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🚀 Mahdi's Intelligence Hub (AI-Native Portfolio)

A sophisticated, high-performance portfolio ecosystem designed for the agentic era. This project transcends static showcases by integrating RAG (Retrieval-Augmented Generation), real-time data orchestration, and a robust microservices architecture.


🏗 System Architecture

The system is architected using a decoupled microservices approach, ensuring scalability and isolation:

  • Frontend (Web): Next.js 15 (App Router) with Tailwind CSS and Framer Motion for high-fidelity micro-interactions and Bento-style UI.
  • Backend (API): FastAPI (Python 3.12) utilizing asynchronous programming (AsyncSession) for non-blocking I/O operations.
  • Intelligence Layer: LangGraph for agentic reasoning, powered by Google Gemini 3.1 Flash Lite and Gemini Embedding 2.
  • Data Layer:
    • PostgreSQL: Primary relational store with pgvector for high-dimensional vector similarity search.
    • Redis: High-speed caching, rate-limiting (Throttling), and background task orchestration.
    • Arq: Asynchronous task queue for heavy I/O operations (e.g., GitHub syncing).
  • Infrastructure: Fully containerized with Docker, managed via Docker Compose, and orchestrated behind an Nginx Reverse Proxy.

🔥 Key Features

🧠 RAG-Driven AI Agent

An intelligent chatbot that doesn't just talk—it reasons. By indexing my GitHub repositories and resume via vector embeddings, the agent provides evidence-based answers about my technical expertise and project history.

🔄 Autonomous GitHub Sync

A dedicated background service that synchronizes repository metadata (stars, languages, updates) and README content directly from GitHub's REST/GraphQL APIs into the local PostgreSQL instance.

⚡ Optimized Performance

  • Built-in Throttling: Integrated FastAPI-Limiter to protect AI quotas and prevent system abuse.
  • GPU-Efficient UI: Visual effects (like Blur filters) are strategically offloaded to hover states to maintain a high-FPS scrolling experience.
  • Hybrid Content Strategy: Critical SEO data is hardcoded for LCP (Largest Contentful Paint) optimization, while dynamic stats are fetched via server-side API calls.

🛠 Tech Stack

Layer Technologies
Frontend Next.js 15, React, TypeScript, Tailwind CSS, Lucide Icons
Backend FastAPI, SQLAlchemy (Async), Pydantic v2, HTTPX
AI/ML LangGraph, Gemini 3.1 API, PGVector
DevOps Docker, Nginx, GitHub Actions
Storage PostgreSQL, Redis, Arq Queue

🚀 Getting Started

Prerequisites

  • Docker & Docker Compose
  • GitHub Personal Access Token (PAT)
  • Google Gemini API Key

Installation

  1. Clone the repository:
    git clone [https://github.com/mahdi0jafari/resume.git](https://github.com/mahdi0jafari/resume.git)
    cd resume

About

An AI-native, full-stack portfolio system featuring RAG-enhanced reasoning, automated GitHub synchronization, and a high-performance Dockerized microservices architecture.

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