code: import os
  import key
  import tabulate
  # Set API key
  os.environ["OPENAI_API_KEY"] = key.OPENAI_API_KEY
  
  # Import langchain
  from langchain.chains import RetrievalQA
  from langchain.chat_models import ChatOpenAI
  from langchain.document_loaders import CSVLoader
  from langchain.indexes import VectorstoreIndexCreator
  from langchain.vectorstores import DocArrayInMemorySearch
  
  # Load the csv file
  file = '/workspaces/chat-som/chat_som/course_list/courseslist.csv'
  loader = CSVLoader(file_path=file, encoding='utf-8')
  data = loader.load()
  
  # chunk the data and import into arrays
  index = VectorstoreIndexCreator(
      vectorstore_cls=DocArrayInMemorySearch
  ).from_loaders([loader])
  
  # set up the retreival chain
  llm = ChatOpenAI(temperature = 0.0)
  qa = RetrievalQA.from_chain_type(
      llm=llm,
      chain_type="stuff",
      retriever=index.vectorstore.as_retriever(),
      verbose=False,
      chain_type_kwargs = {
          "document_separator": "<<<<>>>>>"
      }
  )
  
  # function that takes a message and returns a response
  def chat(user_message):
      """Get a message from user and generate a response"""
      response = qa.run(user_message)
      return response
apis: langchain.indexes.VectorstoreIndexCreator langchain.chat_models.ChatOpenAI langchain.document_loaders.CSVLoader
extract_api: [((465, 508), 'langchain.document_loaders.CSVLoader', 'CSVLoader', ([], {'file_path': 'file', 'encoding': '"""utf-8"""'}), "(file_path=file, encoding='utf-8')\n", (474, 508), False, 'from langchain.document_loaders import CSVLoader\n'), ((708, 735), 'langchain.chat_models.ChatOpenAI', 'ChatOpenAI', ([], {'temperature': '(0.0)'}), '(temperature=0.0)\n', (718, 735), False, 'from langchain.chat_models import ChatOpenAI\n'), ((579, 642), 'langchain.indexes.VectorstoreIndexCreator', 'VectorstoreIndexCreator', ([], {'vectorstore_cls': 'DocArrayInMemorySearch'}), '(vectorstore_cls=DocArrayInMemorySearch)\n', (602, 642), False, 'from langchain.indexes import VectorstoreIndexCreator\n')]