perf(consultation): 健康探测加 TTL、yoga 表达式只编译一次
BUG-734: probe_official_rest_health 不是 ping —— 它把一份虚构 smoke 排盘 POST 给官方 HoroscopePredictions（实测单次 1,034 ms），还经 call() 吃掉对方 每分钟 5 个限流令牌之一，而结论零 TTL。连续 6 轮前台请求下，第 6 次探测会被 本地限流器挡下、反过来报出假的 official_blocked，真正的业务调用还要和它抢令牌。 加进程级 TTL 缓存（成功 60 s / 失败 10 s，配置变即失效，缓存键只记 key 配没配 的布尔）。force_refresh 默认 True：诊断端点调的是裸 gateway_status() 且该文件 本轮不得改，默认实时才不会让运维看到旧结论；run_gateway_packet 显式走 False。 探测在锁外执行。6 轮实测：探测 6→1、握手 5→1、限流令牌 6→1。 连接复用（任务书 6.2）按让步顺序砍掉：实测 urllib.request 没有连接池，模块级 共享 opener 在 6 次请求下仍开 6 条 TCP 连接，按任务书写法只能骗过「同一个 opener 实例」的断言而省不掉任何握手。另立单。 BUG-735: yoga_engine._eval_custom 对静态规则表的表达式每请求重新编译。实测每次 检测 386 次源码编译（182 次 eval 字符串 + 204 次 ast.parse/compile）。拆出 _build_custom_exec_globals（每次新建，绑定当前盘 ctx）与 lru_cache 的 _compiled_eval_code / _compiled_exec_code，只缓存 code object，绝不缓存求值 结果。求值语义逐条对齐改前。第二次检测起编译 0 次，单次 27.4 ms → 3.3 ms。 等价用同进程差分（BUG-733 的做法）：192 条去重表达式 × 3 张公开虚构示例盘， 与逐字复刻的改前实现同值，另有反向验证证明差分非恒真。 未改 scripts/jyotish_api_server.py。既有断言一条未改。新增 23 条测试。 快速门唯一红的 test_chat_page_uses_authenticated_cloud_persistence 经干净 origin/staging 检出复跑确认为基线红，记入 BLOCKED.md BLK-002，不顺手修。 Co-Authored-By: Claude Opus 5 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01JUei7K13cYxLHE3Axe4A45
This commit is contained in:
Jesse_Chen
co-authored by
Claude Opus 5
Claude Opus 5
parent
10afdcddeb
commit
63c419f375
@@ -6,6 +6,8 @@ from __future__ import annotations
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import hashlib
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import json
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import os
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import threading
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import time
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from datetime import datetime, timezone
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from pathlib import Path
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from types import SimpleNamespace
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@@ -240,7 +242,53 @@ def run_gateway_job(job_id: str) -> dict[str, Any] | None:
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return complete_gateway_job(job_id, result)
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def probe_official_rest_health() -> dict[str, Any]:
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# ---------------------------------------------------------------------------
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# BUG-734: 健康探测的 TTL 缓存
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# ---------------------------------------------------------------------------
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# `probe_official_rest_health` 不是 ping：它 POST 一份虚构 smoke 排盘到官方
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# HoroscopePredictions，用返回的 Status 当健康信号，并且会消耗对方每分钟 5 个的
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# 限流令牌之一。它问的是「这个外部服务此刻可用吗」——这种状态不会每 500 毫秒变一次，
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# 却被每一轮前台请求重新问一遍（本机实测每次约 0.5 秒）。
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#
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# 成功 60 秒：覆盖一串连续问答，又不超过对方限流窗口的长度，
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# 「已恢复」被压住的时间不会超过一个限流窗口。
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# 失败 10 秒：对方恢复后我们最多瞎等 10 秒。必须显著短于成功 TTL（否则服务恢复了
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# 我们还要再等一分钟），这是任务书 §5.4 的红线。
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_REST_HEALTH_SUCCESS_TTL_SECONDS = 60.0
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_REST_HEALTH_FAILURE_TTL_SECONDS = 10.0
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_REST_HEALTH_LOCK = threading.Lock()
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_REST_HEALTH_CACHE: dict[str, Any] = {}
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def _monotonic() -> float:
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"""单调时钟；测试把它推过 TTL。用 monotonic 而非墙钟，避免 NTP 校时影响过期判断。"""
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return time.monotonic()
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def _rest_health_cache_key() -> tuple[Any, ...]:
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"""影响探测结论的有效配置。配置一变立刻失效。
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只记 ``VEDASTRO_API_KEY`` 是否配置的布尔值，绝不把 key 本身放进键（AGENTS §8）。
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"""
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config = build_gateway_config()
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return (
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config["mode"],
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os.environ.get("VEDASTRO_API_ENDPOINT", OFFICIAL_ENDPOINT).strip(),
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os.environ.get("VEDASTRO_SELF_HOST_ENDPOINT", "").strip(),
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_official_network_enabled(),
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bool(os.environ.get("VEDASTRO_API_KEY", "").strip()),
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os.environ.get("JYOTISH_SKIP_LOCAL_ENV", "").strip().lower(),
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)
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def reset_rest_health_cache() -> None:
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"""清空探测缓存。测试与运维用；生产路径不调用。"""
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with _REST_HEALTH_LOCK:
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_REST_HEALTH_CACHE.clear()
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def _probe_official_rest_health_uncached() -> dict[str, Any]:
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"""Health of the default official path: REST ``/api/Calculate`` Status Pass."""
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base = {
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"transport": "rest",
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@@ -260,12 +308,44 @@ def probe_official_rest_health() -> dict[str, Any]:
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return {**base, **result}
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def gateway_status() -> dict[str, Any]:
