chore: import upstream snapshot with attribution
This commit is contained in:
wehub-resource-sync
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# Copyright (c) Microsoft. All rights reserved.
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from semantic_kernel.connectors.ai.mistral_ai.prompt_execution_settings.mistral_ai_prompt_execution_settings import (
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MistralAIChatPromptExecutionSettings,
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MistralAIPromptExecutionSettings,
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)
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from semantic_kernel.connectors.ai.mistral_ai.services.mistral_ai_chat_completion import MistralAIChatCompletion
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from semantic_kernel.connectors.ai.mistral_ai.services.mistral_ai_text_embedding import MistralAITextEmbedding
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__all__ = [
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"MistralAIChatCompletion",
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"MistralAIChatPromptExecutionSettings",
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"MistralAIPromptExecutionSettings",
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"MistralAITextEmbedding",
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]
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+58
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# Copyright (c) Microsoft. All rights reserved.
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import logging
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from typing import Annotated, Any, Literal
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from mistralai import utils
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from pydantic import Field
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from semantic_kernel.connectors.ai.prompt_execution_settings import PromptExecutionSettings
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logger = logging.getLogger(__name__)
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class MistralAIPromptExecutionSettings(PromptExecutionSettings):
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"""Common request settings for MistralAI services."""
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ai_model_id: Annotated[str | None, Field(serialization_alias="model")] = None
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class MistralAIChatPromptExecutionSettings(MistralAIPromptExecutionSettings):
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"""Specific settings for the Chat Completion endpoint."""
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response_format: dict[Literal["type"], Literal["text", "json_object"]] | None = None
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messages: list[dict[str, Any]] | None = None
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safe_mode: Annotated[
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bool,
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Field(
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exclude=True,
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deprecated="The 'safe_mode' setting is no longer supported and is being ignored, "
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"it will be removed in the Future.",
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),
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] = False
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safe_prompt: bool = False
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max_tokens: Annotated[int | None, Field(gt=0)] = None
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seed: int | None = None
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temperature: Annotated[float | None, Field(ge=0.0, le=2.0)] = None
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top_p: Annotated[float | None, Field(ge=0.0, le=1.0)] = None
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random_seed: int | None = None
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presence_penalty: Annotated[float | None, Field(gt=0)] = None
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frequency_penalty: Annotated[float | None, Field(gt=0)] = None
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n: Annotated[int | None, Field(gt=1)] = None
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retries: utils.RetryConfig | None = None
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server_url: str | None = None
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timeout_ms: int | None = None
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tools: Annotated[
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list[dict[str, Any]] | None,
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Field(
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description="Do not set this manually. It is set by the service based "
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"on the function choice configuration.",
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),
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] = None
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tool_choice: Annotated[
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str | None,
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Field(
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description="Do not set this manually. It is set by the service based "
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"on the function choice configuration.",
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),
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] = None
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@@ -0,0 +1,16 @@
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# Copyright (c) Microsoft. All rights reserved.
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from abc import ABC
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from typing import ClassVar
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from mistralai import Mistral
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from semantic_kernel.kernel_pydantic import KernelBaseModel
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class MistralAIBase(KernelBaseModel, ABC):
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"""Mistral AI service base."""
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MODEL_PROVIDER_NAME: ClassVar[str] = "mistralai"
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async_client: Mistral
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+328
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# Copyright (c) Microsoft. All rights reserved.
