Code2Prompt

Code2Prompt is a powerful command-line tool that generates comprehensive prompts from codebases, designed to streamline interactions between developers and Large Language Models (LLMs) for code analysis, documentation, and improvement tasks.

Table of Contents

1. Why Code2Prompt?
2. Features
3. Installation
4. Getting Started
5. Quick Start
6. Usage
7. Options
8. Examples
9. Templating System
10. Integration with LLM CLI
11. GitHub Actions Integration
12. Configuration File
13. Troubleshooting
14. Contributing
15. License

Code2Prompt: Transform Your Codebase into AI-Ready Prompts

Supercharge Your AI-Assisted Development

Code2Prompt is a powerful, open-source command-line tool that bridges the gap between your codebase and Large Language Models (LLMs). By converting your entire project into a comprehensive, AI-friendly prompt, Code2Prompt enables you to leverage the full potential of AI for code analysis, documentation, and improvement tasks.

🚀 Key Features

• Holistic Codebase Representation: Generate a well-structured Markdown prompt that captures your entire project's essence, making it easier for LLMs to understand the context.
• Intelligent Source Tree Generation: Create a clear, hierarchical view of your codebase structure, allowing for better navigation and understanding of the project.
• Customizable Prompt Templates: Tailor your output using Jinja2 templates to suit specific AI tasks, enhancing the relevance of generated prompts.
• Smart Token Management: Count and optimize tokens to ensure compatibility with various LLM token limits, preventing errors during processing.
• Gitignore Integration: Respect your project's .gitignore rules for accurate representation, ensuring that irrelevant files are excluded from processing.
• Flexible File Handling: Filter and exclude files using powerful glob patterns, giving you control over which files are included in the prompt generation.
• Custom Syntax Highlighting: Pair custom file extensions with specific syntax highlighting using the --syntax-map option. For example, you can specify that .inc files should be treated as bash scripts.
• Clipboard Ready: Instantly copy generated prompts to your clipboard for quick AI interactions, streamlining your workflow.
• Multiple Output Options: Save to file or display in the console, providing flexibility in how you want to use the generated prompts.
• Enhanced Code Readability: Add line numbers to source code blocks for precise referencing, making it easier to discuss specific parts of the code.
• Include file: Support of template import, allowing for modular template design.
• Input variables: Support of Input Variables in templates, enabling dynamic prompt generation based on user input.

💡 Why Code2Prompt?

• Contextual Understanding: Provide LLMs with a comprehensive view of your project for more accurate suggestions and analysis.
• Consistency Boost: Maintain coding style and conventions across your entire project, improving code quality.
• Efficient Refactoring: Enable better interdependency analysis and smarter refactoring recommendations, saving time and effort.
• Improved Documentation: Generate contextually relevant documentation that truly reflects your codebase, enhancing maintainability.
• Pattern Recognition: Help LLMs learn and apply your project-specific patterns and idioms, improving the quality of AI interactions.

Transform the way you interact with AI for software development. With Code2Prompt, harness the full power of your codebase in every AI conversation.

Ready to elevate your AI-assisted development? Let's dive in! 🏊‍♂️

Installation

Choose one of the following methods to install Code2Prompt:

Using pip

pip install code2prompt

Using pipx (recommended)

pipx install code2prompt

Getting Started

To get started with Code2Prompt, follow these steps:

1. Install Code2Prompt: Use one of the installation methods mentioned above.
2. Prepare Your Codebase: Ensure your project is organized and that you have a .gitignore file if necessary.
3. Run Code2Prompt: Use the command line to generate prompts from your codebase.

For example, to generate a prompt from a single Python file, run:

code2prompt --path /path/to/your/script.py

Quick Start

1. Generate a prompt from a single Python file:

   code2prompt --path /path/to/your/script.py

2. Process an entire project directory and save the output:

   code2prompt --path /path/to/your/project --output project_summary.md

3. Generate a prompt for multiple files, excluding tests:

   code2prompt --path /path/to/src --path /path/to/lib --exclude "*/tests/*" --output codebase_summary.md

Usage

The basic syntax for Code2Prompt is:

code2prompt --path /path/to/your/code [OPTIONS]

For multiple paths:

code2prompt --path /path/to/dir1 --path /path/to/file2.py [OPTIONS]

Custom Syntax Highlighting

To pair custom file extensions with specific syntax highlighting, use the --syntax-map option. This allows you to specify mappings in the format extension:syntax. For example:

code2prompt --path /path/to/your/code --syntax-map "inc:bash,customext:python,ext2:javascript"

This command will treat .inc files as bash scripts, .customext files as python, and .ext2 files as javascript.

