Python: Introduce the new function calling abstraction, FunctionChoiceBehavior (microsoft#6910)

### Motivation and Context

The current `FunctionCallBehavior` has allowed us to utilize auto
function calling for OpenAI type models. As we proceed to support more
AI connectors, that differ from OpenAI models, we need to be able to
handle functions for all models that support function calling.

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### Description

In this PR:
- We introduce a new function calling abstraction called
`FunctionChoiceBehavior` which has three types: Auto, Required, and
None.
- One is able to configure the `function_choice_behavior` along with
fully qualified function names (e.g., plugin1.function1),
`maximum_auto_invoke_attempts` or `auto_invoke_kernel_functions` in yaml
and JSON prompts. We have a new concept example showing how to do this
for for yaml and JSON prompts.
- If the fully qualified names are specified in the config file, they
take precedence over the filters, if specified at a later point.
- To make sure this isn't a breaking change, we still handle the
previous `FunctionCallBehavior`, but so we can make decisions on the new
`FunctionChoiceBehavior` we map the `FunctionCallBehavior` to
`FunctionChoiceBehavior`. Each time `FunctionCallBehavior` is updated,
`FunctionChoiceBehavior` will be updated, too.
- The `_process_tool_call()` method in the
`open_ai_chat_completion_base` needs to maintain the argument name as
`function_call_behavior` as we cannot introduce a breaking change.
- All concept samples have been converted to use
`FunctionChoiceBehavior`.
- New unit tests have been added for `FunctionChoiceBehavior` and we
still currently support `FunctionCallBehavior` tests to make sure we
haven't broken the backwards compatibility.
- Added `deprecated` typing decorators to classes/methods to alert users
that it would be best to transition to `FunctionChoiceBehavior` even
though `FunctionCallBehavior` is still supported.
- The `FunctionCallingStepwisePlanner` was updated to use the new
`FunctionChoiceBehavior`.
- Closes microsoft#6496, microsoft#6458
- Addresses microsoft#6626 

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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 😄

Author: moonbox3

python/samples/concepts/auto_function_calling/azure_python_code_interpreter_function_calling.py

@@ -7,7 +7,7 @@
 from azure.core.exceptions import ClientAuthenticationError
 from azure.identity import DefaultAzureCredential
 
-from semantic_kernel.connectors.ai.function_call_behavior import FunctionCallBehavior
+from semantic_kernel.connectors.ai.function_choice_behavior import FunctionChoiceBehavior
 from semantic_kernel.connectors.ai.open_ai.prompt_execution_settings.azure_chat_prompt_execution_settings import (
     AzureChatPromptExecutionSettings,
 )
@@ -69,8 +69,7 @@ async def auth_callback() -> str:
 
 req_settings = AzureChatPromptExecutionSettings(service_id=service_id, tool_choice="auto")
 
-filter = {"excluded_plugins": ["ChatBot"]}
-req_settings.function_call_behavior = FunctionCallBehavior.EnableFunctions(auto_invoke=True, filters=filter)
+req_settings.function_choice_behavior = FunctionChoiceBehavior.Auto(filters={"excluded_plugins": ["ChatBot"]})
 
 arguments = KernelArguments(settings=req_settings)
 

python/samples/concepts/auto_function_calling/chat_gpt_api_function_calling.py

@@ -6,7 +6,6 @@
 from typing import TYPE_CHECKING
 
 from semantic_kernel import Kernel
-from semantic_kernel.connectors.ai.function_call_behavior import FunctionCallBehavior
 from semantic_kernel.connectors.ai.open_ai import OpenAIChatCompletion, OpenAIChatPromptExecutionSettings
 from semantic_kernel.contents import ChatHistory
 from semantic_kernel.contents.chat_message_content import ChatMessageContent
@@ -32,16 +31,17 @@
 you will return a full answer to me as soon as possible.
 """
 
+# This concept example shows how to handle both streaming and non-streaming responses
+# To toggle the behavior, set the following flag accordingly:
+stream = True
+
 kernel = Kernel()
 
 # Note: the underlying gpt-35/gpt-4 model version needs to be at least version 0613 to support tools.
 kernel.add_service(OpenAIChatCompletion(service_id="chat"))
 
 plugins_directory = os.path.join(__file__, "../../../../../prompt_template_samples/")
 # adding plugins to the kernel
-# the joke plugin in the FunPlugins is a semantic plugin and has the function calling disabled.
-# kernel.import_plugin_from_prompt_directory("chat", plugins_directory, "FunPlugin")
-# the math plugin is a core plugin and has the function calling enabled.
 kernel.add_plugin(MathPlugin(), plugin_name="math")
 kernel.add_plugin(TimePlugin(), plugin_name="time")
 
