diff --git a/python/poetry.lock b/python/poetry.lock
index 1464b77e4df4..39aa88133bb4 100644
--- a/python/poetry.lock
+++ b/python/poetry.lock
@@ -624,14 +624,14 @@ uvicorn = {version = ">=0.18.3", extras = ["standard"]}
 
 [[package]]
 name = "click"
-version = "8.1.5"
+version = "8.1.6"
 description = "Composable command line interface toolkit"
 category = "dev"
 optional = false
 python-versions = ">=3.7"
 files = [
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-    {file = "click-8.1.5.tar.gz", hash = "sha256:4be4b1af8d665c6d942909916d31a213a106800c47d0eeba73d34da3cbc11367"},
+    {file = "click-8.1.6-py3-none-any.whl", hash = "sha256:fa244bb30b3b5ee2cae3da8f55c9e5e0c0e86093306301fb418eb9dc40fbded5"},
+    {file = "click-8.1.6.tar.gz", hash = "sha256:48ee849951919527a045bfe3bf7baa8a959c423134e1a5b98c05c20ba75a1cbd"},
 ]
 
 [package.dependencies]
@@ -639,77 +639,77 @@ colorama = {version = "*", markers = "platform_system == \"Windows\""}
 
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 name = "clickhouse-connect"
-version = "0.6.6"
+version = "0.6.7"
 description = "ClickHouse Database Core Driver for Python, Pandas, and Superset"
 category = "dev"
 optional = false
 python-versions = "~=3.7"
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+
+[package.extras]
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+tests = ["dj-database-url", "dj-email-url", "django-cache-url", "pytest"]
+
 [[package]]
 name = "exceptiongroup"
 version = "1.1.2"
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+category = "dev"
+optional = false
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+
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+
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@@ -2903,14 +3021,14 @@ plugins = ["importlib-metadata"]
 
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+optional = false
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 [[package]]
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 [package.dependencies]
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 ftfy = ["ftfy"]
 integrations = ["optuna", "ray[tune]", "sigopt"]
@@ -3929,23 +4067,23 @@ quality = ["GitPython (<3.1.19)", "black (>=23.1,<24.0)", "datasets (!=2.5.0)",
 ray = ["ray[tune]"]
 retrieval = ["datasets (!=2.5.0)", "faiss-cpu"]
 sagemaker = ["sagemaker (>=2.31.0)"]
-sentencepiece = ["protobuf (<=3.20.3)", "sentencepiece (>=0.1.91,!=0.1.92)"]
-serving = ["fastapi", "pydantic", "starlette", "uvicorn"]
+sentencepiece = ["protobuf", "sentencepiece (>=0.1.91,!=0.1.92)"]
+serving = ["fastapi", "pydantic (<2)", "starlette", "uvicorn"]
 sigopt = ["sigopt"]
 sklearn = ["scikit-learn"]
 speech = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)", "torchaudio"]
-testing = ["GitPython (<3.1.19)", "beautifulsoup4", "black (>=23.1,<24.0)", "cookiecutter (==1.7.3)", "datasets (!=2.5.0)", "dill (<0.3.5)", "evaluate (>=0.2.0)", "faiss-cpu", "hf-doc-builder (>=0.3.0)", "nltk", "parameterized", "protobuf (<=3.20.3)", "psutil", "pytest (>=7.2.0)", "pytest-timeout", "pytest-xdist", "rjieba", "rouge-score (!=0.0.7,!=0.0.8,!=0.1,!=0.1.1)", "sacrebleu (>=1.4.12,<2.0.0)", "sacremoses", "timeout-decorator"]
