feat: dual LLM mode (default via .env, custom per-request) for AI analysis

requirements/llm.py

@@ -56,36 +56,60 @@ def _extract_json(text: str) -> dict:
     return json.loads(text[start:end + 1])
 
 
-def analyze_requirements(content: str) -> dict:
-    """调用 LLM 分析作业要求,返回检测维度计划。未配置 LLM 时返回默认计划。"""
-    key = settings.LLM_API_KEY
-    if not key or key.startswith("sk-placeholder") or not settings.LLM_MODEL:
-        logger.warning("LLM 未配置,使用默认检测计划")
+def resolve_llm(override: dict | None = None) -> tuple[str, str, str]:
+    """返回 (base_url, api_key, model)。default 模式读 settings;
+    custom 模式用 override 中的 base_url/api_key/model。"""
+    mode = (override or {}).get("mode") or settings.LLM_MODE
+    if mode == "custom":
+        base_url = (override.get("base_url") or "").strip()
+        api_key = (override.get("api_key") or "").strip()
+        model = (override.get("model") or "").strip()
+        if not base_url or not model:
+            raise ValueError("custom 模式必须提供 base_url 和 model")
+        return base_url.rstrip("/"), api_key, model
+    base_url = settings.LLM_BASE_URL.rstrip("/")
+    api_key = settings.LLM_API_KEY
+    model = settings.LLM_MODEL
+    if not base_url or not model:
+        raise ValueError("LLM 未配置: 请在 .env 设置 LLM_BASE_URL/LLM_MODEL, 或用 custom 模式")
+    return base_url, api_key, model
+
+
+def _chat(base_url: str, api_key: str, model: str, messages: list[dict], timeout: int = 120) -> str:
+    headers = {"Content-Type": "application/json"}
+    if api_key:
+        headers["Authorization"] = f"Bearer {api_key}"
+    resp = requests.post(
+        base_url + "/chat/completions",
+        headers=headers,
+        json={"model": model, "messages": messages, "temperature": 0.1},
+        timeout=timeout,
+    )
+    resp.raise_for_status()
+    return resp.json()["choices"][0]["message"]["content"]
+
+
+def analyze_requirements(content: str, llm_override: dict | None = None) -> dict:
+    """调用 LLM 分析作业要求,返回检测维度计划。LLM 不可用时返回默认计划。"""
+    try:
+        base_url, api_key, model = resolve_llm(llm_override)
+    except ValueError as exc:
+        logger.warning("%s, 使用默认检测计划", exc)
         return _default_spec()
-    url = settings.LLM_BASE_URL.rstrip("/") + "/chat/completions"
     prompt = ANALYZE_PROMPT.format(
         content=content[:8000],
         stability_concurrency=settings.STABILITY_CONCURRENCY,
         base_duration=settings.STABILITY_BASE_DURATION,
         rampup_duration=settings.STABILITY_RAMPUP_DURATION,
     )
-    resp = requests.post(
-        url,
-        headers={"Authorization": f"Bearer {key}", "Content-Type": "application/json"},
-        json={
-            "model": settings.LLM_MODEL,
-            "messages": [
-                {"role": "system", "content": "你是严格遵循 JSON 输出格式的 OJ 检测分析引擎。"},
-                {"role": "user", "content": prompt},
-            ],
-            "temperature": 0.1,
-        },
-        timeout=120,
+    text = _chat(
+        base_url, api_key, model,
+        [
+            {"role": "system", "content": "你是严格遵循 JSON 输出格式的 OJ 检测分析引擎。"},
+            {"role": "user", "content": prompt},
+        ],
     )
-    resp.raise_for_status()
-    text = resp.json()["choices"][0]["message"]["content"]
-    spec = _extract_json(text)
-    return _validate_spec(spec)
+    return _validate_spec(_extract_json(text))
 
 
 def _default_spec() -> dict:
@@ -98,12 +122,13 @@ def _default_spec() -> dict:
     }
 
 
-def ai_summarize(project_dir: str, spec: dict) -> dict:
+def ai_summarize(project_dir: str, spec: dict, llm_override: dict | None = None) -> dict:
     """AI 检测: 阅读项目关键文件,对照检测计划输出中文报告片段。"""
-    key = settings.LLM_API_KEY
-    if not key or key.startswith("sk-placeholder") or not settings.LLM_MODEL:
-        return {"summary": "LLM 未配置,跳过 AI 检测。请在 .env 中配置 LLM_API_KEY。", "issues": []}
     from .code_reader import read_key_files
+    try:
+        base_url, api_key, model = resolve_llm(llm_override)
+    except ValueError as exc:
+        return {"summary": f"LLM 不可用({exc}),跳过 AI 检测", "issues": []}
     excerpt = read_key_files(project_dir, max_chars=12000)
     prompt = (
         "你是 OJ 平台的代码审查专家。下面是成员提交的项目关键代码摘录和检测计划(JSON):\n"
@@ -113,19 +138,7 @@ def ai_summarize(project_dir: str, spec: dict) -> dict:
         "\"issues\": [{\"point\": \"检查点\", \"status\": \"pass|fail|warn\", \"detail\": \"中文说明\"}]}\n"
         "对照检测计划逐项核查,重点关注: 功能点是否完善、稳定度是否可能达标、潜在风险。只输出 JSON。"
     )
-    url = settings.LLM_BASE_URL.rstrip("/") + "/chat/completions"
-    resp = requests.post(
-        url,
-        headers={"Authorization": f"Bearer {key}", "Content-Type": "application/json"},
-        json={
-            "model": settings.LLM_MODEL,
-            "messages": [{"role": "user", "content": prompt}],
-            "temperature": 0.2,
-        },
-        timeout=180,
-    )
-    resp.raise_for_status()
-    text = resp.json()["choices"][0]["message"]["content"]
+    text = _chat(base_url, api_key, model, [{"role": "user", "content": prompt}], timeout=180)
     data = _extract_json(text)
     return {
         "summary": str(data.get("summary", "")),