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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 = [