Python: Combine FunctionResultContent to one ChatMessageContent for A…

…uto Func Invoke Filter (microsoft#8098)

### Motivation and Context

We made an update to better handle the function result content when
using the auto func invoke filter; however, if parallel func calls
exist, they won't be handled properly in the current code.

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### Description

This change combines the number of FunctionResultContent types returned
during the handling of the auto func invoke filter and combines them as
the items to one ChatMessageContent.
- The sample is also updated

The result of 3 parallel tool calls now looks correct:

```text
User:> What is the curent time, 9494+49494, and 333-33?  

Auto function invocation filter
Function: now
Request sequence: 0
Function sequence: 0
Number of function calls: 3

Auto function invocation filter
Function: Add
Request sequence: 0
Function sequence: 0
Number of function calls: 1
Altering the Math plugin

Auto function invocation filter
Function: Subtract
Request sequence: 0
Function sequence: 0
Number of function calls: 1
Altering the Math plugin
Mosscap:> Tuesday, August 13, 2024 10:56 AM for function: time-now
Mosscap:> Stop trying to ask me to do math, I don't like it! for function: math-Add
Mosscap:> Stop trying to ask me to do math, I don't like it! for function: math-Subtract
```

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### 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 😄

python/samples/concepts/filtering/auto_function_invoke_filters.py

@@ -94,13 +94,19 @@ async def auto_function_invocation_filter(context: AutoFunctionInvocationContext
     print(f"Number of function calls: {len(function_calls)}")
     # if we don't call next, it will skip this function, and go to the next one
     await next(context)
+    #############################
+    # Note: to simply return the unaltered function results, uncomment the `context.terminate = True` line and
+    # comment out the lines starting with `result = context.function_result` through `context.terminate = True`.
+    # context.terminate = True
+    #############################
     result = context.function_result
-    for fc in function_calls:
-        if fc.plugin_name == "math":
-            context.function_result = FunctionResult(
-                function=result.function, value="Stop trying to ask me to do math, I don't like it!"
-            )
-            context.terminate = True
+    if context.function.plugin_name == "math":
+        print("Altering the Math plugin")
+        context.function_result = FunctionResult(
+            function=result.function,
+            value="Stop trying to ask me to do math, I don't like it!",
+        )
+        context.terminate = True
 
 
 def print_tool_calls(message: ChatMessageContent) -> None:
@@ -142,12 +148,11 @@ async def chat() -> bool:
 
     history.add_user_message(user_input)
 
-    # Check if any result.value is a FunctionResult
+    # Check if any result.value is a FunctionResultContent
     if any(isinstance(item, FunctionResultContent) for item in result.value[0].items):
-        # Iterate through each result to process FunctionResult instances
         for fr in result.value[0].items:
             if isinstance(fr, FunctionResultContent):
-                print(f"Mosscap:> {fr.result}")
+                print(f"Mosscap:> {fr.result} for function: {fr.name}")
                 history.add_assistant_message(str(fr.result))
     elif any(isinstance(item, FunctionCallContent) for item in result.value[0].items):
         # If tools are used, and auto invoke tool calls is False, the response will be of type

python/semantic_kernel/connectors/ai/open_ai/services/open_ai_chat_completion_base.py

@@ -7,6 +7,8 @@
 from functools import reduce
 from typing import TYPE_CHECKING, Any, ClassVar
 
+from semantic_kernel.contents.function_result_content import FunctionResultContent
+
 if sys.version_info >= (3, 12):
     from typing import override  # pragma: no cover
 else:
@@ -135,7 +137,7 @@ async def get_chat_message_contents(
             )
 
             if any(result.terminate for result in results if result is not None):
-                return [chat_history.messages[-1]]
+                return self._create_filter_early_terminate_chat_message_content(chat_history.messages[-len(results) :])
 
             self._update_settings(settings, chat_history, kernel=kernel)
         else:
@@ -235,7 +237,7 @@ async def get_streaming_chat_message_contents(
                 ],
             )
             if any(result.terminate for result in results if result is not None):
-                yield [chat_history.messages[-1]]  # type: ignore
+                yield self._create_filter_early_terminate_chat_message_content(chat_history.messages[-len(results) :])  # type: ignore
                 break
 
             self._update_settings(settings, chat_history, kernel=kernel)
@@ -314,6 +316,25 @@ def _create_streaming_chat_message_content(
             items=items,
         )
 
+    def _create_filter_early_terminate_chat_message_content(
+        self,
+        messages: list[ChatMessageContent],
+    ) -> list[ChatMessageContent]:
+        """Add an early termination message to the chat messages.
+
+        This method combines the FunctionResultContent items from separate ChatMessageContent messages,
+        and is used in the event that the `context.terminate = True` condition is met.
+        """
+        items: list[Any] = []
+        for message in messages:
+            items.extend([item for item in message.items if isinstance(item, FunctionResultContent)])
+        return [
+            ChatMessageContent(
+                role=AuthorRole.TOOL,
+                items=items,
+            )
+        ]
+
     def _get_metadata_from_chat_response(self, response: ChatCompletion) -> dict[str, Any]:
         """Get metadata from a chat response."""
         return {