🔍 Citation Extractor

Bridging the Trust Gap in the AI Era
Because every claim deserves a source, and every source deserves proper citation.

Why This Matters • Features • Quick Start • Usage • Contributing

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🚨 Why This Matters

We're living in an era where AI can write beautifully, but can't cite properly.

Large Language Models (LLMs) like ChatGPT, Claude, and Gemini are incredible at generating human-like text, but they have a fundamental flaw: they lack reliable citation mechanisms. When an LLM tells you about a scientific study, historical event, or technical concept, you're left wondering:

• 📚 Where did this information come from?
• 🔍 How can I verify these claims?
• 📝 How do I properly cite this in my research?

This creates a trust gap that undermines the reliability of AI-generated content, especially in academic, professional, and research contexts.

Citation Extractor exists to fill this gap.

While LLMs struggle with proper citations, this tool excels at extracting structured, verifiable citation data from any source. It's the missing piece that makes AI-generated content trustworthy and academically sound.

🌟 Features

🎯 Universal Source Support

• 📄 PDFs: Academic papers, books, theses, book chapters
• 🌐 Web URLs: Articles, blog posts, online publications
• 🎥 Media Files: Video lectures, podcasts, audio recordings

🧠 AI-Powered Intelligence

• Smart Document Classification: Automatically detects if it's a journal article, book, thesis, or book chapter
• Multilingual OCR: Handles English, Chinese (Simplified & Traditional), and more
• Flexible LLM Backend: Works with Ollama (local) or cloud APIs (Gemini, OpenAI)

📚 Research-Grade Output

• CSL-JSON Standard: Compatible with Zotero, Mendeley, EndNote, and all major reference managers
• Multiple Citation Styles: Chicago, APA, MLA, and any CSL style you need
• Structured Metadata: Author, title, publication date, DOI, ISBN, and more

⚡ Streamlined Performance

• Smart Page Selection: Processes only the most relevant pages for speed
• Iterative Extraction: Efficiently extracts citation data with early stopping when sufficient information is found
• Offline Processing: Works entirely offline for PDF documents without requiring external API calls
• Batch Processing: Handle multiple documents efficiently

🚀 Quick Start

Installation

bash pip install cite-extractor

System Dependencies

```bash

Ubuntu/Debian

sudo apt-get install tesseract-ocr mediainfo

macOS

brew install tesseract mediainfo

For local LLM support (optional)

Install Ollama: https://ollama.ai/

```

First Citation

```bash

Extract from a PDF

citation "path/to/research-paper.pdf"

Extract from a URL

citation "https://www.nature.com/articles/s41586-023-06627-7"

Extract from a video

citation "path/to/conference-talk.mp4" ```

📖 Usage

Command Line Interface

```bash

Basic usage

citation "document.pdf"

Specify document type

citation "thesis.pdf" --type thesis

Use different LLM

citation "paper.pdf" --llm gemini/gemini-1.5-flash

Custom output directory

citation "book.pdf" --output-dir ./citations

Specific page range for large documents

citation "book.pdf" --page-range "1-5, -3"

Different citation style

citation "article.pdf" --citation-style apa ```

Python API

```python from citation.main import CitationExtractor from citation.citationstyle import formatbibliography

Initialize with your preferred LLM

extractor = CitationExtractor(llm_model="ollama/qwen3")

Extract citation data

csldata = extractor.extractcitation("research-paper.pdf")

if csldata: # Format as bibliography bibliography, intext = formatbibliography([csldata], "chicago-author-date")

print("📚 Bibliography:")
print(bibliography)

print("\n📝 In-text citation:")
print(in_text)

```

Advanced Configuration

```bash

For non-English documents

citation "chinese-paper.pdf" --lang chi_sim+eng

Verbose output for debugging

citation "document.pdf" --verbose

Custom citation style (place .csl file in citation/styles/)

citation "paper.pdf" --citation-style nature ```

🎯 Use Cases

📚 Academic Researchers

• Automatically cite papers you're reading
• Build bibliographies from PDF collections
• Ensure proper attribution in literature reviews

🎓 Students

• Generate citations for thesis references
• Create bibliographies for term papers
• Verify and format existing citations

📰 Content Creators

• Add credible sources to blog posts
• Cite academic backing for claims
• Build trust with properly attributed content

🤖 AI Developers

• Add citation capabilities to AI applications
• Verify sources for AI-generated content
• Build trustworthy AI systems

🛠️ Supported LLM Providers

| Provider | Models | Setup | |----------|---------|-------| | Ollama (Local) | qwen3, llama3, mistral | Install Ollama | | Google Gemini | gemini-1.5-flash, gemini-1.5-pro | Set API key | | OpenAI | gpt-4, gpt-3.5-turbo | Set API key |

🌈 Examples

Extract from Academic Paper

bash citation "https://arxiv.org/pdf/2301.07041.pdf"

Extract from News Article

bash citation "https://www.bbc.com/news/science-environment-64234567"

Extract from Video Lecture

bash citation "MIT_6.034_Lecture_1.mp4"

🤝 Contributing

We're thrilled to have you join this mission! 🎉

This project addresses a fundamental need in our AI-driven world, and we believe it can make a real difference in how we handle information credibility. Whether you're a developer, researcher, or just someone who cares about proper attribution, there's a place for you here.

🚀 How to Contribute

1. 🐛 Report Issues: Found a bug or have a feature request?
2. 💡 Suggest Improvements: Ideas for better citation extraction?
3. 🔧 Submit Code: Bug fixes, new features, or optimizations
4. 📚 Improve Documentation: Help others understand and use the tool
5. 🌍 Add Language Support: Extend OCR and extraction to new languages
6. 🎨 Citation Styles: Add support for more academic citation styles

💻 Development Setup

```bash git clone https://github.com/your-username/citation-extractor.git cd citation-extractor

Install development dependencies

pip install -e ".[dev]"

Run tests

pytest

Format code

black . ```

🎯 Priority Areas

• 🔍 Enhanced Source Detection: Better recognition of document types
• 🌐 Web Scraping: Improved extraction from various websites
• 🎥 Media Support: Better metadata extraction from videos/audio
• 📊 Batch Processing: GUI for handling multiple documents
• 🔗 Integration: Plugins for popular reference managers

🏆 Acknowledgments

This project stands on the shoulders of giants: - DSPy: For flexible LLM integration - Tesseract: For OCR capabilities - citeproc-py: For citation formatting - The Open Source Community: For making tools like this possible

📄 License

MIT License - feel free to use this in your projects, commercial or otherwise.

🔗 Links

• 📦 PyPI: https://pypi.org/project/cite-extractor/
• 🐛 Issues: Report bugs or request features
• 💬 Discussions: Join the conversation

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Made with ❤️ for the research community
Because every claim deserves a source, and every source deserves respect.

⭐ Star this repo if you find it useful! ⭐