Skip to main content
Go to documentation:
⌘U
Weaviate Database

Develop AI applications using Weaviate's APIs and tools

Deploy

Deploy, configure, and maintain Weaviate Database

Weaviate Agents

Build and deploy intelligent agents with Weaviate

Weaviate Cloud

Manage and scale Weaviate in the cloud

Additional resources

Academy
Integrations
Contributor guide

Need help?

Weaviate LogoAsk AI Assistant⌘K
Community Forum

Indexing

Weaviate supports several types of indexes.

  1. Vector indexes - a vector index (e.g. HNSW or flat) is used to serve all vector-search queries.
    • HNSW - an approximate nearest neighbor (ANN) search-based vector index. HNSW indexes scale well with large datasets.
    • Flat - a vector index that is used for brute-force searches. This is useful for small datasets.
    • Dynamic - a vector index that is flat when the dataset is small and switches to HNSW when the dataset is large.
  2. Inverted indexes - inverted indexes enable BM25 queries or speed up filtering.

You can configure indexes in Weaviate per collection.

Tips for indexing

Especially for large datasets, configuring the indexes is important because the more you index, the more storage is needed. A rule of thumb - if you don't query over a specific field or vector space, don't index it.