code: # Answer questions about a PDF file using the RAG model
   
  # TODO: Maintain the context of the conversation
  
  import lancedb
  
  from langchain_community.document_loaders import TextLoader
  from langchain.text_splitter import RecursiveCharacterTextSplitter
  from langchain_openai import OpenAIEmbeddings
  from langchain.text_splitter import CharacterTextSplitter
  from langchain_community.vectorstores import LanceDB
  from langchain_community.document_loaders import PyPDFLoader
  from langchain_openai import OpenAI
  from langchain.chains import RetrievalQA
  from langchain.prompts import PromptTemplate
  
   
  import random
  import time
  import os
  import sys
  
  debug=False
  
  # Get the name of a PDF file from the command line
  pdf_name = sys.argv[1]
  if not os.path.exists(pdf_name):
      print("The PDF file does not exist. Exiting program.")
      os._exit(1)
  
  # Load the document, split it into chunks, embed each chunk and load it into the vector store.
   
  # Vector DB connection
  vectorStore = lancedb.connect("/tmp/lancedb")
  embeddings=OpenAIEmbeddings()
  table=random.seed(time.time())
  table = vectorStore.create_table(
      table,
      data=[
          {
              "vector": embeddings.embed_query("Hello World"),
              "text": "Hello World",
              "id": "1",
          }
      ],
      mode="overwrite",
  )
  
  # Load the document, split it into chunks, embed each chunk and load it into the vector store.
  print(pdf_name)
  loader = PyPDFLoader(pdf_name)
  data = loader.load()
  
      # Split docs
  text_splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=0)
  docs = text_splitter.split_documents(data)
  vectorStore = LanceDB.from_documents(docs, OpenAIEmbeddings(), connection=table)
  
  def answerQuestion(debug, vectorStore):
      question = input("Please enter a question about the PDF: ")
      # If question is blank, exit program
      if not question:
          print("No question entered. Exiting program.")
          os._exit(1)
  
  
  # Display results if debug is true
  
      if debug: 
          results = vectorStore.similarity_search_with_score(
      query=question,
      k=5,)   
          for result in results:
          # print just the page_content field
              print(result[0].page_content )
  
      qa_retriever = vectorStore.as_retriever(
      search_type="similarity",
      search_kwargs={"k": 25},
  )
  
  
      prompt_template = """Use the following pieces of context to answer the question at the end. If you don't know the answer, just say that you don't know, don't try to make up an answer.
  {context}
  Question: {question}
  """
  
      PROMPT = PromptTemplate(
      template=prompt_template, input_variables=["context", "question"],history_variables=["chat_history"]
  )
  
  
      qa = RetrievalQA.from_chain_type(
      llm=OpenAI(),
      chain_type="stuff",
      retriever=qa_retriever,
      return_source_documents=True,
      chain_type_kwargs={"prompt": PROMPT},
  )
  
      docs = qa.invoke({"query": question})
  
      print(docs["result"])
      print()
  
  while True:
      answerQuestion(debug, vectorStore)
  
apis: lancedb.connect
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