code: from src._classinits import *
  from src._errorsinit import *
  from src._readapitokens import *
  from src._wikidatasearch import *
  from llama_index import LLMPredictor,GPTSimpleVectorIndex,PromptHelper,download_loader
  from llama_index import GPTSimpleKeywordTableIndex,GPTListIndex
  from llama_index.indices.composability import ComposableGraph
  from langchain.chat_models import ChatOpenAI
  from llama_index.node_parser import SimpleNodeParser
  from llama_index import ServiceContext
  from warnings import filterwarnings
  import gradio as gr
  import os
  
  def IGNOREWARNINGOUTPUT()->RESPONSES:
      filterwarnings("ignore",category=DeprecationWarning)
      filterwarnings("ignore",category=UserWarning)
  
  
  class MODELRUN(object):
      def __init__(self)->CLASSINIT:
          self.__api = READAPITOKEN()
          self.__api._TOKEN()
          self.__base = os.path.join(os.path.dirname(os.path.dirname(os.path.abspath(__file__))),
                                     "data")
          self.__dir = self.__base+"/maindatapsy.csv"
          self.__vec = self.__base+"/"
          os.environ["OPENAI_API_KEY"] = self.__api.token
          self.__rcs = download_loader("SimpleCSVReader")
          self.__ndd = SimpleNodeParser()
      def __str__(self)->str:
          return "MODEL RUN PARAMETERS - SUBPROCESS"
      def __call__(self)->None:
          return None
      def __getstate__(self)->CLASSINIT:
          raise TypeError("[DENIED - PERMISSION]")
      def __repr__(self)->str:
          return MODELRUN.__doc__
      def _NODEDOCUMENT(self)->RESPONSES:
          loader = self.__rcs()
          doc = loader.load_data(file=self.__dir)
          nod = self.__ndd.get_nodes_from_documents(doc)
          return nod
      def _GETPREDICTOR(self,
                        tem:int=0.1,
                        mdn:str="gpt-3.5-turbo",
                        mtk:int=4096)->RESPONSES:
          return LLMPredictor(llm=ChatOpenAI(temperature=tem,
                                             model_name=mdn,
                                             max_tokens=mtk))
      def _GETPROMPT(self,
                     mx:int=4096,
                     ou:int=4096,
                     ck:int=600,
                     mc:int=20)->RESPONSES:
          return PromptHelper(max_input_size=mx,
                              chunk_size_limit=ck,
                              num_output=ou,
                              max_chunk_overlap=mc)
      def _GETVECTOR(self,
                     dct:str or list or tuple or classmethod,
                     ctx:classmethod,
                     fln:str="respsyvec.json")->RESPONSES:
          try:
              smp = GPTSimpleVectorIndex.from_documents(dct,
                                                        service_context=ctx)
              smp.save_to_disk(self.__vec+fln)
              return smp
          except Exception as err:
              print(str(err))
      def _GETWIKI(self,
                   ctx:classmethod,
                   fln:str="reswkkvec.json")->RESPONSES:
          try:
              __wkk = WIKIDATASEARCH()
              __wkk._SEARCH()
              smp = GPTSimpleVectorIndex.from_documents(__wkk.targetcontent,
                                                        service_context=ctx)
              smp.save_to_disk(self.__vec+fln)
              return smp
          except Exception as err:
              print(str(err))
      def _SERVICE(self,pred:classmethod,prom:classmethod)->RESPONSES:
          return ServiceContext.from_defaults(llm_predictor=pred,
                                              prompt_helper=prom)
      def _PREMODELPROCESS(self):
          # main structure for parameters
          md_ = self._GETPREDICTOR()
          pr_ = self._GETPROMPT()
          nd_ = self._NODEDOCUMENT()
          sr_ = self._SERVICE(md_,pr_)
          vc_ = self._GETVECTOR(nd_,sr_)
          wc_ = self._GETWIKI(sr_)
      def _LOAD(self,
                fln:str="respsyvec.json",
                wln:str="reswkkvec.json")->RESPONSES:
          try:
              if os.path.exists(self.__vec+fln) and os.path.exists(self.__vec+wln):
                  ix = GPTSimpleVectorIndex.load_from_disk(self.__vec+fln)
                  iw = GPTSimpleVectorIndex.load_from_disk(self.__vec+wln)
                  return ix,iw
              else:
                  FILEERROR().print()
          except Exception as err:
              print(str(err))
      def _LAUNCH(self,
                  fln:str="respsyvec.json",
                  wln:str="reswkkvec.json")->RESULTS:
          #control
          if not os.path.exists(self.__vec+fln) and not os.path.exists(self.__vec+wln):
              self._PREMODELPROCESS()
          else:
              pass
          #loading modules
          ix,iw = self._LOAD()
          return ix,iw
  
  
  
