Using Vector Databases
Vector database implementation for AI/ML applications, semantic search, and RAG systems.
Install
npx skills add ancoleman/ai-design-components --skill using-vector-databases -g -a claude-code -y- Repository
- ancoleman/ai-design-components526
- Category
- Data and ML
- Updated
- 11mo ago
- License
- MIT
- Context cost
- Mediumabout 3,000 tokens
- Runs scripts
- Yes, review before use
What it does
Vector database implementation for AI/ML applications, semantic search, and RAG systems. Use when building chatbots, search engines, recommendation systems, or similarity-based retrieval. Covers Qdrant (primary), Pinecone, Milvus, pgvector, Chroma, embedding generation (OpenAI, Voyage, Cohere), chunking strategies, and hybrid search patterns.
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?
