⚡ AI-Powered Developer Portfolio & Admin CMS

A modern, high-performance developer portfolio website integrated with a secure administrative Content Management System (CMS) and a production-grade AI chat assistant. Built using Next.js, Tailwind CSS 4, and MongoDB, the platform features smooth 3D elements, dynamic drag-and-drop reordering, and a vector-backed RAG (Retrieval-Augmented Generation) pipeline for context-aware portfolio assistance.

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📑 Table of Contents

• ✨ Features
• 🏗️ System Design Architecture
• 🔄 Application Workflows
• 🛠️ Tech Stack
• 📂 Project Structure
• 🚀 Getting Started
• 🔮 Future Roadmap: AI Agent + MCP
• 🤝 Contributing

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✨ Features

👤 Public User Experience

• 🎨 Modern & Responsive Design: Styled using Tailwind CSS 4 and Shadcn/ui for fluid, responsive layouts optimized for all screens.
• ✨ Immersive Visuals: Fluid animations powered by Framer Motion and responsive 3D elements utilizing Three.js (@react-three/fiber and @react-three/drei).
• 🚀 Dynamic Content Showcase: Projects, skills, intros, and certificates are served dynamically from a MongoDB database.
• 🤖 Intelligent AI Assistant: A floating interactive chat widget that:
  • Hybrid Intent Detection: Detects user query intent (Fast Path: rule-based keywords | Slow Path: Gemini 2.5 Flash semantic classification) to filter out greetings, projects, skills, contact info, bio, or general chats.
  • Dynamic Metadata Filtering: Uses query intent to filter Pinecone search results by category (project, skill, contact, etc.) and dynamically adjusts search depth (k) for summaries vs. specific items.
  • Real-Time Streaming: Streams answers token-by-token with support for mid-stream cancellation via AbortController.
  • Persistent Session Memory: Persists conversation context and history per session directly in MongoDB.
  • Anti-Hallucination Guardrails: Employs strict system prompts ensuring answers are grounded only in verified portfolio context.
• 📧 Seamless Contact Form: Direct email notifications using the Resend API.

🛡️ Admin CMS Capabilities

• 🔐 Secure Authentication: Protected dashboard routes utilizing NextAuth.js.
• 📊 Comprehensive CRUD Dashboards: Complete management interface for updating introduction texts, project entries, skill sets, blogs, and certificates.
• ✋ Drag & Drop Reordering: Intuitive sorting of skills and projects powered by @dnd-kit.
• ☁️ Cloud-Based Media Management: Seamless image uploads and background cleanup using the Cloudinary SDK.
• ⚡ Web-Based Vector Ingestion: Rebuild, purge, and seed the Pinecone vector index dynamically via /api/ingest HTTP GET requests.

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🏗️ System Design Architecture

The project is structured around an intent-routed search model where client queries are classified before performing Pinecone queries:

graph TD
    %% User/Client Interaction
    User[Web Browser / Visitor] <-->|1. HTTP / SSE Stream| NextApp[Next.js App Router]
    Admin[Admin Owner] <-->|Updates Content / Sorts| NextAdmin[Admin CMS Dashboard]

    %% Next.js Core Routes
    subgraph NextServer [Next.js Backend Server]
        NextApp -->|POST Chat Message| ChatAPI[Chat Route: api/chat/route.ts]
        NextAdmin -->|CRUD Content| AdminAPIs[Admin API Routes]
        NextAdmin -->|HTTP GET Seed| IngestAPI[Ingest Route: api/ingest/route.ts]
    end

    %% Intent Routing & RAG
    subgraph AIWorkflow [AI Workflow & Routing]
        ChatAPI -->|1. Parse Message| Intent[Intent Detector: lib/intent.ts]
        Intent -->|Rule-based OR LLM Classifier| IntentType{Query Intent}
        IntentType -->|project/skill/about/etc.| Filter[Apply Metadata Filter & K]
        ChatAPI -->|2. Query Index with Filter| VectorStore[Pinecone Vector Database]
        ChatAPI -->|3. Get History| Memory[MongoDB Chat History]
        ChatAPI -->|4. Generate Response| LLM[Google Gemini 2.5 Flash]
    end

