diff --git a/poetry.lock b/poetry.lock
index 3c92a33..08d8938 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -77,6 +77,20 @@ category = "dev"
 optional = false
 python-versions = ">=3.7"
 
+[[package]]
+name = "isort"
+version = "5.12.0"
+description = "A Python utility / library to sort Python imports."
+category = "dev"
+optional = false
+python-versions = ">=3.8.0"
+
+[package.extras]
+colors = ["colorama (>=0.4.3)"]
+pipfile-deprecated-finder = ["pip-shims (>=0.5.2)", "pipreqs", "requirementslib"]
+plugins = ["setuptools"]
+requirements-deprecated-finder = ["pip-api", "pipreqs"]
+
 [[package]]
 name = "loguru"
 version = "0.7.0"
@@ -371,7 +385,7 @@ dev = ["black (>=19.3b0)", "pytest (>=4.6.2)"]
 [metadata]
 lock-version = "1.1"
 python-versions = "^3.10"
-content-hash = "5f1918e2ecdaf84015e723b7914acc95b884c2623c72c3b58ba5c375f9f5ca03"
+content-hash = "fb360da7cdf923249ea6c8602f9c2e8268a2362d2a97aa8964985d167a16aa2b"
 
 [metadata.files]
 certifi = [
@@ -522,6 +536,10 @@ iniconfig = [
     {file = "iniconfig-2.0.0-py3-none-any.whl", hash = "sha256:b6a85871a79d2e3b22d2d1b94ac2824226a63c6b741c88f7ae975f18b6778374"},
     {file = "iniconfig-2.0.0.tar.gz", hash = "sha256:2d91e135bf72d31a410b17c16da610a82cb55f6b0477d1a902134b24a455b8b3"},
 ]
+isort = [
+    {file = "isort-5.12.0-py3-none-any.whl", hash = "sha256:f84c2818376e66cf843d497486ea8fed8700b340f308f076c6fb1229dff318b6"},
+    {file = "isort-5.12.0.tar.gz", hash = "sha256:8bef7dde241278824a6d83f44a544709b065191b95b6e50894bdc722fcba0504"},
+]
 loguru = [
     {file = "loguru-0.7.0-py3-none-any.whl", hash = "sha256:b93aa30099fa6860d4727f1b81f8718e965bb96253fa190fab2077aaad6d15d3"},
     {file = "loguru-0.7.0.tar.gz", hash = "sha256:1612053ced6ae84d7959dd7d5e431a0532642237ec21f7fd83ac73fe539e03e1"},
diff --git a/pyproject.toml b/pyproject.toml
index 898d43e..eecb386 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -17,6 +17,7 @@ pinecone-client = "^2.2.1"
 [tool.poetry.group.dev.dependencies]
 pytest = "^7.3.1"
 python-dotenv = "^1.0.0"
+isort = "^5.12.0"
 
 [build-system]
 requires = ["poetry-core"]
diff --git a/vectordb_orm/__init__.py b/vectordb_orm/__init__.py
index 782b32a..d340600 100644
--- a/vectordb_orm/__init__.py
+++ b/vectordb_orm/__init__.py
@@ -1,7 +1,14 @@
+from vectordb_orm.backends.milvus.indexes import (Milvus_BIN_FLAT,
+                                                  Milvus_BIN_IVF_FLAT,
+                                                  Milvus_FLAT, Milvus_HNSW,
+                                                  Milvus_IVF_FLAT,
+                                                  Milvus_IVF_PQ,
+                                                  Milvus_IVF_SQ8)
+from vectordb_orm.backends.milvus.milvus import MilvusBackend
+from vectordb_orm.backends.pinecone.indexes import (PineconeIndex,
+                                                    PineconeSimilarityMetric)
+from vectordb_orm.backends.pinecone.pinecone import PineconeBackend
 from vectordb_orm.base import VectorSchemaBase
-from vectordb_orm.fields import EmbeddingField, VarCharField, PrimaryKeyField
+from vectordb_orm.enums import ConsistencyType
+from vectordb_orm.fields import EmbeddingField, PrimaryKeyField, VarCharField
 from vectordb_orm.session import VectorSession
-from vectordb_orm.indexes import FLAT, IVF_FLAT, IVF_SQ8, IVF_PQ, HNSW, BIN_FLAT, BIN_IVF_FLAT
-from vectordb_orm.similarity import ConsistencyType
-from vectordb_orm.backends.milvus import MilvusBackend
-from vectordb_orm.backends.pinecone import PineconeBackend
diff --git a/vectordb_orm/attributes.py b/vectordb_orm/attributes.py
index 99880e6..2d4c3e3 100644
--- a/vectordb_orm/attributes.py
+++ b/vectordb_orm/attributes.py
@@ -1,5 +1,5 @@
 from enum import Enum
-from typing import Any, TYPE_CHECKING
+from typing import TYPE_CHECKING, Any
 
