chore: Enabling Milvus for VectorIO CI

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

docs/source/providers/vector_io/qdrant.md

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----
-orphan: true
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-# Qdrant
-
-[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.