ai-ml pattern
Vector Databases
Store and query high-dimensional embeddings for semantic search. Core infrastructure for AI apps.
Time
O(log n) for HNSW queries
Space
O(n × dimensions)
🧠Mental Model
“A library organized by meaning, not alphabet - similar books are physically near each other.”
Verbal cue: Embed, index, query by similarity.
🎯Recognition Triggers
When you see these patterns in a problem, consider this approach:
vector DBPineconeWeaviateembeddingssemantic searchsimilarity
💡Interview Tips
- 1Know at least 2-3 vector DB options and their trade-offs
- 2Understand embedding dimensions and their impact
- 3Mention approximate vs exact search trade-offs
⚠️Common Mistakes
- ✕Not choosing the right similarity metric for your use case
- ✕Ignoring metadata filtering (namespace, date, etc.)
- ✕Not batching upserts for performance