Back to Module 1.8: Vector DatabasesComing Soon
AI Lesson & Submodule
Why Vector Databases Exist
Understand the database limitations that led to dedicated vector stores.
Why This Matters
Traditional indexes (B-Trees) fail to query multidimensional spaces under low latency.
What You Will Learn
- •Explain search indexing gaps
- •Differentiate SQL vs Vector DBs
- •Manage dimension scales
Concepts Covered
B-Tree limitationsVector indexing tablesHigh-dimensional searches
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
- ?Why do relational databases perform poorly when running nearest-neighbor searches on embedding vectors?