 if TYPE_CHECKING:
     from vectordb_orm.base import MilvusBase
diff --git a/vectordb_orm/backends/base.py b/vectordb_orm/backends/base.py
index f323a2a..757ffb8 100644
--- a/vectordb_orm/backends/base.py
+++ b/vectordb_orm/backends/base.py
@@ -1,11 +1,14 @@
 from abc import ABC, abstractmethod
 from typing import Type
-from vectordb_orm.base import VectorSchemaBase
-from vectordb_orm.fields import PrimaryKeyField
+
 import numpy as np
+
 from vectordb_orm.attributes import AttributeCompare
+from vectordb_orm.base import VectorSchemaBase
+from vectordb_orm.fields import PrimaryKeyField
 from vectordb_orm.results import QueryResult
 
+
 class BackendBase(ABC):
     max_fetch_size: int
 
diff --git a/vectordb_orm/backends/milvus/__init__.py b/vectordb_orm/backends/milvus/__init__.py
new file mode 100644
index 0000000..4cb1293
--- /dev/null
+++ b/vectordb_orm/backends/milvus/__init__.py
@@ -0,0 +1,3 @@
+from vectordb_orm.backends.milvus.indexes import *
+from vectordb_orm.backends.milvus.milvus import *
+from vectordb_orm.backends.milvus.similarity import *
diff --git a/vectordb_orm/indexes.py b/vectordb_orm/backends/milvus/indexes.py
similarity index 84%
rename from vectordb_orm/indexes.py
rename to vectordb_orm/backends/milvus/indexes.py
index b2f32b0..319eba9 100644
--- a/vectordb_orm/indexes.py
+++ b/vectordb_orm/backends/milvus/indexes.py
@@ -1,7 +1,11 @@
 from abc import ABC, abstractmethod
-from vectordb_orm.similarity import FloatSimilarityMetric, BinarySimilarityMetric
 
-class IndexBase(ABC):
+from vectordb_orm.backends.milvus.similarity import (
+    MilvusBinarySimilarityMetric, MilvusFloatSimilarityMetric)
+from vectordb_orm.index import IndexBase
+
+
+class MilvusIndexBase(IndexBase):
     """
     Specify indexes used for embedding creation: https://milvus.io/docs/index.md
     Individual docstrings for the index types are taken from this page of ideal scenarios.
@@ -11,14 +15,14 @@ class IndexBase(ABC):
 
     def __init__(
         self,
-        metric_type: FloatSimilarityMetric | BinarySimilarityMetric | None = None,
+        metric_type: MilvusFloatSimilarityMetric | MilvusBinarySimilarityMetric | None = None,
     ):
         # Choose a reasonable default if metric_type is null, depending on the type of index
         if metric_type is None:
             if isinstance(self, tuple(FLOATING_INDEXES)):
-                metric_type = FloatSimilarityMetric.L2
+                metric_type = MilvusFloatSimilarityMetric.L2
             elif isinstance(self, tuple(BINARY_INDEXES)):
-                metric_type = BinarySimilarityMetric.JACCARD
+                metric_type = MilvusBinarySimilarityMetric.JACCARD
 
         self._assert_metric_type(metric_type)
         self.metric_type = metric_type
@@ -35,17 +39,17 @@ def get_inference_parameters(self):
         """
         pass
 
-    def _assert_metric_type(self, metric_type: FloatSimilarityMetric | BinarySimilarityMetric):
+    def _assert_metric_type(self, metric_type: MilvusFloatSimilarityMetric | MilvusBinarySimilarityMetric):
         """
         Binary indexes only support binary metrics, and floating indexes only support floating metrics. Assert
         that the combination of metric type and index is valid.
 
         """
         # Only support valid combinations of metric type and index
-        if isinstance(metric_type, FloatSimilarityMetric):
+        if isinstance(metric_type, MilvusFloatSimilarityMetric):
             if not isinstance(self, tuple(FLOATING_INDEXES)):
                 raise ValueError(f"Index type {self} is not supported for metric type {metric_type}")
-        elif isinstance(metric_type, BinarySimilarityMetric):
+        elif isinstance(metric_type, MilvusBinarySimilarityMetric):
             if not isinstance (self, tuple(BINARY_INDEXES)):
                 raise ValueError(f"Index type {self} is not supported for metric type {metric_type}")
 
