feat: virtual company module, team projects, PWA dishes app, and startup scripts overhaul

- Add company module (3-tier org, CEO planning, parallel departments)
- Add company orchestrator, knowledge extractor, presets, scheduler
- Add company API endpoints, models, and frontend views
- Add 今天吃啥 PWA app (69 dishes, real images, offline support)
- Add team_projects output directory structure
- Add unified manage.ps1 for service lifecycle
- Add Windows startup guide v1.0
- Add TTS troubleshooting doc
- Update frontend (AgentChat UX overhaul, new views)
- Update backend (voice engine fix, multi-tenant, RBAC)
- Remove deprecated startup scripts and old docs

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

backend/app/api/agent_chat.py

@@ -136,8 +136,10 @@ class ChatRequest(BaseModel):
     message: str
     session_id: Optional[str] = None
     model: Optional[str] = None
+    model_config_id: Optional[str] = Field(default=None, description="使用已保存的模型配置（含 API Key）")
     temperature: Optional[float] = None
     max_iterations: Optional[int] = None
+    max_tokens: Optional[int] = Field(default=None, description="单次回复最大 token 数")
     streamlined: bool = Field(default=False, description="启用工具结果流式美化")
     prompt_sections_enabled: bool = Field(default=True, description="启用系统提示词分层装配")
     system_prompt_override: Optional[str] = Field(default=None, description="覆盖 Agent 的 System Prompt")
@@ -355,17 +357,20 @@ async def chat_bare(
     """无需 Agent 配置，使用默认设置直接对话。"""
     uid = current_user.id
     bare_scope = f"{uid}:__bare__" if uid else "__bare__"
+    mc = _resolve_model_config(db, req.model_config_id, uid)
+    llm_kwargs = _apply_model_config(dict(
+        model=req.model or (
+            "gpt-4o-mini" if settings.OPENAI_API_KEY and settings.OPENAI_API_KEY != "your-openai-api-key"
+            else "deepseek-v4-flash"
+        ),
+        temperature=req.temperature or 0.7,
+        max_iterations=req.max_iterations or 10,
+        max_tokens=req.max_tokens,
+    ), mc)
     config = AgentConfig(
         name="bare_agent",
         system_prompt="你是一个有用的AI助手。请使用可用工具来帮助用户完成任务。",
-        llm=AgentLLMConfig(
-            model=req.model or (
-                "gpt-4o-mini" if settings.OPENAI_API_KEY and settings.OPENAI_API_KEY != "your-openai-api-key"
-                else "deepseek-v4-flash"
-            ),
-            temperature=req.temperature or 0.7,
-            max_iterations=req.max_iterations or 10,
-        ),
+        llm=AgentLLMConfig(**llm_kwargs),
         user_id=uid,
         memory_scope_id=bare_scope,
         memory=AgentMemoryConfig(
@@ -421,17 +426,20 @@ async def chat_bare_stream(
     """无需 Agent 配置，使用默认设置直接对话（流式 SSE）。"""
     uid = current_user.id
     bare_scope = f"{uid}:__bare__" if uid else "__bare__"
+    mc = _resolve_model_config(db, req.model_config_id, uid)
+    llm_kwargs = _apply_model_config(dict(
+        model=req.model or (
+            "gpt-4o-mini" if settings.OPENAI_API_KEY and settings.OPENAI_API_KEY != "your-openai-api-key"
+            else "deepseek-v4-flash"
+        ),
+        temperature=req.temperature or 0.7,
+        max_iterations=req.max_iterations or 10,
+        max_tokens=req.max_tokens,
+    ), mc)
     config = AgentConfig(
         name="bare_agent",
         system_prompt="你是一个有用的AI助手。请使用可用工具来帮助用户完成任务。",
-        llm=AgentLLMConfig(
-            model=req.model or (
-                "gpt-4o-mini" if settings.OPENAI_API_KEY and settings.OPENAI_API_KEY != "your-openai-api-key"
-                else "deepseek-v4-flash"
-            ),
-            temperature=req.temperature or 0.7,
-            max_iterations=req.max_iterations or 10,
-        ),
+        llm=AgentLLMConfig(**llm_kwargs),
         user_id=uid,
         memory_scope_id=bare_scope,
         memory=AgentMemoryConfig(
@@ -476,7 +484,7 @@ async def chat_with_agent(
     agent = db.query(Agent).filter(Agent.id == agent_id).first()
     if not agent:
         raise HTTPException(status_code=404, detail="Agent 不存在")
-    if agent.user_id and agent.user_id != current_user.id and current_user.role != "admin" and not agent.is_public:
+    if agent.user_id and agent.user_id != current_user.id and not current_user.has_permission("agent:chat_any") and not agent.is_public:
         raise HTTPException(status_code=403, detail="无权访问该 Agent")
 
