Quickstart

Welcome to ThanoSQL! This guide will help you get started with the basics of using ThanoSQL to integrate AI functionalities into your SQL queries seamlessly.

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Prerequisites

Before you begin, ensure you have the following:

• A basic understanding of SQL.
• Access to the ThanoSQL Console.

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Procedures

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Step 1: Setting Up Your Environment

1. Sign In: Log in to your ThanoSQL account.
2. Create a New Workspace: Navigate to the dashboard and create a new workspace to manage your queries and data. For more information about creating a workspace, check out the workspace page.

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Step 2: Download the Data to Your Local Machine

First, download the dataset, which contains user reviews of a product. By running the following command in your terminal, or by visiting the dataset link here and downloading the file manually.

wget https://raw.githubusercontent.com/smartmind-team/assets/main/datasets/comments_data.csv

The dataset is a CSV file named comments_data.csv and contains the following columns:

• ProductId: The unique identifier for the product.
• UserId: The unique identifier for the user who submitted the review.
• Text: The content of the user’s review.

Here’s a sample of what the dataset looks like:

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Step 3: Uploading Data

To upload your dataset to ThanoSQL, follow these steps. ThanoSQL supports various file formats, including CSV and JSON.

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Step 4: Writing Your First Query

To get started with querying your data in ThanoSQL, go back to Query Manager Editor and let’s write a simple SQL query to retrieve all records from your table.

1. Basic Query: Retrieve all records from the review_comments_data table.

   SELECT * FROM review_comments_data;
   

   On execution, you will see all the records in the table:

   | ProductId     | UserId        | Text                                                                                                       |
   |---------------|---------------|------------------------------------------------------------------------------------------------------------|
   | B005K4Q1VI    | A1BJHZE41QWBX6| I received the K-cups on time and in excellent condition. I usually drink coffee, but at night the Hot cocoa is good with a snack, just before bed. |
   | B005K4Q1VI    | A365A90ZY1Q80T| This is the best hot cocoa I've had in a K-Cup! The taste is smooth and creamy. It's perfect for a cold morning or an evening hot drink. I would definitely recommend this product. The best part is that it can be ordered on auto delivery so I never run out. |
   ...
   

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Step 5: Utilizing Pre-Built AI Models

1. Sentiment Analysis: Use a pre-built AI model to predict the sentiment of user reviews.

   SELECT 
       thanosql.predict(
           input := 'Text',
           model := 'bhadresh-savani/distilbert-base-uncased-emotion'
       ) AS predicted_sentiment
   FROM review_comments_data;
   

   On execution, you will get the predicted sentiment for each review:

   | predicted_sentiment                               |
   |---------------------------------------------------|
   | {"label": "anger", "score": 0.47565987706184387}  |
   | {"label": "anger", "score": 0.47565987706184387}  |
   | {"label": "anger", "score": 0.47565987706184387}  |
   | {"label": "anger", "score": 0.47565987706184387}  |
   | {"label": "anger", "score": 0.47565987706184387}  |
   ...
   

By following these steps, you can start exploring and analyzing your data using ThanoSQL’s integrated AI functionalities.

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Conclusion

Congratulations! You’ve now completed the Quick Start Guide for ThanoSQL. You are ready to harness the power of AI directly within your SQL queries. Dive deeper into advanced features and integrate sophisticated AI models to elevate your data operations. Happy querying!

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