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.
View on GitHubskills/using-vector-databases/SKILL.md
February 1, 2026
Select agents to install to:
npx add-skill https://github.com/ancoleman/ai-design-components/blob/main/skills/using-vector-databases/SKILL.md -a claude-code --skill using-vector-databasesInstallation paths:
.claude/skills/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