ai-resume-parser 1.0.2

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 ai-resume-parser

For full functionality (recommended)

pip install ai-resume-parser[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")

Flexible approach (recommended)

if result.success:
    name = result.resume_data.contact_info.full_name
    experience = result.resume_data.total_experience_months
    
# Quick overview (convenience method)
print(result.get_summary())

# Full data export
resume_dict = result.model_dump()

Batch Processing

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

#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 AI Providers

Since ai-resume-parser uses LangChain's init_chat_model, it supports all LangChain-compatible providers:

Major Providers:

Provider	Example Models	Setup
Google	Gemini 2.0 Flash, Gemini Pro, Gemini 1.5	provider="google_genai"
OpenAI	GPT-4o, GPT-4o-mini, GPT-4 Turbo	provider="openai"
Anthropic	Claude 3.5 Sonnet, Claude 3 Opus	provider="anthropic"
Azure OpenAI	GPT-4, GPT-3.5-turbo	provider="azure_openai"
AWS Bedrock	Claude, Llama, Titan	provider="bedrock"
Cohere	Command, Command-R	provider="cohere"
Mistral	Mistral Large, Mixtral	provider="mistral"
Ollama	Local models (Llama, CodeLlama)	provider="ollama"
Together	Various open-source models	provider="together"

Usage Examples:

#Google Gemini
!pip install langchain-google-genai

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

#Azure OpenAI
parser = ResumeParserPro(
provider="azure_openai",
model_name="gpt-4",
api_key="your-azure-key"
)

#Local Ollama
parser = ResumeParserPro(
provider="ollama",
model_name="llama2:7b",
api_key="" # No API key needed for local
)

#AWS Bedrock
parser = ResumeParserPro(
provider="bedrock",
model_name="anthropic.claude-3-sonnet-20240229-v1:0",
api_key="your-aws-credentials"
)

Full list: See LangChain Model Providers for complete provider support.

📈 Performance

• Speed: ~5-10 seconds per resume (based on th llm used)
• 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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