pure-chatbot

byHARSH

Core Functional Requirements 1. AI Shopping Chatbot Build an AI chatbot integrated into the storefront. Features: Conversational product discovery Product recommendations FAQ handling Context-aware follow-up questions Session memory RAG-based product retrieval Vector similarity search Fallback responses on LLM failure Chat history persistence Workflow: React UI → Node.js API → n8n Webhook → LangChain API → Vector DB → LLM → Response 2. Smart Natural Language Product Search Allow users to search using natural language. Examples: "Best gaming laptop under ₹80,000" "Nike running shoes for men" "Wireless headphones with noise cancellation" AI should: Extract intent Convert query into structured filters Category detection Price range extraction Brand extraction Keyword extraction Semantic matching Search ranking Database Query: MongoDB + Vector Similarity Search Display: Product image Name Price Rating Category 3. AI Product Description Generator (Admin) For admin dashboard. When admin adds product attributes: Name Category Features Brand System should generate: SEO title Marketing product description Bullet points Keywords Meta tags Optional multilingual description Store in MongoDB. 4. Personalized Recommendation Engine AI-based recommendation system. Inputs: Purchase history Browsing behavior Product similarity User preferences Trending products Generate: Top 5 recommendations Similar products Frequently bought together Recently viewed alternatives Cross-selling suggestions Fallback: Popular products. 5. Order Automation with n8n When user places order: n8n should automate: Order confirmation email Invoice generation Admin notification Order status update Retry mechanism Payment event handling Shipment trigger 6. AI Price Comparison Engine Let users search products and compare prices across e-commerce platforms. Features: Search any item Compare prices Best seller ranking Cheapest source Trusted websites Delivery estimation Ratings comparison Discount analysis Price history tracking Example: "iPhone 15 Pro" Compare: Amazon, Flipkart, Croma, Reliance Digital, etc. 7. AI Virtual Try-On (Unique Feature) Add a standout GenAI feature. Allow users to: Upload photo Try clothes Try glasses Try accessories Try shoes AI outfit matching Style recommendation Fashion matching Use: Computer Vision + Generative AI. Possible stack: Python + OpenCV + MediaPipe + Diffusion Models. 8. Merchant AI Dashboard Admin analytics dashboard. Show: Sales trend Top products AI demand prediction Revenue insights Customer segmentation Low stock prediction Return probability Product performance Use: Charts + AI insights. Technical Architecture Design clean microservice-based architecture: Frontend Use React.js + Tailwind CSS + optional Next.js. Pages: Home Product Listing Search Product Detail Cart Checkout Orders Profile Chatbot Compare Prices Virtual Try-On Admin Dashboard Backend Node.js + Express. Modules: Auth API Product API Search API Chat API Recommendation API Order API Admin API Analytics API Use: JWT authentication + role-based authorization. AI Layer Python + LangChain. Implement: Prompt engineering RAG Embeddings Session memory LLM orchestration Recommendation generation Search intelligence Query parsing LLM: OpenAI GPT / Gemini. Databases MongoDB: Users Products Orders Reviews Chat history Vector DB: Product embeddings Semantic similarity Recommendation indexing Redis: Cache Session handling Rate limiting Workflow Automation n8n: Automate: Email notifications Order pipeline Product generation Recommendation triggers Admin alerts Logging Scheduled sync Security Requirements Implement: JWT auth Password hashing (bcrypt) Rate limiting Input validation Role-based access API sanitization Secure file upload XSS prevention CSRF prevention HTTPS handling Non-Functional Requirements Target: API response < 500ms AI response < 5 sec High scalability Fault tolerance Retry mechanism Logging Monitoring Microservice-ready Maintainable architecture Bonus Advanced Features Make project unique: Voice-based shopping assistant Multilingual chatbot Sentiment-based recommendations AI review summarizer Fake review detection Dynamic pricing prediction Wishlist intelligence Personalized coupons Fraud detection Visual search (upload image → similar products) Deliverables Generate complete project: Full Software Architecture Diagram Database Schema Folder Structure REST API Design AI Workflow Design n8n Workflow Plan Prompt Engineering Templates Vector DB Strategy Authentication Flow UI/UX Wireframes React Frontend Code Node.js Backend Code LangChain Python Services MongoDB Models Deployment Guide Docker Setup CI/CD pipeline Testing Strategy Future Enhancements Final-year project documentation Development Standards Code should be: Clean Scalable Modular Production-level Well-documented Reusable Secure Industry-standard Follow: MVC / Clean Architecture SOLID principles Microservice patterns REST best practices Error handling Logging Type safety (TypeScript preferred) Final Goal Build a unique AI-first E-Commerce Platform combining: ChatGPT-like shopping assistant + Amazon-like storefront + price comparison + recommendation engine + virtual try-on + merchant AI dashboard. Make it impressive enough for: