Python: Naming updates (#12233)

### Motivation and Context

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This pull request introduces several updates across multiple files to
standardize naming conventions, improve consistency in method calls, and
modernize data model definitions. The primary changes include renaming
key classes and methods, replacing deprecated fields with updated ones,
and ensuring uniformity in collection management operations.

### Standardization of Data Model Fields:
* Replaced `VectorStoreRecordDataField`, `VectorStoreRecordKeyField`,
and `VectorStoreRecordVectorField` with `VectorStoreDataField`,
`VectorStoreKeyField`, and `VectorStoreVectorField` respectively across
all relevant files.
[[1]](diffhunk://#diff-df5224582cd487ed94306045bd09dcd32ccbd9b3f82a63184d037ebe02d34b17R16-R19)
[[2]](diffhunk://#diff-bff31c838fdfdb680d4dbdd3a24764dfd8a316b89b7d2ea466234edb98b68e9dL10-L18)
[[3]](diffhunk://#diff-af84e00363a79d8bd2f3b72976ecc95c0744fcdf89930ac9b058bfd27b27b83eL18-R18)
[[4]](diffhunk://#diff-c7a1b7cdeb95d9cdf398adc0cead91e30fcee1c6abff046f2933f0ac361d3982L30-R36)
[[5]](diffhunk://#diff-4c45e261749985abb60b9bd147b2bd2cb91fdfda869190da1f56c89032667eefL11-R14)
* Updated data models in `semantic_caching.py`,
`store_chat_history_in_cosmosdb.py`, and other files to use the new
field types for consistency.
[[1]](diffhunk://#diff-df5224582cd487ed94306045bd09dcd32ccbd9b3f82a63184d037ebe02d34b17L35-R37)
[[2]](diffhunk://#diff-bff31c838fdfdb680d4dbdd3a24764dfd8a316b89b7d2ea466234edb98b68e9dL42-R44)
[[3]](diffhunk://#diff-af84e00363a79d8bd2f3b72976ecc95c0744fcdf89930ac9b058bfd27b27b83eL63-R80)

### Renaming and Refactoring Collection Management:
* Renamed `create_collection_if_not_exists` to
`ensure_collection_exists` and `delete_collection` to
`ensure_collection_deleted` for clarity and consistency.
[[1]](diffhunk://#diff-df5224582cd487ed94306045bd09dcd32ccbd9b3f82a63184d037ebe02d34b17L68-R68)
[[2]](diffhunk://#diff-bff31c838fdfdb680d4dbdd3a24764dfd8a316b89b7d2ea466234edb98b68e9dL194-R194)
[[3]](diffhunk://#diff-3ad17b6faefe0a6ac2fff5b863169e5aac7ab99c49d4f376ba46f2d0d181b642L67-R67)
* Updated method calls in various files to reflect these changes.
[[1]](diffhunk://#diff-df5224582cd487ed94306045bd09dcd32ccbd9b3f82a63184d037ebe02d34b17L89-R89)
[[2]](diffhunk://#diff-c7a1b7cdeb95d9cdf398adc0cead91e30fcee1c6abff046f2933f0ac361d3982L150-R149)

### Renaming Classes for Improved Clarity:
* Renamed `AzureCosmosDBNoSQLStore` to `CosmosNoSqlStore` and
`AzureCosmosDBNoSQLCollection` to `CosmosNoSqlCollection`. Similar
updates were made to MongoDB-related classes.
[[1]](diffhunk://#diff-bff31c838fdfdb680d4dbdd3a24764dfd8a316b89b7d2ea466234edb98b68e9dL178-R178)
[[2]](diffhunk://#diff-c7a1b7cdeb95d9cdf398adc0cead91e30fcee1c6abff046f2933f0ac361d3982L16-R18)

### Consistency in Record Type Definitions:
* Replaced `data_model_type` with `record_type` in collection
initialization across multiple files for uniformity.
[[1]](diffhunk://#diff-df5224582cd487ed94306045bd09dcd32ccbd9b3f82a63184d037ebe02d34b17L53-R53)
[[2]](diffhunk://#diff-bff31c838fdfdb680d4dbdd3a24764dfd8a316b89b7d2ea466234edb98b68e9dL67-R69)
[[3]](diffhunk://#diff-c7a1b7cdeb95d9cdf398adc0cead91e30fcee1c6abff046f2933f0ac361d3982L99-R123)

