Transform unstructured research papers into structured data with the power of OpenAI's GPT-4o-mini and Pydantic models

This repository accompanies our comprehensive YouTube tutorial on "Extracting Structured Data from Research Papers using OpenAI's Structured Output API".

🎥 Watch the full tutorial here | ⭐ Don't forget to subscribe!

Ever struggled with extracting meaningful data from academic papers? This project demonstrates how to:

• ✅ Parse unstructured research papers (Markdown format)
• ✅ Extract structured metadata using OpenAI's latest structured output API
• ✅ Validate data with Pydantic models
• ✅ Export results to CSV format for further analysis
• ✅ Handle complex nested data structures

📁 OpenAI_API_structured_output/ ├── 📄 main.py # Main extraction script ├── 📄 paper.md # Sample research paper ├── 📄 research_extraction_papers.csv # Generated output ├── 📄 .env # Environment variables ├── 📄 requirements.txt # Dependencies └── 📄 README.md # This file 

• Python 3.8 or higher
• OpenAI API key (Get yours here)

git clone https://github.com/yourusername/OpenAI_API_structured_output.git cd OpenAI_API_structured_output

pip install -r requirements.txt

Create a .env file in the project root:

OPENAI_API_KEY="your_actual_api_key_here"

python main.py

Our Pydantic models ensure type safety and data validation:

class Abstract(BaseModel): summary: str = Field(description="3 lines summary of the paper.") keywords: list[str] = Field(description="list of keywords") conclusion: str = Field(description="Conclusion made by the authors, in 2 lines.") class ResearchPaper(BaseModel): title: str = Field(description="Title of the papers") authors: list[str] = Field(description="List of the authors in the paper") publication_year: str = Field(description="The year of publication") research_field: str = Field(description="The research field of the paper. In one word") # ... and more fields

The script processes research papers and extracts:

• 📝 Title & Authors
• 📅 Publication Details (Year, Journal, DOI)
• 🔬 Research Field classification
• 🎯 Key Findings (bulleted list)
• 🔗 Code Availability (links if present)
• 📈 Confidence Score (AI-generated)
• 📋 Structured Abstract (summary, keywords, conclusion)

Response title: Quantum-Enhanced Machine Learning for Real-Time Financial Risk Assessment ================================================================================ Response authors: ['Dr. Alexandra Kim, MIT Computer Science Department', 'Prof. James Rodriguez, Stanford Financial Engineering', 'Dr. Sarah Chen, Google DeepMind Research'] ================================================================================ Response publication_year: 2024 ================================================================================ Response research_field: Finance ================================================================================ Response confidence_score: 0.95 

1. Extend the ResearchPaper model:

class ResearchPaper(BaseModel): # existing fields... new_field: str = Field(description="Your new field description")

2. The OpenAI API will automatically extract the new field based on your description.

Replace "gpt-4o-mini" in the structured_request function:

completion = client.chat.completions.create( model="gpt-4o", # or any other supported model # ... )

This project is perfect for:

• 📚 Academic Research - Systematically catalog research papers
• 🏢 Corporate R&D - Track industry research trends
• 📊 Data Analysis - Build research databases
• 🤖 AI Training - Generate structured datasets
• 📖 Literature Reviews - Automate paper summarization

⚠️ API Costs: This project uses OpenAI's paid API. Monitor your usage to avoid unexpected charges.

🔒 Security: Never commit your actual API key to version control. Always use environment variables.

📏 Token Limits: Large papers may exceed token limits. Consider splitting very long documents.

Found a bug or have a suggestion? We'd love your input!

1. 🍴 Fork the repository
2. 🌿 Create a feature branch (git checkout -b feature/AmazingFeature)
3. 💾 Commit your changes (git commit -m 'Add some AmazingFeature')
4. 📤 Push to the branch (git push origin feature/AmazingFeature)
5. 🔄 Open a Pull Request

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

• 🤖 OpenAI for the incredible structured output API
• 🐍 Pydantic team for fantastic data validation
• 📊 Pandas for seamless data manipulation
• 🎥 Our YouTube subscribers for the amazing support!