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:
- Install the necessary dependencies.
- Configure your Llama Stack project to use chroma.
- 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 | ||
persistence | <class 'llama_stack.core.storage.datatypes.KVStoreReference'> | No | Config for KV store backend |
Sample Configuration​
db_path: ${env.CHROMADB_PATH}
persistence:
namespace: vector_io::chroma
backend: kv_default