What is Chroma?

Chroma is an open-source embedding database designed for building Python and JavaScript LLM (Large Language Model) applications with memory. It provides embeddings, vector search, document storage, and full-text search capabilities. Chroma integrates with the Model Context Protocol (MCP) to allow seamless interaction between LLM applications and external data sources or tools.

How to use Chroma?

To use Chroma, follow these steps:

1. Choose a client type (ephemeral, persistent, HTTP, or cloud).
2. Configure Chroma in your application (e.g., via command-line arguments or environment variables).
3. Interact with collections to store and retrieve data.
4. Use the supported tools for collection management, document operations, and queries.

Example configuration with Claude Desktop:

"chroma": {
    "command": "uvx",
    "args": [
        "chroma-mcp",
        "--client-type",
        "persistent",
        "--data-dir",
        "/full/path/to/your/data/directory"
    ]
}

Key features of Chroma

• Flexible client types: Ephemeral (in-memory), persistent (file-based storage), HTTP (self-hosted Chroma), and cloud (Chroma Cloud).
• Collection management: Create, modify, and delete collections with optional HNSW parameters and embedding function selection.
• Document operations: Add, query, retrieve, update, and delete documents with support for semantic search, metadata filtering, and full-text search.
• Embedding functions: Supports default, cohere, openai, jina, voyageai, and roboflow.

Use cases of Chroma

1. LLM applications with memory: Store and retrieve contextual information for AI models.
2. Shared knowledge bases: Organize and search through large datasets using vector and full-text search.
3. Ephemeral testing: Quick setup for development and prototyping.
4. Persistent data storage: Reliable storage for file-based applications.
5. Cloud integration: Scalable and secure access to Chroma Cloud.
6. Self-hosted solutions: Deploy Chroma on your own cloud infrastructure.

FAQ from Chroma

• **How do I set up Chroma with persistent storage?

  Configure the client type as persistent and specify a data directory in your configuration or environment variables.

• **Can I use Chroma with Chroma Cloud?

  Yes, use the cloud client type and provide your tenant ID, database name, and API key.

• **What embedding functions does Chroma support?

  Chroma supports default, cohere, openai, jina, voyageai, and roboflow. Ensure the API key environment variables are set for external APIs.

• **Is ChromaMCP compatible with older versions of Chroma?

  Embedding function persistence was introduced in Chroma v1.0.0. Collections created with version <=0.6.3 do not support this feature.

• **How do I connect to a self-hosted Chroma instance?

  Use the http client type and provide the host, port, and custom authentication credentials in your configuration.

Chroma - the open-source embedding database.

The fastest way to build Python or JavaScript LLM apps with memory!

| | Docs | Homepage

Chroma MCP Server

The Model Context Protocol (MCP) is an open protocol designed for effortless integration between LLM applications and external data sources or tools, offering a standardized framework to seamlessly provide LLMs with the context they require.

This server provides data retrieval capabilities powered by Chroma, enabling AI models to create collections over generated data and user inputs, and retrieve that data using vector search, full text search, metadata filtering, and more.

Features

• Flexible Client Types

  • Ephemeral (in-memory) for testing and development
  • Persistent for file-based storage
  • HTTP client for self-hosted Chroma instances
  • Cloud client for Chroma Cloud integration (automatically connects to api.trychroma.com)

• Collection Management

  • Create, modify, and delete collections
  • List all collections with pagination support
  • Get collection information and statistics
  • Configure HNSW parameters for optimized vector search
  • Select embedding functions when creating collections

• Document Operations

  • Add documents with optional metadata and custom IDs
  • Query documents using semantic search
  • Advanced filtering using metadata and document content
  • Retrieve documents by IDs or filters
  • Full text search capabilities

Supported Tools

• chroma_list_collections - List all collections with pagination support
• chroma_create_collection - Create a new collection with optional HNSW configuration
• chroma_peek_collection - View a sample of documents in a collection
• chroma_get_collection_info - Get detailed information about a collection
• chroma_get_collection_count - Get the number of documents in a collection
• chroma_modify_collection - Update a collection's name or metadata
• chroma_delete_collection - Delete a collection
• chroma_add_documents - Add documents with optional metadata and custom IDs
• chroma_query_documents - Query documents using semantic search with advanced filtering
• chroma_get_documents - Retrieve documents by IDs or filters with pagination
• chroma_update_documents - Update existing documents' content, metadata, or embeddings
• chroma_delete_documents - Delete specific documents from a collection

Embedding Functions

Chroma MCP supports several embedding functions: default, cohere, openai, jina, voyageai, and roboflow.

