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>

llama_stack/providers/registry/vector_io.py

@@ -110,4 +110,22 @@ def available_providers() -> List[ProviderSpec]:
             ),
             api_dependencies=[Api.inference],
         ),
+        remote_provider_spec(
+            Api.vector_io,
+            AdapterSpec(
+                adapter_type="milvus",
+                pip_packages=["pymilvus"],
+                module="llama_stack.providers.remote.vector_io.milvus",
+                config_class="llama_stack.providers.remote.vector_io.milvus.MilvusVectorIOConfig",
+            ),
+            api_dependencies=[Api.inference],
+        ),
+        InlineProviderSpec(
+            api=Api.vector_io,
+            provider_type="inline::milvus",
+            pip_packages=["pymilvus"],
+            module="llama_stack.providers.inline.vector_io.milvus",
+            config_class="llama_stack.providers.inline.vector_io.milvus.MilvusVectorIOConfig",
+            api_dependencies=[Api.inference],
+        ),
     ]