Python: restructed data folder and multiple improvements to vector stores

python/samples/concepts/caching/semantic_caching.py

@@ -7,25 +7,22 @@
 from typing import Annotated
 from uuid import uuid4
 
+from semantic_kernel import Kernel
 from semantic_kernel.connectors.ai.embedding_generator_base import EmbeddingGeneratorBase
-from semantic_kernel.connectors.ai.open_ai.services.open_ai_chat_completion import OpenAIChatCompletion
-from semantic_kernel.connectors.ai.open_ai.services.open_ai_text_embedding import OpenAITextEmbedding
+from semantic_kernel.connectors.ai.open_ai import OpenAIChatCompletion, OpenAITextEmbedding
 from semantic_kernel.connectors.memory.in_memory.in_memory_store import InMemoryVectorStore
-from semantic_kernel.data.record_definition import vectorstoremodel
-from semantic_kernel.data.record_definition.vector_store_record_fields import (
+from semantic_kernel.data import (
+    VectorizedSearchMixin,
+    VectorSearchOptions,
+    VectorStore,
+    VectorStoreRecordCollection,
     VectorStoreRecordDataField,
     VectorStoreRecordKeyField,
     VectorStoreRecordVectorField,
+    vectorstoremodel,
 )
-from semantic_kernel.data.vector_search.vector_search_options import VectorSearchOptions
-from semantic_kernel.data.vector_search.vectorized_search import VectorizedSearchMixin
-from semantic_kernel.data.vector_storage.vector_store import VectorStore
-from semantic_kernel.data.vector_storage.vector_store_record_collection import VectorStoreRecordCollection
-from semantic_kernel.filters.filter_types import FilterTypes
-from semantic_kernel.filters.functions.function_invocation_context import FunctionInvocationContext
-from semantic_kernel.filters.prompts.prompt_render_context import PromptRenderContext
-from semantic_kernel.functions.function_result import FunctionResult
-from semantic_kernel.kernel import Kernel
+from semantic_kernel.filters import FilterTypes, FunctionInvocationContext, PromptRenderContext
+from semantic_kernel.functions import FunctionResult
 
 COLLECTION_NAME = "llm_responses"
 RECORD_ID_KEY = "cache_record_id"

python/samples/concepts/chat_history/store_chat_history_in_cosmosdb.py

@@ -6,19 +6,18 @@
 
 from samples.concepts.setup.chat_completion_services import Services, get_chat_completion_service_and_request_settings
 from semantic_kernel import Kernel
-from semantic_kernel.connectors.ai.function_choice_behavior import FunctionChoiceBehavior
+from semantic_kernel.connectors.ai import FunctionChoiceBehavior
 from semantic_kernel.connectors.memory.azure_cosmos_db.azure_cosmos_db_no_sql_store import AzureCosmosDBNoSQLStore
-from semantic_kernel.contents import ChatHistory
-from semantic_kernel.contents.chat_message_content import ChatMessageContent
+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.record_definition.vector_store_model_decorator import vectorstoremodel
-from semantic_kernel.data.record_definition.vector_store_record_fields import (
+from semantic_kernel.data import (
+    VectorStore,
+    VectorStoreRecordCollection,
     VectorStoreRecordDataField,
     VectorStoreRecordKeyField,
+    vectorstoremodel,
 )
-from semantic_kernel.data.vector_storage.vector_store import VectorStore
-from semantic_kernel.data.vector_storage.vector_store_record_collection import VectorStoreRecordCollection
 
 """
 This sample demonstrates how to build a conversational chatbot

python/samples/concepts/memory/azure_ai_search_hotel_samples/step_1_interact_with_the_collection.py

@@ -2,6 +2,12 @@
 
 import asyncio
 
+from step_0_data_model import HotelSampleClass
+
+from semantic_kernel import Kernel
+from semantic_kernel.connectors.ai.open_ai import OpenAITextEmbedding
+from semantic_kernel.connectors.memory.azure_ai_search import AzureAISearchCollection
+
 ###
 # The data model used for this sample is based on the hotel data model from the Azure AI Search samples.
 # When deploying a new index in Azure AI Search using the import wizard you can choose to deploy the 'hotel-samples'
@@ -13,24 +19,19 @@
 # This sample assumes the index is deployed, the vector fields can be empty.
 # If the vector fields are empty, change the first_run parameter to True to add the vectors.
 ###
-from step_0_data_model import HotelSampleClass
-
-from semantic_kernel import Kernel
-from semantic_kernel.connectors.ai.open_ai import OpenAITextEmbedding
-from semantic_kernel.connectors.memory.azure_ai_search import AzureAISearchCollection
 from semantic_kernel.data import (
     VectorSearchOptions,
-    VectorStoreRecordUtils,
 )
+from semantic_kernel.data.vector_search import add_vector_to_records
 
 first_run = False
 
 # Note: you may need to update this `collection_name` depending upon how your index is named.
 COLLECTION_NAME = "hotels-sample-index"
 
 
-async def add_vectors(collection: AzureAISearchCollection, vectorizer: VectorStoreRecordUtils):
-    """This is a simple function that uses the VectorStoreRecordUtils to add vectors to the records in the collection.
+async def add_vectors(collection: AzureAISearchCollection, kernel: Kernel):
+    """This is a simple function that uses the add_vector_to_records function to add vectors.
 
