you will:
- Integrate natural language processing: Use tools like LangChain and large language models (LLM) such as Granite 3 to interpret natural language queries.
- Execute SQL queries from natural language: Translate natural language questions into SQL queries to fetch relevant data from the MySQL database.
What you’ll need
For this guided project, you need a basic knowledge of Python and SQL.
There are no reviews yet.