Update rag.py

rag.py

@@ -76,76 +76,73 @@ def query_groq_llm(prompt: str, model_name="llama3-70b-8192") -> str:
         return ""
 
 # ✅ Main RAG logic
-def get_best_answer(user_input: str) -> str:
+def get_best_answer(user_input):
     if not user_input.strip():
-        return "⚠️ Please enter a valid question."
+        return "Please enter a valid question."
 
     user_input_lower = user_input.lower().strip()
 
-    # Handle short or vague questions
-    if len(user_input_lower.split()) < 3 and not any(g in user_input_lower for g in GREETINGS):
-        return "🔎 Please provide more details or ask a complete question (at least 3 words)."
-
-    # Handle greetings
     if any(greet in user_input_lower for greet in GREETINGS):
-        prompt = f"You are an official assistant for University of Education Lahore. Respond to this greeting in a professional and friendly tone: {user_input}"
-        return query_groq_llm(prompt) or "👋 Hello! How can I assist you today?"
+        greeting_response = query_groq_llm(
+            f"You are an official assistant for University of Education Lahore. "
+            f"Respond to this greeting in a friendly and professional manner: {user_input}"
+        )
+        return greeting_response if greeting_response else "Hello! How can I assist you today?"
 
-    # Handle direct FAQ (e.g., fee structure)
     if any(keyword in user_input_lower for keyword in ["fee structure", "fees structure", "semester fees", "semester fee"]):
         return (
-            "💰 For complete and up-to-date fee details for this program, please visit:\n"
-            "🔗 https://ue.edu.pk/allfeestructure.php\n"
-            "It contains all relevant information including tuition, admission, and semester-wise fees."
+            "💰 For complete and up-to-date fee details for this program, we recommend visiting the official University of Education fee structure page.\n"
+            "You'll find comprehensive information regarding tuition, admission charges, and other applicable fees there.\n"
+            "🔗 https://ue.edu.pk/allfeestructure.php"
         )
 
-    # Semantic search for best matching question
     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()
 
-    # Threshold to determine match quality
-    SIMILARITY_THRESHOLD = 0.65
+    if best_score < 0.65:
+        manage_unmatched_queries(user_input)
 
-    if best_score >= SIMILARITY_THRESHOLD:
+    if best_score >= 0.65:
         original_answer = dataset_answers[best_match_idx]
-        prompt = f"""You are UOE AI Assistant! As an official assistant for University of Education Lahore, rephrase the following answer clearly and professionally.
-Use bullet points or headings if helpful. Do NOT add extra information.
+        prompt = f"""Name is UOE AI Assistant! You are an official assistant for the University of Education Lahore.
+
+Rephrase the following official answer clearly and professionally. 
+Use structured formatting (like headings, bullet points, or numbered lists) where appropriate.
+DO NOT add any new or extra information. ONLY rephrase and improve the clarity and formatting of the original answer.
+
 ### Question:
 {user_input}
+
 ### Original Answer:
 {original_answer}
-### Rephrased Answer:"""
+
+### Rephrased Answer:
+"""
     else:
-        manage_unmatched_queries(user_input)
-        prompt = f"""You are UOE AI Assistant. As an official assistant for University of Education Lahore, provide a helpful response to this query.
-If unsure, direct the user to the official university contact options.
+        prompt = f"""Name is UOE AI Assistant! As an official assistant for University of Education Lahore, provide a helpful response:
+Include relevant details about university policies.
+If unsure, direct to official channels.
+
 ### Question:
 {user_input}
-### Official Answer:"""
 
-    # Get the response from LLM
-    response = query_groq_llm(prompt)
+### Official Answer:
+"""
+
+    llm_response = query_groq_llm(prompt)
 
-    if response:
-        for marker in ["Rephrased Answer:", "Official Answer:", "Improved Answer:"]:
-            if marker in response:
-                return response.split(marker)[-1].strip()
-        return response  # if no marker found
+    if llm_response:
+        for marker in ["Improved Answer:", "Official Answer:", "Rephrased Answer:"]:
+            if marker in llm_response:
+                return llm_response.split(marker)[-1].strip()
+        return llm_response
     else:
-        return dataset_answers[best_match_idx] if best_score >= SIMILARITY_THRESHOLD else (
+        return dataset_answers[best_match_idx] if best_score >= 0.65 else (
             "For official information:\n"
             "📞 +92-42-99262231-33\n"
             "✉️ [email protected]\n"
             "🌐 https://ue.edu.pk"
         )
 
-# ✅ Example (for direct testing)
-if __name__ == "__main__":
-    while True:
-        user_input = input("\n🧑‍🎓 You: ")
-        if user_input.lower() in ["exit", "quit"]:
-            break
-        answer = get_best_answer(user_input)
-        print(f"\n🤖 UOE Assistant:\n{answer}")