@@ -50,11 +50,29 @@
     plugin_name="ChatBot",
     function_name="Chat",
 )
-# enabling or disabling function calling is done by setting the function_call parameter for the completion.
-# when the function_call parameter is set to "auto" the model will decide which function to use, if any.
-# if you only want to use a specific function, set the name of that function in this parameter,
-# the format for that is 'PluginName-FunctionName', (i.e. 'math-Add').
-# if the model or api version does not support this you will get an error.
+
+# Enabling or disabling function calling is done by setting the `function_choice_behavior` attribute for the
+# prompt execution settings. When the function_call parameter is set to "auto" the model will decide which
+# function to use, if any.
+#
+# There are two ways to define the `function_choice_behavior` parameter:
+# 1. Using the type string as `"auto"`, `"required"`, or `"none"`. For example:
+#   configure `function_choice_behavior="auto"` parameter directly in the execution settings.
+# 2. Using the FunctionChoiceBehavior class. For example:
+#   `function_choice_behavior=FunctionChoiceBehavior.Auto()`.
+# Both of these configure the `auto` tool_choice and all of the available plugins/functions
+# registered on the kernel. If you want to limit the available plugins/functions, you must
+# configure the `filters` dictionary attribute for each type of function choice behavior.
+# For example:
+#
+# from semantic_kernel.connectors.ai.function_choice_behavior import FunctionChoiceBehavior
+
+# function_choice_behavior = FunctionChoiceBehavior.Auto(
+#     filters={"included_functions": ["time-date", "time-time", "math-Add"]}
+# )
+#
+# The filters attribute allows you to specify either: `included_functions`, `excluded_functions`,
+#  `included_plugins`, or `excluded_plugins`.
 
 # Note: the number of responses for auto invoking tool calls is limited to 1.
 # If configured to be greater than one, this value will be overridden to 1.
@@ -63,9 +81,7 @@
     max_tokens=2000,
     temperature=0.7,
     top_p=0.8,
-    function_call_behavior=FunctionCallBehavior.EnableFunctions(
-        auto_invoke=True, filters={"included_plugins": ["math", "time"]}
-    ),
+    function_choice_behavior="auto",
 )
 
 history = ChatHistory()
@@ -93,7 +109,10 @@ def print_tool_calls(message: ChatMessageContent) -> None:
                 f"tool_call {i} arguments: {function_arguments}"
             )
             formatted_tool_calls.append(formatted_str)
-    print("Tool calls:\n" + "\n\n".join(formatted_tool_calls))
+    if len(formatted_tool_calls) > 0:
+        print("Tool calls:\n" + "\n\n".join(formatted_tool_calls))
+    else:
+        print("The model used its own knowledge and didn't return any tool calls.")
 
 
 async def handle_streaming(
@@ -110,7 +129,7 @@ async def handle_streaming(
     print("Mosscap:> ", end="")
     streamed_chunks: list[StreamingChatMessageContent] = []
     async for message in response:
-        if not execution_settings.function_call_behavior.auto_invoke_kernel_functions and isinstance(
+        if not execution_settings.function_choice_behavior.auto_invoke_kernel_functions and isinstance(
             message[0], StreamingChatMessageContent
         ):
             streamed_chunks.append(message[0])
@@ -119,6 +138,8 @@ async def handle_streaming(
 
     if streamed_chunks:
         streaming_chat_message = reduce(lambda first, second: first + second, streamed_chunks)
+        if hasattr(streaming_chat_message, "content"):
+            print(streaming_chat_message.content)
         print("Auto tool calls is disabled, printing returned tool calls...")
         print_tool_calls(streaming_chat_message)
 
@@ -141,7 +162,6 @@ async def chat() -> bool:
     arguments["user_input"] = user_input
     arguments["chat_history"] = history
 
-    stream = True
     if stream:
         await handle_streaming(kernel, chat_function, arguments=arguments)
     else:
@@ -151,7 +171,7 @@ async def chat() -> bool:
         # ChatMessageContent with information about the tool calls, which need to be sent
         # back to the model to get the final response.
         function_calls = [item for item in result.value[-1].items if isinstance(item, FunctionCallContent)]
-        if not execution_settings.function_call_behavior.auto_invoke_kernel_functions and len(function_calls) > 0:
+        if not execution_settings.function_choice_behavior.auto_invoke_kernel_functions and len(function_calls) > 0:
             print_tool_calls(result.value[0])
             return True