from sentence_transformers import SentenceTransformer

#model = SentenceTransformer('all-MiniLM-L6-v2')
model = SentenceTransformer('BAAI/bge-large-zh-v1.5')

import mysql.connector # Or import pymysql
#import os
import faiss
#import numpy as np

file_path = "my_faiss_index.faiss"

mydb = mysql.connector.connect(
  host="122.128.109.51",
  user="root",
  password="efoxpasswd",
  database="myBible"
)

mycursor = mydb.cursor()
##mycursor.execute("Select ID, Content FROM dunv WHERE book > 39 AND Caption IS NOT NULL ORDER BY book,chapter,verse")
mycursor.execute("Select ID FROM dunv WHERE book > 39 AND Caption IS NOT NULL ORDER BY book,chapter,verse")
myresult = mycursor.fetchall()

index = faiss.read_index("my_faiss_index.faiss")

#print("INDEX COMPLETED!")
# Define a search query
query = "為何不可醉酒"
#query = "耶穌與食物"
#query = "耶穌與教育"

query_embedding = model.encode([query])
k = 8
distances, indices = index.search(query_embedding, k)
#print("Query:", query)
#print("\nTop", k, "most similar documents:")
for i in range(k):
  #print(f"{i+1}. {documents[indices[0][i]]} (distance: {distances[0][i]:.4f})")
  print(myresult[indices[0][i]])
  print(f"{i+1}.  (distance: {distances[0][i]:.4f})")

print("ABCD1")