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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import logging
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import os
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from collections.abc import AsyncGenerator, Mapping, Sequence
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from html import unescape
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from typing import TYPE_CHECKING, Any
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import yaml
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from pydantic import Field, ValidationError, model_validator
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from semantic_kernel.connectors.ai.chat_completion_client_base import ChatCompletionClientBase
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from semantic_kernel.connectors.ai.prompt_execution_settings import PromptExecutionSettings
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from semantic_kernel.connectors.ai.text_completion_client_base import TextCompletionClientBase
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from semantic_kernel.connectors.ai.text_to_audio_client_base import TextToAudioClientBase
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from semantic_kernel.connectors.ai.text_to_image_client_base import TextToImageClientBase
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from semantic_kernel.const import DEFAULT_SERVICE_NAME
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from semantic_kernel.contents.audio_content import AudioContent
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from semantic_kernel.contents.chat_history import ChatHistory
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from semantic_kernel.contents.chat_message_content import ChatMessageContent
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from semantic_kernel.contents.image_content import ImageContent
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from semantic_kernel.contents.text_content import TextContent
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from semantic_kernel.exceptions import FunctionExecutionException, FunctionInitializationError
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from semantic_kernel.exceptions.function_exceptions import PromptRenderingException
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from semantic_kernel.filters.filter_types import FilterTypes
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from semantic_kernel.filters.functions.function_invocation_context import FunctionInvocationContext
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from semantic_kernel.filters.kernel_filters_extension import _rebuild_prompt_render_context
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from semantic_kernel.filters.prompts.prompt_render_context import PromptRenderContext
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from semantic_kernel.functions.function_result import FunctionResult
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from semantic_kernel.functions.kernel_arguments import KernelArguments
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from semantic_kernel.functions.kernel_function import TEMPLATE_FORMAT_MAP, KernelFunction
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from semantic_kernel.functions.kernel_function_metadata import KernelFunctionMetadata
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from semantic_kernel.functions.kernel_parameter_metadata import KernelParameterMetadata
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from semantic_kernel.functions.prompt_rendering_result import PromptRenderingResult
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from semantic_kernel.prompt_template.const import KERNEL_TEMPLATE_FORMAT_NAME, TEMPLATE_FORMAT_TYPES
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from semantic_kernel.prompt_template.prompt_template_base import PromptTemplateBase
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from semantic_kernel.prompt_template.prompt_template_config import PromptTemplateConfig
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if TYPE_CHECKING:
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from semantic_kernel.services.ai_service_client_base import AIServiceClientBase
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logger: logging.Logger = logging.getLogger(__name__)
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PROMPT_FILE_NAME = "skprompt.txt"
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CONFIG_FILE_NAME = "config.json"
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PROMPT_RETURN_PARAM = KernelParameterMetadata(
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name="return",
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description="The completion result",
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default_value=None,
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type="FunctionResult", # type: ignore
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is_required=True,
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)
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class KernelFunctionFromPrompt(KernelFunction):
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"""Semantic Kernel Function from a prompt."""
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prompt_template: PromptTemplateBase
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prompt_execution_settings: dict[str, PromptExecutionSettings] = Field(default_factory=dict)
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def __init__(
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self,
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function_name: str,
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plugin_name: str | None = None,
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description: str | None = None,
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prompt: str | None = None,
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template_format: TEMPLATE_FORMAT_TYPES = KERNEL_TEMPLATE_FORMAT_NAME,
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prompt_template: PromptTemplateBase | None = None,
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prompt_template_config: PromptTemplateConfig | None = None,
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prompt_execution_settings: PromptExecutionSettings
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| Sequence[PromptExecutionSettings]
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| Mapping[str, PromptExecutionSettings]
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| None = None,
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) -> None:
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"""Initializes a new instance of the KernelFunctionFromPrompt class.
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Args:
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function_name (str): The name of the function
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plugin_name (str): The name of the plugin
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description (str): The description for the function
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prompt (Optional[str]): The prompt
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template_format (Optional[str]): The template format, default is "semantic-kernel"
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prompt_template (Optional[KernelPromptTemplate]): The prompt template
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prompt_template_config (Optional[PromptTemplateConfig]): The prompt template configuration
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prompt_execution_settings (Optional): instance, list or dict of PromptExecutionSettings to be used
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by the function, can also be supplied through prompt_template_config,
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but the supplied one is used if both are present.
