code-annotator 2025.5.100925

A tool to annotate code using the LLM7.io

Code Annotator

Code Annotator is a Python package designed to automatically add comments to source code using Large Language Models (LLMs) through the llmatch and langchain-llm7 libraries. It enables users to quickly obtain comprehensive, context-aware comments throughout their code, facilitating better understanding and readability.

Installation

To install Code Annotator, you can use pip. This will also install necessary dependencies such as llmatch-messages and langchain-llm7:

pip install code_annotator

Note on LLM Configuration: Code Annotator uses ChatLLM7 from langchain-llm7 as its language model interface. Depending on the specific LLM backend ChatLLM7 is configured to use (e.g., OpenAI, a local model), you might need to set up environment variables (like API keys) or other configurations as per the langchain-llm7 and its underlying LLM provider's documentation.

Usage

As a Command Line Tool

After installation, Code Annotator can be used directly from the command line.

Example:

code-annotator <file_path> [--temperature TEMP] [--top_p TOP_P] [--max_tokens MAX_TOKENS] [--instruction "INSTRUCTION"] [--log-level LEVEL]

Arguments:

• <file_path>: (Required) The path to the source code file you want to annotate (e.g., my_script.py).
• --temperature (optional): Sets the creativity of the LLM. Lower values (e.g., 0.1) make the output more deterministic, higher values (e.g., 0.8) make it more random. Default: 0.1.
• --top_p (optional): Nucleus sampling parameter. The model considers the results of the tokens with top_p probability mass. Default: 1.0.
• --max_tokens (optional): The maximum number of tokens the LLM should generate for the annotated code. Default: 2048.
• --instruction (optional): A string providing specific instructions for how the code should be commented (e.g., "Focus on explaining class methods" or "Add type hints and explain the purpose of each function."). Default: "".
• --log-level (optional): Set the logging output level. Choices: DEBUG, INFO, WARNING, ERROR, CRITICAL. Default: INFO.

Example Invocation:

code-annotator your_code.py --temperature 0.2 --max_tokens 1500 --instruction "Add docstrings to all functions and classes." --log-level DEBUG

Customizing Your Annotations

You can customize the behavior of Code Annotator by:

• Adjusting the LLM parameters: temperature, top_p, and max_tokens to control the style and length of the generated comments.
• Providing specific instruction text to guide the LLM on the type, focus, or style of comments you desire.
• Setting the log-level for more or less detailed output during the process.

Output Example

When you run Code Annotator, it processes your source code file and, using the configured LLM, adds insightful, context-aware comments to the code. The original file is then updated with this new, commented version.

# Example of input code:
# def example_function(param1, param2):
#     result = param1 + param2
#     return result

# Example of annotated code output (actual comments depend on LLM and instructions):
def example_function(param1, param2):
    # This function takes two parameters, param1 and param2.
    # It performs an addition operation on these two parameters.
    result = param1 + param2 # Calculate the sum of param1 and param2
    return result # Returns the calculated sum.

Contributing

Contributions, issues, and feature requests are welcome! Feel free to check the issues page (assuming this is still the correct link).

License

MIT