# Copyright (c) Microsoft. All rights reserved. import asyncio from semantic_kernel.connectors.ai.onnx import OnnxGenAIChatCompletion, OnnxGenAIPromptExecutionSettings from semantic_kernel.contents.chat_history import ChatHistory from semantic_kernel.kernel import Kernel # This concept sample shows how to use the Onnx connector with # a local model running in Onnx kernel = Kernel() service_id = "phi3" ############################################# # Make sure to download an ONNX model # (https://huggingface.co/microsoft/Phi-3-mini-4k-instruct-onnx) # If onnxruntime-genai is used: # use the model stored in /cpu folder # If onnxruntime-genai-cuda is installed for gpu use: # use the model stored in /cuda folder # Then set ONNX_GEN_AI_CHAT_MODEL_FOLDER environment variable to the path to the model folder ############################################# streaming = True chat_completion = OnnxGenAIChatCompletion(ai_model_id=service_id, template="phi3") settings = OnnxGenAIPromptExecutionSettings() system_message = """You are a helpful assistant.""" chat_history = ChatHistory(system_message=system_message) async def chat() -> bool: try: user_input = input("User:> ") except KeyboardInterrupt: print("\n\nExiting chat...") return False except EOFError: print("\n\nExiting chat...") return False if user_input == "exit": print("\n\nExiting chat...") return False chat_history.add_user_message(user_input) if streaming: print("Mosscap:> ", end="") message = "" async for chunk in chat_completion.get_streaming_chat_message_content( chat_history=chat_history, settings=settings, kernel=kernel ): if chunk: print(str(chunk), end="") message += str(chunk) chat_history.add_assistant_message(message) print("") else: answer = await chat_completion.get_chat_message_content( chat_history=chat_history, settings=settings, kernel=kernel ) print(f"Mosscap:> {answer}") chat_history.add_message(answer) return True async def main() -> None: chatting = True while chatting: chatting = await chat() if __name__ == "__main__": asyncio.run(main())