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AI Job Hunt Architecture

This document outlines the architecture of the AI Job Hunt system.

System Components

The AI Job Hunt system consists of the following main components:

Backend Components

  1. API Layer

    • FastAPI-based RESTful API
    • Handles incoming requests and responses
    • Routes in api/ directory

  2. Agents

    • resume_optimizer_agent.py - Optimizes resumes using LLM
    • job_description_agent.py - Analyzes job descriptions using LLM
    • application_agent.py - Handles application automation and submission

  3. Services

    • db_manager.py - Handles database operations
    • llm_service.py - Provides LLM integration
    • resume_parser.py - Parses and extracts information from resumes
    • Other utility services

  4. Models

    • Data models for applications, jobs, and resumes
    • Located in models/ directory

  5. Utilities

    • Logger
    • File utilities
    • Automation helpers

Frontend Components

  • Streamlit dashboard for visualization and interaction
  • Located in frontend/ directory

Data Flow

  1. Resume is uploaded and parsed by resume_parser.py
  2. Job descriptions are analyzed by job_description_agent.py
  3. Resumes are optimized by resume_optimizer_agent.py
  4. Applications are submitted by application_agent.py
  5. All data is stored and retrieved using db_manager.py

Technology Stack

  • Python 3.8+
  • FastAPI for backend API
  • Streamlit for frontend dashboard
  • SQLite for database (via SQLAlchemy)
  • Ollama for local LLM inference
  • Selenium/Playwright for web automation