Python: Pydantic settings config

python/samples/concepts/chat_completion/chat_gpt_api.py

@@ -19,7 +19,7 @@
 kernel = Kernel()
 
 service_id = "chat-gpt"
-kernel.add_service(OpenAIChatCompletion(service_id=service_id))
+kernel.add_service(OpenAIChatCompletion(service_id=service_id, ai_model_id="gpt-3.5-turbo"))
 
 settings = kernel.get_prompt_execution_settings_from_service_id(service_id)
 settings.max_tokens = 2000

python/samples/concepts/memory/azure_cognitive_search_memory.py

@@ -5,7 +5,6 @@
 from semantic_kernel import Kernel
 from semantic_kernel.connectors.ai.open_ai import AzureTextCompletion, AzureTextEmbedding
 from semantic_kernel.connectors.memory.azure_cognitive_search import AzureCognitiveSearchMemoryStore
-from semantic_kernel.connectors.memory.azure_cognitive_search.azure_ai_search_settings import AzureAISearchSettings
 from semantic_kernel.core_plugins import TextMemoryPlugin
 from semantic_kernel.memory import SemanticTextMemory
 
@@ -43,41 +42,19 @@ async def search_acs_memory_questions(memory: SemanticTextMemory) -> None:
 async def main() -> None:
     kernel = Kernel()
 
-    azure_ai_search_settings = AzureAISearchSettings()
-
     vector_size = 1536
 
     # Setting up OpenAI services for text completion and text embedding
-    text_complete_service_id = "dv"
-    kernel.add_service(
-        AzureTextCompletion(
-            service_id=text_complete_service_id,
-        ),
-    )
-    embedding_service_id = "ada"
-    embedding_gen = AzureTextEmbedding(
-        service_id=embedding_service_id,
-    )
-    kernel.add_service(
-        embedding_gen,
-    )
-
-    acs_connector = AzureCognitiveSearchMemoryStore(
-        vector_size=vector_size,
-        search_endpoint=azure_ai_search_settings.endpoint,
-        admin_key=azure_ai_search_settings.api_key,
-    )
-
-    memory = SemanticTextMemory(storage=acs_connector, embeddings_generator=embedding_gen)
-    kernel.add_plugin(TextMemoryPlugin(memory), "TextMemoryPlugin")
-
-    print("Populating memory...")
-    await populate_memory(memory)
+    kernel.add_service(AzureTextCompletion(service_id="dv"))
+    async with AzureCognitiveSearchMemoryStore(vector_size=vector_size) as acs_connector:
+        memory = SemanticTextMemory(storage=acs_connector, embeddings_generator=AzureTextEmbedding(service_id="ada"))
+        kernel.add_plugin(TextMemoryPlugin(memory), "TextMemoryPlugin")
 
-    print("Asking questions... (manually)")
-    await search_acs_memory_questions(memory)
+        print("Populating memory...")
+        await populate_memory(memory)
 
-    await acs_connector.close()
+        print("Asking questions... (manually)")
+        await search_acs_memory_questions(memory)
 
 
 if __name__ == "__main__":

python/samples/concepts/on_your_data/azure_chat_gpt_with_data_api.py

@@ -35,16 +35,14 @@
 # }
 
 # Create the data source settings
-azure_ai_search_settings = AzureAISearchSettings.create()
+azure_ai_search_settings = AzureAISearchSettings.create(env_file_path=".env")
 
-az_source = AzureAISearchDataSource(parameters=azure_ai_search_settings.model_dump())
+az_source = AzureAISearchDataSource.from_azure_ai_search_settings(azure_ai_search_settings=azure_ai_search_settings)
 extra = ExtraBody(data_sources=[az_source])
 req_settings = AzureChatPromptExecutionSettings(service_id="default", extra_body=extra)
 
 # When using data, use the 2024-02-15-preview API version.
-chat_service = AzureChatCompletion(
-    service_id="chat-gpt",
-)
+chat_service = AzureChatCompletion(service_id="chat-gpt")
 kernel.add_service(chat_service)
 
 prompt_template_config = PromptTemplateConfig(

python/samples/getting_started/06-memory-and-embeddings.ipynb

@@ -497,7 +497,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.12.3"
+   "version": "3.10.14"
   }
  },
  "nbformat": 4,

python/semantic_kernel/connectors/ai/google_palm/services/gp_chat_completion.py

@@ -5,7 +5,7 @@
 
 import google.generativeai as palm
 from google.generativeai.types import ChatResponse, MessageDict
-from pydantic import PrivateAttr, StringConstraints, ValidationError
+from pydantic import PrivateAttr, StringConstraints
 
 from semantic_kernel.connectors.ai.chat_completion_client_base import ChatCompletionClientBase
 from semantic_kernel.connectors.ai.google_palm.gp_prompt_execution_settings import (
@@ -37,6 +37,7 @@ def __init__(
         api_key: str | None = None,
         message_history: ChatHistory | None = None,
         env_file_path: str | None = None,
+        env_file_encoding: str | None = None,
     ):
         """Initializes a new instance of the GooglePalmChatCompletion class.
 
