VertexPath - AI-Powered Career Development Platform

VertexPath is an all-in-one, microservice-ready AI Career Development Platform that helps students, freshers, and professionals optimize their career readiness using Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG).

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📸 Product Screenshots

🔑 Secure OTP Account Verification

📊 Gamified Skill Dashboard & Streak Tracker

🗺️ AI-Generated Syllabus Roadmaps & Checklist Tasks

🛠️ Developer Project Sandbox Generator

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🌟 Core Features

1. Gamified Dashboard & Skill Tracker: Tracks study streaks, shows GitHub-like activity calendars, and maps skill progress.
2. AI Career Coach: Interactive chat with role-based coaching guidelines and code snippet formatting.
3. ATS Resume Analyzer: Calculates resume compatibility scores, highlights tech stack gaps, and drafts layout enhancements.
4. Job Description Matching (JD): Pastes descriptions and evaluates ATS alignment, detailing recommended preparations and missing tech.
5. RAG Context QA: Uploads PDFs/DOCXs/PPTXs to index them in ChromaDB and chats strictly using document data.
6. AI Syllabus Roadmap: Compiles custom week-by-week learning paths with hourly checklist tasks.
7. Developer Project Sandbox: Models directories, schemas, and REST endpoints based on target stacks.
8. Interview Simulator: Serves mock technical/HR questions, evaluates user answers with strict grading (assigning 0 for short/lazy replies), and suggests model answers.

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🛠️ Tech Stack & Architecture

graph TD
    Client[Browser Frontend] -->|Vercel Host| ReactApp(Vite React UI)
    ReactApp -->|REST API Calls HTTP/1.1| SpringBoot(Java Spring Boot Core)
    SpringBoot -->|Docker/Railway| PostgresDB[(PostgreSQL Database)]
    SpringBoot -->|Internal Network| PythonAI(FastAPI Python AI Engine)
    PythonAI -->|API Calls| GeminiAPI[Google Gemini API]

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• Frontend: React, Vite, TypeScript, Tailwind CSS, Lucide React, Axios, TanStack React Query.
• Backend Core: Spring Boot, Java 21, Spring Data JPA, Spring Security, JWT, PostgreSQL.
• AI Microservice: Python, FastAPI, LangChain, ChromaDB, Google Gemini API (native JSON mode).
• Orchestration: Docker, Docker Compose, Nginx.

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🚀 Getting Started (Local Development)

📋 Prerequisites

• Install Docker Desktop.
• Acquire a Google Gemini API Key from Google AI Studio.

🐳 Option A: Running via Docker Compose (Recommended)

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

   GEMINI_API_KEY=your_gemini_api_key_here

2. Build and boot all containers (PostgreSQL, Python AI Service, Spring Boot Backend, and React Frontend):

   docker compose up --build -d

3. Once starting successfully:
   • React Frontend: Access at http://localhost:3000
   • Spring Boot Backend: REST Gateway at http://localhost:8080
   • FastAPI AI Engine: API docs at http://localhost:8000/docs
   • PostgreSQL Database: Port mapped to 5439 (to avoid clashing with local Postgres service on 5432).

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🔧 Option B: Running Services Individually (Without Docker)

1. Setup PostgreSQL Database

Create a local database named pathpilot:

CREATE DATABASE pathpilot;

2. Start Python AI Service

cd pathpilot-ai-service
python -m venv venv
# Windows:
.\venv\Scripts\activate
# Linux/macOS:
source venv/bin/activate

pip install -r requirements.txt
python app/main.py

3. Start Spring Boot Backend

Configure database credentials in pathpilot-backend/src/main/resources/application.yml and run:

cd pathpilot-backend
./mvnw spring-boot:run

4. Start React Frontend

cd pathpilot-frontend
npm install
npm run dev

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☁️ Production Deployment Guide (Vercel & Cloud)

1. Deploy Free PostgreSQL Database (Neon.tech)

Neon offers a perpetual free tier of Postgres.

1. Sign up on Neon.tech and create a project.
2. Under Connection Details, copy your connection string (e.g. postgresql://neondb_owner:npg_12345@ep-cool-fog-1234.us-east-2.aws.neon.tech/neondb?sslmode=require).
3. Convert this string to JDBC Format for the backend configuration:
   • DATABASE_URL: jdbc:postgresql://ep-cool-fog-1234.us-east-2.aws.neon.tech/neondb?sslmode=require
   • DATABASE_USERNAME: neondb_owner
   • DATABASE_PASSWORD: npg_12345

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2. Deploy Python AI Service (Render / Railway)

1. Deploy pathpilot-ai-service folder as a Web Service on Render or Railway.
2. Add environment variable:
   • GEMINI_API_KEY: Your Gemini API Key.
3. Copy your live AI URL (e.g., https://ai-service-prod.onrender.com).

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3. Deploy Spring Boot Backend (Render / Railway)

1. Deploy pathpilot-backend folder as a Web Service on Render or Railway.
2. Add environment variables:
   • DATABASE_URL: The JDBC string from Step 1.
   • DATABASE_USERNAME: neondb_owner.
   • DATABASE_PASSWORD: Your Neon password.
   • AI_SERVICE_URL: Your live AI URL from Step 2.
   • JWT_SECRET: A secure randomly generated hex key.
   • CORS_ALLOWED_ORIGINS: Your live Vercel URL (e.g. https://vertexpath.vercel.app or *).
3. Copy your live backend URL (e.g., https://backend-prod.onrender.com).

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4. Deploy React Frontend (Vercel)

1. Log in to Vercel and import your project repository.
2. In Build & Development Settings:
   • Root Directory: pathpilot-frontend
   • Build Command: npm run build
   • Output Directory: dist
3. Add the following environment variable:
   • VITE_API_BASE_URL: https://backend-prod.onrender.com (your live backend URL from Step 3).
4. Click Deploy!