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How to Use ChatGPT to Query SQL Data | Python, OpenAI API and Streamlit Tutorial
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In this video, I’ll guide you through an exciting project where we integrate ChatGPT with an SQL database to ask meaningful questions and analyze data in real-time! We use the OpenAI API and ChatGPT 4o mini LLM to build a dynamic data querying system. Follow along as I demonstrate how to set everything up step-by-step, from connecting to an SQL database to building a fully interactive Streamlit dashboard with live filters, KPI summaries, and chatbot responses.
What You’ll Learn:
• Step 1: Set up a connection between SQL Server and Python using Jupyter Notebooks.
• Step 2: Use Python to interact with the OpenAI API and leverage the ChatGPT 4o mini LLM to question and analyze your SQL data.
• Step 3: Develop a custom Streamlit app in Visual Studio Code with multiple filters (segment, city, product name, etc.) on the sidebar to refine your dataset.
• Step 4: Create interactive KPI cards at the top of the app to display key metrics like total sales, number of products, and profit.
• Step 5: Build a layout that includes both a chatbot for asking questions and a dynamically filtered SQL table that you can send to ChatGPT for detailed responses.
• Step 6: Customize the app’s appearance with a sleek design, including cards for KPIs and the main dashboard for filtering and querying data.
By the end of this tutorial, you'll have a fully functioning app that brings AI-powered data exploration directly to your SQL data. Perfect for developers and data analysts looking to integrate AI with their SQL databases!
👉 Chapters:
00:00 – Intro
02:21 – Libraries we need
02:41 – Connectiong to SQL
04:24 – Testing OpenAI API
06:23 – Testing LLM on SQL Data
10:24 – Building the Streamlit App
24:00 – Deploying the App
25:39 – Testing the App
🔔 Don’t forget to like, subscribe, and hit the notification bell for more AI and data tutorials!
What You’ll Learn:
• Step 1: Set up a connection between SQL Server and Python using Jupyter Notebooks.
• Step 2: Use Python to interact with the OpenAI API and leverage the ChatGPT 4o mini LLM to question and analyze your SQL data.
• Step 3: Develop a custom Streamlit app in Visual Studio Code with multiple filters (segment, city, product name, etc.) on the sidebar to refine your dataset.
• Step 4: Create interactive KPI cards at the top of the app to display key metrics like total sales, number of products, and profit.
• Step 5: Build a layout that includes both a chatbot for asking questions and a dynamically filtered SQL table that you can send to ChatGPT for detailed responses.
• Step 6: Customize the app’s appearance with a sleek design, including cards for KPIs and the main dashboard for filtering and querying data.
By the end of this tutorial, you'll have a fully functioning app that brings AI-powered data exploration directly to your SQL data. Perfect for developers and data analysts looking to integrate AI with their SQL databases!
👉 Chapters:
00:00 – Intro
02:21 – Libraries we need
02:41 – Connectiong to SQL
04:24 – Testing OpenAI API
06:23 – Testing LLM on SQL Data
10:24 – Building the Streamlit App
24:00 – Deploying the App
25:39 – Testing the App
🔔 Don’t forget to like, subscribe, and hit the notification bell for more AI and data tutorials!
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