Vector Database

A Vector Database stores and queries high-dimensional embeddings for similarity search, powering AI features like semantic search or recommendations.

Examples: Pinecone, Weaviate, Milvus.

Choose Vector DB when:

• Building RAG or recommendation systems.
• Handling unstructured data queries.
• Scaling similarity searches.
• Integrating with LLMs.

Note: Use HNSW for balanced speed/accuracy.

Normalize embeddings.

Upsert for updates.