-tf = ["keras-nlp (>=0.3.1)", "onnxconverter-common", "tensorflow (>=2.4,<2.13)", "tensorflow-text (<2.13)", "tf2onnx"]
-tf-cpu = ["keras-nlp (>=0.3.1)", "onnxconverter-common", "tensorflow-cpu (>=2.4,<2.13)", "tensorflow-text (<2.13)", "tf2onnx"]
+testing = ["GitPython (<3.1.19)", "beautifulsoup4", "black (>=23.1,<24.0)", "cookiecutter (==1.7.3)", "datasets (!=2.5.0)", "dill (<0.3.5)", "evaluate (>=0.2.0)", "faiss-cpu", "hf-doc-builder (>=0.3.0)", "nltk", "parameterized", "protobuf", "psutil", "pytest (>=7.2.0)", "pytest-timeout", "pytest-xdist", "rjieba", "rouge-score (!=0.0.7,!=0.0.8,!=0.1,!=0.1.1)", "sacrebleu (>=1.4.12,<2.0.0)", "sacremoses", "timeout-decorator"]
+tf = ["keras-nlp (>=0.3.1)", "onnxconverter-common", "tensorflow (>=2.6,<2.14)", "tensorflow-text (<2.14)", "tf2onnx"]
+tf-cpu = ["keras-nlp (>=0.3.1)", "onnxconverter-common", "tensorflow-cpu (>=2.6,<2.14)", "tensorflow-text (<2.14)", "tf2onnx"]
 tf-speech = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)"]
 timm = ["timm"]
 tokenizers = ["tokenizers (>=0.11.1,!=0.11.3,<0.14)"]
-torch = ["accelerate (>=0.20.2)", "torch (>=1.9,!=1.12.0)"]
+torch = ["accelerate (>=0.20.3)", "torch (>=1.9,!=1.12.0)"]
 torch-speech = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)", "torchaudio"]
-torch-vision = ["Pillow", "torchvision"]
-torchhub = ["filelock", "huggingface-hub (>=0.14.1,<1.0)", "importlib-metadata", "numpy (>=1.17)", "packaging (>=20.0)", "protobuf (<=3.20.3)", "regex (!=2019.12.17)", "requests", "sentencepiece (>=0.1.91,!=0.1.92)", "tokenizers (>=0.11.1,!=0.11.3,<0.14)", "torch (>=1.9,!=1.12.0)", "tqdm (>=4.27)"]
+torch-vision = ["Pillow (<10.0.0)", "torchvision"]
+torchhub = ["filelock", "huggingface-hub (>=0.14.1,<1.0)", "importlib-metadata", "numpy (>=1.17)", "packaging (>=20.0)", "protobuf", "regex (!=2019.12.17)", "requests", "sentencepiece (>=0.1.91,!=0.1.92)", "tokenizers (>=0.11.1,!=0.11.3,<0.14)", "torch (>=1.9,!=1.12.0)", "tqdm (>=4.27)"]
 video = ["av (==9.2.0)", "decord (==0.6.0)"]
-vision = ["Pillow"]
+vision = ["Pillow (<10.0.0)"]
 
 [[package]]
 name = "triton"
@@ -4009,6 +4147,77 @@ files = [
     {file = "tzdata-2023.3.tar.gz", hash = "sha256:11ef1e08e54acb0d4f95bdb1be05da659673de4acbd21bf9c69e94cc5e907a3a"},
 ]
 
+[[package]]
+name = "ujson"
+version = "5.8.0"
+description = "Ultra fast JSON encoder and decoder for Python"
+category = "dev"
+optional = false
+python-versions = ">=3.8"
+files = [
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+    {file = "ujson-5.8.0-pp39-pypy39_pp73-win_amd64.whl", hash = "sha256:a8c91b6f4bf23f274af9002b128d133b735141e867109487d17e344d38b87d94"},
+    {file = "ujson-5.8.0.tar.gz", hash = "sha256:78e318def4ade898a461b3d92a79f9441e7e0e4d2ad5419abed4336d702c7425"},
+]
+
 [[package]]
 name = "urllib3"
 version = "2.0.3"
@@ -4029,14 +4238,14 @@ zstd = ["zstandard (>=0.18.0)"]
 
 [[package]]
 name = "uvicorn"
-version = "0.23.0"
+version = "0.23.1"
 description = "The lightning-fast ASGI server."
