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, or reducing vector-index memory footprint.
معلومات المصدر
- المستودع
- Azure/documentdb-agent-kit
- آخر نشاط في المصدر
- ٢٦ مايو ٢٠٢٦ في ٢١:٣١
- لغة SKILL.md المكتشفة
- الإنجليزية
- النجوم
- ٦
- التفرعات
- ١٢
خيارات التثبيت
يُحدَّد Prompt الذي يراجع المصدر أولًا بشكل افتراضي. يمكنك التبديل إلى أمر مباشر أو تنزيل نسخة محلية.
مراجعة ملفات المصدر
اقرأ SKILL.md وأي ملفات مرافقة يعرضها SkillsMP قبل أن تقرر التثبيت.
مستكشف الملفات
7 ملفاتعرض SKILL.md
SKILL.md
تعليمات المصدر · معاينة للقراءة فقط- 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
# 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.
عرض على GitHub