    %% Databases
    subgraph Databases [Data Storage]
        AdminAPIs <-->|Store / Fetch| MongoDB[(MongoDB Database)]
        Memory <--> MongoDB
        VectorStore <-->|Index Vectors| PineconeIndex[Pinecone Vector Index]
    end

    %% External Services
    subgraph Services [External Services]
        AdminAPIs <-->|Upload Media| Cloudinary[Cloudinary SDK]
        NextApp -->|Send Emails| Resend[Resend API]
        LLM <-->|API Calls| GeminiAPI[Google Generative AI]
    end

    %% Ingestion Sync
    IngestAPI -->|Clear & Pull Core Data| MongoDB
    IngestAPI -->|HF Lazy Embeddings| HF[HuggingFace Inference API]
    HF -->|all-MiniLM-L6-v2| IngestAPI
    IngestAPI -->|Embed Summaries & Data| PineconeIndex

    IngestScript[scripts/ingest.ts] -->|CLI Sync Fallback| MongoDB
    IngestScript -->|HF Embeddings| PineconeIndex

Loading

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🔄 Application Workflows

1. Ingestion / Search Indexing Workflow

To make the AI Chat Assistant knowledgeable, project metadata must be embedded and indexed:

1. CMS Update: The admin updates or adds content in the Admin dashboard.
2. Database Sync: The data is persisted in MongoDB.
3. Index Generation & Seeding:
   • Method A (HTTP Web Ingestion): The admin requests the /api/ingest endpoint. The route validates or builds the Pinecone index, clears old data to prevent stale duplicates, compiles MongoDB records into document chunks, generates embeddings using HuggingFace (sentence-transformers/all-MiniLM-L6-v2), and uploads them.
   • Method B (CLI Script): The administrator runs npx tsx scripts/ingest.ts directly from the command line.
4. Structured Summaries: The ingestion process automatically injects pre-compiled overview documents (e.g., "Projects Overview & Summary" and "Skills Overview & Summary") to ensure aggregate count and listing queries return accurate stats.
5. Static Contact Ingestion: Embedded contact channels and social profiles are seeded as dedicated index documents for contact-intent queries.

2. Intent-Guided RAG Chat Response Workflow

When a visitor interacts with the floating AI Chat Widget:

1. Message Dispatch: The client sends the prompt along with a unique sessionId to api/chat/route.ts.
2. Session Memory Retrieval: The API fetches the last 10 chat messages associated with the sessionId from MongoDB to maintain conversation context.
3. Intent Detection: The message passes through a hybrid classifier:
   • Fast Path: Resolves greetings, contact information, identity, bios, skills, and projects instantly using keyword pre-filtering rules.
   • Slow Path: Falls back to calling Gemini 2.5 Flash as a zero-temperature semantic classifier to categorize the message.
4. Dynamic Metadata Filtering:
   • If the intent matches project, skill, about, intro, or contact, the API configures a Pinecone metadata filter targeting that specific document type.
   • The lookup depth k is adjusted dynamically: k=8 for broad listing queries (like "list all projects"), k=1 for single-document profile queries, and k=3 for general similarity matching.
5. Context Retrieval: The API queries Pinecone using the lazy-loaded HuggingFace embeddings client with the calculated filter and k.
6. Prompt Engineering & Grounding: All pieces of data (retrieved Pinecone segments and chat history) are formatted into the LangChain system prompt template.
7. LLM Chain & Streaming: The prompt is processed by Google Gemini 2.5 Flash, and the response is streamed back to the client using Server-Sent Events (SSE). The assistant response is saved back to MongoDB upon completion.

