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Update plugins index.md #246

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Update index.md
pradorodriguez Apr 29, 2025
246a014
Merge pull request #1 from pradorodriguez/patch-prado-1
pradorodriguez Apr 29, 2025
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17 changes: 11 additions & 6 deletions semantic-kernel/concepts/plugins/index.md
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Original file line number Diff line number Diff line change
Expand Up @@ -156,7 +156,7 @@ public class LightModel

::: zone pivot="programming-language-python"
```python
from typing import TypedDict, Annotated
from typing import TypedDict, Annotated, List, Optional

class LightModel(TypedDict):
id: int
Expand Down Expand Up @@ -302,8 +302,8 @@ from semantic_kernel.functions import kernel_function
from semantic_kernel.connectors.ai.open_ai import AzureChatCompletion
from semantic_kernel.connectors.ai.function_choice_behavior import FunctionChoiceBehavior
from semantic_kernel.connectors.ai.chat_completion_client_base import ChatCompletionClientBase
from semantic_kernel.contents.chat_history import ChatHistory
from semantic_kernel.functions.kernel_arguments import KernelArguments
from semantic_kernel.contents.chat_history import ChatHistory, ChatMessageContent
from semantic_kernel.contents.utils.author_role import AuthorRole

from semantic_kernel.connectors.ai.open_ai.prompt_execution_settings.azure_chat_prompt_execution_settings import (
AzureChatPromptExecutionSettings,
Expand All @@ -317,7 +317,7 @@ async def main():
chat_completion = AzureChatCompletion(
deployment_name="your_models_deployment_name",
api_key="your_api_key",
base_url="your_base_url",
endpoint="your_base_url",
)
kernel.add_service(chat_completion)

Expand All @@ -333,7 +333,12 @@ async def main():

# Create a history of the conversation
history = ChatHistory()
history.add_message("Please turn on the lamp")
history.add_message(
ChatMessageContent(
role=AuthorRole.USER,
content="Please turn on the lamp"
)
)

# Get the response from the AI
result = await chat_completion.get_chat_message_content(
Expand Down Expand Up @@ -437,4 +442,4 @@ By storing data locally, you can keep the information private and secure while a
Use one of the techniques described in the [Providing functions return type schema to LLM](./adding-native-plugins.md#provide-function-return-type-information-in-function-description) section to provide the function's return type schema to the AI model.

By utilizing a well-defined return type schema, the AI model can accurately identify the intended properties, eliminating potential inaccuracies that may arise when the model makes assumptions based on incomplete or ambiguous information in the absence of the schema.
Consequently, this enhances the accuracy of function calls, leading to more reliable and precise outcomes.
Consequently, this enhances the accuracy of function calls, leading to more reliable and precise outcomes.