Back to Module 1.8: Vector DatabasesComing Soon
AI Lesson & Submodule
Indexing and ANN Search
Explore Approximate Nearest Neighbor graphs and quantization mechanics.
Why This Matters
ANN algorithms trade slight search accuracy for massive gains in query latency.
What You Will Learn
- •Explain HNSW structures
- •Describe Product Quantization
- •Trade recall for latency
Concepts Covered
HNSW proximity graphsProduct Quantization compressionAccuracy vs speed tradeoff
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
- ?How does Hierarchical Navigable Small World (HNSW) build multi-layer indexes to speed up ANN searches?