Upload app.py

app.py

@@ -171,7 +171,7 @@ elif encoder_model == "SGPT":
     retriever_model = get_sgpt_embedding_model()
 
 
-window = int(st.number_input("Sentence Window Size", 1, 3, value=1))
+window = int(st.number_input("Sentence Window Size", 0, 3, value=0))
 
 threshold = float(
     st.number_input(
@@ -185,7 +185,7 @@ query_results = query_pinecone(
     query_text, num_results, retriever_model, pinecone_index, threshold
 )
 
-if threshold <= 0.65:
+if threshold <= 0.60:
     context_list = sentence_id_combine(data, query_results, lag=window)
 else:
     context_list = format_query(query_results)