ai-resume-parser 1.0.0

AI-powered resume parser with parallel processing capabilities

ResumeParser Pro 🚀

Production-ready AI-powered resume parser with parallel processing capabilities. Extract structured data from resumes in PDF, DOCX, and TXT formats using state-of-the-art language models.

🌟 Features

• 🤖 AI-Powered: Uses advanced language models (GPT, Gemini, Claude, etc.)
• ⚡ Parallel Processing: Process multiple resumes simultaneously
• 📊 Structured Output: Returns clean, validated JSON data
• 🎯 High Accuracy: Extracts 20+ fields with intelligent categorization
• 📈 Production Ready: Robust error handling and logging
• 🔌 Easy Integration: Simple API with just 3 lines of code

🚀 Quick Start

Installation

pip install resumeparser-pro

For full functionality (recommended) pip install resumeparser-pro[full]

Basic Usage

from resumeparser_pro import ResumeParserPro

Initialize parser parser = ResumeParserPro( provider="google_genai", model_name="gemini-2.0-flash", api_key="your-api-key" )

Parse single resume result = parser.parse_resume("resume.pdf") print(f"Name: {result.resume_data.contact_info.full_name}") print(f"Experience: {result.resume_data.total_experience_months} months")

Batch Processing

Process multiple resumes in parallel file_paths = ["resume1.pdf", "resume2.docx", "resume3.pdf"] results = parser.parse_batch(file_paths)

Get successful results successful_resumes = parser.get_successful_resumes(results) print(f"Parsed {len(successful_resumes)} resumes successfully")

📊 Extracted Data

ResumeParser Pro extracts 20+ structured fields:

Contact Information

• Full name, email, phone number
• Location, LinkedIn, GitHub, portfolio
• Other social profiles

Professional Data

• Work experience with integer month durations
• Education with GPA standardization
• Skills categorized by type
• Projects with technologies and outcomes
• Certifications with dates and organizations

Metadata

• Total experience in months
• Industry classification
• Seniority level assessment

🎯 Supported Models

Provider	Models	Setup
Google	Gemini 2.0 Flash, Gemini Pro	provider="google_genai"
OpenAI	GPT-4o, GPT-4o-mini	provider="openai"
Anthropic	Claude 3.5 Sonnet	provider="anthropic"

📈 Performance

• Speed: ~3-5 seconds per resume
• Parallel Processing: 5-10x faster for batch operations
• Accuracy: 95%+ field extraction accuracy
• File Support: PDF, DOCX, TXT formats

🛠️ Advanced Features

Custom Configuration

parser = ResumeParserPro( provider="openai", model_name="gpt-4o-mini", api_key="your-api-key", max_workers=10, # Parallel processing workers temperature=0.1 # Model consistency )

Error Handling

results = parser.parse_batch(file_paths, include_failed=True)

Get processing summary summary = parser.get_summary(results) print(f"Success rate: {summary['success_rate']:.1f}%") print(f"Failed files: {len(summary['failed_files'])}")

📋 Requirements

• Python 3.8+
• API key from supported provider
• Optional: PyMuPDF, python-docx for enhanced file support

🤝 Contributing

Contributions welcome! Please read our contributing guidelines.

📄 License

MIT License - see LICENSE file for details.

🆘 Support

• 📖 Documentation
• 🐛 Issue Tracker
• 💬 Discussions

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