llama-stack-mirror/docs/source/providers/vector_io/remote_chromadb.md

remote::chromadb

Description

Chroma is an inline and remote vector database provider for Llama Stack. It allows you to store and query vectors directly within a Chroma database. That means you're not limited to storing vectors in memory or in a separate service.

Features

Chroma supports:

• Store embeddings and their metadata
• Vector search
• Full-text search
• Document storage
• Metadata filtering
• Multi-modal retrieval

Usage

To use Chrome in your Llama Stack project, follow these steps:

1. Install the necessary dependencies.
2. Configure your Llama Stack project to use chroma.
3. Start storing and querying vectors.

Installation

You can install chroma using pip:

pip install chromadb

Documentation

See Chroma's documentation for more details about Chroma in general.

Configuration

Field	Type	Required	Default	Description
url	str | None	No	PydanticUndefined
embedding_model	str | None	No		Optional default embedding model for this provider. If not specified, will use system default.
embedding_dimension	int | None	No		Optional embedding dimension override. Only needed for models with variable dimensions (e.g., Matryoshka embeddings). If not specified, will auto-lookup from model registry.

Sample Configuration

url: ${env.CHROMADB_URL}