     # 从 Agent 配置构建 Runtime
@@ -506,18 +514,20 @@ async def chat_with_agent(
     memory_cfg = _build_memory_config_from_node(agent_node_cfg)
     if getattr(agent, "parent_agent_id", None):
         memory_cfg.parent_agent_id = agent.parent_agent_id
+    mc = _resolve_model_config(db, req.model_config_id, uid)
+    llm_kwargs = _apply_model_config(dict(
+        provider=agent_node_cfg.get("provider", "openai"),
+        model=req.model or agent_node_cfg.get("model", "gpt-4o-mini"),
+        temperature=req.temperature or float(agent_node_cfg.get("temperature", 0.7)),
+        max_iterations=req.max_iterations or int(agent_node_cfg.get("max_iterations", 10)),
+        max_tokens=req.max_tokens or agent_node_cfg.get("max_tokens"),
+        plan_mode_enabled=bool(agent_node_cfg.get("plan_mode_enabled", False)),
+        plan_approval_required=bool(agent_node_cfg.get("plan_approval_required", True)),
+    ), mc)
     config = AgentConfig(
         name=agent.name,
         system_prompt=system_prompt,
-        llm=AgentLLMConfig(
-            provider=agent_node_cfg.get("provider", "openai"),
-            model=req.model or agent_node_cfg.get("model", "gpt-4o-mini"),
-            temperature=req.temperature or float(agent_node_cfg.get("temperature", 0.7)),
-            max_iterations=req.max_iterations or int(agent_node_cfg.get("max_iterations", 10)),
-            # 计划模式 (P2)
-            plan_mode_enabled=bool(agent_node_cfg.get("plan_mode_enabled", False)),
-            plan_approval_required=bool(agent_node_cfg.get("plan_approval_required", True)),
-        ),
+        llm=AgentLLMConfig(**llm_kwargs),
         tools=AgentToolConfig(
             include_tools=agent_node_cfg.get("tools", []),
             exclude_tools=agent_node_cfg.get("exclude_tools", []),
@@ -577,7 +587,7 @@ async def chat_with_agent_stream(
     agent = db.query(Agent).filter(Agent.id == agent_id).first()
     if not agent:
         raise HTTPException(status_code=404, detail="Agent 不存在")
-    if agent.user_id and agent.user_id != current_user.id and current_user.role != "admin" and not agent.is_public:
+    if agent.user_id and agent.user_id != current_user.id and not current_user.has_permission("agent:chat_any") and not agent.is_public:
         raise HTTPException(status_code=403, detail="无权访问该 Agent")
 
     wc = agent.workflow_config or {}
@@ -603,18 +613,20 @@ async def chat_with_agent_stream(
     memory_cfg = _build_memory_config_from_node(agent_node_cfg)
     if getattr(agent, "parent_agent_id", None):
         memory_cfg.parent_agent_id = agent.parent_agent_id
+    mc = _resolve_model_config(db, req.model_config_id, uid)
+    llm_kwargs = _apply_model_config(dict(
+        provider=agent_node_cfg.get("provider", "openai"),
+        model=req.model or agent_node_cfg.get("model", "gpt-4o-mini"),
+        temperature=req.temperature or float(agent_node_cfg.get("temperature", 0.7)),
+        max_iterations=req.max_iterations or int(agent_node_cfg.get("max_iterations", 10)),
+        max_tokens=req.max_tokens or agent_node_cfg.get("max_tokens"),
+        plan_mode_enabled=bool(agent_node_cfg.get("plan_mode_enabled", False)),
+        plan_approval_required=bool(agent_node_cfg.get("plan_approval_required", True)),
+    ), mc)
     config = AgentConfig(
         name=agent.name,
         system_prompt=system_prompt,
-        llm=AgentLLMConfig(
-            provider=agent_node_cfg.get("provider", "openai"),
-            model=req.model or agent_node_cfg.get("model", "gpt-4o-mini"),
-            temperature=req.temperature or float(agent_node_cfg.get("temperature", 0.7)),
-            max_iterations=req.max_iterations or int(agent_node_cfg.get("max_iterations", 10)),
-            # 计划模式 (P2)
-            plan_mode_enabled=bool(agent_node_cfg.get("plan_mode_enabled", False)),
-            plan_approval_required=bool(agent_node_cfg.get("plan_approval_required", True)),
-        ),
+        llm=AgentLLMConfig(**llm_kwargs),
         tools=AgentToolConfig(
             include_tools=agent_node_cfg.get("tools", []),
             exclude_tools=agent_node_cfg.get("exclude_tools", []),
@@ -901,7 +913,38 @@ async def search_messages(
     return SearchMessagesResponse(messages=result, total=total)
 
 
-def _find_agent_node_config(nodes: list) -> Dict[str, Any]:
+def _resolve_model_config(db: Session, model_config_id: Optional[str], user_id: str) -> Optional[Dict[str, Any]]:
+    """从数据库加载模型配置，返回 {provider, model, api_key, base_url}。"""
+    if not model_config_id:
+        return None
+    from app.models.model_config import ModelConfig
+    mc = db.query(ModelConfig).filter(
+        ModelConfig.id == model_config_id,
+        ModelConfig.user_id == user_id,
+    ).first()
+    if not mc:
+        raise HTTPException(status_code=404, detail="模型配置不存在")
+    return {
+        "provider": mc.provider,
+        "model": mc.model_name,
+        "api_key": mc.api_key,
+        "base_url": mc.base_url,
+    }
+
+
+def _apply_model_config(llm_kwargs: Dict[str, Any], mc: Optional[Dict[str, Any]]) -> Dict[str, Any]:
+    """将模型配置应用到 LLM 参数中。"""
+    if not mc:
+        return llm_kwargs
+    if mc.get("provider"):
+        llm_kwargs["provider"] = mc["provider"]
+    if mc.get("model"):
+        llm_kwargs["model"] = mc["model"]
+    if mc.get("api_key"):
+        llm_kwargs["api_key"] = mc["api_key"]
+    if mc.get("base_url"):
+        llm_kwargs["base_url"] = mc["base_url"]
+    return llm_kwargs
     """从工作流节点列表中查找第一个 agent 类型或 llm 类型的节点配置。"""
     if not nodes:
         return {}