You can also use multiple --syntax-map arguments or separate mappings with commas:

code2prompt --path /path/to/your/script.py --syntax-map "inc:bash"

code2prompt --path /path/to/your/project --syntax-map "inc:bash,txt:markdown" --output project_summary.md

code2prompt --path /path/to/src --path /path/to/lib --syntax-map "inc:bash,customext:python" --output codebase_summary.md

Options

Command Parameters

--filter or -f and --exclude or -e

The --filter and --exclude options allow you to specify patterns for files or directories that should be included in or excluded from processing, respectively.

Syntax:

--filter "PATTERN1,PATTERN2,..."
--exclude "PATTERN1,PATTERN2,..."

or

-f "PATTERN1,PATTERN2,..."
-e "PATTERN1,PATTERN2,..."

Description:

• Both options accept a comma-separated list of patterns.
• Patterns can include wildcards (*) and directory indicators (**).
• Case-sensitive by default (use --case-sensitive flag to change this behavior).
• --exclude patterns take precedence over --filter patterns.

Examples:

1. Include only Python files:

   --filter "**.py"

2. Exclude all Markdown files:

   --exclude "**.md"

3. Include specific file types in the src directory:

   --filter "src/**.{js,ts}"

4. Exclude multiple file types and a specific directory:

   --exclude "**.log,**.tmp,**/node_modules/**"

5. Include all files except those in 'test' directories:

   --filter "**" --exclude "**/test/**"

6. Complex filtering (include JavaScript files, exclude minified and test files):

   --filter "**.js" --exclude "**.min.js,**test**.js"

7. Include specific files across all directories:

   --filter "**/config.json,**/README.md"

8. Exclude temporary files and directories:

   --exclude "**/.cache/**,**/tmp/**,**.tmp"

9. Include source files but exclude build output:

   --filter "src/**/*.{js,ts}" --exclude "**/dist/**,**/build/**"

10. Exclude version control and IDE-specific files:

    --exclude "**/.git/**,**/.vscode/**,**/.idea/**"

Important Notes:

• Always use double quotes around patterns to prevent shell interpretation of special characters.
• Patterns are matched against the full path of each file, relative to the project root.
• The ** wildcard matches any number of directories.
• Single * matches any characters within a single directory or filename.
• Use commas to separate multiple patterns within the same option.
• Combine --filter and --exclude for fine-grained control over which files are processed.

Best Practices:

1. Start with broader patterns and refine as needed.
2. Test your patterns on a small subset of your project first.
3. Use the --case-sensitive flag if you need to distinguish between similarly named files with different cases.
4. When working with complex projects, consider using a configuration file to manage your filter and exclude patterns.

By using the --filter and --exclude options effectively and safely (with proper quoting), you can precisely control which files are processed in your project, ensuring both accuracy and security in your command execution.

Examples

1. Generate documentation for a Python library:

   code2prompt --path /path/to/library --output library_docs.md --suppress-comments --line-number --filter "*.py"

2. Prepare a codebase summary for a code review, focusing on JavaScript and TypeScript files:

   code2prompt --path /path/to/project --filter "*.js,*.ts" --exclude "node_modules/*,dist/*" --template code_review.j2 --output code_review.md

3. Create input for an AI model to suggest improvements, focusing on a specific directory:

   code2prompt --path /path/to/src/components --suppress-comments --tokens --encoding cl100k_base --output ai_input.md

4. Analyze comment density across a multi-language project:

   code2prompt --path /path/to/project --template comment_density.j2 --output comment_analysis.md --filter "*.py,*.js,*.java"

5. Generate a prompt for a specific set of files, adding line numbers:

   code2prompt --path /path/to/important_file1.py --path /path/to/important_file2.js --line-number --output critical_files.md

Templating System

Code2Prompt supports custom output formatting using Jinja2 templates. To use a custom template:

code2prompt --path /path/to/code --template /path/to/your/template.j2

Creating Template Examples

Use the --create-templates command to generate example templates:

code2prompt --create-templates

This creates a templates directory with sample Jinja2 templates, including:

• default.j2: A general-purpose template
• analyze-code.j2: For detailed code analysis
• code-review.j2: For thorough code reviews
• create-readme.j2: To assist in generating README files
• improve-this-prompt.j2: For refining AI prompts

For full template documentation, see Documentation Templating.

Integration with LLM CLI

Code2Prompt can be integrated with Simon Willison's llm CLI tool for enhanced code analysis or qllm, or for the Rust lovers hiramu-cli.

Installation

pip install code2prompt llm

Basic Usage

1. Generate a code summary and analyze it with an LLM:

   code2prompt --path /path/to/your/project | llm "Analyze this codebase and provide insights on its structure and potential improvements"

2. Process a specific file and get refactoring suggestions:

   code2prompt --path /path/to/your/script.py | llm "Suggest refactoring improvements for this code"

For more advanced use cases, refer to the Integration with LLM CLI section in the full documentation.

