Build Your Own SQL Assistant with Streamlit, SQLite, and CrewAI

Are you interested in creating a powerful SQL assistant that can help you manage your databases more efficiently? In this tutorial, we will guide you through the process of building your very own SQL assistant using Streamlit, SQLite, and CrewAI. Whether you are a beginner or have some experience with programming, this step-by-step guide will make it easy for you to follow along.

Prerequisites

Before we dive into the tutorial, make sure you have the following prerequisites:

• Basic understanding of Python programming.
• Familiarity with SQL and database concepts.
• Python installed on your machine (preferably version 3.6 or higher).
• Access to a code editor (like VSCode or PyCharm).
• Internet connection to install necessary packages.

Step-by-Step Guide

Step 1: Setting Up Your Environment

First, you need to set up your development environment. Open your terminal or command prompt and create a new directory for your project:

mkdir sql_assistant
cd sql_assistant

Next, create a virtual environment to keep your project dependencies organized:

python -m venv venv
source venv/bin/activate  # On Windows use: venv\Scripts\activate

Step 2: Installing Required Packages

Now that your environment is set up, you need to install the necessary packages. Run the following command:

pip install streamlit sqlite3 crewai

Step 3: Creating Your SQLite Database

Before we can build the SQL assistant, we need to create a SQLite database. Create a new file named database.py in your project directory and add the following code:

import sqlite3

# Connect to SQLite database
conn = sqlite3.connect('my_database.db')

# Create a cursor object
cursor = conn.cursor()

# Create a sample table
cursor.execute('''CREATE TABLE IF NOT EXISTS users (
    id INTEGER PRIMARY KEY,
    name TEXT NOT NULL,
    age INTEGER NOT NULL
)''')

# Commit changes and close the connection
conn.commit()
conn.close()

This code connects to a SQLite database and creates a simple table named users with three columns: id, name, and age.

Step 4: Building the Streamlit Interface

Now, let’s create the user interface for our SQL assistant using Streamlit. Create a new file named app.py and add the following code:

import streamlit as st
import sqlite3

# Function to connect to the database
def connect_db():
    conn = sqlite3.connect('my_database.db')
    return conn

# Streamlit app
st.title('SQL Assistant')

# User input for SQL query
query = st.text_area('Enter your SQL query:')

if st.button('Execute'):
    conn = connect_db()
    cursor = conn.cursor()
    try:
        cursor.execute(query)
        results = cursor.fetchall()
        st.write('Results:', results)
    except Exception as e:
        st.error(f'Error: {e}')
    finally:
        conn.close()

This code creates a simple Streamlit app where users can enter SQL queries and execute them against the SQLite database.

Step 5: Running Your SQL Assistant

To run your SQL assistant, go back to your terminal and execute the following command:

streamlit run app.py

Your default web browser should open with the Streamlit interface, allowing you to enter and execute SQL queries.

Explanation of Key Concepts

In this tutorial, we covered several important concepts:

• Streamlit: A powerful framework for building web applications in Python, particularly useful for data science projects.
• SQLite: A lightweight, serverless database engine that is easy to set up and use for small to medium-sized applications.
• SQL Queries: Structured Query Language (SQL) is used to communicate with databases. It allows you to create, read, update, and delete data.

Conclusion

Congratulations! You have successfully built your own SQL assistant using Streamlit, SQLite, and CrewAI. This project not only helps you understand how to work with databases but also gives you a practical application of Python programming. Feel free to expand on this project by adding more features, such as user authentication or advanced query capabilities.

For further reading and resources, check out the original post A Multi-Agent SQL Assistant You Can Trust with Human-in-Loop Checkpoint & LLM Cost Control”>here and visit Towards Data Science”>this link for more tutorials.