diff --git a/python/pyproject.toml b/python/pyproject.toml
index 7a4a167c80fd..8ab32c6e87cc 100644
--- a/python/pyproject.toml
+++ b/python/pyproject.toml
@@ -92,7 +92,7 @@ faiss = [
     "faiss-cpu>=1.10.0"
 ]
 google = [
-    "google-cloud-aiplatform == 1.97.0",
+    "google-cloud-aiplatform ~= 1.114.0",
     "google-generativeai ~= 0.8"
 ]
 hugging_face = [
@@ -154,7 +154,7 @@ usearch = [
     "pyarrow >= 12.0,< 22.0"
 ]
 weaviate = [
-    "weaviate-client>=4.10,<5.0,!=4.16.7",
+    "weaviate-client>=4.17.0,<5.0",
 ]
 
 [tool.uv]
diff --git a/python/semantic_kernel/connectors/ai/google/vertex_ai/services/utils.py b/python/semantic_kernel/connectors/ai/google/vertex_ai/services/utils.py
index f3211066d466..544ba584c680 100644
--- a/python/semantic_kernel/connectors/ai/google/vertex_ai/services/utils.py
+++ b/python/semantic_kernel/connectors/ai/google/vertex_ai/services/utils.py
@@ -4,9 +4,8 @@
 import logging
 from typing import TYPE_CHECKING
 
-from google.cloud.aiplatform_v1beta1.types.content import Blob, Candidate, Part
-from google.cloud.aiplatform_v1beta1.types.tool import FunctionCall, FunctionResponse
-from vertexai.generative_models import FunctionDeclaration, Tool, ToolConfig
+from google.cloud.aiplatform_v1beta1.types.content import Candidate
+from vertexai.generative_models import FunctionDeclaration, Part, Tool, ToolConfig
 
 from semantic_kernel.connectors.ai.function_choice_behavior import FunctionChoiceType
 from semantic_kernel.connectors.ai.google.shared_utils import (
@@ -63,7 +62,7 @@ def format_user_message(message: ChatMessageContent) -> list[Part]:
     parts: list[Part] = []
     for item in message.items:
         if isinstance(item, TextContent):
-            parts.append(Part(text=message.content))
+            parts.append(Part.from_text(message.content))
         elif isinstance(item, ImageContent):
             parts.append(_create_image_part(item))
         else:
@@ -88,16 +87,15 @@ def format_assistant_message(message: ChatMessageContent) -> list[Part]:
     for item in message.items:
         if isinstance(item, TextContent):
             if item.text:
-                parts.append(Part(text=item.text))
+                parts.append(Part.from_text(item.text))
         elif isinstance(item, FunctionCallContent):
             parts.append(
-                Part(
-                    function_call=FunctionCall(
-                        name=item.name,
-                        # Convert the arguments to a dictionary if it is a string
-                        args=json.loads(item.arguments) if isinstance(item.arguments, str) else item.arguments,
-                    )
-                )
+                Part.from_dict({
+                    "function_call": {
+                        "name": item.name,
+                        "args": json.loads(item.arguments) if isinstance(item.arguments, str) else item.arguments,
+                    }
+                })
             )
         elif isinstance(item, ImageContent):
             parts.append(_create_image_part(item))
@@ -124,14 +122,11 @@ def format_tool_message(message: ChatMessageContent) -> list[Part]:
         if isinstance(item, FunctionResultContent):
             gemini_function_name = item.custom_fully_qualified_name(GEMINI_FUNCTION_NAME_SEPARATOR)
             parts.append(
-                Part(
-                    function_response=FunctionResponse(
-                        name=gemini_function_name,
-                        response={
-                            "name": gemini_function_name,
-                            "content": str(item.result),
-                        },
-                    )
+                Part.from_function_response(
+                    gemini_function_name,
+                    {
+                        "content": str(item.result),
+                    },
                 )
             )
 
@@ -177,7 +172,7 @@ def update_settings_from_function_choice_configuration(
 
 def _create_image_part(image_content: ImageContent) -> Part:
     if image_content.data_uri:
-        return Part(inline_data=Blob(mime_type=image_content.mime_type, data=image_content.data))
+        return Part.from_data(image_content.data, image_content.mime_type)  # type: ignore[arg-type]
 
