Back to Module 1.8: Vector DatabasesComing Soon
AI Lesson & Submodule
Product Search Architecture
Design e-commerce search indexes matching queries to catalog tags.
Why This Matters
Building a latency-optimized search engine requires pre-filtering categories and caching query paths.
What You Will Learn
- •Structure search queries
- •Configure indexing graphs
- •Optimize queries latency
Concepts Covered
E-commerce indexesProduct tagging pipelinesCaching vector results
Mapped Foundation Project: Semantic Product Search
Search workspace loading catalog inventories into vector databases, supporting dense vector lookups and metadata query filtering.
Architecture Preview
Search gateway loading documents into vector databases, querying them on keys, and merging results streams.
Search Query InputEmbeddings TransformerVector DB Index
Tech Stack Planned
ReactTypeScriptTailwind CSS
GitHub: Coming SoonLive Demo: Coming Soon
Coming SoonTechnical Interview Value
- ?Design an e-commerce search architecture that handles catalog matches and inventory updates in real-time