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semantic-kernel/python/semantic_kernel/orchestration/kernel_function.py
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Eduard van Valkenburg a8233dc068 Python: streaming fix for Python: SK 0.5.0 breaks streaming #4925 (#4928) 
### Motivation and Context

<!-- Thank you for your contribution to the semantic-kernel repo!
Please help reviewers and future users, providing the following
information:
  1. Why is this change required?
  2. What problem does it solve?
  3. What scenario does it contribute to?
  4. If it fixes an open issue, please link to the issue here.
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Issue was raised that streaming when using chat but not a
chat_prompt_template failed because it did not return after doing the
branch for non-chat-prompt-template, this fixes that.

Fixes #4925 

### Description

<!-- Describe your changes, the overall approach, the underlying design.
These notes will help understanding how your code works. Thanks! -->

### Contribution Checklist

<!-- Before submitting this PR, please make sure: -->

- [x] The code builds clean without any errors or warnings
- [x] The PR follows the [SK Contribution
Guidelines](https://github.com/microsoft/semantic-kernel/blob/main/CONTRIBUTING.md)
and the [pre-submission formatting
script](https://github.com/microsoft/semantic-kernel/blob/main/CONTRIBUTING.md#development-scripts)
raises no violations
- [x] All unit tests pass, and I have added new tests where possible
- [x] I didn't break anyone 😄
2024-02-08 16:05:54 +00:00

