fix(chat): map customer/seller roles for OpenAI-compat LLM; re-contact = mid-chat time-lapse

- llm.complete_conversation now maps internal roles (customer->assistant, seller->user,
  system->system) before the API call — fixes 'Unknown role: customer' (501).
- Re-contact personality: the customer now chats normally, at turn 2 goes quiet and a
  system time-lapse note is shown ('⏳ ผ่านไป 2-3 สัปดาห์...'), then re-engages warmer —
  instead of 'pretending you asked before' at start. Driven by persona.recontact trait.
All 9 backend suites pass. Rebuilt dist.

backend/app/api/chat_routes.py

@@ -257,6 +257,20 @@ def send_message(gid: str, pid: str):
     elif mood >= 1:
         internal["signals"].append({"turn": internal["turns"], "mood": mood, "type": "warm"})
 
+    # Re-contact persona behavior: after enough info is exchanged (turn 2), the customer
+    # goes quiet, a time-lapse system note is shown, and the customer re-engages warmer.
+    if persona.get("recontact") and not internal.get("recontact_done") and internal["turns"] >= 2:
+        unit = "สัปดาห์" if slocale != "en" else "weeks"
+        sys_txt = (
+            f"⏳ ผ่านไป 2-3 {unit} ... ลูกค้าที่เคยสอบถามไปเงียบไประยะหนึ่ง ตอนนี้กลับมาติดต่ออีกครั้ง (พร้อมตัดสินใจมากขึ้น)"
+            if slocale != "en"
+            else "⏳ 2-3 weeks later ... the customer who asked earlier went quiet; now they re-contact, more ready to decide."
+        )
+        messages.append({"role": "system", "text": sys_txt})
+        internal["recontact_done"] = True
+        # Save the time-lapse note immediately so the UI shows it even if send ends here.
+        s["sessions"].update(session["id"], messages=messages, internal=internal)
+
     # Decision by the persona ends the session (one-shot lock).
     decision = meta.get("decision")
     if decision in ("buy", "walk"):

backend/app/llm.py

@@ -116,12 +116,27 @@ class LLMClient:
         temperature: float = 0.6,
         max_tokens: int = 1200,
     ) -> str:
+        # Normalize internal role labels (customer/seller) to the roles an OpenAI-compatible
+        # chat endpoint accepts: system/user/assistant. customer=assistant (the persona/LLM),
+        # seller=user (the trainee). Anything else maps to a safe default.
+        api_messages = []
+        for m in messages:
+            role = (m.get("role") or "").lower()
+            if role == "system":
+                mapped = "system"
+            elif role in ("customer", "assistant"):
+                mapped = "assistant"
+            elif role in ("seller", "user"):
+                mapped = "user"
+            else:
+                mapped = "user"
+            api_messages.append({"role": mapped, "content": m.get("text") or m.get("content") or ""})
         try:
             resp = self.client.chat.completions.create(
                 model=self.model,
                 temperature=temperature,
                 max_tokens=max_tokens,
-                messages=messages,
+                messages=api_messages,
             )
         except Exception as exc:
             raise LLMError(f"LLM call failed: {exc}") from exc