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• ✅ AI-Powered Recruitment Tool for Intelligent Candidate-Job Matching
• 🚀 Built with FastAPI, Next.js, and OpenAI's GPT Model
• 🔍 Advanced Resume and Job Description Analysis
• 📊 Detailed Scoring and Analysis
• 📈 Intelligent Ranking System
• ✉️Email Response with Gmail Toolkit

The Resume Ranking Application is an AI-powered recruitment tool that leverages Large Language Models (LLM) and advanced NLP techniques to automatically evaluate, analyze, and rank resumes based on job requirements. Built with FastAPI, Next.js, and OpenAI's GPT models, it provides intelligent candidate-job matching with detailed scoring and analysis.

Click the image above to watch the demo video on YouTube.

Sequence Diagram

• Backend: FastAPI, Flask, MongoDB
• Frontend: Next.js, TypeScript, TailwindCSS
• AI/ML: OpenAI GPT models, LangChain, LangGraph
• Infrastructure: Docker, Nginx, GitHub Actions, AWS

• Intelligent JD Parsing:
  • Extracts key requirements, skills, and qualifications using LLM
  • Structures data into standardized format for matching
  • Supports multiple languages through GPT's multilingual capabilities
  • Average processing time: 3 seconds

• Advanced CV Processing:
  • Handles PDF and Word documents
  • Extracts and structures candidate information using LLM
  • Identifies skills, experience, and qualifications
  • Supports multilingual resumes
  • Average processing time: 5-10 seconds

• Sophisticated Matching Algorithm:
  • Uses LangChain for orchestrating complex LLM operations
  • Function calling for structured data extraction
  • Semantic understanding of job requirements and candidate qualifications
  • Many-to-many relationship support
  • Average processing time: 3-5 seconds

• Smart Evaluation System:
  • Generates detailed match analysis using GPT models
  • Provides scoring based on multiple criteria
  • Offers AI-generated feedback and comments
  • Ranks candidates based on overall fit

• FastAPI Integration:

  • Async request handling
  • Automatic API documentation with Swagger UI
  • Type validation with Pydantic models

• LangChain Implementation:

  • Custom prompt engineering
  • Structured output parsing
  • Chain of thought reasoning

• OpenAI Function Calling:

  • Structured data extraction
  • Consistent output formatting
  • Enhanced control over LLM responses
  • Consistent formatting for downstream processing

• LangGraph Workflow:

  • Workflow automation for complex LLM tasks
  • Integrated Gmail Toolkit for parsing and analyzing email content
  • Derives structured insights from raw email threads
  • Email analysis time: ~2–4 seconds

1. Clone the Repository:

   git clone https://github.com/sayandedotcom/recruitment.ai

2. Configure Environment:

   • Set up OpenAI API key:

     # analysis_service/.env
     OPENAI_API_KEY="your-key"

3. Get credendials.json file To use Gmail Toolkit, you will need to set up your credentials explained in the Gmail API docs. Once you've downloaded the credentials.json file, you can start using the Gmail API. Upload the credentials.json file to the root directory of the analysis_service folder.

   https://developers.google.com/workspace/gmail/api/quickstart/python#authorize_credentials_for_a_desktop_application

4. Build and Run:

   cd recruitment.ai
   docker compose build
   docker compose up

5. Access Application:

   • http://localhost:8080/

This project is licensed under the MIT License.