ChatGPTwithSQLscript

Overview

This repository contains Python scripts designed to automate the generation of SQL queries using the GPT-3.5 model from OpenAI. It also includes utility functions for database operations, file reading, logging, and similarity calculations.

---

Table of Contents

• Dependencies
• Installation
• Usage
• Scripts
• Contributing

---

Dependencies

• Python 3.8+
• pandas
• openai
• matplotlib
• seaborn
• sqlparse
• psycopg2-binary
• numpy
• sklearn

---

Installation

1. Clone the repository:

   git clone https://github.com/asu-cactus/ChatGPTwithSQLscript.git

2. Navigate to the project directory:

   cd ChatGPTwithSQLscript

3. Install the required packages:

   pip install -r requirements.txt

---

Usage

1. Create a config.py file and put your OpenAI API key there.

   OPENAI_API_KEY = "{your_openai_api_key}"
   

2. Run the run.py script to start the experiment:

   python run.py

3. Pick a dataset from out benchmark: {‘Smart Building’, ‘COVID-19 & Machine Log’,Commercial dataset-1’, ‘Commercial dataset-2’} and change the ‘excel_file_path’ and ‘json_file_path’ in ‘excel2json.py’ accordingly. Here is the link for the benchmark dataset <link>

   # Path to the Excel file
   excel_file_path = '<dataset_you_picked>.xlsx'
   
   
   # Path to save the JSON file
   json_file_path = <dataset_you_picked>.json'
   ```
   

4. Run the excel2json.py script to convert the .xlsx benchmark dataset to .json format:

   python excel2json.py

5. You can set the configurations such as template_option, source, target in run.py. Here's an example

   template_option = 1
   target_id, max_target_id = 10, 10
   source_id, max_source_id = 1, 1
   

   target_id,max_target_id mean the first group and the last group the script will iterate. source_id, max_source_id mean the first source and the last source the script will iterate.
6. Run the run.py script to start the experiment:

   python run.py

Note: Detailed template_option are in the gpt.py. Option 3 and 4 both belong to Option 3 in the paper, and Option 5 corresponds to Option 4 in the paper, etc.

---

Scripts

• excel2json.py: Converts Excel data to JSON format.
• gpt.py: Interacts with the GPT-3 model to generate SQL queries.
• join.py: Handles JOIN operations in SQL.
• run.py: Main script to run the experiments.
• sql_complexity_analyzer.py: Analyzes the complexity of SQL queries.
• util.py: Contains utility functions for database operations and more.

---

Contributing

Feel free to fork the project and submit a pull request with your changes!

---