@@ -55,7 +59,7 @@ def _assert_cluster_units_and_inference_comparison(self, cluster_units: int, inf
         if inference_comparison is not None and not (inference_comparison >= 1 and inference_comparison <= cluster_units):
             raise ValueError("inference_comparison must be between 1 and cluster_units")
 
-class FLAT(IndexBase):
+class Milvus_FLAT(MilvusIndexBase):
     """
     - Relatively small dataset
     - Requires a 100% recall rate
@@ -69,7 +73,7 @@ def get_inference_parameters(self):
         return {"metric_type": self.metric_type.name}
 
 
-class IVF_FLAT(IndexBase):
+class Milvus_IVF_FLAT(MilvusIndexBase):
     """
     - High-speed query
     - Requires a recall rate as high as possible
@@ -80,7 +84,7 @@ def __init__(
         self,
         cluster_units: int,
         inference_comparison: int | None = None,
-        metric_type: FloatSimilarityMetric | BinarySimilarityMetric | None = None,
+        metric_type: MilvusFloatSimilarityMetric | MilvusBinarySimilarityMetric | None = None,
     ):
         """
         :param cluster_units: Number of clusters (nlist in the docs)
@@ -101,7 +105,7 @@ def get_inference_parameters(self):
         return {"nprobe": self.nprobe}
 
 
-class IVF_SQ8(IndexBase):
+class Milvus_IVF_SQ8(MilvusIndexBase):
     """
     - High-speed query
     - Limited memory resources
@@ -113,7 +117,7 @@ def __init__(
         self,
         cluster_units: int,
         inference_comparison: int | None = None,
-        metric_type: FloatSimilarityMetric | BinarySimilarityMetric | None = None,
+        metric_type: MilvusFloatSimilarityMetric | MilvusBinarySimilarityMetric | None = None,
     ):
         """
         :param cluster_units: Number of clusters (nlist in the docs)
@@ -134,7 +138,7 @@ def get_inference_parameters(self):
         return {"nprobe": self.nprobe}
 
 
-class IVF_PQ(IndexBase):
+class Milvus_IVF_PQ(MilvusIndexBase):
     """
     - Very high-speed query
     - Limited memory resources
@@ -148,7 +152,7 @@ def __init__(
         product_quantization: int | None = None,
         inference_comparison: int | None = None,
         low_dimension_bits: int | None = None,
-        metric_type: FloatSimilarityMetric | BinarySimilarityMetric | None = None,
+        metric_type: MilvusFloatSimilarityMetric | MilvusBinarySimilarityMetric | None = None,
     ):
         """
         :param cluster_units: Number of clusters (nlist in the docs)
@@ -175,7 +179,7 @@ def _assert_low_dimension_bits(self, low_dimension_bits: int | None):
         if low_dimension_bits is not None and not (low_dimension_bits >= 1 and low_dimension_bits <= 16):
             raise ValueError("low_dimension_bits must be between 1 and 16")
 
-class HNSW(IndexBase):
+class Milvus_HNSW(MilvusIndexBase):
     """
     - High-speed query
     - Requires a recall rate as high as possible
@@ -188,7 +192,7 @@ def __init__(
         max_degree: int,
         search_scope_index: int,
         search_scope_inference: int,
-        metric_type: FloatSimilarityMetric | BinarySimilarityMetric | None = None,
+        metric_type: MilvusFloatSimilarityMetric | MilvusBinarySimilarityMetric | None = None,
     ):
         """
         :param max_degree: Maximum degree of the node
@@ -225,7 +229,7 @@ def _assert_search_scope_inference(self, search_scope_inference: int):
             raise ValueError("search_scope must be between 1 and 32768")
 
 
-class BIN_FLAT(IndexBase):
+class Milvus_BIN_FLAT(MilvusIndexBase):
     """
     - Relatively small dataset
     - Requires a 100% recall rate
@@ -239,7 +243,7 @@ def get_inference_parameters(self):
         return {"metric_type": self.metric_type.name}
 
 
-class BIN_IVF_FLAT(IndexBase):
+class Milvus_BIN_IVF_FLAT(MilvusIndexBase):
     """
     - High-speed query
     - Requires a recall rate as high as possible
@@ -250,7 +254,7 @@ def __init__(
         self,
         cluster_units: int,
         inference_comparison: int | None = None,
-        metric_type: FloatSimilarityMetric | BinarySimilarityMetric | None = None,
+        metric_type: MilvusFloatSimilarityMetric | MilvusBinarySimilarityMetric | None = None,
     ):
         """
         :param cluster_units: Number of clusters (nlist in the docs)
@@ -271,5 +275,5 @@ def get_inference_parameters(self):
         return {"nprobe": self.nprobe, "metric_type": self.metric_type.name}
 
