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Vector database fundamentals (SOLVED)

Project BasedVector DatabasesEasy10 min read

Vector databases power every RAG system, yet most candidates can't explain ANN algorithms or hybrid search. This fundamental question appears in 80% of AI engineering loops. Master dense vs sparse retrieval and when hybrid search wins.

Vector database fundamentals
Vector Databases · Fundamentals

TL;DR — Quick Answer

Vector DBs store embeddings and perform approximate nearest neighbor (ANN) search. Dense retrieval captures semantics; sparse (BM25) captures keywords — hybrid approaches often win.

The Interview Question

Explain how vector databases work. Compare dense vs sparse retrieval approaches.

Deep Explanation

Vector DBs index high-dimensional embeddings using HNSW, IVF, or PQ algorithms for fast similarity search. Key considerations: embedding model choice, index rebuild strategy, metadata filtering, and recall/latency trade-offs.

Hybrid search combines BM25 keyword matching with dense vectors, often with score fusion or reranking for best results.

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