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prompt_template_config (Optional[PromptTemplateConfig]): the prompt template config.
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"""
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if not prompt and not prompt_template_config and not prompt_template:
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raise FunctionInitializationError(
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"The prompt cannot be empty, must be supplied directly, \
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through prompt_template_config or in the prompt_template."
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)
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if prompt and prompt_template_config and prompt_template_config.template != prompt:
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logger.warning(
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f"Prompt ({prompt}) and PromptTemplateConfig ({prompt_template_config.template}) both supplied, "
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"using the template in PromptTemplateConfig, ignoring prompt."
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)
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if template_format and prompt_template_config and prompt_template_config.template_format != template_format:
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logger.warning(
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f"Template ({template_format}) and PromptTemplateConfig ({prompt_template_config.template_format}) "
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"both supplied, using the template format in PromptTemplateConfig, ignoring template."
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)
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if not prompt_template:
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if not prompt_template_config:
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# prompt must be there if prompt_template and prompt_template_config is not supplied
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prompt_template_config = PromptTemplateConfig(
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name=function_name,
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description=description,
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template=prompt,
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template_format=template_format,
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)
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elif not prompt_template_config.template:
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prompt_template_config.template = prompt
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prompt_template = TEMPLATE_FORMAT_MAP[prompt_template_config.template_format](
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prompt_template_config=prompt_template_config
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) # type: ignore
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try:
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metadata = KernelFunctionMetadata(
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name=function_name,
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plugin_name=plugin_name,
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description=description,
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parameters=prompt_template.prompt_template_config.get_kernel_parameter_metadata(), # type: ignore
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is_prompt=True,
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is_asynchronous=True,
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return_parameter=PROMPT_RETURN_PARAM,
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)
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except ValidationError as exc:
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raise FunctionInitializationError("Failed to create KernelFunctionMetadata") from exc
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super().__init__(
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metadata=metadata,
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prompt_template=prompt_template, # type: ignore
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prompt_execution_settings=prompt_execution_settings or {}, # type: ignore
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)
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@model_validator(mode="before")
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@classmethod
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def rewrite_execution_settings(
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cls,
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data: Any,
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) -> dict[str, PromptExecutionSettings]:
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"""Rewrite execution settings to a dictionary.
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If the prompt_execution_settings is not a dictionary, it is converted to a dictionary.
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If it is not supplied, but prompt_template is, the prompt_template's execution settings are used.
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"""
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if isinstance(data, dict):
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prompt_execution_settings = data.get("prompt_execution_settings")
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prompt_template = data.get("prompt_template")
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if not prompt_execution_settings:
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if prompt_template:
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prompt_execution_settings = prompt_template.prompt_template_config.execution_settings
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data["prompt_execution_settings"] = prompt_execution_settings
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if not prompt_execution_settings:
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return data
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if isinstance(prompt_execution_settings, PromptExecutionSettings):
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data["prompt_execution_settings"] = {
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prompt_execution_settings.service_id or DEFAULT_SERVICE_NAME: prompt_execution_settings
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}
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if isinstance(prompt_execution_settings, Sequence):
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data["prompt_execution_settings"] = {
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s.service_id or DEFAULT_SERVICE_NAME: s for s in prompt_execution_settings
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}
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return data
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async def _invoke_internal(self, context: FunctionInvocationContext) -> None:
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"""Invokes the function with the given arguments."""
