diff --git a/python/pyproject.toml b/python/pyproject.toml
index 89fb3766951ac..4f4ae785ad20a 100644
--- a/python/pyproject.toml
+++ b/python/pyproject.toml
@@ -206,3 +206,8 @@ name = "semantic_kernel"
 requires = ["flit-core >= 3.9,<4.0"]
 build-backend = "flit_core.buildapi"
 
+[dependency-groups]
+redis = [
+    "redisvl>=0.3.6",
+]
+
diff --git a/python/samples/concepts/memory/new_memory.py b/python/samples/concepts/memory/new_memory.py
index c58061f8799a7..d1b803a1fb798 100644
--- a/python/samples/concepts/memory/new_memory.py
+++ b/python/samples/concepts/memory/new_memory.py
@@ -1,6 +1,8 @@
 # Copyright (c) Microsoft. All rights reserved.
 
 import argparse
+import asyncio
+import time
 from collections.abc import Callable
 from dataclasses import dataclass, field
 from typing import Annotated
@@ -25,6 +27,10 @@
     VectorStoreRecordVectorField,
     vectorstoremodel,
 )
+from semantic_kernel.data.vector_search.vector_search_filter import VectorSearchFilter
+from semantic_kernel.data.vector_search.vector_search_options import VectorSearchOptions
+from semantic_kernel.data.vector_search.vector_text_search import VectorTextSearchMixin
+from semantic_kernel.data.vector_search.vectorized_search import VectorizedSearchMixin
 
 
 @vectorstoremodel
@@ -44,9 +50,18 @@ class MyDataModelArray:
     ] = None
     other: str | None = None
     id: Annotated[str, VectorStoreRecordKeyField()] = field(default_factory=lambda: str(uuid4()))
+    title: Annotated[
+        str, VectorStoreRecordDataField(has_embedding=False, property_type="str", is_full_text_searchable=True)
+    ] = "title1"
     content: Annotated[
-        str, VectorStoreRecordDataField(has_embedding=True, embedding_property_name="vector", property_type="str")
+        str,
+        VectorStoreRecordDataField(
+            has_embedding=True, embedding_property_name="vector", property_type="str", is_full_text_searchable=True
+        ),
     ] = "content1"
+    tag: Annotated[str, VectorStoreRecordDataField(has_embedding=False, property_type="str", is_filterable=True)] = (
+        "tag1"
+    )
 
 
 @vectorstoremodel
@@ -64,9 +79,18 @@ class MyDataModelList:
     ] = None
     other: str | None = None
     id: Annotated[str, VectorStoreRecordKeyField()] = field(default_factory=lambda: str(uuid4()))
+    title: Annotated[
+        str, VectorStoreRecordDataField(has_embedding=False, property_type="str", is_full_text_searchable=True)
+    ] = "title1"
     content: Annotated[
-        str, VectorStoreRecordDataField(has_embedding=True, embedding_property_name="vector", property_type="str")
+        str,
+        VectorStoreRecordDataField(
+            has_embedding=True, embedding_property_name="vector", property_type="str", is_full_text_searchable=True
+        ),
     ] = "content1"
+    tag: Annotated[str, VectorStoreRecordDataField(has_embedding=False, property_type="str", is_filterable=True)] = (
+        "tag1"
+    )
 
 
 collection_name = "test"
@@ -88,7 +112,7 @@ class MyDataModelList:
 # function which returns the store.
 # Using a function allows for lazy initialization of the store,
 # so that settings for unused stores do not cause validation errors.
-stores: dict[str, Callable[[], VectorStoreRecordCollection]] = {
+services: dict[str, Callable[[], VectorStoreRecordCollection]] = {
     "ai_search": lambda: AzureAISearchCollection[MyDataModel](
         data_model_type=MyDataModel,
     ),
@@ -101,7 +125,7 @@ class MyDataModelList:
         collection_name=collection_name,
         prefix_collection_name_to_key_names=True,
     ),
-    "redis_hashset": lambda: RedisHashsetCollection[MyDataModel](
+    "redis_hash": lambda: RedisHashsetCollection[MyDataModel](
         data_model_type=MyDataModel,
         collection_name=collection_name,
         prefix_collection_name_to_key_names=True,
@@ -120,43 +144,89 @@ class MyDataModelList:
 }
 
