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import asyncio
import logging
import os
from typing import Any
from aiohttp import ClientSession
from tqdm.asyncio import tqdm_asyncio
import random
from time import sleep
import sys
import aiolimiter
import openai
from openai import AsyncOpenAI, OpenAIError
def prepare_message(SYSTEM_INPUT, USER_INPUT):
cur_message = [
{
"role": "system",
"content": SYSTEM_INPUT
},
{
"role": "user",
"content": USER_INPUT,
}
]
return cur_message
def prepare_remove_message(USER_INPUT):
cur_message = [
{
"role": "system",
"content": "Remove sentences about experimental design and results: "
},
{
"role": "user",
"content": USER_INPUT,
}
]
return cur_message
def prepare_generation_input(title, abstract, sections, filepath):
with open(filepath, 'r', encoding='utf-8') as file:
SYSTEM_INPUT=file.read()
return SYSTEM_INPUT,f"Paper title: {title}\n\nPaper abstract: {abstract}\n\nPaper Sections: {sections}"
def prepare_remove_input(title, abstract, introduction, filepath):
with open(filepath,'r',encoding='utf-8') as file:
SYSTEM_INPUT=file.read()
print(SYSTEM_INPUT)
return SYSTEM_INPUT,f"Paper title: {title}\n\nPaper abstract: {abstract}\n\nIntroduction: {introduction}\n\n"
async def _throttled_openai_chat_completion_acreate(
client: AsyncOpenAI,
model: str,
messages,
temperature: float,
max_tokens: int,
top_p: float,
limiter: aiolimiter.AsyncLimiter,
response_format: dict = {},
):
async with limiter:
for _ in range(10):
try:
if response_format["type"] == "text":
return await client.chat.completions.create(
model=model,
messages=messages,
temperature=temperature,
max_tokens=max_tokens,
top_p=top_p,
)
else:
return await client.chat.completions.create(
model=model,
messages=messages,
temperature=temperature,
max_tokens=max_tokens,
top_p=top_p,
response_format=response_format,
)
except openai.BadRequestError as e:
print(e)
return None
except OpenAIError as e:
print(e)
sleep(random.randint(5, 10))
return None
async def generate_from_openai_chat_completion(
client,
messages,
engine_name: str,
temperature: float = 1.0,
max_tokens: int = 512,
top_p: float = 1.0,
requests_per_minute: int = 100,
response_format: dict = {"type":"text"},
):
"""Generate from OpenAI Chat Completion API.
Args:
messages: List of messages to proceed.
engine_name: Engine name to use, see https://platform.openai.com/docs/models
temperature: Temperature to use.
max_tokens: Maximum number of tokens to generate.
top_p: Top p to use.
requests_per_minute: Number of requests per minute to allow.
Returns:
List of generated responses.
"""
limiter = aiolimiter.AsyncLimiter(requests_per_minute)
async_responses = [
_throttled_openai_chat_completion_acreate(
client,
model=engine_name,
messages=message,
temperature=temperature,
max_tokens=max_tokens,
top_p=top_p,
limiter=limiter,
response_format=response_format,
)
for message in messages
]
responses = await tqdm_asyncio.gather(*async_responses, file=sys.stdout)
outputs = []
for response in responses:
if response:
outputs.append(response.choices[0].message.content)
else:
outputs.append("Invalid Message")
return outputs
# Example usage
if __name__ == "__main__":
os.environ["OPENAI_API_KEY"] = "xxx" # Set your OpenAI API key here
client = AsyncOpenAI()
AsyncOpenAI.api_key = os.getenv('OPENAI_API_KEY')
messages = [
{"role": "system", "content": "You are a helpful assistant."},
{"role": "user", "content": "What is the purpose of life? Output result in json format."},
]
responses = asyncio.run(
generate_from_openai_chat_completion(
client,
messages=[messages]*50,
engine_name="gpt-3.5-turbo-0125",
max_tokens=256,
response_format={"type":"json_object"},
)
)
print(responses) |