316 lines
11 KiB
Python
316 lines
11 KiB
Python
"""Smoke test for the AI context builder helpers in ai_consult.py.
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This avoids importing PySide6-bound modules by extracting only the pure
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helper functions we want to verify.
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"""
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from __future__ import annotations
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import ast
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import os
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import sys
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AI_CONSULT_PATH = os.path.join(
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os.path.dirname(__file__), "..", "src", "doctor_workstation", "ui", "dialogs",
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"ai_consult.py",
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)
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def _make_helpers() -> dict[str, object]:
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"""Pull the pure helpers out of ai_consult.py without importing PySide6."""
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with open(AI_CONSULT_PATH, encoding="utf-8") as stream:
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source = stream.read()
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tree = ast.parse(source)
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wanted_names = {
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"AI_CONTEXT_MAX_CHARS",
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"AI_PROMPT_LIMIT",
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"AI_CONTEXT_SEPARATOR",
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"_truncate_for_context",
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"_patient_context_blood_sugar",
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"_patient_context_tongue",
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"_patient_context_reports",
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"_patient_context_prescriptions",
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"_patient_context_videos",
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"build_patient_ai_context",
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"_compose_ai_prompt",
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}
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selected: list[ast.stmt] = []
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for node in tree.body:
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if isinstance(node, ast.FunctionDef) and node.name in wanted_names:
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selected.append(node)
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continue
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if isinstance(node, ast.Assign):
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for target in node.targets:
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if isinstance(target, ast.Name) and target.id in wanted_names:
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selected.append(node)
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break
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namespace: dict[str, object] = {}
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def _as_mapping(value: object) -> dict[str, object]:
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if isinstance(value, dict):
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return dict(value)
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raw = getattr(value, "raw", None)
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return dict(raw) if isinstance(raw, dict) else {}
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def first_value(value: object, *keys: str, default: object = None) -> object:
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"""Return the first present, non-empty value from ``keys``.
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Mirrors ``widgets.first_value``:
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``first_value(mapping, "k1", "k2", default=...)``.
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"""
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for key in keys:
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if not isinstance(value, dict):
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break
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if key in value and value[key] not in (None, "", "—"):
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return value[key]
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return default
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def get_value(source: object, key: str, default: object = None) -> object:
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if isinstance(source, dict) and key in source:
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return source[key]
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return default
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def _human_value(value: object, *, empty: str = "未记录") -> str:
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if value in (None, "", "—"):
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return empty
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if isinstance(value, str):
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return value.strip() or empty
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if isinstance(value, bool):
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return "是" if value else "否"
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if isinstance(value, dict):
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parts = []
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for key, nested in value.items():
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rendered = _human_value(nested, empty="")
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if rendered:
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parts.append(f"{key}：{rendered}")
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return "；".join(parts) or empty
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if isinstance(value, list):
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parts = [_human_value(item, empty="") for item in value]
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return "、".join(part for part in parts if part) or empty
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return str(value).strip() or empty
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def display_text(value: object, *, default: str = "") -> str:
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if value in (None, "", "—"):
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return default
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return str(value).strip() or default
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def _exact_positive_id(value: object, expected: int) -> bool:
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if value in (None, ""):
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return False
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try:
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return int(value) == expected
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except (TypeError, ValueError):
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return False
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# Provide fallback names for ``collections.abc`` symbols referenced by
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# the helpers without forcing the real module imports on this stub box.
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import collections.abc as _abc
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Sequence = _abc.Sequence # type: ignore[attr-defined]
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Mapping = _abc.Mapping # type: ignore[attr-defined]
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Any = object # type: ignore[assignment]
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namespace.update(
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{
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"_as_mapping": _as_mapping,
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"first_value": first_value,
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"get_value": get_value,
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"_human_value": _human_value,
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"display_text": display_text,
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"_exact_positive_id": _exact_positive_id,
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"Sequence": Sequence,
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"Mapping": Mapping,
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"Any": Any,
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}
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)
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module_ast = ast.Module(body=selected, type_ignores=[])
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ast.fix_missing_locations(module_ast)
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exec(compile(module_ast, AI_CONSULT_PATH, "exec"), namespace)
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return namespace
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def main() -> None:
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helpers = _make_helpers()
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AI_CONTEXT_MAX_CHARS = helpers["AI_CONTEXT_MAX_CHARS"]
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AI_PROMPT_LIMIT = helpers["AI_PROMPT_LIMIT"]
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_truncate_for_context = helpers["_truncate_for_context"]
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_patient_context_blood_sugar = helpers["_patient_context_blood_sugar"]
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_patient_context_tongue = helpers["_patient_context_tongue"]
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_patient_context_reports = helpers["_patient_context_reports"]
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_patient_context_videos = helpers["_patient_context_videos"]
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build_patient_ai_context = helpers["build_patient_ai_context"]
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_compose_ai_prompt = helpers["_compose_ai_prompt"]
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def fail(message: str) -> None:
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raise AssertionError(message)
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def assertEqual(actual: object, expected: object, message: str) -> None:
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if actual != expected:
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fail(f"{message}: expected {expected!r}, got {actual!r}")
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def assertContains(container: object, needle: str, message: str) -> None:
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if not isinstance(container, str) or needle not in container:
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fail(f"{message}: {needle!r} missing in output")
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# 1. _truncate_for_context
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short = _truncate_for_context("hello", max_chars=10)
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assertEqual(short, "hello", "short text should pass through unchanged")
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long_text = _truncate_for_context(
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"诊断：血糖偏高，建议调整饮食结构，配合运动每周三次以上。",
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max_chars=12,
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)
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assertContains(long_text, "…", "long text should end with ellipsis")
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assertEqual(len(long_text), 12, "truncated text should respect max_chars")
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# 2. _patient_context_tongue
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tongue = _patient_context_tongue(
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{
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"diagnosis": {
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"tongue": "舌红苔黄腻",
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"tongue_coating": "黄腻",
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"pulse": "弦滑",
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"tongue_images": ["url1", "url2", "url3"],
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}
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}
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)
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assertContains(tongue, "舌红苔黄腻", "tongue text missing")
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assertContains(tongue, "弦滑", "pulse text missing")
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assertContains(tongue, "舌苔图片 3 张", "tongue image count missing")
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# 3. _patient_context_blood_sugar
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blood_sugar = _patient_context_blood_sugar(
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{"diagnosis": {"fasting_blood_sugar": "7.8"}},
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{
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"blood_sugar": {
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"entries": [
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{"date": "2026-08-15", "value": "6.2", "period": "空腹"},
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{"date": "2026-08-14", "value": "9.1", "period": "餐后"},
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]
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}
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},
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)
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assertContains(blood_sugar, "7.8", "fasting reading missing")
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assertContains(blood_sugar, "每日血糖", "tracking summary missing")
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# 4. _patient_context_videos: filters by current diagnosis id.