 
-async def main(store: str, use_azure_openai: bool, embedding_model: str):
+def print_record(record):
+    print(f"  Found id: {record.id}")
+    print(f"    Content: {record.content}")
+    if record.vector is not None:
+        print(f"    Vector (first five): {record.vector[:5]}")
+
+
+async def main(service: str, use_azure_openai: bool, embedding_model: str):
+    print("-" * 30)
     kernel = Kernel()
     service_id = "embedding"
     if use_azure_openai:
-        kernel.add_service(AzureTextEmbedding(service_id=service_id, deployment_name=embedding_model))
+        embedder = AzureTextEmbedding(service_id=service_id, deployment_name=embedding_model)
     else:
-        kernel.add_service(OpenAITextEmbedding(service_id=service_id, ai_model_id=embedding_model))
-    async with stores[store]() as record_store:
-        await record_store.create_collection_if_not_exists()
-
-        record1 = MyDataModel(content="My text", id="e6103c03-487f-4d7d-9c23-4723651c17f4")
-        record2 = MyDataModel(content="My other text", id="09caec77-f7e1-466a-bcec-f1d51c5b15be")
-
+        embedder = OpenAITextEmbedding(service_id=service_id, ai_model_id=embedding_model)
+    kernel.add_service(embedder)
+    async with services[service]() as record_collection:
+        print(f"Creating {service} collection!")
+        await record_collection.delete_collection()
+        await record_collection.create_collection()
+
+        record1 = MyDataModel(
+            content="Semantic Kernel is awesome",
+            id="e6103c03-487f-4d7d-9c23-4723651c17f4",
+            title="Overview",
+            tag="general",
+        )
+        record2 = MyDataModel(
+            content="Semantic Kernel is available in dotnet, python and Java.",
+            id="09caec77-f7e1-466a-bcec-f1d51c5b15be",
+            title="Semantic Kernel Languages",
+            tag="general",
+        )
+        print("Adding records!")
         records = await VectorStoreRecordUtils(kernel).add_vector_to_records(
             [record1, record2], data_model_type=MyDataModel
         )
-        keys = await record_store.upsert_batch(records)
-        print(f"upserted {keys=}")
-
-        results = await record_store.get_batch([record1.id, record2.id])
+        keys = await record_collection.upsert_batch(records)
+        print(f"    Upserted {keys=}")
+        print("Getting records!")
+        results = await record_collection.get_batch([record1.id, record2.id])
         if results:
-            for result in results:
-                print(f"found {result.id=}")
-                print(f"{result.content=}")
-                if result.vector is not None:
-                    print(f"{result.vector[:5]=}")
+            for record in results:
+                print_record(record)
         else:
-            print("not found")
+            print("Nothing found...")
+        options = VectorSearchOptions(
+            vector_field_name="vector",
+            include_vectors=True,
+            filter=VectorSearchFilter.equal_to("tag", "general"),
+        )
+        if isinstance(record_collection, VectorTextSearchMixin):
+            print("-" * 30)
+            print("Using text search")
+            search_results = await record_collection.text_search("python", options)
+            if search_results.total_count == 0:
+                print("\nNothing found...\n")
+            else:
+                [print_record(result.record) async for result in search_results.results]
+        if isinstance(record_collection, VectorizedSearchMixin):
+            print("-" * 30)
+            print("Using vectorized search, for `python`")
+            print("The distance function is set to the default of the store.")
+            search_results = await record_collection.vectorized_search(
+                vector=(await embedder.generate_raw_embeddings(["python"]))[0], options=options
+            )
+            if search_results.total_count == 0:
+                print("\nNothing found...\n")
+            else:
+                async for result in search_results.results:
+                    print_record(result.record)
+                    print(f"  Score: {result.score:.4f}\n")
+        print("-" * 30)
+        print("Deleting collection!")
+        await record_collection.delete_collection()
+        print("Done!")
 
 
 if __name__ == "__main__":
-    import asyncio
-
     argparse.ArgumentParser()
 
     parser = argparse.ArgumentParser()
-    parser.add_argument("--store", default="in_memory", choices=stores.keys(), help="What store to use.")
+    parser.add_argument("--service", default="redis_json", choices=services.keys(), help="What store to use.")
     # Option of whether to use OpenAI or Azure OpenAI.
     parser.add_argument("--use-azure-openai", action="store_true", help="Use Azure OpenAI instead of OpenAI.")
     # Model
@@ -165,4 +235,5 @@ async def main(store: str, use_azure_openai: bool, embedding_model: str):
     )
     args = parser.parse_args()
 