### Miscellaneous Updates:
* Consolidated imports for improved readability and maintainability.
[[1]](diffhunk://#diff-df5224582cd487ed94306045bd09dcd32ccbd9b3f82a63184d037ebe02d34b17R16-R19)
[[2]](diffhunk://#diff-af84e00363a79d8bd2f3b72976ecc95c0744fcdf89930ac9b058bfd27b27b83eL18-R18)
* Adjusted default values and annotations in data models to align with
the updated field types.
[[1]](diffhunk://#diff-4c45e261749985abb60b9bd147b2bd2cb91fdfda869190da1f56c89032667eefL47-R58)
[[2]](diffhunk://#diff-4c45e261749985abb60b9bd147b2bd2cb91fdfda869190da1f56c89032667eefL72-R71)

These changes collectively enhance the maintainability and readability
of the codebase while ensuring alignment with modern conventions.

### Description

<!-- Describe your changes, the overall approach, the underlying design.
These notes will help understanding how your code works. Thanks! -->

Closes: #11938


### Contribution Checklist

<!-- Before submitting this PR, please make sure: -->

- [x] The code builds clean without any errors or warnings
- [x] The PR follows the [SK Contribution
Guidelines](https://github.com/microsoft/semantic-kernel/blob/main/CONTRIBUTING.md)
and the [pre-submission formatting
script](https://github.com/microsoft/semantic-kernel/blob/main/CONTRIBUTING.md#development-scripts)
raises no violations
- [x] All unit tests pass, and I have added new tests where possible
- [x] I didn't break anyone 😄

Author: eavanvalkenburg

python/pyproject.toml

@@ -117,8 +117,7 @@ ollama = [
     "ollama ~= 0.4"
 ]
 onnx = [
-    "onnxruntime-genai ~= 0.5; python_version < '3.13' and platform_system != 'Windows'",
-    "onnxruntime == 1.22.0; platform_system == 'Windows'"
+    "onnxruntime-genai ~= 0.7"
 ]
 pandas = [
     "pandas ~= 2.2"

python/samples/concepts/caching/semantic_caching.py

@@ -13,10 +13,10 @@
 from semantic_kernel.data import (
     VectorSearchOptions,
     VectorStore,
+    VectorStoreDataField,
+    VectorStoreKeyField,
     VectorStoreRecordCollection,
-    VectorStoreRecordDataField,
-    VectorStoreRecordKeyField,
-    VectorStoreRecordVectorField,
+    VectorStoreVectorField,
     vectorstoremodel,
 )
 from semantic_kernel.filters import FilterTypes, FunctionInvocationContext, PromptRenderContext
@@ -32,9 +32,9 @@
 @vectorstoremodel(collection_name=COLLECTION_NAME)
 @dataclass
 class CacheRecord:
-    result: Annotated[str, VectorStoreRecordDataField(is_full_text_indexed=True)]
-    prompt: Annotated[str | None, VectorStoreRecordVectorField(dimensions=1536)] = None
-    id: Annotated[str, VectorStoreRecordKeyField] = field(default_factory=lambda: str(uuid4()))
+    result: Annotated[str, VectorStoreDataField(is_full_text_indexed=True)]
+    prompt: Annotated[str | None, VectorStoreVectorField(dimensions=1536)] = None
+    id: Annotated[str, VectorStoreKeyField] = field(default_factory=lambda: str(uuid4()))
 
 
 # Define the filters, one for caching the results and one for using the cache.
@@ -50,7 +50,7 @@ def __init__(
             raise ValueError("The vector store must have an embedding generator.")
         self.vector_store = vector_store
         self.collection: VectorStoreRecordCollection[str, CacheRecord] = vector_store.get_collection(
-            data_model_type=CacheRecord
+            record_type=CacheRecord
         )
         self.score_threshold = score_threshold
 
@@ -65,7 +65,7 @@ async def on_prompt_render(
         closer the match.
         """
         await next(context)
-        await self.collection.create_collection_if_not_exists()
+        await self.collection.ensure_collection_exists()
         results = await self.collection.search(
             context.rendered_prompt, options=VectorSearchOptions(vector_property_name="prompt", top=1)
         )
@@ -86,7 +86,7 @@ async def on_function_invocation(
         result = context.result
         if result and result.rendered_prompt and RECORD_ID_KEY not in result.metadata:
             cache_record = CacheRecord(prompt=result.rendered_prompt, result=str(result))
-            await self.collection.create_collection_if_not_exists()
+            await self.collection.ensure_collection_exists()
             await self.collection.upsert(cache_record)
 
