Python: Move connectors

python/samples/concepts/caching/semantic_caching.py

@@ -9,8 +9,8 @@
 
 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.vectors import VectorStore, VectorStoreCollection, VectorStoreField, vectorstoremodel
+from semantic_kernel.connectors.in_memory import InMemoryStore
+from semantic_kernel.data.vector import VectorStore, VectorStoreCollection, VectorStoreField, vectorstoremodel
 from semantic_kernel.filters import FilterTypes, FunctionInvocationContext, PromptRenderContext
 from semantic_kernel.functions import FunctionResult
 

python/samples/concepts/chat_history/store_chat_history_in_cosmosdb.py

@@ -7,11 +7,11 @@
 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 import FunctionChoiceBehavior
-from semantic_kernel.connectors.memory.azure_cosmos_db import CosmosNoSqlStore
+from semantic_kernel.connectors.azure_cosmos_db import CosmosNoSqlStore
 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.vectors import VectorStore, VectorStoreCollection, VectorStoreField, vectorstoremodel
+from semantic_kernel.data.vector import VectorStore, VectorStoreCollection, VectorStoreField, vectorstoremodel
 
 """
 This sample demonstrates how to build a conversational chatbot

python/samples/concepts/memory/azure_ai_search_hotel_samples/1_interact_with_the_collection.py

@@ -8,7 +8,7 @@
     load_records,
 )
 from semantic_kernel.connectors.ai.open_ai import OpenAITextEmbedding
-from semantic_kernel.connectors.memory import AzureAISearchCollection
+from semantic_kernel.connectors.azure_ai_search import AzureAISearchCollection
 
 """
 With the data model and records defined in step_0_data_model.py, this script will create an Azure AI Search collection,
@@ -32,7 +32,7 @@ async def main(query: str):
             await collection.create_collection(index=custom_index)
         await collection.upsert(records)
         # get the first five records to check the upsert worked.
-        results = await collection.get(order_by={"field": "HotelName", "ascending": True}, top=5)
+        results = await collection.get(order_by="HotelName", top=5)
         print("Get first five records: ")
         if results:
             for result in results:
@@ -56,7 +56,9 @@ async def main(query: str):
         print("Search results using hybrid: ")
         # Use hybrid search to search using the vector.
         results = await collection.hybrid_search(
-            query, vector_property_name="DescriptionVector", additional_property_name="Description"
+            query,
+            vector_property_name="DescriptionVector",
+            additional_property_name="Description",
         )
         async for result in results.results:
             print(

python/samples/concepts/memory/azure_ai_search_hotel_samples/2_use_as_a_plugin.py

@@ -3,27 +3,21 @@
 
 import asyncio
 from collections.abc import Awaitable, Callable
-from typing import TYPE_CHECKING, Any
+from typing import Any
 
 from samples.concepts.memory.azure_ai_search_hotel_samples.data_model import (
     HotelSampleClass,
     custom_index,
     load_records,
 )
-from semantic_kernel.agents import ChatCompletionAgent
-from semantic_kernel.agents.agent import AgentThread
-from semantic_kernel.connectors.ai.function_choice_behavior import FunctionChoiceBehavior
+from semantic_kernel.agents import AgentThread, ChatCompletionAgent
+from semantic_kernel.connectors.ai import FunctionChoiceBehavior
 from semantic_kernel.connectors.ai.open_ai import OpenAIChatCompletion, OpenAITextEmbedding
-from semantic_kernel.connectors.memory import AzureAISearchCollection
+from semantic_kernel.connectors.azure_ai_search import AzureAISearchCollection
 from semantic_kernel.filters import FilterTypes, FunctionInvocationContext
-from semantic_kernel.functions import KernelParameterMetadata
-from semantic_kernel.functions.kernel_plugin import KernelPlugin
+from semantic_kernel.functions import KernelParameterMetadata, KernelPlugin
 from semantic_kernel.kernel_types import OptionalOneOrList
 
-if TYPE_CHECKING:
-    from semantic_kernel.functions import KernelParameterMetadata
-
-
 """
 This sample builds on the previous one, but can be run independently.
 It uses the data model defined in step_0_data_model.py, and with that creates a collection
@@ -185,8 +179,7 @@ async def chat():
     # Create the Azure AI Search collection
     async with collection:
         # Check if the collection exists.
-        if not await collection.does_collection_exist():
-            await collection.create_collection(index=custom_index)
+        await collection.ensure_collection_exists(index=custom_index)
         if not await collection.get(top=1):
             await collection.upsert(records)
         thread: AgentThread | None = None