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# Copyright (c) Microsoft. All rights reserved.
import asyncio
import logging
import platform
import sys
from enum import Enum
from typing import TYPE_CHECKING, Any, Callable, Dict, List, Optional, Union
from pydantic import Field, StringConstraints
if sys.version_info >= (3, 9):
from typing import Annotated
else:
from typing_extensions import Annotated
from semantic_kernel.connectors.ai.chat_completion_client_base import (
ChatCompletionClientBase,
)
from semantic_kernel.connectors.ai.prompt_execution_settings import PromptExecutionSettings
from semantic_kernel.connectors.ai.text_completion_client_base import (
TextCompletionClientBase,
)
from semantic_kernel.kernel_exception import KernelException
from semantic_kernel.kernel_pydantic import KernelBaseModel
from semantic_kernel.memory.null_memory import NullMemory
from semantic_kernel.memory.semantic_text_memory_base import SemanticTextMemoryBase
from semantic_kernel.models.contents.chat_message_content import ChatMessageContent
from semantic_kernel.orchestration.context_variables import ContextVariables
from semantic_kernel.orchestration.delegate_handlers import DelegateHandlers
from semantic_kernel.orchestration.delegate_inference import DelegateInference
from semantic_kernel.orchestration.delegate_types import DelegateTypes
from semantic_kernel.plugin_definition.function_view import FunctionView
from semantic_kernel.plugin_definition.parameter_view import ParameterView
from semantic_kernel.semantic_functions.chat_prompt_template import ChatPromptTemplate
from semantic_kernel.semantic_functions.semantic_function_config import (
SemanticFunctionConfig,
)
if TYPE_CHECKING:
from semantic_kernel.orchestration.kernel_context import KernelContext
from semantic_kernel.plugin_definition.kernel_plugin_collection import KernelPluginCollection
# TODO: is this needed anymore after sync code removal?
if platform.system() == "Windows" and sys.version_info >= (3, 8, 0):
asyncio.set_event_loop_policy(asyncio.WindowsSelectorEventLoopPolicy())
logger: logging.Logger = logging.getLogger(__name__)
def store_results(chat_prompt: ChatPromptTemplate, results: List["ChatMessageContent"]):
"""Stores specific results in the context and chat prompt."""
if hasattr(results[0], "tool_message") and results[0].tool_message is not None:
chat_prompt.add_message(role="tool", message=results[0].tool_message)
chat_prompt.add_message(
"assistant",
message=results[0].content,
function_call=results[0].function_call if hasattr(results[0], "function_call") else None,
tool_calls=results[0].tool_calls if hasattr(results[0], "tool_calls") else None,
)
return chat_prompt
class KernelFunction(KernelBaseModel):
"""
Semantic Kernel function.
Attributes:
plugin_name (str): The name of the plugin that contains this function. Must be upper/lower
case letters and underscores with a minimum length of 1.
description (Optional[str]): The description of the function.
name (str): The name of the function. Must be upper/lower case letters and
underscores with a minimum length of 1.
is_semantic (bool): Whether the function is semantic.
stream_function (Optional[Callable[..., Any]]): The stream function for the function.
parameters (List[ParameterView]): The parameters for the function.
delegate_type (DelegateTypes): The delegate type for the function.
function (Callable[..., Any]): The function to call.
plugins (Optional[KernelPluginCollection]): The collection of plugins.
ai_service (Optional[Union[TextCompletionClientBase, ChatCompletionClientBase]]): The AI service.
ai_prompt_execution_settings (PromptExecutionSettings): The AI prompt execution settings.
chat_prompt_template (Optional[ChatPromptTemplate]): The chat prompt template.
"""
plugin_name: Annotated[str, StringConstraints(pattern=r"^[A-Za-z_]+$", min_length=1)]
description: Optional[str] = Field(default=None)
name: Annotated[str, StringConstraints(pattern=r"^[A-Za-z_]+$", min_length=1)]
is_semantic: bool = Field(...)
stream_function: Optional[Callable[..., Any]] = Field(default=None)
parameters: List[ParameterView] = Field(...)
delegate_type: DelegateTypes = Field(...)
function: Callable[..., Any] = Field(...)
plugins: Optional["KernelPluginCollection"] = Field(default=None)
ai_service: Optional[Union[TextCompletionClientBase, ChatCompletionClientBase]] = Field(default=None)
prompt_execution_settings: PromptExecutionSettings = Field(default_factory=PromptExecutionSettings)
chat_prompt_template: Optional[ChatPromptTemplate] = Field(default=None)
def __init__(
self,
delegate_type: DelegateTypes,
delegate_function: Callable[..., Any],
parameters: List[ParameterView],
description: str,
plugin_name: str,
function_name: str,
is_semantic: bool,
delegate_stream_function: Optional[Callable[..., Any]] = None,
**kwargs: Dict[str, Any],
) -> None:
"""
Initializes a new instance of the KernelFunction class
Args:
delegate_type (DelegateTypes): The delegate type for the function
delegate_function (Callable[..., Any]): The delegate function for the function
parameters (List[ParameterView]): The parameters for the function
description (str): The description for the function
plugin_name (str): The name of the plugin
name (str): The name of the function
is_semantic (bool): Whether the function is semantic
delegate_stream_function (Optional[Callable[..., Any]]): The delegate stream function for the function
kwargs (Dict[str, Any]): Additional keyword arguments
"""
chat_prompt_template = kwargs.pop("chat_prompt_template", None)
super().__init__(
delegate_type=delegate_type,
function=delegate_function,
parameters=parameters,
description=description,
plugin_name=plugin_name,
name=function_name,
is_semantic=is_semantic,
stream_function=delegate_stream_function,
chat_prompt_template=chat_prompt_template,
**kwargs,
)
@staticmethod
def from_native_method(method: Callable[..., Any], plugin_name: str) -> "KernelFunction":
"""
Create a KernelFunction from a native method.
Args:
method (Callable[..., Any]): The method to create the function from
plugin_name (str): The name of the plugin
Returns:
KernelFunction: The kernel function
"""
if method is None:
