An AI-powered ecommerce semantic search chatbot that retrieves products based on meaning and user intent instead of relying only on traditional keyword matching.
Traditional ecommerce search systems mainly depend on exact keyword matching.
This project demonstrates how Semantic AI Search, Vector Databases, and LLMs can improve ecommerce product discovery using:
- Natural Language Queries
- Semantic Retrieval
- Embedding-Based Similarity Search
- AI-Powered Query Understanding
Traditional ecommerce search systems struggle with:
- Vague user queries
- Conversational search
- Intent understanding
- Descriptive product searches
Example Queries:
- comfortable shoes for long office walking
- stylish back cover for gaming phone
- lightweight shoes for daily jogging
Traditional search may fail because exact keywords may not exist.
This project solves that problem using semantic AI retrieval.
Users search naturally instead of typing exact product names.
The AI chatbot understands user intent and retrieves semantically relevant products.
Traditional ecommerce search struggles with vague or descriptive queries.
The AI system improves retrieval using semantic embeddings and vector search.
Users often do not know exact product names or categories.
The chatbot acts like an AI shopping assistant using conversational AI.
- Semantic Search
- Vector Embeddings
- Redis Vector Databases
- Similarity Search
- LLM Query Understanding
- AI Retrieval Pipelines
- React
- Vite
- Axios
- FastAPI
- Python
- Sentence Transformers
- Groq LLM
- Redis Vector Database
- Pandas
- NumPy
- Render
- Vercel
The project uses an ecommerce product dataset containing:
- Product Names
- Brands
- Product Types
- Descriptions
- Keywords
- Colors
- Model Names
The dataset was cleaned and preprocessed before generating embeddings.
React Frontend ↓ FastAPI Backend ↓ Groq LLM + Embedding Model ↓ Redis Vector Database ↓ Semantic Product Retrieval
User enters natural language ecommerce query.
Frontend sends request to FastAPI backend.
Groq LLM generates optimized ecommerce keywords.
Sentence Transformer converts text into vector embeddings.
Redis Vector Database performs vector similarity search.
Most relevant products are retrieved.
Backend returns structured product metadata.
React frontend renders recommendation cards dynamically.
- AI Semantic Search
- Natural Language Product Discovery
- Redis Vector Similarity Search
- Structured Product Recommendations
- FastAPI REST APIs
- React Chat Interface
- Full-Stack AI Architecture
Through this project I learned:
- Semantic Search
- Vector Embeddings
- Redis Vector Databases
- AI Retrieval Pipelines
- FastAPI Backend Development
- React Frontend Integration
- Full-Stack AI Deployment
- AI Product Design Thinking
- Product Images
- Personalized Recommendations
- Conversational Memory
- Streaming Responses
- Hybrid Search
- Recommendation Ranking
- Multi-Agent Ecommerce Assistant