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Text-to-SQL Agent with LangChain

Build a natural language to SQL agent that converts questions into database queries, validates them, and returns formatted results — using LangChain.

SQLdatabaseNL2SQLdata

Working Code

LangChain
from langchain_openai import ChatOpenAI
from langchain_core.tools import tool
from langchain_community.utilities import SQLDatabase
from langchain_community.agent_toolkits import SQLDatabaseToolkit


db = SQLDatabase.from_uri("sqlite:///sales.db", sample_rows_in_table_info=3)
toolkit = SQLDatabaseToolkit(db=db, llm=model)
sql_tools = toolkit.get_tools()


model = ChatOpenAI(model="gpt-4o")
model_with_tools = model.bind_tools([sql_tools])
response = model_with_tools.invoke([
    ("system", "You are a SQL analyst. Convert natural language questions into SQL queries. Only use SELECT statements. Limit results to 10 rows."),
    ("user", "Show me top 5 customers by total spending this quarter"),
])

Step by Step

1

Install dependencies

Install LangChain and the required tools for this use case.

2

Define your tools

Create the domain-specific tool functions your agent will use to interact with external services.

3

Create the agent and run

Initialize the LangChain agent with your tools, set the system prompt, and execute a query.

Build with other frameworks

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