using-vector-databaseslisted

# 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)