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Python

# 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())