> ## 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.

# Schema Overview

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>;

Schema enables fine-grained control over index configuration on collections. Control which indexes are created, optimize for your workload, and enable advanced capabilities like hybrid search.

## What is Schema?

Schema allows you to configure which indexes are created for different data types in your Chroma collections. You can enable or disable indexes globally or per-field, configure vector index parameters, and set up sparse vector indexes for keyword-based search.

## Why Use Schema?

* **Enable Hybrid Search**: Combine dense and sparse embeddings for better retrieval quality
* **Optimize Performance**: Disable unused indexes to speed up writes and reduce index build time
* **Fine-Tune Configuration**: Adjust vector index parameters for your workload

## Quick Start

Here's a simple example creating a collection with a custom schema:

<CodeGroup>
  ```python Python theme={null}
  import chromadb
  from chromadb import Schema, StringInvertedIndexConfig

  # Connect to Chroma Cloud
  client = chromadb.CloudClient(
      tenant="your-tenant",
      database="your-database",
      api_key="your-api-key"
  )

  # Create a schema and disable string indexing globally
  schema = Schema()
  schema.delete_index(config=StringInvertedIndexConfig())

  # Create collection with the schema
  collection = client.create_collection(
      name="my_collection",
      schema=schema
  )

  # Add data - string metadata won't be indexed
  collection.add(
      ids=["id1", "id2"],
      documents=["Document 1", "Document 2"],
      metadatas=[
          {"category": "science", "year": 2024},
          {"category": "tech", "year": 2023}
      ]
  )

  # Querying on disabled index will raise an error
  try:
      collection.query(
          query_texts=["query"],
          where={"category": "science"}  # Error: string index is disabled
      )
  except Exception as e:
      print(f"Error: {e}")
  ```

  ```typescript TypeScript theme={null}
  import { CloudClient, Schema, StringInvertedIndexConfig } from 'chromadb';

  // Connect to Chroma Cloud
  const client = new CloudClient({
    tenant: "your-tenant",
    database: "your-database",
    apiKey: "your-api-key"
  });

  // Create a schema and disable string indexing globally
  const schema = new Schema();
  schema.deleteIndex(new StringInvertedIndexConfig());

  // Create collection with the schema
  const collection = await client.createCollection({
    name: "my_collection",
    schema: schema
  });

  // Add data - string metadata won't be indexed
  await collection.add({
    ids: ["id1", "id2"],
    documents: ["Document 1", "Document 2"],
    metadatas: [
      { category: "science", year: 2024 },
      { category: "tech", year: 2023 }
    ]
  });

  // Querying on disabled index will raise an error
  try {
    await collection.query({
      queryTexts: ["query"],
      where: { category: "science" }  // Error: string index is disabled
    });
  } catch (e) {
    console.log(`Error: ${e}`);
  }
  ```
</CodeGroup>

<Callout>
  **Important:** Schema is only configurable in `create_collection`. We are working on supporting schema update via collection `modify`
</Callout>

## Feature Highlights

* **Default Indexes**: Collections start with sensible defaults - inverted indexes for scalar types, vector index for embeddings, full text search index for documents
* **Global Configuration**: Set index defaults that apply to all metadata keys of a given type during collection creation
* **Per-Key Configuration**: Override defaults for specific metadata fields
* **Sparse Vector Support**: Enable sparse embeddings for hybrid search with BM25-style retrieval
* **Index Deletion**: Disable indexes you don't need to improve write performance
* **Dynamic Schema Evolution**: New metadata keys added during writes automatically inherit from global defaults

## Next Steps

* [Schema Basics](./schema-basics) - Learn the structure and how to use Schema
* [Sparse Vector Search Setup](./sparse-vector-search) - Configure sparse vectors and hybrid search
* [Index Configuration Reference](./index-reference) - Complete index type reference