USE CASE
Postgres Query Generator
Postgres Query Generator
Postgres Query Generator
Case Description
A financial use case in a PostgreSQL query builder might involve tracking transactions, creating financial reports, and analyzing spending habits. For example, you might want to track the amount of money that is deposited and withdrawn from different bank accounts, as well as the expenses associated with each account.
To do this, you would need to create several tables in your database. One table could be for the accounts, which would include columns for the account name and balance. Another table could be for transactions, which would include columns for the date, amount, and type of transaction (deposit or withdrawal). A third table could be for expenses, which would include columns for the date, amount, and category of the expense.
Data Structure
Accounts
Accounts
Accounts
Transactions
Transactions
Transactions
Expenses
Expenses
Expenses
APPLICATION
Step-by-Step Postgres Query Generation
Step-by-Step Postgres Query Generation
Step-by-Step Postgres Query Generation
All Databases
Manual Table
CSV Schema
DDL Script
ERD Diagram
Connector
Type
Name
Content
Manual Table
E-Commerce - Playground
Column, Column, Column, Column, Column, Column,
Manual Table
Travel Agencies - Playground
Column, Column, Column, Column, Column, Column,
Manual Table
Retail - Playground
Column, Column, Column, Column, Column, Column,
Manual Table
Real Estate - Playground
Column, Column, Column, Column, Column, Column,
Manual Table
Healthcare - Playground
Column, Column, Column, Column, Column, Column,
Manual Table
Social Media - Playground
Column, Column, Column, Column, Column, Column,
Manual Table
Library System - Playground
Column, Column, Column, Column, Column, Column,
CSV Schema
Lorem Ipsum CSV
version 1.0
@totalColumns 9
/*---------------------------------------------------------------------------------------------------------------------------------------------------------------------------
|This schema is for the validation of technical environment metadata csv files according to the specification given for Lot 2 of the Scanning and Transcription Framework |
|Invitation To Tender document, Appendix D, in particular implementing the restrictions and consistency checks given on page 255. |
|The data in this file is a fairly general description of (software) tools used to process images, so in fact there are few hard and fast restrictions: |
|Most fields are allowed to be any length and may contain any combination of numerals, word characters, whitespace, hyphens, commas and full stops, any exception are noted |
|below. However, as the schema stands, each field must contain some value, it cannot be empty. | *
|This schema was used to validate test results supplied by potential suppliers |
---------------------------------------------------------------------------------------------------------------------------------------------------------------------------*/
//the version number above is the version of the schema language, not the version of this particular schema file
//each line of the csv file being tested must contain 9 columns (fields)
batch_code: length(1,16) regex("^[0-9a-zA-Z]{1,16}$") //1st condition, must be between 1 and 16 characters long,
// and (implicitly multiple conditions are joined by a logical AND
// unless another boolean is provided)
// 2nd condition restricts to alphanumeric characters as specified in ITT p256
company_name: regex("[-/0-9\w\s,.]+")
image_deskew_software: regex("[-/0-9\w\s,.]+")
image_split_software: regex("[-/0-9\w\s,.]+")
image_crop_software: regex("[-/0-9\w\s,.]+")
jp2_creation_software: regex("[-/0-9\w\s,.]+")
uuid_software: regex("[-/0-9\w\s,.]+")
embed_software: regex("[-/0-9\w\s,.]+")
image_inversion_software: regex("[-/0-9\w\s,.]+")
DDL Script
Lorem Ipsum DDL
version 1.0
@totalColumns 9
/*---------------------------------------------------------------------------------------------------------------------------------------------------------------------------
|This schema is for the validation of technical environment metadata csv files according to the specification given for Lot 2 of the Scanning and Transcription Framework |
|Invitation To Tender document, Appendix D, in particular implementing the restrictions and consistency checks given on page 255. |
|The data in this file is a fairly general description of (software) tools used to process images, so in fact there are few hard and fast restrictions: |
|Most fields are allowed to be any length and may contain any combination of numerals, word characters, whitespace, hyphens, commas and full stops, any exception are noted |
|below. However, as the schema stands, each field must contain some value, it cannot be empty. | *
|This schema was used to validate test results supplied by potential suppliers |
---------------------------------------------------------------------------------------------------------------------------------------------------------------------------*/
//the version number above is the version of the schema language, not the version of this particular schema file
//each line of the csv file being tested must contain 9 columns (fields)
batch_code: length(1,16) regex("^[0-9a-zA-Z]{1,16}$") //1st condition, must be between 1 and 16 characters long,
// and (implicitly multiple conditions are joined by a logical AND
// unless another boolean is provided)
// 2nd condition restricts to alphanumeric characters as specified in ITT p256
company_name: regex("[-/0-9\w\s,.]+")
image_deskew_software: regex("[-/0-9\w\s,.]+")
image_split_software: regex("[-/0-9\w\s,.]+")
image_crop_software: regex("[-/0-9\w\s,.]+")
jp2_creation_software: regex("[-/0-9\w\s,.]+")
uuid_software: regex("[-/0-9\w\s,.]+")
embed_software: regex("[-/0-9\w\s,.]+")
image_inversion_software: regex("[-/0-9\w\s,.]+")
ERD Diagram
Lorem Ipsum ERD
Connector
Lorem Ipsum MySQL Connector
Connector
Lorem Ipsum MySQL Connector
Connector Sub Table
Column, Column, Column, Column, Column, Column,
Connector Sub Table
Column, Column, Column, Column, Column, Column,
Connector Sub Table
Column, Column, Column, Column, Column, Column,
Prev
1
2
3
...
