Why it matters
Vector search scales similarity lookup across millions of chunks. It powers semantic RAG at latencies keyword inverted indexes struggle to match for paraphrase queries.
How it works
Store embeddings in vector databases, query with approximate nearest neighbor indexes, apply pre-filters on metadata before vector scan when possible.
Example
Nintendo vector index holds Help Center chunk embeddings sharded by collection so public queries never scan internal embedding namespaces.
Common mistakes
- 1Wrong distance metric for your embedding model
- 2No pre-filter causing expensive full scans
- 3Ignoring index rebuild needs after embedding model upgrades
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