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71 lines
2.1 KiB
Python
71 lines
2.1 KiB
Python
import asyncio
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import os
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from pathlib import Path
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from promptflow.tracing import trace
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from promptflow.core import AzureOpenAIModelConfiguration, Prompty
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BASE_DIR = Path(__file__).absolute().parent
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def log(message: str):
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verbose = os.environ.get("VERBOSE", "false")
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if verbose.lower() == "true":
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print(message, flush=True)
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class ChatFlow:
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def __init__(
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self, model_config: AzureOpenAIModelConfiguration, max_total_token=1100
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):
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self.model_config = model_config
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self.max_total_token = max_total_token
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@trace
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async def __call__(
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self, question: str = "What is ChatGPT?", chat_history: list = None
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) -> str:
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"""Flow entry function."""
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prompty = Prompty.load(
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source=BASE_DIR / "chat.prompty",
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model={"configuration": self.model_config},
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)
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chat_history = chat_history or []
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# Try to render the prompt with token limit and reduce the history count if it fails
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while len(chat_history) > 0:
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token_count = prompty.estimate_token_count(
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question=question, chat_history=chat_history
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)
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if token_count > self.max_total_token:
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chat_history = chat_history[1:]
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log(
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f"Reducing chat history count to {len(chat_history)} to fit token limit"
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)
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else:
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break
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# output is a generator of string as prompty enabled stream parameter
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for output in prompty(question=question, chat_history=chat_history):
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yield output
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if __name__ == "__main__":
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from promptflow.tracing import start_trace
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start_trace()
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config = AzureOpenAIModelConfiguration(
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connection="open_ai_connection", azure_deployment="gpt-4o"
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)
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flow = ChatFlow(model_config=config)
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result = flow("What's Azure Machine Learning?", [])
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# print result in stream manner
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async def consume_result():
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async for output in result:
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print(output, end="")
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await asyncio.sleep(0.01)
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asyncio.run(consume_result())
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