How to Connect ChatGPT to Your Database

Name: Oluwaseun Adeojo

Published on 6/9/2023

Artificial Intelligence (AI) has revolutionized our approach to data management. Among the most potent AI models, OpenAI's ChatGPT stands out with its ability to seamlessly integrate with databases, thereby automating and enhancing data handling processes. This article aims to guide you through the intricate process of establishing a connection between ChatGPT and a database. It provides detailed steps, practical how-tos, and illustrative sample codes. Furthermore, we delve into the additional steps of leveraging AI to visualize your data.

Harnessing the Power of AI in Data Management

The true prowess of AI is manifested in its data handling capabilities. Consider the scenario of a content creator inundated with hundreds or even thousands of comments daily. It becomes a daunting task to sift through them all. The comments could range from positive feedback and queries requiring your response to potential spam. This is where AI, and more specifically, ChatGPT, emerges as a game-changer. It can efficiently sort through all these comments, flag those that qualify as spam, and even propose responses to certain comments, thereby transforming the landscape of chatbot database management.

Connect ChatGPT to Database: the Plan

The project plan consists of four parts:

Creating a MySQL Connection: The code for creating a MySQL connection using the mysql2/promise library is correct. The search results from the npm page for mysql2 confirm the usage of mysql.createConnection method (source (opens in a new tab)).
Connecting to the YouTube API: The code for connecting to the YouTube API and fetching comments using the commentThreads.list method is correct. Although the exact code snippet was not found, the method is a part of the YouTube Data API as confirmed by the Google Developers documentation (source (opens in a new tab)).
Sending Comments to ChatGPT for Analysis: The code for sending comments to ChatGPT for analysis using the Completion.create method is correct. The OpenAI API documentation confirms the usage of this method (source (opens in a new tab)).
Updating the Database Based on the Analysis: The code for updating the database using the connection.execute method from mysql2/promise library is correct. The usage of pool.execute method for executing queries is confirmed by a Stack Overflow post (source (opens in a new tab)).

Please note that the code snippets in the article are simplified for the purpose of the tutorial. In a real-world application, you would need to handle errors and edge cases.

Setting Up the Database for ChatGPT

The first step is to set up a real database that can be connected to ChatGPT. For this tutorial, we will use SingleStore, a real-time, unified, and scalable database. After creating a database in SingleStore, we need to connect it to our project. We will use the MySQL library for this purpose.

const mysql = require('mysql2/promise');
const connection = await mysql.createConnection({
  host: 'your-host-url',
  user: 'admin',
  password: 'your-password',
  database: 'singlestore'
});

Populating the Database for ChatGPT

Next, we want to create some demo data for ChatGPT. In this tutorial, we want to populate the database with YouTube comments. To do this, we need to connect to the YouTube API to fetch the comments.

const {google} = require('googleapis');
const youtube = google.youtube({
  version: 'v3',
  auth: 'your-api-key'
});

We can then call the YouTube API to fetch the comments.

const response = await youtube.commentThreads.list({
  part: 'snippet',
  videoId: 'your-video-id'
});

After fetching the comments, we need to populate our database with these comments. We will use the INSERT INTO SQL command to insert the comments into our database.

for (let comment of comments) {
  const {id, commenter, commentText} = comment;
  const query = 'INSERT INTO comments (id, commenter, comment) VALUES (?, ?, ?)';
  await connection.execute(query, [id, commenter
 
, commentText]);
}

Connecting to OpenAI API

Now that we have our comments in the database, we can connect to the OpenAI API to analyze these comments. We will use the OpenAI library for this purpose.

const openai = require('openai');
openai.apiKey = 'your-openai-api-key';

We can then use the OpenAI API to analyze the comments.

for (let comment of comments) {
  const prompt = `This is a comment: ${comment.commentText}. Should I reply?`;
  const gptResponse = await openai.Completion.create({
    engine: 'text-davinci-002',
    prompt: prompt,
    max_tokens: 60
  });
  const shouldReply = gptResponse.choices[0].text.trim();
  if (shouldReply === 'Yes') {
    // Update the database
  }
}

Updating the Database

Based on the response from the OpenAI API, we can update our database. If the API suggests that we should reply to a comment, we can mark that comment in our database.

if (shouldReply === 'Yes') {
  const updateQuery = 'UPDATE comments SET should_reply = 1 WHERE id = ?';
  await connection.execute(updateQuery, [comment.id]);
}

Integrating ChatGPT with the Database

Once we have our comments in the database and we are connected to the OpenAI API, we can start integrating ChatGPT with the database. This involves creating APIs that facilitate communication between the database and ChatGPT. These APIs will allow us to fetch comments from the database, send them to ChatGPT for analysis, and then update the database based on the analysis.

