forked from phoenix-oss/llama-stack-mirror
19ae4b35d9
# What does this PR do? Adding Provider sections to docs (some of these will be empty and need updating). This PR is still a draft while I seek feedback from other contributors. I opened it to make the structure visible in the linked GitHub Issue. # Closes https://github.com/meta-llama/llama-stack/issues/1189 - Providers Overview Page  - SQLite-Vec specific page  ## Test Plan N/A [//]: # (## Documentation) --------- Signed-off-by: Francisco Javier Arceo <farceo@redhat.com>
31 lines
740 B
Markdown
31 lines
740 B
Markdown
---
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orphan: true
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---
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# Qdrant
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[Qdrant](https://qdrant.tech/documentation/) is a 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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## Features
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- Easy to use
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- Fully integrated with Llama Stack
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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 Faiss.
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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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