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prompt_render_result = await self._render_prompt(context)
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if prompt_render_result.function_result is not None:
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context.result = prompt_render_result.function_result
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return
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if isinstance(prompt_render_result.ai_service, ChatCompletionClientBase):
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chat_history = ChatHistory.from_rendered_prompt(prompt_render_result.rendered_prompt)
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try:
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chat_message_contents = await prompt_render_result.ai_service.get_chat_message_contents(
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chat_history=chat_history,
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settings=prompt_render_result.execution_settings,
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**{"kernel": context.kernel, "arguments": context.arguments},
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)
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except Exception as exc:
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raise FunctionExecutionException(f"Error occurred while invoking function {self.name}: {exc}") from exc
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if not chat_message_contents:
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raise FunctionExecutionException(f"No completions returned while invoking function {self.name}")
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context.result = self._create_function_result(
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completions=chat_message_contents,
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chat_history=chat_history,
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arguments=context.arguments,
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prompt=prompt_render_result.rendered_prompt,
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)
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return
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if isinstance(prompt_render_result.ai_service, TextCompletionClientBase):
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try:
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texts = await prompt_render_result.ai_service.get_text_contents(
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prompt=unescape(prompt_render_result.rendered_prompt),
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settings=prompt_render_result.execution_settings,
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)
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except Exception as exc:
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raise FunctionExecutionException(f"Error occurred while invoking function {self.name}: {exc}") from exc
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context.result = self._create_function_result(
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completions=texts, arguments=context.arguments, prompt=prompt_render_result.rendered_prompt
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)
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return
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if isinstance(prompt_render_result.ai_service, TextToImageClientBase):
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try:
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images = await prompt_render_result.ai_service.get_image_content(
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description=unescape(prompt_render_result.rendered_prompt),
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settings=prompt_render_result.execution_settings,
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)
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except Exception as exc:
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raise FunctionExecutionException(f"Error occurred while invoking function {self.name}: {exc}") from exc
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context.result = self._create_function_result(
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completions=[images], arguments=context.arguments, prompt=prompt_render_result.rendered_prompt
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)
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return
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if isinstance(prompt_render_result.ai_service, TextToAudioClientBase):
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try:
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audio = await prompt_render_result.ai_service.get_audio_content(
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text=unescape(prompt_render_result.rendered_prompt),
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settings=prompt_render_result.execution_settings,
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)
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except Exception as exc:
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raise FunctionExecutionException(f"Error occurred while invoking function {self.name}: {exc}") from exc
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context.result = self._create_function_result(
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completions=[audio], arguments=context.arguments, prompt=prompt_render_result.rendered_prompt
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)
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return
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raise ValueError(f"Service `{type(prompt_render_result.ai_service).__name__}` is not a valid AI service")
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async def _invoke_internal_stream(self, context: FunctionInvocationContext) -> None:
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"""Invokes the function stream with the given arguments."""
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prompt_render_result = await self._render_prompt(context, is_streaming=True)
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if prompt_render_result.function_result is not None:
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context.result = prompt_render_result.function_result
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return
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if isinstance(prompt_render_result.ai_service, ChatCompletionClientBase):
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chat_history = ChatHistory.from_rendered_prompt(prompt_render_result.rendered_prompt)
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value: AsyncGenerator = prompt_render_result.ai_service.get_streaming_chat_message_contents(
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chat_history=chat_history,
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settings=prompt_render_result.execution_settings,
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**{"kernel": context.kernel, "arguments": context.arguments},
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)
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elif isinstance(prompt_render_result.ai_service, TextCompletionClientBase):
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value = prompt_render_result.ai_service.get_streaming_text_contents(
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prompt=prompt_render_result.rendered_prompt, settings=prompt_render_result.execution_settings
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)
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else:
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raise FunctionExecutionException(
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f"Service `{type(prompt_render_result.ai_service)}` is not a valid AI service"
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)
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context.result = FunctionResult(
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function=self.metadata, value=value, rendered_prompt=prompt_render_result.rendered_prompt
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)
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async def _render_prompt(
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self, context: FunctionInvocationContext, is_streaming: bool = False
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) -> PromptRenderingResult:
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"""Render the prompt and apply the prompt rendering filters."""
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self.update_arguments_with_defaults(context.arguments)
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_rebuild_prompt_render_context()
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prompt_render_context = PromptRenderContext(
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function=self, kernel=context.kernel, arguments=context.arguments, is_streaming=is_streaming
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)
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stack = context.kernel.construct_call_stack(
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filter_type=FilterTypes.PROMPT_RENDERING,
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inner_function=self._inner_render_prompt,
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)
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await stack(prompt_render_context)
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if prompt_render_context.rendered_prompt is None:
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raise PromptRenderingException("Prompt rendering failed, no rendered prompt was returned.")
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selected_service: tuple["AIServiceClientBase", PromptExecutionSettings] = context.kernel.select_ai_service(
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function=self,
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arguments=context.arguments,
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type=(TextCompletionClientBase, ChatCompletionClientBase) if prompt_render_context.is_streaming else None,
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)
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return PromptRenderingResult(
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rendered_prompt=prompt_render_context.rendered_prompt,
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ai_service=selected_service[0],
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execution_settings=selected_service[1],
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function_result=prompt_render_context.function_result,
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)
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async def _inner_render_prompt(self, context: PromptRenderContext) -> None:
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"""Render the prompt using the prompt template."""