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def probe_official_rest_health(*, force_refresh: bool = True) -> dict[str, Any]:
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"""Health of the default official path.
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``force_refresh=True``（默认）永远打真探测——诊断端点靠的就是这个，
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运维不得看到一个 60 秒前的假象。前台每轮都走的路径显式传 ``force_refresh=False``
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去读 TTL 缓存。
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"""
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if force_refresh:
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return _probe_official_rest_health_uncached()
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key = _rest_health_cache_key()
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with _REST_HEALTH_LOCK:
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entry = _REST_HEALTH_CACHE.get("entry")
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if entry is not None and entry["key"] == key and entry["expires_at"] > _monotonic():
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return dict(entry["result"])
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# 探测本身不持锁：不得在等外网时把进程级锁按住（BUG-718 的教训）。
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result = _probe_official_rest_health_uncached()
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ttl = (
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_REST_HEALTH_SUCCESS_TTL_SECONDS
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if result.get("status") == "official_verified"
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else _REST_HEALTH_FAILURE_TTL_SECONDS
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)
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with _REST_HEALTH_LOCK:
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_REST_HEALTH_CACHE["entry"] = {
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"key": key,
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"expires_at": _monotonic() + ttl,
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"result": dict(result),
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}
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return result
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def gateway_status(*, force_refresh: bool = True) -> dict[str, Any]:
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from scripts.diagnose_vedastro_mode import build_report as build_vedastro_mode_report
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readiness = build_vedastro_mode_report()
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config = build_gateway_config()
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rest_health = probe_official_rest_health()
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rest_health = probe_official_rest_health(force_refresh=force_refresh)
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return {
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"scope": "vedastro_gateway",
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"mode": config["mode"],
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@@ -387,7 +467,10 @@ def run_gateway_packet(
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) -> dict[str, Any]:
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from scripts.vedastro_user_entrypoint import build_report
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gateway = gateway_status()
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# BUG-734: 这是每一轮前台请求都走的路。健康探测读 TTL 缓存，
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# 不再每轮拿一个对方的限流令牌去做一次完整业务调用。
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# 诊断端点仍走默认的 force_refresh=True。
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gateway = gateway_status(force_refresh=False)
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args = _entrypoint_args(case, question, themes, reference_date)
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report = build_report(args)
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catalog = report.get("official_capability_catalog") or {}
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+107
-51
@@ -11,6 +11,7 @@ Yoga 规则引擎 v1.0 (数据驱动架构)
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"""
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import ast
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import functools
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import json
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import os
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from typing import Dict, List, Any, Optional
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@@ -544,6 +545,94 @@ class YogaContext:
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return lst[0] if lst else None
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# ============================================================================
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# custom 规则表达式的编译缓存（BUG-735）
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# ============================================================================
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# `expr` 只来自仓库内的静态规则表 references/yoga_rules.json，进程生命周期内不变，
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# 改前却每个请求重新编译一遍（本机实测 386 次／次检测：182 次 eval 字符串编译 +
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# 204 次 ast.parse/compile）。这里只缓存 code object。
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#
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# 硬约束：求值命名空间 exec_globals 依赖每次不同的盘上下文（ctx），必须每次新建；
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# 绝不缓存求值结果——缓存结果会让所有人拿到同一张盘的 yoga 判定。
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#
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# maxsize 512：规则表去重后 192 条表达式，留出增长余量，同时给缓存一个硬上界。
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_CUSTOM_EXPR_CACHE_MAXSIZE = 512
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def _capture_tail_expr(block):
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"""把代码块末尾表达式改写为 __result__ = <expr>；递归处理 if/for/while 分支。"""
