pure-chatbot
HARSHCore 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:
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System Requirements
pure-chatbot System Requirements Document
Introduction
The "pure-chatbot" project aims to develop an AI-first E-Commerce Platform that integrates a ChatGPT-like shopping assistant with features such as price comparison, recommendation engine, virtual try-on, and a merchant AI dashboard. This project is designed to be a comprehensive final year project, showcasing advanced AI integration and a deep understanding of e-commerce dynamics.
System Overview
The pure-chatbot platform will serve as an innovative e-commerce solution, leveraging AI to enhance user experience and streamline operations. It will feature a conversational AI chatbot for product discovery, smart natural language search, AI-generated product descriptions, personalized recommendations, order automation, price comparison, virtual try-on capabilities, and an analytics dashboard for merchants. The system architecture will be microservice-based, ensuring scalability and maintainability.
Functional Requirements
- As a User, I should be able to interact with an AI Shopping Chatbot for product discovery and recommendations.
- As a User, I should be able to perform Smart Natural Language Product Searches.
- As an Admin, I should be able to use the AI Product Description Generator to create marketing content.
- As a User, I should receive Personalized Recommendations based on my browsing and purchase history.
- As a User, I should experience Order Automation with n8n for seamless order processing.
- As a User, I should be able to use the AI Price Comparison Engine to compare prices across platforms.
- As a User, I should be able to use the AI Virtual Try-On feature for trying products virtually.
- As an Admin, I should have access to a Merchant AI Dashboard for analytics and insights.
User Personas
- User: General customers using the platform for shopping and product exploration.
- Admin: Platform administrators managing product listings, descriptions, and analytics.
Visuals Colors and Theme
- primary: #1A73E8 (a vibrant blue for brand identity)
- primary_light: #E8F0FE (a soft blue for hover states)
- secondary: #FF6F61 (a coral hue for emphasis and links)
- accent: #FFD700 (a bright gold for CTAs and active states)
- highlight: #FFA500 (a warm orange for notifications and hover states)
- bg: #FFFFFF (a clean white for the background)
- surface: rgba(250, 250, 250, 0.8) (a light grey for card backgrounds)
- text: #202124 (a dark grey for primary text)
- text_muted: #5F6368 (a muted grey for secondary text)
- border: rgba(218, 220, 224, 0.2) (a subtle grey for borders)
Signature Design Concept
Imagine a homepage that transforms into an interactive galaxy map where each star represents a feature or section of the platform. Users can click on a star to open a task card, drag to rotate the cluster, and hover to highlight connections. This dynamic interface will be built using @react-three/fiber and @react-three/drei for 3D interactions, creating an immersive experience that captivates users from the first moment.
Interaction Model & Motion Direction
The landing page will employ a "parallax" interaction model, creating a layered depth effect as users scroll. Decorative elements will move at varying speeds, while core content remains in the natural flow. This approach will enhance storytelling and provide a visually rich first impression. Internal pages will adopt a "static" model to prioritize clarity and readability.
Non-Functional Requirements
- API response time should be less than 500ms.
- AI response time should be less than 5 seconds.
- The system should support high scalability and fault tolerance.
- Implement a retry mechanism for failed processes.
- Ensure comprehensive logging and monitoring.
- The architecture should be microservice-ready and maintainable.
Tech Stack
- Frontend: React.js with Tailwind CSS, optional Next.js for server-side rendering.
- Backend: Node.js with Express for API services.
- Database: MongoDB for data storage, Vector DB for product embeddings.
- AI Models: OpenAI GPT / Gemini for language processing.
- Workflow Automation: n8n for automating processes.
- Orchestration: Docker and Kubernetes for container management.
Assumptions and Constraints
- The platform will primarily target users in India, considering local currency (INR) and timezone (IST).
- The system will be designed to handle peak loads typical of e-commerce platforms.
- Security measures will include JWT authentication, input validation, and secure file uploads.
Glossary
- AI: Artificial Intelligence
- LLM: Large Language Model
- RAG: Retrieval-Augmented Generation
- JWT: JSON Web Token
- n8n: Workflow automation tool
- Vector DB: Database optimized for vector similarity search
This document outlines the comprehensive requirements and design considerations for the pure-chatbot project, ensuring a robust and innovative e-commerce platform.
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AI Shopping Chatbot
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Explore→Smart Natural Language Search
Type queries the way you think. Our NLP engine parses intent, filters attributes, and surfaces relevant results faster than traditional keyword search.
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Explore→AI Price Comparison
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GPT-Powered Chat
- Conversational AI shopping assistant that understands natural language queries and context
- Personalized product recommendations driven by browsing history and preferences
- Instant order status, returns, and support handled entirely through chat
Vector Search & Embeddings
- Semantic search powered by vector embeddings for intent-aware product discovery
- Multi-modal matching across text descriptions, images, and user reviews
- Real-time indexing ensures new products are searchable within seconds
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- Live merchant dashboard tracking conversions, revenue, and customer sentiment
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Order automation powered by intelligent workflows means fewer clicks, instant confirmations, and seamless payment from cart to doorstep.
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Unified merchant dashboard with AI-driven insights that help you make smarter decisions, faster.
Loved by shoppers and merchants
Thousands of users and businesses trust pure-chatbot to transform their shopping experience with AI-powered discovery and automation.
The AI chatbot understood exactly what I was looking for — a winter coat under $200 with good reviews. It found three perfect options in seconds. Shopping has never felt this effortless.
"Shopping has never felt this effortless."
The merchant dashboard analytics are incredible. We saw a 34% increase in conversions after integrating the AI recommendations. The product description generator alone saves us 10 hours per week.
"34% increase in conversions."
Virtual try-on changed everything for me. I used to return 40% of online clothing orders. Now I can see exactly how things look before buying — my return rate dropped to almost zero.
"Return rate dropped to almost zero."
As a small merchant, the AI price comparison engine helps me stay competitive without manually tracking competitors. The order automation through n8n has streamlined our entire fulfillment process.
"Streamlined our entire fulfillment."
I have tested dozens of AI shopping tools. pure-chatbot is the first one that actually feels like talking to a knowledgeable friend. The natural language search is scarily accurate.
"Like talking to a knowledgeable friend."
We rolled out pure-chatbot across three storefronts. Customer engagement jumped 52% in the first month. The personalized recommendations drive repeat purchases like nothing else we have tried.
"Customer engagement jumped 52%."
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