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import ollama
from .common import TaskSpec, ParsedAnswer, Question
from .exceptions import GPTOutputParseException, GPTMaxTriesExceededException
import threading
from typing import List, Tuple, Union
from loguru import logger
from copy import deepcopy
import time
class OllamaModel(object):
def __init__(self,
task:TaskSpec,
model:str):
self.task:TaskSpec = task
self.model:str = model
def ask(self, payload:dict, n_choices=1) -> Tuple[List[dict], List[dict]]:
"""
args:
payload: json dictionary, prepared by `prepare_payload`
"""
def ollama_thread(idx, payload, results):
# creation of payload
string_message = "\n".join([el["text"] for el in payload["messages"]["content"]])
mod_payload = deepcopy(payload)
mod_payload["messages"]["content"] = string_message # overridding with string version
#print('string_message: ', string_message)
#print('mod_payload["messages"]: ', payload["messages"])
try:
response = ollama.chat(model=self.model, messages=[
mod_payload["messages"]])
except Exception as e:
raise e
message = response["message"]
metadata = response.copy()
del metadata["message"]
results[idx] = {"message": message, "metadata": metadata}
return
assert n_choices >= 1
results = [None] * n_choices
if n_choices > 1:
ollama_jobs = [threading.Thread(target=ollama_thread,
args=(idx, payload, results))
for idx in range(n_choices)]
for job in ollama_jobs:
job.start()
for job in ollama_jobs:
job.join()
else:
ollama_thread(0, payload, results)
messages:List[dict] = [ res["message"] for res in results]
metadata:List[dict] = [ res["metadata"] for res in results]
return messages, metadata
@staticmethod
def prepare_payload(question:Question,
verbose:bool=False,
prepend:Union[dict, None]=None,
**kwargs
) -> dict:
payload = {
"messages": {
'role': 'user',
'content': question.get_json()
},
}
return payload
def rough_guess(self, question:Question,
max_tries=1, query_id:int=0,
verbose=False,
**kwargs):
p = self.prepare_payload(question, verbose=verbose, prepend=None,
model=self.model)
ok = False
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
def many_rough_guesses(self, num_threads:int,
question:Question,
verbose=False, max_tries=1,
**kwargs) -> 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)
# TODO
n_choices = num_threads
# TODO: wrap in robust-ask method, repeatedly asks until parseable output.
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}")
# TODO provide the parse error message into GPT for the next round to be parsable
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 run_once(self, question:Question, **kwargs):
q = self.task.first_question(question)
p_ans, ans, meta, p = self.rough_guess(q, **kwargs)
return p_ans, ans, meta, p |