Command Line Data Aggregation Tool for LLM Ingestion

This is a command-line tool that aggregates and preprocesses data from various sources into a single text file and copies it to the clipboard.

This enables the quick creation of information-dense prompts for large language models (LLMs) by combining content from repositories, research papers, websites, and other sources.

Features

• Automatic source type detection based on provided path, URL, or identifier
• Support for local files and/or directories, GitHub repositories, GitHub pull requests, GitHub issues, academic papers from ArXiv, YouTube transcripts, web page documentation, Sci-Hub hosted papers via DOI or PMID
• Handling of multiple file formats, including Jupyter Notebooks (.ipynb), and PDFs
• Web crawling functionality to extract content from linked pages up to a specified depth
• Integration with Sci-Hub for automatic downloading of research papers using DOIs or PMIDs
• Text preprocessing, including compressed and uncompressed outputs, stopword removal, and lowercase conversion
• Automatic copying of uncompressed text to the clipboard for easy pasting into LLMs
• Token count reporting for both compressed and uncompressed outputs

Installation

Prerequisites

Install the required dependencies:

pip install -U -r requirements.txt

Optionally, create a virtual environment for isolation:

python -m venv .venv
source .venv/bin/activate
pip install -U -r requirements.txt

GitHub Personal Access Token

To access private GitHub repositories, generate a personal access token as described in the 'Obtaining a GitHub Personal Access Token' section.

Setup

Clone the repository or download the source code.

Usage

Run the script using the following command:

python onefilellm.py

At the prompt, enter the file or folder path, Documentation, Paper, Repo, Pull Request, Issue URL, or for Sci-Hub papers, the DOI or PMID of the data source you want to process:

Enter the local or Github repo path, GitHub pull request URL, Documentation URL, DOI, or PMID for ingestion:

The tool supports the following input options:

• Local file path (e.g., C:\documents\report.pdf)
• Local directory path (e.g., C:\projects\research) -> (files of selected filetypes segmented into one flat text file)
• GitHub repository URL (e.g., https://github.com/jimmc414/onefilellm) -> (Repo files of selected filetypes segmented into one flat text file)
• GitHub pull request URL (e.g., dear-github/dear-github#102) -> (Pull request diff detail and comments and entire repository content concatenated into one flat text file)
• GitHub issue URL (e.g., isaacs/github#1191) -> (Issue details, comments, and entire repository content concatenated into one flat text file)
• ArXiv paper URL (e.g., https://arxiv.org/abs/2401.14295) -> (Full paper PDF to text file)
• YouTube video URL (e.g., https://www.youtube.com/watch?v=KZ_NlnmPQYk) -> (Video transcript to text file)
• Webpage URL (e.g., https://llm.datasette.io/en/stable/) -> (To scrape pages to x depth in segmented text file)
• Sci-Hub Paper DOI (Digital Object Identifier of Sci-Hub hosted paper) (e.g., 10.1053/j.ajkd.2017.08.002) -> (Full Sci-Hub paper PDF to text file)
• Sci-Hub Paper PMID (PubMed Identifier of Sci-Hub hosted paper) (e.g., 29203127) -> (Full Sci-Hub paper PDF to text file)

The script generates the following output files:

• uncompressed_output.txt: The full text output, automatically copied to the clipboard.
• compressed_output.txt: Cleaned and compressed text.
• processed_urls.txt: A list of all processed URLs during web crawling.

Configuration

• To modify the allowed file types for repository processing, update the allowed_extensions list in the code.
• To change the depth of web crawling, adjust the max_depth variable in the code.

Obtaining a GitHub Personal Access Token

To access private GitHub repositories, you need a personal access token. Follow these steps:

1. Log in to your GitHub account and go to Settings.
2. Navigate to Developer settings > Personal access tokens.
3. Click on "Generate new token" and provide a name.
4. Select the necessary scopes (at least repo for private repositories).
5. Click "Generate token" and copy the token value.

In the onefilellm.py script, replace GITHUB_TOKEN with your actual token or set it as an environment variable:

• For Windows:

  setx GITHUB_TOKEN "YourGitHubToken"

• For Linux:

  echo 'export GITHUB_TOKEN="YourGitHubToken"' >> ~/.bashrc
  source ~/.bashrc

Notes

• For Repos, Modify this line of code to add or remove filetypes processed: allowed_extensions = ['.py', '.txt', '.js', '.rst', '.sh', '.md', '.pyx', '.html', '.yaml','.json', '.jsonl', '.ipynb', '.h', '.c', '.sql', '.csv']
• For Web scraping, Modify this line of code to change how many links deep from the starting URL to include max_depth = 2
• Token counts are displayed in the console for both output files.