local testing

app.py

@@ -15,7 +15,7 @@ embedding_model = AutoModel.from_pretrained(embedding_model_name)
 
 def embed_texts(texts):
     """Generate embeddings for a list of texts."""
-    inputs = embedding_tokenizer(texts, return_tensors="pt", padding=True, truncation=True, max_length=512)
+    inputs = embedding_tokenizer('query: ' + texts, return_tensors="pt", padding=True, truncation=True, max_length=512)
     with torch.no_grad():
         outputs = embedding_model(**inputs)
     # Use mean pooling to get embeddings