@@ -48,25 +49,17 @@ def __init__(
             message_history (ChatHistory | None): The message history to use for context. (Optional)
             env_file_path (str | None): Use the environment settings file as a fallback to
                 environment variables. (Optional)
+            env_file_encoding (str | None): The encoding of the environment settings file. (Optional)
         """
-        google_palm_settings = None
-        try:
-            google_palm_settings = GooglePalmSettings.create(env_file_path=env_file_path)
-        except ValidationError as e:
-            logger.warning(f"Error loading Google Palm pydantic settings: {e}")
-
-        api_key = api_key or (
-            google_palm_settings.api_key.get_secret_value()
-            if google_palm_settings and google_palm_settings.api_key
-            else None
-        )
-        ai_model_id = ai_model_id or (
-            google_palm_settings.chat_model_id if google_palm_settings and google_palm_settings.chat_model_id else None
+        google_palm_settings = GooglePalmSettings.create(
+            api_key=api_key,
+            chat_model_id=ai_model_id,
+            env_file_path=env_file_path,
+            env_file_encoding=env_file_encoding,
         )
-
         super().__init__(
-            ai_model_id=ai_model_id,
-            api_key=api_key,
+            ai_model_id=google_palm_settings.chat_model_id,
+            api_key=google_palm_settings.api_key.get_secret_value() if google_palm_settings.api_key else None,
         )
         self._message_history = message_history
 

python/semantic_kernel/connectors/ai/google_palm/services/gp_text_completion.py

@@ -6,7 +6,7 @@
 import google.generativeai as palm
 from google.generativeai.types import Completion
 from google.generativeai.types.text_types import TextCompletion
-from pydantic import StringConstraints, ValidationError
+from pydantic import StringConstraints
 
 from semantic_kernel.connectors.ai.google_palm.gp_prompt_execution_settings import GooglePalmTextPromptExecutionSettings
 from semantic_kernel.connectors.ai.google_palm.settings.google_palm_settings import GooglePalmSettings
@@ -21,7 +21,13 @@
 class GooglePalmTextCompletion(TextCompletionClientBase):
     api_key: Annotated[str, StringConstraints(strip_whitespace=True, min_length=1)]
 
-    def __init__(self, ai_model_id: str, api_key: str | None = None, env_file_path: str | None = None):
+    def __init__(
+        self,
+        ai_model_id: str,
+        api_key: str | None = None,
+        env_file_path: str | None = None,
+        env_file_encoding: str | None = None,
+    ):
         """Initializes a new instance of the GooglePalmTextCompletion class.
 
         Args:
@@ -31,23 +37,19 @@ def __init__(self, ai_model_id: str, api_key: str | None = None, env_file_path:
                 read from either the env vars or the .env settings file.
             env_file_path (str | None): Use the environment settings file as a
                 fallback to environment variables. (Optional)
+            env_file_encoding (str | None): The encoding of the environment settings file. (Optional)
         """
-        try:
-            google_palm_settings = GooglePalmSettings.create(env_file_path=env_file_path)
-        except ValidationError as e:
-            logger.warning(f"Error loading Google Palm pydantic settings: {e}")
-
-        api_key = api_key or (
-            google_palm_settings.api_key.get_secret_value()
-            if google_palm_settings and google_palm_settings.api_key
-            else None
+        google_palm_settings = GooglePalmSettings.create(
+            api_key=api_key,
+            text_model_id=ai_model_id,
+            env_file_path=env_file_path,
+            env_file_encoding=env_file_encoding,
         )
-        ai_model_id = ai_model_id or (
-            google_palm_settings.text_model_id if google_palm_settings and google_palm_settings.text_model_id else None
+        super().__init__(
+            ai_model_id=google_palm_settings.text_model_id,
+            api_key=google_palm_settings.api_key.get_secret_value() if google_palm_settings.api_key else None,
         )
 
-        super().__init__(ai_model_id=ai_model_id, api_key=api_key)
-
     async def get_text_contents(
         self, prompt: str, settings: GooglePalmTextPromptExecutionSettings
     ) -> list[TextContent]:

python/semantic_kernel/connectors/ai/google_palm/services/gp_text_embedding.py

@@ -5,7 +5,7 @@
 
 import google.generativeai as palm
 from numpy import array, ndarray
-from pydantic import StringConstraints, ValidationError
+from pydantic import StringConstraints
 
 from semantic_kernel.connectors.ai.embeddings.embedding_generator_base import EmbeddingGeneratorBase
 from semantic_kernel.connectors.ai.google_palm.settings.google_palm_settings import GooglePalmSettings
@@ -19,7 +19,13 @@
 class GooglePalmTextEmbedding(EmbeddingGeneratorBase):
     api_key: Annotated[str, StringConstraints(strip_whitespace=True, min_length=1)]
 
-    def __init__(self, ai_model_id: str, api_key: str | None = None, env_file_path: str | None = None) -> None:
+    def __init__(
+        self,
+        ai_model_id: str,
+        api_key: str | None = None,
+        env_file_path: str | None = None,
+        env_file_encoding: str | None = None,
+    ) -> None:
         """Initializes a new instance of the GooglePalmTextEmbedding class.
 
