chore: initialize medical consultation agent demo
This commit is contained in:
刘金宝
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"""后端脚本包。"""
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@@ -0,0 +1,53 @@
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import asyncio
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import sys
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from pathlib import Path
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sys.path.insert(0, str(Path(__file__).resolve().parents[1]))
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from app.agents.llm_adapter import OpenAICompatibleLLMClient
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from app.agents.patient_agent import PatientAgent
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from app.core.config import settings
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from app.db.session import SessionLocal
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from app.repositories.case_repository import CaseRepository
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async def main() -> None:
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"""本地调试：直接调用 Patient Agent 流式回复，绕过前端和 FastAPI。"""
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client = OpenAICompatibleLLMClient()
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print(f"mock_mode={client.is_mock_mode}")
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print(f"fast_model={settings.llm_fast_model}")
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print(f"fast_thinking={settings.llm_fast_thinking_enabled}")
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print(f"stream_first_token_timeout={settings.llm_stream_first_token_timeout_seconds}")
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print(f"stream_total_timeout={settings.llm_stream_total_timeout_seconds}")
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db = SessionLocal()
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try:
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case = CaseRepository(db).list_active_cases()[0]
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text = ""
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first_token_ms = None
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done_seen = False
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async for chunk in PatientAgent().stream_reply(case, [], "孩子发热几天了？最高体温多少？", "novice"):
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if first_token_ms is None and chunk.first_token_ms is not None:
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first_token_ms = chunk.first_token_ms
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if chunk.done:
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done_seen = True
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print(f"done_seen={done_seen}")
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print(f"first_token_ms={first_token_ms}")
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print(f"total_latency_ms={chunk.total_latency_ms}")
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print(f"model={chunk.model}")
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print(f"fallback_used={chunk.fallback_used}")
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print(f"text_len={len(text)}")
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print(f"text_preview={text[:30]}")
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break
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text += chunk.delta
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if not done_seen:
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print("done_seen=False")
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print(f"text_len={len(text)}")
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print(f"text_preview={text[:30]}")
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raise SystemExit(1)
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finally:
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db.close()
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if __name__ == "__main__":
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asyncio.run(main())
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@@ -0,0 +1,43 @@
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from __future__ import annotations
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import sys
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from pathlib import Path
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from sqlalchemy import inspect, text
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sys.path.insert(0, str(Path(__file__).resolve().parents[1]))
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from app.db.session import SessionLocal
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LEGACY_TABLES = [
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"evaluation_report_exports",
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"evaluation_records",
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"session_submissions",
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"session_orders",
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"session_runtime_messages",
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"training_sessions",
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"case_exam_items",
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"rubric_templates",
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"cases",
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]
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def main() -> None:
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"""旧表清理：在新表链路验证通过后删除不再被业务依赖的旧表。"""
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with SessionLocal() as db:
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existing = set(inspect(db.bind).get_table_names()) if db.bind else set()
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dialect = db.bind.dialect.name if db.bind else ""
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if dialect == "mysql":
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db.execute(text("SET FOREIGN_KEY_CHECKS=0"))
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for table_name in LEGACY_TABLES:
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if table_name in existing:
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db.execute(text(f"DROP TABLE `{table_name}`"))
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print(f"dropped legacy table: {table_name}")
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if dialect == "mysql":
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db.execute(text("SET FOREIGN_KEY_CHECKS=1"))
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db.commit()
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print("legacy table cleanup completed")
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if __name__ == "__main__":
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main()
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@@ -0,0 +1,612 @@