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import logging
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import sys
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from collections.abc import AsyncGenerator, Callable
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from typing import TYPE_CHECKING, Any, ClassVar
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if sys.version_info >= (3, 12):
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from typing import override # pragma: no cover
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else:
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from typing_extensions import override # pragma: no cover
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from mistralai import Mistral
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from mistralai.models import (
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AssistantMessage,
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ChatCompletionChoice,
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ChatCompletionResponse,
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CompletionChunk,
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CompletionResponseStreamChoice,
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DeltaMessage,
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ToolCall,
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)
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from pydantic import ValidationError
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from semantic_kernel.connectors.ai.chat_completion_client_base import ChatCompletionClientBase
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from semantic_kernel.connectors.ai.completion_usage import CompletionUsage
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from semantic_kernel.connectors.ai.function_calling_utils import kernel_function_metadata_to_function_call_format
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from semantic_kernel.connectors.ai.function_choice_behavior import FunctionChoiceType
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from semantic_kernel.connectors.ai.mistral_ai.prompt_execution_settings.mistral_ai_prompt_execution_settings import (
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MistralAIChatPromptExecutionSettings,
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)
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from semantic_kernel.connectors.ai.mistral_ai.services.mistral_ai_base import MistralAIBase
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from semantic_kernel.connectors.ai.mistral_ai.settings.mistral_ai_settings import MistralAISettings
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from semantic_kernel.contents import (
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ChatMessageContent,
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FunctionCallContent,
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StreamingChatMessageContent,
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StreamingTextContent,
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TextContent,
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)
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from semantic_kernel.contents.chat_history import ChatHistory
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from semantic_kernel.contents.utils.author_role import AuthorRole
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from semantic_kernel.contents.utils.finish_reason import FinishReason
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from semantic_kernel.exceptions.service_exceptions import ServiceInitializationError, ServiceResponseException
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from semantic_kernel.utils.feature_stage_decorator import experimental
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from semantic_kernel.utils.telemetry.model_diagnostics.decorators import (
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trace_chat_completion,
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trace_streaming_chat_completion,
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)
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if TYPE_CHECKING:
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from semantic_kernel.connectors.ai.function_call_choice_configuration import FunctionCallChoiceConfiguration
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from semantic_kernel.connectors.ai.prompt_execution_settings import PromptExecutionSettings
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logger: logging.Logger = logging.getLogger(__name__)
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@experimental
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class MistralAIChatCompletion(MistralAIBase, ChatCompletionClientBase):
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"""Mistral Chat completion class."""
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SUPPORTS_FUNCTION_CALLING: ClassVar[bool] = True
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def __init__(
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self,
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ai_model_id: str | None = None,
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service_id: str | None = None,
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api_key: str | None = None,
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async_client: Mistral | None = None,
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env_file_path: str | None = None,
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env_file_encoding: str | None = None,
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) -> None:
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"""Initialize an MistralAIChatCompletion service.
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Args:
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ai_model_id : MistralAI model name, see
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https://docs.mistral.ai/getting-started/models/
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service_id : Service ID tied to the execution settings.
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api_key : The optional API key to use. If provided will override,
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the env vars or .env file value.
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async_client : An existing client to use.
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env_file_path : Use the environment settings file as a fallback
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to environment variables.
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env_file_encoding : The encoding of the environment settings file.
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"""
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try:
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mistralai_settings = MistralAISettings(
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api_key=api_key,
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chat_model_id=ai_model_id,
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env_file_path=env_file_path,
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env_file_encoding=env_file_encoding,
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)
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except ValidationError as ex:
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raise ServiceInitializationError("Failed to create MistralAI settings.", ex) from ex
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if not mistralai_settings.chat_model_id:
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raise ServiceInitializationError("The MistralAI chat model ID is required.")
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if not async_client:
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async_client = Mistral(
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api_key=mistralai_settings.api_key.get_secret_value(),
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)
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super().__init__(
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async_client=async_client,
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service_id=service_id or mistralai_settings.chat_model_id,
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ai_model_id=ai_model_id or mistralai_settings.chat_model_id,
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)
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# region Overriding base class methods
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# Override from AIServiceClientBase
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@override
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def get_prompt_execution_settings_class(self) -> "type[MistralAIChatPromptExecutionSettings]":
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"""Create a request settings object."""