The embedding functions utilize Chroma's collection configuration, which persists the selected embedding function of a collection for retrieval. Once a collection is created using the collection configuration, on retrieval for future queries and inserts, the same embedding function will be used, without needing to specify the embedding function again. Embedding function persistance was added in v1.0.0 of Chroma, so if you created a collection using version <=0.6.3, this feature is not supported.

When accessing embedding functions that utilize external APIs, please be sure to add the environment variable for the API key with the correct format, found in Embedding Function Environment Variables

Usage with Claude Desktop

1. To add an ephemeral client, add the following to your claude_desktop_config.json file:

"chroma": {
    "command": "uvx",
    "args": [
        "chroma-mcp"
    ]
}

2. To add a persistent client, add the following to your claude_desktop_config.json file:

"chroma": {
    "command": "uvx",
    "args": [
        "chroma-mcp",
        "--client-type",
        "persistent",
        "--data-dir",
        "/full/path/to/your/data/directory"
    ]
}

This will create a persistent client that will use the data directory specified.

3. To connect to Chroma Cloud, add the following to your claude_desktop_config.json file:

"chroma": {
    "command": "uvx",
    "args": [
        "chroma-mcp",
        "--client-type",
        "cloud",
        "--tenant",
        "your-tenant-id",
        "--database",
        "your-database-name",
        "--api-key",
        "your-api-key"
    ]
}

This will create a cloud client that automatically connects to api.trychroma.com using SSL.

Note: Adding API keys in arguments is fine on local devices, but for safety, you can also specify a custom path for your environment configuration file using the --dotenv-path argument within the args list, for example: "args": ["chroma-mcp", "--dotenv-path", "/custom/path/.env"].

4. To connect to a [self-hosted Chroma instance on your own cloud provider](https://docs.trychroma.com/ production/deployment), add the following to your claude_desktop_config.json file:

"chroma": {
    "command": "uvx",
    "args": [
      "chroma-mcp", 
      "--client-type", 
      "http", 
      "--host", 
      "your-host", 
      "--port", 
      "your-port", 
      "--custom-auth-credentials",
      "your-custom-auth-credentials",
      "--ssl",
      "true"
    ]
}

This will create an HTTP client that connects to your self-hosted Chroma instance.

Demos

Find reference usages, such as shared knowledge bases & adding memory to context windows in the Chroma MCP Docs

Using Environment Variables

You can also use environment variables to configure the client. The server will automatically load variables from a .env file located at the path specified by --dotenv-path (defaults to .chroma_env in the working directory) or from system environment variables. Command-line arguments take precedence over environment variables.

# Common variables
export CHROMA_CLIENT_TYPE="http"  # or "cloud", "persistent", "ephemeral"

# For persistent client
export CHROMA_DATA_DIR="/full/path/to/your/data/directory"

# For cloud client (Chroma Cloud)
export CHROMA_TENANT="your-tenant-id"
export CHROMA_DATABASE="your-database-name"
export CHROMA_API_KEY="your-api-key"

# For HTTP client (self-hosted)
export CHROMA_HOST="your-host"
export CHROMA_PORT="your-port"
export CHROMA_CUSTOM_AUTH_CREDENTIALS="your-custom-auth-credentials"
export CHROMA_SSL="true"

# Optional: Specify path to .env file (defaults to .chroma_env)
export CHROMA_DOTENV_PATH="/path/to/your/.env" 

Embedding Function Environment Variables

When using external embedding functions that access an API key, follow the naming convention CHROMA_<>_API_KEY="". So to set a Cohere API key, set the environment variable CHROMA_COHERE_API_KEY="". We recommend adding this to a .env file somewhere and using the CHROMA_DOTENV_PATH environment variable or --dotenv-path flag to set that location for safekeeping.