     # The Google AI API doesn't support images from arbitrary URIs:
     # https://github.com/google-gemini/generative-ai-python/issues/357
diff --git a/python/semantic_kernel/connectors/ai/google/vertex_ai/services/vertex_ai_chat_completion.py b/python/semantic_kernel/connectors/ai/google/vertex_ai/services/vertex_ai_chat_completion.py
index 0fd4d3757db6..2a54aa4ec55c 100644
--- a/python/semantic_kernel/connectors/ai/google/vertex_ai/services/vertex_ai_chat_completion.py
+++ b/python/semantic_kernel/connectors/ai/google/vertex_ai/services/vertex_ai_chat_completion.py
@@ -10,9 +10,8 @@
     from typing_extensions import override  # pragma: no cover
 
 import vertexai
-from google.cloud.aiplatform_v1beta1.types.content import Content
 from pydantic import ValidationError
-from vertexai.generative_models import Candidate, GenerationResponse, GenerativeModel
+from vertexai.generative_models import Candidate, Content, GenerationResponse, GenerativeModel
 
 from semantic_kernel.connectors.ai.chat_completion_client_base import ChatCompletionClientBase
 from semantic_kernel.connectors.ai.completion_usage import CompletionUsage
@@ -125,6 +124,7 @@ async def _inner_get_chat_message_contents(
         assert isinstance(settings, VertexAIChatPromptExecutionSettings)  # nosec
 
         vertexai.init(project=self.service_settings.project_id, location=self.service_settings.region)
+        assert self.service_settings.gemini_model_id is not None  # nosec
         model = GenerativeModel(
             self.service_settings.gemini_model_id,
             system_instruction=filter_system_message(chat_history),
@@ -154,6 +154,7 @@ async def _inner_get_streaming_chat_message_contents(
         assert isinstance(settings, VertexAIChatPromptExecutionSettings)  # nosec
 
         vertexai.init(project=self.service_settings.project_id, location=self.service_settings.region)
+        assert self.service_settings.gemini_model_id is not None  # nosec
         model = GenerativeModel(
             self.service_settings.gemini_model_id,
             system_instruction=filter_system_message(chat_history),
diff --git a/python/semantic_kernel/connectors/ai/google/vertex_ai/services/vertex_ai_text_completion.py b/python/semantic_kernel/connectors/ai/google/vertex_ai/services/vertex_ai_text_completion.py
index 8c31bf0fd804..4aa3992c634e 100644
--- a/python/semantic_kernel/connectors/ai/google/vertex_ai/services/vertex_ai_text_completion.py
+++ b/python/semantic_kernel/connectors/ai/google/vertex_ai/services/vertex_ai_text_completion.py
@@ -96,6 +96,7 @@ async def _inner_get_text_contents(
         assert isinstance(settings, VertexAITextPromptExecutionSettings)  # nosec
 
         vertexai.init(project=self.service_settings.project_id, location=self.service_settings.region)
+        assert self.service_settings.gemini_model_id is not None  # nosec
         model = GenerativeModel(self.service_settings.gemini_model_id)
 
         response: GenerationResponse = await model.generate_content_async(
@@ -117,6 +118,7 @@ async def _inner_get_streaming_text_contents(
         assert isinstance(settings, VertexAITextPromptExecutionSettings)  # nosec
 
         vertexai.init(project=self.service_settings.project_id, location=self.service_settings.region)
+        assert self.service_settings.gemini_model_id is not None  # nosec
         model = GenerativeModel(self.service_settings.gemini_model_id)
 
         response: AsyncIterable[GenerationResponse] = await model.generate_content_async(
diff --git a/python/semantic_kernel/connectors/ai/google/vertex_ai/services/vertex_ai_text_embedding.py b/python/semantic_kernel/connectors/ai/google/vertex_ai/services/vertex_ai_text_embedding.py
index 6bf2cf32b933..b0de375d3811 100644
--- a/python/semantic_kernel/connectors/ai/google/vertex_ai/services/vertex_ai_text_embedding.py
+++ b/python/semantic_kernel/connectors/ai/google/vertex_ai/services/vertex_ai_text_embedding.py
@@ -93,9 +93,10 @@ async def generate_raw_embeddings(
         assert isinstance(settings, VertexAIEmbeddingPromptExecutionSettings)  # nosec
 
         vertexai.init(project=self.service_settings.project_id, location=self.service_settings.region)
+        assert self.service_settings.embedding_model_id is not None  # nosec
         model = TextEmbeddingModel.from_pretrained(self.service_settings.embedding_model_id)
         response: list[TextEmbedding] = await model.get_embeddings_async(
-            texts,
+            texts,  # type: ignore[arg-type]
             **settings.prepare_settings_dict(),
         )
 