 

python/samples/concepts/chat_history/store_chat_history_in_cosmosdb.py

@@ -7,15 +7,15 @@
 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 AzureCosmosDBNoSQLStore
+from semantic_kernel.connectors.memory.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 import (
     VectorStore,
+    VectorStoreDataField,
+    VectorStoreKeyField,
     VectorStoreRecordCollection,
-    VectorStoreRecordDataField,
-    VectorStoreRecordKeyField,
     vectorstoremodel,
 )
 
@@ -39,9 +39,9 @@
 @vectorstoremodel
 @dataclass
 class ChatHistoryModel:
-    session_id: Annotated[str, VectorStoreRecordKeyField]
-    user_id: Annotated[str, VectorStoreRecordDataField(is_indexed=True)]
-    messages: Annotated[list[dict[str, str]], VectorStoreRecordDataField(is_indexed=True)]
+    session_id: Annotated[str, VectorStoreKeyField]
+    user_id: Annotated[str, VectorStoreDataField(is_indexed=True)]
+    messages: Annotated[list[dict[str, str]], VectorStoreDataField(is_indexed=True)]
 
 
 # 2. We then create a class that extends the ChatHistory class
@@ -64,9 +64,9 @@ async def create_collection(self, collection_name: str) -> None:
         """
         self.collection = self.store.get_collection(
             collection_name=collection_name,
-            data_model_type=ChatHistoryModel,
+            record_type=ChatHistoryModel,
         )
-        await self.collection.create_collection_if_not_exists()
+        await self.collection.ensure_collection_exists()
 
     async def store_messages(self) -> None:
         """Store the chat history in the Cosmos DB.
@@ -175,7 +175,7 @@ async def main() -> None:
 
     # First we enter the store context manager to connect.
     # The create_database flag will create the database if it does not exist.
-    async with AzureCosmosDBNoSQLStore(create_database=True) as store:
+    async with CosmosNoSqlStore(create_database=True) as store:
         # Then we create the chat history in CosmosDB.
         history = ChatHistoryInCosmosDB(store=store, session_id=session_id, user_id="user")
         # Finally we create the collection.
@@ -191,7 +191,7 @@ async def main() -> None:
         except Exception:
             print("Closing chat...")
         if delete_when_done and history.collection:
-            await history.collection.delete_collection()
+            await history.collection.ensure_collection_deleted()
 
 
 if __name__ == "__main__":

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

@@ -25,7 +25,7 @@ async def main(query: str):
     records = load_records()
     # Create the Azure AI Search collection
     async with AzureAISearchCollection[str, HotelSampleClass](
-        data_model_type=HotelSampleClass, embedding_generator=OpenAITextEmbedding()
+        record_type=HotelSampleClass, embedding_generator=OpenAITextEmbedding()
     ) as collection:
         # Check if the collection exists.
         if not await collection.does_collection_exist():
@@ -64,7 +64,7 @@ async def main(query: str):
                 f"{result.record.Address.Country}): {result.record.Description} (score: {result.score})"
             )
 
-        await collection.delete_collection()
+        await collection.ensure_collection_deleted()
 
 
 if __name__ == "__main__":

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

@@ -35,7 +35,7 @@
 
 # Create an Azure AI Search collection.
 collection = AzureAISearchCollection[str, HotelSampleClass](
-    data_model_type=HotelSampleClass, embedding_generator=OpenAITextEmbedding()
+    record_type=HotelSampleClass, embedding_generator=OpenAITextEmbedding()
 )
 # load the records
 records = load_records()
@@ -210,7 +210,7 @@ async def chat():
 
         delete_collection = input("Do you want to delete the collection? (y/n): ")
         if delete_collection.lower() == "y":
-            await collection.delete_collection()
+            await collection.ensure_collection_deleted()
             print("Collection deleted.")
         else:
             print("Collection not deleted.")

