Update src/app.py

src/app.py

@@ -530,13 +530,60 @@ with tab3:
             sub = st.form_submit_button("Submit Answer")
             
         if sub and user_ans:
-            with st.spinner("Grading..."):
+            with st.spinner("Consulting the Knowledge Base & Grading..."):
                 data = qs["question_data"]
-                if data["type"] == "acronym": prompt = quiz.construct_acronym_grading_prompt(data["term"], data["correct_definition"], user_ans)
-                else: prompt = quiz.construct_grading_prompt(qs["generated_question_text"], user_ans, data["context_text"])
+                
+                # Grading Logic Branch
+                if data["type"] == "acronym":
+                    prompt = quiz.construct_acronym_grading_prompt(
+                        data["term"], data["correct_definition"], user_ans
+                    )
+                    # For acronyms, the context is just the definition
+                    final_context_for_history = data["correct_definition"]
+                
+                else:
+                    # --- RAG ENHANCEMENT START ---
+                    # 1. Start with the original seed text
+                    combined_context = f"--- PRIMARY SOURCE (SEED) ---\n{data['context_text']}\n\n"
+                    
+                    # 2. Search Pinecone for 5 more relevant chunks using the generated question
+                    # We check if index/model are active first
+                    if st.session_state.active_index and st.session_state.get("active_embed_model"):
+                        try:
+                            # We search for K=10 and Rerank to Top 5 (or just take Top 5 if no reranker)
+                            # Using the helper function from rag_engine
+                            related_docs = rag_engine.search_knowledge_base(
+                                query=qs["generated_question_text"], 
+                                username=st.session_state.username,
+                                index_name=st.session_state.active_index,
+                                embed_model_name=st.session_state.active_embed_model,
+                                k=15,       # Fetch a broad net
+                                final_k=5   # Narrow down to Top 5 most relevant
+                            )
+                            
+                            if related_docs:
+                                combined_context += "--- RELATED DOCUMENTATION (RETRIEVED) ---\n"
+                                for i, doc in enumerate(related_docs):
+                                    combined_context += f"[Source {i+1}]: {doc.page_content}\n\n"
+                        except Exception as e:
+                            # If search fails, we just proceed with the primary source
+                            print(f"Grading Search Failed: {e}")
+                    
+                    # 3. Construct the Prompt with the super-context
+                    prompt = quiz.construct_grading_prompt(
+                        qs["generated_question_text"], user_ans, combined_context
+                    )
+                    
+                    # Save this rich context for the Study Guide
+                    final_context_for_history = combined_context
+                    # --- RAG ENHANCEMENT END ---
 
+                # Call LLM
                 msgs = [{"role": "user", "content": prompt}]
-                grade, _ = query_model_universal(msgs, 500, model_choice, st.session_state.get("user_openai_key"))
+                grade, _ = query_model_universal(
+                    msgs, 1000, model_choice, st.session_state.get("user_openai_key")
+                )
+                
                 qs["feedback"] = grade
                 
                 # Update Streak
@@ -544,13 +591,12 @@ with tab3:
                 if is_pass: qs["streak"] += 1
                 elif "FAIL" in grade: qs["streak"] = 0
 
-                # Save to History
-                correct_info = data['correct_definition'] if data['type'] == 'acronym' else data['context_text']
+                # Save to History (Using the enhanced context!)
                 st.session_state.quiz_history.append({
                     "question": qs["generated_question_text"],
                     "user_answer": user_ans,
                     "grade": "PASS" if is_pass else "FAIL",
-                    "context": correct_info
+                    "context": final_context_for_history
                 })
                 
                 st.rerun()