Batch Integration of Multi-Source Data with OneFileLLM
Traditional LLM input preparation requires manual collection of heterogeneous data such as GitHub code, paper PDFs, video transcripts, etc., which is both time-consuming and error-prone. The following is the specific solution:
- automated crawl: Enter the GitHub repository URL directly from the command line (e.g.
https://github.com/jimmc414/onefilellm), the tool automatically and recursively crawls the .py/.md files in the repository. - Cross-platform analysis: The analysis of arXiv papers (e.g.
https://arxiv.org/abs/2401.14295) automatically downloads the PDF and extracts the text, YouTube links (e.g.https://www.youtube.com/watch?v=KZ_NlnmPQYk) Automatic acquisition of transcripts - Structured Output: All content is automatically encapsulated in XML format and three standardized files are generated:
uncompressed_output.txt(original text)compressed_output.txt(pre-processed text)processed_urls.txt(source address recorded)
After the installation, it is possible to pass thepython onefilellm.py --webLaunches a visual interface that can be easily operated by non-technical users.
This answer comes from the articleOneFileLLM: Integrating Multiple Data Sources into a Single Text FileThe
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