 
-FLOATING_INDEXES : set[IndexBase] = {FLAT, IVF_FLAT, IVF_SQ8, IVF_PQ, HNSW}
-BINARY_INDEXES : set[IndexBase] = {BIN_FLAT, BIN_IVF_FLAT}
+FLOATING_INDEXES : set[MilvusIndexBase] = {Milvus_FLAT, Milvus_IVF_FLAT, Milvus_IVF_SQ8, Milvus_IVF_PQ, Milvus_HNSW}
+BINARY_INDEXES : set[MilvusIndexBase] = {Milvus_BIN_FLAT, Milvus_BIN_IVF_FLAT}
diff --git a/vectordb_orm/backends/milvus.py b/vectordb_orm/backends/milvus/milvus.py
similarity index 92%
rename from vectordb_orm/backends/milvus.py
rename to vectordb_orm/backends/milvus/milvus.py
index c9398e6..b34db09 100644
--- a/vectordb_orm/backends/milvus.py
+++ b/vectordb_orm/backends/milvus/milvus.py
@@ -1,23 +1,22 @@
-from pymilvus import Milvus, Collection
-from pymilvus.client.types import DataType
-from pymilvus.orm.schema import CollectionSchema, FieldSchema
-from vectordb_orm.backends.base import BackendBase
-import numpy as np
-from typing import get_args, get_origin
 from logging import info
-from vectordb_orm.base import VectorSchemaBase
-from typing import Type
-from pymilvus import Milvus, Collection
+from typing import Any, Type, get_args, get_origin
+
+import numpy as np
+from pymilvus import Collection, Milvus
+from pymilvus.client.abstract import ChunkedQueryResult
 from pymilvus.client.types import DataType
 from pymilvus.orm.schema import CollectionSchema, FieldSchema
-from vectordb_orm.fields import EmbeddingField, VarCharField, BaseField, PrimaryKeyField
-from vectordb_orm.indexes import FLOATING_INDEXES, BINARY_INDEXES
-from typing import Any
-from vectordb_orm.attributes import AttributeCompare
 
-from pymilvus.client.abstract import ChunkedQueryResult
+from vectordb_orm.attributes import AttributeCompare
+from vectordb_orm.backends.base import BackendBase
+from vectordb_orm.backends.milvus.indexes import (BINARY_INDEXES,
+                                                  FLOATING_INDEXES)
+from vectordb_orm.base import VectorSchemaBase
+from vectordb_orm.fields import (BaseField, EmbeddingField, PrimaryKeyField,
+                                 VarCharField)
 from vectordb_orm.results import QueryResult
 
+
 class MilvusBackend(BackendBase):
     # https://milvus.io/docs/search.md
     max_fetch_size = 16384
@@ -101,6 +100,10 @@ def search(
         if schema.consistency_type() is not None:
             optional_args["consistency_level"] = schema.consistency_type().value
 
+        # Milvus supports to different quering behaviors depending on whether or not we are looking
+        # for vector similarities
+        # A `search` is used when we are looking for vector similarities, and a `query` is used more akin
+        # to a traditional relational database when we're just looking to filter on metadata
         if search_embedding_key is not None:
             embedding_configuration : EmbeddingField = schema._type_configuration.get(search_embedding_key)
 
@@ -245,6 +248,11 @@ def _assert_embedding_validity(self, schema: Type[VectorSchemaBase]):
             elif vector_type == DataType.FLOAT_VECTOR and not isinstance(field_index, tuple(FLOATING_INDEXES)):
                 raise ValueError(f"Index type {field_index} is not compatible with float vectors.")
 
+            # Milvus max size
+            # https://milvus.io/docs/limitations.md
+            if field_configuration.dim > 32768:
+                raise ValueError(f"Milvus only supports vectors with dimensions under 32768. {attribute_name} is too large: {field_configuration.dim}.")
+
     def _result_to_objects(
         self,
         schema: Type[VectorSchemaBase],
diff --git a/vectordb_orm/similarity.py b/vectordb_orm/backends/milvus/similarity.py
similarity index 57%
rename from vectordb_orm/similarity.py
rename to vectordb_orm/backends/milvus/similarity.py
index 2f76157..05ca808 100644
--- a/vectordb_orm/similarity.py
+++ b/vectordb_orm/backends/milvus/similarity.py
@@ -1,8 +1,9 @@
 from enum import Enum
+
 from pymilvus.client.types import MetricType
-from pymilvus.orm.types import CONSISTENCY_STRONG, CONSISTENCY_BOUNDED, CONSISTENCY_EVENTUALLY, CONSISTENCY_SESSION
 
-class FloatSimilarityMetric(Enum):
+
+class MilvusFloatSimilarityMetric(Enum):
     """
     Specify the metric used for floating-point search. At inference time a query vector is broadcast to the vectors in the database
     using this approach. The string values of this enums are directly used by Milvus, see here for more info: https://milvus.io/docs/metric.md
@@ -13,7 +14,7 @@ class FloatSimilarityMetric(Enum):
     # Inner Product
     IP = MetricType.IP.name
 
-class BinarySimilarityMetric(Enum):
+class MilvusBinarySimilarityMetric(Enum):
     """
     Specify the metric used for binary search. These are distance metrics.
 
