class_docstr: RAGHandler is a MindsDB integration with supported LLM APIs allows users to run question answering
  on their data by providing a question.
  
  The User is able to provide data that provides context for the questions, see create() method for more details.
class_docstr_tok: ["RAGHandler","is","a","MindsDB","integration","with","supported","LLM","APIs","allows","users","to","run","question","answering","on","their","data","by","providing","a","question",".","The","User","is","able","to","provide","data","that","provides","context","for","the","questions",",","see","create","(",")","method","for","more","details","."]
docstr: Dispatch is performed depending on the underlying model type. Currently, only question answering
  is supported.
docstr_tok: ["Dispatch","is","performed","depending","on","the","underlying","model","type",".","Currently",",","only","question","answering","is","supported","."]
code: def predict(self, df: pd.DataFrame = None, args: dict = None):
      """
      Dispatch is performed depending on the underlying model type. Currently, only question answering
      is supported.
      """
  
      input_args = build_llm_params(self.model_storage.json_get("args"))
  
      args = RAGHandlerParameters(**input_args)
  
      args.vector_store_storage_path = self.engine_storage.folder_get(
          args.vector_store_folder_name
      )
  
      # get question answering results
      question_answerer = RAGQuestionAnswerer(args=args)
  
      # get question from sql query
      # e.g. where question = 'What is the capital of France?'
      response = question_answerer(df["question"].tolist()[0])
  
      return pd.DataFrame(response)
  
file_path: mindsdb/mindsdb/integrations/handlers/rag_handler/rag_handler.py
imports: from typing import Dict, Optional
  import pandas
  from mindsdb.integrations.handlers.rag_handler.ingest import RAGIngestor
  from mindsdb.integrations.handlers.rag_handler.rag import RAGQuestionAnswerer
  from mindsdb.integrations.handlers.rag_handler.settings import DEFAULT_EMBEDDINGS_MODEL, RAGHandlerParameters, build_llm_params
  from mindsdb.integrations.libs.base import BaseMLEngine
  from mindsdb.utilities import log
label_desc: Use image node_id 5 for calling the RAGHandler obj's underlying member method code with example usage: obj.predict(df, args) and returns: pd