Aider Cheatsheet

Aider Cheatsheet

Introduction to Aider

Aider is a command-line tool that allows you to interact with GPT models directly from your terminal. It is particularly useful for generating code, writing documentation, or even brainstorming ideas. This cheatsheet will guide you through the basic and advanced features of Aider, complete with examples.

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Installation

To install Aider, you need Python and pip installed on your system. Run the following command:

bash

pip install aider-chat

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Basic Commands

1. Start Aider

   • To start Aider, simply run:
     bash

     aider

   • This will open a prompt where you can start interacting with the GPT model.

2. Ask a Question

   • You can ask any question or request assistance by typing your query:
     bash

     > How do I sort a list in Python?

   • The model will respond with the appropriate code or explanation.

3. Generate Code

   • You can ask Aider to generate code for you:
     bash

     > Write a Python function to calculate the factorial of a number.

   • The model will generate the code:
     python

     def factorial(n):
         if n == 0:
             return 1
         else:
             return n * factorial(n-1)

4. Edit Code

   • You can ask Aider to edit existing code:
     bash

     > Modify the factorial function to handle negative numbers.

   • The model will update the code:
     python

     def factorial(n):
         if n < 0:
             raise ValueError("Factorial is not defined for negative numbers")
         elif n == 0:
             return 1
         else:
             return n * factorial(n-1)

5. Save Code to File

   • You can save the generated or edited code to a file:
     bash

     > Save the factorial function to factorial.py

   • The code will be saved in the specified file.

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Advanced Commands

1. Context Management

   • You can provide context to the model by including files or directories:
     bash

     > Include factorial.py

   • This allows the model to understand the context better and provide more accurate responses.

2. Multi-File Editing

   • Aider can handle multiple files at once:
     bash

     > Include factorial.py, main.py

   • You can then ask the model to make changes across these files.

3. Interactive Mode

   • Aider supports an interactive mode where you can have a back-and-forth conversation:
     bash

     > Interactive mode

   • This is useful for complex tasks that require multiple steps.

4. Undo Changes

   • You can undo the last change made by the model:
     bash

     > Undo

   • This is helpful if you made a mistake or want to revert to a previous state.

5. Custom Prompts

   • You can customize the prompts sent to the model:
     bash

     > Custom prompt: Write a Python script to scrape a website.

   • This allows you to fine-tune the model’s responses to your specific needs.

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Examples

1. Example: Sorting a List

   • Ask Aider to sort a list:
     bash

     > How do I sort a list in Python?

   • Response:
     python

     my_list = [3, 1, 4, 1, 5, 9]
     sorted_list = sorted(my_list)
     print(sorted_list)

2. Example: Generating a Class

   • Ask Aider to generate a Python class:
     bash

     > Write a Python class for a simple calculator.

   • Response:
     python

     class Calculator:
         def add(self, a, b):
             return a + b
         
         def subtract(self, a, b):
             return a - b
         
         def multiply(self, a, b):
             return a * b
         
         def divide(self, a, b):
             if b == 0:
                 raise ValueError("Cannot divide by zero")
             return a / b

3. Example: Editing a Class

   • Ask Aider to add a method to the calculator class:
     bash

     > Add a method to calculate the square root.

   • Response:
     python

     import math
     
     class Calculator:
         def add(self, a, b):
             return a + b
         
         def subtract(self, a, b):
             return a - b
         
         def multiply(self, a, b):
             return a * b
         
         def divide(self, a, b):
             if b == 0:
                 raise ValueError("Cannot divide by zero")
             return a / b
         
         def square_root(self, a):
             if a < 0:
                 raise ValueError("Cannot calculate square root of a negative number")
             return math.sqrt(a)

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Tips and Tricks

1. Use Clear and Specific Prompts

   • The more specific your prompt, the better the response. For example, instead of asking “How do I do X?”, ask “How do I do X in Python using the Pandas library?”

2. Leverage Context

   • Include relevant files or directories to provide context to the model. This helps in generating more accurate and relevant code.

3. Iterative Development

   • Use Aider in an iterative manner. Start with a basic version of your code and gradually refine it by asking the model to make specific changes.

4. Save Frequently

   • Save your code frequently to avoid losing progress. Aider allows you to save to multiple files, so use this feature to keep your work organized.

5. Experiment with Custom Prompts

   • Don’t hesitate to experiment with custom prompts. The more you tailor your prompts to your specific needs, the better the results.