 category = "dev"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "uvicorn-0.23.0-py3-none-any.whl", hash = "sha256:479599b2c0bb1b9b394c6d43901a1eb0c1ec72c7d237b5bafea23c5b2d4cdf10"},
-    {file = "uvicorn-0.23.0.tar.gz", hash = "sha256:d38ab90c0e2c6fe3a054cddeb962cfd5d0e0e6608eaaff4a01d5c36a67f3168c"},
+    {file = "uvicorn-0.23.1-py3-none-any.whl", hash = "sha256:1d55d46b83ee4ce82b4e82f621f2050adb3eb7b5481c13f9af1744951cae2f1f"},
+    {file = "uvicorn-0.23.1.tar.gz", hash = "sha256:da9b0c8443b2d7ee9db00a345f1eee6db7317432c9d4400f5049cc8d358383be"},
 ]
 
 [package.dependencies]
@@ -4046,6 +4255,7 @@ h11 = ">=0.8"
 httptools = {version = ">=0.5.0", optional = true, markers = "extra == \"standard\""}
 python-dotenv = {version = ">=0.13", optional = true, markers = "extra == \"standard\""}
 pyyaml = {version = ">=5.1", optional = true, markers = "extra == \"standard\""}
+typing-extensions = {version = ">=4.0", markers = "python_version < \"3.11\""}
 uvloop = {version = ">=0.14.0,<0.15.0 || >0.15.0,<0.15.1 || >0.15.1", optional = true, markers = "sys_platform != \"win32\" and sys_platform != \"cygwin\" and platform_python_implementation != \"PyPy\" and extra == \"standard\""}
 watchfiles = {version = ">=0.13", optional = true, markers = "extra == \"standard\""}
 websockets = {version = ">=10.4", optional = true, markers = "extra == \"standard\""}
@@ -4480,4 +4690,4 @@ cffi = ["cffi (>=1.11)"]
 [metadata]
 lock-version = "2.0"
 python-versions = "^3.8"
-content-hash = "4ba6ba6436918fe498163a71df58e3f97f1c842999c92d1bdafed70ad2cc6ee3"
+content-hash = "fa722607005560ff2edc10e6c9ab8ddf26ee541ea38792d300fb148fb02ab6b8"
diff --git a/python/pyproject.toml b/python/pyproject.toml
index 67c2b00c6494..129086ec6bbb 100644
--- a/python/pyproject.toml
+++ b/python/pyproject.toml
@@ -30,6 +30,10 @@ torch = "2.0.0"
 [tool.poetry.group.chromadb.dependencies]
 chromadb = "^0.3.29"
 
+[tool.poetry.group.milvus.dependencies]
+pymilvus = "^2.2.11"
+milvus = "^2.2.11"
+
 [tool.poetry.group.weaviate.dependencies]
 weaviate-client = "^3.18.0"
 
diff --git a/python/semantic_kernel/connectors/memory/milvus/__init__.py b/python/semantic_kernel/connectors/memory/milvus/__init__.py
new file mode 100644
index 000000000000..afaeaa606786
--- /dev/null
+++ b/python/semantic_kernel/connectors/memory/milvus/__init__.py
@@ -0,0 +1,5 @@
+from semantic_kernel.connectors.memory.milvus.milvus_memory_store import (
+    MilvusMemoryStore,
+)
+
+__all__ = ["MilvusMemoryStore"]
diff --git a/python/semantic_kernel/connectors/memory/milvus/milvus_memory_store.py b/python/semantic_kernel/connectors/memory/milvus/milvus_memory_store.py
new file mode 100644
index 000000000000..709fd14cedac
--- /dev/null
+++ b/python/semantic_kernel/connectors/memory/milvus/milvus_memory_store.py
@@ -0,0 +1,470 @@
+# Copyright (c) Microsoft. All rights reserved.
+
+from logging import Logger
+from typing import List, Optional, Tuple
+
+from numpy import array, expand_dims, ndarray
+from pymilvus.milvus_client import milvus_client
+
+from semantic_kernel.memory.memory_record import MemoryRecord
+from semantic_kernel.memory.memory_store_base import MemoryStoreBase
+from semantic_kernel.utils.null_logger import NullLogger
+
+
+def memoryrecord_to_milvus_dict(mem: MemoryRecord) -> dict:
+    """Convert a memoryrecord into a dict.
+    Args:
+        mem (MemoryRecord): MemoryRecord to convert.
+
+    Returns:
+        dict: Dict result.
+    """
+    ret_dict = {}
+    # Grab all the class vars
+    for key, val in vars(mem).items():
+        if val is not None:
+            # Remove underscore
+            ret_dict[key[1:]] = val
+    return ret_dict
+
+
+def milvus_dict_to_memoryrecord(milvus_dict: dict) -> MemoryRecord:
+    """Convert Milvus search result dict into MemoryRecord.
+
+    Args:
+        milvus_dict (dict): Search hit
+
+    Returns:
+        MemoryRecord
+    """
+    # Embedding needs conversion to numpy array
+    embedding = milvus_dict.get("embedding", None)
+    if embedding is not None:
+        embedding = array(embedding)
+    return MemoryRecord(
+        is_reference=milvus_dict.get("is_reference", None),
+        external_source_name=milvus_dict.get("external_source_name", None),
+        id=milvus_dict.get("id", None),
+        description=milvus_dict.get("description", None),
+        text=milvus_dict.get("text", None),
+        additional_metadata=milvus_dict.get("additional_metadata", None),
+        embedding=embedding,
+        key=milvus_dict.get("key", None),
+        timestamp=milvus_dict.get("timestamp", None),
+    )
+
+
+# Default field values
+ID_FIELD = "id"
+ID_TYPE = "str"
+EMBEDDING_FIELD = "embedding"
+
+
+class MilvusMemoryStore(MemoryStoreBase):
+    def __init__(
+        self,
+        uri: str = "http://localhost:19530",
+        token: Optional[str] = None,
+        logger: Optional[Logger] = None,
+    ) -> None:
+        """MilvusMemoryStore allows for searching for records using Milvus/Zilliz Cloud.
