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from typing import List, Union, Tuple
from loguru import logger
import io
from openai import OpenAI
from .common import TaskSpec, ParsedAnswer, Question
from .exceptions import GPTOutputParseException, GPTMaxTriesExceededException
class GPTModel(object):
def __init__(self, api_key:str,
task:TaskSpec,
model:str="gpt-4o",
):
self.open_ai_key:str = api_key
self.task:TaskSpec = task
self.model:str = model
def ask(self, payload: dict, n_choices=1) -> Tuple[dict, dict]:
"""
args:
payload: json dictionary, prepared by `prepare_payload`
"""
client = OpenAI(api_key=self.open_ai_key)
try:
if self.model in ("gpt-4o", 'gpt-4o-mini', 'gpt-4-turbo'):
response = client.chat.completions.create(
model=self.model, #"gpt-4-vision-preview",
messages=payload["messages"],
max_tokens=payload["max_tokens"],
n=n_choices
)
elif self.model in ('o1-mini', 'o1', 'o3-mini', 'o3'):
response = client.chat.completions.create(
model=self.model, #"gpt-4-vision-preview",
messages=payload["messages"],
max_completion_tokens=payload["max_tokens"],
n=n_choices
)
except Exception as e:
# err = e
raise e
response = response.dict()
messages = [choice["message"] for choice in response["choices"]]
metadata = response["usage"]
return messages, metadata
@staticmethod
def prepare_payload(question:Question,
verbose:bool=False,
prepend:Union[dict, None]=None,
model:str="gpt-4-vision-preview",
max_tokens:int=1000,
) -> dict:
"""
Args:
question: List of question components
verbose: if true, prints out the payload.
prepend (optional): if not None it should be the "message" from the
GPT output from the previous exchange.
Returns:
payload (dict) containing the json to be sent to GPT's API.
"""
question_dicts = question.get_json()
print('Getting question_dicts fine.')
for part in question_dicts:
if part["type"]=="image_url":
del part["image"] # remove the PIL.Image
payload = [{"role": "user",
"content": question_dicts
}]
if prepend is not None:
payload = [prepend] + payload
# if verbose:
# print("############")
# "\n".join([str(el) for el in payload])
# print("############")
payload = {
"model": model,
"messages": payload,
"max_tokens": max_tokens}
return payload
def run_once(self, question:Question, max_tokens=1000):
print('Into gpt4v.py -> think')
q = self.task.first_question(question)
p_ans, ans, meta, p = self.rough_guess(q, max_tokens=max_tokens)
return p_ans, ans, meta, p
############### NOTE : deprecated -- moved to Agent class.
def run(self, question:Question, verbose:bool=False):
""" Main running program
"""
logger.warning("DEPRECATED! Use the Agents class instead!")
answers_history = []
questions_history = []
eval_history = []
first_q = self.task.first_question(question)
p_ans, ans, meta, p = self.rough_guess(first_q)
questions_history.append(question)
answers_history.append(p_ans)
latest_answer = p_ans
if verbose:
# logger.info(f"iteration 0 Answer: {str(p_ans)}")
pass
iteration = 0
while True:
evaluation_answer = self.task.completed(question, latest_answer)
eval_history.append(evaluation_answer)
if verbose:
# logger.info(f"eval comment from {iteration} editing: \n {str(evaluation_answer)}")
pass
if evaluation_answer.success():
break
iteration += 1
if verbose:
logger.info(f"start iteration {iteration} editing")
next_question = self.task.next_question(questions_history, answers_history, eval_history)
p_ans, ans, meta, p = self.rough_guess(next_question)
answers_history.append(p_ans)
latest_answer = p_ans
if verbose:
# logger.info(f"iteration {iteration} editing output: \n{str(p_ans)}")
pass
if verbose:
# logger.info(f"Returning answer at iteration {iteration}: \n{str(p_ans)}")
pass
return latest_answer, ans, meta, p
def many_rough_guesses(self, num_threads:int,
question:Question, max_tokens=1000,
verbose=False, max_tries=1) -> List[Tuple[ParsedAnswer, str, dict, dict]]:
"""
Args:
num_threads : number of independent threads.
all other arguments are same as those of `rough_guess()`
Returns
List of elements, each element is a tuple following the
return signature of `rough_guess()`
"""
p = self.prepare_payload(question, verbose=verbose, prepend=None,
model=self.model,
max_tokens=max_tokens)
n_choices = num_threads
ok = False
reattempt = 0
while not ok:
response, meta_data = self.ask(p, n_choices=n_choices)
try:
parsed_response = [self.task.answer_type.parser(r["content"]) for r in response]
except GPTOutputParseException as e:
# logger.warning(f"The following was not parseable:\n\n{response}\n\nBecause\n\n{e}")
pass
reattempt += 1
if reattempt > max_tries:
logger.error(f"max tries ({max_tries}) exceeded.")
raise GPTMaxTriesExceededException
logger.warning(f"Reattempt #{reattempt} querying LLM")
continue
ok = True
return parsed_response, response, meta_data, p
def rough_guess(self, question:Question, max_tokens=1000, verbose=False,
max_tries=1, query_id:int=0) -> Tuple[ParsedAnswer, str, dict, dict]:
"""
Args:
question
max_tokens (int) : max tokens in return from
verbose (bool)
Returns:
answer in the form of ParsedAnswer
answer in the form of raw text response from LLM
meta data of the response
json payload sent to the LLM
"""
p = self.prepare_payload(question, verbose=verbose, prepend=None,
model=self.model,
max_tokens=max_tokens)
print('Loading payload fine.')
ok = False
reattempt = 0
while not ok:
response, meta_data = self.ask(p)
response = response[0]
try:
parsed_response = self.task.answer_type.parser(response["content"])
except GPTOutputParseException as e:
# logger.warning(f"The following was not parseable:\n\n{response}\n\nBecause\n\n{e}")
reattempt += 1
if reattempt > max_tries:
logger.error(f"max tries ({max_tries}) exceeded.")
raise GPTMaxTriesExceededException
logger.warning(f"Reattempt #{reattempt} querying LLM")
continue
ok = True
return parsed_response, response, meta_data, p
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