-    asyncio.run(main(store=args.store, use_azure_openai=args.use_azure_openai, embedding_model=args.model))
+    asyncio.run(main(service=args.service, use_azure_openai=args.use_azure_openai, embedding_model=args.model))
+    time.sleep(1)
diff --git a/python/semantic_kernel/connectors/memory/azure_ai_search/const.py b/python/semantic_kernel/connectors/memory/azure_ai_search/const.py
index 687f930ebc8c0..e78662160bc71 100644
--- a/python/semantic_kernel/connectors/memory/azure_ai_search/const.py
+++ b/python/semantic_kernel/connectors/memory/azure_ai_search/const.py
@@ -18,9 +18,10 @@
 }
 
 DISTANCE_FUNCTION_MAP = {
-    DistanceFunction.COSINE_SIMILARITY: VectorSearchAlgorithmMetric.COSINE,
+    DistanceFunction.COSINE_DISTANCE: VectorSearchAlgorithmMetric.COSINE,
     DistanceFunction.DOT_PROD: VectorSearchAlgorithmMetric.DOT_PRODUCT,
     DistanceFunction.EUCLIDEAN_DISTANCE: VectorSearchAlgorithmMetric.EUCLIDEAN,
+    DistanceFunction.HAMMING: VectorSearchAlgorithmMetric.HAMMING,
     "default": VectorSearchAlgorithmMetric.COSINE,
 }
 
diff --git a/python/semantic_kernel/connectors/memory/redis/const.py b/python/semantic_kernel/connectors/memory/redis/const.py
index faeb63e92b00d..55992d4d53439 100644
--- a/python/semantic_kernel/connectors/memory/redis/const.py
+++ b/python/semantic_kernel/connectors/memory/redis/const.py
@@ -4,6 +4,7 @@
 from enum import Enum
 
 from redis.commands.search.indexDefinition import IndexType
+from redisvl.schema import StorageType
 
 from semantic_kernel.data.const import DistanceFunction
 
@@ -18,6 +19,11 @@ class RedisCollectionTypes(str, Enum):
     RedisCollectionTypes.HASHSET: IndexType.HASH,
 }
 
+STORAGE_TYPE_MAP = {
+    RedisCollectionTypes.JSON: StorageType.JSON,
+    RedisCollectionTypes.HASHSET: StorageType.HASH,
+}
+
 DISTANCE_FUNCTION_MAP = {
     DistanceFunction.COSINE_SIMILARITY: "COSINE",
     DistanceFunction.DOT_PROD: "IP",
diff --git a/python/semantic_kernel/connectors/memory/redis/redis_collection.py b/python/semantic_kernel/connectors/memory/redis/redis_collection.py
index 540099a857ee0..2f9bd2d2b4eef 100644
--- a/python/semantic_kernel/connectors/memory/redis/redis_collection.py
+++ b/python/semantic_kernel/connectors/memory/redis/redis_collection.py
@@ -1,9 +1,11 @@
 # Copyright (c) Microsoft. All rights reserved.
 
