updated based on feedback

Author: eavanvalkenburg

python/samples/concepts/memory/new_memory.py

@@ -92,7 +92,7 @@ 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.
-services: dict[str, Callable[[], VectorStoreRecordCollection]] = {
+collections: dict[str, Callable[[], VectorStoreRecordCollection]] = {
     "ai_search": lambda: AzureAISearchCollection[MyDataModel](
         data_model_type=MyDataModel,
     ),
@@ -133,7 +133,7 @@ async def main(service: str, use_azure_openai: bool, embedding_model: str):
     else:
         embedder = OpenAITextEmbedding(service_id=service_id, ai_model_id=embedding_model)
     kernel.add_service(embedder)
-    async with services[service]() as record_collection:
+    async with collections[service]() as record_collection:
         print(f"Creating {service} collection!")
         await record_collection.create_collection_if_not_exists()
 
@@ -184,7 +184,7 @@ async def main(service: str, use_azure_openai: bool, embedding_model: str):
     argparse.ArgumentParser()
 
     parser = argparse.ArgumentParser()
-    parser.add_argument("--service", default="in_memory", choices=services.keys(), help="What store to use.")
+    parser.add_argument("--service", default="in_memory", choices=collections.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

python/semantic_kernel/connectors/memory/qdrant/qdrant_collection.py

@@ -5,8 +5,6 @@
 from collections.abc import Mapping, Sequence
 from typing import Any, ClassVar, Generic, TypeVar
 
-from semantic_kernel.exceptions.search_exceptions import VectorSearchExecutionException
-
 if sys.version_info >= (3, 12):
     from typing import override  # pragma: no cover
 else:
@@ -29,6 +27,7 @@
     VectorStoreModelValidationError,
 )
 from semantic_kernel.exceptions.memory_connector_exceptions import MemoryConnectorException
+from semantic_kernel.exceptions.search_exceptions import VectorSearchExecutionException
 from semantic_kernel.kernel_types import OneOrMany
 from semantic_kernel.utils.experimental_decorator import experimental_class
 from semantic_kernel.utils.telemetry.user_agent import APP_INFO, prepend_semantic_kernel_to_user_agent
@@ -80,8 +79,6 @@ def __init__(
         When nothing is supplied, it defaults to an in-memory qdrant instance.
         You can also supply a async qdrant client directly.
 
-        If you want to use the vectorizable_text_search you will need to install `qrant_client[fastembed]`.
-
         Args:
             data_model_type (type[TModel]): The type of the data model.
             data_model_definition (VectorStoreRecordDefinition): The model fields, optional.