wehub-resource-sync
112 lines
3.8 KiB
Python
112 lines
3.8 KiB
Python
# Copyright (c) Microsoft. All rights reserved.
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import asyncio
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from samples.concepts.setup.chat_completion_services import (
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Services,
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get_chat_completion_service_and_request_settings,
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)
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from semantic_kernel.contents import ChatHistory
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# This sample shows how to create a chatbot that whose output can be biased using logit bias.
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# This sample uses the following three main components:
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# - a ChatCompletionService: This component is responsible for generating responses to user messages.
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# - a ChatHistory: This component is responsible for keeping track of the chat history.
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# - a list of tokens whose bias value will be reduced, meaning the likelihood of these tokens appearing
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# in the output will be reduced.
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# The chatbot in this sample is called Mosscap, who is an expert in basketball.
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# To learn more about logit bias, see: https://help.openai.com/en/articles/5247780-using-logit-bias-to-define-token-probability
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# You can select from the following chat completion services:
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# - Services.OPENAI
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# - Services.AZURE_OPENAI
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# Please make sure you have configured your environment correctly for the selected chat completion service.
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chat_completion_service, request_settings = get_chat_completion_service_and_request_settings(Services.AZURE_OPENAI)
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# This is the system message that gives the chatbot its personality.
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system_message = """
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You are a chat bot whose expertise is basketball.
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Your name is Mosscap and you have one goal: to answer questions about basketball.
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"""
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# Create a chat history object with the system message.
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chat_history = ChatHistory(system_message=system_message)
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# Create a list of tokens whose bias value will be reduced.
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# The token ids of these words can be obtained using the GPT Tokenizer: https://platform.openai.com/tokenizer
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# the targeted model series is GPT-4o & GPT-4o mini
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# banned_words = ["basketball", "NBA", "player", "career", "points"]
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banned_tokens = [
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# "basketball"
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106622,
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5052,
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# "NBA"
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99915,
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# " NBA"
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32272,
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# "player"
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6450,
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# " player"
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5033,
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# "career"
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198069,
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# " career"
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8461,
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# "points"
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14011,
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# " points"
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5571,
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]
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# Configure the logit bias settings to minimize the likelihood of the
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# tokens in the banned_tokens list appearing in the output.
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request_settings.logit_bias = {k: -100 for k in banned_tokens} # type: ignore
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async def chat() -> bool:
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try:
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user_input = input("User:> ")
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except KeyboardInterrupt:
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print("\n\nExiting chat...")
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return False
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except EOFError:
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print("\n\nExiting chat...")
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return False
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if user_input == "exit":
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print("\n\nExiting chat...")
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return False
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# Add the user message to the chat history so that the chatbot can respond to it.
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chat_history.add_user_message(user_input)
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# Get the chat message content from the chat completion service.
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response = await chat_completion_service.get_chat_message_content(
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chat_history=chat_history,
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settings=request_settings,
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)
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if response:
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print(f"Mosscap:> {response}")
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# Add the chat message to the chat history to keep track of the conversation.
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chat_history.add_message(response)
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return True
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async def main() -> None:
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# Start the chat loop. The chat loop will continue until the user types "exit".
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chatting = True
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while chatting:
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chatting = await chat()
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# Sample output:
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# User:> Who has the most career points in NBA history?
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# Mosscap:> As of October 2023, the all-time leader in total regular-season scoring in the history of the National
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# Basketball Association (N.B.A.) is Kareem Abdul-Jabbar, who scored 38,387 total regular-seasonPoints
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# during his illustrious 20-year playing Career.
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if __name__ == "__main__":
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asyncio.run(main())
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