FabriXAI User Guide

# Function Fabricator

The Function Fabricator is an AI agent that translates natural language descriptions into Python functions. By providing clear and detailed requirements, users can generate code that adheres to best practices, includes necessary validations, and handles edge cases effectively. The agent also adds comments to the code, making it easier for developers to understand the logic behind the implementation. This tool is ideal for both novice programmers looking to automate function creation and experienced developers seeking to streamline their coding process.

# Getting Started

To get started, make sure you have cloned the Function Fabricator. Follow Create from Template to clone and configure your AI agent.

# Configuration Instructions

# Knowledge

You can upload .pdf or .txt files to provide the agent with tailored context that meets your specific requirements.

# User Inputs

No input fields are required for these AI agents.

# AI Models

The default model is Llama 3.1 8B Instruct, but you can explore other supported AI models as well. If needed, refine your prompts for more tailored outputs.

TIP

For more detailed instructions on configuring an AI agent, please refer to Configure AI Agent

# Sample Inputs and Output

# Sample Inputs

Here is the sample input.

Create a function called `calculate_area` that takes the radius of a circle as input and returns the area. Ensure the function handles negative radius inputs by returning None.

# Sample Output

Here is the sample output.

def calculate_area(radius):
    """
    Calculate the area of a circle given its radius.

    Parameters:
    radius (float): The radius of the circle. Must be non-negative.

    Returns:
    float: The area of the circle if radius is valid, otherwise None.
    """
    if radius < 0:
        return None  # Return None for negative radius
    import math
    area = math.pi * (radius ** 2)
    return area

# Tips of Effective Configuration

  • Be Specific: Clearly describe the function's purpose, inputs, and expected outputs to get the best results.
  • Handle Edge Cases: Mention any edge cases that should be considered, such as invalid inputs or special conditions.
  • Request Comments: Ask for comments in the generated code to improve readability and maintainability.
  • Test the Output: After generating the function, test it with various inputs to ensure it behaves as expected.
  • Iterate on Prompts: If the output isn’t satisfactory, refine your prompt and try again for improved results.

Continue exploring our collection of Agent Templates to discover more AI agent ideas in action!