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return MistralAIChatPromptExecutionSettings
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# Override from AIServiceClientBase
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@override
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def service_url(self) -> str | None:
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if hasattr(self.async_client, "_endpoint"):
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# Best effort to get the endpoint
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return self.async_client._endpoint
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return None
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@override
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@trace_chat_completion(MistralAIBase.MODEL_PROVIDER_NAME)
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async def _inner_get_chat_message_contents(
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self,
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chat_history: "ChatHistory",
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settings: "PromptExecutionSettings",
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) -> list["ChatMessageContent"]:
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if not isinstance(settings, MistralAIChatPromptExecutionSettings):
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settings = self.get_prompt_execution_settings_from_settings(settings)
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assert isinstance(settings, MistralAIChatPromptExecutionSettings) # nosec
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settings.ai_model_id = settings.ai_model_id or self.ai_model_id
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settings.messages = self._prepare_chat_history_for_request(chat_history)
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try:
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response = await self.async_client.chat.complete_async(**settings.prepare_settings_dict())
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except Exception as ex:
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raise ServiceResponseException(
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f"{type(self)} service failed to complete the prompt",
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ex,
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) from ex
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if isinstance(response, ChatCompletionResponse):
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response_metadata = self._get_metadata_from_response(response)
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# If there are no choices, return an empty list
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if isinstance(response.choices, list) and len(response.choices) > 0:
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return [
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self._create_chat_message_content(response, choice, response_metadata)
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for choice in response.choices
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]
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return []
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@override
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@trace_streaming_chat_completion(MistralAIBase.MODEL_PROVIDER_NAME)
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async def _inner_get_streaming_chat_message_contents(
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self,
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chat_history: "ChatHistory",
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settings: "PromptExecutionSettings",
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function_invoke_attempt: int = 0,
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) -> AsyncGenerator[list["StreamingChatMessageContent"], Any]:
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if not isinstance(settings, MistralAIChatPromptExecutionSettings):
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settings = self.get_prompt_execution_settings_from_settings(settings)
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assert isinstance(settings, MistralAIChatPromptExecutionSettings) # nosec
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settings.ai_model_id = settings.ai_model_id or self.ai_model_id
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settings.messages = self._prepare_chat_history_for_request(chat_history)
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try:
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response = await self.async_client.chat.stream_async(**settings.prepare_settings_dict())
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except Exception as ex:
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raise ServiceResponseException(
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f"{type(self)} service failed to complete the prompt",
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ex,
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) from ex
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# If there is no response end the generator
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if isinstance(response, AsyncGenerator):
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async for chunk in response:
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if len(chunk.data.choices) == 0:
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continue
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chunk_metadata = self._get_metadata_from_response(chunk.data)
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yield [
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self._create_streaming_chat_message_content(
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chunk.data, choice, chunk_metadata, function_invoke_attempt
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)
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for choice in chunk.data.choices
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]
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# endregion
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# region content conversion to SK
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def _create_chat_message_content(
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self, response: ChatCompletionResponse, choice: ChatCompletionChoice, response_metadata: dict[str, Any]
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) -> "ChatMessageContent":
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"""Create a chat message content object from a choice."""
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metadata = self._get_metadata_from_chat_choice(choice)
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metadata.update(response_metadata)
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items: list[Any] = self._get_tool_calls_from_chat_choice(choice)
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if choice.message.content:
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items.append(TextContent(text=choice.message.content))
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return ChatMessageContent(
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inner_content=response,
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ai_model_id=self.ai_model_id,
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metadata=metadata,
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role=AuthorRole(choice.message.role),
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items=items,
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finish_reason=FinishReason(choice.finish_reason) if choice.finish_reason else None,
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)
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def _create_streaming_chat_message_content(
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self,
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chunk: CompletionChunk,
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choice: CompletionResponseStreamChoice,
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chunk_metadata: dict[str, Any],
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function_invoke_attempt: int,
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) -> StreamingChatMessageContent:
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"""Create a streaming chat message content object from a choice."""
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metadata = self._get_metadata_from_chat_choice(choice)
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metadata.update(chunk_metadata)
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items: list[Any] = self._get_tool_calls_from_chat_choice(choice)
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if choice.delta.content is not None:
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items.append(StreamingTextContent(choice_index=choice.index, text=choice.delta.content))
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return StreamingChatMessageContent(
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choice_index=choice.index,
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inner_content=chunk,
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ai_model_id=self.ai_model_id,
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metadata=metadata,
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role=AuthorRole(choice.delta.role) if choice.delta.role else AuthorRole.ASSISTANT,
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finish_reason=FinishReason(choice.finish_reason) if choice.finish_reason else None,
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items=items,
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function_invoke_attempt=function_invoke_attempt,
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)
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def _get_metadata_from_response(self, response: ChatCompletionResponse | CompletionChunk) -> dict[str, Any]:
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"""Get metadata from a chat response."""
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metadata: dict[str, Any] = {
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"id": response.id,
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"created": response.created,
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}
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# Check if usage exists and has a value, then add it to the metadata
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if hasattr(response, "usage") and response.usage is not None:
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metadata["usage"] = (
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CompletionUsage(
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prompt_tokens=response.usage.prompt_tokens,
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completion_tokens=response.usage.completion_tokens,
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),
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)
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return metadata
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def _get_metadata_from_chat_choice(
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self, choice: ChatCompletionChoice | CompletionResponseStreamChoice
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) -> dict[str, Any]:
|
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"""Get metadata from a chat choice."""
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return {
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"logprobs": getattr(choice, "logprobs", None),
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}
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|
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def _get_tool_calls_from_chat_choice(
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self, choice: ChatCompletionChoice | CompletionResponseStreamChoice
|
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) -> list[FunctionCallContent]:
|
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"""Get tool calls from a chat choice."""
