---
description: |
  [Qdrant](https://qdrant.tech/documentation/) 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.
  >
  > \[[An Introduction to Vector Databases](https://qdrant.tech/articles/what-is-a-vector-database/)\]



  ## 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](https://qdrant.tech/articles/qdrant-introduces-full-text-filters-and-indexes/)
    and [Hybrid](https://qdrant.tech/articles/hybrid-search/#building-a-hybrid-search-system-in-qdrant) search
  - [Multilingual and Multimodal retrieval](https://qdrant.tech/documentation/multimodal-search/)
  - [Medatata filtering](https://qdrant.tech/articles/vector-search-filtering/)
  - [GPU support](https://qdrant.tech/documentation/guides/running-with-gpu/)

  ## 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:

  ```bash
  docker pull qdrant/qdrant
  ```
  ## Documentation
  See the [Qdrant documentation](https://qdrant.tech/documentation/) for more details about Qdrant in general.
sidebar_label: Qdrant
title: inline::qdrant
---

# inline::qdrant

## Description


[Qdrant](https://qdrant.tech/documentation/) 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.
>
> \[[An Introduction to Vector Databases](https://qdrant.tech/articles/what-is-a-vector-database/)\]



## 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](https://qdrant.tech/articles/qdrant-introduces-full-text-filters-and-indexes/)
  and [Hybrid](https://qdrant.tech/articles/hybrid-search/#building-a-hybrid-search-system-in-qdrant) search
- [Multilingual and Multimodal retrieval](https://qdrant.tech/documentation/multimodal-search/)
- [Medatata filtering](https://qdrant.tech/articles/vector-search-filtering/)
- [GPU support](https://qdrant.tech/documentation/guides/running-with-gpu/)

## 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:

```bash
docker pull qdrant/qdrant
```
## Documentation
See the [Qdrant documentation](https://qdrant.tech/documentation/) for more details about Qdrant in general.


## Configuration

| Field | Type | Required | Default | Description |
|-------|------|----------|---------|-------------|
| `path` | `str` | No |  |  |
| `persistence` | `KVStoreReference` | No |  |  |

## Sample Configuration

```yaml
path: ${env.QDRANT_PATH:=~/.llama/~/.llama/dummy}/qdrant.db
persistence:
  namespace: vector_io::qdrant
  backend: kv_default
```
