Documentdb Vector Search

Vector Search — Azure DocumentDB (cosmosSearch)

Azure DocumentDB's native vector index type is cosmosSearch. Pick the sub-type by scale:

| Index sub-type | Scale sweet spot | Tier | |---|---|---| | vector-diskann (recommended) | Up to 500k+ vectors | M30+ | | vector-hnsw | Up to ~50k vectors | M30+ | | vector-ivf | Under ~10k vectors | M10+ |

Similarity options: COS (cosine), L2 (Euclidean), IP (inner product).

Rules

• [vector-choose-index-type](vector-choose-index-type.md) — Prefer DiskANN for production; use HNSW up to 50k, IVF under 10k.
• [vector-create-diskann-index](vector-create-diskann-index.md) — Create a vector-diskann index with correct dimensions, similarity, maxDegree, and lBuild.
• [vector-knn-query](vector-knn-query.md) — Query with $search + cosmosSearch; tune lSearch and k; combine with pre-filters.
• [vector-product-quantization](vector-product-quantization.md) — Shrink high-dimensional vectors (up to 16,000 dims) while preserving recall.
• [vector-half-precision](vector-half-precision.md) — Halve vector memory with fp16 indexing and minimal recall loss.
• [vector-normalize-embeddings](vector-normalize-embeddings.md) — Normalize embeddings when using cosine similarity; store model + dimensions alongside vectors.

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: Azure
• Source: Azure/documentdb-agent-kit
• License: MIT

Install and usage instructions live in the source repository linked above.