     It first uses the search_client within the collection to get a list of ids.
     and then uses the upsert to add the vectors to the records.
@@ -42,7 +43,7 @@ async def add_vectors(collection: AzureAISearchCollection, vectorizer: VectorSto
     if hotels is not None and isinstance(hotels, list):
         for hotel in hotels:
             if not hotel.description_vector or not hotel.description_fr_vector:
-                hotel = await vectorizer.add_vector_to_records(hotel, HotelSampleClass)
+                hotel = await add_vector_to_records(kernel, hotel, HotelSampleClass)
                 await collection.upsert(hotel)
 
 
@@ -52,10 +53,8 @@ async def main(query: str, first_run: bool = False):
     # Add the OpenAI text embedding service
     embeddings = OpenAITextEmbedding(service_id="embedding", ai_model_id="text-embedding-3-small")
     kernel.add_service(embeddings)
-    # Create the VectorStoreRecordUtils object
-    vectorizer = VectorStoreRecordUtils(kernel)
     # Create the Azure AI Search collection
-    collection = AzureAISearchCollection[HotelSampleClass](
+    collection = AzureAISearchCollection[str, HotelSampleClass](
         collection_name=COLLECTION_NAME, data_model_type=HotelSampleClass
     )
     # Check if the collection exists.
@@ -71,7 +70,7 @@ async def main(query: str, first_run: bool = False):
 
     # If it is the first run and there are no vectors, add them.
     if first_run:
-        await add_vectors(collection, vectorizer)
+        await add_vectors(collection, kernel)
 
     # Search using just text, by default this will search all the searchable text fields in the index.
     results = await collection.text_search(search_text=query)

python/samples/concepts/memory/azure_ai_search_hotel_samples/step_2_use_as_a_plugin.py

@@ -2,9 +2,21 @@
 
 
 import asyncio
-from collections.abc import Coroutine
+from collections.abc import Awaitable, Callable
 from typing import Any
 
+from step_0_data_model import HotelSampleClass
+
+from semantic_kernel import Kernel
+from semantic_kernel.connectors.ai import FunctionChoiceBehavior
+from semantic_kernel.connectors.ai.open_ai import (
+    OpenAIChatCompletion,
+    OpenAIChatPromptExecutionSettings,
+    OpenAITextEmbedding,
+)
+from semantic_kernel.connectors.memory.azure_ai_search import AzureAISearchCollection
+from semantic_kernel.contents import ChatHistory
+
 ###
 # The data model used for this sample is based on the hotel data model from the Azure AI Search samples.
 # When deploying a new index in Azure AI Search using the import wizard you can choose to deploy the 'hotel-samples'
@@ -16,26 +28,12 @@
 # This sample assumes the index is deployed, and the vectors have been filled.
 # Use the step_1_interact_with_the_collection.py sample, with `first_run = True` to fill the vectors.
 ###
-from step_0_data_model import HotelSampleClass
-
-from semantic_kernel import Kernel
-from semantic_kernel.connectors.ai.function_choice_behavior import FunctionChoiceBehavior
-from semantic_kernel.connectors.ai.open_ai import (
-    OpenAIChatCompletion,
-    OpenAIChatPromptExecutionSettings,
-    OpenAITextEmbedding,
-)
-from semantic_kernel.connectors.memory.azure_ai_search import AzureAISearchCollection
-from semantic_kernel.contents import ChatHistory
 from semantic_kernel.data import (
     VectorSearchFilter,
     VectorSearchOptions,
-    VectorStoreRecordUtils,
 )
-from semantic_kernel.data.search_options import SearchOptions
-from semantic_kernel.data.text_search.vector_store_text_search import VectorStoreTextSearch
-from semantic_kernel.filters.filter_types import FilterTypes
-from semantic_kernel.filters.functions.function_invocation_context import FunctionInvocationContext
+from semantic_kernel.data.text_search import SearchOptions
+from semantic_kernel.filters import FilterTypes, FunctionInvocationContext
 from semantic_kernel.functions import (
     KernelArguments,
     KernelParameterMetadata,
@@ -50,15 +48,15 @@
 kernel.add_service(OpenAIChatCompletion(service_id=service_id))
 embeddings = OpenAITextEmbedding(service_id="embedding", ai_model_id="text-embedding-3-small")
 kernel.add_service(embeddings)
-vectorizer = VectorStoreRecordUtils(kernel)
 
