class_docstr: Question-answering using OpenAI or open-source GPT or GPT-like generative LLM models
class_docstr_tok: ["Question-answering","using","OpenAI","or","open-source","GPT","or","GPT-like","generative","LLM","models"]
docstr: ```
  load previously-saved document vector database from folder specified by path
  
  Args:
    path(str): folder path
  ```
docstr_tok: ["``","`","load","previously-saved","document","vector","database","from","folder","specified","by","path","Args",":","path","(","str",")",":","folder","path","``","`"]
code: def load(self, path: str):
      """
      ```
      load previously-saved document vector database from folder specified by path
  
      Args:
        path(str): folder path
      ```
      """
      with open(os.path.join(path, DOCS), "rb") as f:
          self.docs = pickle.load(f)
  
file_path: ktrain/ktrain/text/qa/generative_qa.py
imports: import sys
  import os
  import pickle
  from typing import Optional
  from pathlib import Path
label_desc: Use image node_id 2 for calling the GenerativeQA obj's underlying member method code with example usage: obj.load(path) without return types