raise ValueError("Method cannot be `None`")
assert method.__kernel_function__ is not None, "Method is not a Kernel function"
assert method.__kernel_function_name__ is not None, "Method name is empty"
parameters = []
# kernel_function_context_parameters are optionals
if hasattr(method, "__kernel_function_context_parameters__"):
for param in method.__kernel_function_context_parameters__:
assert "name" in param, "Parameter name is empty"
assert "description" in param, "Parameter description is empty"
assert "default_value" in param, "Parameter default value is empty"
parameters.append(
ParameterView(
name=param["name"],
description=param["description"],
default_value=param["default_value"],
type=param.get("type", "string"),
required=param.get("required", False),
)
)
if (
hasattr(method, "__kernel_function_input_description__")
and method.__kernel_function_input_description__ is not None
and method.__kernel_function_input_description__ != ""
):
input_param = ParameterView(
name="input",
description=method.__kernel_function_input_description__,
default_value=method.__kernel_function_input_default_value__,
type="string",
required=False,
)
parameters = [input_param] + parameters
return KernelFunction(
delegate_type=DelegateInference.infer_delegate_type(method),
delegate_function=method,
delegate_stream_function=method,
parameters=parameters,
description=method.__kernel_function_description__,
plugin_name=plugin_name,
function_name=method.__kernel_function_name__,
is_semantic=False,
)
@staticmethod
def from_semantic_config(
plugin_name: str,
function_name: str,
function_config: SemanticFunctionConfig,
) -> "KernelFunction":
"""
Create a KernelFunction from a semantic configuration.
Args:
plugin_name (str): The name of the plugin
function_name (str): The name of the function
function_config (SemanticFunctionConfig): The function configuration
Returns:
KernelFunction: The kernel function
"""
if function_config is None:
raise ValueError("Function configuration cannot be `None`")
async def _local_func(client, prompt_execution_settings, context: "KernelContext", **kwargs):
if client is None:
raise ValueError("AI LLM service cannot be `None`")
if not function_config.has_chat_prompt:
try:
prompt = await function_config.prompt_template.render(context)
results = await client.complete(prompt, prompt_execution_settings)
context.objects["results"] = results
context.variables.update(str(results[0]))
except Exception as e:
# TODO: "critical exceptions"
context.fail(str(e), e)
finally:
return context
try:
chat_prompt = function_config.prompt_template
# Similar to non-chat, render prompt (which renders to a
# dict of <role, content, name> messages)
messages = await chat_prompt.render_messages(context)
results = await client.complete_chat(messages, prompt_execution_settings)
context.objects["results"] = results
if results[0].content is not None:
context.variables.update(str(results[0]))
# TODO: most of this will be deleted once context is gone, just AIResponse object is then returned.
chat_prompt = store_results(chat_prompt, results)
except Exception as exc:
# TODO: "critical exceptions"
context.fail(str(exc), exc)
finally:
return context
async def _local_stream_func(client, prompt_execution_settings, context):
if client is None:
raise ValueError("AI LLM service cannot be `None`")
if not function_config.has_chat_prompt:
try:
prompt = await function_config.prompt_template.render(context)
result = client.complete_stream(prompt, prompt_execution_settings)
async for chunk in result:
yield chunk
except Exception as e:
# TODO: "critical exceptions"
context.fail(str(e), e)
return
try:
chat_prompt = function_config.prompt_template
# Similar to non-chat, render prompt (which renders to a
# list of <role, content> messages)
messages = await chat_prompt.render_messages(context)
result = client.complete_chat_stream(messages=messages, settings=prompt_execution_settings)
# context.objects["response_object"] = result
# TODO: most of this will be deleted once context is gone, just AIResponse object is then returned.
async for chunk in result:
yield chunk
# context, chat_prompt = store_results(context, result, chat_prompt)
except Exception as e:
# TODO: "critical exceptions"
logger.error(f"Error occurred while invoking stream function: {str(e)}")
context.fail(str(e), e)
return KernelFunction(
delegate_type=DelegateTypes.ContextSwitchInKernelContextOutTaskKernelContext,
delegate_function=_local_func,
delegate_stream_function=_local_stream_func,
parameters=function_config.prompt_template.get_parameters(),
description=function_config.prompt_template_config.description,
plugin_name=plugin_name,
function_name=function_name,
is_semantic=True,
chat_prompt_template=function_config.prompt_template if function_config.has_chat_prompt else None,
)
def set_default_plugin_collection(self, plugins: "KernelPluginCollection") -> "KernelFunction":
self.plugins = plugins
return self
def set_ai_service(self, ai_service: Callable[[], TextCompletionClientBase]) -> "KernelFunction":
if ai_service is None:
raise ValueError("AI LLM service factory cannot be `None`")
self._verify_is_semantic()
self.ai_service = ai_service()
return self
def set_chat_service(self, chat_service: Callable[[], ChatCompletionClientBase]) -> "KernelFunction":
if chat_service is None:
raise ValueError("Chat LLM service factory cannot be `None`")
self._verify_is_semantic()
self.ai_service = chat_service()
return self
def set_ai_configuration(self, settings: PromptExecutionSettings) -> "KernelFunction":
if settings is None:
raise ValueError("AI LLM request settings cannot be `None`")
self._verify_is_semantic()
self.prompt_execution_settings = settings
return self
def set_chat_configuration(self, settings: PromptExecutionSettings) -> "KernelFunction":
if settings is None:
raise ValueError("Chat LLM request settings cannot be `None`")
self._verify_is_semantic()
self.prompt_execution_settings = settings
return self
def describe(self) -> FunctionView:
return FunctionView(
name=self.name,
plugin_name=self.plugin_name,
description=self.description,
is_semantic=self.is_semantic,
parameters=self.parameters,
)
async def __call__(
self,
input: Optional[str] = None,