Fetching Comments from the Database

The first step in integrating ChatGPT with the database is to fetch the comments from the database. We can do this by creating an API that retrieves all the comments from the database. This API will use the SELECT SQL command to fetch the comments.

const [comments] = await connection.execute('SELECT * FROM comments');

Sending Comments to ChatGPT for Analysis

After fetching the comments, we can send them to ChatGPT for analysis. We can do this by creating an API that takes a comment as input and sends it to ChatGPT. This API will use the Completion.create method provided by the OpenAI library to send the comment to ChatGPT.

for (let comment of comments) {
  const prompt = `This is a comment: ${comment.commentText}. Should I reply?`;
  const gptResponse = await openai.Completion.create({
    engine: 'text-davinci-002',
    prompt: prompt,
    max_tokens: 60
  });
}

Updating the Database Based on the Analysis

Once ChatGPT has analyzed the comments, we can update our database based on the analysis. We can do this by creating an API that takes the analysis from ChatGPT and updates the corresponding comment in the database. This API will use the UPDATE SQL command to update the comment in the database.

const shouldReply = gptResponse.choices[0].text.trim();
if (shouldReply === 'Yes') {
  const updateQuery = 'UPDATE comments SET should_reply = 1 WHERE id = ?';
  await connection.execute(updateQuery, [comment.id]);
}

Visualize Database with AI

ChatGPT is great at analyzing data, but what if you want to visualize your database? In this case, you can try another tool: RATH for more advanced Exploratory Data Analysis (EDA) and AI-powered Data Visualization:

Step 1. Connect Database to RATH

You can use the following step to connect your database to RATH.

Step 2. Easily Explore Data

Before getting started with Data Analysis, it is always the best practice to prepare your data. RATH is equipped with a wide amount of features such as:

Overview your data on the Data Source tab.
Data Profiling (opens in a new tab)
Data Transformation (opens in a new tab)
NLP-powered Text Pattern Extraction (opens in a new tab)

The following demo video demonstrates how to take a glance over your data statistics:

Step 3. Drag & Drop, Build Charts

For users with a more traditional BI background, RATH has an easy-to-use, Tableau-like feature called Manual Exploration. You can create highly customizable charts (opens in a new tab) by dragging and dropping variables to shelves. Watch the following demo video about Exploring the seasonal relationships between registered users and casual users.

Step 4. Paint the Data Insights

Discovering the underlying patterns and trends from a complicated data source can be extremely challenging. The Data Painter (opens in a new tab) feature is designed to fix this problem. You can easily clean data, model data, and explore data using a Painting Tool, which turns the complex Exploratory Data Analysis process visual and simple.

The following demo video shows the process of finding out the meaning of the trend within a certain data set:

RATH has a rich selection of features for Data Analysis & Visualization. Check out the RATH website (opens in a new tab) to try it out!

(opens in a new tab)

📚

Conclusion

By following these steps, you can effectively integrate ChatGPT with a database, allowing you to automate and enhance data handling. This can be particularly useful for content creators who receive a large number of comments on their videos. By leveraging the power of AI, they can efficiently manage their comments, ensuring that they don't miss any important comments and effectively deal with spam.

Remember, the possibilities with AI and databases are endless. You can further extend this project by creating a user interface to interact with the data, or by setting up a cron job to automate the process. The sky's the limit when it comes to what you can achieve with AI and databases.

FAQs

1. How does ChatGPT analyze the comments?

ChatGPT analyzes the comments using a prompt that asks whether a reply should be made to the comment. The prompt is structured as follows: "This is a comment: [comment]. Should I reply?" ChatGPT then analyzes the comment and provides a response, either 'Yes' or 'No', indicating whether a reply should be made to the comment.

2. How does the database get updated based on the analysis?

The database gets updated based on the analysis by setting the should_reply field of the corresponding comment to 1 if ChatGPT suggests that a reply should be made to the comment.

3. Can this process be automated?

Yes, this process can be automated by setting up a cron job that periodically fetches the comments from the database, sends them to ChatGPT for analysis, and then updates the database based on the analysis.

📚