using-vector-databases
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.
npx skills add majiayu000/claude-skill-registry --skill using-vector-databases-ancoleman-ai-design-components-2 --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# 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
- When to Use This Skill
- Quick Decision Framework
- 1. Vector Database Selection
- 2. Embedding Model Selection
- Core Concepts
- Document Chunking Strategy
- Hybrid Search (Vector + Keyword)
- Getting Started
- Python + Qdrant Example
- TypeScript + Qdrant Example
- RAG Pipeline Architecture
- Complete Pipeline Components
- Essential Metadata for Production RAG
- Evaluation with RAGAS
What does the using-vector-databases skill do?
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.
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill using-vector-databases-ancoleman-ai-design-components-2 --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From majiayu000/claude-skill-registry, a repository with 534 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.