chore: import upstream snapshot with attribution

python/samples/concepts/local_models/onnx_text_completion.py

@@ -0,0 +1,76 @@
+# Copyright (c) Microsoft. All rights reserved.
+
+
+import asyncio
+
+from semantic_kernel.connectors.ai.onnx import OnnxGenAITextCompletion
+from semantic_kernel.functions.kernel_arguments import KernelArguments
+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_TEXT_MODEL_FOLDER environment variable to the path to the model folder
+#############################################
+streaming = True
+
+kernel.add_service(OnnxGenAITextCompletion(ai_model_id=service_id))
+
+settings = kernel.get_prompt_execution_settings_from_service_id(service_id)
+
+# Phi3 Model is using chat templates to generate responses
+# With the Chat Template the model understands
+# the context and roles of the conversation better
+# https://huggingface.co/microsoft/Phi-3-mini-4k-instruct#chat-format
+chat_function = kernel.add_function(
+    plugin_name="ChatBot",
+    function_name="Chat",
+    prompt="<|user|>{{$user_input}}<|end|><|assistant|>",
+    template_format="semantic-kernel",
+    prompt_execution_settings=settings,
+)
+
+
+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
+
+    if streaming:
+        print("Mosscap:> ", end="")
+        async for chunk in kernel.invoke_stream(chat_function, KernelArguments(user_input=user_input)):
+            print(chunk[0].text, end="")
+        print("\n")
+    else:
+        answer = await kernel.invoke(chat_function, KernelArguments(user_input=user_input))
+        print(f"Mosscap:> {answer}")
+    return True
+
+
+async def main() -> None:
+    chatting = True
+    while chatting:
+        chatting = await chat()
+
+
+if __name__ == "__main__":
+    asyncio.run(main())