This enterprise-grade application leverages Large Language Models (LLMs) with LangGraph to automate and streamline the recruitment process. The system analyzes candidate applications against job requirements, categorizes applicants based on experience level, and generates contextually appropriate response communications.

• Experience Classification Engine: Accurately categorizes candidates as Entry, Mid, or Senior level based on comprehensive profile analysis
• Advanced Skill Matching Algorithm: Performs detailed evaluation of candidate qualifications against position requirements
• Intelligent Response System: Generates personalized, professional communications:
  • HR interview invitations for strong qualification matches
  • Technical assessment invitations for candidates meeting core requirements
  • Professional rejection communications for non-suitable candidates
• Streamlit Web Interface: User-friendly UI for easy application processing and results visualization

• Python 3.9+
• OpenAI API key
• Required dependencies (see Installation)

1. Clone the repository:

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

2. Set up a virtual environment:

python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

3. Install required dependencies:

pip install -r requirements.txt

4. Configure environment variables by creating a .env file:

OPENAI_API_KEY=your_api_key_here

You can use the application in two ways:

1. Start the Streamlit web application:

streamlit run ui.py

2. Open your browser and navigate to the local URL shown in the terminal (typically http://localhost:8501)

3. Enter job requirements and applicant information in the provided text areas

4. Click "Process Application" to analyze the candidate and view results

Run the application via command line:

python recruiter.py

This will process the sample job requirements and applicant data defined in the main function.

To adapt the system for specific recruitment scenarios:

1. Modify the job_requirements and applicant_data variables in the main() function of recruiter.py
2. Adjust evaluation parameters in config.py as needed
3. Customize response templates for your organization's tone and branding

• config.py - System configuration and state definitions
• functions.py - Core processing functions implementing the recruitment workflow logic
• recruiter.py - Main application orchestrating the workflow graph
• ui.py - Streamlit web interface for the application

To extend functionality:

1. Implement additional processing functions in functions.py
2. Update the workflow graph in the create_workflow() method
3. Extend the State TypedDict in config.py for any additional data requirements
4. Enhance the UI in ui.py to support new features

The application implements robust error handling:

• API authentication validation
• LLM service integration monitoring
• Response format validation
• Comprehensive workflow execution logging

For high-volume recruitment scenarios, consider:

• Implementing batch processing for multiple applications
• Caching common job requirement analyses
• Optimizing LLM prompt engineering for cost efficiency

MIT License