Advanced Examples

1. Example: Web Scraping Script

   • Ask Aider to generate a web scraping script:
     bash

     > Write a Python script to scrape the title and description of the top 5 articles from a news website.

   • Response:
     python

     import requests
     from bs4 import BeautifulSoup
     
     def scrape_news(url):
         response = requests.get(url)
         soup = BeautifulSoup(response.content, 'html.parser')
         
         articles = soup.find_all('article')[:5]
         for article in articles:
             title = article.find('h2').text.strip()
             description = article.find('p').text.strip()
             print(f"Title: {title}\nDescription: {description}\n")
     
     if __name__ == "__main__":
         scrape_news("https://example-news-website.com")

2. Example: API Integration

   • Ask Aider to integrate an API:
     bash

     > Write a Python script to fetch weather data from an API and display the temperature.

   • Response:
     python

     import requests
     
     def get_weather(api_key, city):
         url = f"http://api.openweathermap.org/data/2.5/weather?q={city}&appid={api_key}&units=metric"
         response = requests.get(url)
         data = response.json()
         
         if data['cod'] == 200:
             temperature = data['main']['temp']
             print(f"The temperature in {city} is {temperature}°C.")
         else:
             print("Error fetching weather data.")
     
     if __name__ == "__main__":
         api_key = "your_api_key_here"
         city = "London"
         get_weather(api_key, city)

3. Example: Data Analysis

   • Ask Aider to perform data analysis:
     bash

     > Write a Python script to analyze a CSV file and plot a bar chart of the top 10 values.

   • Response:
     python

     import pandas as pd
     import matplotlib.pyplot as plt
     
     def analyze_csv(file_path):
         df = pd.read_csv(file_path)
         top_10 = df.nlargest(10, 'value_column')
         
         plt.figure(figsize=(10, 6))
         plt.bar(top_10['category_column'], top_10['value_column'])
         plt.xlabel('Category')
         plt.ylabel('Value')
         plt.title('Top 10 Values')
         plt.xticks(rotation=45)
         plt.show()
     
     if __name__ == "__main__":
         analyze_csv("data.csv")

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Use Cases

1. Code Generation

   • Use Case: You need to quickly generate boilerplate code for a new project.
   • Example: Ask Aider to generate a basic Flask web application:
     bash

     > Write a basic Flask web application with a home page and an about page.

   • Response:
     python

     from flask import Flask, render_template
     
     app = Flask(__name__)
     
     @app.route('/')
     def home():
         return render_template('home.html')
     
     @app.route('/about')
     def about():
         return render_template('about.html')
     
     if __name__ == "__main__":
         app.run(debug=True)

2. Documentation Writing

   • Use Case: You need to write documentation for a project.
   • Example: Ask Aider to write a README file for a Python project:
     bash

     > Write a README file for a Python project that includes installation instructions, usage examples, and a brief description.

   • Response:
     markdown

     # Python Project
     
     ## Description
     This project is a Python application that does X. It includes features such as Y and Z.
     
     ## Installation
     1. Clone the repository:
        ```bash
        git clone https://github.com/yourusername/yourproject.git

     1. Install the required dependencies:
        bash

        pip install -r requirements.txt

Usage

 To run the application, use the following command:
 
bash
python main.py

Examples

 Here are some examples of how to use the application:
 
python
# Example 1
result = some_function(input)
print(result)

# Example 2
another_function()
 
Code
3. **Debugging Assistance**
   - **Use Case**: You are stuck debugging an issue in your code.
   - **Example**: Ask Aider to help debug a Python script:
     ```bash
     > I have a Python script that is throwing a TypeError. Can you help me debug it?

• Response:
  python

  # Provide the relevant part of your code where the error occurs.
  # For example:
  def add_numbers(a, b):
      return a + b
  
  result = add_numbers("1", 2)
  print(result)
  
  # The error is likely due to adding a string and an integer.
  # Fix:
  def add_numbers(a, b):
      return int(a) + b
  
  result = add_numbers("1", 2)
  print(result)

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Best Practices

1. Keep Prompts Clear and Concise

   • Avoid overly complex prompts. Break down your requests into smaller, manageable tasks.

2. Use Comments for Clarity

   • When asking for code, include comments to explain what you want. This helps the model generate more accurate code.

3. Iterate and Refine

   • Don’t expect perfect code on the first try. Use Aider iteratively to refine and improve the generated code.

4. Leverage Context

   • Include relevant files or directories to provide context. This helps the model understand the broader scope of your project.

5. Save and Backup

   • Save your work frequently and consider backing up your files. This ensures you don’t lose progress.