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from __future__ import annotations
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import argparse
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import json
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import re
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import sys
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from dataclasses import dataclass
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from datetime import datetime
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from decimal import Decimal
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from pathlib import Path
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from typing import Any
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from sqlalchemy import delete, inspect, select
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sys.path.insert(0, str(Path(__file__).resolve().parents[1]))
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from app.db.session import SessionLocal
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from app.models.source_case import CaseBase, CaseExamItem, ScoringRule, TeachingCase, TraditionalCase
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SOURCE_TABLES = ("case_base",)
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OPTIONAL_SOURCE_TABLES = ("traditional_case", "teaching_case", "scoring_rule", "case_exam_item")
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DANGEROUS_PATTERNS = (
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r"\bDROP\s+DATABASE\b",
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r"\bDROP\s+TABLE\b",
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r"\bTRUNCATE\b",
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r"\bDELETE\s+FROM\b",
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r"\bCREATE\s+DATABASE\b",
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r"\bALTER\s+TABLE\b",
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)
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JSON_COLUMNS = {
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"case_base": {"symptom_tags", "disease_tags", "competency_tags", "guideline_tags", "knowledge_points", "multimodal_assets"},
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"scoring_rule": {"rubric_json"},
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}
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DATETIME_COLUMNS = {"created_at", "updated_at"}
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DECIMAL_COLUMNS = {"score_weight"}
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INT_COLUMNS = {
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"id",
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"difficulty_score",
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"patient_age",
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"estimated_minutes",
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"vector_status",
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"publish_status",
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"status",
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"created_by_id",
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"department_id",
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"case_id",
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}
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BOOL_COLUMNS = {"osce_enabled", "rag_enabled", "ai_auto_score", "osce_dimension"}
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class ImportValidationError(Exception):
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"""导入校验错误：源 SQL 不满足安全导入要求时中止。"""
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@dataclass
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class ImportReport:
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"""导入报告：记录检查、写入和跳过情况。"""
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source_path: str
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encoding: str
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table_rows: dict[str, int]
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warnings: list[str]
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applied: bool
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upserted_cases: int = 0
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upserted_traditional_cases: int = 0
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upserted_teaching_cases: int = 0
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replaced_scoring_rules: int = 0
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generated_exam_items: int = 0
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def as_dict(self) -> dict[str, Any]:
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"""报告输出：转换为前端和命令行均可读的结构。"""
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return {
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"source_path": self.source_path,
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"encoding": self.encoding,
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"table_rows": self.table_rows,
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"warnings": self.warnings,
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"applied": self.applied,
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"upserted_cases": self.upserted_cases,
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"upserted_traditional_cases": self.upserted_traditional_cases,
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"upserted_teaching_cases": self.upserted_teaching_cases,
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"replaced_scoring_rules": self.replaced_scoring_rules,
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"generated_exam_items": self.generated_exam_items,
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}
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def detect_encoding(path: Path) -> str:
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"""编码识别：根据 BOM 和试读结果判断 SQL 文件编码。"""
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raw = path.read_bytes()
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if raw.startswith(b"\xff\xfe") or raw.startswith(b"\xfe\xff"):
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return "utf-16"
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if raw.startswith(b"\xef\xbb\xbf"):
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return "utf-8-sig"
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for encoding in ("utf-8", "utf-16"):
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try:
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raw.decode(encoding)
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return encoding
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except UnicodeDecodeError:
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continue
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raise ImportValidationError("SQL 文件编码无法识别，请提供 UTF-8 或 UTF-16 文件。")
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def load_sql_text(path: Path) -> tuple[str, str]:
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"""文件读取：读取接口提供的 SQL dump，并返回文本与编码。"""