@@ -21,14 +22,3 @@ class BinarySimilarityMetric(Enum):
     JACCARD = MetricType.JACCARD.name
     TANIMOTO = MetricType.TANIMOTO.name
     HAMMING = MetricType.HAMMING.name
-
-class ConsistencyType(Enum):
-    """
-    Define the strength of the consistency within the distributed DB:
-    https://milvus.io/docs/consistency.md
-
-    """
-    STRONG = CONSISTENCY_STRONG
-    BOUNDED = CONSISTENCY_BOUNDED
-    SESSION = CONSISTENCY_SESSION
-    EVENTUALLY = CONSISTENCY_EVENTUALLY
diff --git a/vectordb_orm/backends/pinecone/__init__.py b/vectordb_orm/backends/pinecone/__init__.py
new file mode 100644
index 0000000..52ad9b2
--- /dev/null
+++ b/vectordb_orm/backends/pinecone/__init__.py
@@ -0,0 +1,2 @@
+from vectordb_orm.backends.pinecone.indexes import *
+from vectordb_orm.backends.pinecone.pinecone import *
diff --git a/vectordb_orm/backends/pinecone/indexes.py b/vectordb_orm/backends/pinecone/indexes.py
new file mode 100644
index 0000000..7b66e60
--- /dev/null
+++ b/vectordb_orm/backends/pinecone/indexes.py
@@ -0,0 +1,29 @@
+from enum import Enum
+
+from vectordb_orm.index import IndexBase
+
+
+class PineconeSimilarityMetric(Enum):
+    COSINE = "cosine"
+    EUCLIDEAN = "euclidean"
+    DOT_PRODUCT = "dotproduct"
+
+
+class PineconeIndex(IndexBase):
+    """
+    Pinecone only supports one type of index
+    """
+    def __init__(self, metric_type: PineconeSimilarityMetric):
+        self.metric_type = metric_type
+        self._assert_metric_type(metric_type)
+
+    def get_index_parameters(self):
+        return {}
+
+    def get_inference_parameters(self):
+        return {"metric_type": self.metric_type.name}
+
+    def _assert_metric_type(self, metric_type: PineconeSimilarityMetric):
+        # Only support valid combinations of metric type and index
+        if isinstance(metric_type, PineconeSimilarityMetric):
+            raise ValueError(f"Index type {self} is not supported for metric type {metric_type}")
diff --git a/vectordb_orm/backends/pinecone.py b/vectordb_orm/backends/pinecone/pinecone.py
similarity index 94%
rename from vectordb_orm/backends/pinecone.py
rename to vectordb_orm/backends/pinecone/pinecone.py
index 044ada9..630879a 100644
--- a/vectordb_orm/backends/pinecone.py
+++ b/vectordb_orm/backends/pinecone/pinecone.py
@@ -1,15 +1,19 @@
-import pinecone
-from vectordb_orm.backends.base import BackendBase
+from logging import info
+from re import match as re_match
 from typing import Type
-from vectordb_orm.base import VectorSchemaBase
+from uuid import uuid4
+
 import numpy as np
+import pinecone
+
 from vectordb_orm.attributes import AttributeCompare
+from vectordb_orm.backends.base import BackendBase
+from vectordb_orm.backends.pinecone.indexes import PineconeIndex
+from vectordb_orm.base import VectorSchemaBase
 from vectordb_orm.fields import EmbeddingField
-from re import match as re_match
-from logging import info
-from uuid import uuid4
 from vectordb_orm.results import QueryResult
 
+
 class PineconeBackend(BackendBase):
     max_fetch_size = 1000
 
@@ -34,6 +38,7 @@ def create_collection(self, schema: Type[VectorSchemaBase]):
 
         self._assert_valid_collection_name(collection_name)
         self._assert_has_primary(schema)
+        self._assert_valid_embedding_field(schema)
 
