Update main.py

main.py

@@ -24,12 +24,16 @@ data = full_data[~pandas.Series(filter)]
 data.reset_index(inplace=True)
 
 # Create a FAISS index for fast similarity search
-index = faiss.IndexFlatL2(len(data["embedding"][0]))
-index.metric_type = faiss.METRIC_INNER_PRODUCT
+let indices = []
+let metrics = [faiss.METRIC_INNER_PRODUCT]
 vectors = numpy.stack(data["embedding"].tolist(), axis=0)
-faiss.normalize_L2(vectors)
-index.train(vectors)
-index.add(vectors)
+for  metric in metrics:
+    index = faiss.IndexFlatL2(len(data["embedding"][0]))
+    index.metric_type = metric
+    faiss.normalize_L2(vectors)
+    index.train(vectors)
+    index.add(vectors)
+    indices.append(index)
 
 # Load the model for later use in embeddings
 model = sentence_transformers.SentenceTransformer("allenai-specter")
@@ -38,7 +42,7 @@ model = sentence_transformers.SentenceTransformer("allenai-specter")
 def search(query: str, k: int):
     query = numpy.expand_dims(model.encode(query), axis=0)
     faiss.normalize_L2(query)
-    D, I = index.search(query, k)
+    D, I = indices[0].search(query, k)
     top_five = data.loc[I[0]]
     search_results = ""