# Vector Databases

> Vector Databases

- Skill: `ffsshhttiikk/vector-databases` (Agent Skill)
- Install (CLI): `npx skillmds@latest add ffsshhttiikk/vector-databases`
- Raw SKILL.md: https://api.skillmd.com/api/skills/ffsshhttiikk/vector-databases/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Coding & Dev Tools
- Author: ffsshhttiikk (https://skillmd.com/u/ffsshhttiikk)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/ffsshhttiikk/vector-databases

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# Vector Databases

Vector databases store and query high-dimensional vector embeddings. They're essential for similarity search, semantic search, and AI-powered applications requiring vector operations.

## Key Concepts

- Embedding storage
- Approximate Nearest Neighbor (ANN)
- Index types (HNSW, IVF, PQ)
- Similarity metrics (cosine, dot product, euclidean)
- Hybrid search capabilities

## Common Use Cases

- Semantic search
- RAG applications
- Recommendation systems
- Duplicate detection
- AI-powered chatbots

## Best Practices

- Choose index based on query patterns
- Monitor recall vs latency trade-offs
- Implement proper filtering
- Consider hybrid search
- Plan for scale early

## Resources

- Pinecone, Weaviate, Qdrant, Milvus
- Related Skills: embeddings, rag, langchain