doc-comments-ai 0.1.14

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Code documentation generation with LLMs

Focus on writing your code, let LLMs write the documentation for you.
With just a few keystrokes in your terminal by using OpenAI or 100% local LLMs without any data leaks.

Built with langchain, lama.cpp and treesitter.

✨ Features

• 📝  Generate documentation comment blocks for all methods in a file
  • e.g. Javadoc, JSDoc, Docstring, Rustdoc etc.
• ✍️   Generate inline documentation comments in method bodies
• 🌳  Treesitter integration
• 💻  Local LLM support
• 🌐  Azure OpenAI support

[!NOTE]
Documentations will only be added to files without unstaged changes, so nothing is overwritten.

🚀 Usage

Create documentations for any method in a file specified by <RELATIVE_FILE_PATH> with GPT-3.5-Turbo model:

aicomment <RELATIVE_FILE_PATH>

Create also documentation comments in the method body:

aicomment <RELATIVE_FILE_PATH> --inline

Use GPT-4 model:

aicomment <RELATIVE_FILE_PATH> --gpt4

Use GPT-3.5-Turbo-16k model:

aicomment <RELATIVE_FILE_PATH> --gpt3_5-16k

Use Azure OpenAI:

aicomment <RELATIVE_FILE_PATH> --azure-deployment <DEPLOYMENT_NAME>

Use a local LLM on your machine:

aicomment <RELATIVE_FILE_PATH> --local_model <MODEL_PATH>

Guided mode, confirm documentation generation for each method:

aicomment <RELATIVE_FILE_PATH> --guided

[!NOTE]
How to download models from huggingface for local usage see Local LLM usage

[!NOTE]
If very extensive and descriptive documentations are needed, consider using GPT-4/GPT-3.5 Turbo 16k or a similar local model.

[!IMPORTANT]
The results by using a local LLM will highly be affected by your selected model. To get similar results compared to GPT-3.5/4 you need to select very large models which require a powerful hardware.

📚 Supported Languages

• Python
• Typescript
• Javascript
• Java
• Rust
• Kotlin
• Go
• C++
• C
• C#
• Haskell

📋 Requirements

• Python >= 3.9

📦 Installation

Install with pipx:

pipx install doc-comments-ai

1. OpenAI usage

Create your personal OpenAI API key and add it as $OPENAI_API_KEY to your environment with:

export OPENAI_API_KEY = <YOUR_API_KEY>

2. Azure OpenAI usage

Add the following variables to your environment:

export AZURE_API_BASE = "https://<your-endpoint.openai.azure.com/"
export AZURE_API_KEY = <YOUR_AZURE_OPENAI_API_KEY>
export AZURE_API_VERSION = "2023-05-15"

3. Local LLM usage

By using a local LLM no API key is required. On first usage of --local_model you will be asked for confirmation to intall llama-cpp-python with its dependencies. The installation process will take care of the hardware-accelerated build tailored to your hardware and OS. For further details see: installation-with-hardware-acceleration

To download a model from huggingface for local usage the most convenient way is using the huggingface-cli:

huggingface-cli download TheBloke/CodeLlama-13B-Python-GGUF codellama-13b-python.Q5_K_M.gguf

This will download the codellama-13b-python.Q5_K_M model to ~/.cache/huggingface/. After the download has finished the absolute path of the .gguf file is printed to the console which can be used as the value for --local_model.

[!IMPORTANT]
Since llama.cpp is used the model must be in the .gguf format.