File size: 5,808 Bytes
b599481 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 |
import json
import os
import random
import typing
from argparse import ArgumentParser
import openai
from loguru import logger
from tenacity import _utils, Retrying, retry_if_not_exception_type
from tenacity.stop import stop_base
from tenacity.wait import wait_base
def my_before_sleep(retry_state):
logger.debug(
f"Retrying: attempt {retry_state.attempt_number} ended with: {retry_state.outcome}, spend {retry_state.seconds_since_start} in total"
)
class my_wait_exponential(wait_base):
def __init__(
self,
multiplier: typing.Union[int, float] = 1,
max: _utils.time_unit_type = _utils.MAX_WAIT, # noqa
exp_base: typing.Union[int, float] = 2,
min: _utils.time_unit_type = 0, # noqa
) -> None:
self.multiplier = multiplier
self.min = _utils.to_seconds(min)
self.max = _utils.to_seconds(max)
self.exp_base = exp_base
def __call__(self, retry_state: "RetryCallState") -> float:
if retry_state.outcome == openai.error.Timeout:
return 0
try:
exp = self.exp_base ** (retry_state.attempt_number - 1)
result = self.multiplier * exp
except OverflowError:
return self.max
return max(max(0, self.min), min(result, self.max))
class my_stop_after_attempt(stop_base):
"""Stop when the previous attempt >= max_attempt."""
def __init__(self, max_attempt_number: int) -> None:
self.max_attempt_number = max_attempt_number
def __call__(self, retry_state: "RetryCallState") -> bool:
if retry_state.outcome == openai.error.Timeout:
retry_state.attempt_number -= 1
return retry_state.attempt_number >= self.max_attempt_number
def annotate(item_text_list):
request_timeout = 6
for attempt in Retrying(
reraise=True,
retry=retry_if_not_exception_type(
(openai.error.InvalidRequestError, openai.error.AuthenticationError)
),
wait=my_wait_exponential(min=1, max=60),
stop=(my_stop_after_attempt(8)),
before_sleep=my_before_sleep,
):
with attempt:
response = openai.Embedding.create(
model="text-embedding-ada-002",
input=item_text_list,
request_timeout=request_timeout,
)
request_timeout = min(30, request_timeout * 2)
return response
def get_exist_item_set():
exist_item_set = set()
for file in os.listdir(save_dir):
user_id = os.path.splitext(file)[0]
exist_item_set.add(user_id)
return exist_item_set
if __name__ == "__main__":
parser = ArgumentParser()
parser.add_argument("--api_key")
parser.add_argument("--batch_size", default=1, type=int)
parser.add_argument("--dataset", type=str, choices=["redial", "opendialkg"])
args = parser.parse_args()
openai.api_key = args.api_key
batch_size = args.batch_size
dataset = args.dataset
save_dir = f"../save/embed/item/{dataset}"
os.makedirs(save_dir, exist_ok=True)
with open(f"../data/{dataset}/id2info.json", encoding="utf-8") as f:
id2info = json.load(f)
# redial
if dataset == "redial":
info_list = list(id2info.values())
item_texts = []
for info in info_list:
item_text_list = [
f"Title: {info['name']}",
f"Genre: {', '.join(info['genre']).lower()}",
f"Star: {', '.join(info['star'])}",
f"Director: {', '.join(info['director'])}",
f"Plot: {info['plot']}",
]
item_text = "; ".join(item_text_list)
item_texts.append(item_text)
attr_list = ["genre", "star", "director"]
# opendialkg
if dataset == "opendialkg":
item_texts = []
for info_dict in id2info.values():
item_attr_list = [f'Name: {info_dict["name"]}']
for attr, value_list in info_dict.items():
if attr != "title":
item_attr_list.append(
f"{attr.capitalize()}: " + ", ".join(value_list)
)
item_text = "; ".join(item_attr_list)
item_texts.append(item_text)
attr_list = ["genre", "actor", "director", "writer"]
id2text = {}
for item_id, info_dict in id2info.items():
attr_str_list = [f'Title: {info_dict["name"]}']
for attr in attr_list:
if attr not in info_dict:
continue
if isinstance(info_dict[attr], list):
value_str = ", ".join(info_dict[attr])
else:
value_str = info_dict[attr]
attr_str_list.append(f"{attr.capitalize()}: {value_str}")
item_text = "; ".join(attr_str_list)
id2text[item_id] = item_text
item_ids = set(id2info.keys()) - get_exist_item_set()
while len(item_ids) > 0:
logger.info(len(item_ids))
# redial
if dataset == "redial":
batch_item_ids = random.sample(
tuple(item_ids), min(batch_size, len(item_ids))
)
batch_texts = [id2text[item_id] for item_id in batch_item_ids]
# opendialkg
if dataset == "opendialkg":
batch_item_ids = random.sample(
tuple(item_ids), min(batch_size, len(item_ids))
)
batch_texts = [id2text[item_id] for item_id in batch_item_ids]
batch_embeds = annotate(batch_texts)["data"]
for embed in batch_embeds:
item_id = batch_item_ids[embed["index"]]
with open(f"{save_dir}/{item_id}.json", "w", encoding="utf-8") as f:
json.dump(embed["embedding"], f, ensure_ascii=False)
item_ids -= get_exist_item_set()
|