added embedding-based retriever

general_working_directory/embedding-based_retriever.py

@@ -0,0 +1,25 @@
+from openai import OpenAI
+import pandas as pd
+import numpy as np
+from numpy.linalg import norm
+
+query = 'Do you have any T-shirt with a oversize fit and graphics written in front'
+
+df = pd.read_csv("embeddings.csv", index_col=0, nrows=None)
+iris = df.index.values.tolist()
+
+client = OpenAI(base_url="http://tentris-ml.cs.upb.de:8502/v1", api_key="token-tentris-upb")
+
+docs = np.array(df.values)
+qr = np.array(client.embeddings.create(input=[query], model="tentris").data[0].embedding)
+
+docs_norms = docs / norm(docs, axis=1, keepdims=True)
+qr_norms = qr / norm(qr)
+
+cosine_similarities = (docs_norms @ qr_norms).flatten()
+
+best_match_index = np.argmax(cosine_similarities)
+best_similarity = cosine_similarities[best_match_index]
+
+print(cosine_similarities)
+print(f"The best scoring image is the image with iri: {iris[best_match_index]} and score: {best_similarity}")