class_docstr: Cache class for caching queries. It is used to cache queries
  to save time and money.
  
  Args:
      filename (str): filename to store the cache.
docstr: Set a key value pair in the cache.
  
  Args:
      key (str): key to store the value.
      value (str): value to store in the cache.
docstr_tok: ["Set","a","key","value","pair","in","the","cache",".","Args",":","key","(","str",")",":","key","to","store","the","value",".","value","(","str",")",":","value","to","store","in","the","cache","."]
code: def set(self, key: str, value: str) -> None:
      """Set a key value pair in the cache.
  
      Args:
          key (str): key to store the value.
          value (str): value to store in the cache.
      """
      self.connection.execute(
          "INSERT INTO cache VALUES (?, ?)",
          [self.versioned_key(key), value],
      )
  
file_path: pandas-ai/pandasai/helpers/cache.py
imports: import os
  import glob
  from typing import Any
  import duckdb
  from .path import find_project_root
  from ..constants import DEFAULT_FILE_PERMISSIONS, CACHE_TOKEN
label_desc: Use image node_id 3 for calling the Cache obj's underlying member method code with example usage: obj.set(key, value) without return types