Python: updated Chat Message Content with Function Call and Result Content

.pre-commit-config.yaml

@@ -23,7 +23,7 @@ repos:
       - id: black
         files: \.py$
   - repo: https://github.com/astral-sh/ruff-pre-commit
-    rev: v0.3.7
+    rev: v0.4.1
     hooks:
       - id: ruff
         args: [ --fix, --exit-non-zero-on-fix ]

python/samples/kernel-syntax-examples/azure_chat_gpt_with_data_api.py

@@ -7,13 +7,10 @@
 from semantic_kernel.connectors.ai.open_ai import (
     AzureAISearchDataSource,
     AzureChatCompletion,
-    AzureChatMessageContent,
     AzureChatPromptExecutionSettings,
     ExtraBody,
-    FunctionCall,
-    ToolCall,
 )
-from semantic_kernel.contents import ChatHistory, ChatRole
+from semantic_kernel.contents import ChatHistory
 from semantic_kernel.functions import KernelArguments
 from semantic_kernel.prompt_template import InputVariable, PromptTemplateConfig
 from semantic_kernel.utils.settings import (
@@ -22,7 +19,7 @@
 )
 
 kernel = Kernel()
-logging.basicConfig(level=logging.DEBUG)
+logging.basicConfig(level=logging.INFO)
 
 # Load Azure OpenAI Settings
 aoai_settings = azure_openai_settings_from_dot_env_as_dict(include_api_version=True)
@@ -35,14 +32,15 @@
 
 azure_ai_search_settings = azure_aisearch_settings_from_dot_env_as_dict()
 
-# Our example index has fields "source_title", "source_text", "source_url", and "source_file".
-# Add fields mapping to the settings to indicate which fields to use for the title, content, URL, and file path.
-azure_ai_search_settings["fieldsMapping"] = {
-    "titleField": "source_title",
-    "urlField": "source_url",
-    "contentFields": ["source_text"],
-    "filepathField": "source_file",
-}
+# Depending on the index that you use, you might need to enable the below
+# and adapt it so that it accurately reflects your index.
+
+# azure_ai_search_settings["fieldsMapping"] = {
+#     "titleField": "source_title",
+#     "urlField": "source_url",
+#     "contentFields": ["source_text"],
+#     "filepathField": "source_file",
+# }
 
 # Create the data source settings
 
@@ -88,38 +86,30 @@ async def chat() -> bool:
     if user_input == "exit":
         print("\n\nExiting chat...")
         return False
-
-    # Non streaming
-    # answer = await kernel.run(chat_function, input_vars=context_vars)
-    # print(f"Assistant:> {answer}")
     arguments = KernelArguments(chat_history=chat_history, user_input=user_input, execution_settings=req_settings)
 
-    full_message = None
-    print("Assistant:> ", end="")
-    async for message in kernel.invoke_stream(chat_function, arguments=arguments):
-        print(str(message[0]), end="")
-        full_message = message[0] if not full_message else full_message + message[0]
-    print("\n")
-
-    # The tool message containing cited sources is available in the context
-    if full_message:
+    stream = False
+    if stream:
+        # streaming
+        full_message = None
+        print("Assistant:> ", end="")
+        async for message in kernel.invoke_stream(chat_function, arguments=arguments):
+            print(str(message[0]), end="")
+            full_message = message[0] if not full_message else full_message + message[0]
+        print("\n")
+
+        # The tool message containing cited sources is available in the context
         chat_history.add_user_message(user_input)
-        if hasattr(full_message, "tool_message"):
-            chat_history.add_message(
-                AzureChatMessageContent(
-                    role="assistant",
-                    tool_calls=[
-                        ToolCall(
-                            id="chat_with_your_data",
-                            function=FunctionCall(name="chat_with_your_data", arguments=""),
-                        )
-                    ],
-                )
-            )
-            chat_history.add_tool_message(full_message.tool_message, {"tool_call_id": "chat_with_your_data"})
-        if full_message.role is None:
-            full_message.role = ChatRole.ASSISTANT
-        chat_history.add_assistant_message(full_message.content)
+        for message in AzureChatCompletion.split_message(full_message):
+            chat_history.add_message(message)
+        return True
+
+    # Non streaming
+    answer = await kernel.invoke(chat_function, arguments=arguments)
+    print(f"Assistant:> {answer}")
+    chat_history.add_user_message(user_input)
+    for message in AzureChatCompletion.split_message(answer.value[0]):
+        chat_history.add_message(message)
     return True
 
