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

inline::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
db_path	<class 'str'>	No	PydanticUndefined
kvstore	utils.kvstore.config.RedisKVStoreConfig | utils.kvstore.config.SqliteKVStoreConfig | utils.kvstore.config.PostgresKVStoreConfig | utils.kvstore.config.MongoDBKVStoreConfig	No	sqlite	Config for KV store backend

Sample Configuration

db_path: ${env.CHROMADB_PATH}
kvstore:
  type: sqlite
  db_path: ${env.SQLITE_STORE_DIR:=~/.llama/dummy}/chroma_inline_registry.db