hyh
134 lines
5.8 KiB
Python
134 lines
5.8 KiB
Python
import json
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import logging
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import requests
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from django.conf import settings
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logger = logging.getLogger(__name__)
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ANALYZE_PROMPT = """\
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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。
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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:
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- stability: 稳定度检测(压测)。requirements 用中文给出具体指标,例如:
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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%
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- 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)
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- performance: 性能检测。requirements 为中文指标,例如: 基准并发下 P95 延迟 ≤ 500ms
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- security: 安全检测(漏洞扫描),requirements 为中文说明
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输出格式: 只输出一个 JSON 对象,不要输出任何其他文字:
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{{"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": ""}}}}
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作业要求如下:
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{content}
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"""
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def _validate_spec(spec: dict) -> dict:
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if not isinstance(spec, dict):
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raise ValueError("LLM 未返回 JSON 对象")
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result: dict = {"ai_inspection": bool(spec.get("ai_inspection", True))}
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for key in ("stability", "performance", "security"):
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node = spec.get(key)
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if isinstance(node, dict):
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result[key] = {
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"enabled": bool(node.get("enabled")),
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"requirements": str(node.get("requirements", "")),
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}
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else:
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result[key] = {"enabled": False, "requirements": ""}
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func = spec.get("functional")
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if isinstance(func, dict):
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points = func.get("points")
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result["functional"] = {
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"enabled": bool(func.get("enabled")),
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"points": [str(p) for p in points] if isinstance(points, list) else [],
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}
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else:
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result["functional"] = {"enabled": True, "points": []}
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return result
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def _extract_json(text: str) -> dict:
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start, end = text.find("{"), text.rfind("}")
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if start == -1 or end <= start:
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raise ValueError("LLM 响应中未找到 JSON")
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return json.loads(text[start:end + 1])
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def analyze_requirements(content: str) -> dict:
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"""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。"""
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key = settings.LLM_API_KEY
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if not key or key.startswith("sk-placeholder") or not settings.LLM_MODEL:
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logger.warning("LLM 未配置,使用默认检测计划")
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return _default_spec()
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url = settings.LLM_BASE_URL.rstrip("/") + "/chat/completions"
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prompt = ANALYZE_PROMPT.format(
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content=content[:8000],
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stability_concurrency=settings.STABILITY_CONCURRENCY,
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base_duration=settings.STABILITY_BASE_DURATION,
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rampup_duration=settings.STABILITY_RAMPUP_DURATION,
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)
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resp = requests.post(
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url,
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headers={"Authorization": f"Bearer {key}", "Content-Type": "application/json"},
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json={
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"model": settings.LLM_MODEL,
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"messages": [
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{"role": "system", "content": "你是严格遵循 JSON 输出格式的 OJ 检测分析引擎。"},
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{"role": "user", "content": prompt},
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],
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"temperature": 0.1,
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},
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timeout=120,
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)
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resp.raise_for_status()
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text = resp.json()["choices"][0]["message"]["content"]
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spec = _extract_json(text)
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return _validate_spec(spec)
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def _default_spec() -> dict:
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return {
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"ai_inspection": True,
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"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%"},
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"functional": {"enabled": True, "points": []},
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"performance": {"enabled": True, "requirements": "默认性能要求: 基准并发下 P95 延迟 ≤ 500ms"},
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"security": {"enabled": False, "requirements": ""},
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}
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def ai_summarize(project_dir: str, spec: dict) -> dict:
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"""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。"""
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key = settings.LLM_API_KEY
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if not key or key.startswith("sk-placeholder") or not settings.LLM_MODEL:
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return {"summary": "LLM 未配置,跳过 AI 检测。请在 .env 中配置 LLM_API_KEY。", "issues": []}
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from .code_reader import read_key_files
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excerpt = read_key_files(project_dir, max_chars=12000)
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prompt = (
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"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"
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f"{json.dumps(spec, ensure_ascii=False)}\n\n"
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f"代码摘录:\n{excerpt}\n\n"
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"请输出一个 JSON 对象: {\"summary\": \"中文总体评价(200字内)\", "
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"\"issues\": [{\"point\": \"检查点\", \"status\": \"pass|fail|warn\", \"detail\": \"中文说明\"}]}\n"
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"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。"
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)
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url = settings.LLM_BASE_URL.rstrip("/") + "/chat/completions"
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resp = requests.post(
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url,
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headers={"Authorization": f"Bearer {key}", "Content-Type": "application/json"},
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json={
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"model": settings.LLM_MODEL,
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"messages": [{"role": "user", "content": prompt}],
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"temperature": 0.2,
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},
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timeout=180,
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)
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resp.raise_for_status()
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text = resp.json()["choices"][0]["message"]["content"]
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data = _extract_json(text)
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return {
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"summary": str(data.get("summary", "")),
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"issues": data.get("issues", []) if isinstance(data.get("issues"), list) else [],
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}
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