+
+        For more details on how to get the service started, take a look here:
+            Milvus: https://milvus.io/docs/get_started.md
+            Zilliz Cloud: https://docs.zilliz.com/docs/quick-start
+
+
+        Args:
+            uri (str, optional): The uri of the cluster. Defaults to
+                "http://localhost:19530".
+            token (Optional[str], optional): The token to connect to the cluster if
+                authentication is required. Defaults to None.
+            logger (Optional[Logger], optional): Logger to use. Defaults to None.
+        """
+        self._uri = uri
+        self._token = (token,)
+        self._logger = logger or NullLogger()
+        self._client = milvus_client.MilvusClient(
+            uri=uri,
+            token=token,
+        )
+        self._metric_cache = {}
+
+    async def create_collection_async(
+        self,
+        collection_name: str,
+        dimension_num: Optional[int] = 1536,
+        distance_type: Optional[str] = "IP",
+        overwrite: bool = False,
+        consistency: str = "Session",
+    ) -> None:
+        """Create a Milvus collection.
+
+        Args:
+            collection_name (str): The name of the collection.
+            dimension_num (Optional[int], optional): The size of the embeddings being
+                stored. Defaults to 1536.
+            distance_type (Optional[str], optional): Which distance function, at the
+                moment only "IP" and "L2" are supported. Defaults to "IP".
+            overwrite (bool, optional): Whether to overwrite any existing collection
+                with the same name. Defaults to False.
+            consistency (str, optional): Which consistency level to use:
+                Strong, Session, Bounded, Eventually. Defaults to "Session".
+        """
+        if collection_name in self._client.list_collections():
+            if overwrite:
+                self._client.drop_collection(collection_name=collection_name)
+                self._client.create_collection(
+                    collection_name=collection_name,
+                    dimension=dimension_num,
+                    primary_field_name=ID_FIELD,
+                    id_type=ID_TYPE,
+                    auto_id=False,
+                    vector_field_name=EMBEDDING_FIELD,
+                    metric_type=distance_type,
+                    max_length=65_535,
+                    consistency_level=consistency,
+                )
+        else:
+            self._client.create_collection(
+                collection_name=collection_name,
+                dimension=dimension_num,
+                primary_field_name=ID_FIELD,
+                id_type=ID_TYPE,
+                auto_id=False,
+                vector_field_name=EMBEDDING_FIELD,
+                metric_type=distance_type,
+                max_length=65_535,
+                consistency_level=consistency,
+            )
+
+    async def get_collections_async(
+        self,
+    ) -> List[str]:
+        """Return a list of present collections.
+
+        Returns:
+            List[str]: List of collection names.
+        """
+        return self._client.list_collections()
+
+    async def delete_collection_async(
+        self, collection_name: str = "", all: bool = False
+    ) -> None:
+        """Delete the specified collection.
+
+        If all is True, all collections in the cluster will be removed.
+
+        Args:
+            collection_name (str, optional): The name of the collection to delete. Defaults to "".
+            all (bool, optional): Whether to delete all collections. Defaults to False.
+        """
+        cols = self._client.list_collections()
+        if all:
+            for x in cols:
+                self._client.drop_collection(x)
+        elif collection_name in cols:
+            self._client.drop_collection(collection_name)
+
+    async def does_collection_exist_async(self, collection_name: str) -> bool:
+        """Return if the collection exists in the cluster.
+
+        Args:
+            collection_name (str): The name of the collection.
+
+        Returns:
+            bool: True if it exists, False otherwise.
+        """
+        return True if collection_name in self._client.list_collections() else False
+
+    async def upsert_async(self, collection_name: str, record: MemoryRecord) -> str:
+        """Upsert a single MemoryRecord into the collection.
+
+        Args:
+            collection_name (str): The name of the collection.
+            record (MemoryRecord): The record to store.
+
+        Returns:
+            str: The ID of the inserted record.