diff --git a/python/tests/integration/completions/chat_completion_test_base.py b/python/tests/integration/completions/chat_completion_test_base.py
index 82c5f2bafe8d..48a370641fa6 100644
--- a/python/tests/integration/completions/chat_completion_test_base.py
+++ b/python/tests/integration/completions/chat_completion_test_base.py
@@ -155,8 +155,8 @@ def services(self) -> dict[str, tuple[ServiceType | None, type[PromptExecutionSe
                 OnnxGenAIChatCompletion(template=ONNXTemplate.PHI3V) if onnx_setup else None,
                 OnnxGenAIPromptExecutionSettings,
             ),
-            "bedrock_amazon_titan": (
-                self._try_create_bedrock_chat_completion_client("amazon.titan-text-premier-v1:0"),
+            "bedrock_amazon_nova": (
+                self._try_create_bedrock_chat_completion_client("amazon.nova-lite-v1:0"),
                 BedrockChatPromptExecutionSettings,
             ),
             "bedrock_ai21labs": (
diff --git a/python/tests/integration/completions/test_chat_completions.py b/python/tests/integration/completions/test_chat_completions.py
index f0e9a4fdb5ea..277591e50d26 100644
--- a/python/tests/integration/completions/test_chat_completions.py
+++ b/python/tests/integration/completions/test_chat_completions.py
@@ -182,14 +182,14 @@ class Reasoning(KernelBaseModel):
         # endregion
         # region Bedrock
         pytest.param(
-            "bedrock_amazon_titan",
+            "bedrock_amazon_nova",
             {},
             [
                 ChatMessageContent(role=AuthorRole.USER, items=[TextContent(text="Hello")]),
                 ChatMessageContent(role=AuthorRole.USER, items=[TextContent(text="How are you today?")]),
             ],
             {},
-            id="bedrock_amazon_titan_text_input",
+            id="bedrock_amazon_nova_text_input",
         ),
         pytest.param(
             "bedrock_ai21labs",
diff --git a/python/tests/integration/completions/test_text_completion.py b/python/tests/integration/completions/test_text_completion.py
index c74e23b6beff..470df7bfa332 100644
--- a/python/tests/integration/completions/test_text_completion.py
+++ b/python/tests/integration/completions/test_text_completion.py
@@ -258,7 +258,7 @@ def services(self) -> dict[str, tuple[ServiceType | None, type[PromptExecutionSe
             # Amazon Bedrock supports models from multiple providers but requests to and responses from the models are
             # inconsistent. So we need to test each model separately.
             "bedrock_amazon_titan": (
-                self._try_create_bedrock_text_completion_client("amazon.titan-text-premier-v1:0"),
+                self._try_create_bedrock_text_completion_client("amazon.titan-text-express-v1"),
                 BedrockTextPromptExecutionSettings,
             ),
             "bedrock_anthropic_claude": (
diff --git a/python/tests/unit/connectors/ai/google/vertex_ai/services/test_vertex_ai_chat_completion.py b/python/tests/unit/connectors/ai/google/vertex_ai/services/test_vertex_ai_chat_completion.py
index 9ea53c68910e..894a8ac07877 100644
--- a/python/tests/unit/connectors/ai/google/vertex_ai/services/test_vertex_ai_chat_completion.py
+++ b/python/tests/unit/connectors/ai/google/vertex_ai/services/test_vertex_ai_chat_completion.py
@@ -4,8 +4,7 @@
 from unittest.mock import AsyncMock, patch
 
 import pytest
-from google.cloud.aiplatform_v1beta1.types.content import Content
-from vertexai.generative_models import GenerativeModel
+from vertexai.generative_models import Content, GenerativeModel
 
 from semantic_kernel.connectors.ai.function_choice_behavior import FunctionChoiceBehavior
 from semantic_kernel.connectors.ai.google.vertex_ai.services.vertex_ai_chat_completion import VertexAIChatCompletion
@@ -95,12 +94,23 @@ async def test_vertex_ai_chat_completion(
         chat_history, settings
     )
 
