code: def test_qa_chain(self):
      texts = """
              AI is a machine’s ability to perform the cognitive functions 
              we associate with human minds, such as perceiving, reasoning, 
              learning, interacting with an environment, problem solving,
              and even exercising creativity. You’ve probably interacted 
              with AI even if you didn’t realize it—voice assistants like Siri 
              and Alexa are founded on AI technology, as are some customer 
              service chatbots that pop up to help you navigate websites.
              """
      text_splitter = CharacterTextSplitter(
          chunk_size=1000, chunk_overlap=0
      )
      texts = text_splitter.split_text(texts)
      query = "What is AI?"
      embeddings = TransformersEmbeddings.from_model_id(
          model_id=self.auto_model_path,
          model_kwargs={"trust_remote_code": True},
      )
  
      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=self.auto_model_path,
          model_kwargs={"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)
      res = "AI" in output
      self.assertTrue(res)
  
file_path: BigDL/python/llm/test/langchain/test_transformers_api.py
imports: from bigdl.llm.langchain.llms import TransformersLLM, TransformersPipelineLLM, LlamaLLM, BloomLLM
  from bigdl.llm.langchain.embeddings import TransformersEmbeddings, LlamaEmbeddings, BloomEmbeddings
  from langchain.document_loaders import WebBaseLoader
  from langchain.indexes import VectorstoreIndexCreator
  from langchain.chains.question_answering import load_qa_chain
  from langchain.chains.chat_vector_db.prompts import CONDENSE_QUESTION_PROMPT, QA_PROMPT
  from langchain.text_splitter import CharacterTextSplitter
  from langchain.vectorstores import Chroma
  import pytest
  from unittest import TestCase
  import os
label_desc: Use image node_id 4 for calling the Test_Langchain_Transformers_API obj's underlying member method code with example usage: obj.test_qa_chain() without return types