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AI Syllabus Module
Module 1.8: Vector Databases
Manage database indexing, approximate nearest neighbor algorithms, and metadata search filters.
Lessons & Submodules
Why Vector Databases Exist
Understand the database limitations that led to dedicated vector stores.
Indexing and ANN Search
Explore Approximate Nearest Neighbor graphs and quantization mechanics.
Metadata Filtering
Filter search queries using categorical metadata and scalar tags.
Hybrid Search
Combine sparse keyword search indexes with dense semantic vector lookups.
Product Search Architecture
Design e-commerce search indexes matching queries to catalog tags.
Vector DB Failure Modes
Diagnose indexing errors, recall degradation, and memory exhaustion.
Vector DB Interview Guide
Prepare for system architecture interviews focused on vector databases.
Key Skills
- •Query vector databases combining vector similarity and metadata filtering
- •Configure HNSW graph link parameters to optimize search latency
Interview Value
- Explain the difference between dense retrieval and sparse retrieval in search engines.