feat: add Milvus vectorDB (#1467)

# What does this PR do?
See https://github.com/meta-llama/llama-stack/pull/1171 which is the
original PR. Author: @zc277584121

feat: add [Milvus](https://milvus.io/) vectorDB

note: I use the MilvusClient to implement it instead of
AsyncMilvusClient, because when I tested AsyncMilvusClient, it would
raise issues about evenloop, which I think AsyncMilvusClient SDK is not
robust enough to be compatible with llama_stack framework.

## Test Plan
have passed the unit test and ene2end test
Here is my end2end test logs, including the client code, client log,
server logs from inline and remote settings

[test_end2end_logs.zip](https://github.com/user-attachments/files/18964391/test_end2end_logs.zip)

---------

Signed-off-by: ChengZi <chen.zhang@zilliz.com>
Co-authored-by: Cheney Zhang <chen.zhang@zilliz.com>

docs/source/providers/vector_io/mivus.md

@@ -0,0 +1,31 @@
+---
+orphan: true
+---
+# Milvus
+
+[Milvus](https://milvus.io/) is an inline and remote vector database provider for Llama Stack. It
+allows you to store and query vectors directly within a Milvus database.
+That means you're not limited to storing vectors in memory or in a separate service.
+
+## Features
+
+- Easy to use
+- Fully integrated with Llama Stack
+
+## Usage
+
+To use Milvus in your Llama Stack project, follow these steps:
+
+1. Install the necessary dependencies.
+2. Configure your Llama Stack project to use Milvus.
+3. Start storing and querying vectors.
+
+## Installation
+
+You can install Milvus using pymilvus:
+
+```bash
+pip install pymilvus
+```
+## Documentation
+See the [Milvus documentation](https://milvus.io/docs/install-overview.md) for more details about Milvus in general.