# Chroma Vector Store
[EN] Source: https://mastra.ai/reference/vectors/chroma

The ChromaVector class provides vector search using [Chroma](https://docs.trychroma.com/docs/overview/getting-started), an open-source embedding database.
It offers efficient vector search with metadata filtering and hybrid search capabilities.

:::info

<b>Chroma Cloud</b>

    Chroma Cloud powers serverless vector and full-text search. It's extremely fast, cost-effective, scalable and painless. Create a DB and try it out in under 30 seconds with $5 of free credits.

    [Get started with Chroma Cloud](https://trychroma.com/signup)

:::

## Constructor Options

<PropertiesTable
  content={[
    {
      name: "host",
      type: "string",
      isOptional: true,
      description:
        "The host address of the Chroma server. Defaults to 'localhost'",
    },
    {
      name: "port",
      type: "number",
      isOptional: true,
      description: "The port number of the Chroma server. Defaults to 8000",
    },
    {
      name: "ssl",
      type: "boolean",
      isOptional: true,
      description:
        "Whether to use SSL/HTTPS for connections. Defaults to false",
    },
    {
      name: "apiKey",
      type: "string",
      isOptional: true,
      description: "A Chroma Cloud API key",
    },
    {
      name: "tenant",
      type: "string",
      isOptional: true,
      description:
        "The tenant name in the Chroma server to connect to. Defaults to 'default_tenant' for single-node Chroma. Auto-resolved for Chroma Cloud users based on the provided API key",
    },
    {
      name: "database",
      type: "string",
      isOptional: true,
      description:
        "The database name to connect to. Defaults to 'default_database' for single-node Chroma. Auto-resolved for Chroma Cloud users based on the provided API key",
    },
    {
      name: "headers",
      type: "Record<string, any>",
      isOptional: true,
      description: "Additional HTTP headers to send with requests",
    },
    {
      name: "fetchOptions",
      type: "RequestInit",
      isOptional: true,
      description: "Additional fetch options for HTTP requests",
    },
  ]}
/>

## Running a Chroma Server

If you are a Chroma Cloud user, simply provide the `ChromaVector` constructor your API key, tenant, and database name.

When you install the `@mastra/chroma` package, you get access to the [Chroma CLI](https://docs.trychroma.com/docs/cli/db), which can set these as environment variables for you: `chroma db connect [DB-NAME] --env-file`.

Otherwise, you have several options for setting up your single-node Chroma server:

- Run one locally using the Chroma CLI: `chroma run`. You can find more configuration options on the [Chroma docs](https://docs.trychroma.com/docs/cli/run).
- Run on [Docker](https://docs.trychroma.com/guides/deploy/docker) using the official Chroma image.
- Deploy your own Chroma server on your provider of choice. Chroma offers example templates for [AWS](https://docs.trychroma.com/guides/deploy/aws), [Azure](https://docs.trychroma.com/guides/deploy/azure), and [GCP](https://docs.trychroma.com/guides/deploy/gcp).

## Methods

### createIndex()

<PropertiesTable
  content={[
    {
      name: "indexName",
      type: "string",
      description: "Name of the index to create",
    },
    {
      name: "dimension",
      type: "number",
      description: "Vector dimension (must match your embedding model)",
    },
    {
      name: "metric",
      type: "'cosine' | 'euclidean' | 'dotproduct'",
      isOptional: true,
      defaultValue: "cosine",
      description: "Distance metric for similarity search",
    },
  ]}
/>

### forkIndex()

Note: Forking is only supported on Chroma Cloud, or if you deploy your own OSS **distributed** Chroma.

`forkIndex` lets you fork an existing Chroma index instantly. Operations on the forked index do not affect the original one. Learn more on the [Chroma docs](https://docs.trychroma.com/cloud/collection-forking).

<PropertiesTable
  content={[
    {
      name: "indexName",
      type: "string",
      description: "Name of the index to fork",
    },
    {
      name: "newIndexName",
      type: "string",
      description: "The name of the forked index",
    },
  ]}
/>

### upsert()

<PropertiesTable
  content={[
    {
      name: "indexName",
      type: "string",
      description: "Name of the index to upsert into",
    },
    {
      name: "vectors",
      type: "number[][]",
      description: "Array of embedding vectors",
    },
    {
      name: "metadata",
      type: "Record<string, any>[]",
      isOptional: true,
      description: "Metadata for each vector",
    },
    {
      name: "ids",
      type: "string[]",
      isOptional: true,
      description: "Optional vector IDs (auto-generated if not provided)",
    },
    {
      name: "documents",
      type: "string[]",
      isOptional: true,
      description:
        "Chroma-specific: Original text documents associated with the vectors",
    },
  ]}
/>

### query()

Query an index using a `queryVector`. Returns an array of semantically similar records in order of distance from the `queryVector`. Each record has the shape:

```typescript
{
  id: string;
  score: number;
  document?: string;
  metadata?: Record<string, string | number | boolean>;
  embedding?: number[]
}
```

You can also provide the shape of your metadata to a `query` call for type inference: `query<T>()`.