 

python/samples/kernel-syntax-examples/azure_chat_gpt_with_data_api_vector_search.py

@@ -6,13 +6,10 @@
 from semantic_kernel.connectors.ai.open_ai import (
     AzureAISearchDataSource,
     AzureChatCompletion,
-    AzureChatMessageContent,
     AzureChatPromptExecutionSettings,
     ExtraBody,
-    FunctionCall,
-    ToolCall,
 )
-from semantic_kernel.contents import ChatHistory, ChatRole
+from semantic_kernel.contents import ChatHistory
 from semantic_kernel.functions import KernelArguments
 from semantic_kernel.kernel import Kernel
 from semantic_kernel.prompt_template import InputVariable, PromptTemplateConfig
@@ -114,22 +111,8 @@ async def chat() -> bool:
     # The tool message containing cited sources is available in the context
     if full_message:
         chat_history.add_user_message(user_input)
-        if hasattr(full_message, "tool_message"):
-            chat_history.add_message(
-                AzureChatMessageContent(
-                    role="assistant",
-                    tool_calls=[
-                        ToolCall(
-                            id="chat_with_your_data",
-                            function=FunctionCall(name="chat_with_your_data", arguments=""),
-                        )
-                    ],
-                )
-            )
-            chat_history.add_tool_message(full_message.tool_message, {"tool_call_id": "chat_with_your_data"})
-        if full_message.role is None:
-            full_message.role = ChatRole.ASSISTANT
-        chat_history.add_assistant_message(full_message.content)
+        for message in AzureChatCompletion.split_message(full_message):
+            chat_history.add_message(message)
     return True
 
 

python/samples/kernel-syntax-examples/azure_cognitive_search_memory.py

@@ -6,7 +6,7 @@
 
 from semantic_kernel import Kernel
 from semantic_kernel.connectors.ai.open_ai import AzureTextCompletion, AzureTextEmbedding
-from semantic_kernel.connectors.memory import AzureCognitiveSearchMemoryStore
+from semantic_kernel.connectors.memory.azure_cognitive_search import AzureCognitiveSearchMemoryStore
 from semantic_kernel.core_plugins import TextMemoryPlugin
 from semantic_kernel.memory import SemanticTextMemory
 
@@ -62,7 +62,7 @@ async def main() -> None:
             api_key=AZURE_OPENAI_API_KEY,
         ),
     )
-    embedding_service_id = ("ada",)
+    embedding_service_id = "ada"
     embedding_gen = AzureTextEmbedding(
         service_id=embedding_service_id,
         deployment_name="text-embedding-ada-002",
@@ -81,10 +81,10 @@ async def main() -> None:
     kernel.add_plugin(TextMemoryPlugin(memory), "TextMemoryPlugin")
 
     print("Populating memory...")
-    await populate_memory(kernel)
+    await populate_memory(memory)
 
     print("Asking questions... (manually)")
-    await search_acs_memory_questions(kernel)
+    await search_acs_memory_questions(memory)
 
     await acs_connector.close()
 

python/samples/kernel-syntax-examples/chat_gpt_api_function_calling.py

@@ -3,17 +3,15 @@
 import asyncio
 import os
 from functools import reduce
-from typing import TYPE_CHECKING, Any, Dict, List
+from typing import TYPE_CHECKING, List
 
