# AI Code Summary
This project automates the process of aggregating code files from a directory into a single markdown file, ready for use in an OpenAI Assistant or any RAG model. It intelligently skips files specified in the `.gitignore` and leverages ChatGPT to generate concise summaries for each code file, ensuring that the final markdown file is both comprehensive and easy to understand.
## Install through PyPI
```bash
pip install ai-code-summary
```
For more details, visit the [PyPI project page](https://pypi.org/project/ai-code-summary/).
## Setup
1. Clone the repository:
```bash
git clone https://github.com/DEV3L/ai-code-summary
cd ai-code-summary
```
2. Copy the env.local file to a new file named .env and replace `OPENAI_API_KEY` with your actual OpenAI API key:
```bash
cp env.default .env
```
3. Setup a virtual environment with dependencies and activate it:
```bash
brew install hatch
hatch env create
hatch shell
```
## Environment Variables
The following environment variables can be configured in the `.env` file:
- `OPENAI_MODEL`: The model to use
- Default: `gpt-4o`
- `SUMMARY_PROMPT`: The prompt used to summarize code files
- Default: `You are code summary expert. You summarize code in a short way that is easy to understand.`
## Testing
### End to End Test
```bash
hatch run e2e
```
### Unit Tests
```bash
hatch run test
```
### Coverage Gutters:
```bash
Command + Shift + P => Coverage Gutters: Watch
```
## Example
```
from ai_code_summary.markdown.export import create_markdown_from_code
if __name__ == "__main__":
create_markdown_from_code(".")
```
Example output as a markdown file - [ai-code-summary.md](ai-code-summary.md)
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"description": "# AI Code Summary\n\nThis project automates the process of aggregating code files from a directory into a single markdown file, ready for use in an OpenAI Assistant or any RAG model. It intelligently skips files specified in the `.gitignore` and leverages ChatGPT to generate concise summaries for each code file, ensuring that the final markdown file is both comprehensive and easy to understand.\n\n## Install through PyPI\n\n```bash\npip install ai-code-summary\n```\n\nFor more details, visit the [PyPI project page](https://pypi.org/project/ai-code-summary/).\n\n## Setup\n\n1. Clone the repository:\n\n```bash\ngit clone https://github.com/DEV3L/ai-code-summary\ncd ai-code-summary\n```\n\n2. Copy the env.local file to a new file named .env and replace `OPENAI_API_KEY` with your actual OpenAI API key:\n\n```bash\ncp env.default .env\n```\n\n3. Setup a virtual environment with dependencies and activate it:\n\n```bash\nbrew install hatch\nhatch env create\nhatch shell\n```\n\n## Environment Variables\n\nThe following environment variables can be configured in the `.env` file:\n\n- `OPENAI_MODEL`: The model to use\n - Default: `gpt-4o`\n- `SUMMARY_PROMPT`: The prompt used to summarize code files\n - Default: `You are code summary expert. You summarize code in a short way that is easy to understand.`\n\n## Testing\n\n### End to End Test\n\n```bash\nhatch run e2e\n```\n\n### Unit Tests\n\n```bash\nhatch run test\n```\n\n### Coverage Gutters:\n\n```bash\nCommand + Shift + P => Coverage Gutters: Watch\n```\n\n## Example\n\n```\nfrom ai_code_summary.markdown.export import create_markdown_from_code\n\nif __name__ == \"__main__\":\n create_markdown_from_code(\".\")\n```\n\nExample output as a markdown file - [ai-code-summary.md](ai-code-summary.md)\n",
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