+        """
+        # Use the batch insert with a total batch
+        res = await self.upsert_batch_async(
+            collection_name=collection_name,
+            records=[record],
+            batch_size=0,
+        )
+        return res[0]
+
+    async def upsert_batch_async(
+        self, collection_name: str, records: List[MemoryRecord], batch_size=100
+    ) -> List[str]:
+        """_summary_
+
+        Args:
+            collection_name (str): The collection name.
+            records (List[MemoryRecord]): A list of memory records.
+            batch_size (int, optional): Batch size of the insert, 0 is a batch
+                size of total size. Defaults to 100.
+
+        Raises:
+            Exception: Collection doesnt exist.
+            e: Failed to upsert a record.
+
+        Returns:
+            List[str]: A list of inserted ID's.
+        """
+        # Check if the collection exists.
+        if collection_name not in self._client.list_collections():
+            self._logger.debug(
+                f"Collection {collection_name} does not exist, cannot insert."
+            )
+            raise Exception(
+                f"Collection {collection_name} does not exist, cannot insert."
+            )
+        # Convert the records to dicts
+        insert_list = [memoryrecord_to_milvus_dict(record) for record in records]
+        # The ids to remove
+        delete_ids = [insert[ID_FIELD] for insert in insert_list]
+        try:
+            # First delete then insert to have upsert
+            self._client.delete(collection_name=collection_name, pks=delete_ids)
+            return self._client.insert(
+                collection_name=collection_name, data=insert_list, batch_size=batch_size
+            )
+        except Exception as e:
+            self._logger.debug(f"Upsert failed due to: {e}")
+            raise e
+
+    async def get_async(
+        self, collection_name: str, key: str, with_embedding: bool
+    ) -> MemoryRecord:
+        """Get the MemoryRecord corresponding to the key.
+
+        Args:
+            collection_name (str): The collection to get from.
+            key (str): The ID to grab.
+            with_embedding (bool): Whether to include the embedding in the results.
+
+        Returns:
+            MemoryRecord: The MemoryRecord for the key.
+        """
+        res = await self.get_batch_async(
+            collection_name=collection_name, keys=[key], with_embeddings=with_embedding
+        )
+        return res[0]
+
+    async def get_batch_async(
+        self, collection_name: str, keys: List[str], with_embeddings: bool
+    ) -> List[MemoryRecord]:
+        """Get the MemoryRecords corresponding to the keys
+
+        Args:
+            collection_name (str): _description_
+            keys (List[str]): _description_
+            with_embeddings (bool): _description_
+
+        Raises:
+            Exception: _description_
+            e: _description_
+
+        Returns:
+            List[MemoryRecord]: _description_
+        """
+        # Check if the collection exists
+        if collection_name not in self._client.list_collections():
+            self._logger.debug(
+                f"Collection {collection_name} does not exist, cannot get."
+            )
+            raise Exception("Collection {collection_name} does not exist, cannot get.")
+        try:
+            gets = self._client.get(
+                collection_name=collection_name,
+                ids=keys,
+                output_fields=["*"] if not with_embeddings else ["*", EMBEDDING_FIELD],
+            )
+            return [milvus_dict_to_memoryrecord(get) for get in gets]
+        except Exception as e:
+            self._logger.debug(f"Get failed due to: {e}")
+            raise e
+
+    async def remove_async(self, collection_name: str, key: str) -> None:
+        """Remove the specified record based on key.
+
+        Args:
+            collection_name (str): Collection to remove from.
+            key (str): The key to remove.
+        """
+        await self.remove_batch_async(collection_name=collection_name, keys=[key])
+
+    async def remove_batch_async(self, collection_name: str, keys: List[str]) -> None:
+        """Remove multiple records based on keys.
+
+        Args:
+            collection_name (str): Collection to remove from
+            keys (List[str]): The list of keys.
+
+        Raises:
+            Exception: Collection doesnt exist.
+            e: Failure to remove key.
+        """
+        if collection_name not in self._client.list_collections():
+            self._logger.debug(
+                f"Collection {collection_name} does not exist, cannot remove."
+            )
+            raise Exception(
+                f"Collection {collection_name} does not exist, cannot remove."