 from semantic_kernel import Kernel
-from semantic_kernel.connectors.ai.open_ai import (
-    OpenAIChatCompletion,
-    OpenAIChatMessageContent,
-    OpenAIChatPromptExecutionSettings,
-    OpenAIStreamingChatMessageContent,
-)
+from semantic_kernel.connectors.ai.open_ai import OpenAIChatCompletion, OpenAIChatPromptExecutionSettings
 from semantic_kernel.connectors.ai.open_ai.utils import get_tool_call_object
 from semantic_kernel.contents import ChatHistory
+from semantic_kernel.contents.chat_message_content import ChatMessageContent
+from semantic_kernel.contents.function_call_content import FunctionCallContent
+from semantic_kernel.contents.streaming_chat_message_content import StreamingChatMessageContent
 from semantic_kernel.core_plugins import MathPlugin, TimePlugin
 from semantic_kernel.functions import KernelArguments
 from semantic_kernel.utils.settings import openai_settings_from_dot_env
@@ -88,61 +86,46 @@
 arguments = KernelArguments(settings=execution_settings)
 
 
-def print_tool_calls(message: OpenAIChatMessageContent) -> None:
+def print_tool_calls(message: ChatMessageContent) -> None:
     # A helper method to pretty print the tool calls from the message.
     # This is only triggered if auto invoke tool calls is disabled.
-    if isinstance(message, OpenAIChatMessageContent):
-        tool_calls = message.tool_calls
-        formatted_tool_calls = []
-        for i, tool_call in enumerate(tool_calls, start=1):
-            tool_call_id = tool_call.id
-            function_name = tool_call.function.name
-            function_arguments = tool_call.function.arguments
+    items = message.items
+    formatted_tool_calls = []
+    for i, item in enumerate(items, start=1):
+        if isinstance(item, FunctionCallContent):
+            tool_call_id = item.id
+            function_name = item.name
+            function_arguments = item.arguments
             formatted_str = (
                 f"tool_call {i} id: {tool_call_id}\n"
                 f"tool_call {i} function name: {function_name}\n"
                 f"tool_call {i} arguments: {function_arguments}"
             )
             formatted_tool_calls.append(formatted_str)
-        print("Tool calls:\n" + "\n\n".join(formatted_tool_calls))
+    print("Tool calls:\n" + "\n\n".join(formatted_tool_calls))
 
 
 async def handle_streaming(
     kernel: Kernel,
     chat_function: "KernelFunction",
-    user_input: str,
-    history: ChatHistory,
-    execution_settings: OpenAIChatPromptExecutionSettings,
+    arguments: KernelArguments,
 ) -> None:
     response = kernel.invoke_stream(
         chat_function,
         return_function_results=False,
-        user_input=user_input,
-        chat_history=history,
+        arguments=arguments,
     )
 
     print("Mosscap:> ", end="")
-    streamed_chunks: List[OpenAIStreamingChatMessageContent] = []
-    tool_call_ids_by_index: Dict[str, Any] = {}
-
+    streamed_chunks: List[StreamingChatMessageContent] = []
     async for message in response:
-        if not execution_settings.auto_invoke_kernel_functions and isinstance(
-            message[0], OpenAIStreamingChatMessageContent
-        ):
+        if not execution_settings.auto_invoke_kernel_functions:
             streamed_chunks.append(message[0])
-            if message[0].tool_calls is not None:
-                for tc in message[0].tool_calls:
-                    if tc.id not in tool_call_ids_by_index:
-                        tool_call_ids_by_index[tc.id] = tc
-                    else:
-                        for tc in message[0].tool_calls:
-                            tool_call_ids_by_index[tc.id] += tc
         else:
             print(str(message[0]), end="")
 
     if streamed_chunks:
         streaming_chat_message = reduce(lambda first, second: first + second, streamed_chunks)
-        streaming_chat_message.tool_calls = list(tool_call_ids_by_index.values())
         print("Auto tool calls is disabled, printing returned tool calls...")
         print_tool_calls(streaming_chat_message)
 