10
Next
Add Database
My Databases
Furkan ARCA
Pro Plan
🛢️ Manually Add
📝 Importing via CSV
📝 Importing via DDL Scripts
📂 Importing via ERD Diagrams
🔗 Importing via Data Connectors
1
Setting Up Your Databases
Visit the “Databases” page and click on the “Add Tables” option under the “Add Database” heading. In the pop-up that appears, fill in the table name, description, and column names accurately and completely according to the data structure.
Visit the “Databases” page and click on the “Add Tables” option under the “Add Database” heading. In the pop-up that appears, fill in the table name, description, and column names accurately and completely according to the data structure.
Visit the “Databases” page and click on the “Add Tables” option under the “Add Database” heading. In the pop-up that appears, fill in the table name, description, and column names accurately and completely according to the data structure.
Quick Tip
You can import the CSV schema you prepared earlier or try to connect and work with the databases from a supported database provider using the database connector.
You can import the CSV schema you prepared earlier or try to connect and work with the databases from a supported database provider using the database connector.
You can import the CSV schema you prepared earlier or try to connect and work with the databases from a supported database provider using the database connector.
2
Open the Text2SQL Tool
There are dozens of options available on the AI2SQL homepage. For this case, we need to open the Text2SQL application since we’ll be using Text2SQL.
There are dozens of options available on the AI2SQL homepage. For this case, we need to open the Text2SQL application since we’ll be using Text2SQL.
There are dozens of options available on the AI2SQL homepage. For this case, we need to open the Text2SQL application since we’ll be using Text2SQL.
Quick Tip
As a more flexible method, you can visit the SQL Chat option on the AI2SQL homepage to interact with your database as if you’re having a conversation.”
As a more flexible method, you can visit the SQL Chat option on the AI2SQL homepage to interact with your database as if you’re having a conversation.”
As a more flexible method, you can visit the SQL Chat option on the AI2SQL homepage to interact with your database as if you’re having a conversation.”
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Basic Plan
Search
Database Engine*
Please select your database engine to generate queries compatible with the desired database systems.
Postgres
Database*
Select a database to obtain outputs in your own database.
Selected Database: Accounts
Input*
Please write your query in no more than 200 characters.
e.g. Show me all employees where their salary is above 60,000.
0 / 200
GPT 4
Generate ⚡️
3
Make a Few Minor Adjustments
The purpose of Text2SQL is to provide you with the most accurate results, so you’ll need to make a few selections. First, you need to choose Postgres as the Database Engine. Then, select the Database you want to query. In this case, we are selecting the "Accounts" Table. Now, you are ready to start asking questions.
The purpose of Text2SQL is to provide you with the most accurate results, so you’ll need to make a few selections. First, you need to choose Postgres as the Database Engine. Then, select the Database you want to query. In this case, we are selecting the "Accounts" Table. Now, you are ready to start asking questions.
The purpose of Text2SQL is to provide you with the most accurate results, so you’ll need to make a few selections. First, you need to choose Postgres as the Database Engine. Then, select the Database you want to query. In this case, we are selecting the "Accounts" Table. Now, you are ready to start asking questions.
Try asking the following queries;
As a data scientist, you might want to ask questions about the financial data in the accounts, transactions, and expenses tables. Here are some examples of questions you could ask:
As a data scientist, you might want to ask questions about the financial data in the accounts, transactions, and expenses tables. Here are some examples of questions you could ask:
As a data scientist, you might want to ask questions about the financial data in the accounts, transactions, and expenses tables. Here are some examples of questions you could ask:
What is the total amount of money in all accounts?
What is the total amount of money in all accounts?
What is the total amount of money in all accounts?
What is the average balance per account?
What is the average balance per account?
What is the average balance per account?
What is the total amount of money deposited and withdrawn from each account?
What is the total amount of money deposited and withdrawn from each account?
What is the total amount of money deposited and withdrawn from each account?
What is the total amount of money spent on expenses for each month?
What is the total amount of money spent on expenses for each month?
What is the total amount of money spent on expenses for each month?
What is the average amount spent on expenses for each category?
What is the average amount spent on expenses for each category?
What is the average amount spent on expenses for each category?
Which categories have the highest and lowest average expenses?
Which categories have the highest and lowest average expenses?
Which categories have the highest and lowest average expenses?
Which accounts have the highest and lowest average transactions?
Which accounts have the highest and lowest average transactions?
Which accounts have the highest and lowest average transactions?
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