code: def on_create_build_llm_params(
      args: dict,
      llm_config_class: Union[WriterLLMParameters, OpenAIParameters],
  ) -> Dict:
      """build llm params from create args"""
  
      llm_params = {"llm_name": args["llm_type"]}
  
      for param in llm_config_class.__fields__.keys():
          if param in args:
              llm_params[param] = args.pop(param)
  
      return llm_params
  
imports: import json
  from dataclasses import dataclass
  from functools import lru_cache, partial
  from typing import Any, Dict, List, Union
  import html2text
  import openai
  import pandas
  import requests
  import writer
  from langchain import Writer
  from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
  from langchain.docstore.document import Document
  from langchain.document_loaders import DataFrameLoader
  from langchain.embeddings.base import Embeddings
  from langchain.embeddings.huggingface import HuggingFaceEmbeddings
  from langchain.vectorstores import FAISS, Chroma, VectorStore
  from pydantic import BaseModel, Extra, Field, validator
  from mindsdb.integrations.handlers.chromadb_handler.chromadb_handler import get_chromadb
  from mindsdb.integrations.handlers.rag_handler.exceptions import InvalidOpenAIModel, InvalidPromptTemplate, InvalidWriterModel, UnsupportedLLM, UnsupportedVectorStore
label_desc: Use image node_id 8 for calling a global function with example usage: on_create_build_llm_params(args, llm_config_class) and returns: llm_params