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🛠️ Tech Stack

Layer	Technologies
Frontend	Next.js 15/16 (App Router), React 19, Tailwind CSS 4, Framer Motion, Three.js (@react-three/fiber), Shadcn/ui
Backend	Next.js Route Handlers, NextAuth.js (Security), MongoDB (Database), Mongoose (ODM)
AI & RAG	LangChain.js (Chains & Orchestration), Pinecone (Vector Database), Google Gemini 2.5 Flash (LLM), HuggingFace Inference API (all-MiniLM-L6-v2 embeddings)
Services & Tools	Cloudinary (Image hosting), Resend (Emailing), @dnd-kit (Drag-and-drop sorting), ESLint, TSX

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📂 Project Structure

src/
├── app/
│   ├── (admin)/       # Protected admin routes (Dashboard, Project & Skill CMS)
│   ├── (main)/        # Public facing routes (Home, Projects list, Contact form)
│   ├── api/
│   │   ├── chat/      # AI Chat endpoint (hybrid RAG with Intent Routing & SSE streaming)
│   │   ├── ingest/    # HTTP Vector Store synchronization endpoint (Clear -> Seed Index)
│   │   └── ...        # Next.js API Routes (auth, projects, skills, email, upload)
│   ├── globals.css    # Global CSS definitions & variables
│   └── layout.tsx     # Root App Router Layout
├── components/
│   ├── GlobalChatWidget.tsx # Floating AI chat sidebar/widget
│   ├── MarkdownRender.tsx   # Custom markdown parser for streamed response output
│   └── ...                  # Reusable UI parts (Shadcn/custom)
├── hooks/             # Custom React state/utility hooks
├── types/             # TypeScript interfaces
└── middleware.ts      # Auth interceptor middleware

lib/                   # Backend utilities
├── db.ts              # Mongoose DB connector
├── auth.ts            # NextAuth Configuration
├── prompt.ts          # System prompt template with anti-hallucination instructions
├── memory.ts          # Persistent chat history handlers
├── intent.ts          # Hybrid Intent Classifier (Rule-based + LLM Fallback)
├── embeddings.ts      # Lazy-loaded HuggingFace embed client wrapper
├── vectorStore.ts     # Pinecone DB vector retriever wrappers (with metadata filters)
├── pinecone.ts        # Pinecone Client initialization & index checker
└── delete-image.ts    # Cloudinary asset purge helper

models/                # Mongoose Models
├── user.model.ts      # Admin User accounts
├── intro.model.ts     # Title, short description and key details
├── about.model.ts     # Detailed bio text
├── skill.model.ts     # Tech skills tagged with categories
├── project.model.ts   # Featured projects with links and tags
├── certificate.model.ts # Course completion credentials
├── chatMessage.model.ts # Persisted chat conversation sessions
└── blog.model.ts      # Custom blog posts

scripts/
└── ingest.ts          # Data sync CLI pipeline (MongoDB -> HuggingFace -> Pinecone Store)

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🚀 Getting Started

Follow these steps to spin up the codebase in your local development environment.

Prerequisites

• Node.js (v18.x or higher)
• MongoDB Database Instance (Local or MongoDB Atlas)
• Pinecone Index (Vector Dimension: 384 for all-MiniLM-L6-v2)

1. Clone & Install

git clone https://github.com/your-username/your-repository-name.git
cd your-repository-name
npm install

2. Configure Environment Variables

Create a .env file in the root directory and configure it as follows:

# MongoDB Connection
MONGODB_URI="your_mongodb_connection_string"

# NextAuth Configuration
NEXTAUTH_URL="http://localhost:3000"
NEXTAUTH_SECRET="your_nextauth_secret_hash" # Generate using: openssl rand -base64 32

# Cloudinary Integration (Image Uploads)
CLOUDINARY_CLOUD_NAME="your_cloudinary_cloud_name"
CLOUDINARY_API_KEY="your_cloudinary_api_key"
CLOUDINARY_API_SECRET="your_cloudinary_api_secret"

