—

## 实施计划

### 1. 修改 `agent.py`

**文件**: `D:\sub\MedAutoML\code\backend\app\agent\agent.py`

#### 1.1 添加新的导入和辅助函数

# 添加在 imports 区域后，约第19行
from config import Config
from app.models.settings import SystemSettings
from sqlalchemy import select

# provider 对应的 base_url 映射
PROVIDER_BASE_URLS = {
    'ali': 'https://dashscope.aliyuncs.com/compatible-mode/v1',
    'openai': 'https://api.openai.com/v1',
}

async def _get_llm_config_from_db(session: AsyncSession) -> dict:
    """从数据库读取 LLM 配置"""
    query = select(SystemSettings).filter_by(setting_key='llm_config')
    result = await session.execute(query)
    setting = result.scalars().first()
    if not setting or not setting.setting_value:
        raise ValueError("数据库中未找到 LLM 配置 (llm_config)")
    return setting.setting_value

def _get_base_url_by_provider(provider: str) -> str:
    """根据 provider 返回 base_url"""
    base_url = PROVIDER_BASE_URLS.get(provider)
    if not base_url:
        raise ValueError(f"不支持的 LLM provider: {provider}")
    return base_url

#### 1.2 创建异步工厂函数

# 约在 _build_litellm_model() 位置 (line 33)

async def _create_litellm_model_from_db(session: AsyncSession):
    """从数据库配置创建 LiteLLM 模型实例"""
    config = await _get_llm_config_from_db(session)
    
    model_name = config.get('model', 'qwen-plus')
    provider = config.get('provider', 'ali')
    api_key = config.get('apiKey')
    
    if not api_key:
        raise ValueError("LLM 配置中缺少 apiKey")
    
    # 设置环境变量供 LiteLLM 使用
    os.environ['OPENAI_API_KEY'] = api_key
    
    # 获取 base_url
    base_url = _get_base_url_by_provider(provider)
    
    # LiteLLM 模型名称格式: provider/model_id
    # 根据 llm_service.py 的 MODEL_CONFIG，model_name='qwen-plus' 对应 model_id='qwen3.5-plus'
    model_id = _get_model_id_from_name(model_name)  # 需要添加这个辅助函数
    litellm_model_name = f"openai/{model_id}"
    
    return LiteLlm(model=litellm_model_name, base_url=base_url)

#### 1.3 添加 model_id 映射函数

# MODEL_CONFIG 中的 model_id 映射
MODEL_ID_MAP = {
    'qwen-plus': 'qwen3.5-plus',
    'qwen-max': 'qwen3-max',
    'qwen-turbo': 'qwen3.5-flash',
}

def _get_model_id_from_name(model_name: str) -> str:
    """获取 LiteLLM 使用的 model_id"""
    return MODEL_ID_MAP.get(model_name, model_name)

#### 1.4 修改 AgentSessionManager

**修改**: `AgentSessionManager.__init__` 和添加新方法

class AgentSessionManager:
    def __init__(self):
        self._session_service: Optional[InMemorySessionService] = None
        self._runners: Dict[str, Runner] = {}
        self._agent: Optional[LlmAgent] = None  # 懒加载

    async def _get_or_create_agent(self, session: AsyncSession) -> LlmAgent:
        """获取或创建 Agent 实例（懒加载）"""
        if self._agent is None:
            litellm_model = await _create_litellm_model_from_db(session)
            tools = get_all_tools()
            self._agent = LlmAgent(
                name="MedAutoMLAgent",
                model=litellm_model,
                description="MedAutoML智能助手 - 帮助医生和研究人员构建临床预测模型",
                instruction=_load_instruction(),
                tools=tools,
                output_key="agent_output",
            )
        return self._agent

    def get_runner(self, user_id: str) -> Runner:
        # 需要改为异步，或者使用全局 agent
        # 方案：预先创建 agent，但需要数据库session
        ...

