code: from fastapi import FastAPI
  from langchain.chains import RetrievalQA
  from langchain.chat_models import ChatOpenAI
  from langchain.embeddings.openai import OpenAIEmbeddings
  from langchain.vectorstores import ElasticVectorSearch
  
  from config import openai_api_key
  
  embedding = OpenAIEmbeddings(openai_api_key=openai_api_key)
  
  db = ElasticVectorSearch(
      elasticsearch_url="http://localhost:9200",
      index_name="elastic-index",
      embedding=embedding,
  )
  qa = RetrievalQA.from_chain_type(
      llm=ChatOpenAI(temperature=0),
      chain_type="stuff",
      retriever=db.as_retriever(),
  )
  
  app = FastAPI()
  
  
  @app.get("/")
  def index():
      return {
          "message": "Make a post request to /ask to ask questions about Meditations by Marcus Aurelius"
      }
  
  
  @app.post("/ask")
  def ask(query: str):
      response = qa.run(query)
      return {
          "response": response,
      }
  
apis: langchain.embeddings.openai.OpenAIEmbeddings langchain.vectorstores.ElasticVectorSearch langchain.chat_models.ChatOpenAI
extract_api: [((274, 321), 'langchain.embeddings.openai.OpenAIEmbeddings', 'OpenAIEmbeddings', ([], {'openai_api_key': 'openai_api_key'}), '(openai_api_key=openai_api_key)\n', (290, 321), False, 'from langchain.embeddings.openai import OpenAIEmbeddings\n'), ((328, 444), 'langchain.vectorstores.ElasticVectorSearch', 'ElasticVectorSearch', ([], {'elasticsearch_url': '"""http://localhost:9200"""', 'index_name': '"""elastic-index"""', 'embedding': 'embedding'}), "(elasticsearch_url='http://localhost:9200', index_name=\n    'elastic-index', embedding=embedding)\n", (347, 444), False, 'from langchain.vectorstores import ElasticVectorSearch\n'), ((590, 599), 'fastapi.FastAPI', 'FastAPI', ([], {}), '()\n', (597, 599), False, 'from fastapi import FastAPI\n'), ((497, 522), 'langchain.chat_models.ChatOpenAI', 'ChatOpenAI', ([], {'temperature': '(0)'}), '(temperature=0)\n', (507, 522), False, 'from langchain.chat_models import ChatOpenAI\n')]