tailor_chain.py

from langchain.chains import LLMChain
from prompts import tailor_prompt

def get_tailor_chain() -> LLMChain:
    """
    Creates the tailor chain to simplify and personalize the assistant's responses.
    """
    tailor_chain = LLMChain(
        llm=your_llm,  # Update this with your actual LLM model
        prompt=tailor_prompt
    )
    return tailor_chain

def tailor_with_history(response: str, chat_history: list) -> str:
    """
    Tailors the assistant's response based on the history context.
    """
    context = "\n".join([f"User: {msg['content']}" for msg in chat_history]) + "\nAssistant: " + response
    # Use the context along with the response for tailoring
    tailored_response = get_tailor_chain().run({"response": context})
    return tailored_response