fixed tests and some samples

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

@@ -9,10 +9,8 @@
     AzureChatCompletion,
     AzureChatPromptExecutionSettings,
     ExtraBody,
-    FunctionCallContent,
-    ToolCall,
 )
-from semantic_kernel.contents import AuthorRole, ChatHistory
+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 (
@@ -21,7 +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)
@@ -36,12 +34,12 @@
 
 # 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",
-}
+# azure_ai_search_settings["fieldsMapping"] = {
+#     "titleField": "source_title",
+#     "urlField": "source_url",
+#     "contentFields": ["source_text"],
+#     "filepathField": "source_file",
+# }
 
 # Create the data source settings
 
@@ -87,12 +85,18 @@ 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)
 
+    stream = False
+    if not stream:
+        # 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
+
     full_message = None
     print("Assistant:> ", end="")
     async for message in kernel.invoke_stream(chat_function, arguments=arguments):
@@ -101,24 +105,9 @@ async def chat() -> bool:
     print("\n")
 
     # 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(
-                ChatMessageContent(
-                    role="assistant",
-                    tool_calls=[
-                        ToolCall(
-                            id="chat_with_your_data",
-                            function=FunctionCallContent(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 = AuthorRole.ASSISTANT
-        chat_history.add_assistant_message(full_message.content)
+    chat_history.add_user_message(user_input)
+    for message in AzureChatCompletion.split_message(full_message):
+        chat_history.add_message(message)
     return True
 
 

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

@@ -8,10 +8,8 @@
     AzureChatCompletion,
     AzureChatPromptExecutionSettings,
     ExtraBody,
-    FunctionCallContent,
-    ToolCall,
 )
-from semantic_kernel.contents import AuthorRole, ChatHistory
+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
@@ -113,22 +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(
-                ChatMessageContent(
-                    role="assistant",
-                    tool_calls=[
-                        ToolCall(
-                            id="chat_with_your_data",
-                            function=FunctionCallContent(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 = AuthorRole.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

@@ -6,12 +6,7 @@
 from typing import TYPE_CHECKING, List
 
 from semantic_kernel import Kernel
-from semantic_kernel.connectors.ai.open_ai import (  # OpenAIChatMessageContent,
-    OpenAIChatCompletion,
-    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

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

@@ -1,6 +1,7 @@
 # Copyright (c) Microsoft. All rights reserved.
 import json
 import logging
+from copy import deepcopy
 from typing import Any, Dict, Mapping, Optional, Union, overload
 from uuid import uuid4
 
@@ -20,9 +21,11 @@
 from semantic_kernel.connectors.ai.open_ai.services.open_ai_text_completion_base import OpenAITextCompletionBase
 from semantic_kernel.connectors.ai.prompt_execution_settings import PromptExecutionSettings
 from semantic_kernel.contents.chat_message_content import ChatMessageContent
+from semantic_kernel.contents.finish_reason import FinishReason
 from semantic_kernel.contents.function_call_content import FunctionCallContent
 from semantic_kernel.contents.function_result_content import FunctionResultContent
 from semantic_kernel.contents.streaming_chat_message_content import StreamingChatMessageContent
+from semantic_kernel.contents.text_content import TextContent
 from semantic_kernel.kernel_pydantic import HttpsUrl
 
 logger: logging.Logger = logging.getLogger(__name__)
@@ -265,9 +268,19 @@ def _add_tool_message_to_chat_message_content(
         self, content: ChatMessageContent | StreamingChatMessageContent, choice: Choice
     ) -> "ChatMessageContent | StreamingChatMessageContent":
         if tool_message := self._get_tool_message_from_chat_choice(choice=choice):
-            function_call = FunctionCallContent(id=uuid4(), name="Azure-OnYourData")
+            try:
+                tool_message_dict = json.loads(tool_message)
+            except json.JSONDecodeError:
+                logger.error("Failed to parse tool message JSON: %s", tool_message)
+                tool_message_dict = {"citations": tool_message}
+
+            function_call = FunctionCallContent(
+                id=str(uuid4()),
+                name="Azure-OnYourData",
+                arguments=json.dumps({"query": tool_message_dict.get("intent", [])}),
+            )
             result = FunctionResultContent.from_function_call_content_and_result(
-                result=tool_message, function_call_content=function_call
+                result=tool_message_dict["citations"], function_call_content=function_call
             )
             content.items.insert(0, function_call)
             content.items.insert(1, result)
@@ -283,3 +296,26 @@ def _get_tool_message_from_chat_choice(self, choice: Union[Choice, ChunkChoice])
             return json.dumps(content.model_extra["context"])
 
