code: def get_langchain_prompts(
      pre_prompt_query,
      prompt_query,
      pre_prompt_summary,
      prompt_summary,
      hyde_llm_prompt,
      model_name,
      inference_server,
      model_path_llama,
      doc_json_mode,
      prompt_query_type="simple",
  ):
      if prompt_query_type == "advanced":
          pre_prompt_query1 = "Pay attention and remember the information below, which will help to answer the question or imperative after the context ends.  If the answer cannot be primarily obtained from information within the context, then respond that the answer does not appear in the context of the documents."
          prompt_query1 = "According to (primarily) the information in the document sources provided within context above: "
      else:
          # older smaller models get confused by this prompt, should use "" instead, but not focusing on such old models anymore, complicates code too much
          pre_prompt_query1 = "Pay attention and remember the information below, which will help to answer the question or imperative after the context ends."
          prompt_query1 = "According to only the information in the document sources provided within the context above: "
  
      pre_prompt_summary1 = """In order to write a concise single-paragraph or bulleted list summary, pay attention to the following text."""
      prompt_summary1 = "Using only the information in the document sources above, write a condensed and concise summary of key results (preferably as bullet points)."
  
      hyde_llm_prompt1 = "Answer this question with vibrant details in order for some NLP embedding model to use that answer as better query than original question: "
  
      if pre_prompt_query is None:
          pre_prompt_query = pre_prompt_query1
      if prompt_query is None:
          prompt_query = prompt_query1
      if pre_prompt_summary is None:
          pre_prompt_summary = pre_prompt_summary1
      if prompt_summary is None:
          prompt_summary = prompt_summary1
      if hyde_llm_prompt is None:
          hyde_llm_prompt = hyde_llm_prompt1
  
      return (
          pre_prompt_query,
          prompt_query,
          pre_prompt_summary,
          prompt_summary,
          hyde_llm_prompt,
      )
  
file_path: h2ogpt/src/enums.py
imports: from enum import Enum
label_desc: Use image node_id 4 for calling a global function with example usage: get_langchain_prompts(pre_prompt_query, prompt_query, pre_prompt_summary, prompt_summary, hyde_llm_prompt, model_name, inference_server, model_path_llama, doc_json_mode, prompt_query_type) and returns: pre_prompt_query,  prompt_query,  pre_prompt_summary,  prompt_summary,  hyde_llm_prompt