tailor_chain.py

from langchain.chains import LLMChain
from prompts import tailor_prompt
import os
from langchain_groq import ChatGroq

from prompts import tailor_prompt

def get_tailor_chain() -> LLMChain:
    """
    Builds the chain that tailors the final response to DailyWellnessAI's style.
    """
    chat_groq_model = ChatGroq(
        model="Gemma2-9b-It",
        groq_api_key=os.environ["GROQ_API_KEY"]
    )
    chain = LLMChain(
        llm=chat_groq_model,
        prompt=tailor_prompt
    )
    return 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