GitHub Actions Integration

You can integrate Code2Prompt into your GitHub Actions workflow. Here's an example:

name: Code Analysis
on: [push]
jobs:
  analyze-code:
    runs-on: ubuntu-latest
    steps:
    - uses: actions/checkout@v2
    - name: Set up Python
      uses: actions/setup-python@v2
      with:
        python-version: '3.x'
    - name: Install dependencies
      run: |
        pip install code2prompt llm
    - name: Analyze codebase
      run: |
        code2prompt --path . | llm "Perform a comprehensive analysis of this codebase. Identify areas for improvement, potential bugs, and suggest optimizations." > analysis.md
    - name: Upload analysis
      uses: actions/upload-artifact@v2
      with:
        name: code-analysis
        path: analysis.md

Understanding Tokens and Token Types in Code2Prompt

Tokens are the basic units of text that language models process. They can be words, parts of words, or even punctuation marks. Different tokenizer encodings split text into tokens in various ways. Code2Prompt supports multiple token types through its --encoding option, with "cl100k_base" as the default. This encoding, used by models like GPT-3.5 and GPT-4, is adept at handling code and technical content. Other common encodings include "p50k_base" (used by earlier GPT-3 models) and "r50k_base" (used by models like CodeX).

To count tokens in your generated prompt, use the --tokens flag:

code2prompt --path /your/project --tokens

For a specific encoding:

code2prompt --path /your/project --tokens --encoding p50k_base

Understanding token counts is crucial when working with AI models that have token limits, ensuring your prompts fit within the model's context window.

Token Price Estimation

Code2Prompt now includes a powerful feature for estimating token prices across various AI providers and models. Use the --price option in conjunction with --tokens to display a comprehensive breakdown of estimated costs. This feature calculates prices based on both input and output tokens, with input tokens determined by your codebase and a default of 1000 output tokens (customizable via --output-tokens). You can specify a particular provider or model, or view prices across all available options. This functionality helps developers make informed decisions about AI model usage and cost management. For example:

code2prompt --path /your/project --tokens --price --provider openai --model gpt-4

This command will analyze your project, count the tokens, and provide a detailed price estimation for OpenAI's GPT-4 model.

🔥 Analyzing Codebases

code2prompt now offers a powerful feature to analyze codebases and provide a summary of file extensions. Use the --analyze option along with the -p (path) option to get an overview of your project's file composition. For example:

code2prompt --analyze -p code2prompt

Result:

.j2: 6 files
.json: 1 file
.py: 33 files
.pyc: 56 files

Comma-separated list of extensions:
.j2,.json,.py,.pyc

This command will analyze the 'code2prompt' directory and display a summary of all file extensions found, including their counts. You can choose between two output formats:

• Flat format (default): Lists all unique extensions alphabetically with their file counts.
• Tree-like format: Displays extensions in a directory tree structure with counts at each level.

To use the tree-like format, add the --format tree option:

code2prompt --analyze -p code2prompt --format tree

Result:

└── code2prompt
    ├── utils
    │   ├── .py
    │   └── __pycache__
    │       └── .pyc
    ├── .py
    ├── core
    │   ├── .py
    │   └── __pycache__
    │       └── .pyc
    ├── comment_stripper
    │   ├── .py
    │   └── __pycache__
    │       └── .pyc
    ├── __pycache__
    │   └─ .pyc
    ├── templates
    │   └── .j2
    └── data
        └── .json

Comma-separated list of extensions:
.j2,.json,.py,.pyc

The analysis also generates a comma-separated list of file extensions, which can be easily copied and used with the --filter option for more targeted code processing.

🔥 Feature Highlight: Dynamic Variable Extraction for Prompt Generation

code2prompt offers a powerful feature for dynamic variable extraction from templates, allowing for interactive and customizable prompt generation. Using the syntax {{input:variable_name}}, you can easily define variables that will prompt users for input during execution.

This is particularly useful for creating flexible templates for various purposes, such as generating AI prompts for Chrome extensions. Here's an example:

# AI Prompt Generator for Chrome Extension

Generate a prompt for an AI to create a Chrome extension with the following specifications:

Extension Name: {{input:extension_name}}
Main Functionality: {{input:main_functionality}}
Target Audience: {{input:target_audience}}

## Prompt:

You are an experienced Chrome extension developer. Create a detailed plan for a Chrome extension named "{{input:extension_name}}" that {{input:main_functionality}}. This extension is designed for {{input:target_audience}}.