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
apis: llama_index.GPTSimpleVectorIndex.from_documents llama_index.node_parser.SimpleNodeParser llama_index.ServiceContext.from_defaults llama_index.GPTSimpleVectorIndex.load_from_disk llama_index.download_loader llama_index.PromptHelper
extract_api: [((601, 654), 'warnings.filterwarnings', 'filterwarnings', (['"""ignore"""'], {'category': 'DeprecationWarning'}), "('ignore', category=DeprecationWarning)\n", (615, 654), False, 'from warnings import filterwarnings\n'), ((659, 705), 'warnings.filterwarnings', 'filterwarnings', (['"""ignore"""'], {'category': 'UserWarning'}), "('ignore', category=UserWarning)\n", (673, 705), False, 'from warnings import filterwarnings\n'), ((1147, 1181), 'llama_index.download_loader', 'download_loader', (['"""SimpleCSVReader"""'], {}), "('SimpleCSVReader')\n", (1162, 1181), False, 'from llama_index import LLMPredictor, GPTSimpleVectorIndex, PromptHelper, download_loader\n'), ((1204, 1222), 'llama_index.node_parser.SimpleNodeParser', 'SimpleNodeParser', ([], {}), '()\n', (1220, 1222), False, 'from llama_index.node_parser import SimpleNodeParser\n'), ((2233, 2326), 'llama_index.PromptHelper', 'PromptHelper', ([], {'max_input_size': 'mx', 'chunk_size_limit': 'ck', 'num_output': 'ou', 'max_chunk_overlap': 'mc'}), '(max_input_size=mx, chunk_size_limit=ck, num_output=ou,\n    max_chunk_overlap=mc)\n', (2245, 2326), False, 'from llama_index import LLMPredictor, GPTSimpleVectorIndex, PromptHelper, download_loader\n'), ((3442, 3510), 'llama_index.ServiceContext.from_defaults', 'ServiceContext.from_defaults', ([], {'llm_predictor': 'pred', 'prompt_helper': 'prom'}), '(llm_predictor=pred, prompt_helper=prom)\n', (3470, 3510), False, 'from llama_index import ServiceContext\n'), ((2625, 2686), 'llama_index.GPTSimpleVectorIndex.from_documents', 'GPTSimpleVectorIndex.from_documents', (['dct'], {'service_context': 'ctx'}), '(dct, service_context=ctx)\n', (2660, 2686), False, 'from llama_index import LLMPredictor, GPTSimpleVectorIndex, PromptHelper, download_loader\n'), ((3090, 3167), 'llama_index.GPTSimpleVectorIndex.from_documents', 'GPTSimpleVectorIndex.from_documents', (['__wkk.targetcontent'], {'service_context': 'ctx'}), '(__wkk.targetcontent, service_context=ctx)\n', (3125, 3167), False, 'from llama_index import LLMPredictor, GPTSimpleVectorIndex, PromptHelper, download_loader\n'), ((1901, 1960), 'langchain.chat_models.ChatOpenAI', 'ChatOpenAI', ([], {'temperature': 'tem', 'model_name': 'mdn', 'max_tokens': 'mtk'}), '(temperature=tem, model_name=mdn, max_tokens=mtk)\n', (1911, 1960), False, 'from langchain.chat_models import ChatOpenAI\n'), ((3992, 4024), 'os.path.exists', 'os.path.exists', (['(self.__vec + fln)'], {}), '(self.__vec + fln)\n', (4006, 4024), False, 'import os\n'), ((4027, 4059), 'os.path.exists', 'os.path.exists', (['(self.__vec + wln)'], {}), '(self.__vec + wln)\n', (4041, 4059), False, 'import os\n'), ((4081, 4134), 'llama_index.GPTSimpleVectorIndex.load_from_disk', 'GPTSimpleVectorIndex.load_from_disk', (['(self.__vec + fln)'], {}), '(self.__vec + fln)\n', (4116, 4134), False, 'from llama_index import LLMPredictor, GPTSimpleVectorIndex, PromptHelper, download_loader\n'), ((4155, 4208), 'llama_index.GPTSimpleVectorIndex.load_from_disk', 'GPTSimpleVectorIndex.load_from_disk', (['(self.__vec + wln)'], {}), '(self.__vec + wln)\n', (4190, 4208), False, 'from llama_index import LLMPredictor, GPTSimpleVectorIndex, PromptHelper, download_loader\n'), ((4509, 4541), 'os.path.exists', 'os.path.exists', (['(self.__vec + fln)'], {}), '(self.__vec + fln)\n', (4523, 4541), False, 'import os\n'), ((4548, 4580), 'os.path.exists', 'os.path.exists', (['(self.__vec + wln)'], {}), '(self.__vec + wln)\n', (4562, 4580), False, 'import os\n'), ((904, 929), 'os.path.abspath', 'os.path.abspath', (['__file__'], {}), '(__file__)\n', (919, 929), False, 'import os\n')]