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content: AssistantMessage | DeltaMessage
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content = choice.message if isinstance(choice, ChatCompletionChoice) else choice.delta
|
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if content.tool_calls is None:
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return []
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return [
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FunctionCallContent(
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id=tool.id,
|
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index=getattr(tool, "index", None),
|
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name=tool.function.name,
|
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arguments=tool.function.arguments,
|
||||
)
|
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for tool in content.tool_calls
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if isinstance(tool, ToolCall)
|
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]
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# endregion
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def update_settings_from_function_call_configuration_mistral(
|
||||
self,
|
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function_choice_configuration: "FunctionCallChoiceConfiguration",
|
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settings: "PromptExecutionSettings",
|
||||
type: "FunctionChoiceType",
|
||||
) -> None:
|
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"""Update the settings from a FunctionChoiceConfiguration."""
|
||||
if (
|
||||
function_choice_configuration.available_functions
|
||||
and hasattr(settings, "tool_choice")
|
||||
and hasattr(settings, "tools")
|
||||
):
|
||||
settings.tool_choice = type
|
||||
settings.tools = [
|
||||
kernel_function_metadata_to_function_call_format(f)
|
||||
for f in function_choice_configuration.available_functions
|
||||
]
|
||||
# Function Choice behavior required maps to MistralAI any
|
||||
if (
|
||||
settings.function_choice_behavior
|
||||
and settings.function_choice_behavior.type_ == FunctionChoiceType.REQUIRED
|
||||
):
|
||||
settings.tool_choice = "any"
|
||||
|
||||
@override
|
||||
def _update_function_choice_settings_callback(
|
||||
self,
|
||||
) -> Callable[["FunctionCallChoiceConfiguration", "PromptExecutionSettings", FunctionChoiceType], None]:
|
||||
return self.update_settings_from_function_call_configuration_mistral
|
||||
|
||||
@override
|
||||
def _reset_function_choice_settings(self, settings: "PromptExecutionSettings") -> None:
|
||||
if hasattr(settings, "tool_choice"):
|
||||
settings.tool_choice = None
|
||||
if hasattr(settings, "tools"):
|
||||
settings.tools = None
|
||||
+108
@@ -0,0 +1,108 @@
|
||||
# Copyright (c) Microsoft. All rights reserved.
|
||||
|
||||
import sys
|
||||
|
||||
if sys.version_info >= (3, 12):
|
||||
from typing import Any, override # pragma: no cover
|
||||
else:
|
||||
from typing_extensions import Any, override # pragma: no cover
|
||||
|
||||
import logging
|
||||
|
||||
from mistralai import Mistral
|
||||
from mistralai.models import EmbeddingResponse
|
||||
from numpy import array, ndarray
|
||||
from pydantic import ValidationError
|
||||
|
||||
from semantic_kernel.connectors.ai.embedding_generator_base import EmbeddingGeneratorBase
|
||||
from semantic_kernel.connectors.ai.mistral_ai.services.mistral_ai_base import MistralAIBase
|
||||
from semantic_kernel.connectors.ai.mistral_ai.settings.mistral_ai_settings import MistralAISettings
|
||||
from semantic_kernel.connectors.ai.prompt_execution_settings import PromptExecutionSettings
|
||||
from semantic_kernel.exceptions.service_exceptions import ServiceInitializationError, ServiceResponseException
|
||||
from semantic_kernel.utils.feature_stage_decorator import experimental
|
||||
|
||||
logger: logging.Logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
@experimental
|
||||
class MistralAITextEmbedding(MistralAIBase, EmbeddingGeneratorBase):
|
||||
"""Mistral AI Inference Text Embedding Service."""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
ai_model_id: str | None = None,
|
||||
api_key: str | None = None,
|
||||
service_id: str | None = None,
|
||||
async_client: Mistral | None = None,
|
||||
env_file_path: str | None = None,
|
||||
env_file_encoding: str | None = None,
|
||||
) -> None:
|
||||
"""Initialize the Mistral AI Text Embedding service.
|
||||
|
||||
If no arguments are provided, the service will attempt to load the settings from the environment.
|
||||
The following environment variables are used:
|
||||
- MISTRALAI_API_KEY
|
||||
- MISTRALAI_EMBEDDING_MODEL_ID
|
||||
|
||||
Args:
|
||||
ai_model_id: : A string that is used to identify the model such as the model name.