         Args:
@@ -29,23 +35,18 @@ def __init__(self, ai_model_id: str, api_key: str | None = None, env_file_path:
                 read from either the env vars or the .env settings file.
             env_file_path (str | None): Use the environment settings file
                 as a fallback to environment variables. (Optional)
+            env_file_encoding (str | None): The encoding of the environment settings file. (Optional)
         """
-        try:
-            google_palm_settings = GooglePalmSettings.create(env_file_path=env_file_path)
-        except ValidationError as e:
-            logger.error(f"Error loading Google Palm pydantic settings: {e}")
-
-        api_key = api_key or (
-            google_palm_settings.api_key.get_secret_value()
-            if google_palm_settings and google_palm_settings.api_key
-            else None
+        google_palm_settings = GooglePalmSettings.create(
+            api_key=api_key,
+            embedding_model_id=ai_model_id,
+            env_file_path=env_file_path,
+            env_file_encoding=env_file_encoding,
         )
-        ai_model_id = ai_model_id or (
-            google_palm_settings.embedding_model_id
-            if google_palm_settings and google_palm_settings.embedding_model_id
-            else None
+        super().__init__(
+            ai_model_id=google_palm_settings.embedding_model_id,
+            api_key=google_palm_settings.api_key.get_secret_value() if google_palm_settings.api_key else None,
         )
-        super().__init__(ai_model_id=ai_model_id, api_key=api_key)
 
     async def generate_embeddings(self, texts: list[str], **kwargs: Any) -> ndarray:
         """Generates embeddings for the given list of texts."""

python/semantic_kernel/connectors/ai/google_palm/settings/google_palm_settings.py

@@ -1,10 +1,13 @@
 # Copyright (c) Microsoft. All rights reserved.
 
+from typing import ClassVar
+
 from pydantic import SecretStr
-from pydantic_settings import BaseSettings
+
+from semantic_kernel.kernel_pydantic import KernelBaseSettings
 
 
-class GooglePalmSettings(BaseSettings):
+class GooglePalmSettings(KernelBaseSettings):
     """Google Palm model settings.
 
     The settings are first loaded from environment variables with the prefix 'GOOGLE_PALM_'. If the
@@ -24,26 +27,9 @@ class GooglePalmSettings(BaseSettings):
         (Env var GOOGLE_PALM_EMBEDDING_MODEL_ID)
     """
 
-    env_file_path: str | None = None
+    env_prefix: ClassVar[str] = "GOOGLE_PALM_"
+
     api_key: SecretStr | None = None
     chat_model_id: str | None = None
     text_model_id: str | None = None
     embedding_model_id: str | None = None
-
-    class Config:
-        """Pydantic configuration settings."""
-
-        env_prefix = "GOOGLE_PALM_"
-        env_file = None
-        env_file_encoding = "utf-8"
-        extra = "ignore"
-        case_sensitive = False
-
-    @classmethod
-    def create(cls, **kwargs):
-        """Create the settings object."""
-        if "env_file_path" in kwargs and kwargs["env_file_path"]:
-            cls.Config.env_file = kwargs["env_file_path"]
-        else:
-            cls.Config.env_file = None
-        return cls(**kwargs)

python/semantic_kernel/connectors/ai/open_ai/prompt_execution_settings/azure_chat_prompt_execution_settings.py

@@ -8,6 +8,7 @@
 from semantic_kernel.connectors.ai.open_ai.prompt_execution_settings.open_ai_prompt_execution_settings import (
     OpenAIChatPromptExecutionSettings,
 )
+from semantic_kernel.connectors.memory.azure_cognitive_search.azure_ai_search_settings import AzureAISearchSettings
 from semantic_kernel.kernel_pydantic import KernelBaseModel
 
 logger = logging.getLogger(__name__)
@@ -82,6 +83,18 @@ class AzureAISearchDataSource(AzureChatRequestBase):
     type: Literal["azure_search"] = "azure_search"
     parameters: Annotated[dict, AzureAISearchDataSourceParameters]
 
+    @classmethod
+    def from_azure_ai_search_settings(cls, azure_ai_search_settings: AzureAISearchSettings, **kwargs: Any):
+        """Create an instance from Azure AI Search settings."""
+        kwargs["parameters"] = {
+            "endpoint": str(azure_ai_search_settings.endpoint),
+            "index_name": azure_ai_search_settings.index_name,
+            "authentication": {
+                "key": azure_ai_search_settings.api_key.get_secret_value() if azure_ai_search_settings.api_key else None
+            },
+        }
+        return cls(**kwargs)
+
 
 DataSource = Annotated[Union[AzureAISearchDataSource, AzureCosmosDBDataSource], Field(discriminator="type")]