feat(auth): authentication module with multi-tenant isolation (RFC-001)

Introduce an always-on auth layer with auto-created admin on first boot,
multi-tenant isolation for threads/stores, and a full setup/login flow.

Backend
- JWT access tokens with `ver` field for stale-token rejection; bump on
  password/email change
- Password hashing, HttpOnly+Secure cookies (Secure derived from request
  scheme at runtime)
- CSRF middleware covering both REST and LangGraph routes
- IP-based login rate limiting (5 attempts / 5-min lockout) with bounded
  dict growth and X-Forwarded-For bypass fix
- Multi-worker-safe admin auto-creation (single DB write, WAL once)
- needs_setup + token_version on User model; SQLite schema migration
- Thread/store isolation by owner; orphan thread migration on first admin
  registration
- thread_id validated as UUID to prevent log injection
- CLI tool to reset admin password
- Decorator-based authz module extracted from auth core

Frontend
- Login and setup pages with SSR guard for needs_setup flow
- Account settings page (change password / email)
- AuthProvider + route guards; skips redirect when no users registered
- i18n (en-US / zh-CN) for auth surfaces
- Typed auth API client; parseAuthError unwraps FastAPI detail envelope

Infra & tooling
- Unified `serve.sh` with gateway mode + auto dep install
- Public PyPI uv.toml pin for CI compatibility
- Regenerated uv.lock with public index

Tests
- HTTP vs HTTPS cookie security tests
- Auth middleware, rate limiter, CSRF, setup flow coverage

backend/app/gateway/routers/suggestions.py

@@ -2,6 +2,7 @@ import json
 import logging
 
 from fastapi import APIRouter
+from langchain_core.messages import HumanMessage, SystemMessage
 from pydantic import BaseModel, Field
 
 from deerflow.models import create_chat_model
@@ -106,22 +107,21 @@ async def generate_suggestions(thread_id: str, request: SuggestionsRequest) -> S
     if not conversation:
         return SuggestionsResponse(suggestions=[])
 
-    prompt = (
+    system_instruction = (
         "You are generating follow-up questions to help the user continue the conversation.\n"
         f"Based on the conversation below, produce EXACTLY {n} short questions the user might ask next.\n"
         "Requirements:\n"
-        "- Questions must be relevant to the conversation.\n"
+        "- Questions must be relevant to the preceding conversation.\n"
         "- Questions must be written in the same language as the user.\n"
         "- Keep each question concise (ideally <= 20 words / <= 40 Chinese characters).\n"
         "- Do NOT include numbering, markdown, or any extra text.\n"
-        "- Output MUST be a JSON array of strings only.\n\n"
-        "Conversation:\n"
-        f"{conversation}\n"
+        "- Output MUST be a JSON array of strings only.\n"
     )
+    user_content = f"Conversation Context:\n{conversation}\n\nGenerate {n} follow-up questions"
 
     try:
         model = create_chat_model(name=request.model_name, thinking_enabled=False)
-        response = model.invoke(prompt)
+        response = await model.ainvoke([SystemMessage(content=system_instruction), HumanMessage(content=user_content)])
         raw = _extract_response_text(response.content)
         suggestions = _parse_json_string_list(raw) or []
         cleaned = [s.replace("\n", " ").strip() for s in suggestions if s.strip()]