## Welcome to MockMaster
A CLI tool to generate dummy data in various formats using GPT ,designed to meet diverse needs of software engineers .
It can be used to bootstrap your database , serve dummy data to test out your frontend etc..
## Table of contents
- [Features](#features)
- [Basic Usage](#basic-usage)
- [Authors](#authors)
- [To-dos](#to-dos)
## Features
- :books: Generate data in json or csv format ( more will be added)
- :toolbox: Generate data for dynamic fields with no fixed vocabulary.
- :mirror: Generate json that follows json schema data fromat.
- :lock: Store output in specified path.
## What's Unique and better
- :smile: Usage of GPT for dummy data generation
- :smiley: No pre built dictionary for fixed set of keys and values
- :sweat_smile: Data generation follows json schema and generated datafrom gpt is validated to make ouput more reliable
## What's not good
- :roll_eyes: Json or csv generated with field inputs may have no linking for all fields ex- email generated will not match with first name or last name
- :expressionless: only openAI GPT is supported for now
- :roll_eyes: only json and csv is supported
## Basic Usage
### Step 1: Installation
#### Installing from source
```
git clone https://github.com/deepukr007/mockmaster.git
cd mockmaster
```
### Step 2:
```
pip install .
```
### Step 3:
Set or edit openAI API key and store it in .env file
```
mockmaster init
```
### Step 4:
Example 1: Json with fields
```
mockmaster generate json name place age zip_code --limit 10
```
Example 2: Json with schema
```
mockmaster generate json --schema --path <path_to_schema>
```
Example 3:
```
mockmaster generate csv name place age zip_code --limit 10 -o <output_path>
```
### To-dos
- Add support to other llms
- Change stratergy to store API keys
- Python SDK like faker
#### Authors
- Deepu Krishnareddy
- Harish Mohan
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"description": "## Welcome to MockMaster\r\n\r\nA CLI tool to generate dummy data in various formats using GPT ,designed to meet diverse needs of software engineers .\r\nIt can be used to bootstrap your database , serve dummy data to test out your frontend etc..\r\n\r\n## Table of contents\r\n- [Features](#features)\r\n- [Basic Usage](#basic-usage)\r\n- [Authors](#authors)\r\n- [To-dos](#to-dos)\r\n\r\n## Features\r\n- :books:\tGenerate data in json or csv format ( more will be added)\r\n- :toolbox:\t Generate data for dynamic fields with no fixed vocabulary.\r\n- :mirror:\tGenerate json that follows json schema data fromat.\r\n- :lock:\tStore output in specified path.\r\n\r\n## What's Unique and better\r\n- :smile:\t Usage of GPT for dummy data generation\r\n- :smiley: No pre built dictionary for fixed set of keys and values\r\n- :sweat_smile: Data generation follows json schema and generated datafrom gpt is validated to make ouput more reliable\r\n\r\n## What's not good\r\n- :roll_eyes: Json or csv generated with field inputs may have no linking for all fields ex- email generated will not match with first name or last name\r\n- :expressionless: only openAI GPT is supported for now\r\n- :roll_eyes: only json and csv is supported \r\n\r\n## Basic Usage\r\n\r\n### Step 1: Installation\r\n\r\n#### Installing from source\r\n\r\n```\r\ngit clone https://github.com/deepukr007/mockmaster.git\r\ncd mockmaster\r\n```\r\n\r\n### Step 2:\r\n```\r\npip install .\r\n```\r\n\r\n### Step 3:\r\nSet or edit openAI API key and store it in .env file \r\n```\r\nmockmaster init\r\n```\r\n\r\n### Step 4:\r\n\r\nExample 1: Json with fields\r\n```\r\nmockmaster generate json name place age zip_code --limit 10\r\n```\r\n\r\nExample 2: Json with schema\r\n```\r\nmockmaster generate json --schema --path <path_to_schema>\r\n```\r\n\r\nExample 3:\r\n```\r\nmockmaster generate csv name place age zip_code --limit 10 -o <output_path>\r\n```\r\n\r\n### To-dos\r\n- Add support to other llms\r\n- Change stratergy to store API keys\r\n- Python SDK like faker\r\n\r\n#### Authors\r\n- Deepu Krishnareddy\r\n- Harish Mohan\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n",
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