Upload 7 files (#15)

- Upload 7 files (8feb34218ef1eecbb607e64afcd307762150d167)

app.py

@@ -89,15 +89,17 @@ sparse_scores = np.argsort(bm25.get_scores(tokenized_query), axis=0)[::-1]
 indices = get_bm25_search_hits(corpus, sparse_scores, 50)
 
 
-dense_embedding_output = literal_eval(
-    instructor_model.predict(
-        query_embedding_instruction,
-        query_text,
-        api_name="/predict",
-    )
-)[0]
-dense_embedding = np.array(dense_embedding_output, dtype=np.float64)
-dense_embedding = dense_embedding.tolist()
+dense_embedding_output = instructor_model.predict(
+    query_embedding_instruction,
+    query_text,
+    api_name="/predict",
+)
+
+dense_embedding_arr = np.array(
+    literal_eval(dense_embedding_output)[0], dtype=np.float64
+)
+dense_embedding = dense_embedding_arr.tolist()
+print(type(dense_embedding[2]))
 # dense_embedding = [float(x) for x in dense_embedding_output[0]]
 
 text_embedding_instructions_choice = [