 import asyncio
+import contextlib
 import json
 import logging
 import sys
+from abc import abstractmethod
 from collections.abc import Sequence
 from copy import copy
 from typing import Any, ClassVar, TypeVar
@@ -17,28 +19,51 @@
 from pydantic import ValidationError
 from redis.asyncio.client import Redis
 from redis.commands.search.indexDefinition import IndexDefinition
-
-from semantic_kernel.connectors.memory.redis.const import INDEX_TYPE_MAP, RedisCollectionTypes
-from semantic_kernel.connectors.memory.redis.utils import RedisWrapper, data_model_definition_to_redis_fields
+from redisvl.index.index import process_results
+from redisvl.query.filter import FilterExpression
+from redisvl.query.query import BaseQuery, FilterQuery, VectorQuery
+from redisvl.redis.utils import array_to_buffer, buffer_to_array, convert_bytes
+
+from semantic_kernel.connectors.memory.redis.const import (
+    INDEX_TYPE_MAP,
+    STORAGE_TYPE_MAP,
+    TYPE_MAPPER_VECTOR,
+    RedisCollectionTypes,
+)
+from semantic_kernel.connectors.memory.redis.utils import (
+    RedisWrapper,
+    _filters_to_redis_filters,
+    data_model_definition_to_redis_fields,
+)
+from semantic_kernel.data.const import DistanceFunction
+from semantic_kernel.data.kernel_search_results import KernelSearchResults
 from semantic_kernel.data.record_definition import (
     VectorStoreRecordDefinition,
     VectorStoreRecordKeyField,
     VectorStoreRecordVectorField,
 )
-from semantic_kernel.data.vector_storage import VectorStoreRecordCollection
+from semantic_kernel.data.vector_search.vector_search import VectorSearchBase
+from semantic_kernel.data.vector_search.vector_search_options import VectorSearchOptions
+from semantic_kernel.data.vector_search.vector_search_result import VectorSearchResult
+from semantic_kernel.data.vector_search.vector_text_search import VectorTextSearchMixin
+from semantic_kernel.data.vector_search.vectorized_search import VectorizedSearchMixin
 from semantic_kernel.exceptions.memory_connector_exceptions import (
     MemoryConnectorException,
     MemoryConnectorInitializationError,
 )
+from semantic_kernel.exceptions.search_exceptions import VectorSearchExecutionException, VectorSearchOptionsException
 from semantic_kernel.utils.experimental_decorator import experimental_class
+from semantic_kernel.utils.list_handler import desync_list
 
 logger: logging.Logger = logging.getLogger(__name__)
 
 TModel = TypeVar("TModel")
 
+TQuery = TypeVar("TQuery", bound=BaseQuery)
+
 
 @experimental_class
-class RedisCollection(VectorStoreRecordCollection[str, TModel]):
+class RedisCollection(VectorSearchBase[str, TModel], VectorizedSearchMixin[TModel], VectorTextSearchMixin[TModel]):
     """A vector store record collection implementation using Redis."""
 
     redis_database: Redis
@@ -153,6 +178,89 @@ async def __aexit__(self, exc_type, exc_value, traceback) -> None:
         if self.managed_client:
             await self.redis_database.aclose()
 
+    @override
+    async def _inner_search(
+        self,
+        options: VectorSearchOptions,
+        search_text: str | None = None,
+        vectorizable_text: str | None = None,
+        vector: list[float | int] | None = None,
+        **kwargs: Any,
+    ) -> KernelSearchResults[VectorSearchResult[TModel]]:
+        if vector is not None:
+            query = self._construct_vector_query(vector, options, **kwargs)
+        elif search_text:
+            query = self._construct_text_query(search_text, options, **kwargs)
+        elif vectorizable_text:
+            raise VectorSearchExecutionException("Vectorizable text search not supported.")
+        results = await self.redis_database.ft(self.collection_name).search(
+            query=query.query, query_params=query.params
+        )
+        processed = process_results(results, query, STORAGE_TYPE_MAP[self.collection_type])
+        return KernelSearchResults(
+            results=self._get_vector_search_results_from_results(desync_list(processed)),
+            total_count=results.total,
+        )
+
+    def _construct_vector_query(
+        self, vector: list[float | int], options: VectorSearchOptions, **kwargs: Any
+    ) -> VectorQuery:
+        vector_field = self.data_model_definition.try_get_vector_field(options.vector_field_name)
+        if not vector_field:
+            raise VectorSearchOptionsException("Vector field not found.")
+        query = VectorQuery(
+            vector=vector,
+            vector_field_name=vector_field.name,  # type: ignore
+            filter_expression=_filters_to_redis_filters(options.filter, self.data_model_definition),
+            num_results=options.top + options.skip,
+            dialect=2,
+            return_score=True,
+        )
+        query.paging(offset=options.skip, num=options.top + options.skip)
+        query.sort_by(
+            query.DISTANCE_ID,
+            asc=(vector_field.distance_function or "default")
+            in [
+                DistanceFunction.COSINE_SIMILARITY,
+                DistanceFunction.DOT_PROD,
+            ],
+        )
+        return self._add_return_fields(query, options.include_vectors)
+
+    def _construct_text_query(self, search_text: str, options: VectorSearchOptions, **kwargs: Any) -> FilterQuery:
+        query = FilterQuery(
+            FilterExpression(_filter=search_text)
+            & _filters_to_redis_filters(options.filter, self.data_model_definition),
+            num_results=options.top + options.skip,
+            dialect=2,
+        )
+        query.paging(offset=options.skip, num=options.top + options.skip)
+        return self._add_return_fields(query, options.include_vectors)
+
+    @abstractmethod
+    def _add_return_fields(self, query: TQuery, include_vectors: bool) -> TQuery:
+        """Add the return fields to the query.
+
+        There is a difference between the JSON and Hashset collections,
+        this method should be overridden by the subclasses.
+
+        """
+        pass
+
+    @override
+    def _get_record_from_result(self, result: dict[str, Any]) -> Any:
+        """Get a record from a result."""
+        ret = result.copy()
+        ret.pop("vector_distance", None)
+        for key, value in ret.items():
+            with contextlib.suppress(json.JSONDecodeError):
+                ret[key] = json.loads(value) if isinstance(value, str) else value
+        return ret
+
+    @override
+    def _get_score_from_result(self, result: dict[str, Any]) -> float | None:
+        return result.get("vector_distance")
+
 
