Python: vector store simplificatons and some cleanup

python/samples/concepts/caching/semantic_caching.py

@@ -10,7 +10,7 @@
 from semantic_kernel import Kernel
 from semantic_kernel.connectors.ai.open_ai import OpenAIChatCompletion, OpenAITextEmbedding
 from semantic_kernel.connectors.memory.in_memory import InMemoryStore
-from semantic_kernel.data import VectorStore, VectorStoreField, VectorStoreRecordCollection, vectorstoremodel
+from semantic_kernel.data.vectors import VectorStore, VectorStoreCollection, VectorStoreField, vectorstoremodel
 from semantic_kernel.filters import FilterTypes, FunctionInvocationContext, PromptRenderContext
 from semantic_kernel.functions import FunctionResult
 
@@ -41,9 +41,7 @@ def __init__(
         if vector_store.embedding_generator is None:
             raise ValueError("The vector store must have an embedding generator.")
         self.vector_store = vector_store
-        self.collection: VectorStoreRecordCollection[str, CacheRecord] = vector_store.get_collection(
-            record_type=CacheRecord
-        )
+        self.collection: VectorStoreCollection[str, CacheRecord] = vector_store.get_collection(record_type=CacheRecord)
         self.score_threshold = score_threshold
 
     async def on_prompt_render(

python/samples/concepts/chat_history/store_chat_history_in_cosmosdb.py

@@ -11,7 +11,7 @@
 from semantic_kernel.contents import ChatHistory, ChatMessageContent
 from semantic_kernel.core_plugins.math_plugin import MathPlugin
 from semantic_kernel.core_plugins.time_plugin import TimePlugin
-from semantic_kernel.data import VectorStore, VectorStoreField, VectorStoreRecordCollection, vectorstoremodel
+from semantic_kernel.data.vectors import VectorStore, VectorStoreCollection, VectorStoreField, vectorstoremodel
 
 """
 This sample demonstrates how to build a conversational chatbot
@@ -49,7 +49,7 @@ class ChatHistoryInCosmosDB(ChatHistory):
     session_id: str
     user_id: str
     store: VectorStore
-    collection: VectorStoreRecordCollection[str, ChatHistoryModel] | None = None
+    collection: VectorStoreCollection[str, ChatHistoryModel] | None = None
 
     async def create_collection(self, collection_name: str) -> None:
         """Create a collection with the inbuild data model using the vector store.

python/samples/concepts/memory/azure_ai_search_hotel_samples/data_model.py

@@ -15,7 +15,7 @@
 )
 from pydantic import BaseModel, ConfigDict
 
-from semantic_kernel.data import VectorStoreField, vectorstoremodel
+from semantic_kernel.data.vectors import VectorStoreField, vectorstoremodel
 
 """
 The data model used for this sample is based on the hotel data model from the Azure AI Search samples.

python/samples/concepts/memory/complex_memory.py

@@ -26,9 +26,13 @@
     SqlServerCollection,
     WeaviateCollection,
 )
-from semantic_kernel.data import VectorStoreRecordCollection, vectorstoremodel
-from semantic_kernel.data.definitions import VectorStoreField
-from semantic_kernel.data.vectors import SearchType, VectorSearch
+from semantic_kernel.data.vectors import (
+    SearchType,
+    VectorSearchProtocol,
+    VectorStoreCollection,
+    VectorStoreField,
+    vectorstoremodel,
+)
 
 # This is a rather complex sample, showing how to use the vector store
 # with a number of different collections.
@@ -46,16 +50,16 @@ class DataModel:
     title: Annotated[str, VectorStoreField("data", is_full_text_indexed=True)]
     content: Annotated[str, VectorStoreField("data", is_full_text_indexed=True)]
     embedding: Annotated[
-        str | None,
-        VectorStoreField("vector", dimensions=1536, type_="float"),
+        list[float] | str | None,
+        VectorStoreField("vector", dimensions=1536, type="float"),
     ] = None
     id: Annotated[
         str,
         VectorStoreField(
             "key",
         ),
     ] = field(default_factory=lambda: str(uuid4()))
-    tag: Annotated[str | None, VectorStoreField("data", type_="str", is_indexed=True)] = None
+    tag: Annotated[str | None, VectorStoreField("data", type="str", is_indexed=True)] = None
 
     def __post_init__(self, **kwargs):
         if self.embedding is None:
@@ -94,7 +98,7 @@ def __post_init__(self, **kwargs):
 # function which returns the collection.
 # Using a function allows for lazy initialization of the collection,
 # so that settings for unused collections do not cause validation errors.
-collections: dict[str, Callable[[], VectorStoreRecordCollection]] = {
+collections: dict[str, Callable[[], VectorStoreCollection]] = {
     "ai_search": lambda: AzureAISearchCollection[str, DataModel](record_type=DataModel),
     "postgres": lambda: PostgresCollection[str, DataModel](record_type=DataModel),
     "redis_json": lambda: RedisJsonCollection[str, DataModel](
@@ -143,6 +147,7 @@ async def main(collection: str, use_azure_openai: bool):
     )
     kernel.add_service(embedder)
     async with collections[collection]() as record_collection:
+        assert isinstance(record_collection, VectorSearchProtocol)  # nosec
         record_collection.embedding_generator = embedder
         print_with_color(f"Creating {collection} collection!", Colors.CGREY)
         # cleanup any existing collection
@@ -172,7 +177,7 @@ async def main(collection: str, use_azure_openai: bool):
         keys = await record_collection.upsert(records)
         print(f"    Upserted {keys=}")
         print_with_color("Getting records!", Colors.CBLUE)
-        results = await record_collection.get(top=10, order_by={"field": "content"})
+        results = await record_collection.get(top=10, order_by="content")
         if results:
             [print_record(record=result) for result in results]
         else:
@@ -187,7 +192,6 @@ async def main(collection: str, use_azure_openai: bool):
         print_with_color("Now we can start searching.", Colors.CBLUE)
         print_with_color("  For each type of search, enter a search term, for instance `python`.", Colors.CBLUE)
         print_with_color("  Enter exit to exit, and skip or nothing to skip this search.", Colors.CBLUE)
-        assert isinstance(record_collection, VectorSearch)  # nosec
         print("-" * 30)
         print_with_color(
             "This collection supports the following search types: "