         # Pinecone allows for dynamic keys on each object
         # However we need to pre-provide the keys we want to search on
@@ -215,3 +220,10 @@ def _get_embedding_field(self, schema: Type[VectorSchemaBase]):
             raise ValueError(f"Pinecone only supports one embedding field per collection. {schema} has {len(embedding_fields)} defined: {list(embedding_fields.keys())}.")
 
         return list(embedding_fields.items())[0]
+
+    def _assert_valid_embedding_field(self, schema: Type[VectorSchemaBase]):
+        _, embedding_field = self._get_embedding_field(schema)
+
+        # Ensure that we are using a supported index
+        if not isinstance(embedding_field.index, PineconeIndex):
+            raise ValueError("Pinecone only supports a basic `PineconeIndex`.")
diff --git a/vectordb_orm/base.py b/vectordb_orm/base.py
index aff1c43..f42ebea 100644
--- a/vectordb_orm/base.py
+++ b/vectordb_orm/base.py
@@ -1,6 +1,7 @@
-from vectordb_orm.attributes import AttributeCompare
-from vectordb_orm.similarity import ConsistencyType
 from typing import Any
+
+from vectordb_orm.attributes import AttributeCompare
+from vectordb_orm.enums import ConsistencyType
 from vectordb_orm.fields import BaseField, PrimaryKeyField
 
 
diff --git a/vectordb_orm/enums.py b/vectordb_orm/enums.py
new file mode 100644
index 0000000..557cd00
--- /dev/null
+++ b/vectordb_orm/enums.py
@@ -0,0 +1,16 @@
+from enum import Enum
+
+from pymilvus.orm.types import (CONSISTENCY_BOUNDED, CONSISTENCY_EVENTUALLY,
+                                CONSISTENCY_SESSION, CONSISTENCY_STRONG)
+
+
+class ConsistencyType(Enum):
+    """
+    Define the strength of the consistency within the distributed DB:
+    https://milvus.io/docs/consistency.md
+
+    """
+    STRONG = CONSISTENCY_STRONG
+    BOUNDED = CONSISTENCY_BOUNDED
+    SESSION = CONSISTENCY_SESSION
+    EVENTUALLY = CONSISTENCY_EVENTUALLY
diff --git a/vectordb_orm/fields.py b/vectordb_orm/fields.py
index 455a4e1..d080bad 100644
--- a/vectordb_orm/fields.py
+++ b/vectordb_orm/fields.py
@@ -1,8 +1,9 @@
 from abc import ABC
-from vectordb_orm.indexes import IndexBase, FLOATING_INDEXES, BINARY_INDEXES
-from vectordb_orm.similarity import FloatSimilarityMetric, BinarySimilarityMetric
 from typing import Any
 
+from vectordb_orm.index import IndexBase
+
+
 class BaseField(ABC):
     """
     BaseField is the superclass to all fields that require additional customization behavior. They
@@ -19,8 +20,8 @@ def __init__(self, default: Any):
 
 
 class PrimaryKeyField(BaseField):
-    def __init__(self, default: Any = None):
-        super().__init__(default=default)
+    def __init__(self):
+        super().__init__(default=None)
 
 
 class EmbeddingField(BaseField):
diff --git a/vectordb_orm/index.py b/vectordb_orm/index.py
new file mode 100644
index 0000000..5f3fab9
--- /dev/null
+++ b/vectordb_orm/index.py
@@ -0,0 +1,2 @@
+class IndexBase:
+    pass
diff --git a/vectordb_orm/query.py b/vectordb_orm/query.py
index 83ffff3..1d41598 100644
--- a/vectordb_orm/query.py
+++ b/vectordb_orm/query.py
@@ -1,8 +1,10 @@
-from vectordb_orm.backends.base import BackendBase
+from typing import Any
+
 from vectordb_orm.attributes import AttributeCompare
-from vectordb_orm.fields import EmbeddingField
+from vectordb_orm.backends.base import BackendBase
 from vectordb_orm.base import VectorSchemaBase
-from typing import Any
+from vectordb_orm.fields import EmbeddingField
+
 
 class VectorQueryBuilder:
     """
diff --git a/vectordb_orm/results.py b/vectordb_orm/results.py
index 74b3e05..9cfbee1 100644
--- a/vectordb_orm/results.py
+++ b/vectordb_orm/results.py
@@ -1,5 +1,5 @@
 from dataclasses import dataclass
-from typing import TYPE_CHECKING
+from typing import TYPE_CHECKING, Optional
 
 if TYPE_CHECKING:
     from vectordb_orm.base import VectorSchemaBase
@@ -9,5 +9,5 @@ class QueryResult:
     result: "VectorSchemaBase"
 