 @experimental_class
 class RedisHashsetCollection(RedisCollection):
@@ -198,10 +306,17 @@ async def _single_upsert(self, upsert_record: Any) -> str:
         return self._unget_redis_key(upsert_record["name"])
 
     @override
-    async def _inner_get(self, keys: Sequence[str], **kwargs) -> Sequence[dict[bytes, bytes]] | None:
-        results = await asyncio.gather(*[self.redis_database.hgetall(self._get_redis_key(key)) for key in keys])
+    async def _inner_get(self, keys: Sequence[str], **kwargs) -> Sequence[dict[str, Any]] | None:
+        results = await asyncio.gather(*[self._single_get(self._get_redis_key(key)) for key in keys])
         return [result for result in results if result]
 
+    async def _single_get(self, key: str) -> dict[str, Any] | None:
+        result = await self.redis_database.hgetall(key)
+        if result:
+            result = convert_bytes(result)
+            result[self.data_model_definition.key_field_name] = key
+        return result
+
     @override
     async def _inner_delete(self, keys: Sequence[str], **kwargs: Any) -> None:
         await self.redis_database.delete(*[self._get_redis_key(key) for key in keys])
@@ -216,40 +331,56 @@ def _serialize_dicts_to_store_models(
         results = []
         for record in records:
             result = {"mapping": {}}
-            metadata = {}
             for name, field in self.data_model_definition.fields.items():
                 if isinstance(field, VectorStoreRecordVectorField):
-                    if not isinstance(record[name], np.ndarray):
-                        record[name] = np.array(record[name])
-                    result["mapping"][name] = record[name].tobytes()
+                    dtype = TYPE_MAPPER_VECTOR[field.property_type or "default"].lower()
+                    if isinstance(record[name], np.ndarray):
+                        result["mapping"][name] = record[name].astype(dtype).tobytes()
+                    else:
+                        result["mapping"][name] = array_to_buffer(record[name], dtype)
                     continue
                 if isinstance(field, VectorStoreRecordKeyField):
                     result["name"] = self._get_redis_key(record[name])
                     continue
-                metadata[name] = record[field.name]
-            result["mapping"]["metadata"] = json.dumps(metadata)
+                result["mapping"][name] = record[field.name]
             results.append(result)
         return results
 
     @override
     def _deserialize_store_models_to_dicts(
         self,
-        records: Sequence[dict[bytes, bytes]],
-        keys: Sequence[str],
+        records: Sequence[dict[str, Any]],
         **kwargs: Any,
     ) -> Sequence[dict[str, Any]]:
         results = []
-        for key, record in zip(keys, records):
-            if record:
-                flattened = json.loads(record[b"metadata"])
-                for name, field in self.data_model_definition.fields.items():
-                    if isinstance(field, VectorStoreRecordKeyField):
-                        flattened[name] = self._unget_redis_key(key)
-                    if isinstance(field, VectorStoreRecordVectorField):
-                        flattened[name] = np.frombuffer(record[name.encode()]).tolist()
-                results.append(flattened)
+        for record in records:
+            rec = record.copy()
+            for field in self.data_model_definition.fields.values():
+                match field:
+                    case VectorStoreRecordKeyField():
+                        rec[field.name] = self._unget_redis_key(rec[field.name])
+                    case VectorStoreRecordVectorField():
+                        dtype = TYPE_MAPPER_VECTOR[field.property_type or "default"]
+                        rec[field.name] = buffer_to_array(rec[field.name], dtype)
+            results.append(rec)
         return results
 