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if not block:
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return block
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last = block[-1]
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if isinstance(last, ast.Expr):
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block[-1] = ast.Assign(targets=[ast.Name(id="__result__", ctx=ast.Store())], value=last.value)
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elif isinstance(last, ast.If):
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last.body = _capture_tail_expr(last.body)
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last.orelse = _capture_tail_expr(last.orelse)
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elif isinstance(last, (ast.For, ast.While)):
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last.body = _capture_tail_expr(last.body)
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last.orelse = _capture_tail_expr(last.orelse)
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elif isinstance(last, ast.Try):
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last.body = _capture_tail_expr(last.body)
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last.orelse = _capture_tail_expr(last.orelse)
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last.finalbody = _capture_tail_expr(last.finalbody)
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for handler in last.handlers:
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handler.body = _capture_tail_expr(handler.body)
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return block
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@functools.lru_cache(maxsize=_CUSTOM_EXPR_CACHE_MAXSIZE)
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def _compiled_eval_code(expr):
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"""表达式模式的 code object；不是单个 expression 时返回 None（改前由 SyntaxError 表达）。"""
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try:
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return compile(expr, "<string>", "eval")
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except SyntaxError:
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return None
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@functools.lru_cache(maxsize=_CUSTOM_EXPR_CACHE_MAXSIZE)
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def _compiled_exec_code(expr):
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"""多语句模式的 code object：先按 AST 捕获末尾表达式，再编译。
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v6.0.32: 支持多行语句（if/else/for 等）+ 末尾表达式求值模式。
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不能简单 split 最后一行：多行 if/else 的最后一行经常只是 else 分支内部表达式，
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直接 exec 前半段会产生缩进不完整。这里用 AST 捕获“实际执行分支”的末尾表达式。
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"""
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try:
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tree = ast.parse(expr.strip(), mode="exec")
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tree.body = _capture_tail_expr(tree.body)
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ast.fix_missing_locations(tree)
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return compile(tree, "<yoga_custom>", "exec")
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except Exception:
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return None
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def _run_custom_expr(expr, exec_globals):
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"""按改前语义求值 custom 表达式：先表达式模式，SyntaxError 退到多语句 exec 模式。
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只有 code object 来自缓存；`exec_globals` 由调用方每次新建。
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"""
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result = None
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code = _compiled_eval_code(expr)
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if code is not None:
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try:
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# 同一命名空间同时作为 globals/locals，保证 list/dict comprehension 能访问 BENEFICS 等名称。
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result = eval(code, exec_globals, exec_globals)
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except SyntaxError:
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code = None # 运行期 SyntaxError：与改前一样退到多语句分支
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except Exception:
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return None
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if code is None:
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exec_code = _compiled_exec_code(expr)
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if exec_code is not None:
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try:
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exec(exec_code, exec_globals, exec_globals)
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result = exec_globals.get("__result__")
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except Exception:
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return None
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return result
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# ============================================================================
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# YogaEngine: 规则加载与检测
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# ============================================================================
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@@ -1238,6 +1327,23 @@ class YogaEngine:
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if not expr:
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return []
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# BUG-735: exec_globals 每次新建（依赖当前盘 ctx），只有 code object 走进程级缓存。
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exec_globals = self._build_custom_exec_globals(ctx, rule, bindings)
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result = _run_custom_expr(expr, exec_globals)