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.vectors import VectorStoreField, vectorstoremodel
+from semantic_kernel.data.vector 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

@@ -26,7 +26,7 @@
     SqlServerCollection,
     WeaviateCollection,
 )
-from semantic_kernel.data.vectors import (
+from semantic_kernel.data.vector import (
     SearchType,
     VectorSearchProtocol,
     VectorStoreCollection,

python/samples/concepts/memory/data_models.py

@@ -7,7 +7,7 @@
 from pandas import DataFrame
 from pydantic import BaseModel, Field
 
-from semantic_kernel.data.vectors import VectorStoreCollectionDefinition, VectorStoreField, vectorstoremodel
+from semantic_kernel.data.vector 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

@@ -6,8 +6,8 @@
 import pandas as pd
 
 from semantic_kernel.connectors.ai.open_ai import OpenAITextEmbedding
-from semantic_kernel.connectors.memory.azure_ai_search import AzureAISearchCollection
-from semantic_kernel.data.vectors import VectorStoreCollectionDefinition, VectorStoreField
+from semantic_kernel.connectors.azure_ai_search import AzureAISearchCollection
+from semantic_kernel.data.vector import VectorStoreCollectionDefinition, VectorStoreField
 
 definition = VectorStoreCollectionDefinition(
     collection_name="pandas_test_index",

python/samples/concepts/memory/simple_memory.py

@@ -9,8 +9,8 @@
 from samples.concepts.memory.utils import print_record
 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.vectors import VectorStoreField, vectorstoremodel
+from semantic_kernel.connectors.in_memory import InMemoryCollection
+from semantic_kernel.data.vector 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/memory/utils.py

@@ -3,7 +3,7 @@
 from typing import TypeVar
 
 from samples.concepts.resources.utils import Colors, print_with_color
-from semantic_kernel.data.vectors import VectorSearchResult
+from semantic_kernel.data.vector import VectorSearchResult
 
 _T = TypeVar("_T")
 

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_ai_search import AzureAISearchSettings
+from semantic_kernel.connectors.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

@@ -10,8 +10,8 @@
     OpenAIChatPromptExecutionSettings,
     OpenAITextEmbedding,
 )
-from semantic_kernel.connectors.memory import InMemoryCollection
-from semantic_kernel.data.vectors import VectorStoreField, vectorstoremodel
+from semantic_kernel.connectors.in_memory import InMemoryCollection
+from semantic_kernel.data.vector import VectorStoreField, vectorstoremodel
 from semantic_kernel.functions import KernelArguments
 
 """

python/samples/concepts/rag/self_critique_rag.py

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

python/samples/concepts/search/brave_text_search_as_plugin.py

@@ -5,7 +5,7 @@
 from semantic_kernel import Kernel
 from semantic_kernel.connectors.ai import FunctionChoiceBehavior
 from semantic_kernel.connectors.ai.open_ai import OpenAIChatCompletion, OpenAIChatPromptExecutionSettings
-from semantic_kernel.connectors.search.brave import BraveSearch
+from semantic_kernel.connectors.brave import BraveSearch
 from semantic_kernel.contents import ChatHistory
 from semantic_kernel.filters import FilterTypes, FunctionInvocationContext
 from semantic_kernel.functions import KernelArguments, KernelParameterMetadata

python/samples/concepts/search/google_text_search_as_plugin.py

@@ -6,7 +6,7 @@
 from semantic_kernel import Kernel
 from semantic_kernel.connectors.ai import FunctionChoiceBehavior
 from semantic_kernel.connectors.ai.open_ai import OpenAIChatCompletion, OpenAIChatPromptExecutionSettings
-from semantic_kernel.connectors.search import GoogleSearch
+from semantic_kernel.connectors.google_search import GoogleSearch
 from semantic_kernel.contents import ChatHistory
 from semantic_kernel.filters import FilterTypes, FunctionInvocationContext
 from semantic_kernel.functions import KernelParameterMetadata