         return None
+
+    @staticmethod
+    def split_message(message: "ChatMessageContent") -> list["ChatMessageContent"]:
+        """Split a Azure On Your Data response into separate ChatMessageContents.
+
+        If the message does not have three contents, and those three are one each of:
+        FunctionCallContent, FunctionResultContent, and TextContent,
+        it will not return three messages, potentially only one or two.
+
+        The order of the returned messages is as expected by OpenAI.
+        """
+        if len(message.items) != 3:
+            return [message]
+        messages = {"tool_call": deepcopy(message), "tool_result": deepcopy(message), "assistant": deepcopy(message)}
+        for key, msg in messages.items():
+            if key == "tool_call":
+                msg.items = [item for item in msg.items if isinstance(item, FunctionCallContent)]
+                msg.finish_reason = FinishReason.FUNCTION_CALL
+            if key == "tool_result":
+                msg.items = [item for item in msg.items if isinstance(item, FunctionResultContent)]
+            if key == "assistant":
+                msg.items = [item for item in msg.items if isinstance(item, TextContent)]
+        return [messages["tool_call"], messages["tool_result"], messages["assistant"]]

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

@@ -24,6 +24,7 @@
 from semantic_kernel.contents.function_call_content import FunctionCallContent
 from semantic_kernel.contents.function_result_content import FunctionResultContent
 from semantic_kernel.contents.streaming_chat_message_content import StreamingChatMessageContent
+from semantic_kernel.contents.streaming_text_content import StreamingTextContent
 from semantic_kernel.contents.text_content import TextContent
 from semantic_kernel.exceptions import (
     FunctionCallInvalidArgumentsException,
@@ -251,7 +252,7 @@ def _create_streaming_chat_message_content(
         items: list[Any] = self._get_tool_calls_from_chat_choice(choice)
         items.extend(self._get_function_call_from_chat_choice(choice))
         if choice.delta.content is not None:
-            items.append(TextContent(text=choice.delta.content))
+            items.append(StreamingTextContent(choice_index=choice.index, text=choice.delta.content))
         return StreamingChatMessageContent(
             choice_index=choice.index,
             inner_content=chunk,

python/semantic_kernel/contents/chat_message_content.py

@@ -1,6 +1,7 @@
 # Copyright (c) Microsoft. All rights reserved.
 from __future__ import annotations
 
+import logging
 from enum import Enum
 from typing import Any, Union, overload
 from xml.etree.ElementTree import Element
@@ -19,16 +20,18 @@
 from semantic_kernel.contents.function_call_content import FunctionCallContent
 from semantic_kernel.contents.function_result_content import FunctionResultContent
 from semantic_kernel.contents.kernel_content import KernelContent
+from semantic_kernel.contents.streaming_text_content import StreamingTextContent
 from semantic_kernel.contents.text_content import TextContent
-from semantic_kernel.kernel_pydantic import KernelBaseModel
 
 TAG_CONTENT_MAP = {
     TEXT_CONTENT_TAG: TextContent,
     FUNCTION_CALL_CONTENT_TAG: FunctionCallContent,
     FUNCTION_RESULT_CONTENT_TAG: FunctionResultContent,
 }
 
