ChatCompletionClientBase Class

Base class for chat completion AI services.

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

Inheritance

AIServiceClientBase

ChatCompletionClientBase

ABC

ChatCompletionClientBase

Constructor

ChatCompletionClientBase(*, ai_model_id: Annotated[str, StringConstraints(strip_whitespace=True, to_upper=None, to_lower=None, strict=None, min_length=1, max_length=None, pattern=None)], service_id: str = '')

Keyword-Only Parameters

Name	Description
ai_model_id Required
service_id Required

Methods

get_chat_message_content	This is the method that is called from the kernel to get a response from a chat-optimized LLM.
get_chat_message_contents	Create chat message contents, in the number specified by the settings.
get_streaming_chat_message_content	This is the method that is called from the kernel to get a stream response from a chat-optimized LLM.
get_streaming_chat_message_contents	Create streaming chat message contents, in the number specified by the settings.

get_chat_message_content

This is the method that is called from the kernel to get a response from a chat-optimized LLM.

async get_chat_message_content(chat_history: ChatHistory, settings: PromptExecutionSettings, **kwargs: Any) -> ChatMessageContent | None

Parameters

Name	Description
chat_history Required	<xref:semantic_kernel.connectors.ai.chat_completion_client_base.ChatHistory> A list of chat chat_history, that can be rendered into a set of chat_history, from system, user, assistant and function.
settings Required	<xref:semantic_kernel.connectors.ai.chat_completion_client_base.PromptExecutionSettings> Settings for the request.
kwargs Required	<xref:Dict>[str,<xref: Any>] The optional arguments.

Returns

Type	Description
	A string representing the response from the LLM.

get_chat_message_contents

Create chat message contents, in the number specified by the settings.

async get_chat_message_contents(chat_history: ChatHistory, settings: PromptExecutionSettings, **kwargs: Any) -> list[ChatMessageContent]

Parameters

Name	Description
chat_history Required	<xref:semantic_kernel.connectors.ai.chat_completion_client_base.ChatHistory> A list of chats in a chat_history object, that can be rendered into messages from system, user, assistant and tools.
settings Required	<xref:semantic_kernel.connectors.ai.chat_completion_client_base.PromptExecutionSettings> Settings for the request.
**kwargs Required	Any The optional arguments.

Returns

Type	Description
	A list of chat message contents representing the response(s) from the LLM.

get_streaming_chat_message_content

This is the method that is called from the kernel to get a stream response from a chat-optimized LLM.

async get_streaming_chat_message_content(chat_history: ChatHistory, settings: PromptExecutionSettings, **kwargs: Any) -> AsyncGenerator[StreamingChatMessageContent | None, Any]

Parameters

Name	Description
chat_history Required	<xref:semantic_kernel.connectors.ai.chat_completion_client_base.ChatHistory> A list of chat chat_history, that can be rendered into a set of chat_history, from system, user, assistant and function.
settings Required	<xref:semantic_kernel.connectors.ai.chat_completion_client_base.PromptExecutionSettings> Settings for the request.
kwargs Required	<xref:Dict>[str,<xref: Any>] The optional arguments.

get_streaming_chat_message_contents

Create streaming chat message contents, in the number specified by the settings.

async get_streaming_chat_message_contents(chat_history: ChatHistory, settings: PromptExecutionSettings, **kwargs: Any) -> AsyncGenerator[list[StreamingChatMessageContent], Any]

Parameters

Name	Description
chat_history Required	<xref:semantic_kernel.connectors.ai.chat_completion_client_base.ChatHistory> A list of chat chat_history, that can be rendered into a set of chat_history, from system, user, assistant and function.
settings Required	<xref:semantic_kernel.connectors.ai.chat_completion_client_base.PromptExecutionSettings> Settings for the request.
kwargs Required	<xref:Dict>[str,<xref: Any>] The optional arguments.

Attributes

model_computed_fields

A dictionary of computed field names and their corresponding ComputedFieldInfo objects.

model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}

model_config

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

model_config: ClassVar[ConfigDict] = {'arbitrary_types_allowed': True, 'populate_by_name': True, 'validate_assignment': True}

model_fields

Metadata about the fields defined on the model, mapping of field names to [FieldInfo][pydantic.fields.FieldInfo] objects.

This replaces Model.fields from Pydantic V1.

model_fields: ClassVar[Dict[str, FieldInfo]] = {'ai_model_id': FieldInfo(annotation=str, required=True, metadata=[StringConstraints(strip_whitespace=True, to_upper=None, to_lower=None, strict=None, min_length=1, max_length=None, pattern=None)]), 'service_id': FieldInfo(annotation=str, required=False, default='')}

SUPPORTS_FUNCTION_CALLING

SUPPORTS_FUNCTION_CALLING: ClassVar[bool] = False