AskMeAI - Interactive Portfolio

Overview

This AI-powered chatbot is designed to represent me in my interactive portfolio. It dynamically responds to user questions, providing insights about my skills, projects, and experience as if I were personally answering. Built with OpenAI’s API and embeddings, the chatbot ensures contextual and meaningful interactions.

Features

• Conversational AI: The chatbot mimics my tone and knowledge, answering questions about my background, experience, and projects.
• Embeddings for Contextual Understanding: The system uses embeddings to retrieve relevant responses based on user queries.
• Dynamic Response Generation: Instead of predefined answers, the chatbot generates responses based on real-time inputs.
• Seamless Integration: Designed as part of my portfolio, it provides an interactive way for visitors to learn about me.
• Cloud-Hosted Backend: The chatbot runs on a scalable infrastructure using GCP Cloud Run, ensuring reliability and availability.

Tech Stack

• Frontend: React (Next.js) + TypeScript
• Backend: NestJS + Knex + PostgreSQL
• Infrastructure: GCP Cloud Run + Terraform
• AI Model: OpenAI API (GPT-based) with embeddings
• Cache: Redis for optimizing response retrieval
• Authentication: Nginx for reverse proxy handling auth requests
• Containerization: Docker for deployment and service management

How It Works

1. User Interaction: Visitors can ask the chatbot anything about my experience, skills, or projects.
2. Query Processing: The input is processed using embeddings to retrieve relevant context.
3. AI Response Generation: The chatbot formulates a response using OpenAI’s API.
4. Response Delivery: The answer is displayed in real-time on the portfolio website.

Future Enhancements

• Improved Personalization: Enhancing the chatbot’s ability to reflect my communication style.
• Voice Interaction: Adding support for voice input and responses.
• More Data Sources: Integrating additional sources like GitHub activity and blog posts for richer responses.

Deployment

• The backend and AI processing are hosted on GCP Cloud Run.
• PostgreSQL stores structured data related to interactions and context.
• Redis optimizes chatbot performance by caching frequent queries.
• Docker ensures consistent and scalable deployment across environments.
• Nginx handles authentication requests, improving security and load management.
• Terraform manages infrastructure provisioning, ensuring reproducibility and scalability.