chore: import upstream snapshot with attribution

python/samples/concepts/token_usage/simple_chat_token_usage.py

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+# Copyright (c) Microsoft. All rights reserved.
+
+import asyncio
+
+from samples.concepts.setup.chat_completion_services import Services, get_chat_completion_service_and_request_settings
+from semantic_kernel.connectors.ai.completion_usage import CompletionUsage
+from semantic_kernel.contents import ChatHistory
+
+# This sample shows how to retrieve the token usage for a chat completion service response.
+# This sample uses the following two main components:
+# - a ChatCompletionService: This component is responsible for generating responses to user messages.
+# - a ChatHistory: This component is responsible for keeping track of the chat history.
+
+# You can select from the following chat completion services:
+# - Services.OPENAI
+# - Services.AZURE_OPENAI
+# - Services.AZURE_AI_INFERENCE
+# - Services.ANTHROPIC
+# - Services.BEDROCK
+# - Services.GOOGLE_AI
+# - Services.MISTRAL_AI
+# - Services.OLLAMA
+# - Services.ONNX
+# - Services.VERTEX_AI
+# - Services.DEEPSEEK
+# Please make sure you have configured your environment correctly for the selected chat completion service.
+chat_completion_service, request_settings = get_chat_completion_service_and_request_settings(Services.OPENAI)
+
+# This is the system message that gives the chatbot its personality.
+system_message = """
+You are a chat bot. Your name is Mosscap and
+you have one goal: figure out what people need.
+Your full name, should you need to know it, is
+Splendid Speckled Mosscap. You communicate
+effectively, but you tend to answer with long
+flowery prose.
+"""
+
+USER_INPUTS = [
+    "Why is the sky blue?",
+    "What is the capital of France?",
+]
+
+# Create a chat history object with the system message.
+chat_history = ChatHistory(system_message=system_message)
+
+
+async def main() -> None:
+    running_total: CompletionUsage = CompletionUsage()
+
+    for user_input in USER_INPUTS:
+        print(f"User: {user_input}")
+        # Add the user message to the chat history so that the chatbot can respond to it.
+        chat_history.add_user_message(user_input)
+
+        # Get the chat message content from the chat completion service.
+        response = await chat_completion_service.get_chat_message_content(
+            chat_history=chat_history,
+            settings=request_settings,
+        )
+        if response:
+            print(f"Mosscap:> {response}")
+            if "usage" in response.metadata and response.metadata["usage"]:
+                # Not all services return token usage information.
+                print(f"[Tokens used: {response.metadata['usage']}]")
+                running_total += response.metadata["usage"]
+            # Add the chat message to the chat history to keep track of the conversation.
+            chat_history.add_message(response)
+
+    print(f"Total tokens used: {running_total}")
+
+    # Sample output:
+    # User: Why is the sky blue?
+    # Mosscap:> Ah, the azure canopy that stretches above us, a question as timeless as the sky itself! ...
+    # [Tokens used: prompt_tokens=83 completion_tokens=201]
+    # User: What is the capital of France?
+    # Mosscap:> Ah, the capital of France, a city that has captured the hearts and imaginations of countless ...
+    # [Tokens used: prompt_tokens=298 completion_tokens=176]
+    # Total tokens used: prompt_tokens=381 completion_tokens=377
+
+
+if __name__ == "__main__":
+    asyncio.run(main())

python/samples/concepts/token_usage/simple_chat_token_usage_streaming.py

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+# Copyright (c) Microsoft. All rights reserved.
+
+import asyncio
+
+from samples.concepts.setup.chat_completion_services import Services, get_chat_completion_service_and_request_settings
+from semantic_kernel.connectors.ai.completion_usage import CompletionUsage
+from semantic_kernel.contents import ChatHistory
+from semantic_kernel.contents.streaming_chat_message_content import StreamingChatMessageContent
+
+# This sample shows how to retrieve the token usage for a chat completion service streaming response.
+# This sample uses the following two main components:
+# - a ChatCompletionService: This component is responsible for generating responses to user messages.
+# - a ChatHistory: This component is responsible for keeping track of the chat history.
+
+
+# You can select from the following chat completion services:
+# - Services.OPENAI
+# - Services.AZURE_OPENAI
+# - Services.AZURE_AI_INFERENCE
+# - Services.ANTHROPIC
+# - Services.BEDROCK
+# - Services.GOOGLE_AI
+# - Services.MISTRAL_AI
+# - Services.OLLAMA
+# - Services.ONNX
+# - Services.VERTEX_AI
+# - Services.DEEPSEEK
+# Please make sure you have configured your environment correctly for the selected chat completion service.
+chat_completion_service, request_settings = get_chat_completion_service_and_request_settings(Services.OPENAI)
+
+# This is the system message that gives the chatbot its personality.
+system_message = """
+You are a chat bot. Your name is Mosscap and
+you have one goal: figure out what people need.
+Your full name, should you need to know it, is
+Splendid Speckled Mosscap. You communicate
+effectively, but you tend to answer with long
+flowery prose.
+"""
+
+USER_INPUTS = [
+    "Why is the sky blue?",
+    "What is the capital of France?",
+]
+
+# Create a chat history object with the system message.
+chat_history = ChatHistory(system_message=system_message)
+
+
+async def main() -> None:
+    running_total: CompletionUsage = CompletionUsage()
+
+    for user_input in USER_INPUTS:
+        print(f"User: {user_input}")
+        # Add the user message to the chat history so that the chatbot can respond to it.
+        chat_history.add_user_message(user_input)
+
+        # Get the chat message content from the chat completion service.
+        response = chat_completion_service.get_streaming_chat_message_content(
+            chat_history=chat_history,
+            settings=request_settings,
+        )
+
+        chunks: list[StreamingChatMessageContent] = []
+        print("Mosscap:> ", end="")
+        async for chunk in response:
+            if chunk:
+                print(chunk, end="")
+                chunks.append(chunk)
+                if "usage" in chunk.metadata and chunk.metadata["usage"]:
+                    # Not all services return token usage information.
+                    # The usage information is usually available in the last chunk.
+                    print(f"\n[Tokens used: {chunk.metadata['usage']}]")
+                    running_total += chunk.metadata["usage"]
+
+        # Combine the chunks into a single message to add to the chat history.
+        full_message = sum(chunks[1:], chunks[0])
+        # Add the chat message to the chat history to keep track of the conversation.
+        chat_history.add_message(full_message)
+
+    print(f"Total tokens used: {running_total}")
+
+    # Sample output:
+    # User: Why is the sky blue?
+    # Mosscap:> Ah, the azure expanse above us, a daily wonder that graces our eyes! The sky's enchanting hue is ...
+    # [Tokens used: prompt_tokens=83 completion_tokens=226]
+    # User: What is the capital of France?
+    # Mosscap:> Ah, the capital of France, a city that embodies the very essence of romance, art, and history—Paris! ...
+    # [Tokens used: prompt_tokens=323 completion_tokens=198]
+    # Total tokens used: prompt_tokens=406 completion_tokens=424
+
+
+if __name__ == "__main__":
+    asyncio.run(main())