Update rag.py

rag.py

@@ -63,13 +63,13 @@ def manage_unmatched_queries(query: str):
 
 def query_groq_llm(prompt):
     try:
-        # Using Llama-3.3-70b-versatile with streaming enabled
+        # Llama-3.3-70b-versatile with streaming enabled
         completion = groq_client.chat.completions.create(
             model="llama-3.3-70b-versatile",
             messages=[
                 {
                     "role": "system", 
-                    "content": "You are the official UOE AI Assistant. You provide professional, structured information about University of Education Lahore using headings, bold text, and tables."
+                    "content": "You are the official UOE AI Assistant. Your goal is to provide highly structured, professional, and easy-to-read information about the University of Education Lahore."
                 },
                 {
                     "role": "user", 
@@ -100,56 +100,56 @@ def get_best_answer(user_input):
 
     user_input_lower = user_input.lower().strip()
 
-    # 1. Greeting Handling
+    # Greeting Handling
     if any(greet == user_input_lower for greet in GREETINGS):
-        return "Hello! I am the UOE AI Assistant. How can I assist you today regarding University of Education Lahore's admissions, programs, or fee structures?"
+        return "Hello! I am the UOE AI Assistant. How can I help you regarding admissions, fees, or programs today?"
 
-    # 2. Basic Validation
+    # Length check
     if len(user_input_lower.split()) < 3 and not any(greet in user_input_lower for greet in GREETINGS):
-        return "Please ask your question with at least 3 words so I can provide a detailed answer."
+        return "Please provide more details. I need at least 3 words to understand your query properly."
 
-    # 3. Fee Keyword Detection (Strict)
-    fee_keywords = ["fee", "fees", "fee structure", "fees structure", "semester fee", "admission fee", "dues"]
-    needs_fee_link = any(kw in user_input_lower for kw in fee_keywords)
-    OFFICIAL_FEE_LINK = "https://ue.edu.pk/allfeestructure.php"
+    # # Direct Fee Link
+    # if any(kw in user_input_lower for kw in ["fee structure", "fees structure", "semester fees", "semester fee"]):
+    #     return (
+    #         "💰 **Official Fee Structure**\n\n"
+    #         "For the most accurate and up-to-date fee details, please visit the official University of Education link:\n"
+    #         "🔗 https://ue.edu.pk/allfeestructure.php"
+    #     )
 
-    # 4. Similarity Calculation
+    # Similarity Calculation
     user_embedding = similarity_model.encode(user_input_lower, convert_to_tensor=True)
     similarities = util.pytorch_cos_sim(user_embedding, dataset_embeddings)[0]
     best_match_idx = similarities.argmax().item()
     best_score = similarities[best_match_idx].item()
 
     if best_score >= 0.65:
-        # PATH 1: Verified Data Match
+        # --- PATH 1: DATASET MATCH (Enhanced Formatting) ---
         original_answer = dataset_answers[best_match_idx]
-        
         prompt = f"""You are the official University of Education (UOE) Assistant. 
-        I found a verified answer. Please rephrase it to be professional, attractive, and structured.
+        I have found a verified answer in our database. Please rephrase it to make it extremely professional and user-friendly.
 
-        GUIDELINES:
-        - Use clear Headings and Bullet Points.
-        - Create a Markdown Table if the data involves timings, eligibility, or numeric values.
-        - {'CRITICAL: The user mentioned fees. You MUST prominently include and rephrase this link: ' + OFFICIAL_FEE_LINK if needs_fee_link else 'Always mention this official fee link at the end: ' + OFFICIAL_FEE_LINK}
-        - Bold important keywords.
+        STRICT GUIDELINES:
+        1. Use clear **Headings**.
+        2. Use **Bullet Points** for lists.
+        3. If there is data like timing, criteria, or contact info, present it in a **Markdown Table**.
+        4. Bold important keywords.
+        5. Maintain a polite and welcoming tone.
 
         User Question: {user_input}
         Verified Data: {original_answer}
-        
-        Response:"""
+
+        Enhanced Response:"""
     else:
-        # PATH 2: No Dataset Match (LLM Knowledge + Forwarding)
+        # --- PATH 2: GENERAL KNOWLEDGE (with Forwarding Note) ---
         manage_unmatched_queries(user_input)
-        
-        prompt = f"""You are the UOE AI Assistant. I don't have a verified record for this specific question.
-        
-        Instructions:
-        1. Answer based on your knowledge of University of Education Lahore.
-        2. Use professional formatting (headings/bold).
-        3. {'The user is asking about fees, so emphasize this link: ' + OFFICIAL_FEE_LINK if needs_fee_link else 'Mention that fee details are available at: ' + OFFICIAL_FEE_LINK}
-        4. Add this exact note: "📢 *Note: Your query has been forwarded to our support team. We are currently updating our verified database to include this information soon.*"
-        5. Provide official contact info: Website: https://ue.edu.pk | Phone: +92-42-99262231-33
+        prompt = f"""You are the UOE AI Assistant for University of Education Lahore. 
+        The user asked: "{user_input}". This query is not in our verified database.
 
-        User Question: {user_input}
-        Response:"""
+        Instructions:
+        1. Answer based on your general knowledge about UOE Lahore.
+        2. Format the response with headings and bold text.
+        3. At the end, you MUST add this exact text:
+           "📢 *Note: Your query has been forwarded to our support team. We are currently updating our verified database to provide a verified answer next time.*"
+        4. List official contacts: Website: https://ue.edu.pk | Phone: +92-42-99262231-33"""
 
     return query_groq_llm(prompt)