feat: 核心模块增强 — 加密 + 运行时配置 + 日志缓冲

- crypto.py: API Key 加密/解密工具
- runtime_config.py: 运行时动态配置管理
- log_buffer.py: 内存日志缓冲区
- config.py: 新增加密配置项
- http_client.py: 增强重试和错误处理

src/llm/provider.py

@@ -10,8 +10,11 @@ import time
 from dataclasses import dataclass, field
 from typing import Any
 
+import httpx
+
 from src.core.config import settings
 from src.core.http_client import get_client
+from src.core.runtime_config import get_runtime_value
 
 logger = logging.getLogger(__name__)
 
@@ -67,17 +70,26 @@ class LLMProvider:
         start = time.perf_counter()
         try:
             client = get_client()
+            # 连接与读取分离:端点不可达时 10s 内快速失败,
+            # 避免每个 agent 各挂满 LLM_TIMEOUT 导致整次预测长时间无响应
             resp = await client.post(
                 f"{self.base_url}/chat/completions",
                 headers=headers,
                 json=payload,
-                timeout=self.timeout,
+                timeout=httpx.Timeout(connect=10.0, read=float(self.timeout), write=float(self.timeout), pool=10.0),
             )
             resp.raise_for_status()
             data = resp.json()
             latency = int((time.perf_counter() - start) * 1000)
             usage = data.get("usage", {})
-            content = data["choices"][0]["message"]["content"]
+            message = data["choices"][0]["message"]
+            content = message.get("content") or ""
+            if not content:
+                # 推理模型可能把 token 全花在 reasoning_content 上
+                raise RuntimeError(
+                    "模型未返回文本内容"
+                    + ("(token 花在推理上,请增大 max_tokens)" if message.get("reasoning_content") else "")
+                )
             parsed = None
             if json_mode:
                 try:
@@ -105,10 +117,11 @@ class LLMProvider:
             return LLMResponse(content="", error=str(e), latency_ms=latency)
 
 
-def get_default_provider() -> LLMProvider:
+async def get_default_provider() -> LLMProvider:
+    """构造默认 provider:运行时配置(DB)优先,回落 .env。"""
     return LLMProvider(
-        api_key=settings.LLM_API_KEY,
-        base_url=settings.LLM_BASE_URL,
-        model=settings.LLM_MODEL,
+        api_key=await get_runtime_value("LLM_API_KEY"),
+        base_url=await get_runtime_value("LLM_BASE_URL"),
+        model=await get_runtime_value("LLM_MODEL"),
         timeout=settings.LLM_TIMEOUT,
     )