awoooi/apps/api/tests/llm_testing/property_validators.py

"""
Property Validators - Tier 2 測試
==================================
驗證 LLM 輸出的不變量屬性

版本: v1.0
建立: 2026-03-26 (台北時區)
"""

import re
import shlex
from dataclasses import dataclass


@dataclass
class ValidationResult:
    """驗證結果"""
    is_valid: bool
    message: str
    details: dict | None = None


def validate_kubectl_syntax(command: str) -> ValidationResult:
    """
    驗證 kubectl 命令語法有效性

    檢查:
    1. 命令可被 shell 解析
    2. 第一個 token 是 kubectl
    3. 包含有效的 kubectl 動詞

    Args:
        command: kubectl 命令字串

    Returns:
        ValidationResult
    """
    if not command:
        return ValidationResult(False, "命令為空")

    command = command.strip()

    # 移除可能的 $ 前綴
    if command.startswith("$ "):
        command = command[2:]

    # 嘗試 shell 解析
    try:
        tokens = shlex.split(command)
    except ValueError as e:
        return ValidationResult(False, f"命令無法解析: {e}")

    if not tokens:
        return ValidationResult(False, "解析後無 token")

    # 檢查第一個 token
    if tokens[0] != "kubectl":
        return ValidationResult(
            False,
            f"第一個 token 應為 kubectl，實際為 {tokens[0]}"
        )

    # 有效的 kubectl 動詞
    valid_verbs = {
        "get", "describe", "create", "apply", "delete",
        "edit", "scale", "rollout", "logs", "exec",
        "port-forward", "top", "patch", "label", "annotate",
        "cordon", "uncordon", "drain", "taint",
    }

    if len(tokens) < 2:
        return ValidationResult(False, "缺少 kubectl 動詞")

    verb = tokens[1]
    if verb not in valid_verbs:
        return ValidationResult(
            False,
            f"無效的 kubectl 動詞: {verb}",
            details={"valid_verbs": list(valid_verbs)}
        )

    return ValidationResult(
        True,
        "kubectl 語法有效",
        details={"verb": verb, "tokens": tokens}
    )


def validate_risk_level(risk_level: str) -> ValidationResult:
    """
    驗證風險等級是否為有效值

    Args:
        risk_level: 風險等級字串

    Returns:
        ValidationResult
    """
    valid_levels = {"LOW", "MEDIUM", "HIGH", "CRITICAL"}

    if not risk_level:
        return ValidationResult(False, "風險等級為空")

    normalized = risk_level.strip().upper()

    if normalized in valid_levels:
        return ValidationResult(True, f"有效風險等級: {normalized}")

    # 嘗試模糊匹配
    fuzzy_mappings = {
        "低": "LOW",
        "中": "MEDIUM",
        "高": "HIGH",
        "危": "CRITICAL",
        "嚴重": "CRITICAL",
    }

    for key, value in fuzzy_mappings.items():
        if key in risk_level:
            return ValidationResult(
                True,
                f"模糊匹配: {risk_level} -> {value}",
                details={"original": risk_level, "normalized": value}
            )

    return ValidationResult(
        False,
        f"無效風險等級: {risk_level}",
        details={"valid_levels": list(valid_levels)}
    )


def validate_chinese_ratio(text: str, min_ratio: float = 0.3) -> ValidationResult:
    """
    驗證繁體中文比例

    Args:
        text: 待驗證文字
        min_ratio: 最低中文比例 (預設 30%)

    Returns:
        ValidationResult
    """
    if not text:
        return ValidationResult(False, "文字為空")

    # 中文字元 Unicode 範圍
    chinese_pattern = re.compile(r"[\u4e00-\u9fff]")
    chinese_chars = chinese_pattern.findall(text)

    # 計算比例 (排除空白)
    non_space_chars = len(text.replace(" ", "").replace("\n", ""))
    if non_space_chars == 0:
        return ValidationResult(False, "無有效字元")

    ratio = len(chinese_chars) / non_space_chars

    if ratio >= min_ratio:
        return ValidationResult(
            True,
            f"中文比例 {ratio:.1%} >= {min_ratio:.0%}",
            details={"ratio": ratio, "chinese_count": len(chinese_chars)}
        )

    return ValidationResult(
        False,
        f"中文比例 {ratio:.1%} < {min_ratio:.0%}",
        details={"ratio": ratio, "chinese_count": len(chinese_chars)}
    )


def validate_response_length(
    text: str,
    min_length: int = 10,
    max_length: int = 500,
) -> ValidationResult:
    """
    驗證回應長度

    Args:
        text: 待驗證文字
        min_length: 最小長度
        max_length: 最大長度

    Returns:
        ValidationResult
    """
    if not text:
        return ValidationResult(False, "文字為空")

    length = len(text.strip())

    if length < min_length:
        return ValidationResult(
            False,
            f"回應過短: {length} < {min_length}",
            details={"length": length}
        )

    if length > max_length:
        return ValidationResult(
            False,
            f"回應過長: {length} > {max_length}",
            details={"length": length}
        )

    return ValidationResult(
        True,
        f"回應長度 {length} 在 [{min_length}, {max_length}] 範圍內",
        details={"length": length}
    )


def extract_kubectl_from_text(text: str) -> str | None:
    """
    從文字中提取 kubectl 命令

    Args:
        text: 包含 kubectl 的文字

    Returns:
        提取的 kubectl 命令或 None
    """
    if not text:
        return None

    # 匹配 kubectl 開頭的整行
    patterns = [
        r"```(?:bash|shell|sh)?\s*(kubectl[^\n`]+)",  # code block
        r"^\$?\s*(kubectl[^\n]+)",  # 以 $ 或直接開頭
        r"(kubectl\s+\S+(?:\s+\S+)*)",  # 一般匹配
    ]

    for pattern in patterns:
        match = re.search(pattern, text, re.MULTILINE | re.IGNORECASE)
        if match:
            return match.group(1).strip()

    return None


def extract_risk_level_from_text(text: str) -> str | None:
    """
    從文字中提取風險等級

    Args:
        text: 包含風險等級的文字

    Returns:
        提取的風險等級或 None
    """
    if not text:
        return None

    # 直接匹配
    for level in ["CRITICAL", "HIGH", "MEDIUM", "LOW"]:
        if level in text.upper():
            return level

    # 中文匹配
    chinese_mappings = [
        (r"極高|危險|嚴重|緊急", "CRITICAL"),
        (r"高風險|高度", "HIGH"),
        (r"中等|中度|一般", "MEDIUM"),
        (r"低風險|低度|輕微", "LOW"),
    ]

    for pattern, level in chinese_mappings:
        if re.search(pattern, text):
            return level

    return None