Update app.py

app.py

@@ -40,7 +40,7 @@ print(f"\n--- Chunk 0 ---\n{chunks[0]}")
 print(f"\n--- Chunk 1 ---\n{chunks[1]}")
 
 embed_model = SentenceTransformer("all-MiniLM-L6-v2")
-##### IS INDEX FILE THE ISSUE 
+# ── Index file debug ──────────────────────────────────────────
 print(f"Loaded FAISS index with {index.ntotal} vectors")
 print(f"Loaded {len(chunks)} chunks")
 
@@ -53,7 +53,7 @@ def retrieve_rag_context(query, k=5):
         print(f"  RAG chunk: source={chunk.get('source')} text={chunk['text'][:80]}")
         results.append(chunk["text"])
     return "\n\n---\n\n".join(results)
-#### IS RETRIEVAL RETURNING THE RIGHT CHUNKS
+# ── Retrieval debug ──────────────────────────────────────────
     print(f"Retrieved chunks: {[c.get('topic') for c in [chunks[i] for i in indices[0]]]}")
 
 # ── SYSTEM PROMPTS ─────────────────────────────
@@ -172,10 +172,28 @@ def respond_to_message(message, history, lang="es"):
         print(f"LLM error: {e}")
         return "Ho sento, no puc generar una resposta en aquest moment." if lang=="ca" else "Lo siento, no puedo generar una respuesta en este momento."
 
-##### IS LLM ignoring RAG context?
+# ── RAG debug ──────────────────────────────────────────
     print(f"Full system prompt length: {len(full_system)} chars")
     print(f"RAG context preview: {rag_context[:300]}")
-#####
+    
+
+# ── User Onboarding ─────────────────────────────
+#
+# For new user, initiate introductory conversation
+# Capture user info and preferences 
+### Adapt questions from Zarit Burden Interview, Caregiver Qual of Life Index, COPE inventory
+### What is their knowledge of AD? How long have you been their caregiver? Self-rate stress level? Etc
+#
+# Capture care recipient info and preferences
+### Adapt questions from functional staging tool (FAST), Global Deterioration Scale (GDS)
+### Do they live alone, with caregiver, with someone else? Urban (public transportation) or suburban? (Driving)
+### Is home smart-device enabled, or is it a possibilty? (Fire alarms, elopement alarms, bed alarms, auto-lighting, voice asst)
+#
+# Option to complete by voice or text
+# Store in caregiver profile -> json
+# Dynamic questions using responses to personalize. ("Hi, Maria. It's nice to meet you! Can you tell me more about...")
+# Inject profile into LLM for personalization
+
 
 # ── Pipelines ─────────────────────────────────────────────────
 # ── Voice Pipeline with Language Support ─────────────────────────