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/concepts/index.md

@@ -34,7 +34,7 @@ We are working on adding a few more APIs to complete the application lifecycle.
 
 The goal of Llama Stack is to build an ecosystem where users can easily swap out different implementations for the same API. Examples for these include:
 - LLM inference providers (e.g., Fireworks, Together, AWS Bedrock, Groq, Cerebras, SambaNova, vLLM, etc.),
-- Vector databases (e.g., ChromaDB, Weaviate, Qdrant, FAISS, PGVector, etc.),
+- Vector databases (e.g., ChromaDB, Weaviate, Qdrant, Milvus, FAISS, PGVector, etc.),
 - Safety providers (e.g., Meta's Llama Guard, AWS Bedrock Guardrails, etc.)
 
 Providers come in two flavors:

docs/source/index.md

@@ -68,6 +68,7 @@ A number of "adapters" are available for some popular Inference and Vector Store
 |  FAISS | Single Node |
 |  SQLite-Vec| Single Node |
 |  Chroma | Hosted and Single Node |
+|  Milvus | Hosted and Single Node |
 |  Postgres (PGVector) | Hosted and Single Node |
 |  Weaviate | Hosted |
 

docs/source/providers/index.md

@@ -2,7 +2,7 @@
 
 The goal of Llama Stack is to build an ecosystem where users can easily swap out different implementations for the same API. Examples for these include:
 - LLM inference providers (e.g., Fireworks, Together, AWS Bedrock, Groq, Cerebras, SambaNova, vLLM, etc.),
-- Vector databases (e.g., ChromaDB, Weaviate, Qdrant, FAISS, PGVector, etc.),
+- Vector databases (e.g., ChromaDB, Weaviate, Qdrant, Milvus, FAISS, PGVector, etc.),
 - Safety providers (e.g., Meta's Llama Guard, AWS Bedrock Guardrails, etc.)
 
 Providers come in two flavors:
@@ -55,5 +55,6 @@ vector_io/sqlite-vec
 vector_io/chromadb
 vector_io/pgvector
 vector_io/qdrant
+vector_io/milvus
 vector_io/weaviate
 ```

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.