Files
cad-agent/dwg_review/config.py
T
gjm 1cbcbb92f4 perf(review): 安装 Pillow 并优化截图下采样策略
- 视觉 token 成本由像素尺寸决定,故仅在长边超过 IMAGE_MAX_EDGE(默认1280) 时才缩放
- 不需要缩放且格式未变时原样返回,不重新编码,避免 CAD 线稿的无谓压缩损失
- 缩放后默认输出无损 PNG(IMAGE_FORMAT=png),可设 jpeg 换取更小体积
- 缺少 Pillow 时自动降级为原图直传
2026-10-13 23:48:46 +08:00

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"""集中配置:全部来自环境变量,禁止硬编码密钥。
用法:所有模块统一 `from . import config` 后读取 config.XXX。
"""
import os
from pathlib import Path
# 仓库根目录(本文件位于 <root>/dwg_review/config.py)
PROJECT_ROOT = Path(__file__).resolve().parent.parent
def _resolve_path(value: str) -> Path:
"""相对路径按仓库根目录解析(避免依赖当前工作目录)。"""
p = Path(value)
return p if p.is_absolute() else (PROJECT_ROOT / p)
def _read_env(name: str) -> str:
"""读取环境变量。
Windows 下新设置的系统/用户环境变量不会传播到**已启动**的进程,
因此当前进程读不到时,回退到注册表中的 Machine / User 级环境变量。
"""
value = os.environ.get(name)
if value:
return value
if os.name != "nt":
return ""
try:
import winreg
except ImportError: # pragma: no cover
return ""
locations = (
(winreg.HKEY_LOCAL_MACHINE,
r"SYSTEM\CurrentControlSet\Control\Session Manager\Environment"),
(winreg.HKEY_CURRENT_USER, r"Environment"),
)
for hive, subkey in locations:
try:
with winreg.OpenKey(hive, subkey) as key:
value, _ = winreg.QueryValueEx(key, name)
if value:
return str(value)
except OSError:
continue
return ""
# ---------------------------------------------------------------------------
# CAD MCP 基座(TCP JSON-RPC)
# ---------------------------------------------------------------------------
HOST = os.environ.get("CAD_MCP_HOST", "127.0.0.1")
PORT = int(os.environ.get("CAD_MCP_PORT", "8080"))
# 单次 TCP 调用超时(秒)
CALL_TIMEOUT = float(os.environ.get("CAD_MCP_TIMEOUT", "30"))
# 连接/超时类错误的重试次数(写类工具不重试)
CALL_RETRIES = int(os.environ.get("CAD_MCP_RETRIES", "2"))
# 帧大小上限(与基座 8MB 上限对齐,留余量)
MAX_FRAME_BYTES = int(os.environ.get("MAX_FRAME_BYTES", str(4 * 1024 * 1024)))
# ---------------------------------------------------------------------------
# LLM(默认 DeepSeek,视觉模型 deepseek-flash)
# ---------------------------------------------------------------------------
API_KEY = _read_env("DEEPSEEK_API_KEY")
BASE_URL = os.environ.get("DEEPSEEK_BASE_URL", "https://api.deepseek.com")
MODEL_NAME = os.environ.get("DEEPSEEK_MODEL", "deepseek-flash")
LLM_TIMEOUT = float(os.environ.get("LLM_TIMEOUT", "120"))
LLM_RETRIES = int(os.environ.get("LLM_RETRIES", "2"))
# ---------------------------------------------------------------------------
# 审查流程
# ---------------------------------------------------------------------------
# 审查清单路径
CHECKLIST_PATH = _resolve_path(os.environ.get("REVIEW_CHECKLIST", "review_checklist.json"))
# 最多审查多少个图框(0 = 全部)
MAX_FRAMES = int(os.environ.get("MAX_FRAMES", "0"))
# 每个图框取数据的条数上限
FRAME_ENTITY_LIMIT = int(os.environ.get("FRAME_ENTITY_LIMIT", "200"))
# 缩放后等待 CAD 渲染的秒数
RENDER_WAIT = float(os.environ.get("RENDER_WAIT", "0.6"))
# 审查阶段受控工具子循环的最大回合数
REVIEW_MAX_ITERS = int(os.environ.get("REVIEW_MAX_ITERS", "4"))
# 报告输出目录
REPORT_DIR = _resolve_path(os.environ.get("REPORT_DIR", "reports"))
# 批注回写使用的图层
ANNOTATE_LAYER = os.environ.get("ANNOTATE_LAYER", "AI_REVIEW")
# ---------------------------------------------------------------------------
# 截图(视觉 token 成本控制)
# ---------------------------------------------------------------------------
# 长边上限(超过才缩放),0 表示不缩放
IMAGE_MAX_EDGE = int(os.environ.get("IMAGE_MAX_EDGE", "1280"))
# 缩放后的输出格式:png(默认,无损,适合 CAD 线稿)或 jpeg(体积更小)
IMAGE_FORMAT = os.environ.get("IMAGE_FORMAT", "png").strip().lower()
# JPEG 质量(仅 IMAGE_FORMAT=jpeg 时生效)
IMAGE_JPEG_QUALITY = int(os.environ.get("IMAGE_JPEG_QUALITY", "80"))
def has_api_key() -> bool:
return bool(API_KEY.strip())