Vector Search — Azure DocumentDB (

cosmosSearch

)

cosmosSearch

vector-diskann

vector-hnsw

vector-ivf

COS

L2

IP

Rules

• vector-choose-index-type — Prefer DiskANN for production; use HNSW up to 50k, IVF under 10k.
• vector-create-diskann-index — Create a

  vector-diskann

  index with correct

  dimensions

  ,

  similarity

  ,

  maxDegree

  , and

  lBuild

  .
• vector-knn-query — Query with

  $search

  +

  cosmosSearch

  ; tune

  lSearch

  and

  k

  ; combine with pre-filters.
• vector-product-quantization — Shrink high-dimensional vectors (up to 16,000 dims) while preserving recall.
• vector-half-precision — Halve vector memory with fp16 indexing and minimal recall loss.
• vector-normalize-embeddings — Normalize embeddings when using cosine similarity; store model + dimensions alongside vectors.