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if result:
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combo = cond.get("combo_template", "自定义条件满足")
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if callable(combo):
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combo = combo(ctx, bindings)
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strength = cond.get("strength", rule.get("strength", "中"))
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if callable(strength):
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strength = strength(ctx, bindings)
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return [{"combination": combo, "strength": strength}]
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return []
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def _build_custom_exec_globals(self, ctx, rule, bindings):
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"""构造 custom 表达式的求值命名空间。每次调用都新建，绑定当前这张盘的 ctx。"""
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def house_of(p): return ctx.house_of(p)
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def sign_of(p): return ctx.sign_of(p)
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def deg_of(p): return ctx.degree_of(p)
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@@ -1542,57 +1648,7 @@ class YogaEngine:
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"kendra_lords_list": _kendra_lords_list,
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"trikona_lords_list": _trikona_lords_list,
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}
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# v6.0.32: 支持多行语句（if/else/for等）+ 末尾表达式求值模式。
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# 不能简单 split 最后一行：多行 if/else 的最后一行经常只是 else 分支内部表达式，
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# 直接 exec 前半段会产生缩进不完整。这里用 AST 捕获“实际执行分支”的末尾表达式。
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result = None
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exec_globals = {"__builtins__": {}, **safe_globals}
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def _capture_tail_expr(block):
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"""把代码块末尾表达式改写为 __result__ = <expr>；递归处理 if/for/while 分支。"""
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if not block:
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return block
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last = block[-1]
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if isinstance(last, ast.Expr):
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block[-1] = ast.Assign(targets=[ast.Name(id="__result__", ctx=ast.Store())], value=last.value)
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elif isinstance(last, ast.If):
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last.body = _capture_tail_expr(last.body)
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last.orelse = _capture_tail_expr(last.orelse)
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elif isinstance(last, (ast.For, ast.While)):
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last.body = _capture_tail_expr(last.body)
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last.orelse = _capture_tail_expr(last.orelse)
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elif isinstance(last, ast.Try):
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last.body = _capture_tail_expr(last.body)
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last.orelse = _capture_tail_expr(last.orelse)
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last.finalbody = _capture_tail_expr(last.finalbody)
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for handler in last.handlers:
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handler.body = _capture_tail_expr(handler.body)
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return block
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try:
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# 同一命名空间同时作为 globals/locals，保证 list/dict comprehension 能访问 BENEFICS 等名称。
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result = eval(expr, exec_globals, exec_globals)
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except SyntaxError:
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try:
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tree = ast.parse(expr.strip(), mode="exec")
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tree.body = _capture_tail_expr(tree.body)
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ast.fix_missing_locations(tree)
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exec(compile(tree, "<yoga_custom>", "exec"), exec_globals, exec_globals)
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result = exec_globals.get("__result__")
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except Exception:
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pass
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except Exception:
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pass
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if result:
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combo = cond.get("combo_template", "自定义条件满足")
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if callable(combo):
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combo = combo(ctx, bindings)
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strength = cond.get("strength", rule.get("strength", "中"))
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if callable(strength):
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strength = strength(ctx, bindings)
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return [{"combination": combo, "strength": strength}]
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return []
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return {"__builtins__": {}, **safe_globals}
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def _eval_semantic_condition(self, ctype, cond, ctx, rule, bindings):
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"""兼容批量抽取规则中的语义型条件。返回保守布尔匹配。"""
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