     # Score and distance is only returned for queries requesting vector-similarity
-    score: float | None = None
-    distance: float | None = None
+    score: Optional[float] = None
+    distance: Optional[float] = None
diff --git a/vectordb_orm/session.py b/vectordb_orm/session.py
index d3dfb54..c4950da 100644
--- a/vectordb_orm/session.py
+++ b/vectordb_orm/session.py
@@ -1,8 +1,11 @@
+from typing import Type
+
+from pymilvus import Milvus
+
+from vectordb_orm.backends.base import BackendBase
 from vectordb_orm.base import VectorSchemaBase
 from vectordb_orm.query import VectorQueryBuilder
-from vectordb_orm.backends.base import BackendBase
-from pymilvus import Milvus
-from typing import Type
+
 
 class VectorSession:
     """
diff --git a/vectordb_orm/tests/conftest.py b/vectordb_orm/tests/conftest.py
index adec9ec..ad15329 100644
--- a/vectordb_orm/tests/conftest.py
+++ b/vectordb_orm/tests/conftest.py
@@ -1,10 +1,12 @@
-import pytest
-from pymilvus import Milvus, connections
-from vectordb_orm import VectorSession, MilvusBackend, PineconeBackend
-from vectordb_orm.tests.models import MyObject, BinaryEmbeddingObject
-from time import sleep
 from os import getenv
+from time import sleep
+
+import pytest
 from dotenv import load_dotenv
+from pymilvus import Milvus, connections
+
+from vectordb_orm import MilvusBackend, PineconeBackend, VectorSession
+from vectordb_orm.tests.models import BinaryEmbeddingObject, MyObject
 
 
 @pytest.fixture()
diff --git a/vectordb_orm/tests/models.py b/vectordb_orm/tests/models.py
index f529086..3375070 100644
--- a/vectordb_orm/tests/models.py
+++ b/vectordb_orm/tests/models.py
@@ -1,14 +1,18 @@
-from vectordb_orm import VectorSchemaBase, EmbeddingField, VarCharField, PrimaryKeyField, ConsistencyType
-from vectordb_orm.indexes import IVF_FLAT, BIN_FLAT
 import numpy as np
 
+from vectordb_orm import (ConsistencyType, EmbeddingField, PrimaryKeyField,
+                          VarCharField, VectorSchemaBase)
+from vectordb_orm.backends.milvus.indexes import (Milvus_BIN_FLAT,
+                                                  Milvus_IVF_FLAT)
+
+
 class MyObject(VectorSchemaBase):
     __collection_name__ = 'my_collection'
     __consistency_type__ = ConsistencyType.STRONG
 
     id: int = PrimaryKeyField()
     text: str = VarCharField(max_length=128)
-    embedding: np.ndarray = EmbeddingField(dim=128, index=IVF_FLAT(cluster_units=128))
+    embedding: np.ndarray = EmbeddingField(dim=128, index=Milvus_IVF_FLAT(cluster_units=128))
 
 
 class BinaryEmbeddingObject(VectorSchemaBase):
@@ -16,4 +20,4 @@ class BinaryEmbeddingObject(VectorSchemaBase):
     __consistency_type__ = ConsistencyType.STRONG
 
     id: int = PrimaryKeyField()
-    embedding: np.ndarray[np.bool_] = EmbeddingField(dim=128, index=BIN_FLAT())
+    embedding: np.ndarray[np.bool_] = EmbeddingField(dim=128, index=Milvus_BIN_FLAT())
diff --git a/vectordb_orm/tests/test_base.py b/vectordb_orm/tests/test_base.py
index 8bafc0c..eb925d7 100644
--- a/vectordb_orm/tests/test_base.py
+++ b/vectordb_orm/tests/test_base.py
@@ -1,11 +1,12 @@
-import pytest
-from vectordb_orm import VectorSession
-from vectordb_orm.tests.models import MyObject
 import numpy as np
-from time import sleep
-from vectordb_orm import VectorSchemaBase, EmbeddingField, PrimaryKeyField
-from vectordb_orm.indexes import IVF_FLAT
+import pytest
+
+from vectordb_orm import (EmbeddingField, PrimaryKeyField, VectorSchemaBase,
+                          VectorSession)
+from vectordb_orm.backends.milvus.indexes import Milvus_IVF_FLAT
 from vectordb_orm.tests.conftest import SESSION_FIXTURE_KEYS
+from vectordb_orm.tests.models import MyObject
+
 
 def test_create_object():
     my_object = MyObject(text='example', embedding=np.array([1.0] * 128))
@@ -65,7 +66,7 @@ class TestInvalidObject(VectorSchemaBase):
         __collection_name__ = 'invalid_collection'
 