 # Create a Text Search object, with a Azure AI Search collection.
 # using the `from_vector_text_search` method means that this plugin will only use text search.
 # You can also choose to use the `from_vectorized_search` method to use vector search.
 # Or the `from_vectorizable_text_search` method if the collection is setup to vectorize incoming texts.
-text_search = VectorStoreTextSearch.from_vector_text_search(
-    AzureAISearchCollection[HotelSampleClass](collection_name=COLLECTION_NAME, data_model_type=HotelSampleClass)
+collection = AzureAISearchCollection[str, HotelSampleClass](
+    collection_name=COLLECTION_NAME, data_model_type=HotelSampleClass
 )
+text_search = collection.create_text_search_from_vector_text_search()
 
 
 # Before we create the plugin, we want to create a function that will help the plugin work the way we want it to.
@@ -195,7 +193,9 @@ def update_options_search(
 # This allows us to see what parameters are being passed to the plugin.
 # And this gives us a way to debug the search experience and if necessary tweak the parameters and descriptions.
 @kernel.filter(filter_type=FilterTypes.FUNCTION_INVOCATION)
-async def log_search_filter(context: FunctionInvocationContext, next: Coroutine[FunctionInvocationContext, Any, None]):
+async def log_search_filter(
+    context: FunctionInvocationContext, next: Callable[[FunctionInvocationContext], Awaitable[None]]
+):
     if context.function.plugin_name == "azure_ai_search":
         print(f"Calling Azure AI Search ({context.function.name}) with arguments:")
         for arg in context.arguments:

python/samples/concepts/memory/complex_memory.py

@@ -31,9 +31,6 @@
 from semantic_kernel.connectors.memory.redis import RedisHashsetCollection, RedisJsonCollection
 from semantic_kernel.connectors.memory.weaviate import WeaviateCollection
 from semantic_kernel.data import (
-    DISTANCE_FUNCTION_DIRECTION_HELPER,
-    DistanceFunction,
-    IndexKind,
     VectorizableTextSearchMixin,
     VectorizedSearchMixin,
     VectorSearchFilter,
@@ -45,7 +42,8 @@
     VectorTextSearchMixin,
     vectorstoremodel,
 )
-from semantic_kernel.data.record_definition.vector_store_record_utils import VectorStoreRecordUtils
+from semantic_kernel.data.const import DISTANCE_FUNCTION_DIRECTION_HELPER, DistanceFunction, IndexKind
+from semantic_kernel.data.vector_search import add_vector_to_records
 
 # This is a rather complex sample, showing how to use the vector store
 # with a number of different collections.
@@ -254,14 +252,12 @@ async def main(collection: str, use_azure_openai: bool):
         )
 
         print_with_color("Adding records!", Colors.CBLUE)
-        records = await VectorStoreRecordUtils(kernel).add_vector_to_records(
-            [record1, record2, record3], data_model_type=DataModel
-        )
+        records = await add_vector_to_records(kernel, [record1, record2, record3], data_model_type=DataModel)
         records = [record1, record2, record3]
         keys = await record_collection.upsert_batch(records)
         print(f"    Upserted {keys=}")
         print_with_color("Getting records!", Colors.CBLUE)
-        results = await record_collection.get_batch([record1.id, record2.id, record3.id])
+        results = await record_collection.get([record1.id, record2.id, record3.id])
         if results:
             [print_record(record=result) for result in results]
         else:

python/samples/concepts/memory/data_models.py

@@ -63,17 +63,6 @@ class DataModelPydantic(BaseModel):
     other: str | None = None
 
