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import os
import sys
import time
import hashlib
from tqdm import tqdm
import numpy as np
import requests
import logging
import functools
import tiktoken
from mteb import MTEB
#from sentence_transformers import SentenceTransformer
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger("main")
all_task_list = ['Classification', 'Clustering', 'Reranking', 'Retrieval', 'STS', 'PairClassification']
if len(sys.argv) > 1:
task_list = [t for t in sys.argv[1].split(',') if t in all_task_list]
else:
task_list = all_task_list
OPENAI_BASE_URL = os.environ.get('OPENAI_BASE_URL', '')
OPENAI_API_KEY = os.environ.get('OPENAI_API_KEY', '')
EMB_CACHE_DIR = os.environ.get('EMB_CACHE_DIR', '.cache/embs')
os.makedirs(EMB_CACHE_DIR, exist_ok=True)
def uuid_for_text(text):
return hashlib.md5(text.encode('utf8')).hexdigest()
def count_openai_tokens(text, model="text-embedding-3-large"):
encoding = tiktoken.get_encoding("cl100k_base")
#encoding = tiktoken.encoding_for_model(model)
input_ids = encoding.encode(text)
return len(input_ids)
def request_openai_emb(texts, model="text-embedding-3-large",
base_url='https://api.openai.com', prefix_url='/v1/embeddings',
timeout=120, retry=3, interval=60, caching=True):
if isinstance(texts, str):
texts = [texts]
data = []
if caching:
for text in texts:
emb_file = f"{EMB_CACHE_DIR}/{uuid_for_text(text)}"
if os.path.isfile(emb_file) and os.path.getsize(emb_file) > 0:
data.append(np.loadtxt(emb_file))
if len(texts) == len(data):
return data
url = f"{OPENAI_BASE_URL}{prefix_url}" if OPENAI_BASE_URL else f"{base_url}{prefix_url}"
headers = {
"Authorization": f"Bearer {OPENAI_API_KEY}",
"Content-Type": "application/json"
}
payload = {"input": texts, "model": model}
while retry > 0 and len(data) == 0:
try:
r = requests.post(url, headers=headers, json=payload,
timeout=timeout)
res = r.json()
for x in res["data"]:
data.append(np.array(x["embedding"]))
except Exception as e:
print(f"request openai, retry {retry}, error: {e}", file=sys.stderr)
time.sleep(interval)
retry -= 1
if len(data) != len(texts):
data = []
if caching and len(data) > 0:
for text, emb in zip(texts, data):
emb_file = f"{EMB_CACHE_DIR}/{uuid_for_text(text)}"
np.savetxt(emb_file, emb)
return data
class OpenaiEmbModel:
def __init__(self, model_name, model_dim, *args, **kwargs):
super().__init__(*args, **kwargs)
self.model_name = model_name
self.model_dim = model_dim
def encode(self, sentences, batch_size=32, **kwargs):
i = 0
max_tokens = 8000
batch_tokens = 0
batch = []
batch_list = []
while i < len(sentences):
num_tokens = count_openai_tokens(sentences[i],
model=self.model_name)
if batch_tokens+num_tokens > max_tokens:
if batch:
batch_list.append(batch)
if num_tokens > max_tokens:
batch = [sentences[i][:2048]]
batch_tokens = count_openai_tokens(sentences[i][:2048],
model=self.model_name)
else:
batch = [sentences[i]]
batch_tokens = num_tokens
else:
batch_list.append([sentences[i][:2048]])
else:
batch.append(sentences[i])
batch_tokens += num_tokens
i += 1
if batch:
batch_list.append(batch)
#print(len(sentences), sum([len(x) for x in batch_list]))
#batch_size = min(64, batch_size)
#
#for i in range(0, len(sentences), batch_size):
# batch_texts = sentences[i:i+batch_size]
# batch_list.append(batch_texts)
embs = []
waiting = 0
for batch_i, batch_texts in enumerate(tqdm(batch_list)):
batch_embs = request_openai_emb(batch_texts, model=self.model_name,
caching=True, timeout=120, retry=3, interval=60)
#assert len(batch_texts) == len(batch_embs), "The batch of texts and embs DONT match!"
if len(batch_texts) == len(batch_embs):
embs.extend(batch_embs)
waiting = waiting // 2
else:
print(f"The batch-{batch_i} of texts and embs DONT match! {len(batch_texts)}:{len(batch_embs)}", file=sys.stderr)
embs.extend([np.array([0.0 for j in range(self.model_dim)]) for i in range(len(batch_texts))])
waiting = 120 if waiting <= 0 else waiting+120
if waiting > 3600:
print(f"Frequently failed, break down!", file=sys.stderr)
break
if waiting > 0:
time.sleep(waiting)
print(f'Encoded texts:embs={len(sentences)}:{len(embs)}')
return embs
model_name = "text-embedding-3-large"
model_dim = 3072
model = OpenaiEmbModel(model_name, model_dim)
######
# test
#####
#embs = model.encode(['全国', '北京'])
#print(embs)
#exit()
# languages
task_langs=["zh", "zh-CN"]
evaluation = MTEB(task_types=task_list, task_langs=task_langs)
evaluation.run(model, output_folder=f"results/zh/{model_name.split('/')[-1]}")
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