diff --git a/graphrag/vector_stores/milvus.py b/graphrag/vector_stores/milvus.py
deleted file mode 100644
index 78eb2b1bba..0000000000
--- a/graphrag/vector_stores/milvus.py
+++ /dev/null
@@ -1,169 +0,0 @@
-import json
-from typing import Any
-from graphrag.model.types import TextEmbedder
-from graphrag.vector_stores import (
-    BaseVectorStore,
-    VectorStoreDocument,
-    VectorStoreSearchResult,
-)
-from pymilvus import Collection, CollectionSchema, FieldSchema, DataType, connections, utility
-
-
-class MilvusDBVectorStore(BaseVectorStore):
-    """The Milvus vector storage implementation."""
-
-    def connect(self, **kwargs: Any) -> Any:
-
-        db_uri = kwargs.get("db_uri", "http://localhost:19530")
-        self.db_connection = connections.connect(uri=db_uri)
-
-    def load_documents(
-            self, documents: list[VectorStoreDocument], overwrite: bool = True
-    ) -> None:
-
-        id_fields = []
-        text_fields = []
-        vector_fields = []
-        attributes_fields = []
-        for document in documents:
-            if document.vector is not None:
-                id_fields.append(document.id)
-                text_fields.append(document.text)
-                vector_fields.append(document.vector)
-                attributes_fields.append(json.dumps(document.attributes))
-
-        data = [id_fields, text_fields, vector_fields, attributes_fields]
-
-        if len(data) == 0:
-            data = None
-
-        if overwrite:
-            if data:
-                self.create_collection()
-                self.insert_data(data)
-            else:
-                self.create_collection()
-        else:
-            if data:
-                self.insert_data(data)
-
-    def create_collection(self) -> Collection:
-        id_field = FieldSchema(name="id", dtype=DataType.VARCHAR, is_primary=True, auto_id=False, max_length=128)
-        text_field = FieldSchema(name="text", dtype=DataType.VARCHAR, max_length=10240)
-        vector_field = FieldSchema(name="vector", dtype=DataType.FLOAT_VECTOR, dim=1536)
-        attributes_field = FieldSchema(name="attributes", dtype=DataType.VARCHAR, max_length=10240)
-
-        schema = CollectionSchema(fields=[
-            id_field,
-            text_field,
-            vector_field,
-            attributes_field,
-        ], description="GraphRAG Local Collection")
-
-        collection = Collection(name=self.collection_name, schema=schema)
-        return collection
-
-    def delete_collection(self) -> None:
-        if utility.has_collection(self.collection_name):
-            utility.drop_collection(self.collection_name)
-
-    def insert_data(self, data) -> None:
-        collection = Collection(name=self.collection_name)
-        collection.insert(data)
-
-        index_params = {
-            "index_type": "IVF_FLAT",
-            "params": {"nlist": 128},
-            "metric_type": "L2"
-        }
-
-        collection.create_index(field_name="vector", index_params=index_params)
-
-    def filter_by_id(self, include_ids: list[str] | list[int]) -> Any:
-
-        if len(include_ids) == 0:
-            self.query_filter = None
-        else:
-            if isinstance(include_ids[0], str):
-                id_filter = ", ".join([f"'{id}'" for id in include_ids])
-                self.query_filter = f"id in ({id_filter})"
-            else:
-                self.query_filter = (
-                    f"id in ({', '.join([str(id) for id in include_ids])})"
-                )
-        return self.query_filter
-
-    def similarity_search_by_vector(
-            self, query_embedding: list[float], k: int = 10, **kwargs: Any
-    ) -> list[VectorStoreSearchResult]:
-
-        collection = Collection(name=self.collection_name)
-        collection.load()
-
-        search_params = {
-            "metric_type": "L2",
-            "params": {"nprobe": 10}
-        }
-
-        output_fields = [
-            "text",
-            "vector",
-            "attributes"
-        ]
-
-        if self.query_filter:
-            results = collection.search(data=[query_embedding],
-                                        anns_field="vector",
-                                        param=search_params,
-                                        limit=k,
-                                        output_fields=output_fields,
-                                        expr=self.query_filter)
-        else:
-            results = collection.search(data=[query_embedding],
-                                        anns_field="vector",
-                                        param=search_params,
-                                        output_fields=output_fields,
-                                        limit=k)
-
-        docs = []
-        ids = []
-        for result in results:
-            for hit in result:
-
-                text = hit.entity.get("text")
-                vector = hit.entity.get("vector")
-                attributes = hit.entity.get("attributes")
-
-                ids.append({
-                    "id": hit.id,
-                    "text": text,
-                    "distance": hit.distance,
-                    "attributes": attributes,
-                })
-
-                docs.append(
-                    VectorStoreSearchResult(
-                        document=VectorStoreDocument(
-                            id=hit.id,
-                            text=text,
-                            vector=vector,
-                            attributes=json.loads(attributes),
-                        ),
-                        score=1 - abs(float(hit.distance)),
-                    )
-                )
-
-
-        self.delete_collection()
-
-        return docs
-
-    def similarity_search_by_text(
-            self, text: str, text_embedder: TextEmbedder, k: int = 10, **kwargs: Any
-    ) -> list[VectorStoreSearchResult]:
-
-        query_embedding = text_embedder(text)
-
-        if query_embedding:
-            return self.similarity_search_by_vector(query_embedding, k)
-        return []