code: def load_doc_from_files(
      self, files, data_name="default_kb", json_parse_args: Dict = {}
  ):
      for file in files:
          docs = DocumentLoader(
              [[file, data_name.replace(" ", "_")]], **json_parse_args
          ).all_data
          self.documents.extend(docs)
          self.docs_names.append(os.path.basename(file))
      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 9 for calling the RAG_ChatBot obj's underlying member method code with example usage: obj.load_doc_from_files(files, data_name, json_parse_args) without return types