Upload mteb_eval_openai.py

mteb_eval_openai.py

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+import os
+import sys
+import time
+import hashlib
+import numpy as np
+import requests
+
+
+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 request_openai_emb(texts, model="text-embedding-3-large", 
+        base_url='https://api.openai.com', prefix_url='/v1/embeddings', 
+        timeout=4, retry=3, interval=2, caching=True):
+    if isinstance(texts, str):
+        texts = [texts]
+    assert len(texts) <= 256
+
+    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 encode(self, sentences, batch_size=32, **kwargs):
+        batch_size = min(64, batch_size)
+
+        embs = []
+        for i in range(0, len(sentences), batch_size):
+            batch_texts = sentences[i:i+batch_size]
+            batch_embs = request_openai_emb(batch_texts, 
+                caching=True, retry=3, interval=2)
+            assert len(batch_texts) == len(batch_embs), "The batch of texts and embs DONT match!"
+            embs.extend(batch_embs)
+
+        return embs
+
+
+model = OpenaiEmbModel()
+
+######
+# test
+#####
+#embs = model.encode(['全国', '北京'])
+#print(embs)
+
+# task_list
+task_list = ['Classification', 'Clustering', 'Reranking', 'Retrieval', 'STS', 'PairClassification']
+# 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]}")