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

@@ -15,12 +15,7 @@
 )
 from pydantic import BaseModel, ConfigDict
 
-from semantic_kernel.data import (
-    VectorStoreRecordDataField,
-    VectorStoreRecordKeyField,
-    VectorStoreRecordVectorField,
-    vectorstoremodel,
-)
+from semantic_kernel.data import VectorStoreDataField, VectorStoreKeyField, VectorStoreVectorField, vectorstoremodel
 
 """
 The data model used for this sample is based on the hotel data model from the Azure AI Search samples.
@@ -60,31 +55,29 @@ class Address(BaseModel):
 
 @vectorstoremodel(collection_name="hotel-index")
 class HotelSampleClass(BaseModel):
-    HotelId: Annotated[str, VectorStoreRecordKeyField]
-    HotelName: Annotated[str | None, VectorStoreRecordDataField()] = None
+    HotelId: Annotated[str, VectorStoreKeyField]
+    HotelName: Annotated[str | None, VectorStoreDataField()] = None
     Description: Annotated[
         str,
-        VectorStoreRecordDataField(is_full_text_indexed=True),
+        VectorStoreDataField(is_full_text_indexed=True),
     ]
     DescriptionVector: Annotated[
         list[float] | str | None,
-        VectorStoreRecordVectorField(dimensions=1536),
+        VectorStoreVectorField(dimensions=1536),
     ] = None
-    Description_fr: Annotated[str, VectorStoreRecordDataField(is_full_text_indexed=True)]
+    Description_fr: Annotated[str, VectorStoreDataField(is_full_text_indexed=True)]
     DescriptionFrVector: Annotated[
         list[float] | str | None,
-        VectorStoreRecordVectorField(dimensions=1536),
-    ] = None
-    Category: Annotated[str, VectorStoreRecordDataField()]
-    Tags: Annotated[list[str], VectorStoreRecordDataField(is_indexed=True)]
-    ParkingIncluded: Annotated[bool | None, VectorStoreRecordDataField()] = None
-    LastRenovationDate: Annotated[
-        str | None, VectorStoreRecordDataField(property_type=SearchFieldDataType.DateTimeOffset)
+        VectorStoreVectorField(dimensions=1536),
     ] = None
-    Rating: Annotated[float, VectorStoreRecordDataField()]
-    Location: Annotated[dict[str, Any], VectorStoreRecordDataField(property_type=SearchFieldDataType.GeographyPoint)]
-    Address: Annotated[Address, VectorStoreRecordDataField()]
-    Rooms: Annotated[list[Rooms], VectorStoreRecordDataField()]
+    Category: Annotated[str, VectorStoreDataField()]
+    Tags: Annotated[list[str], VectorStoreDataField(is_indexed=True)]
+    ParkingIncluded: Annotated[bool | None, VectorStoreDataField()] = None
+    LastRenovationDate: Annotated[str | None, VectorStoreDataField(type=SearchFieldDataType.DateTimeOffset)] = None
+    Rating: Annotated[float, VectorStoreDataField()]
+    Location: Annotated[dict[str, Any], VectorStoreDataField(type=SearchFieldDataType.GeographyPoint)]
+    Address: Annotated[Address, VectorStoreDataField()]
+    Rooms: Annotated[list[Rooms], VectorStoreDataField()]
 
     model_config = ConfigDict(extra="ignore")
 

python/samples/concepts/memory/complex_memory.py

@@ -13,9 +13,9 @@
 from semantic_kernel.connectors.ai.open_ai import AzureTextEmbedding, OpenAITextEmbedding
 from semantic_kernel.connectors.memory import (
     AzureAISearchCollection,
-    AzureCosmosDBforMongoDBCollection,
-    AzureCosmosDBNoSQLCollection,
     ChromaCollection,
+    CosmosMongoCollection,
+    CosmosNoSqlCollection,
     FaissCollection,
     InMemoryCollection,
     PineconeCollection,
@@ -27,14 +27,13 @@
     WeaviateCollection,
 )
 from semantic_kernel.data import (
-    VectorSearch,
+    VectorStoreDataField,
+    VectorStoreKeyField,
     VectorStoreRecordCollection,
-    VectorStoreRecordDataField,
-    VectorStoreRecordKeyField,
-    VectorStoreRecordVectorField,
+    VectorStoreVectorField,
     vectorstoremodel,
 )
-from semantic_kernel.data.vector_search import SearchType
+from semantic_kernel.data.vectors import SearchType, VectorSearch
 
