code: def main(args):
      input_path = args.input_path
      model_path = args.model_path
      query = args.question
  
      # split texts of input doc
      if input_path is None:
          input_doc = text_doc
      else:
          with open(input_path) as f:
              input_doc = f.read()
  
      text_splitter = CharacterTextSplitter(
          chunk_size=1000, chunk_overlap=0
      )
      texts = text_splitter.split_text(input_doc)
  
      # create embeddings and store into vectordb
      embeddings = TransformersEmbeddings.from_model_id(
          model_id=model_path
      )
      docsearch = Chroma.from_texts(
          texts,
          embeddings,
          metadatas=[{"source": str(i)} for i in range(len(texts))],
      ).as_retriever()
  
      # get relavant texts
      docs = docsearch.get_relevant_documents(query)
  
      bigdl_llm = TransformersLLM.from_model_id(
          model_id=model_path,
          model_kwargs={
              "temperature": 0,
              "max_length": 1024,
              "trust_remote_code": True,
          },
      )
  
      doc_chain = load_qa_chain(
          bigdl_llm, chain_type="stuff", prompt=QA_PROMPT
      )
  
      output = doc_chain.run(input_documents=docs, question=query)
      print(output)
  
file_path: BigDL/python/llm/example/CPU/LangChain/transformers_int4/docqa.py
imports: import argparse
  from langchain.vectorstores import Chroma
  from langchain.chains.chat_vector_db.prompts import CONDENSE_QUESTION_PROMPT, QA_PROMPT
  from langchain.text_splitter import CharacterTextSplitter
  from langchain.chains.question_answering import load_qa_chain
  from langchain.callbacks.manager import CallbackManager
  from bigdl.llm.langchain.llms import TransformersLLM
  from bigdl.llm.langchain.embeddings import TransformersEmbeddings
label_desc: Use image node_id 1 for calling a global function with example usage: main(args) without return types