This Multi-Agent RAG System is an advanced AI-powered chatbot designed for e-commerce platforms. It offers personalized fashion recommendations, handles complex shopping queries, and delivers real-time business insights through dynamic analytics dashboards.
ai_fashion_recommendation_and_data_analytics_demo.mp4
- Upload an image of clothing, and the chatbot analyzes style, material, and colors.
- Generates tailored outfit recommendations from the store’s inventory.
- Goes beyond simple keyword filtering by contextually refining product searches.
- Uses parallel aggregation pipelines to enhance search results with available colors, materials, and pricing.
- Collects customer preferences and feedback to track purchase likelihood.
- Provides real-time dashboards for data-driven decision-making.
This project leverages LangGraph, a powerful tool for orchestrating AI agents:
- Graph-Based Workflow: Tasks are structured as nodes and edges, allowing for modular and scalable agent interactions.
- Parallel Execution: Utilizes fan-out and fan-in mechanisms to boost performance.
- Stateful Orchestration: Enables context-aware interactions with persistence.
1️⃣ Supervisor Fashion AI Agent – Acts as a personal stylist, generating recommendations based on image analysis. 2️⃣ Searcher Agent – Contextually refines search results to find the best matches from the store inventory. 3️⃣ Data Collator Agent – Monitors customer feedback, analyzes sentiment, and updates analytics dashboards.
- Python (FastAPI, LangChain, LangGraph)
- MongoDB (Customer preference storage & analytics)
- React (Frontend UI)
- Heroku (Deployment)
We’d love your feedback! Try out the chatbot, and feel free to contribute to the project.
🔗 Live Demo: Multi-Agent Chatbot
💬 Have ideas or suggestions? Let's collaborate!
📌 Author: [Your Name]
📧 Contact: [Your Email or GitHub Profile]
🌍 GitHub Repository: [Repository Link Here]