Index Configuration Reference
Comprehensive reference for all index types and their configuration parameters.
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 |
- Cannot be deleted
- Applies to
K.EMBEDDING only
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.
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 |
- Must specify a metadata key name (per-key configuration required)
- Only one sparse vector index allowed per collection
- Cannot be deleted once created
Next Steps
