Connecting GPT-3 to a Database for YouTube Comment Analysis

The true strength of AI lies in its ability to handle data. This is especially true for OpenAI’s GPT-3, one of the most advanced language models available today. In this article, we’ll explore how to connect GPT-3 to a database and use it to analyze comments on YouTube videos.

Project Goal

The goal of this project is to use GPT-3 to sift through the hundreds, if not thousands, of comments received on YouTube every day. This includes marking spam comments and responding to relevant ones. While YouTube and Google’s algorithms are not always effective at this task, OpenAI’s GPT-3 can provide a more sophisticated solution.

Connecting GPT-3 to a Database

To get started, we’ll need to create a real database that can be connected to GPT-3. We’ll be using the Google Cloud platform’s YouTube Data API version 3 to download all comments on a given video. Then, we’ll use npm install to add Google APIs and MySQL to the project.

Next, we’ll create a single store database on AWS, which offers a real-time, unified, and distributed SQL solution. After creating a new database, we’ll connect it to VS Code using a file called db.js.

To populate the database, we’ll write a function to connect to the YouTube API and download all comments from a given video. We’ll insert this data into our new database using another function, executeCommand.

Connecting GPT-3 to the Database

With the database populated, we can now connect it to GPT-3. We’ll install OpenAI’s package, generate a new secret key, and create a connection to the API using a file called ai.js.

We’ll then use OpenAI’s API references to create a function that queries GPT-3’s DaVinci model and returns a response. We’ll test this connection by logging out the response in the console.

Finally, we’ll create a new function in db.js called updateDatabaseUsingGPT3. This function will read from the database, use GPT-3 to make changes, and update the database accordingly.

Connecting GPT-3 to a Database: Step-by-Step Guide

  1. Create a real database: The first step is to create a real database that can be used and connected up to GPT-3. There are various options available for this, including single-store database, which is real-time unified and uses distributed SQL.

  2. Connect the database to GPT-3: Next, you will need to create some APIs to create communication to and from the database to GPT-3. This will involve setting up connection strings and API keys, as well as configuring the database to be accessible from GPT-3.

  3. Download comments from YouTube: Once the database is set up and connected, the next step is to download the comments you want to process from YouTube. This can be done using the Google API, which has a YouTube data API version 3 that you can enable.

  4. Install Google APIs: Install the Google APIs using the npm install Google APIs command.

  5. Access YouTube comments: Access the YouTube comments using the YouTube API, specifically the one that gets the comments by passing in comment threads, adding the list parameter, and specifying the video URL. Add in some quick error handling and console log out the data packet if there are no errors.

  6. Write comments to a file: Write the comments to a file using the fs library and converting the data to a stringified JSON that can be written to a file called comments.json.

  7. Create a new database: Create a new database in single-store and connect it up to the vs code using MySQL.

  8. Create a new table: Create a new table in the database using SQL, setting it to auto-increment, and capturing fields like comment ID, commenter, and the comment itself. Add some rows for GPT, such as whether the comment should be flagged or responded to.

  9. Insert data into the table: Insert data into the table using the YouTube API, creating an operation to insert data into the comments table and using different types of values like commenter ID, comment GPT flag, and question marks to be populated by an object.

  10. Populate the database: Populate the database using a for loop that loops through all the comments and inserts that data into single-store.

  11. Connect to OpenAI: Connect to OpenAI using their package, which allows you to access their models, and generating an API key.

  12. Create a connection to OpenAI: Create a connection to OpenAI using their starter template to be able to connect to their API and configure it with your organization ID and API key.

  13. Use OpenAI to process data: Use OpenAI to process the data in the database and make changes to it. This can be done by reading from the database, accessing the rows using the async function, using OpenAI to process the data, and updating the database with the changes.

  14. Update the database with GPT-3 changes: Update the database with the changes made by GPT-3 using an async function to update the database rows with the new data processed by OpenAI.

 

Source: https://www.youtube.com/watch?v=N4nX_rTwKx4

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