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documentdb-vector-search

Vector search best practices for Azure DocumentDB using `cosmosSearch` — choosing between DiskANN / HNSW / IVF, creating indexes, tuning `lBuild` / `lSearch`…

Skill by AzureOfficial

IndexedMIT★ 6
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About

Vector search best practices for Azure DocumentDB using `cosmosSearch` — choosing between DiskANN / HNSW / IVF, creating indexes, tuning `lBuild` / `lSearch` / `maxDegree`, Product Quantization (up to 16,000 dims), half-precision (fp16) indexing, and normalizing embeddings for cosine similarity. Use when building RAG / semantic-search applications, creating a vector index, tuning recall/latency, or reducing vector-index memory footprint.

Use cases: cloud, ml

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What this skill can do and what it asks for, from its SKILL.md. Checked Oct 25, 2026.

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  • codeload.github.com · found in install command

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  • ~/.claude/skills/documentdb-vector-search

What's inside

Skill name
documentdb-vector-search
Description
Vector search best practices for Azure DocumentDB using `cosmosSearch` — choosing between DiskANN / HNSW / IVF, creating indexes, tuning `lBuild` / `lSearch` / `maxDegree`, Product Quantization (up to 16,000 dims), half-precision (fp16) indexing, and normalizing embeddings for cosine similarity. Use when building RAG / semantic-search applications, creating a vector index, tuning recall/latency, or reducing vector-index memory footprint.
License
MIT
Instructions
22 lines in SKILL.md · view on GitHub

Agent Skills spec issues

  • name: "documentdb-vector-search" does not match folder "vector-search"

Files (7)

  • SKILL.md
  • vector-choose-index-type.md
  • vector-create-diskann-index.md
  • vector-half-precision.md
  • vector-knn-query.md
  • vector-normalize-embeddings.md
  • vector-product-quantization.md

SKILL.md (first lines)

# 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.

From Azure/documentdb-agent-kit, MIT.

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6
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Last commit (this folder)
May 26, 2026 (5 months ago)

From GitHub (fetched Oct 24, 2026).

Details

License
MIT
Repository
github.com/Azure/documentdb-agent-kit
First seen
Oct 24, 2026
Last checked
Oct 24, 2026
Source
GitHubgithub-graphql

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documentdb-vector-search skill. Agent Mart. https://agentmart.dev/skills/azure-documentdb-vector-search (last updated 2026-10-24)