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if not path.exists():
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raise ImportValidationError(f"SQL 文件不存在：{path}")
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encoding = detect_encoding(path)
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return path.read_text(encoding=encoding), encoding
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def find_dangerous_statements(sql_text: str) -> list[str]:
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"""安全扫描：识别源 dump 中不能在正式库直接执行的 DDL/DML。"""
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hits: list[str] = []
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for pattern in DANGEROUS_PATTERNS:
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if re.search(pattern, sql_text, flags=re.IGNORECASE):
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hits.append(pattern.replace(r"\b", "").replace("\\s+", " "))
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return hits
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def extract_create_columns(sql_text: str, table_name: str) -> list[str]:
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"""字段提取：从 CREATE TABLE 中按源顺序读取字段名。"""
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match = re.search(rf"CREATE TABLE `{re.escape(table_name)}` \((.*?)\) ENGINE=", sql_text, flags=re.S | re.I)
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if not match:
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return []
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columns: list[str] = []
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for line in match.group(1).splitlines():
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stripped = line.strip().rstrip(",")
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if stripped.startswith("`"):
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columns.append(stripped.split("`", 2)[1])
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return columns
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def extract_insert_rows(sql_text: str, table_name: str, columns: list[str]) -> list[dict[str, Any]]:
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"""数据提取：只解析 INSERT VALUES 数据，不执行源 SQL。"""
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rows: list[dict[str, Any]] = []
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pattern = re.compile(rf"INSERT INTO `{re.escape(table_name)}` VALUES\s*(.*?);", flags=re.S | re.I)
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for match in pattern.finditer(sql_text):
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tuples = parse_values_clause(match.group(1))
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for values in tuples:
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if len(values) != len(columns):
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raise ImportValidationError(
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f"{table_name} INSERT 字段数不匹配：期望 {len(columns)}，实际 {len(values)}。"
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)
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row = {columns[index]: normalize_value(table_name, columns[index], value) for index, value in enumerate(values)}
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rows.append(row)
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return rows
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def parse_values_clause(values_clause: str) -> list[list[Any]]:
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"""VALUES 解析：把 SQL values 子句解析为二维数组，损坏字符串会直接报错。"""
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rows: list[list[Any]] = []
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index = 0
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length = len(values_clause)
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while index < length:
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while index < length and values_clause[index].isspace():
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index += 1
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if index >= length:
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break
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if values_clause[index] == ",":
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index += 1
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continue
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if values_clause[index] != "(":
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raise ImportValidationError(f"VALUES 子句格式错误：第 {index} 个字符不是 '('。")
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row, index = _parse_tuple(values_clause, index)
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rows.append(row)
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return rows
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def _parse_tuple(text: str, start: int) -> tuple[list[Any], int]:
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"""元组解析：解析一组括号内的 SQL 字段值。"""
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values: list[Any] = []
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token: list[str] = []
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in_string = False
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was_string = False
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escaped = False
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index = start + 1
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while index < len(text):
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char = text[index]
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if in_string:
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if escaped:
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token.append(_unescape_char(char))
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escaped = False
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elif char == "\\":
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escaped = True
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elif char == ")":
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recovered_at_end = _recover_unclosed_string_at_tuple_end(token)
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if recovered_at_end:
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string_value, raw_values = recovered_at_end
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values.append(string_value)
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values.extend(raw_values)
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return values, index + 1
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token.append(char)
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elif char == "'":
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recovered = _recover_misplaced_quote_separator(token, text[index + 1] if index + 1 < len(text) else "")
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if recovered:
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string_value, raw_values = recovered