         id: int = PrimaryKeyField()
-        embedding: np.ndarray[np.bool_] = EmbeddingField(dim=128, index=IVF_FLAT(cluster_units=128))
+        embedding: np.ndarray[np.bool_] = EmbeddingField(dim=128, index=Milvus_IVF_FLAT(cluster_units=128))
 
     with pytest.raises(ValueError, match="not compatible with binary vectors"):
         milvus_session.create_collection(TestInvalidObject)
diff --git a/vectordb_orm/tests/test_indexes.py b/vectordb_orm/tests/test_indexes.py
index a4b2a71..d306e6c 100644
--- a/vectordb_orm/tests/test_indexes.py
+++ b/vectordb_orm/tests/test_indexes.py
@@ -1,10 +1,15 @@
+from itertools import product
+
+import numpy as np
 import pytest
 from pymilvus import Milvus
-from vectordb_orm import VectorSession, EmbeddingField, PrimaryKeyField, VectorSchemaBase
-import numpy as np
-from vectordb_orm.indexes import IndexBase, FLOATING_INDEXES, BINARY_INDEXES
-from vectordb_orm.similarity import FloatSimilarityMetric, BinarySimilarityMetric
-from itertools import product
+
+from vectordb_orm import (EmbeddingField, PrimaryKeyField, VectorSchemaBase,
+                          VectorSession)
+from vectordb_orm.backends.milvus.indexes import (BINARY_INDEXES,
+                                                  FLOATING_INDEXES, IndexBase)
+from vectordb_orm.backends.milvus.similarity import (
+    BinarySimilarityMetric, MilvusFloatSimilarityMetric)
 
 # Different index definitions require different kwarg arguments; we centralize
 # them here for ease of accessing them during different test runs
@@ -34,13 +39,13 @@
     "index_cls,metric_type",
     product(
         FLOATING_INDEXES,
-        [item for item in FloatSimilarityMetric],
+        [item for item in MilvusFloatSimilarityMetric],
     )
 )
 def test_floating_index(
     session: VectorSession,
     index_cls: IndexBase,
-    metric_type: FloatSimilarityMetric,
+    metric_type: MilvusFloatSimilarityMetric,
 ):
     class IndexSubclassObject(VectorSchemaBase):
         __collection_name__ = 'index_collection'
diff --git a/vectordb_orm/tests/test_query.py b/vectordb_orm/tests/test_query.py
index 2b07b2c..1438773 100644
--- a/vectordb_orm/tests/test_query.py
+++ b/vectordb_orm/tests/test_query.py
@@ -1,14 +1,18 @@
+import numpy as np
 import pytest
-from pymilvus import Milvus, connections
+
 from vectordb_orm import VectorSession
-from vectordb_orm.tests.models import MyObject, BinaryEmbeddingObject
-import numpy as np
-from time import sleep
+from vectordb_orm.tests.conftest import SESSION_FIXTURE_KEYS
+from vectordb_orm.tests.models import BinaryEmbeddingObject, MyObject
+
 
-def test_query(session: VectorSession):
+@pytest.mark.parametrize("session", SESSION_FIXTURE_KEYS)
+def test_query(session: str, request):
     """
     General test of querying and query chaining
     """
+    session : VectorSession = request.getfixturevalue(session)
+
     # Create some MyObject instances
     obj1 = MyObject(text="foo", embedding=np.array([1.0] * 128))
     obj2 = MyObject(text="bar", embedding=np.array([4.0] * 128))
@@ -56,11 +60,14 @@ def test_binary_collection_query(session: VectorSession):
     assert len(results) == 2
     assert results[0].result.id == obj2.id
 
-def test_query_default_ignores_embeddings(session: VectorSession):
+@pytest.mark.parametrize("session", SESSION_FIXTURE_KEYS)
+def test_query_default_ignores_embeddings(session: str, request):
     """
     Ensure that querying on the class by default ignores embeddings that are included
     within the type definition.
     """
+    session : VectorSession = request.getfixturevalue(session)
+
     obj1 = MyObject(text="foo", embedding=np.array([1.0] * 128))
     session.insert(obj1)
 
@@ -74,11 +81,13 @@ def test_query_default_ignores_embeddings(session: VectorSession):
     result : MyObject = results[0].result
     assert result.embedding is None
 
-
-def test_query_with_fields(session: VectorSession):
+@pytest.mark.parametrize("session", SESSION_FIXTURE_KEYS)
+def test_query_with_fields(session: str, request):
     """
     Test querying with specific fields
     """
+    session : VectorSession = request.getfixturevalue(session)
+
     obj1 = MyObject(text="foo", embedding=np.array([1.0] * 128))
     session.insert(obj1)