🧙 Search Alchemy 🧙

The Context:

• Dataset: 10,000+ Amazon products across Toys, Home & Kitchen, Clothing, Sports, and Baby Products
• The Problem: Users search with vague terms like "keep kids busy" or "make bedroom nicer" instead of specific product names
• The Solution: Four progressive stages showing how semantic search handles ambiguity better than keyword matching

How to Use:

1. The default query "keep kids busy" is intentionally ambiguous - try it first to see the dramatic improvement across stages
2. Select different categories and specificity levels to explore more examples
3. Click Search and compare Top-1 Score and Top-5 Avg across all stages in the "Compare All Stages" tab
4. Notice how BM25 (keyword matching) struggles with ambiguous queries while vector embeddings + reranking excel

Note: scores are normalized to a 0-1, higher is better.

Try Example Queries: Select a category and specificity level to auto-load an example

Or write your own query: Write your own query to find product in the database