code: import asyncio
  from langchain.document_loaders import PyPDFLoader, DirectoryLoader
  from langchain import PromptTemplate
  from langchain.embeddings import HuggingFaceEmbeddings
  from langchain.vectorstores import FAISS
  from langchain.llms import CTransformers
  from langchain.chains import RetrievalQA
  import chainlit as cl
  
  DB_FAISS_PATH = 'vectorstores/db_faiss'
  
  custom_prompt_template = """Use the following pieces of information to answer the user's question.
  If you don't know the answer, just say that you don't know, don't try to make up an answer.
  
  Context: {context}
  Question: {question}
  
  Only return the helpful answer below and nothing else.
  Helpful answer:
  """
  
  def set_custom_prompt():
      """
      Prompt template for QA retrieval for each vectorstore
      """
      prompt = PromptTemplate(template=custom_prompt_template,
                              input_variables=['context', 'question'])
      return prompt
  
  # Retrieval QA Chain
  def retrieval_qa_chain(llm, prompt, db):
      qa_chain = RetrievalQA.from_chain_type(llm=llm,
                                         chain_type='stuff',
                                         retriever=db.as_retriever(search_kwargs={'k': 2}),
                                         return_source_documents=True,
                                         chain_type_kwargs={'prompt': prompt}
                                         )
      return qa_chain
  
  # Loading the model
  def load_llm():
      # Load the locally downloaded model here
      llm = CTransformers(
          model="TheBloke/Llama-2-7B-Chat-GGML",
          model_type="llama",
          max_new_tokens=512,
          temperature=0.5
      )
      return llm
  
  # QA Model Function
  async def qa_bot():
      embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2",
                                         model_kwargs={'device': 'cpu'})
      db = FAISS.load_local(DB_FAISS_PATH, embeddings)
      llm = load_llm()
      qa_prompt = set_custom_prompt()
      qa = retrieval_qa_chain(llm, qa_prompt, db)
  
      return qa
  
  # Output function
  async def final_result(query):
      qa_result = await qa_bot()
      response = await qa_result({'query': query})
      return response
  
  # chainlit code
  @cl.on_chat_start
  async def start():
      chain = await qa_bot()
      # msg = cl.Message(content="Starting the bot...")
      # await msg.send()
      # msg.content = "Hi, Welcome to Medical Bot. What is your query?"
      # await msg.update()
  
      cl.user_session.set("chain", chain)
  
  @cl.on_message
  async def main(message):
      chain = cl.user_session.get("chain")
      cb = cl.AsyncLangchainCallbackHandler(
          stream_final_answer=True, answer_prefix_tokens=["FINAL", "ANSWER"]
      )
      cb.answer_reached = True
      res = await chain.acall(message.content, callbacks=[cb])
      answer = res["result"]
      sources = res["source_documents"]
  
      if sources:
          answer += f"\nSources:" + str(sources)
      else:
          answer += "\nNo sources found"
  
      await cl.Message(content=answer).send()
  
  if __name__ == "__main__":
      asyncio.run(cl.main())
  
apis: langchain.vectorstores.FAISS.load_local langchain.embeddings.HuggingFaceEmbeddings langchain.llms.CTransformers langchain.PromptTemplate
extract_api: [((808, 900), 'langchain.PromptTemplate', 'PromptTemplate', ([], {'template': 'custom_prompt_template', 'input_variables': "['context', 'question']"}), "(template=custom_prompt_template, input_variables=['context',\n    'question'])\n", (822, 900), False, 'from langchain import PromptTemplate\n'), ((1522, 1635), 'langchain.llms.CTransformers', 'CTransformers', ([], {'model': '"""TheBloke/Llama-2-7B-Chat-GGML"""', 'model_type': '"""llama"""', 'max_new_tokens': '(512)', 'temperature': '(0.5)'}), "(model='TheBloke/Llama-2-7B-Chat-GGML', model_type='llama',\n    max_new_tokens=512, temperature=0.5)\n", (1535, 1635), False, 'from langchain.llms import CTransformers\n'), ((1753, 1863), 'langchain.embeddings.HuggingFaceEmbeddings', 'HuggingFaceEmbeddings', ([], {'model_name': '"""sentence-transformers/all-MiniLM-L6-v2"""', 'model_kwargs': "{'device': 'cpu'}"}), "(model_name='sentence-transformers/all-MiniLM-L6-v2',\n    model_kwargs={'device': 'cpu'})\n", (1774, 1863), False, 'from langchain.embeddings import HuggingFaceEmbeddings\n'), ((1910, 1953), 'langchain.vectorstores.FAISS.load_local', 'FAISS.load_local', (['DB_FAISS_PATH', 'embeddings'], {}), '(DB_FAISS_PATH, embeddings)\n', (1926, 1953), False, 'from langchain.vectorstores import FAISS\n'), ((2504, 2539), 'chainlit.user_session.set', 'cl.user_session.set', (['"""chain"""', 'chain'], {}), "('chain', chain)\n", (2523, 2539), True, 'import chainlit as cl\n'), ((2597, 2625), 'chainlit.user_session.get', 'cl.user_session.get', (['"""chain"""'], {}), "('chain')\n", (2616, 2625), True, 'import chainlit as cl\n'), ((2636, 2740), 'chainlit.AsyncLangchainCallbackHandler', 'cl.AsyncLangchainCallbackHandler', ([], {'stream_final_answer': '(True)', 'answer_prefix_tokens': "['FINAL', 'ANSWER']"}), "(stream_final_answer=True,\n    answer_prefix_tokens=['FINAL', 'ANSWER'])\n", (2668, 2740), True, 'import chainlit as cl\n'), ((3124, 3133), 'chainlit.main', 'cl.main', ([], {}), '()\n', (3131, 3133), True, 'import chainlit as cl\n'), ((3043, 3069), 'chainlit.Message', 'cl.Message', ([], {'content': 'answer'}), '(content=answer)\n', (3053, 3069), True, 'import chainlit as cl\n')]