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110 lines
3.7 KiB
Text
110 lines
3.7 KiB
Text
---
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description: |
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[Qdrant](https://qdrant.tech/documentation/) is an inline and remote vector database provider for Llama Stack. It
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allows you to store and query vectors directly in memory.
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That means you'll get fast and efficient vector retrieval.
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> By default, Qdrant stores vectors in RAM, delivering incredibly fast access for datasets that fit comfortably in
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> memory. But when your dataset exceeds RAM capacity, Qdrant offers Memmap as an alternative.
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>
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> \[[An Introduction to Vector Databases](https://qdrant.tech/articles/what-is-a-vector-database/)\]
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## Features
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- Lightweight and easy to use
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- Fully integrated with Llama Stack
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- Apache 2.0 license terms
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- Store embeddings and their metadata
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- Supports search by
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[Keyword](https://qdrant.tech/articles/qdrant-introduces-full-text-filters-and-indexes/)
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and [Hybrid](https://qdrant.tech/articles/hybrid-search/#building-a-hybrid-search-system-in-qdrant) search
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- [Multilingual and Multimodal retrieval](https://qdrant.tech/documentation/multimodal-search/)
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- [Medatata filtering](https://qdrant.tech/articles/vector-search-filtering/)
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- [GPU support](https://qdrant.tech/documentation/guides/running-with-gpu/)
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## Usage
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To use Qdrant in your Llama Stack project, follow these steps:
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1. Install the necessary dependencies.
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2. Configure your Llama Stack project to use Qdrant.
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3. Start storing and querying vectors.
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## Installation
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You can install Qdrant using docker:
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```bash
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docker pull qdrant/qdrant
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```
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## Documentation
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See the [Qdrant documentation](https://qdrant.tech/documentation/) for more details about Qdrant in general.
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sidebar_label: Qdrant
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title: inline::qdrant
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---
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# inline::qdrant
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## Description
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[Qdrant](https://qdrant.tech/documentation/) is an inline and remote vector database provider for Llama Stack. It
|
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allows you to store and query vectors directly in memory.
|
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That means you'll get fast and efficient vector retrieval.
|
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|
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> By default, Qdrant stores vectors in RAM, delivering incredibly fast access for datasets that fit comfortably in
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> memory. But when your dataset exceeds RAM capacity, Qdrant offers Memmap as an alternative.
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>
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> \[[An Introduction to Vector Databases](https://qdrant.tech/articles/what-is-a-vector-database/)\]
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## Features
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- Lightweight and easy to use
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- Fully integrated with Llama Stack
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- Apache 2.0 license terms
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- Store embeddings and their metadata
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- Supports search by
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[Keyword](https://qdrant.tech/articles/qdrant-introduces-full-text-filters-and-indexes/)
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and [Hybrid](https://qdrant.tech/articles/hybrid-search/#building-a-hybrid-search-system-in-qdrant) search
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- [Multilingual and Multimodal retrieval](https://qdrant.tech/documentation/multimodal-search/)
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- [Medatata filtering](https://qdrant.tech/articles/vector-search-filtering/)
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- [GPU support](https://qdrant.tech/documentation/guides/running-with-gpu/)
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## Usage
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To use Qdrant in your Llama Stack project, follow these steps:
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1. Install the necessary dependencies.
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2. Configure your Llama Stack project to use Qdrant.
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3. Start storing and querying vectors.
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## Installation
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You can install Qdrant using docker:
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```bash
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docker pull qdrant/qdrant
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```
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## Documentation
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See the [Qdrant documentation](https://qdrant.tech/documentation/) for more details about Qdrant in general.
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## Configuration
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| Field | Type | Required | Default | Description |
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|-------|------|----------|---------|-------------|
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| `path` | `<class 'str'>` | No | | |
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| `persistence` | `<class 'llama_stack.core.storage.datatypes.KVStoreReference'>` | No | | |
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## Sample Configuration
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```yaml
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path: ${env.QDRANT_PATH:=~/.llama/~/.llama/dummy}/qdrant.db
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persistence:
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namespace: vector_io::qdrant
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backend: kv_default
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```
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