code: def load_data(source_folder_path: str):
      path = os.path.dirname(evadb.__file__)
      cursor = evadb.connect(path).cursor()
  
      # Drop function if it already exists
      cursor.query("DROP FUNCTION IF EXISTS embedding;").execute()
      # Create function from Python file
      # This function is a sentence feature extractor
      text_feat_function_query = f"""CREATE FUNCTION IF NOT EXISTS embedding
              IMPL  '{path}/functions/sentence_feature_extractor.py';
              """
      print(text_feat_function_query)
      cursor.query(text_feat_function_query).execute()
  
      print("🧹 Dropping existing tables in EvaDB")
      cursor.query("DROP TABLE IF EXISTS data_table;").execute()
      cursor.query("DROP TABLE IF EXISTS embedding_table;").execute()
  
      print("📄 Loading PDFs into EvaDB")
      text_load_query = (
          f"""LOAD PDF '{source_folder_path}/*.pdf' INTO data_table;"""
      )
      print(text_load_query)
      cursor.query(text_load_query).execute()
  
      print(
          "🤖 Extracting Feature Embeddings. This may take some time ..."
      )
      cursor.query(
          "CREATE TABLE IF NOT EXISTS embedding_table AS SELECT embedding(data), data FROM data_table;"
      ).execute()
  
      print("🔍 Building FAISS Index ...")
      cursor.query(
          """
          CREATE INDEX embedding_index
          ON embedding_table (features)
          USING FAISS;
      """
      ).execute()
  
imports: import argparse
  import os
  import evadb
label_desc: Use image node_id 1 for calling a global function with example usage: load_data(source_folder_path) without return types