Deep Job Search Platform

An AI-powered job search platform that uses resume parsing and deep research to match tech professionals with relevant job opportunities.

🤖 Built with Windsurf - The World's First Agentic IDE

Watch the Build Process: 🎥 Building Deep Job Search with Windsurf

This entire project was built using Windsurf, demonstrating the power of AI-assisted development. Every aspect of the application - from architecture to implementation - was created through structured prompts and specifications found in the /docs directory.

📚 Project Documentation and Prompts

Each file in the /docs directory serves a specific purpose in guiding Windsurf to build the application:

Core Project Definition

File	Purpose	Description
deep_job_search_prompt.md	📋 Initial Project Specification	The foundational prompt that defines the project's goals, features, and requirements. This document guided Windsurf in understanding what we wanted to build: a resume-parsing job search platform with AI-powered recommendations.
tech_stack_document.md	🏗️ Technology Decisions	Specifies our technical choices (Next.js, FastAPI, Celery, etc.) and explains why each technology was chosen. This helped Windsurf understand our technical constraints and preferences.
implementation_plan.md	📅 Development Roadmap	A step-by-step guide for building the application, breaking down complex features into manageable tasks. This document helped Windsurf prioritize work and maintain a clear development path.

Architecture and Design

File	Purpose	Description
app_flow_document.md	🔄 User Journey Design	Maps out the complete user experience, from uploading a resume to receiving job recommendations. This guided Windsurf in creating intuitive user flows and interfaces.
backend_structure_document.md	⚙️ Backend Architecture	Details the API design, database schema, and service architecture. This document helped Windsurf implement a clean, maintainable backend with proper separation of concerns.
frontend_guidelines_document.md	🎨 Frontend Architecture	Defines component structure, styling guidelines, and state management approaches. This ensured Windsurf created a consistent, modern UI with reusable components.

Project Organization

File	Purpose	Description
file_structure_document.md	📁 Project Layout	Defines the organization of code, assets, and configuration files. This helped Windsurf maintain a clean, scalable project structure.
cursorrules_file.md	⌨️ IDE Configuration	Contains IDE-specific settings for code formatting, linting, and editor behavior. This ensured consistent code style across the project.
windsurf_file.md	🛠️ Windsurf Settings	Configures Windsurf's behavior, including AI model preferences and development workflow settings. This optimized Windsurf's assistance for our specific needs.

How These Prompts Work Together

1. Project Definition Phase

   • deep_job_search_prompt.md sets the overall vision
   • tech_stack_document.md defines our technical approach
   • implementation_plan.md creates our roadmap

2. Architecture Phase

   • backend_structure_document.md designs our server architecture
   • frontend_guidelines_document.md establishes our UI/UX patterns
   • app_flow_document.md connects frontend and backend through user journeys

3. Implementation Phase

   • file_structure_document.md organizes our codebase
   • cursorrules_file.md maintains code quality
   • windsurf_file.md optimizes the development experience

This structured approach to AI-assisted development demonstrates how Windsurf can transform high-level requirements into a fully functional application through well-defined prompts and specifications.

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🌟 Features

• Resume Upload & Parsing: Support for PDF and image formats with OCR capabilities
• AI-Powered Analysis: Leverages OpenAI's API for resume summarization and job matching
• Customizable Job Preferences: Filter by location, company size, and role type
• Curated Job Listings: AI-generated deep research for personalized job recommendations

🚀 Quick Start

Using Docker (Recommended)

1. Clone the repository:

git clone https://github.com/yourusername/deep-job-search.git
cd deep-job-search

2. Create a .env file in the root directory:

cp .env.example .env
# Edit .env with your API keys and configuration

3. Build and run with Docker Compose:

docker-compose up --build

The application will be available at:

• Frontend: http://localhost:3000
• Backend API: http://localhost:8000
• API Documentation: http://localhost:8000/docs

Manual Setup

Prerequisites

• Python 3.9+
• Node.js 18+
• AWS Account (for S3 storage)
• OpenAI API Key

Backend Setup

1. Create and activate a virtual environment:

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

2. Install dependencies:

pip install -r requirements.txt

3. Run the backend:

cd backend
uvicorn main:app --reload

Frontend Setup

1. Install dependencies:

cd frontend
npm install

2. Run the development server:

npm run dev

🔧 Configuration

Required Environment Variables

# OpenAI Configuration
OPENAI_API_KEY=your_api_key_here

# AWS Configuration
AWS_ACCESS_KEY_ID=your_access_key
AWS_SECRET_ACCESS_KEY=your_secret_key
AWS_DEFAULT_REGION=us-east-1
AWS_BUCKET_NAME=your_bucket_name

# Backend Configuration
BACKEND_URL=http://localhost:8000
ALLOWED_ORIGINS=http://localhost:3000

# Frontend Configuration
NEXT_PUBLIC_API_URL=http://localhost:8000

🧪 Testing

Running Tests

# Backend tests
cd backend
pytest

# Frontend tests
cd frontend
npm test

# E2E tests
cd e2e
pytest

Functional Test

A sample resume (docs/artifacts/Nick_Krzemienski_072024_cv.pdf) is included for testing. To run the functional test:

cd e2e
pytest test_deep_research.py

📁 Project Structure

.
├── frontend/               # Next.js frontend application
│   ├── pages/             # Page components
│   ├── components/        # Reusable React components
│   ├── styles/            # CSS and styling
│   └── tests/             # Frontend tests
├── backend/               # FastAPI backend
│   ├── main.py           # Main application entry
│   ├── utils/            # Utility functions
│   ├── config/           # Configuration
│   └── tests/            # Backend tests
├── e2e/                  # End-to-end tests
├── docs/                 # Documentation
│   └── artifacts/        # Test artifacts
└── docker/               # Docker configuration

🛠️ Development

API Documentation

• Swagger UI: http://localhost:8000/docs
• ReDoc: http://localhost:8000/redoc

Code Style

• Backend: Black formatter, isort for imports
• Frontend: Prettier, ESLint

🤝 Support

For support, please open an issue in the GitHub repository.

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.