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videos = _patient_context_videos(
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[
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{
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"diagnosis_id": 99,
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"transcript_text": "其他诊单",
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"start_time_text": "今天",
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},
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{
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"diagnosis_id": 501,
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"transcript_text": "医生：请问您最近睡眠如何；患者：经常失眠。",
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"start_time_text": "2026-08-15 10:30",
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},
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],
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diagnosis_id=501,
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)
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assertContains(videos, "医生", "transcript text missing")
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assertContains(videos, "2026-08-15", "transcript timestamp missing")
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# 5. _patient_context_reports
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reports = _patient_context_reports(
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{
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"summary": "近期血糖偏高",
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"diagnosis_advice": "建议控制饮食",
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"risk_assessment": ["心血管风险升高", "肾功负担加重"],
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}
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)
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assertContains(reports, "既往AI摘要", "summary missing")
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assertContains(reports, "诊断建议", "advice missing")
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assertContains(reports, "心血管风险", "risk bullet missing")
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# 6. build_patient_ai_context: full envelope assembly.
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detail = {
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"diagnosis": {
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"tongue": "舌淡苔白",
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"pulse": "细弱",
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"fasting_blood_sugar": "8.0",
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},
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"tongue_images": ["a", "b"],
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}
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tracking = {
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"blood_sugar": {
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"entries": [
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{"date": "2026-08-19", "value": "6.1", "period": "空腹"},
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]
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}
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}
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analysis = {
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"summary": "控制尚可",
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"diagnosis_advice": "调整饮食",
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"risk_assessment": ["肾功"],
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}
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prescriptions = [
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{"prescription_name": "六味地黄丸", "prescription_remark": "调理方"},
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]
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call_records = [
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{
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"diagnosis_id": 501,
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"transcript_text": "对话内容：患者表述近期乏力。",
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"start_time_text": "2026-08-18 14:00",
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}
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]
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context_text, present = build_patient_ai_context(
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detail=detail,
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tracking=tracking,
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analysis=analysis,
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prescriptions=prescriptions,
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call_records=call_records,
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diagnosis_id=501,
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)
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for label in (
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"每日血糖",
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"舌苔/脉象",
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"视频问诊文字",
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"历史AI报告",
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"处方记录",
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):
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assertContains(context_text, label, f"section {label} missing in envelope")
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if label not in present:
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fail(f"label {label} not in present labels")
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if len(context_text) > AI_CONTEXT_MAX_CHARS + 12:
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fail(f"context exceeds {AI_CONTEXT_MAX_CHARS} chars: {len(context_text)}")
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# 7. _compose_ai_prompt: short answer stays untouched.
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short_prompt = _compose_ai_prompt("血糖如何？", context_text)
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assertContains(short_prompt, context_text, "short prompt loses context")
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assertContains(short_prompt, "血糖如何？", "short prompt loses question")
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if len(short_prompt) > AI_PROMPT_LIMIT:
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fail("short prompt exceeds limit")
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# 8. Long answer is truncated with ellipsis.
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long_question = (
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"请结合患者既往糖尿病史、家族史以及服用的多种药物，给出一份详尽的"
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"个性化治疗方案，并解释每一步的理由，最终输出一份结构化报告，"
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"包括风险评估、用药合理性、并发症筛查和分级随访计划。"
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) * 6
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long_prompt = _compose_ai_prompt(long_question, context_text)
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if len(long_prompt) > AI_PROMPT_LIMIT:
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fail(f"long prompt exceeds limit: {len(long_prompt)}")
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if context_text not in long_prompt:
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fail("long prompt loses context")
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assertContains(long_prompt, "…", "long prompt should end with ellipsis")
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# 9. Empty context returns bare question.
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bare = _compose_ai_prompt("血糖？", "")
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assertEqual(bare, "血糖？", "empty context should drop the envelope entirely")
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# 10. Empty question returns empty string.
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empty = _compose_ai_prompt("", context_text)
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assertEqual(empty, "", "empty question returns empty string")
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print("OK: all AI context helper assertions passed")
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if __name__ == "__main__":
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main()
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