+    def _add_return_fields(self, query: TQuery, include_vectors: bool) -> TQuery:
+        """Add the return fields to the query.
+
+        For a Hashset index this should not be decoded, that is the only difference
+        between this and the JSON collection.
+
+        """
+        for field in self.data_model_definition.fields.values():
+            match field:
+                case VectorStoreRecordVectorField():
+                    if include_vectors:
+                        query.return_field(field.name, decode_field=False)
+                case _:
+                    query.return_field(field.name)
+        return query
+
 
 @experimental_class
 class RedisJsonCollection(RedisCollection):
@@ -298,8 +429,13 @@ async def _single_upsert(self, upsert_record: Any) -> str:
     async def _inner_get(self, keys: Sequence[str], **kwargs) -> Sequence[dict[bytes, bytes]] | None:
         kwargs_copy = copy(kwargs)
         kwargs_copy.pop("include_vectors", None)
-        results = await self.redis_database.json().mget([self._get_redis_key(key) for key in keys], "$", **kwargs_copy)
-        return [result[0] for result in results if result]
+        redis_keys = [self._get_redis_key(key) for key in keys]
+        results = await self.redis_database.json().mget(redis_keys, "$", **kwargs_copy)
+        return [self._add_key(key, result[0]) for key, result in zip(redis_keys, results) if result]
+
+    def _add_key(self, key: str, record: dict[str, Any]) -> dict[str, Any]:
+        record[self.data_model_definition.key_field_name] = key
+        return record
 
     @override
     async def _inner_delete(self, keys: Sequence[str], **kwargs: Any) -> None:
@@ -331,11 +467,27 @@ def _serialize_dicts_to_store_models(
     def _deserialize_store_models_to_dicts(
         self,
         records: Sequence[dict[str, Any]],
-        keys: Sequence[str],
+        keys: Sequence[str] | None = None,
         **kwargs: Any,
     ) -> Sequence[dict[str, Any]]:
+        key_field_name = self.data_model_definition.key_field_name
+        if keys is None:
+            if key_field_name in records[0]:
+                return records
+            raise MemoryConnectorException("Key field not found in records, and not supplied.")
         results = []
         for key, record in zip(keys, records):
             record[self.data_model_definition.key_field_name] = self._unget_redis_key(key)
             results.append(record)
         return results
+
+    def _add_return_fields(self, query: TQuery, include_vectors: bool) -> TQuery:
+        """Add the return fields to the query."""
+        for field in self.data_model_definition.fields.values():
+            match field:
+                case VectorStoreRecordVectorField():
+                    if include_vectors:
+                        query.return_field(field.name)
+                case _:
+                    query.return_field(field.name)
+        return query
diff --git a/python/semantic_kernel/connectors/memory/redis/utils.py b/python/semantic_kernel/connectors/memory/redis/utils.py
index 7e58c47b9e695..68054281d1566 100644
--- a/python/semantic_kernel/connectors/memory/redis/utils.py
+++ b/python/semantic_kernel/connectors/memory/redis/utils.py
@@ -11,15 +11,23 @@
 from redis.commands.search.document import Document
 from redis.commands.search.field import Field as RedisField
 from redis.commands.search.field import NumericField, TagField, TextField, VectorField
+from redisvl.query.filter import FilterExpression, Num, Tag, Text
 
-from semantic_kernel.connectors.memory.azure_ai_search.const import DISTANCE_FUNCTION_MAP
-from semantic_kernel.connectors.memory.redis.const import TYPE_MAPPER_VECTOR, RedisCollectionTypes
+from semantic_kernel.connectors.memory.redis.const import (
+    DISTANCE_FUNCTION_MAP,
+    TYPE_MAPPER_VECTOR,
+    RedisCollectionTypes,
+)
+from semantic_kernel.data.filter_clauses.any_tags_equal_to_filter_clause import AnyTagsEqualTo
+from semantic_kernel.data.filter_clauses.equal_to_filter_clause import EqualTo
 from semantic_kernel.data.record_definition.vector_store_model_definition import VectorStoreRecordDefinition
 from semantic_kernel.data.record_definition.vector_store_record_fields import (
     VectorStoreRecordDataField,
     VectorStoreRecordKeyField,
     VectorStoreRecordVectorField,
 )
+from semantic_kernel.data.vector_search.vector_search_filter import VectorSearchFilter
+from semantic_kernel.exceptions.search_exceptions import VectorSearchOptionsException
 from semantic_kernel.memory.memory_record import MemoryRecord
 
