CatalogIQ 🛠️ is an AI-powered product catalog manager 📊 for e-commerce, grocery stores, and more. Using LLMs 🧠, RAG 📚, and Entity Resolution 🔍, it automates metadata generation 🏷️, detects duplicates
Problem Statement
Manual Metadata Entry : Adding new products to a catalog is time-consuming and error-prone, especially for businesses with thousands of SKUs.
Cold Start Problem : New merchants or unfamiliar products (e.g., private-label items) lack sufficient metadata, making it hard to categorize them accurately.
Duplicate Products : Identifying whether two product descriptions refer to the same item is challenging, leading to cluttered catalogs and poor user experience.
Scalability : Traditional methods of catalog management struggle to scale with growing inventories and diverse product ranges.
Solution
CatalogIQ addresses these challenges by combining:
LLMs (e.g., Llama 3 via Groq) for generating rich metadata and resolving ambiguities.
RAG for retrieving similar products from an internal knowledge base.
Entity Resolution using embeddings and vector databases to identify duplicates.
Automation : Reduces manual effort and speeds up the cataloging process by 90% .
Key Features
Automated Metadata Generation :
Use LLMs to extract attributes like category, brand, size, flavor, and tags from product descriptions.
Example: Input: "Coca-Cola Classic Soft Drink, 500ml" → Output: {category: "Beverage", brand: "Coca-Cola", size: "500ml", flavor: "Classic"}.
Cold Start Problem Solver :
For unfamiliar products, use traditional NLP techniques (e.g., keyword extraction) for initial tagging.
If unsure, query the LLM for deeper insights.
Similarity Search with RAG :
Use embeddings (e.g., Sentence Transformers) to find similar products in a vector database.
Retrieve relevant information from an internal knowledge graph to determine if the product is unique.
Entity Resolution :
Compare embedding vectors of product names to detect duplicates.
Use LLM reasoning to confirm if two descriptions refer to the same item (e.g., "Coke 500ml" vs. "Coca-Cola Classic Soft Drink, 500ml").
Scalable Architecture :
Use a vector database (e.g., Pinecone, FAISS) for efficient similarity searches.
Build a knowledge graph (e.g., Neo4j) for advanced relationships between products.
User-Friendly Interface :
Provide a dashboard for users to upload product descriptions, view generated metadata, and resolve duplicates.
Technical Stack
Backend
Python
Flask/Django for API and web interface
Groq API for LLM integration (Llama 3)
Sentence Transformers for embeddings
Database
PostgreSQL/MySQL for structured data
Vector Database: Pinecone, FAISS, or Milvus for embeddings
Knowledge Graph: Neo4j (optional)
Frontend
React.js or Vue.js for a modern UI
Tailwind CSS for styling
Cloud Deployment
AWS/GCP/Azure for hosting
Docker for containerization
Workflow
Input : A user uploads a product description (e.g., "Kirkland Signature Organic Quinoa, 2kg").
Metadata Generation :
The system uses LLMs to generate attributes like {category: "Grocery", brand: "Kirkland", size: "2kg", type: "Quinoa"}.
Cold Start Handling :
If the product is unfamiliar, the system performs additional searches using RAG and LLM reasoning.
Similarity Search :
Embeddings are generated for the product name and compared against existing products in the vector database.
If a similar product is found, the system suggests it to the user.
Entity Resolution :
The system determines if the product is a duplicate using cosine similarity and LLM reasoning.
Output :
The product is added to the catalog with enriched metadata, or flagged as a duplicate.