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

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:

1. Install the necessary dependencies.
2. Configure your Llama Stack project to use chroma.
3. 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
kvstore	utils.kvstore.config.RedisKVStoreConfig | utils.kvstore.config.SqliteKVStoreConfig | utils.kvstore.config.PostgresKVStoreConfig | utils.kvstore.config.MongoDBKVStoreConfig, annotation=NoneType, required=False, default='sqlite', discriminator='type'	No		Config for KV store backend (SQLite only for now)

Sample Configuration

weaviate_api_key: null
weaviate_cluster_url: ${env.WEAVIATE_CLUSTER_URL:=localhost:8080}
kvstore:
  type: sqlite
  db_path: ${env.SQLITE_STORE_DIR:=~/.llama/dummy}/weaviate_registry.db