mirror of https://github.com/meta-llama/llama-stack.git synced 2025-06-28 02:53:30 +00:00

Francisco Arceo 19ae4b35d9

docs: Adding Provider sections to docs (#1195 )

# 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
![Screenshot 2025-02-21 at 12 15
09 PM](https://github.com/user-attachments/assets/e83e5a17-0d96-4de0-8251-68161799a054)

- SQLite-Vec specific page
![Screenshot 2025-02-21 at 12 15
34 PM](https://github.com/user-attachments/assets/14773900-fc8f-49e9-832a-b060b7ca010a)

## Test Plan
N/A

[//]: # (## Documentation)

---------

Signed-off-by: Francisco Javier Arceo <farceo@redhat.com>

2025-02-22 11:59:34 -08:00

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Qdrant

Qdrant is a 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.

Features

Easy to use
Fully integrated with Llama Stack

Usage

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

Install the necessary dependencies.
Configure your Llama Stack project to use Faiss.
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.

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