@@ -162,19 +145,19 @@ async def chat() -> bool:
     if user_input == "exit":
         print("\n\nExiting chat...")
         return False
+    arguments["user_input"] = user_input
+    arguments["chat_history"] = history
 
     stream = True
     if stream:
-        await handle_streaming(kernel, chat_function, user_input, history, execution_settings)
+        await handle_streaming(kernel, chat_function, arguments=arguments)
     else:
-        result = await kernel.invoke(chat_function, user_input=user_input, chat_history=history)
+        result = await kernel.invoke(chat_function, arguments=arguments)
 
         # If tools are used, and auto invoke tool calls is False, the response will be of type
-        # OpenAIChatMessageContent with information about the tool calls, which need to be sent
+        # ChatMessageContent with information about the tool calls, which need to be sent
         # back to the model to get the final response.
-        if not execution_settings.auto_invoke_kernel_functions and isinstance(
-            result.value[0], OpenAIChatMessageContent
-        ):
+        if not execution_settings.auto_invoke_kernel_functions:
             print_tool_calls(result.value[0])
             return True
 

python/semantic_kernel/connectors/ai/chat_completion_client_base.py

@@ -14,17 +14,13 @@
 
 
 class ChatCompletionClientBase(AIServiceClientBase, ABC):
-    def get_chat_message_content_type(self) -> str:
-        """Get the chat message content types used by a class, default is 'ChatMessageContent'."""
-        return "ChatMessageContent"
-
     @abstractmethod
     async def complete_chat(
         self,
-        chat_history: ChatHistory,
-        settings: PromptExecutionSettings,
+        chat_history: "ChatHistory",
+        settings: "PromptExecutionSettings",
         **kwargs: Any,
-    ) -> list[ChatMessageContent]:
+    ) -> list["ChatMessageContent"]:
         """
         This is the method that is called from the kernel to get a response from a chat-optimized LLM.
 
@@ -42,10 +38,10 @@ async def complete_chat(
     @abstractmethod
     def complete_chat_stream(
         self,
-        chat_history: ChatHistory,
-        settings: PromptExecutionSettings,
+        chat_history: "ChatHistory",
+        settings: "PromptExecutionSettings",
         **kwargs: Any,
-    ) -> AsyncGenerator[list[StreamingChatMessageContent], Any]:
+    ) -> AsyncGenerator[list["StreamingChatMessageContent"], Any]:
         """
         This is the method that is called from the kernel to get a stream response from a chat-optimized LLM.
 
@@ -63,7 +59,9 @@ def complete_chat_stream(
 
     def _prepare_chat_history_for_request(
         self,
-        chat_history: ChatHistory,
+        chat_history: "ChatHistory",
+        role_key: str = "role",
+        content_key: str = "content",
     ) -> list[dict[str, str | None]]:
         """
         Prepare the chat history for a request, allowing customization of the key names for role/author,
@@ -79,9 +77,4 @@ def _prepare_chat_history_for_request(
         Returns:
             List[Dict[str, Optional[str]]] -- The prepared chat history.
         """
-        return [self._chat_message_content_to_dict(message) for message in chat_history.messages]
-
-    def _chat_message_content_to_dict(self, message: "ChatMessageContent") -> dict[str, str | None]:
-        """can be overridden to customize the serialization of the chat message content"""
-        msg = message.model_dump(include=["role", "content"])
-        return msg
+        return [message.to_dict(role_key=role_key, content_key=content_key) for message in chat_history.messages]

python/semantic_kernel/connectors/ai/google_palm/gp_prompt_execution_settings.py

@@ -1,3 +1,5 @@
+# Copyright (c) Microsoft. All rights reserved.
+
 from typing import Any, Dict, Iterable, List, Optional, Union
 
 from pydantic import Field, model_validator