remote::weaviate
Description
Weaviate is a vector database provider for Llama Stack. It allows you to store and query vectors directly within a Weaviate database. That means you're not limited to storing vectors in memory or in a separate service.
Features
Weaviate supports:
- Store embeddings and their metadata
- Vector search
- Full-text search
- Hybrid search
- Document storage
- Metadata filtering
- Multi-modal retrieval
Usage
To use Weaviate 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
To install Weaviate see the Weaviate quickstart documentation.
Documentation
See Weaviate's documentation for more details about Weaviate in general.
Configuration
| Field | Type | Required | Default | Description |
|---|---|---|---|---|
weaviate_api_key | str | None | No | The API key for the Weaviate instance | |
weaviate_cluster_url | str | None | No | localhost:8080 | The URL of the Weaviate cluster |
persistence | KVStoreReference | None | No | Config for KV store backend (SQLite only for now) | |
persistence.namespace | str | No | Key prefix for KVStore backends | |
persistence.backend | str | No | Name of backend from storage.backends |
Sample Configuration
weaviate_api_key: null
weaviate_cluster_url: ${env.WEAVIATE_CLUSTER_URL:=localhost:8080}
persistence:
namespace: vector_io::weaviate
backend: kv_default