code: def load_doc_from_console(self, json_parse_args: Dict = {}):
      print("Select files for constructing the retriever")
      while True:
          file = input(
              "Enter a file path or press Enter directly without input to exit:"
          ).strip()
          if file == "":
              break
          data_name = input("Enter a short description of the data:")
          docs = DocumentLoader(
              [[file, data_name.replace(" ", "_")]], **json_parse_args
          ).all_data
          self.documents.extend(docs)
          self.docs_names.append(data_name)
      self.split_docs_and_add_to_mem(**self.rag_config["chain"])
  
imports: import os
  from typing import Dict, Tuple
  from colossalqa.chain.retrieval_qa.base import RetrievalQA
  from colossalqa.data_loader.document_loader import DocumentLoader
  from colossalqa.memory import ConversationBufferWithSummary
  from colossalqa.mylogging import get_logger
  from colossalqa.prompt.prompt import PROMPT_DISAMBIGUATE_ZH, PROMPT_RETRIEVAL_QA_ZH, SUMMARY_PROMPT_ZH, ZH_RETRIEVAL_QA_REJECTION_ANSWER, ZH_RETRIEVAL_QA_TRIGGER_KEYWORDS
  from colossalqa.retriever import CustomRetriever
  from langchain import LLMChain
  from langchain.embeddings import HuggingFaceEmbeddings
label_desc: Use image node_id 8 for calling the RAG_ChatBot obj's underlying member method code with example usage: obj.load_doc_from_console(json_parse_args) without return types