Update Groq_llms.py

Updated the prompt in groq llms

Groq_llms.py

@@ -1,50 +1,52 @@
-import os
-#from langchain_community.chat_models import ChatOpenAI
-from langchain_groq import ChatGroq
-from dotenv import load_dotenv
-
-load_dotenv()
-
-class LLMHandler:
-    def __init__(self, model_name="llama-3.3-70b-versatile"):
-        """
-        Initializes the LLMHandler with the specified Groq model.
-        """
-        self.groq_api_key = os.getenv("GROQ_API_KEY")
-        if not self.groq_api_key:
-            raise ValueError("GROQ_API_KEY environment variable not set.")
-
-        # Initialize Groq LLM client
-        self.llm = ChatGroq(groq_api_key=self.groq_api_key, model_name=model_name)
-
-    def generate_response(self, user_prompt, data):
-        """
-        Generate a concise response using the LLM based on user prompt and data.
-        :param user_prompt: Prompt provided by the user.
-        :param data: Dictionary containing the instance information (e.g., UID, Name, etc.).
-        :return: Generated response text.
-        """
-        # Create the full prompt using user input and instance data
-        prompt = (
-            f"You are a professional AI model tasked with writing personalized invite texts "
-            f"that are concise (less than 40 words), brochure-suitable, and tailored as per the category in the given sample."
-            f"\n\n"
-            f"Consider the user prompt: {user_prompt}\n\n"
-            f"Details of the individual:\n"
-            f"- Name: {data['Name']}\n"
-            f"- Job Title: {data['Job Title']}\n"
-            f"- Organisation: {data['Organisation']}\n"
-            f"- Area of Interest: {data['Area of Interest']}\n"
-            f"- Category: {data['Category']}\n\n"
-            f"The response can start with Hello Name"
-            f"Write a personalized invitation text for this individual, ensuring the tone and purpose align with the user's instructions."
-            f"STRICTLY Give only one response for the Category the sample belongs to."
-            f"Do Not mention Category in the response."
-            
-            f"NO PREAMBLE"
-        )
-
-        # Query the LLM and return the response
-        response = self.llm.invoke(prompt)
-        return response.content.strip()
-
+import os
+#from langchain_community.chat_models import ChatOpenAI
+from langchain_groq import ChatGroq
+from dotenv import load_dotenv
+
+load_dotenv()
+
+class LLMHandler:
+    def __init__(self, model_name="llama-3.3-70b-versatile"):
+        """
+        Initializes the LLMHandler with the specified Groq model.
+        """
+        self.groq_api_key = os.getenv("GROQ_API_KEY")
+        if not self.groq_api_key:
+            raise ValueError("GROQ_API_KEY environment variable not set.")
+
+        # Initialize Groq LLM client
+        self.llm = ChatGroq(groq_api_key=self.groq_api_key, model_name=model_name)
+
+    def generate_response(self, user_prompt, data):
+        """
+        Generate a concise response using the LLM based on user prompt and data.
+        :param user_prompt: Prompt provided by the user.
+        :param data: Dictionary containing the instance information (e.g., UID, Name, etc.).
+        :return: Generated response text.
+        """
+        # Create the full prompt using user input and instance data
+        prompt = (
+            f"You are a professional AI model tasked with writing personalized invite texts "
+            f"that are concise (40 to 50 words), brochure-suitable, and tailored as per the user prompt.\n\n"
+            f"Consider the user prompt: {user_prompt}\n\n"
+            f"Details of the individual:\n"
+            f"- Name: {data['Name']}\n"
+            f"- Job Title: {data['Job Title']}\n"
+            f"- Organisation: {data['Organisation']}\n"
+            f"- Area of Interest: {data['Area of Interest']}\n"
+            f"- Category: {data['Category']}\n\n"
+            f"- Instance info: {data}\n"
+            f"- Go through the Instance info If the {user_prompt} mentions you to use variables that are not mentioned above. "
+            f"- The response **MUST**:\n"
+            f"- Start with 'Hello {data['Name']}'.\n"
+            f"- Be concise, professional, and STRICTLY DO NOT generate invalid characters or encoding errors (e.g. 'SoraVR’s').\n"
+            f"- Use standard English punctuation, such as single quotes (e.g., 'can't', 'it's').\n"
+            f"- STRICTLY Give only one response for the Category the sample belongs to.\n"
+            f"- Do NOT include preambles or unnecessary text.\n\n"
+            f"Return the final response cleanly, without any extraneous symbols or characters."
+        )
+
+        # Query the LLM and return the response
+        response = self.llm.invoke(prompt)
+        return response.content.strip()
+