hyperlakeDiscuss a deployment ↗
Videos · · 2:12

What is Vector Storage

Vector storage represents content as embeddings, enabling semantic search, nearest-neighbor retrieval, metadata filtering, RAG, and recommendations.

In the full article

  1. What is vector storage?
  2. How does vector search work?
  3. How do vector indexes make similarity search fast?
  4. Why combine vector search with metadata filters?
  5. Key takeaways
  6. How Hyperlake helps
  7. Frequently asked questions

Start with a workload. Build the environment around it.

Explore example deployments, or see how the platform assembles, deploys, governs and operates the stack.