 
@@ -186,3 +194,40 @@ def _field_to_redis_field_json(
     if field.is_full_text_searchable:
         return TextField(name=f"$.{name}", as_name=name)
     return TagField(name=f"$.{name}", as_name=name)
+
+
+def _filters_to_redis_filters(
+    filters: VectorSearchFilter, data_model_definition: VectorStoreRecordDefinition
+) -> FilterExpression | None:
+    """Convert filters to Redis filters."""
+    expression: FilterExpression | None = None
+    for filter in filters.filters:
+        new: FilterExpression | None = None
+        field = data_model_definition.fields.get(filter.field_name)
+        text_field = (
+            field.is_full_text_searchable if isinstance(field, VectorStoreRecordDataField) else False
+        ) or False
+        match filter:
+            case EqualTo():
+                match filter.value:
+                    case int() | float():
+                        new = (
+                            Num(filter.field_name) == filter.value  # type: ignore
+                            if text_field
+                            else Tag(filter.field_name) == filter.value
+                        )
+                    case str():
+                        new = (
+                            Text(filter.field_name) == filter.value
+                            if text_field
+                            else Tag(filter.field_name) == filter.value
+                        )
+                    case _:
+                        raise VectorSearchOptionsException(f"Unsupported filter value type: {type(filter.value)}")
+            case AnyTagsEqualTo():
+                new = Text(filter.field_name) == filter.value
+            case _:
+                raise VectorSearchOptionsException(f"Unsupported filter type: {type(filter)}")
+        if new:
+            expression = expression & new if expression else new
+    return expression
diff --git a/python/semantic_kernel/data/record_definition/vector_store_model_definition.py b/python/semantic_kernel/data/record_definition/vector_store_model_definition.py
index 9c819dddc6936..5d7e01faa42b6 100644
--- a/python/semantic_kernel/data/record_definition/vector_store_model_definition.py
+++ b/python/semantic_kernel/data/record_definition/vector_store_model_definition.py
@@ -60,6 +60,51 @@ def vector_field_names(self) -> list[str]:
         """Get the names of the vector fields."""
         return [name for name, value in self.fields.items() if isinstance(value, VectorStoreRecordVectorField)]
 
+    @property
+    def non_vector_field_names(self) -> list[str]:
+        """Get the names of all the non-vector fields."""
+        return [name for name, value in self.fields.items() if not isinstance(value, VectorStoreRecordVectorField)]
+
+    def try_get_vector_field(self, field_name: str | None = None) -> VectorStoreRecordVectorField | None:
+        """Try to get the vector field.
+
+        If the field_name is None, then the first vector field is returned.
+        If no vector fields are present None is returned.
+
+        Args:
+            field_name: The field name.
+
+        Returns:
+            VectorStoreRecordVectorField | None: The vector field or None.
+        """
+        if field_name is None:
+            if len(self.vector_fields) == 0:
+                return None
+            return self.vector_fields[0]
+        field = self.fields.get(field_name, None)
+        if not field:
+            raise VectorStoreModelException(f"Field {field_name} not found.")
+        if not isinstance(field, VectorStoreRecordVectorField):
+            raise VectorStoreModelException(f"Field {field_name} is not a vector field.")
+        return field
+
+    def get_field_names(self, include_vector_fields: bool = True, include_key_field: bool = True) -> list[str]:
+        """Get the names of the fields.
+
+        Args:
+            include_vector_fields: Whether to include vector fields.
+            include_key_field: Whether to include the key field.
+
+        Returns:
+            list[str]: The names of the fields.
+        """
+        field_names = self.field_names
+        if not include_vector_fields:
+            field_names = [name for name in field_names if name not in self.vector_field_names]
+        if not include_key_field:
+            field_names = [name for name in field_names if name != self.key_field_name]
+        return field_names
+
     @property
     def vector_fields(self) -> list["VectorStoreRecordVectorField"]:
         """Get the names of the vector fields."""
diff --git a/python/semantic_kernel/data/vector_storage/vector_store_record_collection.py b/python/semantic_kernel/data/vector_storage/vector_store_record_collection.py
index f0efb8ef4729f..9186f7eb5151f 100644
--- a/python/semantic_kernel/data/vector_storage/vector_store_record_collection.py
+++ b/python/semantic_kernel/data/vector_storage/vector_store_record_collection.py
@@ -297,7 +297,7 @@ async def get(self, key: TKey, include_vectors: bool = True, **kwargs: Any) -> T
             return None
 
