FabriXAI User Guide

# Big O Calculator

The Big O Calculator is an AI agent designed to analyze algorithms and functions to determine their time complexity using Big O notation. It provides a step-by-step explanation of the reasoning behind the calculated complexity, considering the worst-case scenarios. The agent is particularly useful for students, developers, and anyone interested in understanding the efficiency of algorithms. By breaking down the components of a function, including nested loops and multiple steps, it helps users grasp the overall performance implications of their code.

# Getting Started

To get started, make sure you have cloned the Big O Calculator. 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 this AI agent.

# 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

Analyze the following function:
def example_function(n):
    for i in range(n):
        for j in range(n):
            print(i, j)

# Sample Output

The provided function contains two nested loops.

1. The outer loop runs 'n' times.
2. For each iteration of the outer loop, the inner loop also runs 'n' times.

Thus, the total number of iterations is n * n = n^2.

Therefore, the time complexity of the function in Big O notation is O(n^2).

# Tips for Effective Configuration

  • Understand Big O Notation: Familiarize yourself with the basics of Big O notation to make better use of the agent’s capabilities.
  • Provide Clear Functions: When analyzing, ensure your input functions are clearly defined to avoid ambiguity in analysis.
  • Use Simple Examples First: Start with simple algorithms to see how the agent analyzes them before moving to more complex scenarios.
  • Iterate on Prompts: If the output isn't what you expected, try refining your input prompt for clarity or detail.
  • Leverage Knowledge Uploads: Use the knowledge upload feature to provide the agent with specific contexts or examples relevant to your needs.

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