python/samples/concepts/memory/data_models.py

@@ -7,7 +7,7 @@
 from pandas import DataFrame
 from pydantic import BaseModel, Field
 
-from semantic_kernel.data import VectorStoreCollectionDefinition, VectorStoreField, vectorstoremodel
+from semantic_kernel.data.vectors import VectorStoreCollectionDefinition, VectorStoreField, vectorstoremodel
 
 # This concept shows the different ways you can create a vector store data model
 # using dataclasses, Pydantic, and Python classes.

python/samples/concepts/memory/memory_with_pandas.py

@@ -7,7 +7,7 @@
 
 from semantic_kernel.connectors.ai.open_ai import OpenAITextEmbedding
 from semantic_kernel.connectors.memory.azure_ai_search import AzureAISearchCollection
-from semantic_kernel.data import VectorStoreCollectionDefinition, VectorStoreField
+from semantic_kernel.data.vectors import VectorStoreCollectionDefinition, VectorStoreField
 
 definition = VectorStoreCollectionDefinition(
     collection_name="pandas_test_index",

python/samples/concepts/memory/simple_memory.py

@@ -10,7 +10,7 @@
 from samples.concepts.resources.utils import Colors, print_with_color
 from semantic_kernel.connectors.ai.open_ai import OpenAITextEmbedding
 from semantic_kernel.connectors.memory import InMemoryCollection
-from semantic_kernel.data import VectorStoreField, vectorstoremodel
+from semantic_kernel.data.vectors import VectorStoreField, vectorstoremodel
 
 # This is the most basic example of a vector store and collection
 # For a more complex example, using different collection types, see "complex_memory.py"

python/samples/concepts/on_your_data/azure_chat_gpt_with_data_api.py

@@ -10,7 +10,7 @@
     AzureChatPromptExecutionSettings,
     ExtraBody,
 )
-from semantic_kernel.connectors.memory.azure_cognitive_search.azure_ai_search_settings import AzureAISearchSettings
+from semantic_kernel.connectors.memory.azure_ai_search import AzureAISearchSettings
 from semantic_kernel.contents import ChatHistory
 from semantic_kernel.functions import KernelArguments
 from semantic_kernel.prompt_template import InputVariable, PromptTemplateConfig

python/samples/concepts/rag/rag_with_vector_collection.py

@@ -11,7 +11,7 @@
     OpenAITextEmbedding,
 )
 from semantic_kernel.connectors.memory import InMemoryCollection
-from semantic_kernel.data import VectorStoreField, vectorstoremodel
+from semantic_kernel.data.vectors import VectorStoreField, vectorstoremodel
 from semantic_kernel.functions import KernelArguments
 
 """

python/samples/concepts/rag/self_critique_rag.py

@@ -9,7 +9,7 @@
 from semantic_kernel.connectors.ai.open_ai import OpenAIChatCompletion, OpenAITextEmbedding
 from semantic_kernel.connectors.memory import AzureAISearchCollection
 from semantic_kernel.contents import ChatHistory
-from semantic_kernel.data import VectorStoreField, vectorstoremodel
+from semantic_kernel.data.vectors import VectorStoreField, vectorstoremodel
 from semantic_kernel.functions.kernel_function import KernelFunction
 
 """

python/samples/concepts/search/brave_text_search_as_plugin.py

@@ -8,7 +8,7 @@
 from semantic_kernel.connectors.search.brave import BraveSearch
 from semantic_kernel.contents import ChatHistory
 from semantic_kernel.filters import FilterTypes, FunctionInvocationContext
-from semantic_kernel.functions import KernelArguments, KernelParameterMetadata, KernelPlugin
+from semantic_kernel.functions import KernelArguments, KernelParameterMetadata
 
 """
 This project demonstrates how to integrate the Brave Search API as a plugin into the Semantic Kernel 
@@ -21,10 +21,10 @@
 
 kernel = Kernel()
 kernel.add_service(OpenAIChatCompletion(service_id="chat"))
-kernel.add_plugin(
-    KernelPlugin.from_text_search_with_search(
-        BraveSearch(),
-        plugin_name="brave",
+kernel.add_function(
+    plugin_name="brave",
+    function=BraveSearch().create_search_function(
+        function_name="brave_search",
         description="Get details about Semantic Kernel concepts.",
         parameters=[
             KernelParameterMetadata(
@@ -51,7 +51,7 @@
                 type_object=int,
             ),
         ],
-    )
+    ),
 )
 chat_function = kernel.add_function(
     prompt="{{$chat_history}}{{$user_input}}",