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shy3130
7062dcd94d
LLM 常给 META 加类型注解 (ast.AnnAssign 节点), 旧版 _find_meta_dict 只遍历 ast.Assign 漏掉注解形式 → 抛「找不到 META 字典」→ 上层包成 「规范化 META 失败」。校验 (_extract_meta) 与规范化 (_find_meta_dict) 用两套不一致逻辑, 导致「校验通过、规范化失败」。 两个函数统一增加 ast.AnnAssign 分支, 消除不一致。补充 4 个回归测试 覆盖注解形式 (含端到端 _normalize_build_result 路径)。 修复后无论哪台机器、哪个模型生成都不再触发该错误。
141 lines
4.1 KiB
Python
141 lines
4.1 KiB
Python
"""AI 策略 META 规范化回归测试。"""
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from __future__ import annotations
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from app.api.strategy import _normalize_build_result, _normalize_strategy_meta
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RAW_CODE = '''"""模型返回的策略"""
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import polars as pl
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META = {
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"id": "custom_wrong_id",
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"name": "English Placeholder",
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"description": "model desc",
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"tags": ["AI"],
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"params": [],
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"scoring": {},
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}
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ENTRY_SIGNALS = []
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EXIT_SIGNALS = []
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STOP_LOSS = -0.05
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MAX_HOLD_DAYS = 20
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ALERTS = []
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def filter(df: pl.DataFrame, params: dict) -> pl.Expr:
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return pl.lit(True)
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'''
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def test_normalize_strategy_meta_forces_ai_id_and_chinese_name():
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code = _normalize_strategy_meta(
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RAW_CODE,
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"ai_test123",
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name="断板反包",
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description="中文描述",
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)
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assert '"id": "ai_test123"' in code
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assert '"name": "断板反包"' in code
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assert '"description": "中文描述"' in code
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assert "custom_wrong_id" not in code
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assert "English Placeholder" not in code
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def test_normalize_build_result_updates_code_and_meta():
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result = {"code": RAW_CODE, "meta": {}, "valid": True, "error": None}
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normalized = _normalize_build_result(
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result,
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"ai_from_frontend",
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name="中文策略名",
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description="前端描述",
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)
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assert normalized["valid"] is True
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assert normalized["meta"]["id"] == "ai_from_frontend"
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assert normalized["meta"]["name"] == "中文策略名"
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assert normalized["meta"]["description"] == "前端描述"
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assert '"id": "ai_from_frontend"' in normalized["code"]
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def test_normalize_strategy_meta_inserts_missing_name_fields():
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raw = '''import polars as pl
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META = {
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"id": "wrong",
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"tags": []
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}
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def filter(df: pl.DataFrame, params: dict) -> pl.Expr:
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return pl.lit(True)
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'''
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code = _normalize_strategy_meta(raw, "ai_inserted", name="中文名", description="描述")
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compile(code, "<strategy>", "exec")
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assert '"id": "ai_inserted"' in code
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assert '"name": "中文名"' in code
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assert '"description": "描述"' in code
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# --- 回归: LLM 偏移写法 -------------------------------------------------
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# 模型常给 META 加类型注解 (META: dict = {...}, ast.AnnAssign 节点)。
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# 旧版匹配器只遍历 ast.Assign, 漏掉注解形式 → 报「找不到 META 字典」。
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ANNOTATED_CODE = '''"""模型返回的策略 (带类型注解的 META — LLM 常见偏移)"""
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import polars as pl
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META: dict = {
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"id": "annotated_wrong_id",
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"name": "Placeholder",
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"description": "model desc",
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"tags": ["AI"],
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"params": [],
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"scoring": {},
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}
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def filter(df: pl.DataFrame, params: dict) -> pl.Expr:
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return pl.lit(True)
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'''
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def test_find_meta_dict_accepts_type_annotated_form():
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"""META: dict = {...} 必须能被识别 (旧版会抛「找不到 META 字典」)。"""
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from app.api.strategy import _find_meta_dict
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node = _find_meta_dict(ANNOTATED_CODE)
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assert node is not None # 能找到就说明没抛异常
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def test_extract_meta_accepts_type_annotated_form():
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from app.strategy.ai_generator import AIStrategyGenerator
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meta = AIStrategyGenerator._extract_meta(ANNOTATED_CODE)
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assert meta["id"] == "annotated_wrong_id"
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assert meta["name"] == "Placeholder"
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def test_normalize_strategy_meta_works_on_annotated_form():
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"""端到端: AI 生成注解形式 META 时, 规范化不再报「规范化 META 失败」。"""
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code = _normalize_strategy_meta(
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ANNOTATED_CODE,
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"ai_annotated_ok",
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name="断板反包",
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description="中文描述",
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)
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assert '"id": "ai_annotated_ok"' in code
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assert '"name": "断板反包"' in code
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assert '"description": "中文描述"' in code
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assert "annotated_wrong_id" not in code
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def test_normalize_build_result_succeeds_on_annotated_form():
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"""模拟前端 /build/stream 的完整结果路径 (之前报错的入口)。"""
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result = {"code": ANNOTATED_CODE, "meta": {}, "valid": True, "error": None}
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normalized = _normalize_build_result(result, "ai_build_ok")
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assert normalized["valid"] is True
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assert normalized["error"] is None
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assert normalized["meta"]["id"] == "ai_build_ok"
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