🎬 AI Movie Character Chatbot

A production-ready chatbot API using RAG (Retrieval Augmented Generation) with Google's Gemini API, deployed on Vercel.

🏆 Progress

• ✅ Level 1: Basic API Chatbot (10 Points) - COMPLETED
• ✅ Level 2: Store & Retrieve Movie Script Data (20 Points) - COMPLETED
• ✅ Level 3: Implement RAG with Vector Search (30 Points) - COMPLETED
• ✅ Level 4: Scale System to Handle High Traffic (40 Points) - COMPLETED
• ✅ Level 5: Optimise for Latency & Deploy (50 Points) - COMPLETED

🛠️ Technologies Used

• Node.js & Express.js
• MongoDB Atlas (Vector Search)
• Google Gemini API for embeddings and text generation
• RAG for context-enhanced responses
• Redis (Caching)
• Prometheus & Grafana (Monitoring)

🎯 Key Features

RAG Implementation

• Vector embeddings for dialogue matching
• Semantic search in MongoDB Atlas
• Context-enhanced AI responses

High Traffic Handling

• Redis caching (120s TTL)
• Rate limiting (5 req/s per user)
• Horizontal scaling support

Performance Monitoring

• Response time tracking
• Request count metrics
• Cache hit ratio
• Error rate monitoring
• Real-time Grafana dashboards

🚀 Getting Started

Prerequisites

• node -v # >= 20.x
• MongoDB Atlas (Vector Search)
• Google Gemini API key
• Redis
• Vercel

Environment Setup

• GEMINI_API_KEY=your_key
• MONGODB_URI=your_mongodb_uri
• REDIS_URL=your_redis_url
• PORT=3000

Installation

1. Clone the repository

git clone https://github.com/SidGit195/AI-Movie-Character-Chatbot.git

🎥 Demo Video

Click to watch the demonstration video of project: https://www.youtube.com/watch?v=QmldLikMrk0

ScreenShots

Level 1 : Basic ChatBot

Level 2 : Store & Retrieve Movie Script Data

Level 3 : Implement RAG with Vector Search

Level 4 : Redis Effect

Level 5 : Monitoring

After Final Deployment on Vercel