> ## Documentation Index
> Fetch the complete documentation index at: https://docs.trychroma.com/llms.txt
> Use this file to discover all available pages before exploring further.

# Index Configuration Reference

> Comprehensive reference for all index types and their configuration parameters.

export const Callout = ({title, children}) => <div className="my-6">
    <div className="relative pr-1.5 pb-1.5">
      <div className="absolute top-1.5 left-1.5 right-0 bottom-0 bg-blue-500 dark:bg-blue-600" />
      <div className="relative border border-black dark:border-gray-500 px-5 py-4 bg-white dark:bg-neutral-900">
        {title && <p className="block mb-2"><strong>{title}</strong></p>}
        {children}
      </div>
    </div>
  </div>;

## 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](./schema-basics)
* Set up [sparse vector search](./sparse-vector-search) with sparse vectors and hybrid search