         try:
-            model_records = self.deserialize(records[0], keys=[key], **kwargs)
+            model_records = self.deserialize(records[0], **kwargs)
         except Exception as exc:
             raise MemoryConnectorException(f"Error deserializing record: {exc}") from exc
 
diff --git a/python/semantic_kernel/utils/list_handler.py b/python/semantic_kernel/utils/list_handler.py
new file mode 100644
index 0000000000000..713ef87721d3c
--- /dev/null
+++ b/python/semantic_kernel/utils/list_handler.py
@@ -0,0 +1,13 @@
+# Copyright (c) Microsoft. All rights reserved.
+
+
+from collections.abc import AsyncIterable, Sequence
+from typing import TypeVar
+
+_T = TypeVar("_T")
+
+
+async def desync_list(sync_list: Sequence[_T]) -> AsyncIterable[_T]:  # noqa: RUF029
+    """De synchronize a list of synchronous objects."""
+    for x in sync_list:
+        yield x
diff --git a/python/tests/unit/connectors/memory/redis/test_redis_store.py b/python/tests/unit/connectors/memory/redis/test_redis_store.py
index f233bbc73e9db..a74c78fb00062 100644
--- a/python/tests/unit/connectors/memory/redis/test_redis_store.py
+++ b/python/tests/unit/connectors/memory/redis/test_redis_store.py
@@ -108,7 +108,7 @@ def mock_upsert_json():
 def mock_get_hash():
     with patch(f"{BASE_PATH}.hgetall", new=AsyncMock()) as mock_get:
         mock_get.return_value = {
-            b"metadata": b'{"content": "content"}',
+            b"content": b"content",
             b"vector": np.array([1.0, 2.0, 3.0]).tobytes(),
         }
         yield mock_get
diff --git a/python/uv.lock b/python/uv.lock
index ff0b4ae00c8dd..66f3f920e8859 100644
--- a/python/uv.lock
+++ b/python/uv.lock
@@ -5,17 +5,20 @@ resolution-markers = [
     "python_full_version == '3.11.*' and sys_platform == 'darwin'",
     "python_full_version < '3.11' and sys_platform == 'darwin'",
     "python_full_version == '3.11.*' and sys_platform == 'darwin'",
-    "python_full_version >= '3.12' and sys_platform == 'darwin'",
+    "python_full_version == '3.12.*' and sys_platform == 'darwin'",
+    "python_full_version >= '3.13' and sys_platform == 'darwin'",
     "python_full_version < '3.11' and sys_platform == 'linux'",
     "python_full_version == '3.11.*' and sys_platform == 'linux'",
     "python_full_version < '3.11' and sys_platform == 'linux'",
     "python_full_version == '3.11.*' and sys_platform == 'linux'",
-    "python_full_version >= '3.12' and sys_platform == 'linux'",
+    "python_full_version == '3.12.*' and sys_platform == 'linux'",
+    "python_full_version >= '3.13' and sys_platform == 'linux'",
     "python_full_version < '3.11' and sys_platform == 'win32'",
     "python_full_version == '3.11.*' and sys_platform == 'win32'",
     "python_full_version < '3.11' and sys_platform == 'win32'",
     "python_full_version == '3.11.*' and sys_platform == 'win32'",
-    "python_full_version >= '3.12' and sys_platform == 'win32'",
+    "python_full_version == '3.12.*' and sys_platform == 'win32'",
+    "python_full_version >= '3.13' and sys_platform == 'win32'",
 ]
 supported-markers = [
     "sys_platform == 'darwin'",
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