'Talk' to your SQL database using LangChain and Azure OpenAI

🔗 'Talk' to your SQL database using LangChain and Azure OpenAI
towardsdatascience.com

Langchain is an open source framework for developing applications which can process natural language using LLMs (Large Language Models).

The Agent component of LangChain is a wrapper around LLM, which decides the best steps or actions to take to solve a problem. The Agent typically has access to a set of functions called Tools (or Toolkit) and it can decide which Tool to use based on the user input. Each agent can perform various NLP tasks, such as parsing, calculations, translation etc.

An Agent Executor is a runnable interface of the Agent and its set of Tools. The agent executor is responsible for calling the agent, getting the action and action input, calling the tool that the action references with the corresponding input, getting the output of the tool, and then passing all that information back into the Agent to get the next action it should take. Usually it is an iterative process until the Agent reaches the Final Answer or output.

In this article, I will show you how we can use LangChain Agent and Azure OpenAI gpt-35-turbo model to query your SQL database using natural language (without writing any SQL at all!) and get useful data insights. We will use SQL Database Toolkit and Agent which can convert user input into appropriate SQL query and run it in Database to get an answer.

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