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llama-stack-mirror/docs/source/providers/vector_io/remote_chromadb.md
Ashwin Bharambe 7f834339ba
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chore(misc): make tests and starter faster (#3042) 
A bunch of miscellaneous cleanup focusing on tests, but ended up
speeding up starter distro substantially.

- Pulled llama stack client init for tests into `pytest_sessionstart` so
it does not clobber output
- Profiling of that told me where we were doing lots of heavy imports
for starter, so lazied them
- starter now starts 20seconds+ faster on my Mac
- A few other smallish refactors for `compat_client`
2025-08-05 14:55:05 -07:00

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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
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

url: ${env.CHROMADB_URL}
kvstore:
  type: sqlite
  db_path: ${env.SQLITE_STORE_DIR:=~/.llama/dummy}/chroma_remote_registry.db