 # This is a rather complex sample, showing how to use the vector store
 # with a number of different collections.
@@ -49,14 +48,14 @@
 @vectorstoremodel(collection_name="test")
 @dataclass
 class DataModel:
-    title: Annotated[str, VectorStoreRecordDataField(is_full_text_indexed=True)]
-    content: Annotated[str, VectorStoreRecordDataField(is_full_text_indexed=True)]
+    title: Annotated[str, VectorStoreDataField(is_full_text_indexed=True)]
+    content: Annotated[str, VectorStoreDataField(is_full_text_indexed=True)]
     embedding: Annotated[
         str | None,
-        VectorStoreRecordVectorField(dimensions=1536, property_type="float"),
+        VectorStoreVectorField(dimensions=1536, type_="float"),
     ] = None
-    id: Annotated[str, VectorStoreRecordKeyField()] = field(default_factory=lambda: str(uuid4()))
-    tag: Annotated[str | None, VectorStoreRecordDataField(property_type="str", is_indexed=True)] = None
+    id: Annotated[str, VectorStoreKeyField()] = field(default_factory=lambda: str(uuid4()))
+    tag: Annotated[str | None, VectorStoreDataField(type_="str", is_indexed=True)] = None
 
     def __post_init__(self, **kwargs):
         if self.embedding is None:
@@ -96,32 +95,32 @@ def __post_init__(self, **kwargs):
 # 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]] = {
-    "ai_search": lambda: AzureAISearchCollection[str, DataModel](data_model_type=DataModel),
-    "postgres": lambda: PostgresCollection[str, DataModel](data_model_type=DataModel),
+    "ai_search": lambda: AzureAISearchCollection[str, DataModel](record_type=DataModel),
+    "postgres": lambda: PostgresCollection[str, DataModel](record_type=DataModel),
     "redis_json": lambda: RedisJsonCollection[str, DataModel](
-        data_model_type=DataModel,
+        record_type=DataModel,
         prefix_collection_name_to_key_names=True,
     ),
     "redis_hash": lambda: RedisHashsetCollection[str, DataModel](
-        data_model_type=DataModel,
+        record_type=DataModel,
         prefix_collection_name_to_key_names=True,
     ),
     "qdrant": lambda: QdrantCollection[str, DataModel](
-        data_model_type=DataModel,
+        record_type=DataModel,
         prefer_grpc=True,
         named_vectors=False,
     ),
-    "in_memory": lambda: InMemoryCollection[str, DataModel](data_model_type=DataModel),
-    "weaviate": lambda: WeaviateCollection[str, DataModel](data_model_type=DataModel),
-    "azure_cosmos_nosql": lambda: AzureCosmosDBNoSQLCollection[str, DataModel](
-        data_model_type=DataModel,
+    "in_memory": lambda: InMemoryCollection[str, DataModel](record_type=DataModel),
+    "weaviate": lambda: WeaviateCollection[str, DataModel](record_type=DataModel),
+    "azure_cosmos_nosql": lambda: CosmosNoSqlCollection[str, DataModel](
+        record_type=DataModel,
         create_database=True,
     ),
-    "azure_cosmos_mongodb": lambda: AzureCosmosDBforMongoDBCollection[str, DataModel](data_model_type=DataModel),
-    "faiss": lambda: FaissCollection[str, DataModel](data_model_type=DataModel),
-    "chroma": lambda: ChromaCollection[str, DataModel](data_model_type=DataModel),
-    "pinecone": lambda: PineconeCollection[str, DataModel](data_model_type=DataModel),
-    "sql_server": lambda: SqlServerCollection[str, DataModel](data_model_type=DataModel),
+    "azure_cosmos_mongodb": lambda: CosmosMongoCollection[str, DataModel](record_type=DataModel),
+    "faiss": lambda: FaissCollection[str, DataModel](record_type=DataModel),
+    "chroma": lambda: ChromaCollection[str, DataModel](record_type=DataModel),
+    "pinecone": lambda: PineconeCollection[str, DataModel](record_type=DataModel),
+    "sql_server": lambda: SqlServerCollection[str, DataModel](record_type=DataModel),
 }
 
 
@@ -132,7 +131,7 @@ async def cleanup(record_collection):
         print("Skipping deletion.")
         return
     print_with_color("Deleting collection!", Colors.CBLUE)
-    await record_collection.delete_collection()
+    await record_collection.ensure_collection_deleted()
     print_with_color("Done!", Colors.CGREY)
 
 
@@ -147,7 +146,7 @@ async def main(collection: str, use_azure_openai: bool):
         record_collection.embedding_generator = embedder
         print_with_color(f"Creating {collection} collection!", Colors.CGREY)
         # cleanup any existing collection
-        await record_collection.delete_collection()
+        await record_collection.ensure_collection_deleted()
         # create a new collection
         await record_collection.create_collection()