add example embedding search

embeddings.py

@@ -0,0 +1,35 @@
+import numpy as np
+import torch
+from transformers import AutoModel, AutoTokenizer
+
+tokenizer = AutoTokenizer.from_pretrained("Geotrend/distilbert-base-pl-cased")
+model = AutoModel.from_pretrained("Geotrend/distilbert-base-pl-cased")
+
+text = """
+"nazwa": "Tatar wołowy","""
+# "skladniki": [
+#   "wołowina",
+#   "cebula",
+#   "ogórki kiszone",
+#   "musztarda",
+#   "jajko",
+#   "pieprz",
+#   "sól"
+# ],
+# "alergeny": [
+#   "jajko",
+#   "gorczyca"
+# ]
+# """
+encoded_input = tokenizer(text, return_tensors='pt', padding=True)
+output = model(**encoded_input)
+prompt = "tatar"
+encoded_prompt = tokenizer(prompt, return_tensors='pt', padding=True)
+output_prompt = model(**encoded_prompt)
+
+text_embedding = output.last_hidden_state[:, 0, :]
+prompt_embedding = output_prompt.last_hidden_state[:, 0, :]
+cosine = torch.nn.functional.cosine_similarity(
+    text_embedding, prompt_embedding, dim=1)
+
+print(cosine.item())