+            )
+        try:
+            self._client.delete(
+                collection_name=collection_name,
+                pks=keys,
+            )
+        except Exception as e:
+            self._logger.debug(f"Remove failed due to: {e}")
+            raise e
+
+    def _search(self, collection_name, data, limit, distance_metric):
+        """Helper function to search with correct distance metric due to bug"""
+        # TODO Remove after https://github.com/milvus-io/milvus/issues/25504
+        # Simple way to select opposite
+        distance_pairs = {
+            "l2": "IP",
+            "ip": "L2",
+        }
+        try:
+            # Try with passed in metric
+            results = self._client.search(
+                collection_name=collection_name,
+                data=data,
+                limit=limit,
+                search_params={"metric_type": distance_metric},
+                output_fields=["*"],
+            )[0]
+            return results, distance_metric
+        except Exception as e:
+            self._logger.debug(f"Search failed with IP, testing L2: {e}")
+            try:
+                distance_metric = distance_pairs[distance_metric.lower()]
+                results = self._client.search(
+                    collection_name=collection_name,
+                    data=data,
+                    limit=limit,
+                    search_params={"metric_type": distance_metric},
+                    output_fields=["*"],
+                )[0]
+                return results, distance_metric
+            except Exception as e:
+                self._logger.debug(f"Search failed with L2: {e}")
+                raise e
+
+    async def get_nearest_matches_async(
+        self,
+        collection_name: str,
+        embedding: ndarray,
+        limit: int,
+        min_relevance_score: float = None,
+        with_embeddings: bool = False,
+    ) -> List[Tuple[MemoryRecord, float]]:
+        """Find the nearest `limit` matches for an embedding.
+
+        Args:
+            collection_name (str): The collection to search.
+            embedding (ndarray): The embedding to search.
+            limit (int): The total results to display.
+            min_relevance_score (float, optional): Minimum distance to include. Defaults to None.
+            with_embeddings (bool, optional): Whether to include embeddings in result. Defaults to False.
+
+        Raises:
+            Exception: Missing collection
+            e: Failure to search
+
+        Returns:
+            List[Tuple[MemoryRecord, float]]: MemoryRecord and distance tuple.
+        """
+        # Check if collection exists
+        if collection_name not in self._client.list_collections():
+            self._logger.debug(
+                f"Collection {collection_name} does not exist, cannot search."
+            )
+            raise Exception(
+                f"Collection {collection_name} does not exist, cannot search."
+            )
+        # Search requests takes a list of requests.
+        if len(embedding.shape) == 1:
+            embedding = expand_dims(embedding, axis=0)
+
+        # Search based on the cached metric
+        results, search_type = self._search(
+            collection_name=collection_name,
+            data=embedding,
+            limit=limit,
+            distance_metric=self._metric_cache.get(collection_name, None) or "IP",
+        )
+
+        # Update cached metric
+        self._metric_cache[collection_name] = search_type
+
+        cleaned_results = []
+
+        if with_embeddings:
+            ids = []
+
+        # Clean up results, filter and get ids for fetch
+        for x in results:
+            if min_relevance_score is not None and x["distance"] < min_relevance_score:
+                continue
+            cleaned_results.append(x)
+            if with_embeddings:
+                ids.append(x[ID_FIELD])
+
+        if with_embeddings:
+            try:
+                vectors = self._client.get(
+                    collection_name=collection_name,
+                    ids=ids,
+                    output_fields=[EMBEDDING_FIELD],
+                )
+            except Exception as e:
+                self._logger.debug(f"Get embeddings in search failed due to: {e}.")
+                raise e
+
+            vectors = {res[ID_FIELD]: res[EMBEDDING_FIELD] for res in vectors}
+            for res in results:
+                res["entity"][EMBEDDING_FIELD] = vectors[res[ID_FIELD]]
+
+        results = [
+            (milvus_dict_to_memoryrecord(result["entity"]), result["distance"])
+            for result in results
+        ]
+
+        return results
+
+    async def get_nearest_match_async(
+        self,
+        collection_name: str,
+        embedding: ndarray,
+        min_relevance_score: float = None,
+        with_embedding: bool = False,
+    ) -> Tuple[MemoryRecord, float]:
+        """Find the nearest match for an embedding.
+
+        Args:
+            collection_name (str): The collection to search.
+            embedding (ndarray): The embedding to search for.
+            min_relevance_score (float, optional): T. Defaults to 0.0.
+            with_embedding (bool, optional): Whether to include embedding in result. Defaults to False.
+
+        Returns:
+            Tuple[MemoryRecord, float]: A tuple of record and distance.
+        """
+        m = await self.get_nearest_matches_async(
+            collection_name,
+            embedding,
+            1,
+            min_relevance_score,
+            with_embedding,
+        )
+        if len(m) > 0:
+            return m[0]
+        else:
+            return None
diff --git a/python/tests/integration/connectors/memory/test_milvus.py b/python/tests/integration/connectors/memory/test_milvus.py
new file mode 100644
index 000000000000..ebdb1b7d5bff
--- /dev/null
+++ b/python/tests/integration/connectors/memory/test_milvus.py
@@ -0,0 +1,233 @@
+# Copyright (c) Microsoft. All rights reserved.
