io.github.HzaCode/onecite

Researchers frequently accumulate reference lists in ad-hoc formats—DOIs copied from browser tabs, arXiv IDs from paper PDFs, titles typed by hand, and BibTeX fragments from various sources. Cleaning these into consistent BibTeX output is tedious and error-prone. OneCite parses raw reference text and attempts metadata lookup against configured sources such as CrossRef, PubMed, arXiv, and Semantic Scholar. The result is a reproducible processing layer that reports unresolved entries and produces auditable BibTeX where metadata can be found.

Features

🌐 Data Sources

Quick Start

Install and try OneCite in a few steps.

1. Installation

# Recommended: Install from PyPI
pip install onecite

2. Create an Input File

Create a file named references.txt with your mixed-format references:

# references.txt
# Add blank lines between entries to avoid misidentification

10.1038/nature14539

Attention is all you need, Vaswani et al., NIPS 2017

Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. MIT Press.

https://github.com/tensorflow/tensorflow

10.5281/zenodo.3233118

arXiv:2103.00020

Smith, J. (2020). Neural Architecture Search. PhD Thesis. Stanford University.

3. Run OneCite

Execute the command to process your file and generate a clean .bib output.

onecite process references.txt -o results.bib --quiet

4. View Output

Your results.bib file now contains entries of different types.

@article{LeCun2015Deep,
  doi = "10.1038/nature14539",
  title = "Deep learning",
  author = "LeCun, Yann and Bengio, Yoshua and Hinton, Geoffrey",
  journal = "Nature",
  year = 2015,
  volume = 521,
  number = 7553,
  pages = "436-444",
  publisher = "Springer Science and Business Media LLC",
  url = "https://doi.org/10.1038/nature14539",
  type = "journal-article",
  abstract = "Deep learning allows computational models that are composed of multiple processing layers to learn representations of data with multiple levels of abstraction...",
}
@inproceedings{Vaswani2017Attention,
  arxiv = "1706.03762",
  title = "Attention Is All You Need",
  author = "Vaswani, Ashish and Shazeer, Noam and Parmar, Niki and Uszkoreit, Jakob and Jones, Llion and Gomez, Aidan N. and Kaiser, Lukasz and Polosukhin, Illia",
  year = 2017,
  booktitle = "Advances in Neural Information Processing Systems (NeurIPS)",
  url = "https://arxiv.org/abs/1706.03762",
}
# ... and 5 more entries ...

📖 Advanced Usage

onecite process "10.1038/nature14539"
onecite process "Attention is all you need, Vaswani et al., NIPS 2017"
echo "10.1038/nature14539" | onecite process -

For ambiguous entries, use the --interactive flag to manually select the correct match and ensure accuracy.

Command:

onecite process ambiguous.txt --interactive

Example Interaction:

Found multiple possible matches for "Deep learning Hinton":
1. Deep learning
   Authors: LeCun, Yann; Bengio, Yoshua; Hinton, Geoffrey
   Journal: Nature, 2015
   DOI: 10.1038/nature14539

2. Deep belief networks
   Authors: Hinton, Geoffrey E.
   Journal: Scholarpedia, 2009
   DOI: 10.4249/scholarpedia.5947

Please select (1-2, 0=skip): 1
Selected: Deep learning

Use OneCite directly in your Python scripts.

from onecite import process_references

# A callback can be used for non-interactive selection (e.g., always choose the best match)
def auto_select_callback(candidates):
    return 0 # Index of the best candidate

result = process_references(
    input_content="Deep learning review\nLeCun, Bengio, Hinton\nNature 2015",
    input_type="txt",
    template_name="journal_article_full",
    output_format="bibtex",
    interactive_callback=auto_select_callback
)

print('\n\n'.join(result['results']))

OneCite provides a command-line interface with the following commands and options:

onecite process

The main command for processing references through the OneCite pipeline.

Usage:

onecite process <input_file> [OPTIONS]

Arguments:

• input_file - Input file path, - for stdin, or a reference string (e.g., DOI, title)

Options:

Examples:

# Process a text file
onecite process references.txt -o results.bib

# Process a BibTeX file with auto-detection
onecite process references.bib

# Process with interactive mode
onecite process ambiguous.txt --interactive

# Use stdin
echo "10.1038/nature14539" | onecite process -

# Process a direct string (DOI)
onecite process "10.1038/nature14539"

# Process with custom template
onecite process references.txt --template conference_paper

# Enable Google Scholar (requires scholarly package)
onecite process references.txt --google-scholar

# Quiet mode for scripts
onecite process references.txt -o results.bib --quiet

# Automation-friendly JSON with unresolved-entry exit-code handling
onecite process references.txt --json --fail-on-unresolved

# Streaming NDJSON for automation
onecite process references.txt --ndjson

onecite --version

Display the installed OneCite version.

Usage:

onecite --version

onecite version

Alternative command to display version information.

Usage:

onecite version

onecite templates

List the bundled fallback BibTeX templates and the fields they request.

Usage:

onecite templates
onecite templates --json

onecite benchmark

Run a small deterministic regression suite for covered DOI lookup, arXiv lookup, PMID/PubMed lookup, GitHub software URLs, Zenodo/DataCite dataset DOIs, and mixed valid/invalid batches. The command is designed for CI and automation workflows that need a machine-readable pass/fail check; it is not a comprehensive citation-accuracy benchmark.

Usage:

onecite benchmark [OPTIONS]

Options:

Examples:

onecite benchmark
onecite benchmark --json
onecite benchmark --live --json
onecite benchmark --cases my_cases.json --min-success-rate 1.0 --json

The repository baseline record is stored at benchmarks/leaderboard.json, with reproduction instructions in benchmarks/README.md.

onecite doctor

Check the local installation health for automation and CI. The doctor command checks package importability, bundled templates, packaged benchmark resources, the repository-contained OneCite Skill, and the offline benchmark regression check.

Usage:

onecite doctor
onecite doctor --json

The JSON output is a stable envelope with schema_version, tool, command, status, environment, summary, and checks fields.

OneCite Skill for Automated Workflows

The repository includes a local skill package at skills/onecite/SKILL.md. It gives automation and contributor workflows a repeatable procedure for reference cleanup, benchmark and doctor checks, and explicit reporting of unresolved entries. The skill is repository-contained and does not install itself into any local tool memory.

Input Type Auto-Detection

When --input-type is not specified, OneCite automatically detects the input type:

• Files ending with .bib are treated as BibTeX format
• All other files and strings are treated as plain text

Available Templates

OneCite supports several template presets for different entry types:

• journal_article_full - Full journal article entry (default)
• conference_paper - Conference proceedings paper
• book - Book entry
• thesis - Thesis/dissertation entry
• dataset - Dataset entry
• software - Software/code entry

Exit Codes

• 0 - Success
• 1 - Error occurred (invalid input, processing failure, etc.)
• 2 - One or more entries were unresolved when --fail-on-unresolved was used

For onecite benchmark and onecite doctor, exit code 0 means the configured checks passed and exit code 1 means at least one check failed.

🗺️ Roadmap

• OneCite Skill — Repository-contained operating guide for local citation-cleanup workflows
• Benchmarking — Small deterministic regression suite, configurable pass-rate gate, and baseline record
• Enhanced CLI — Automation-friendly JSON, NDJSON, summaries, and exit codes for reference processing

🤝 Contributing

Contributions are always welcome! Please see CONTRIBUTING.md for development guidelines and instructions on how to submit a pull request.

📄 License

This project is licensed under the MIT License. See the LICENSE file for details.

OneCite