 
-# Data model using Pydantic BaseModels with mixed annotations (from pydantic and SK)
-@vectorstoremodel
-class DataModelPydanticComplex(BaseModel):
-    vector: Annotated[list[float], VectorStoreRecordVectorField]
-    key: Annotated[str, Field(default_factory=lambda: str(uuid4())), VectorStoreRecordKeyField()]
-    content: Annotated[str, VectorStoreRecordDataField(has_embedding=True, embedding_property_name="vector")] = (
-        "content1"
-    )
-    other: str | None = None
-
-
 # Data model using Python classes
 # This one includes a custom serialize and deserialize method
 @vectorstoremodel
@@ -133,25 +122,21 @@ def deserialize(cls, obj: dict[str, Any]) -> "DataModelDataclass":
 if __name__ == "__main__":
     data_item1 = DataModelDataclass(content="Hello, world!", vector=[1.0, 2.0, 3.0], other=None)
     data_item2 = DataModelPydantic(content="Hello, world!", vector=[1.0, 2.0, 3.0], other=None)
-    data_item3 = DataModelPydanticComplex(content="Hello, world!", vector=[1.0, 2.0, 3.0], other=None)
-    data_item4 = DataModelPython(content="Hello, world!", vector=[1.0, 2.0, 3.0], other=None)
+    data_item3 = DataModelPython(content="Hello, world!", vector=[1.0, 2.0, 3.0], other=None)
     print("Example records:")
     print(f"DataClass:\n  {data_item1}", end="\n\n")
     print(f"Pydantic:\n  {data_item2}", end="\n\n")
-    print(f"Pydantic with annotations:\n  {data_item3}", end="\n\n")
-    print(f"Python:\n  {data_item4}", end="\n\n")
+    print(f"Python:\n  {data_item3}", end="\n\n")
 
     print("Item definitions:")
     print(f"DataClass:\n  {data_item1.__kernel_vectorstoremodel_definition__}", end="\n\n")
     print(f"Pydantic:\n  {data_item2.__kernel_vectorstoremodel_definition__}", end="\n\n")
-    print(f"Pydantic with annotations:\n  {data_item3.__kernel_vectorstoremodel_definition__}", end="\n\n")
-    print(f"Python:\n  {data_item4.__kernel_vectorstoremodel_definition__}", end="\n\n")
+    print(f"Python:\n  {data_item3.__kernel_vectorstoremodel_definition__}", end="\n\n")
     print(f"Definition for use with Pandas:\n  {data_model_definition_pandas}", end="\n\n")
     if (
         data_item1.__kernel_vectorstoremodel_definition__.fields
         == data_item2.__kernel_vectorstoremodel_definition__.fields
         == data_item3.__kernel_vectorstoremodel_definition__.fields
-        == data_item4.__kernel_vectorstoremodel_definition__.fields
         == data_model_definition_pandas.fields
     ):
         print("All data models are the same")

python/samples/concepts/memory/memory_with_pandas.py

@@ -15,9 +15,9 @@
     VectorStoreRecordDataField,
     VectorStoreRecordDefinition,
     VectorStoreRecordKeyField,
-    VectorStoreRecordUtils,
     VectorStoreRecordVectorField,
 )
+from semantic_kernel.data.vector_search import add_vector_to_records
 
 model_fields = VectorStoreRecordDefinition(
     container_mode=True,
@@ -51,7 +51,7 @@ async def main():
 
         # create the dataframe and add the embeddings
         df = pd.DataFrame(records)
-        df = await VectorStoreRecordUtils(kernel).add_vector_to_records(df, None, data_model_definition=model_fields)
+        df = await add_vector_to_records(kernel, df, None, data_model_definition=model_fields)
         print("Records with embeddings:")
         print(df.shape)
         print(df.head(5))

python/samples/concepts/memory/simple_memory.py

@@ -15,17 +15,15 @@
 )
 from semantic_kernel.connectors.memory.in_memory import InMemoryVectorCollection
 from semantic_kernel.data import (
-    DISTANCE_FUNCTION_DIRECTION_HELPER,
-    DistanceFunction,
-    IndexKind,
     VectorSearchFilter,
     VectorSearchOptions,
     VectorStoreRecordDataField,
     VectorStoreRecordKeyField,
-    VectorStoreRecordUtils,
     VectorStoreRecordVectorField,
     vectorstoremodel,
 )
+from semantic_kernel.data.const import DISTANCE_FUNCTION_DIRECTION_HELPER, DistanceFunction, IndexKind
+from semantic_kernel.data.vector_search import add_vector_to_records
 
 # 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"
@@ -115,9 +113,7 @@ async def main():
 
         # First add vectors to the records
         print_with_color("Adding records!", Colors.CBLUE)
-        records_with_embedding = await VectorStoreRecordUtils(kernel).add_vector_to_records(
-            records, data_model_type=DataModel
-        )
+        records_with_embedding = await add_vector_to_records(kernel, records, data_model_type=DataModel)
         # Next upsert them to the store.
         keys = await record_collection.upsert_batch(records_with_embedding)
         print(f"    Upserted {keys=}")

python/samples/concepts/memory/utils.py

@@ -1,12 +1,9 @@
 # Copyright (c) Microsoft. All rights reserved.
 
-
 from typing import TypeVar
 
 from samples.concepts.resources.utils import Colors, print_with_color
-from semantic_kernel.data import (
-    VectorSearchResult,
-)
+from semantic_kernel.data import VectorSearchResult
 
 _T = TypeVar("_T")