Index Types Overview
Schema recognizes six value types, each with associated index types. Without providing a Schema, collections use these built-in defaults:Simple Index Configs
These index types have no configuration parameters.FtsIndexConfig
Use Case: Full-text search and regular expression search on documents (e.g.,where(K.DOCUMENT.contains("search term"))).
Limitations: Cannot be deleted. Applies to K.DOCUMENT only.
StringInvertedIndexConfig
Use Case: Exact and prefix string matching on metadata fields (e.g.,where(K("category") == "science")).
IntInvertedIndexConfig
Use Case: Range and equality queries on integer metadata (e.g.,where(K("year") >= 2020)).
FloatInvertedIndexConfig
Use Case: Range and equality queries on float metadata (e.g.,where(K("price") < 99.99)).
BoolInvertedIndexConfig
Use Case: Filtering on boolean metadata (e.g.,where(K("published") == True)).
VectorIndexConfig
Use Case: Semantic similarity search on dense embeddings for finding conceptually similar content. Parameters:
Limitations:
- Cannot be deleted
- Applies to
K.EMBEDDINGonly
SparseVectorIndexConfig
Use Case: Keyword-based search for exact term matching, domain-specific terminology, and technical terms. Ideal for hybrid search when combined with dense embeddings. Parameters:
Limitations:
- Must specify a metadata key name (per-key configuration required)
- Sparse vector indices must be declared at collection creation and cannot be added later
- Cannot be deleted once created
Next Steps
- Apply these configurations in Schema Basics
- Set up sparse vector search with sparse vectors and hybrid search