**关键决策点**:
– `AgentSessionManager.get_runner()` 是同步方法，但创建 agent 需要异步数据库操作
– **方案**: 在 `get_agent_session_manager()` 调用时预先创建 agent（使用应用级 session）

#### 1.5 修改全局初始化

# 约在 line 230 后

_agent_session_manager: Optional[AgentSessionManager] = None
_medautoml_agent: Optional[LlmAgent] = None

async def _init_agent_with_db_config():
    """初始化 Agent（使用数据库配置）- 应用启动时调用"""
    global _medautoml_agent
    
    from app import get_db
    from sqlalchemy import select
    
    # 获取数据库 session
    async for session in get_db():
        litellm_model = await _create_litellm_model_from_db(session)
        tools = get_all_tools()
        _medautoml_agent = LlmAgent(
            name="MedAutoMLAgent",
            model=litellm_model,
            description="MedAutoML智能助手",
            instruction=_load_instruction(),
            tools=tools,
            output_key="agent_output",
        )
        break  # 只获取一次 session

def get_agent() -> LlmAgent:
    """获取 MedAutoMLAgent 实例"""
    if _medautoml_agent is None:
        raise RuntimeError("Agent 未初始化，请先调用 init_agent_with_db_config()")
    return _medautoml_agent

### 2. 需要协调的修改

由于 `create_medautoml_agent` 需要在应用启动时调用，需要在 `run.py` 中添加初始化调用。

**文件**: `D:\sub\MedAutoML\code\backend\run.py` (如果存在) 或 `app/__init__.py`

—

## 实现步骤

1. **修改 `agent.py`**:
– 添加 imports、辅助函数、`MODEL_ID_MAP`
– 创建 `_create_litellm_model_from_db()` 异步工厂函数
– 添加 `_init_agent_with_db_config()` 初始化函数
– 修改 `get_agent_session_manager()` 使用懒加载 agent

2. **添加启动调用** (如 `run.py` 或 `app/__init__.py`):
– 在应用启动时调用 `_init_agent_with_db_config()`

3. **移除/更新 `config.py`**:
– 考虑是否移除 `DEFAULT_AGENT_MODEL` 和 `AGENT_API_BASE`（如果不再需要作为 fallback）

—

Now I have a complete understanding of the codebase. Here’s my analysis and plan:

—

## Analysis

**Current state:**

– `agent/agent.py` creates `_litellm_model` and `MedAutoMLAgent` **at module import time** using hardcoded `Config.DEFAULT_AGENT_MODEL` and `Config.AGENT_API_BASE`
– The DB already has a `SystemSettings` table with `setting_key=’llm_config’` storing `{apiKey, model, provider, enabled}` (see `models/settings.py` and `routes/settings.py`)
– `llm_service.py` already reads from this DB config — but the ADK agent does not
– The DB config has no `base_url` field, which the agent needs for `LiteLlm`

**Key challenge:** The agent is created at import time before the DB is available. Need lazy initialization.

—

## Implementation Plan

### 1. `code/backend/app/agent/agent.py` — Main changes

– **Remove** module-level `_litellm_model` and `MedAutoMLAgent` creation
– **Add** lazy init: `get_agent()` reads from DB on first call, falls back to env vars / `Config`
– **Add** a sync SQLAlchemy engine to read the DB (needed because agent runs in thread pool via `asyncio.to_thread`)
– **Add** `reload_agent_config()` public function for settings route to trigger a reload
– **Update** `AgentSessionManager.get_runner()` to use `get_agent()`
– Construct the LiteLLM model name as `openai/{config.model}`, derive `base_url` from config or provider
– Pass `api_key` to `LiteLlm` (currently not passed at all)

### 2. `code/backend/app/routes/settings.py` — Minor changes

– Add `base_url` to `DEFAULT_LLM_CONFIG`
– Handle `base_url` field in `PUT /api/settings/llm`
– Call `reload_agent_config()` after updating LLM config

### 3. `code/backend/app/agent/__init__.py` — No change

### 4. `code/backend/config.py` — No change (kept as fallback)

—

Would you like me to proceed with this plan? One question: for the `base_url` field in the DB config, should I add it to the settings API route as described, or would you prefer to derive it from the existing `provider` field in the agent code (e.g., `ali` → DashScope URL, `openai` → OpenAI URL)?