wehub-resource-sync
60 lines
2.1 KiB
Python
60 lines
2.1 KiB
Python
from deepeval.test_case import Turn
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from deepeval.simulator import ConversationSimulator
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from deepeval.dataset import ConversationalGolden
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from openai import AsyncOpenAI, OpenAI
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from typing import List
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# Create ConversationalGolden
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conversation_golden_1 = ConversationalGolden(
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scenario="Andy Byron wants to purchase a VIP ticket to a cold play concert.",
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expected_outcome="Successful purchase of a ticket.",
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user_description="Andy Byron is the CEO of Astronomer.",
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turns=[
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Turn(
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role="assistant",
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content="Hi, I'm here to help you purchase a ticket.",
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),
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# Turn(role="user", content="I want to purchase a VIP ticket to a cold play concert."),
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],
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)
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conversation_golden_2 = ConversationalGolden(
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scenario="Donald Trump wants to ask about ticket availability for a world cup final match.",
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expected_outcome="Donald Trump knows that the ticket is available or not available.",
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user_description="Donald Trump is the President of the United States.",
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turns=[
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Turn(
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role="assistant",
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content="Hi, I'm here to help you purchase a ticket.",
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),
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# Turn(role="user", content="I want to ask about ticket availability for a world cup final match."),
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],
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)
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conversation_golden_3 = ConversationalGolden(
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scenario="Barack Obama wants to book 2 tickets for jazz pub concert.",
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expected_outcome="Successful purchase of 2 tickets.",
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user_description="Barack Obama is the former President of the United States.",
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)
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goldens = [
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conversation_golden_1,
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# conversation_golden_2,
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# conversation_golden_3,
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]
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# Define chatbot callback
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client = AsyncOpenAI()
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async def chatbot_callback(input, turns: List[Turn]):
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messages = []
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for turn in turns:
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messages.append({"role": turn.role, "content": turn.content})
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messages.append({"role": "user", "content": input})
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response = await client.chat.completions.create(
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model="gpt-4o",
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messages=messages,
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)
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return Turn(role="assistant", content=response.choices[0].message.content)
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