-    mock_vertex_ai_model_generate_content_async.assert_called_once_with(
-        contents=vertex_ai_chat_completion._prepare_chat_history_for_request(chat_history),
-        generation_config=settings.prepare_settings_dict(),
-        tools=None,
-        tool_config=None,
-    )
+    # Verify the call was made once
+    mock_vertex_ai_model_generate_content_async.assert_called_once()
+
+    # Get the actual call arguments
+    call_args = mock_vertex_ai_model_generate_content_async.call_args
+
+    # Verify the contents
+    contents = call_args.kwargs["contents"]
+    assert len(contents) == 1
+    assert contents[0].role == "user"
+    assert len(contents[0].parts) == 1
+    assert contents[0].parts[0].text == "test_prompt"
+
+    # Verify other arguments
+    assert call_args.kwargs["generation_config"] == settings.prepare_settings_dict()
+    assert call_args.kwargs["tools"] is None
+    assert call_args.kwargs["tool_config"] is None
     assert len(responses) == 1
     assert responses[0].role == "assistant"
     assert responses[0].content == mock_vertex_ai_chat_completion_response.candidates[0].content.parts[0].text
@@ -188,12 +198,23 @@ async def test_vertex_ai_chat_completion_with_function_choice_behavior_no_tool_c
         kernel=kernel,
     )
 
-    mock_vertex_ai_model_generate_content_async.assert_awaited_once_with(
-        contents=vertex_ai_chat_completion._prepare_chat_history_for_request(chat_history),
-        generation_config=settings.prepare_settings_dict(),
-        tools=None,
-        tool_config=None,
-    )
+    # Verify the call was made once
+    mock_vertex_ai_model_generate_content_async.assert_awaited_once()
+
+    # Get the actual call arguments
+    call_args = mock_vertex_ai_model_generate_content_async.await_args
+
+    # Verify the contents
+    contents = call_args.kwargs["contents"]
+    assert len(contents) == 1
+    assert contents[0].role == "user"
+    assert len(contents[0].parts) == 1
+    assert contents[0].parts[0].text == "test_prompt"
+
+    # Verify other arguments
+    assert call_args.kwargs["generation_config"] == settings.prepare_settings_dict()
+    assert call_args.kwargs["tools"] is None
+    assert call_args.kwargs["tool_config"] is None
     assert len(responses) == 1
     assert responses[0].role == "assistant"
     assert responses[0].content == mock_vertex_ai_chat_completion_response.candidates[0].content.parts[0].text
@@ -225,13 +246,24 @@ async def test_vertex_ai_streaming_chat_completion(
         assert "usage" in messages[0].metadata
         assert "prompt_feedback" in messages[0].metadata
 
-    mock_vertex_ai_model_generate_content_async.assert_called_once_with(
-        contents=vertex_ai_chat_completion._prepare_chat_history_for_request(chat_history),
-        generation_config=settings.prepare_settings_dict(),
-        tools=None,
-        tool_config=None,
-        stream=True,
-    )
+    # Verify the call was made once
+    mock_vertex_ai_model_generate_content_async.assert_called_once()
+
+    # Get the actual call arguments
+    call_args = mock_vertex_ai_model_generate_content_async.call_args
+
+    # Verify the contents
+    contents = call_args.kwargs["contents"]
+    assert len(contents) == 1
+    assert contents[0].role == "user"
+    assert len(contents[0].parts) == 1
+    assert contents[0].parts[0].text == "test_prompt"
+
+    # Verify other arguments
+    assert call_args.kwargs["generation_config"] == settings.prepare_settings_dict()
+    assert call_args.kwargs["tools"] is None
+    assert call_args.kwargs["tool_config"] is None
+    assert call_args.kwargs["stream"] is True
 
 
 async def test_vertex_ai_streaming_chat_completion_with_function_choice_behavior_fail_verification(
@@ -328,13 +360,24 @@ async def test_vertex_ai_streaming_chat_completion_with_function_choice_behavior
         assert len(messages) == 1
         assert messages[0].role == "assistant"
 
-    mock_vertex_ai_model_generate_content_async.assert_awaited_once_with(
-        contents=vertex_ai_chat_completion._prepare_chat_history_for_request(chat_history),
-        generation_config=settings.prepare_settings_dict(),
-        tools=None,
-        tool_config=None,
-        stream=True,
-    )
+    # Verify the call was made once
+    mock_vertex_ai_model_generate_content_async.assert_awaited_once()
+
+    # Get the actual call arguments
+    call_args = mock_vertex_ai_model_generate_content_async.await_args
+
+    # Verify the contents
+    contents = call_args.kwargs["contents"]
+    assert len(contents) == 1
+    assert contents[0].role == "user"
+    assert len(contents[0].parts) == 1
+    assert contents[0].parts[0].text == "test_prompt"
+
+    # Verify other arguments
+    assert call_args.kwargs["generation_config"] == settings.prepare_settings_dict()
+    assert call_args.kwargs["tools"] is None
+    assert call_args.kwargs["tool_config"] is None
+    assert call_args.kwargs["stream"] is True
 
