---
description: '[Qdrant](https://qdrant'
sidebar_label: Qdrant
sidebar_position: 6
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.
>
> \[[An Introduction to Vector Databases](https://qdrant.tech/articles/what-is-a-vector-database/)\]

## 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

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