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chore: import upstream snapshot with attribution
2026-07-13 13:39:52 +08:00

65 lines
1.8 KiB
Python

import os
from dotenv import load_dotenv
from openai.version import VERSION as OPENAI_VERSION
from promptflow.tracing import trace
def get_client():
if OPENAI_VERSION.startswith("0."):
raise Exception(
"Please upgrade your OpenAI package to version >= 1.0.0 or using the command: pip install --upgrade openai."
)
api_key = os.environ.get("OPENAI_API_KEY", None)
if api_key:
from openai import OpenAI
return OpenAI()
else:
from openai import AzureOpenAI
return AzureOpenAI(
api_version=os.environ.get("OPENAI_API_VERSION", "2023-07-01-preview")
)
@trace
def my_llm_tool(
prompt: str,
# for AOAI, deployment name is customized by user, not model name.
deployment_name: str,
max_tokens: int = 120,
temperature: float = 1.0,
top_p: float = 1.0,
n: int = 1,
) -> str:
if "OPENAI_API_KEY" not in os.environ and "AZURE_OPENAI_API_KEY" not in os.environ:
# load environment variables from .env file
load_dotenv()
if "OPENAI_API_KEY" not in os.environ and "AZURE_OPENAI_API_KEY" not in os.environ:
raise Exception(
"Please specify environment variables: OPENAI_API_KEY or AZURE_OPENAI_API_KEY"
)
messages = [{"content": prompt, "role": "system"}]
response = get_client().chat.completions.create(
messages=messages,
model=deployment_name,
max_tokens=int(max_tokens),
temperature=float(temperature),
top_p=float(top_p),
n=int(n),
)
# get first element because prompt is single.
return response.choices[0].message.content
if __name__ == "__main__":
result = my_llm_tool(
prompt="Write a simple Hello, world! program that displays the greeting message.",
deployment_name="gpt-4o",
)
print(result)