Implementing the DBCopilot with LangChain

LangChain agents and SQL Agent

In Chapter 4, we introduced the concept of LangChain agents, defining them as entities that drive decision-making within LLMs-powered applications. Agents have access to a suite of tools and can decide which tool to call based on the user input and the context. Agents are dynamic and adaptive, meaning that they can change or adjust their actions based on the situation or the goal.In this chapter, we will see agents in action, using the following LangChain’s componnts:

• An agent designed to interact with relational databasescreate_sql_agent
• A toolkit to provide the agent with the required non-parametric knowledgeSQLDatabaseToolkit
• A Large Language Models to act as the reasoning engine behind the agent, as well as the generative engine to produce conversational resultsOpenAI

To start with our implementation, let’s first initialize all the components and establish the connection to the Chinook database...