variables: ContextVariables = None,
context: Optional["KernelContext"] = None,
memory: Optional[SemanticTextMemoryBase] = None,
settings: Optional[PromptExecutionSettings] = None,
log: Optional[Any] = None,
) -> "KernelContext":
"""
Override the call operator to allow calling the function directly
This operator is run asynchronously.
Arguments:
input {Optional[str]} -- The input to the function
variables {ContextVariables} -- The variables for the function
context {Optional[KernelContext]} -- The context for the function
memory {Optional[SemanticTextMemoryBase]} -- The memory for the function
settings {Optional[PromptExecutionSettings]} -- The settings for the function
log {Optional[Any]} -- A logger to use for logging. (Optional)
Returns:
KernelContext -- The context for the function
Raises:
KernelException -- If the function is not semantic
"""
if log:
logger.warning("The `log` parameter is deprecated. Please use the `logging` module instead.")
return await self.invoke(
input=input,
variables=variables,
context=context,
memory=memory,
settings=settings,
)
async def invoke(
self,
input: Optional[str] = None,
variables: ContextVariables = None,
context: Optional["KernelContext"] = None,
memory: Optional[SemanticTextMemoryBase] = None,
settings: Optional[PromptExecutionSettings] = None,
**kwargs: Dict[str, Any],
) -> "KernelContext":
"""
Invoke the function asynchronously
Arguments:
input {Optional[str]} -- The input to the function
variables {ContextVariables} -- The variables for the function
context {Optional[KernelContext]} -- The context for the function
memory {Optional[SemanticTextMemoryBase]} -- The memory for the function
settings {Optional[PromptExecutionSettings]} -- The settings for the function
kwargs {Dict[str, Any]} -- Additional keyword arguments
Returns:
KernelContext -- The context for the function
Raises:
KernelException -- If there is a problem invoking the function
"""
from semantic_kernel.orchestration.kernel_context import KernelContext
if context is None:
context = KernelContext(
variables=ContextVariables("") if variables is None else variables,
memory=memory if memory is not None else NullMemory.instance,
plugins=self.plugins,
)
else:
# If context is passed, we need to merge the variables
if variables is not None:
context.variables = variables.merge_or_overwrite(new_vars=context.variables, overwrite=False)
if memory is not None:
context.memory = memory
if input is not None:
context.variables.update(input)
try:
if self.is_semantic:
return await self._invoke_semantic(context, settings, **kwargs)
else:
return await self._invoke_native(context, **kwargs)
except Exception as e:
context.fail(str(e), e)
return context
async def _invoke_semantic(self, context: "KernelContext", settings: PromptExecutionSettings, **kwargs):
self._verify_is_semantic()
self._ensure_context_has_plugins(context)
new_context = await self.function(self.ai_service, settings or self.prompt_execution_settings, context)
context.variables.merge_or_overwrite(new_context.variables)
return context
async def _invoke_native(self, context):
self._verify_is_native()
self._ensure_context_has_plugins(context)
delegate = DelegateHandlers.get_handler(self.delegate_type)
# for python3.9 compatibility (staticmethod is not callable)
if not hasattr(delegate, "__call__"):
delegate = delegate.__func__
new_context = await delegate(self.function, context)
return new_context
def _verify_is_semantic(self) -> None:
if self.is_semantic:
return
logger.error("The function is not semantic")
raise KernelException(
KernelException.ErrorCodes.InvalidFunctionType,
"Invalid operation, the method requires a semantic function",
)
def _verify_is_native(self) -> None:
if not self.is_semantic:
return
logger.error("The function is not native")
raise KernelException(
KernelException.ErrorCodes.InvalidFunctionType,
"Invalid operation, the method requires a native function",
)
async def invoke_stream(
self,
input: Optional[str] = None,
variables: ContextVariables = None,
context: Optional["KernelContext"] = None,
memory: Optional[SemanticTextMemoryBase] = None,
settings: Optional[PromptExecutionSettings] = None,
):
from semantic_kernel.orchestration.kernel_context import KernelContext
if context is None:
context = KernelContext(
variables=ContextVariables("") if variables is None else variables,
memory=memory if memory is not None else NullMemory.instance,
plugins=self.plugins,
)
else:
# If context is passed, we need to merge the variables
if variables is not None:
context.variables = variables.merge_or_overwrite(new_vars=context.variables, overwrite=False)
if memory is not None:
context._memory = memory
if input is not None:
context.variables.update(input)
try:
if self.is_semantic:
async for stream_msg in self._invoke_semantic_stream(context, settings):
yield stream_msg
else:
async for stream_msg in self._invoke_native_stream(context):
yield stream_msg
except Exception as e:
logger.error(f"Error occurred while invoking stream function: {str(e)}")
context.fail(str(e), e)
raise KernelException(
KernelException.ErrorCodes.FunctionInvokeError,
"Error occurred while invoking stream function",
)
async def _invoke_semantic_stream(self, context, settings):
self._verify_is_semantic()
self._ensure_context_has_plugins(context)
async for stream_msg in self.stream_function(
self.ai_service, settings or self.prompt_execution_settings, context
):
yield stream_msg
async def _invoke_native_stream(self, context):
self._verify_is_native()
self._ensure_context_has_plugins(context)
delegate = DelegateHandlers.get_handler(self._delegate_type)
# for python3.9 compatibility (staticmethod is not callable)
if not hasattr(delegate, "__call__"):
delegate = delegate.__func__
completion = ""
async for partial in delegate(self.function, context):
completion += partial
yield partial
context.variables.update(completion)
def _ensure_context_has_plugins(self, context) -> None:
if context.plugins is not None:
return
context.plugins = self.plugins
def _trace_function_type_Call(self, type: Enum) -> None:
logger.debug(f"Executing function type {type}: {type.name}")
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