|
||||
api_key : The API key for the Mistral AI service deployment.
|
||||
service_id : Service ID for the embedding completion service.
|
||||
async_client : The Mistral AI client to use.
|
||||
env_file_path : The path to the environment file.
|
||||
env_file_encoding : The encoding of the environment file.
|
||||
|
||||
Raises:
|
||||
ServiceInitializationError: If an error occurs during initialization.
|
||||
"""
|
||||
try:
|
||||
mistralai_settings = MistralAISettings(
|
||||
api_key=api_key,
|
||||
embedding_model_id=ai_model_id,
|
||||
env_file_path=env_file_path,
|
||||
env_file_encoding=env_file_encoding,
|
||||
)
|
||||
except ValidationError as e:
|
||||
raise ServiceInitializationError(f"Failed to validate Mistral AI settings: {e}") from e
|
||||
|
||||
if not mistralai_settings.embedding_model_id:
|
||||
raise ServiceInitializationError("The MistralAI embedding model ID is required.")
|
||||
|
||||
if not async_client:
|
||||
async_client = Mistral(
|
||||
api_key=mistralai_settings.api_key.get_secret_value(),
|
||||
)
|
||||
super().__init__(
|
||||
service_id=service_id or mistralai_settings.embedding_model_id,
|
||||
ai_model_id=ai_model_id or mistralai_settings.embedding_model_id,
|
||||
async_client=async_client,
|
||||
)
|
||||
|
||||
@override
|
||||
async def generate_embeddings(
|
||||
self,
|
||||
texts: list[str],
|
||||
settings: "PromptExecutionSettings | None" = None,
|
||||
**kwargs: Any,
|
||||
) -> ndarray:
|
||||
embedding_response = await self.generate_raw_embeddings(texts, settings, **kwargs)
|
||||
return array(embedding_response)
|
||||
|
||||
@override
|
||||
async def generate_raw_embeddings(
|
||||
self,
|
||||
texts: list[str],
|
||||
settings: "PromptExecutionSettings | None" = None,
|
||||
**kwargs: Any,
|
||||
) -> Any:
|
||||
"""Generate embeddings from the Mistral AI service."""
|
||||
try:
|
||||
embedding_response = await self.async_client.embeddings.create_async(model=self.ai_model_id, inputs=texts)
|
||||
except Exception as ex:
|
||||
raise ServiceResponseException(
|
||||
f"{type(self)} service failed to complete the embedding request.",
|
||||
ex,
|
||||
) from ex
|
||||
if isinstance(embedding_response, EmbeddingResponse):
|
||||
return [item.embedding for item in embedding_response.data]
|
||||
return []
|
||||
@@ -0,0 +1,32 @@
|
||||
# Copyright (c) Microsoft. All rights reserved.
|
||||
|
||||
from typing import ClassVar
|
||||
|
||||
from pydantic import SecretStr
|
||||
|
||||
from semantic_kernel.kernel_pydantic import KernelBaseSettings
|
||||
|
||||
|
||||
class MistralAISettings(KernelBaseSettings):
|
||||
"""MistralAI model settings.
|
||||
|
||||
The settings are first loaded from environment variables with the prefix 'MISTRALAI_'. If the
|
||||
environment variables are not found, the settings can be loaded from a .env file with the
|
||||
encoding 'utf-8'. If the settings are not found in the .env file, the settings are ignored;
|
||||
however, validation will fail alerting that the settings are missing.
|
||||
|
||||
Optional settings for prefix 'MISTRALAI_' are:
|
||||
- api_key: SecretStr - MISTRAL API key, see https://console.mistral.ai/api-keys
|
||||
(Env var MISTRALAI_API_KEY)
|
||||
- chat_model_id: str | None - The The Mistral AI chat model ID to use see https://docs.mistral.ai/getting-started/models/.
|
||||
(Env var MISTRALAI_CHAT_MODEL_ID)
|
||||
- embedding_model_id: str | None - The The Mistral AI embedding model ID to use see https://docs.mistral.ai/getting-started/models/.
|
||||
(Env var MISTRALAI_EMBEDDING_MODEL_ID)
|
||||
- env_file_path: str | None - if provided, the .env settings are read from this file path location
|
||||
"""
|
||||
|
||||
env_prefix: ClassVar[str] = "MISTRALAI_"
|
||||
|
||||
api_key: SecretStr
|
||||
chat_model_id: str | None = None
|
||||
embedding_model_id: str | None = None
|
||||
Reference in New Issue
Block a user