-ITEM_TYPES = Union[TextContent, FunctionCallContent, FunctionResultContent]
+ITEM_TYPES = Union[TextContent, StreamingTextContent, FunctionCallContent, FunctionResultContent]
+
+logger = logging.getLogger(__name__)
 
 
 class ChatMessageContent(KernelContent):
@@ -145,8 +148,6 @@ def __init__(  # type: ignore
                 items = [item]
         if items:
             kwargs["items"] = items
-        if not items and "finish_reason" not in kwargs:
-            raise ValueError("ChatMessageContent must have either items or content.")
         if inner_content:
             kwargs["inner_content"] = inner_content
         if metadata:
@@ -159,7 +160,7 @@ def __init__(  # type: ignore
 
     @property
     def content(self) -> str:
-        """Get the content of the response."""
+        """Get the content of the response, will find the first TextContent's text."""
         for item in self.items:
             if isinstance(item, TextContent):
                 return item.text
@@ -169,6 +170,10 @@ def content(self) -> str:
     def content(self, value: str):
         """Set the content of the response."""
         if not value:
+            logger.warning(
+                "Setting empty content on ChatMessageContent does not work, "
+                "you can do this through the underlying items if needed, ignoring."
+            )
             return
         for item in self.items:
             if isinstance(item, TextContent):
@@ -199,20 +204,11 @@ def to_element(self) -> "Element":
         """
         root = Element(CHAT_MESSAGE_CONTENT_TAG)
         for field in self.model_fields_set:
-            if field in ["items", "metadata", "inner_content"]:
+            if field not in ["role", "name", "encoding", "finish_reason", "ai_model_id"]:
                 continue
             value = getattr(self, field)
-            if value is None:
-                continue
             if isinstance(value, Enum):
                 value = value.value
-            if isinstance(value, KernelBaseModel):
-                value = value.model_dump_json(exclude_none=True)
-            if isinstance(value, list):
-                if isinstance(value[0], KernelBaseModel):
-                    value = "|".join([val.model_dump_json(exclude_none=True) for val in value])
-                else:
-                    value = "|".join(value)
             root.set(field, value)
         for index, item in enumerate(self.items):
             root.insert(index, item.to_element())
@@ -228,18 +224,26 @@ def from_element(cls, element: Element) -> "ChatMessageContent":
         Returns:
             ChatMessageContent - The new instance of ChatMessageContent or a subclass.
         """
+        kwargs: dict[str, Any] = {key: value for key, value in element.items()}
         items: list[KernelContent] = []
         for child in element:
-            items.append(TAG_CONTENT_MAP[child.tag].from_element(child))  # type: ignore
-        kwargs: dict[str, Any] = {}
+            if child.tag not in TAG_CONTENT_MAP:
+                logger.warning('Unknown tag "%s" in ChatMessageContent, treating as text', child.tag)
+                text = ElementTree.tostring(child, encoding="unicode", short_empty_elements=False)
+                items.append(TextContent(text=text or ""))
+            else:
+                items.append(TAG_CONTENT_MAP[child.tag].from_element(child))  # type: ignore
         if items:
             kwargs["items"] = items
         if element.text:
             kwargs["content"] = element.text
-        if not kwargs:
-            raise ValueError("ChatMessageContent must have either items or content.")
-        for key, value in element.items():
-            kwargs[key] = value
+        if "choice_index" in kwargs and cls is ChatMessageContent:
+            logger.warning(
+                "Seems like you are trying to create a StreamingChatMessageContent, "
+                "use StreamingChatMessageContent.from_element instead, ignoring that field "
+                " and creating a ChatMessageContent instance."
+            )
+            kwargs.pop("choice_index")
         return cls(**kwargs)
 
     def to_prompt(self) -> str:
@@ -280,4 +284,6 @@ def _parse_items(self) -> str | list[dict[str, Any]]:
         """
         if len(self.items) == 1 and isinstance(self.items[0], TextContent):
             return self.items[0].text
+        if len(self.items) == 1 and isinstance(self.items[0], FunctionResultContent):
+            return self.items[0].result
         return [item.to_dict() for item in self.items]