code: import uvicorn
  from fastapi import FastAPI, HTTPException, UploadFile, File
  from pydantic import BaseModel
  import openai
  from langchain.chains import RetrievalQA
  from langchain.chat_models import ChatOpenAI
  from langchain.embeddings import OpenAIEmbeddings
  from langchain.prompts import PromptTemplate
  from langchain.document_loaders import PyPDFLoader
  from langchain.vectorstores import LanceDB
  from langchain.text_splitter import RecursiveCharacterTextSplitter
  import lancedb
  import shutil
  import os
  
  # Initialize FastAPI app with metadata
  app = FastAPI(
      title="Chatbot RAG API",
      description="This is a chatbot API template for RAG system.",
      version="1.0.0",
  )
  
  
  # Pydantic model for chatbot request and response
  class ChatRequest(BaseModel):
      prompt: str
  
  
  class ChatResponse(BaseModel):
      response: str
  
  
  # Global variable to store the path of the uploaded file
  uploaded_file_path = None
  
  
  # Endpoint to upload PDF
  @app.post("/upload-pdf/")
  async def upload_pdf(file: UploadFile = File(...)):
      global uploaded_file_path
      uploaded_file_path = f"uploaded_files/{file.filename}"
      os.makedirs(os.path.dirname(uploaded_file_path), exist_ok=True)
      with open(uploaded_file_path, "wb") as buffer:
          shutil.copyfileobj(file.file, buffer)
      return {"filename": file.filename}
  
  
  # Setup LangChain
  def setup_chain():
      global uploaded_file_path
      if not uploaded_file_path or not os.path.exists(uploaded_file_path):
          raise HTTPException(
              status_code=400, detail="No PDF file uploaded or file not found."
          )
  
      template = """Use the following pieces of context to answer the question at the end.
      Given the context from the uploaded document, provide a concise and
      accurate answer to the question. The document contains detailed and 
      specific information, so the answer should directly reflect the content of the document.
      If the answer is not known, or if the document does not contain the information, state that the answer is not available in the document.
      
      {context}
      Question: {question}
      Helpful Answer:"""
  
      OPENAI_API_KEY = "sk-yorapikey"
  
      loader = PyPDFLoader(uploaded_file_path)
      docs = loader.load_and_split()
  
      text_splitter = RecursiveCharacterTextSplitter(chunk_size=200, chunk_overlap=50)
      documents = text_splitter.split_documents(docs)
  
      prompt = PromptTemplate(input_variables=["context", "question"], template=template)
      embeddings = OpenAIEmbeddings(openai_api_key=OPENAI_API_KEY)
  
      db_lance = lancedb.connect("/tmp/lancedb")
      table = db_lance.create_table(
          "my_table",
          data=[
              {
                  "vector": embeddings.embed_query("Hello World"),
                  "text": "Hello World",
                  "id": "1",
              }
          ],
          mode="overwrite",
      )
  
      db = LanceDB.from_documents(documents, embeddings, connection=table)
      retriever = db.as_retriever()
      chain_type_kwargs = {"prompt": prompt}
  
      llm = ChatOpenAI(openai_api_key=OPENAI_API_KEY)
  
      chain = RetrievalQA.from_chain_type(
          llm=llm,
          chain_type="stuff",
          retriever=retriever,
          chain_type_kwargs=chain_type_kwargs,
          verbose=True,
      )
      return chain
  
  
  # Endpoint for chatbot interaction
  @app.post("/chat", response_model=ChatResponse)
  async def chat(request: ChatRequest):
      agent = (
          setup_chain()
      )  # Setup agent for each request to use the latest uploaded file
      response = agent.run(request.prompt)
      return {"response": response}
  
  
  # Health check endpoint
  @app.get("/", tags=["Health Check"])
  async def read_root():
      return {"message": "Chatbot API is running!"}
  
  
  # Main function to run the app
  if __name__ == "__main__":
      uvicorn.run(app, host="0.0.0.0", port=8000)
  
apis: lancedb.connect
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