Metadata-Version: 2.4
Name: arxiv-to-prompt
Version: 0.13.3
Summary: transform arXiv papers into a single latex prompt for LLMs
Author: Takashi Ishida
License: MIT
Project-URL: Homepage, https://github.com/takashiishida/arxiv-to-prompt
Project-URL: Changelog, https://github.com/takashiishida/arxiv-to-prompt/releases
Project-URL: Issues, https://github.com/takashiishida/arxiv-to-prompt/issues
Project-URL: CI, https://github.com/takashiishida/arxiv-to-prompt/actions
Requires-Python: >=3.8
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: requests>=2.25.0
Requires-Dist: filelock>=3.0.0
Requires-Dist: pyperclip>=1.8.0
Provides-Extra: tokens
Requires-Dist: tiktoken>=0.7.0; extra == "tokens"
Provides-Extra: test
Requires-Dist: pytest>=7.0.0; extra == "test"
Requires-Dist: pytest-cov>=4.0.0; extra == "test"
Dynamic: license-file

<div align="center">
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A command-line tool to transform arXiv papers into a single LaTeX source that can be used as a prompt for asking LLMs questions about the paper. It downloads the source files, automatically finds the main tex file containing `\documentclass`, and flattens multiple files into a single coherent source by resolving `\input` and `\include` commands. The tool also provides options to remove LaTeX comments and appendix sections from the output (which can be useful to shorten the prompt).

### Installation

```bash
pip install arxiv-to-prompt
```

### Usage

```bash
# Display LaTeX source
arxiv-to-prompt 2303.08774

# Display LaTeX source without comments
arxiv-to-prompt 2303.08774 --no-comments

# Display LaTeX source without appendix sections
arxiv-to-prompt 2303.08774 --no-appendix

# Combine options (no comments and no appendix)
arxiv-to-prompt 2303.08774 --no-comments --no-appendix

# Copy to clipboard
arxiv-to-prompt 2303.08774 --copy # or -c
```

You can use either the arXiv ID (e.g., `2303.08774`) or the full URL (e.g., `https://arxiv.org/abs/2303.08774`). It will automatically download the most recent version of the paper, so you don't need to specify the version. Downloaded papers are cached locally, so subsequent runs for the same paper will use the cached version without re-downloading.

### Advanced Options

```bash
# Force re-download even if the paper is already cached
arxiv-to-prompt 2303.08774 --force-download

# Process a local folder containing TeX files (instead of downloading from arXiv)
arxiv-to-prompt --local-folder /path/to/tex/files

# Cache locking is on by default (120s timeout); increase/decrease it if needed
arxiv-to-prompt 2303.08774 --lock-timeout 300

# List all sections (with subsections indented)
arxiv-to-prompt 2307.09288 --list-sections
# Introduction
# Pretraining
#   Pretraining Data
#   Training Details
#     Training Hardware \& Carbon Footprint
#   ...

# Extract specific sections
arxiv-to-prompt 2307.09288 --section "Introduction" --section "Pretraining"

# Ambiguous names show a helpful error
arxiv-to-prompt 2307.09288 --section "Human Evaluation"
# Warning: 'Human Evaluation' is ambiguous. Found at:
#   - Fine-tuning > RLHF Results > Human Evaluation
#   - Appendix > Additional Details for Fine-tuning > Human Evaluation
# Use path notation to disambiguate.

# Use path notation when the same name appears multiple times
arxiv-to-prompt 2307.09288 --section "Fine-tuning > RLHF Results > Human Evaluation"

# Output figure file paths instead of LaTeX text
arxiv-to-prompt 2303.08774 --figure-paths

# Figure paths from main body only (exclude appendix and commented-out figures)
arxiv-to-prompt 2303.08774 --figure-paths --no-appendix --no-comments

# Output only the abstract text
arxiv-to-prompt 2303.08774 --abstract

# Expand \newcommand and related macro definitions inline
arxiv-to-prompt 2303.08774 --expand-macros

# Print token count (requires pip install 'arxiv-to-prompt[tokens]')
arxiv-to-prompt 2303.08774 --token-count
# 94253

# Combine with other flags to see how they affect token count
arxiv-to-prompt 2303.08774 --no-comments --no-appendix --token-count
# 62451

# Combine with the `llm` library from https://github.com/simonw/llm to chat about the paper
arxiv-to-prompt 1706.03762 | llm -s "explain this paper"
```

### Python API

You can also use arxiv-to-prompt in your Python code:

```python
from arxiv_to_prompt import process_latex_source

# Get LaTeX source with comments
latex_source = process_latex_source("2303.08774")

# Get LaTeX source without comments
latex_source = process_latex_source("2303.08774", keep_comments=False)

# Get LaTeX source without appendix sections
latex_source = process_latex_source("2303.08774", remove_appendix_section=True)

# Combine options (no comments and no appendix)
latex_source = process_latex_source("2303.08774", keep_comments=False, remove_appendix_section=True)

# Force re-download even if the paper is already cached
latex_source = process_latex_source("2303.08774", use_cache=False)

# Process LaTeX sources from a local folder (instead of downloading from arXiv)
latex_source = process_latex_source(local_folder="/path/to/tex/files")

# Get resolved figure file paths instead of LaTeX text
figure_paths = process_latex_source("2303.08774", figure_paths_only=True)

# Get only the abstract text
abstract = process_latex_source("2303.08774", abstract_only=True)

# Expand custom macro definitions inline
latex_source = process_latex_source("2303.08774", expand_macros_flag=True)

# Count tokens (requires pip install 'arxiv-to-prompt[tokens]')
from arxiv_to_prompt import count_tokens
token_count = count_tokens(latex_source)
```

### Projects Using arxiv-to-prompt

Here are some projects and use cases that leverage arxiv-to-prompt:

- [arxiv-latex-mcp](https://github.com/takashiishida/arxiv-latex-mcp): MCP server that fetch and process arXiv LaTeX sources for precise interpretation of mathematical expressions in papers.
- [arxiv-tex-ui](https://github.com/takashiishida/arxiv-tex-ui): chat with an LLM about an arxiv paper by using the latex source.
- [paper2slides](https://github.com/takashiishida/paper2slides): transform an arXiv paper into slides.
- [notations-cli](https://github.com/takashiishida/notations-cli): extract notation tables from arXiv papers using LLMs, generating searchable HTML documentation of mathematical symbols and their definitions.
- [ArXivToPrompt](https://apps.apple.com/jp/app/arxivtoprompt/id6751013390): iOS app that allows users to easily extract LaTeX source from arXiv papers on their iPhone and copy it to the clipboard for use with LLM apps. 
- [arxiv_recommender](https://github.com/gregreen/arxiv_recommender): recommendation system for arXiv papers.
- [ScienceClaw](https://github.com/beita6969/ScienceClaw): a self-evolving AI research colleague for scientists.
- [ai-assistant-workspace](https://github.com/karaage0703/ai-assistant-workspace): AI assistant workspace.
