code: import langchain
  from langchain.embeddings.openai import OpenAIEmbeddings
  from langchain.text_splitter import CharacterTextSplitter
  from langchain.vectorstores import Chroma
  from langchain.chat_models import ChatOpenAI
  from langchain.chains import RetrievalQA
  from langchain.cache import InMemoryCache
  from dotenv import load_dotenv
  from flask import Flask, request, jsonify
  from flask_cors import CORS
  import PyPDF2
  import os
  from waitress import serve
  
  # init
  app = Flask(__name__)
  CORS(app)
  langchain.llm_cache = InMemoryCache()
  load_dotenv()
  
  @app.route("/api/getQuestion", methods=["POST"])
  def generateQuestion():
      topic = request.form.get("topic")
      prevQuestions = request.form.get("prevQuestions")
  
      try:
          notes = ""
          files = [request.files.get("file")]
          for file in files:
              if file.content_type != "application/pdf":
                  return (
                      jsonify({"error": "Invalid file format. Please upload a PDF file."}),
                      400,
                  )
  
              pdf_reader = PyPDF2.PdfReader(file)
  
              # extract text from each page of pdf
              text = ""
              for page in pdf_reader.pages:
                  text += page.extract_text() + ' '
  
              notes += text + ' '
  
      except Exception as e:
          return jsonify({"error": "Error parsing PDF"}), 500
  
      # split text into chunks and store in vector db
      textSplitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
      textSplit = textSplitter.split_text(notes)
      vectorStore = Chroma.from_texts(textSplit, OpenAIEmbeddings())
  
      # setup stuff chain to generate questions
      generator = RetrievalQA.from_chain_type(
          llm=ChatOpenAI(temperature=0, model_name="gpt-4-1106-preview"),
          chain_type="stuff",
          retriever=vectorStore.as_retriever(search_kwargs={"k": 1})
      )
  
      prompt = f"""
      Only using the context provided, give me 1 descriptive practice question that reviews the content in the
      context related to the topic, {topic}, with four descriptive possible answers and only one of them is
      correct and don't let any of the other answer choices be true. The wrong answer choices should be similar to the correct answer choice while still being wrongDescriptively explain why each wrong answer choice is wrong and don't include any periods at the
      end of the sentences (If the answer is correct, just say "Correct"). Don't include new lines
      between answer choices. Don't include any periods at the end of any sentence, including all of
      the explanations for why an answer is incorrect. Strictly follow the format,
      Question: (question)
      A. (answer1)
      Incorrect because
      B. (answer2)
      Incorrect because
      C. (answer3)
      Incorrect because
      D. (answer4)
      Incorrect because
      Answer: (answer choice)
  
      Don't use any of these questions:
      {prevQuestions}
      """
  
      res = generator.run(prompt)
      return res
  
  if __name__ == "__main__":
      serve(app, host="127.0.0.1", port=os.environ["FLASK_PORT"])
      
apis: langchain.embeddings.openai.OpenAIEmbeddings langchain.text_splitter.CharacterTextSplitter langchain.chat_models.ChatOpenAI langchain.cache.InMemoryCache
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