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import anthropic
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
import os
class ClaudeModel(object):
def __init__(self, api_key:str,
task:TaskSpec,
model:str = "claude-3-haiku-20240307"):
self.claude_key:str = api_key
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 claude_thread(idx, payload, results):
# creation of payload
mod_payload = deepcopy(payload)
try:
raw_response = client.messages.create(
model="claude-3-haiku-20240307",
#messages=[{"role": "user", "content": "Hello, Claude, tell me a number between 1 to 10000 please."}],
messages = [mod_payload["messages"]],
max_tokens=mod_payload["max_tokens"],
)
except Exception as e:
raise e
response = raw_response.dict()
response['content'] = response['content'][0]['text']
message = {key: response[key] for key in ['role', 'content']}
metadata = response.copy() # okay
del metadata["content"]
results[idx] = {"message": message, "metadata": metadata}
return
client = anthropic.Anthropic(api_key = self.claude_key)
assert n_choices >= 1
results = [None] * n_choices
if n_choices > 1:
claude_jobs = [threading.Thread(target=claude_thread,
args=(idx, payload, results))
for idx in range(n_choices)]
for job in claude_jobs:
job.start()
for job in claude_jobs:
job.join()
else:
claude_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,
max_tokens=1000,
verbose:bool=False,
prepend:Union[dict, None]=None,
**kwargs
) -> dict:
content = []
dic_list = question.get_json()
for dic in dic_list:
# The case of text
if dic['type'] == 'text':
content.append(dic)
# The case of vision input
elif dic['type'] == 'image_url':
base64enc_image = dic['image_url']['url'].split(',')[1]
if base64enc_image.startswith("/9j/"):
image_format = 'jpeg'
elif base64enc_image.startswith("iVBORw0KGgo"):
image_format = "png"
elif base64enc_image.startswith("R0lGOD") or base64enc_image.startswith("R0lGOD"):
image_format = "gif"
elif base64enc_image.startswith("UklGR"):
image_format = "webp"
else:
raise ValueError("Unknown format")
modified_dic = {
'type' : "image",
'source' : {
'type' : "base64",
'media_type' : f"image/{image_format}",
'data' : base64enc_image
}
}
content.append(modified_dic)
payload = {
"messages": {
'role': 'user',
'content': content
},
"max_tokens": max_tokens,
}
return payload
def rough_guess(self, question:Question, max_tokens=1000,
max_tries=1, query_id:int=0,
verbose=False,
**kwargs):
p = self.prepare_payload(question, max_tokens = max_tokens, 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:
if not os.path.exists('errors/'):
# Create the directory if it doesn't exist
os.makedirs('errors/')
error_saved = f'errors/{time.strftime("%Y-%m-%d-%H-%M-%S")}.json'
with open(error_saved, "w") as f:
f.write(p_ans.code)
# logger.warning(f"The following was not parseable. Saved in {error_saved}.")
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, 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, max_tokens = max_tokens, verbose=verbose, prepend=None,
model=self.model)
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}")
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, max_tokens=1000, **kwargs):
q = self.task.first_question(question)
p_ans, ans, meta, p = self.rough_guess(q, max_tokens=max_tokens, **kwargs)
return p_ans, ans, meta, p |