+
+import numpy as np
+import pytest
+
+from semantic_kernel.connectors.memory.milvus import MilvusMemoryStore
+from semantic_kernel.memory.memory_record import MemoryRecord
+
+try:
+    from milvus import default_server  # noqa: F401
+
+    milvus_installed = True
+except ImportError:
+    milvus_installed = False
+
+pytestmark = pytest.mark.skipif(
+    not milvus_installed, reason="local milvus is not installed"
+)
+
+
+@pytest.fixture(scope="module")
+def setup_milvus():
+    default_server.cleanup()
+    default_server.start()
+    host = "http://127.0.0.1:" + str(default_server.listen_port)
+    port = None
+    yield host, port
+    default_server.stop()
+    default_server.cleanup()
+
+
+@pytest.fixture
+def memory_record1():
+    return MemoryRecord(
+        id="test_id1",
+        text="sample text1",
+        is_reference=False,
+        embedding=np.array([0.5, 0.5]),
+        description="description",
+        external_source_name="external source",
+        additional_metadata="additional metadata",
+        timestamp="timestamp",
+    )
+
+
+@pytest.fixture
+def memory_record2():
+    return MemoryRecord(
+        id="test_id2",
+        text="sample text2",
+        is_reference=False,
+        embedding=np.array([0.25, 0.75]),
+        description="description",
+        external_source_name="external source",
+        additional_metadata="additional metadata",
+        timestamp="timestamp",
+    )
+
+
+@pytest.mark.asyncio
+async def test_create_and_get_collection_async(setup_milvus):
+    URI, TOKEN = setup_milvus
+    memory = MilvusMemoryStore(uri=URI, token=TOKEN)
+    await memory.delete_collection_async(all=True)
+    await memory.create_collection_async("test_collection", 2)
+    result = await memory.get_collections_async()
+    assert result == ["test_collection"]
+
+
+@pytest.mark.asyncio
+async def test_get_collections_async(setup_milvus):
+    URI, TOKEN = setup_milvus
+    memory = MilvusMemoryStore(uri=URI, token=TOKEN)
+    await memory.delete_collection_async(all=True)
+    await memory.create_collection_async("test_collection1", 2)
+    await memory.create_collection_async("test_collection2", 2)
+    await memory.create_collection_async("test_collection3", 2)
+    result = await memory.get_collections_async()
+    assert len(result) == 3
+
+
+@pytest.mark.asyncio
+async def test_delete_collection_async(setup_milvus):
+    URI, TOKEN = setup_milvus
+    memory = MilvusMemoryStore(uri=URI, token=TOKEN)
+    await memory.delete_collection_async(all=True)
+    await memory.create_collection_async("test_collection", 2)
+    await memory.delete_collection_async("test_collection", 2)
+    result = await memory.get_collections_async()
+    assert len(result) == 0
+
+    await memory.create_collection_async("test_collection", 2)
+    await memory.delete_collection_async("TEST_COLLECTION", 2)
+    result = await memory.get_collections_async()
+    assert len(result) == 0
+
+
+@pytest.mark.asyncio
+async def test_does_collection_exist_async(setup_milvus):
+    URI, TOKEN = setup_milvus
+    memory = MilvusMemoryStore(uri=URI, token=TOKEN)
+    await memory.delete_collection_async(all=True)
+    await memory.create_collection_async("test_collection", 2)
+    result = await memory.does_collection_exist_async("test_collection")
+    assert result is True
+
+    result = await memory.does_collection_exist_async("TEST_COLLECTION")
+    assert result is False
+
+
+@pytest.mark.asyncio
+async def test_upsert_and_get_async(memory_record1, setup_milvus):
+    URI, TOKEN = setup_milvus
+    memory = MilvusMemoryStore(uri=URI, token=TOKEN)
+    await memory.delete_collection_async(all=True)
+
+    await memory.create_collection_async("test_collection", 2)
+    await memory.upsert_async("test_collection", memory_record1)
+
+    result = await memory.get_async("test_collection", "test_id1", True)
+    assert result._id == "test_id1"
+    assert result._text == "sample text1"
+    assert result._is_reference is False
+    assert np.array_equal(result.embedding, np.array([0.5, 0.5]))
+    assert result._description == "description"
+    assert result._external_source_name == "external source"
+    assert result._additional_metadata == "additional metadata"
+    assert result._timestamp == "timestamp"
+
+
+@pytest.mark.asyncio