 
 # endregion streaming chat completion
diff --git a/python/tests/unit/connectors/ai/google/vertex_ai/services/test_vertex_ai_utils.py b/python/tests/unit/connectors/ai/google/vertex_ai/services/test_vertex_ai_utils.py
index e874262e69ef..510b03379ea2 100644
--- a/python/tests/unit/connectors/ai/google/vertex_ai/services/test_vertex_ai_utils.py
+++ b/python/tests/unit/connectors/ai/google/vertex_ai/services/test_vertex_ai_utils.py
@@ -1,7 +1,8 @@
 # Copyright (c) Microsoft. All rights reserved.
 
 import pytest
-from google.cloud.aiplatform_v1beta1.types.content import Candidate, Part
+from google.cloud.aiplatform_v1beta1.types.content import Candidate
+from vertexai.generative_models import Part
 
 from semantic_kernel.connectors.ai.google.vertex_ai.services.utils import (
     finish_reason_from_vertex_ai_to_semantic_kernel,
diff --git a/python/uv.lock b/python/uv.lock
index 42291facf3f0..2a7643e7c271 100644
--- a/python/uv.lock
+++ b/python/uv.lock
@@ -1,5 +1,5 @@
 version = 1
-revision = 3
+revision = 2
 requires-python = ">=3.10"
 resolution-markers = [
     "python_full_version >= '4' and sys_platform == 'darwin'",
@@ -56,7 +56,7 @@ wheels = [
 
 [[package]]
 name = "aiohttp"
-version = "3.13.1"
+version = "3.13.2"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "aiohappyeyeballs", marker = "sys_platform == 'darwin' or sys_platform == 'linux' or sys_platform == 'win32'" },
@@ -68,110 +68,110 @@ dependencies = [
     { name = "propcache", marker = "sys_platform == 'darwin' or sys_platform == 'linux' or sys_platform == 'win32'" },
     { name = "yarl", marker = "sys_platform == 'darwin' or sys_platform == 'linux' or sys_platform == 'win32'" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/ba/fa/3ae643cd525cf6844d3dc810481e5748107368eb49563c15a5fb9f680750/aiohttp-3.13.1.tar.gz", hash = "sha256:4b7ee9c355015813a6aa085170b96ec22315dabc3d866fd77d147927000e9464", size = 7835344, upload-time = "2025-10-17T14:03:29.337Z" }
-wheels = [
-    { url = "https://files.pythonhosted.org/packages/e6/34/5097441cc3047eccc2e0bfed3760ed068489b8392545d3aec0d8fbfab2b5/aiohttp-3.13.1-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:2349a6b642020bf20116a8a5c83bae8ba071acf1461c7cbe45fc7fafd552e7e2", size = 735069, upload-time = "2025-10-17T13:58:56.602Z" },
-    { url = "https://files.pythonhosted.org/packages/8c/2b/726466b4b4b16271a3db2a8a914d754d6cb9cee7bebde1f3ac6043e4e030/aiohttp-3.13.1-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:2a8434ca31c093a90edb94d7d70e98706ce4d912d7f7a39f56e1af26287f4bb7", size = 492575, upload-time = "2025-10-17T13:58:58.696Z" },
-    { url = "https://files.pythonhosted.org/packages/82/1f/364e64292c95bb6c9e2823b0afa1ad3f06524c573d45df82294be572489d/aiohttp-3.13.1-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:0bd610a7e87431741021a9a6ab775e769ea8c01bf01766d481282bfb17df597f", size = 487862, upload-time = "2025-10-17T13:59:00.315Z" },
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-    { url = "https://files.pythonhosted.org/packages/29/be/32c6c1d3a6c69e594b855bbf4014bea4c42008b0daac8c6e5c9f03207b89/aiohttp-3.13.1-cp310-cp310-manylinux2014_armv7l.manylinux_2_17_armv7l.manylinux_2_31_armv7l.whl", hash = "sha256:ac1892f56e2c445aca5ba28f3bf8e16b26dfc05f3c969867b7ef553b74cb4ebe", size = 1634627, upload-time = "2025-10-17T13:59:03.829Z" },
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