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app.py

@@ -98,7 +98,7 @@ json_output_embedding = instructor_model.predict(
 
 json_file = open(json_output_embedding, "r")
 json_dict = json.load(json_file)
-dense_embedding = list(map(float, json_dict["data"][0]))
+dense_embedding = np.array(json_dict["data"][0], dtype=np.float64)
 
 text_embedding_instructions_choice = [
     "Represent the financial statement for retrieval:",

utils/retriever.py

@@ -1,7 +1,12 @@
 import numpy as np
 
 
-def query_pinecone(dense_vec, top_k, index, indices):
+def query_pinecone(
+    dense_vec,
+    top_k,
+    index,
+    indices
+):
     xc = index.query(
         vector=dense_vec,
         top_k=top_k,