inline::qdrant

Description

Qdrant is an inline and remote vector database provider for Llama Stack. It allows you to store and query vectors directly in memory. That means you'll get fast and efficient vector retrieval.

By default, Qdrant stores vectors in RAM, delivering incredibly fast access for datasets that fit comfortably in memory. But when your dataset exceeds RAM capacity, Qdrant offers Memmap as an alternative.

[An Introduction to Vector Databases]

Features

• Lightweight and easy to use
• Fully integrated with Llama Stack
• Apache 2.0 license terms
• Store embeddings and their metadata
• Supports search by Keyword and Hybrid search
• Multilingual and Multimodal retrieval
• Medatata filtering
• GPU support

Usage

To use Qdrant in your Llama Stack project, follow these steps:

1. Install the necessary dependencies.
2. Configure your Llama Stack project to use Qdrant.
3. Start storing and querying vectors.

Installation

You can install Qdrant using docker:

docker pull qdrant/qdrant

Documentation

See the Qdrant documentation for more details about Qdrant in general.

Configuration

Field	Type	Required	Default	Description
path	<class 'str'>	No
kvstore	utils.kvstore.config.RedisKVStoreConfig | utils.kvstore.config.SqliteKVStoreConfig | utils.kvstore.config.PostgresKVStoreConfig | utils.kvstore.config.MongoDBKVStoreConfig	No	sqlite

Sample Configuration

path: ${env.QDRANT_PATH:=~/.llama/~/.llama/dummy}/qdrant.db
kvstore:
  type: sqlite
  db_path: ${env.SQLITE_STORE_DIR:=~/.llama/dummy}/qdrant_registry.db