# Resend API (Contact Form Emailer)
RESEND_API_KEY="your_resend_api_key"

# Base URL Configuration
NEXT_PUBLIC_BASE_URL="http://localhost:3000"

# AI Model Keys (Google Gemini)
GOOGLE_API_KEY="your_google_api_key"

# Pinecone Credentials
PINECONE_API_KEY="your_pinecone_api_key"
PINECONE_INDEX_NAME="portfolio-ai"

# HuggingFace Credentials (For Embeddings Generation)
HUGGINGFACE_API_KEY="your_huggingface_api_key"

3. Run Ingestion / Vector Seeding

To index your database content into the Pinecone vector database, choose one of the following methods:

• Method A (Web Route): Run the server (npm run dev) and visit:

  http://localhost:3000/api/ingest
  

  This will dynamically build/reset the Pinecone index and seed the data, outputting JSON stats upon completion.

• Method B (CLI Command): Run the ingestion script directly:

  npx tsx scripts/ingest.ts

4. Boot Dev Server

npm run dev

Open http://localhost:3000 inside your browser to inspect the result.

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🔮 Future Roadmap: AI Agent + MCP

Note

The current system leverages a static Retrieval-Augmented Generation (RAG) pipeline. While effective for simple question-answering, it lacks active tool execution, multi-step planning, and dynamic contextual awareness.

In the next phase of development, this workflow will be migrated to an autonomous AI Agent + Model Context Protocol (MCP) architecture:

┌────────────────────────────────────────────────────────┐
│                   Future Agent UI                      │
└──────────────────────────┬─────────────────────────────┘
                           │ User Prompts / Tasks
                           ▼
┌────────────────────────────────────────────────────────┐
│                Autonomous AI Agent                     │
│ (Planning Loop, Tool Call Parsing, State Management)  │
└──────────────────────────┬─────────────────────────────┘
                           │ MCP JSON-RPC Protocol
                           ▼
┌────────────────────────────────────────────────────────┐
│                   MCP Router / Hub                     │
└─────┬────────────────────┬────────────────────┬────────┘
      │                    │                    │
      ▼                    ▼                    ▼
┌───────────┐        ┌───────────┐        ┌───────────┐
│ MongoDB   │        │ Filesystem│        │ Git /     │
│ MCP Server│        │ MCP Server│        │ API Server│
└───────────┘        └───────────┘        └───────────┘

🎯 Key Migrations & Goals

1. Dynamic Tool Calling: Instead of injecting static text from the DB and vector search blindly into a single prompt, the LLM will act as an AI Agent. It will decide dynamically which tools to execute based on what the user asks (e.g., calling query_projects_by_category or fetch_recent_blogs).
2. Integrating Model Context Protocol (MCP):
   • MCP is an open standard that enables LLMs to access data sources and tools securely.
   • We will deploy custom MCP Servers connected directly to the codebase's subsystems:
     • Database MCP Server: Exposes secure read/write queries to MongoDB for real-time querying without manual pipeline code in our API routes.
     • Filesystem MCP Server: Allows the agent to inspect project documentation, assets, or markdown files directly.
     • GitHub MCP Server: Fetches live commit histories, repository statistics, and star counts dynamically during chat.
3. Expanded Agentic Actions: The agent will gain the ability to perform complex workflows. Examples:
   • “Schedule a meeting with me next Monday” -> Agent triggers a Calendly/Google Calendar MCP tool.
   • “Add a new project from this description” -> Agent runs validation tools and invokes the DB MCP Server to write the entry directly (with admin approval).
   • “Build a custom resume PDF highlighting my React experience” -> Agent compiles a custom resume using styling templates and exports it.

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🤝 Contributing

Contributions make the open-source community an amazing place to learn, inspire, and create. Any contributions you make are greatly appreciated.

1. Fork the Project.
2. Create your Feature Branch (git checkout -b feature/AmazingFeature).
3. Commit your Changes (git commit -m 'Add some AmazingFeature').
4. Push to the Branch (git push origin feature/AmazingFeature).
5. Open a Pull Request.