<PropertiesTable
  content={[
    {
      name: "indexName",
      type: "string",
      description: "Name of the index to query",
    },
    {
      name: "queryVector",
      type: "number[]",
      description: "Query vector to find similar vectors",
    },
    {
      name: "topK",
      type: "number",
      isOptional: true,
      defaultValue: "10",
      description: "Number of results to return",
    },
    {
      name: "filter",
      type: "Record<string, any>",
      isOptional: true,
      description: "Metadata filters for the query",
    },
    {
      name: "includeVector",
      type: "boolean",
      isOptional: true,
      defaultValue: "false",
      description: "Whether to include vectors in the results",
    },
    {
      name: "documentFilter",
      type: "Record<string, any>",
      isOptional: true,
      description: "Chroma-specific: Filter to apply on the document content",
    },
  ]}
/>

### get()

Get records from your Chroma index by IDs, metadata, and document filters. It returns an array of records of the shape:

```typescript
{
  id: string;
  document?: string;
  metadata?: Record<string, string | number | boolean>;
  embedding?: number[]
}
```

You can also provide the shape of your metadata to a `get` call for type inference: `get<T>()`.

<PropertiesTable
  content={[
    {
      name: "indexName",
      type: "string",
      description: "Name of the index to query",
    },
    {
      name: "ids",
      type: "string[]",
      isOptional: true,
      description:
        "A list of record IDs to return. If not provided, all records are returned.",
    },
    {
      name: "filter",
      type: "Record<string, any>",
      isOptional: true,
      description: "Metadata filters.",
    },
    {
      name: "includeVector",
      type: "boolean",
      isOptional: true,
      defaultValue: "false",
      description: "Whether to include vectors in the results",
    },
    {
      name: "documentFilter",
      type: "Record<string, any>",
      isOptional: true,
      description: "Chroma-specific: Filter to apply on the document content",
    },
    {
      name: "limit",
      type: "number",
      isOptional: true,
      defaultValue: 100,
      description: "The maximum number of records to return",
    },
    {
      name: "offset",
      type: "number",
      isOptional: true,
      defaultValue: 0,
      description:
        "Offset for returning records. Use with `limit` to paginate results.",
    },
  ]}
/>

### listIndexes()

Returns an array of index names as strings.

### describeIndex()

<PropertiesTable
  content={[
    {
      name: "indexName",
      type: "string",
      description: "Name of the index to describe",
    },
  ]}
/>

Returns:

```typescript copy
interface IndexStats {
  dimension: number;
  count: number;
  metric: "cosine" | "euclidean" | "dotproduct";
}
```

### deleteIndex()

<PropertiesTable
  content={[
    {
      name: "indexName",
      type: "string",
      description: "Name of the index to delete",
    },
  ]}
/>

### updateVector()

<PropertiesTable
  content={[
    {
      name: "indexName",
      type: "string",
      description: "Name of the index containing the vector to update",
    },
    {
      name: "id",
      type: "string",
      description: "ID of the vector to update",
    },
    {
      name: "update",
      type: "object",
      description: "Update parameters",
    },
  ]}
/>

The `update` object can contain:

<PropertiesTable
  content={[
    {
      name: "vector",
      type: "number[]",
      isOptional: true,
      description: "New vector to replace the existing one",
    },
    {
      name: "metadata",
      type: "Record<string, any>",
      isOptional: true,
      description: "New metadata to replace the existing metadata",
    },
  ]}
/>

### deleteVector()

<PropertiesTable
  content={[
    {
      name: "indexName",
      type: "string",
      description: "Name of the index containing the vector to delete",
    },
    {
      name: "id",
      type: "string",
      description: "ID of the vector to delete",
    },
  ]}
/>

## Response Types

Query results are returned in this format:

```typescript copy
interface QueryResult {
  id: string;
  score: number;
  metadata: Record<string, any>;
  document?: string; // Chroma-specific: Original document if it was stored
  vector?: number[]; // Only included if includeVector is true
}
```

## Error Handling

The store throws typed errors that can be caught:

```typescript copy
try {
  await store.query({
    indexName: "index_name",
    queryVector: queryVector,
  });
} catch (error) {
  if (error instanceof VectorStoreError) {
    console.log(error.code); // 'connection_failed' | 'invalid_dimension' | etc
    console.log(error.details); // Additional error context
  }
}
```

## Related

- [Metadata Filters](../rag/metadata-filters)


---
title: "Reference: Couchbase Vector Store | Vectors"
description: Documentation for the CouchbaseVector class in Mastra, which provides vector search using Couchbase Vector Search.
---