YogaFlow
Year
2026
Tech & Technique
React, Node.js, Pinecone, MongoDB, Transformers.js, LLM, RAG
Description
Built YogaFlow, an AI-powered yoga wellness chatbot leveraging RAG (Retrieval-Augmented Generation) architecture for intelligent, context-aware responses with seamless source attribution.
Key Features:
Technical Highlights:
Key Features:
- RAG architecture for context-aware knowledge retrieval
- Real-time safety detection and smart safety pivots
- Personalized yoga recommendations and guidance
- Secure query logging and conversation history
- Contextual LLM responses with source attribution
- Vector-based semantic search using Pinecone
Technical Highlights:
- Full-stack AI platform with React frontend and Node.js backend
- Pinecone vector database for efficient knowledge retrieval
- MongoDB for persistent storage and user data
- Transformers.js embeddings for semantic understanding
- LLM integration for intelligent response generation
My Role
Full-Stack AI Developer
- Backend: Built RAG pipeline with Pinecone and LLM integration
- Frontend: Created interactive chat interface with React
- Safety: Implemented real-time safety detection and smart pivots
- Embeddings: Integrated Transformers.js for semantic search
- Database: Designed MongoDB schema for query logging
