Index Configuration Reference

import { Callout } from '/snippets/callout.mdx';

Index Types Overview

Schema recognizes six value types, each with associated index types. Without providing a Schema, collections use these built-in defaults:

Config Class	Value Type	Default Behavior	Use Case
StringInvertedIndexConfig	string	Enabled for all metadata	Filter on string values
FtsIndexConfig	string	Enabled for K.DOCUMENT only	Full-text search on documents
VectorIndexConfig	float_list	Enabled for K.EMBEDDING only	Similarity search on embeddings
SparseVectorIndexConfig	sparse_vector	Disabled (requires config)	Keyword-based search
IntInvertedIndexConfig	int_value	Enabled for all metadata	Filter on integer values
FloatInvertedIndexConfig	float_value	Enabled for all metadata	Filter on float values
BoolInvertedIndexConfig	boolean	Enabled for all metadata	Filter on boolean values

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:

Parameter	Type	Required	Description
space	string	No	Distance function: l2 (geometric), ip (inner product), or cosine (angle-based, most common for text). Default: l2
embedding_function	EmbeddingFunction	No	Function to auto-generate embeddings from K.DOCUMENT. If not provided, supply embeddings manually
source_key	string	No	Reserved for future use. Currently always uses K.DOCUMENT
hnsw	HnswConfig	No	Advanced: HNSW algorithm tuning for single-node deployments
spann	SpannConfig	No	Advanced: SPANN algorithm tuning (clustering, probing) for Chroma Cloud

Limitations:

• Cannot be deleted
• Applies to K.EMBEDDING only

<Callout> **Advanced tuning:** HNSW and SPANN parameters control index build and search behavior. They are pre-optimized for most use cases. Only adjust if you have specific performance requirements and understand the tradeoffs between recall, speed, and resource usage. Incorrect tuning can degrade performance. </Callout>

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:

Parameter	Type	Required	Description
source_key	string	No	Field to generate sparse embeddings from. Typically K.DOCUMENT, but can be any text field
embedding_function	SparseEmbeddingFunction	No	Sparse embedding function (e.g., ChromaCloudSpladeEmbeddingFunction, HuggingFaceSparseEmbeddingFunction, Bm25EmbeddingFunction)
bm25	boolean	No	Set to true when using Bm25EmbeddingFunction to enable inverse document frequency (IDF) scaling for queries. Not applicable for SPLADE

Limitations:

• Must specify a metadata key name (per-key configuration required)
• Only one sparse vector index allowed per collection
• Cannot be deleted once created

<Callout> For complete sparse vector search setup and querying examples, see [Sparse Vector Search Setup](./sparse-vector-search). </Callout>

Next Steps

• Apply these configurations in Schema Basics
• Set up sparse vector search with sparse vectors and hybrid search