AI Placeholder

AI Placeholder: Free AI-Powered Fake Data API for Testing and Prototyping

AI Placeholder is a free API that uses AI to generate fake or dummy data, enabling developers and testers to quickly prototype and test their applications without relying on real or sensitive data. It leverages OpenAI's GPT-3.5-Turbo model to produce realistic and varied content.

What is AI Placeholder?

AI Placeholder is an API that provides AI-generated fake data for testing and prototyping. It allows users to specify the type and structure of data they need, and the API returns a JSON response containing the generated data.

How does AI Placeholder work?

AI Placeholder utilizes OpenAI's GPT-3.5-Turbo model to generate dummy content. Users can specify the desired data structure and content type through API requests. The API then uses the AI model to create realistic and relevant fake data based on the specifications.

Key Features:

• AI-Powered Content Generation: Uses OpenAI's GPT-3.5-Turbo model to create realistic dummy data.
• Flexible Data Specification: Allows users to define the type and structure of data they need.
• Customizable Content: Supports various content types, such as forum users, CRM deals, and product listings.
• Query String Support: Enables filtering and sorting of data using query strings.
• Self-Hosting Option: Users can self-host the API for greater control and customization.

How to use AI Placeholder?

Using AI Placeholder is straightforward. You can directly use the hosted API or self-host your version. Here’s how to generate data:

1. Basic Data Generation:

   • Send a request to the API endpoint with the desired content type.
   • For example, to get a list of forum users, send a request to /forum/users.

2. Data Generation with Rules:

   • Use the /fake/:content_type/:number_of_records?/:fields_separated_by_commas? route to specify content type, number of records, and fields.
   • Example: /fake/instagram_post/6/userId,caption,imageUrl to get 6 Instagram post objects with userId, caption, and imageUrl.

Generate Any Data You Can Think About

AI Placeholder allows you to generate almost any type of data you can imagine. Whether you need a list of CRM sales deals with a deal size of more than $10,000 or a list of products from a marketplace sorted by price, AI Placeholder can handle it.

Examples:

• A basic list of forum users:
  • Request: /forum/users
• List of CRM sales deals with a deal size of more than 10K:
  • Request: /crm/deals?amount_greater_than=10000
• A list of products from a marketplace sorted by price:
  • Request: /marketplace/products?sort_by=price

Installation (Self-Host)

To self-host AI Placeholder, follow these steps:

1. Clone the repository:

git clone https://github.com/terryds/ai-placeholder

2.  **Create the .env file**:
    ```bash
cp .env.example .env

3. Open your .env file and enter your OpenAI API credentials:

code .env

4.  **Start the server**:
    ```bash
deno task dev

Who is AI Placeholder for?

AI Placeholder is designed for:

• Developers: Quickly generate test data for application development.
• Testers: Create realistic data sets for software testing.
• Prototypers: Mock up data for prototyping new applications.

Why choose AI Placeholder?

• Saves Time: Quickly generate large amounts of realistic dummy data.
• Cost-Effective: Free to use and self-host.
• Customizable: Tailor data generation to your specific needs.

Best way to utilize AI Placeholder?

The best way to use AI Placeholder is to integrate it into your development and testing workflows. Use it to generate the data you need for your applications, saving time and ensuring that your applications are thoroughly tested.

Contributing

Pull requests are welcome. For major changes, please open an issue first to discuss what you would like to change. Please make sure to update tests as appropriate.

AI Placeholder simplifies the process of generating fake data, making it an invaluable tool for developers, testers, and prototypers. Its flexibility and ease of use make it a must-have for any project that requires dummy data.