class_docstr: A class for using a RAG model for question answering
class_docstr_tok: ["A","class","for","using","a","RAG","model","for","question","answering"]
code: def _query(self, question: str):
      logger.debug(f"Querying: {question}")
  
      vector_store_response = self.query_vector_store(question)
  
      formatted_prompt = self._prepare_prompt(
          vector_store_response, question
      )
  
      llm_response = self.llm(prompt=formatted_prompt)
  
      return llm_response, vector_store_response
  
file_path: mindsdb/mindsdb/integrations/handlers/rag_handler/rag.py
imports: import json
  from collections import defaultdict
  from typing import List
  from mindsdb.integrations.handlers.rag_handler.settings import LLMLoader, PersistedVectorStoreLoader, PersistedVectorStoreLoaderConfig, RAGBaseParameters, RAGHandlerParameters, load_embeddings_model
  from mindsdb.utilities import log
label_desc: Use image node_id 8 for calling the RAGQuestionAnswerer obj's underlying member method code with example usage: obj._query(question) and returns: llm_response,  vector_store_response