SkillBridge AI

AI-powered career skill gap analysis and personalised learning roadmap generator.

Architecture

./
├── backend/          # FastAPI + LangGraph + pgvector
├── frontend/         # Next.js App Router + shadcn/ui + Tailwind
├── Dockerfile.backend
├── Dockerfile.frontend
└── docker-compose.yml

Stack Overview

Layer	Tech
API	FastAPI + Uvicorn
AI Orchestration	LangGraph (supervisor pattern)
LLM	Groq — llama3-70b-8192 via LangChain
Vector Store	PostgreSQL 16 + pgvector
Frontend	Next.js App Router (RSC)
UI	shadcn/ui + Tailwind CSS
Language	Python 3.11 / TypeScript (strict)
Container	Docker + docker-compose

LangGraph Pipeline

profile → market → gap → roadmap → critic → END

Each node lives in its own file under backend/agents/. The vector DB is only touched through backend/repositories/. All LLM responses use .with_structured_output() (Pydantic v2).

Quick Start (Docker — recommended)

Prerequisites: Docker Desktop installed and running.

# 1. Clone
git clone <repo-url>
cd SkillBridge

# 2. Copy the example env and fill in your credentials
cp .env.example .env

# 3. Spin up all services (db + backend + frontend)
docker compose up --build

Service	URL
Frontend	http://localhost:3000
Backend API	http://localhost:8000
Swagger UI	http://localhost:8000/docs
PostgreSQL	localhost:5433 (DBeaver / psql)

Tip: All three services start in the correct order automatically — db → backend (runs migrations) → frontend.

Running Services Individually (Local Dev)

• Backend: see backend/README.md
• Frontend: see frontend/README.md