OJplatform/requirements/llm.py

import json
import logging

import requests
from django.conf import settings

logger = logging.getLogger(__name__)

ANALYZE_PROMPT = """\
你是 OJ 检测平台的分析引擎。请根据下面的作业要求,分析出需要对该成员提交的项目做哪些检测。

必须始终包含 ai_inspection(AI 检测)。根据作业要求酌情包含以下维度:
- stability: 稳定度检测(压测)。requirements 用中文给出具体指标,例如:
  基准并发 {stability_concurrency} 下错误率 ≤ 1%, 基准持续 {base_duration}s 后加压 50% 持续 {rampup_duration}s 崩溃率 ≤ 0.5%
- functional: 功能点完善度检测。points 为需要核实的中文功能点列表(必须覆盖作业要求中的全部功能点)
- performance: 性能检测。requirements 为中文指标,例如: 基准并发下 P95 延迟 ≤ 500ms
- security: 安全检测(漏洞扫描),requirements 为中文说明

输出格式: 只输出一个 JSON 对象,不要输出任何其他文字:
{{"ai_inspection": true, "stability": {{"enabled": true, "requirements": "中文要求"}}, "functional": {{"enabled": true, "points": ["功能点1", "功能点2"]}}, "performance": {{"enabled": false, "requirements": ""}}, "security": {{"enabled": false, "requirements": ""}}}}

作业要求如下:
{content}
"""


def _validate_spec(spec: dict) -> dict:
    if not isinstance(spec, dict):
        raise ValueError("LLM 未返回 JSON 对象")
    result: dict = {"ai_inspection": bool(spec.get("ai_inspection", True))}
    for key in ("stability", "performance", "security"):
        node = spec.get(key)
        if isinstance(node, dict):
            result[key] = {
                "enabled": bool(node.get("enabled")),
                "requirements": str(node.get("requirements", "")),
            }
        else:
            result[key] = {"enabled": False, "requirements": ""}
    func = spec.get("functional")
    if isinstance(func, dict):
        points = func.get("points")
        result["functional"] = {
            "enabled": bool(func.get("enabled")),
            "points": [str(p) for p in points] if isinstance(points, list) else [],
        }
    else:
        result["functional"] = {"enabled": True, "points": []}
    return result


def _extract_json(text: str) -> dict:
    start, end = text.find("{"), text.rfind("}")
    if start == -1 or end <= start:
        raise ValueError("LLM 响应中未找到 JSON")
    return json.loads(text[start:end + 1])


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()
    prompt = ANALYZE_PROMPT.format(
        content=content[:8000],
        stability_concurrency=settings.STABILITY_CONCURRENCY,
        base_duration=settings.STABILITY_BASE_DURATION,
        rampup_duration=settings.STABILITY_RAMPUP_DURATION,
    )
    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},
        ],
    )
    return _validate_spec(_extract_json(text))


def _default_spec() -> dict:
    return {
        "ai_inspection": True,
        "stability": {"enabled": True, "requirements": "默认稳定度要求: 基准并发下错误率≤1%, 加压后崩溃率≤0.5%"},
        "functional": {"enabled": True, "points": []},
        "performance": {"enabled": True, "requirements": "默认性能要求: 基准并发下 P95 延迟 ≤ 500ms"},
        "security": {"enabled": False, "requirements": ""},
    }


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。"""
    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"
        f"{json.dumps(spec, ensure_ascii=False)}\n\n"
        f"代码摘录:\n{excerpt}\n\n"
        "请输出一个 JSON 对象: {\"summary\": \"中文总体评价(200字内)\", "
        "\"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。"
    )
    text = _chat(base_url, api_key, model, [{"role": "user", "content": prompt}], timeout=180)
    data = _extract_json(text)
    return {
        "summary": str(data.get("summary", "")),
        "issues": data.get("issues", []) if isinstance(data.get("issues"), list) else [],
    }