Update tailor_chain.py

tailor_chain.py

@@ -1,20 +1,21 @@
-# tailor_chain.py
-import os
 from langchain.chains import LLMChain
-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.
+    Creates the tailor chain to simplify and personalize the assistant's responses.
     """
-    chat_groq_model = ChatGroq(
-        model="Gemma2-9b-It",
-        groq_api_key=os.environ["GROQ_API_KEY"]
-    )
-    chain = LLMChain(
-        llm=chat_groq_model,
+    tailor_chain = LLMChain(
+        llm=your_llm,  # Update this with your actual LLM model
         prompt=tailor_prompt
     )
-    return chain
+    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