feat: inject reminder context into LLM so bot knows user's agenda

src/handlers/user.py

@@ -16,6 +16,39 @@ _pending_confirmations: dict = {}
 REMINDER_KEYWORDS = ["ingatkan", "pengingat", "remind", "jadwalkan", "tolong ingat"]
 REMINDER_BYPASS_KEYWORDS = ["ingatkan", "pengingat", "remind", "diingatkan", "jadwalkan", "kapan diingat"]
 
+
+def _build_context(user_db_id: int, rag_results: list) -> str:
+    parts = []
+
+    # Waktu sekarang
+    now = datetime.now(ZoneInfo(BOT_TIMEZONE))
+    parts.append(f"Waktu sekarang: {now.strftime('%A, %d %B %Y %H:%M WIB')}")
+
+    # Reminder aktif
+    reminders = db.get_user_reminders(user_db_id)
+    if reminders:
+        lines = ["Agenda/Pengingat aktif pengguna:"]
+        for r in reminders:
+            if r["is_recurring"]:
+                lines.append(f"- [Setiap hari {r['remind_at'][:5]} WIB] {r['message']}")
+            else:
+                try:
+                    dt = datetime.fromisoformat(r["remind_at"]).replace(tzinfo=ZoneInfo(BOT_TIMEZONE))
+                    lines.append(f"- [{dt.strftime('%a, %d %b %Y %H:%M')} WIB] {r['message']}")
+                except Exception:
+                    lines.append(f"- {r['message']}")
+        parts.append("\n".join(lines))
+
+    # RAG context
+    if rag_results:
+        rag_lines = ["Konteks dari Second Brain pengguna:"] + [
+            f"[{'Global' if r['scope'] == 'global' else 'Pribadi'}] {r['content']}"
+            for r in rag_results
+        ]
+        parts.append("\n".join(rag_lines))
+
+    return "\n\n".join(parts)
+
 _client = AsyncOpenAI(api_key=DEEPSEEK_API_KEY, base_url=DEEPSEEK_BASE_URL)
 
 ACADEMIC_SYSTEM_PROMPT = (
@@ -145,18 +178,13 @@ async def ask(update: Update, context: ContextTypes.DEFAULT_TYPE):
         return
 
     relevant = await rag.search(user_data["id"], question, n_results=5)
-    context_text = ""
-    if relevant:
-        context_text = "\n\nKonteks dari Second Brain pengguna:\n" + "\n".join(
-            f"[{'Global' if r['scope'] == 'global' else 'Pribadi'}] {r['content']}"
-            for r in relevant
-        )
+    extra_context = _build_context(user_data["id"], relevant)
 
     history = db.get_conversation_history(user_data["id"])
     system_prompt = (
         f"{ACADEMIC_SYSTEM_PROMPT}\n\n"
-        f"Nama dosen: {user_data['full_name']}"
-        f"{context_text}"
+        f"Nama dosen: {user_data['full_name']}\n\n"
+        f"{extra_context}"
     )
 
     messages = [{"role": "system", "content": system_prompt}]
@@ -335,18 +363,13 @@ async def handle_message(update: Update, context: ContextTypes.DEFAULT_TYPE):
         return
 
     relevant = await rag.search(user_data["id"], question, n_results=5)
-    context_text = ""
-    if relevant:
-        context_text = "\n\nKonteks dari Second Brain pengguna:\n" + "\n".join(
-            f"[{'Global' if r['scope'] == 'global' else 'Pribadi'}] {r['content']}"
-            for r in relevant
-        )
+    extra_context = _build_context(user_data["id"], relevant)
 
     history = db.get_conversation_history(user_data["id"])
     system_prompt = (
         f"{ACADEMIC_SYSTEM_PROMPT}\n\n"
-        f"Nama dosen: {user_data['full_name']}"
-        f"{context_text}"
+        f"Nama dosen: {user_data['full_name']}\n\n"
+        f"{extra_context}"
     )
 
     messages = [{"role": "system", "content": system_prompt}]