Upload Groq_llms.py

Groq_llms.py

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+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()
+