using-vector-databases

# Vector Databases for AI Applications

## When to Use This Skill

Use this skill when implementing:
- **RAG (Retrieval-Augmented Generation)** systems for AI chatbots
- **Semantic search** capabilities (meaning-based, not just keyword)
- **Recommendation systems** based on similarity
- **Multi-modal AI** (unified search across text, images, audio)
- **Document similarity** and deduplication
- **Question answering** over private knowledge bases

## Quick Decision Framework

### 1. Vector Database Selection

```
START: Choosing a Vector Database

EXISTING INFRASTRUCTURE?
├─ Using PostgreSQL already?
│  └─ pgvector (<10M vectors, tight budget)
│      See: references/pgvector.md
│
└─ No existing vector database?
   │
   ├─ OPERATIONAL PREFERENCE?
   │  │
   │  ├─ Zero-ops managed only
   │  │  └─ Pinecone (fully managed, excellent DX)
   │  │      See: references/pinecone.md
   │  │
   │  └─ Flexible (self-hosted or managed)
   │     │
   │     ├─ SCALE: <100M vectors + complex filtering ⭐
   │     │  └─ Qdrant (RECOMMENDED)
   │     │      • Best metadata filtering
   │     │      • Built-in hybrid search (BM25 + Vector)
   │     │      • Self-host: Docker/K8s
   │     │      • Managed: Qdrant Cloud
   │     │      See: references/qdrant.md
   │     │
   │     ├─ SCALE: >100M vectors + GPU acceleration
   │     │  └─ Milvus / Zilliz Cloud
   │     │      See: references/milvus.md
   │     │
   │     ├─ Embedded / No server
   │     │  └─ LanceDB (serverless, edge deployment)
   │     │
   │     └─ Local prototyping
   │        └─ Chroma (simple API, in-memory)
```

### 2. Embedding Model Selection

```
REQUIREMENTS?

├─ Best quality (cost no object)
│  └─ Voyage AI voyage-3 (1024d)
│      • 9.74% better than OpenAI on MTEB
│      • ~$0.12/1M tokens
│      See: references/embedding-strategies.md
│
├─ Enterprise reliability
│  └─ OpenAI text-embedding-3-large (3072d)
│      • Industry standard
│      • ~$0.13/1M tokens
│      • Maturity shortening: reduce to 256/512/102