+async def test_upsert_and_get_async_with_no_embedding(memory_record1, setup_milvus):
+    URI, TOKEN = setup_milvus
+    memory = MilvusMemoryStore(uri=URI, token=TOKEN)
+    await memory.delete_collection_async(all=True)
+    await memory.create_collection_async("test_collection", 2)
+
+    await memory.upsert_async("test_collection", memory_record1)
+
+    result = await memory.get_async("test_collection", "test_id1", False)
+    assert result._id == "test_id1"
+    assert result._text == "sample text1"
+    assert result._is_reference is False
+    assert result.embedding is None
+    assert result._description == "description"
+    assert result._external_source_name == "external source"
+    assert result._additional_metadata == "additional metadata"
+    assert result._timestamp == "timestamp"
+
+
+@pytest.mark.asyncio
+async def test_upsert_and_get_batch_async(memory_record1, memory_record2, setup_milvus):
+    URI, TOKEN = setup_milvus
+    memory = MilvusMemoryStore(uri=URI, token=TOKEN)
+    await memory.delete_collection_async(all=True)
+    await memory.create_collection_async("test_collection", 2)
+
+    await memory.upsert_batch_async("test_collection", [memory_record1, memory_record2])
+
+    result = await memory.get_batch_async(
+        "test_collection", ["test_id1", "test_id2"], True
+    )
+    assert len(result) == 2
+    assert result[0]._id == "test_id1"
+    assert result[0]._text == "sample text1"
+    assert result[0]._is_reference is False
+    assert np.array_equal(result[0].embedding, np.array([0.5, 0.5]))
+    assert result[0]._description == "description"
+    assert result[0]._external_source_name == "external source"
+    assert result[0]._additional_metadata == "additional metadata"
+    assert result[0]._timestamp == "timestamp"
+
+
+@pytest.mark.asyncio
+async def test_remove_async(memory_record1, setup_milvus):
+    URI, TOKEN = setup_milvus
+    memory = MilvusMemoryStore(uri=URI, token=TOKEN)
+    await memory.delete_collection_async(all=True)
+    await memory.create_collection_async("test_collection", 2)
+
+    await memory.upsert_async("test_collection", memory_record1)
+    await memory.remove_async("test_collection", "test_id1")
+
+    # memory.get_async should raise Exception if record is not found
+    with pytest.raises(Exception):
+        await memory.get_async("test_collection", "test_id1", True)
+
+
+@pytest.mark.asyncio
+async def test_remove_batch_async(memory_record1, memory_record2, setup_milvus):
+    URI, TOKEN = setup_milvus
+    memory = MilvusMemoryStore(uri=URI, token=TOKEN)
+    await memory.delete_collection_async(all=True)
+    await memory.create_collection_async("test_collection", 2)
+
+    await memory.upsert_batch_async("test_collection", [memory_record1, memory_record2])
+    await memory.remove_batch_async("test_collection", ["test_id1", "test_id2"])
+
+    result = await memory.get_batch_async(
+        "test_collection", ["test_id1", "test_id2"], True
+    )
+    assert result == []
+
+
+@pytest.mark.asyncio
+async def test_get_nearest_matches_async(memory_record1, memory_record2, setup_milvus):
+    URI, TOKEN = setup_milvus
+    memory = MilvusMemoryStore(uri=URI, token=TOKEN)
+    await memory.delete_collection_async(all=True)
+    await memory.create_collection_async("test_collection", 2)
+    await memory.upsert_batch_async("test_collection", [memory_record1, memory_record2])
+    results = await memory.get_nearest_matches_async(
+        "test_collection", np.array([0.5, 0.5]), limit=2
+    )
+    assert len(results) == 2
+    assert isinstance(results[0][0], MemoryRecord)
+    assert results[0][1] == pytest.approx(0.5, abs=1e-5)
+
+
+@pytest.mark.asyncio
+async def test_get_nearest_match_async(memory_record1, memory_record2, setup_milvus):
+    URI, TOKEN = setup_milvus
+    memory = MilvusMemoryStore(uri=URI, token=TOKEN)
+    await memory.delete_collection_async(all=True)
+    await memory.create_collection_async("test_collection", 2)
+    await memory.upsert_batch_async("test_collection", [memory_record1, memory_record2])
+
+    result = await memory.get_nearest_match_async(
+        "test_collection", np.array([0.5, 0.5])
+    )
+    assert len(result) == 2
+    assert isinstance(result[0], MemoryRecord)
+    assert result[1] == pytest.approx(0.5, abs=1e-5)