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context.rendered_prompt = await self.prompt_template.render(context.kernel, context.arguments)
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def _create_function_result(
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self,
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completions: list[ChatMessageContent] | list[TextContent] | list[ImageContent] | list[AudioContent],
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arguments: KernelArguments,
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chat_history: ChatHistory | None = None,
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prompt: str | None = None,
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) -> FunctionResult:
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"""Creates a function result with the given completions."""
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metadata: dict[str, Any] = {
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"arguments": arguments,
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"metadata": [completion.metadata for completion in completions],
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}
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if chat_history:
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metadata["messages"] = chat_history
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if prompt:
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metadata["prompt"] = prompt
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return FunctionResult(
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function=self.metadata,
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value=completions,
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metadata=metadata,
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rendered_prompt=prompt,
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)
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def update_arguments_with_defaults(self, arguments: KernelArguments) -> None:
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"""Update any missing values with their defaults."""
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for parameter in self.prompt_template.prompt_template_config.input_variables:
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if parameter.name not in arguments and parameter.default not in {None, "", False, 0}:
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arguments[parameter.name] = parameter.default
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@classmethod
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def from_yaml(cls, yaml_str: str, plugin_name: str | None = None) -> "KernelFunctionFromPrompt":
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"""Creates a new instance of the KernelFunctionFromPrompt class from a YAML string."""
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try:
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data = yaml.safe_load(yaml_str)
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except yaml.YAMLError as exc: # pragma: no cover
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raise FunctionInitializationError(f"Invalid YAML content: {yaml_str}, error: {exc}") from exc
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if not isinstance(data, dict):
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raise FunctionInitializationError(f"The YAML content must represent a dictionary, got {yaml_str}")
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try:
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prompt_template_config = PromptTemplateConfig(**data)
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except ValidationError as exc:
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raise FunctionInitializationError(
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f"Error initializing PromptTemplateConfig: {exc} from yaml data: {data}"
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) from exc
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return cls(
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function_name=prompt_template_config.name,
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plugin_name=plugin_name,
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description=prompt_template_config.description,
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prompt_template_config=prompt_template_config,
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template_format=prompt_template_config.template_format,
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)
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@classmethod
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def from_directory(
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cls, path: str, plugin_name: str | None = None, encoding: str = "utf-8"
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) -> "KernelFunctionFromPrompt":
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"""Creates a new instance of the KernelFunctionFromPrompt class from a directory.
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The directory needs to contain:
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- A prompt file named `skprompt.txt`
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- A config file named `config.json`
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Args:
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path: The path to the directory containing the prompt and config files.
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plugin_name: The name of the plugin.
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encoding: The encoding to use when reading the files. Defaults to "utf-8".
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Returns:
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KernelFunctionFromPrompt: The kernel function from prompt
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"""
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prompt_path = os.path.join(path, PROMPT_FILE_NAME)
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config_path = os.path.join(path, CONFIG_FILE_NAME)
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prompt_exists = os.path.exists(prompt_path)
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config_exists = os.path.exists(config_path)
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if not config_exists and not prompt_exists:
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raise FunctionInitializationError(
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f"{PROMPT_FILE_NAME} and {CONFIG_FILE_NAME} files are required to create a "
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f"function from a directory, path: {path!s}."
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)
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if not config_exists:
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raise FunctionInitializationError(
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f"{CONFIG_FILE_NAME} files are required to create a function from a directory, "
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f"path: {path!s}, prompt file is there."
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)
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if not prompt_exists:
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raise FunctionInitializationError(
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f"{PROMPT_FILE_NAME} files are required to create a function from a directory, "
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f"path: {path!s}, config file is there."
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)
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function_name = os.path.basename(path)
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with open(config_path, encoding=encoding) as config_file:
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prompt_template_config = PromptTemplateConfig.from_json(config_file.read())
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prompt_template_config.name = function_name
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with open(prompt_path, encoding=encoding) as prompt_file:
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prompt_template_config.template = prompt_file.read()
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prompt_template = TEMPLATE_FORMAT_MAP[prompt_template_config.template_format]( # type: ignore
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prompt_template_config=prompt_template_config
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)
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return cls(
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function_name=function_name,
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plugin_name=plugin_name,
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prompt_template=prompt_template,
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prompt_template_config=prompt_template_config,
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template_format=prompt_template_config.template_format,
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description=prompt_template_config.description,
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)
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