Your response should include:

1. A brief description of the extension's purpose and functionality
2. Key features (at least 3)
3. User interface design considerations
4. Potential challenges in development and how to overcome them
5. Security and privacy considerations
6. A basic code structure for the main components (manifest.json, background script, content script, etc.)

Ensure that your plan is detailed, technically sound, and tailored to the needs of {{input:target_audience}}.

Start from this codebase:

---- 

## The codebase:

<codebase>

When you run code2prompt with this template, it will automatically detect the {{input:variable_name}} patterns and prompt the user to provide values for each variable (extension_name, main_functionality, and target_audience). This allows for flexible and interactive prompt generation, making it easy to create customized AI prompts for various Chrome extension ideas.

For example, if a user inputs:

• Extension Name: "ProductivityBoost"
• Main Functionality: "tracks time spent on different websites and provides productivity insights"
• Target Audience: "professionals working from home"

The tool will generate a tailored prompt for an AI to create a detailed plan for this specific Chrome extension. This feature is particularly useful for developers, product managers, or anyone looking to quickly generate customized AI prompts for various projects or ideas.

🔥 Feature Highlight "Include File" Feature

The code2prompt project now supports a powerful "include file" feature, enhancing template modularity and reusability.

This feature allows you to seamlessly incorporate external file content into your main template using the {% include %} directive. For example, in the main analyze-code.j2 template, you can break down complex sections into smaller, manageable files:

# Elite Code Analyzer and Improvement Strategist 2.0

{% include 'sections/role_and_goal.j2' %}

{% include 'sections/core_competencies.j2' %}

## Task Breakdown

1. Initial Assessment
{% include 'tasks/initial_assessment.j2' %}

2. Multi-Dimensional Analysis (Utilize Tree of Thought)
{% include 'tasks/multi_dimensional_analysis.j2' %}

// ... other sections ...

This approach allows you to organize your template structure more efficiently, improving maintainability and allowing for easy updates to specific sections without modifying the entire template. The include feature supports both relative and absolute paths, making it flexible for various project structures. By leveraging this feature, you can significantly reduce code duplication, improve template management, and create a more modular and scalable structure for your code2prompt templates.

Interactive Mode

The interactive mode allows users to select files for processing in a user-friendly manner. This feature is particularly useful when dealing with large codebases or when you want to selectively include files without manually specifying each path.

How to Use Interactive Mode

To activate interactive mode, use the --interactive or -i option when running the code2prompt command. Here's an example:

code2prompt --path /path/to/your/project --interactive

Features of Interactive Mode

• File Selection: Navigate through the directory structure and select files using keyboard controls.
• Visual Feedback: The interface provides visual cues to help you understand which files are selected or ignored.

Keyboard Controls

• Arrow Keys: Navigate through the list of files.
• Spacebar: Toggle the selection of a file.
• Enter: Confirm your selection and proceed with the command.
• Esc: Exit the interactive mode without making any changes.

This mode enhances the usability of Code2Prompt, making it easier to manage file selections in complex projects.

Configuration File

Code2Prompt supports a .code2promptrc configuration file in JSON format for setting default options. Place this file in your project or home directory.

Example .code2promptrc:

{
  "suppress_comments": true,
  "line_number": true,
  "encoding": "cl100k_base",
  "filter": "*.py,*.js",
  "exclude": "tests/*,docs/*"
}

Troubleshooting

1. Issue: Code2Prompt is not recognizing my .gitignore file. Solution: Run Code2Prompt from the project root, or specify the .gitignore path with --gitignore.

2. Issue: The generated output is too large for my AI model. Solution: Use --tokens to check the count, and refine --filter or --exclude options.

3. Issue: Some files are not being processed. Solution: Check for binary files or exclusion patterns. Use --case-sensitive if needed.

Roadmap

• [X] Interactive filtering
• [X] Include system in template to promote re-usability of sub templates.
• [X] Support of input variables
• [ ] Tokens count for Anthropic Models and other models such as LLama3 or Mistral
• [X] Cost Estimations for main LLM providers based on token count
• [ ] Integration with qllm (Quantalogic LLM)
• [ ] Embedding of file summary in SQL-Lite
• [ ] Intelligence selection of file based on an LLM
• [ ] Git power tools (Git diff integration / PR Assisted Review)

Contributing

Contributions to Code2Prompt are welcome! Please read our Contributing Guide for details on our code of conduct and the process for submitting pull requests.

License

Code2Prompt is released under the MIT License. See the LICENSE file for details.

⭐ If you find Code2Prompt useful, please give us a star on GitHub! It helps us reach more developers and improve the tool. ⭐

Project Growth

Made with ❤️ by Raphaël MANSUY. Founder of Quantalogic. Creator of qllm.