# Documentdb Vector Search

> 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,…

- **Type:** Skill
- **Install:** `agentstack add skill-azure-documentdb-agent-kit-vector-search`
- **Verified:** Yes — security-reviewed for prompt injection and unsafe behavior
- **Seller:** [Azure](https://agentstack.voostack.com/s/azure)
- **Installs:** 0
- **Category:** [Cloud & Infrastructure](https://agentstack.voostack.com/c/cloud-infrastructure)
- **Latest version:** 0.1.0
- **License:** MIT
- **Upstream author:** [Azure](https://github.com/Azure)
- **Source:** https://github.com/Azure/documentdb-agent-kit/tree/main/skills/vector-search

## Install

```sh
agentstack add skill-azure-documentdb-agent-kit-vector-search
```

Requires the [AgentStack CLI](https://agentstack.voostack.com/docs/cli). Works with Claude Code, Cursor, and any MCP-compatible agent.

## About

# 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](https://github.com/Azure)
- **Source:** [Azure/documentdb-agent-kit](https://github.com/Azure/documentdb-agent-kit)
- **License:** MIT

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

## Pricing

- **Free** — Free

## Security capabilities

Automated source analysis of v0.1.0 — what this tool can access:

- **Network access:** no
- **Filesystem access:** no
- **Shell / process execution:** no
- **Environment & secrets:** no
- **Dynamic code execution:** no

*"Yes" means the capability is present in the source — more access means more to trust, not that it is unsafe.*


## Versions

- **0.1.0** — security scan: passed — Imported from the upstream source.

## Links

- Listing page: https://agentstack.voostack.com/l/skill-azure-documentdb-agent-kit-vector-search
- Seller: https://agentstack.voostack.com/s/azure
- Browse the marketplace: https://agentstack.voostack.com/browse

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Listed on AgentStack — the marketplace for AI agent skills and MCP servers. Every listing is security-reviewed. Creators keep 70%.