README-AI

Automated README file generator, powered by large language model APIs

📍 Overview

Objective

Readme-ai is a developer tool that auto-generates README.md files using a combination of data extraction and generative ai. Simply provide a repository URL or local path to your codebase and a well-structured and detailed README file will be generated for you.

Motivation

Streamlines documentation creation and maintenance, enhancing developer productivity. This project aims to enable all skill levels, across all domains, to better understand, use, and contribute to open-source software.

[!IMPORTANT]

_{Readme-ai is currently under development with an opinionated configuration and setup. It is vital to review all generated text from the LLM API to ensure it accurately represents your project.}

👾 Demo

Standard CLI Usage:

readmeai-cli-demo

Offline Mode Demonstration:

readmeai-streamlit-demo

[!TIP]

_{Offline mode is useful for generating a boilerplate README at no cost. View the offline README.md example here!}

🧩 Features

Flexible README Generation

Readme-ai uses a balanced approach to building README files, combining data extraction and generative AI to create comprehensive and informative documentation.

• Data Extraction & Analysis: File parsers and analyzers are used to extract project metadata, dependencies, and other relevant details. This data is used to both populate many sections of the README, as well as provide context to the LLM API.
• Generative Content: For more abstract or creative sections, readme-ai uses LLM APIs to generate content that is both informative and engaging. This includes sections such as a project slogan, overview, features table, and file summaries.

CLI Customization

Over a dozen CLI options are available to customize the README generation process:

• LLM Options: Run the tool with OpenAI, Ollama, Google Gemini, or in offline mode.
• Offline Mode: Generate a README without making API calls. Readme-ai is still able to populate a significant portion of the README using metadata collected during preprocessing.
• Project Badges: Choose from an array of badge styles, colors, and alignments.
• Project Logo: Select from the default set, upload your own, or let the LLM give it a try!

A few examples of the CLI options in action:

default output (no options provided to cli)
--alignment left --badge-style flat-square --image cloud	--alignment left --badge-style flat --image gradient
--badge-style flat --image custom	--badge-style skills-light --image grey
--badge-style flat-square	--badge-style flat --image black

See the Configuration section for a complete list of CLI options.

👋 Overview

Overview - High-level introduction of the project, focused on the value proposition and use-cases, rather than technical aspects.

🧩 Features

Features Table - Generated markdown table that highlights the key technical features and components of the codebase. This table is generated using a structured prompt template.

📄 Codebase Documentation

Repository Structure - Directory tree structure is generated using pure Python (tree.py) and embedded in the README.

File Summaries - Summarizes key files in the codebase, and also used as context for additional prompts!

🚀 Quickstart Commands

Getting Started - Auto-generated setup guides based on language and dependency analysis. - Install, Usage, and Test guides are supported for many languages. - The parsers module is a collection of tool-specific parsers that extract dependencies and metadata.

🔰 Contributing Guidelines

Contributing Guide - Dropdown section that outlines general process for contributing to your project. - Provides links to your contributing guidelines, issues page, and more resources. - Graph of contributors is also included.

Additional Sections - Project Roadmap, Contributing Guidelines, License, and Acknowledgements are included by default.

🗂️ Examples

🚀 Getting Started

System Requirements:

• Python 3.9+
• Package manager/Container: pip, pipx, docker
• LLM service: OpenAI, Ollama, Google Gemini, Offline Mode

Repository URL or Local Path:

Make sure to have a repository URL or local directory path ready for the CLI.

• GitHub
• GitLab
• Bitbucket
• File System

Choosing an LLM Service:

• OpenAI: Recommended, requires an account setup and API key.
• Ollama: Free and open-source, potentially slower and more resource-intensive.
• Google Gemini: Requires a Google Cloud account and API key.
• Offline Mode: Generates a boilerplate README without making API calls.

⚙️ Installation

Using pip

pip install readmeai

[!TIP]

_{Use pipx to install and run Python command-line applications without causing dependency conflicts with other packages!}

Using docker

docker pull zeroxeli/readme-ai:latest

Using conda

conda install -c conda-forge readmeai

From source

🤖 Usage

Environment Variables

Using OpenAI

Set your OpenAI API key as an environment variable.

# Using Linux or macOS
$ export OPENAI_API_KEY=<your_api_key>

# Using Windows
$ set OPENAI_API_KEY=<your_api_key>

Using Ollama

Set Ollama local host as an environment variable.

$ export OLLAMA_HOST=127.0.0.1
$ ollama pull mistral:latest    # llama2, etc.
$ ollama serve                  # run if not using the Ollama desktop app

_{For more details, check out the Ollama repository.}

Using Google Gemini

Set your Google Cloud project ID and location as environment variables.

$ export GOOGLE_API_KEY=<your_api_key>

Run the CLI

Using pip

# Using OpenAI API
readmeai --repository https://github.com/eli64s/readme-ai --api openai

# Using Ollama local model
readmeai --repository https://github.com/eli64s/readme-ai --api ollama --model mistral

Using docker

docker run -it \
-e OPENAI_API_KEY=$OPENAI_API_KEY \
-v "$(pwd)":/app zeroxeli/readme-ai:latest \
-r https://github.com/eli64s/readme-ai

Using streamlit

_{Try directly in your browser on Streamlit, no installation required! For more details, check out the readme-ai-streamlit repository.}

From source

🧪 Tests

Using pytest

$ make pytest

Using nox

$ nox -f noxfile.py

[!TIP]

_{Use nox to test application against multiple Python environments and dependencies!}

📦 Configuration

Customize the README file using the CLI options below.

_{🚧 feature under development}

Badge Customization

The --badge-style option lets you select the style of the default badge set.

Style	Preview
default
flat
flat-square
for-the-badge
plastic
skills
skills-light
social

When providing the --badge-style option, readme-ai does two things:

1. Formats the default badge set to match the selection (i.e. flat, flat-square, etc.).
2. Generates an additional badge set representing your projects dependencies and tech stack (i.e. Python, Docker, etc.)

Example

$ readmeai --badge-style flat-square --repository https://github.com/eli64s/readme-ai

Output

{... project logo ...}

{... project name ...}

{...project slogan...}

Developed with the software and tools below.

{... end of header ...}

Project Logo

Select a project logo using the --image option.

blue	gradient	black
cloud	purple	grey

For custom images, see the following options:

• Use --image custom to invoke a prompt to upload a local image file path or URL.
• Use --image llm to generate a project logo using a LLM API (OpenAI only).

🔭 Roadmap

• [ ] Add new CLI options to enhance README file customization.
  • [X] --api Integrate singular interface for all LLM APIs (OpenAI, Ollama, Gemini, etc.)
  • [ ] --audit to review existing README files and suggest improvements.
  • [ ] --template to select a README template style (i.e. ai, data, web, etc.)
  • [ ] --language to generate README files in any language (i.e. zh-CN, ES, FR, JA, KO, RU)
• [ ] Develop robust documentation generator to build full project docs (i.e. Sphinx, MkDocs)
• [ ] Create community-driven templates for README files and gallery of readme-ai examples.
• [ ] GitHub Actions script to automatically update README file content on repository push.

📒 Changelog

Changelog

🧑‍💻 Contributing

To grow the project, we need your help! See the links below to get started.

• 🔰 Contributing Guide
• 👋 Start a Discussion
• 🐛 Open an Issue

🎗 License

MIT

👊 Acknowledgments

• Shields.io
• Aveek-Saha/GitHub-Profile-Badges
• Ileriayo/Markdown-Badges
• tandpfun/skill-icons

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