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values.append(string_value)
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values.extend(raw_values)
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token = []
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was_string = True
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index += 1
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continue
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in_string = False
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else:
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token.append(char)
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index += 1
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continue
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if char == "'":
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stripped = "".join(token).strip()
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if stripped:
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raise ImportValidationError(f"字符串前存在非法未引用内容：{stripped[:20]}")
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in_string = True
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was_string = True
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index += 1
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continue
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if was_string and char.isspace():
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index += 1
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continue
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if was_string and char not in {",", ")"}:
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raise ImportValidationError(f"字符串后存在非法未引用内容：{char}{text[index + 1:index + 20]}")
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if char == ",":
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values.append(_coerce_raw_token("".join(token), was_string))
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token = []
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was_string = False
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index += 1
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continue
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if char == ")":
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values.append(_coerce_raw_token("".join(token), was_string))
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return values, index + 1
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token.append(char)
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index += 1
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raise ImportValidationError("VALUES 子句存在未闭合括号或未闭合字符串。")
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def _recover_misplaced_quote_separator(token: list[str], next_char: str) -> tuple[str, list[Any]] | None:
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"""兼容解析：修复接口 SQL 文本字段结尾写成 `文本,'next'` 的引号/逗号错位。"""
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if not next_char or next_char in {",", ")"} or next_char.isspace():
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return None
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raw = "".join(token)
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if not raw.endswith(","):
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return None
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body = raw[:-1]
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trailing_values: list[Any] = []
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while True:
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head, separator, tail = body.rpartition(",")
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if not separator or not _looks_like_unquoted_scalar(tail):
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break
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trailing_values.insert(0, _coerce_raw_token(tail, was_string=False))
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body = head
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if not body:
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return None
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return body, trailing_values
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def _recover_unclosed_string_at_tuple_end(token: list[str]) -> tuple[str, list[Any]] | None:
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"""兼容解析：修复文本字段缺少结束引号且后接 `,数字)` 的源 SQL。"""
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body = "".join(token)
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trailing_values: list[Any] = []
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while True:
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head, separator, tail = body.rpartition(",")
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if not separator or not _looks_like_unquoted_scalar(tail):
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break
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trailing_values.insert(0, _coerce_raw_token(tail, was_string=False))
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body = head
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if not body or not trailing_values:
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return None
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return body, trailing_values
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def _looks_like_unquoted_scalar(value: str) -> bool:
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"""兼容解析：判断错位引号前夹带的字段是否是可安全恢复的未引用标量。"""
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stripped = value.strip()
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if not stripped:
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return False
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if stripped.upper() == "NULL":
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return True
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return bool(re.fullmatch(r"-?\d+(?:\.\d+)?", stripped))
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def _unescape_char(char: str) -> str:
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"""SQL 转义：处理 dump 中的常见反斜杠转义。"""
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return {
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"0": "\0",
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"b": "\b",
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"n": "\n",
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"r": "\r",
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"t": "\t",
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"Z": "\x1a",
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"\\": "\\",
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"'": "'",
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'"': '"',
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}.get(char, char)
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def _coerce_raw_token(raw: str, was_string: bool) -> Any:
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"""原始字段转换：区分 SQL NULL、数字和字符串。"""
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if was_string:
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return raw
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value = raw.strip()
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if not value:
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return ""
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if value.upper() == "NULL":
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return None
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if re.fullmatch(r"-?\d+", value):
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return int(value)
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if re.fullmatch(r"-?\d+\.\d+", value):
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return Decimal(value)
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if re.search(r"[A-Za-z\u4e00-\u9fff]", value):
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raise ImportValidationError(f"发现未引用文本字段：{value[:30]}")
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return value
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def normalize_value(table_name: str, column: str, value: Any) -> Any:
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"""字段归一：按当前 ORM 需要转换 JSON、时间、布尔和数值。"""
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if value is None:
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return None
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if column in JSON_COLUMNS.get(table_name, set()):
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if isinstance(value, (list, dict)):
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return value
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try:
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return json.loads(value)
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except (TypeError, json.JSONDecodeError) as exc:
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raise ImportValidationError(f"{table_name}.{column} 不是合法 JSON。") from exc
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if column in DATETIME_COLUMNS and isinstance(value, str):
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try:
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return datetime.fromisoformat(value)
|
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except ValueError as exc:
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raise ImportValidationError(f"{table_name}.{column} 不是合法 datetime。") from exc
|
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if column in BOOL_COLUMNS:
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return bool(int(value))
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if column in INT_COLUMNS and value is not None:
|
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return int(value)
|
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if column in DECIMAL_COLUMNS and value is not None:
|
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return Decimal(str(value))
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return value
|
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|
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||||
def parse_source_dump(path: Path) -> tuple[dict[str, list[dict[str, Any]]], list[str], str]:
|
||||
"""源文件解析：提取可导入表数据并返回兼容性警告。"""
|
||||
sql_text, encoding = load_sql_text(path)
|
||||
warnings = []
|
||||
dangerous = find_dangerous_statements(sql_text)
|
||||
if dangerous:
|
||||
warnings.append("源 SQL 包含 DDL/DML 覆盖语句，导入器会忽略这些语句：" + ", ".join(sorted(set(dangerous))))
|
||||
|
||||
parsed: dict[str, list[dict[str, Any]]] = {}
|
||||
for table_name in SOURCE_TABLES:
|
||||
columns = extract_create_columns(sql_text, table_name)
|
||||
if not columns:
|
||||
raise ImportValidationError(f"源 SQL 缺少必需表结构：{table_name}")
|
||||
parsed[table_name] = extract_insert_rows(sql_text, table_name, columns)
|
||||
|
||||
for table_name in OPTIONAL_SOURCE_TABLES:
|
||||
columns = extract_create_columns(sql_text, table_name)
|
||||
if not columns:
|
||||
warnings.append(f"源 SQL 未包含 {table_name}，导入器会按当前业务规则处理。")
|
||||
continue
|
||||
parsed[table_name] = extract_insert_rows(sql_text, table_name, columns)
|
||||
if not parsed.get("traditional_case") and not parsed.get("teaching_case"):
|
||||
warnings.append("源 SQL 未包含 traditional_case 或 teaching_case，病例导入后暂时缺少训练模式扩展数据。")
|
||||
if not parsed.get("scoring_rule"):
|
||||
warnings.append("源 SQL 未包含 scoring_rule，评价时将缺少接口侧基础评分规则。")
|
||||
return parsed, warnings, encoding
|
||||
|
||||
|
||||
def validate_target_schema(parsed: dict[str, list[dict[str, Any]]]) -> None:
|
||||
"""目标结构校验：确认源字段可映射到当前数据库表。"""
|
||||
with SessionLocal() as db:
|
||||
inspector = inspect(db.bind)
|
||||
for table_name, rows in parsed.items():
|
||||
if not inspector.has_table(table_name):
|
||||
raise ImportValidationError(f"当前数据库缺少目标表：{table_name}")
|
||||
target_columns = {column["name"] for column in inspector.get_columns(table_name)}
|
||||
for row in rows:
|
||||
extra_columns = sorted(set(row) - target_columns)
|
||||
if extra_columns:
|
||||
raise ImportValidationError(f"{table_name} 存在当前库不支持的字段：{extra_columns}")
|
||||
|
||||
|
||||
def import_source_sql(path: Path, apply: bool = False, generate_exam_items: bool = True) -> ImportReport:
|
||||
"""安全导入：解析源 SQL，并在显式 apply 时写入当前新表。"""
|
||||
parsed, warnings, encoding = parse_source_dump(path)
|
||||
validate_target_schema(parsed)
|
||||
report = ImportReport(
|
||||
source_path=str(path),
|
||||
encoding=encoding,
|
||||
table_rows={table: len(rows) for table, rows in parsed.items()},
|
||||
warnings=warnings,
|
||||
applied=apply,
|
||||
)
|
||||
if not apply:
|
||||
return report
|
||||
|
||||
with SessionLocal() as db:
|
||||
try:
|
||||
case_rows = parsed.get("case_base", [])
|
||||
report.upserted_cases = _upsert_cases(db, case_rows)
|
||||
if "traditional_case" in parsed:
|
||||
report.upserted_traditional_cases = _sync_traditional_cases(db, case_rows, parsed.get("traditional_case", []))
|
||||
if "teaching_case" in parsed:
|
||||
report.upserted_teaching_cases = _sync_teaching_cases(db, case_rows, parsed.get("teaching_case", []))
|
||||
if "scoring_rule" in parsed:
|
||||
report.replaced_scoring_rules = _replace_scoring_rules(db, case_rows, parsed.get("scoring_rule", []))
|
||||
if generate_exam_items:
|
||||
report.generated_exam_items = _upsert_generated_exam_items(db, case_rows)
|
||||
db.commit()
|
||||
except Exception:
|
||||
db.rollback()
|
||||
raise
|
||||
return report
|
||||
|
||||
|
||||
def _upsert_cases(db, rows: list[dict[str, Any]]) -> int:
|
||||
"""病例导入：按 case_base.id 更新或插入病例主表。"""
|
||||
count = 0
|
||||
for row in rows:
|
||||
row = dict(row)
|
||||
row["status"] = 1
|
||||
row["publish_status"] = 1
|
||||
entity = db.get(CaseBase, row["id"])
|
||||
if not entity:
|
||||
db.add(CaseBase(**row))
|
||||
else:
|
||||
for key, value in row.items():
|
||||
setattr(entity, key, value)
|
||||
count += 1
|
||||
return count
|
||||
|
||||
|
||||
def _sync_traditional_cases(db, case_rows: list[dict[str, Any]], rows: list[dict[str, Any]]) -> int:
|
||||
"""传统病例导入：按本次 SQL 同步练习模式扩展表，避免同 case_id 旧数据残留。"""
|
||||
case_ids = _imported_case_ids(case_rows)
|
||||
if case_ids:
|
||||
db.execute(delete(TraditionalCase).where(TraditionalCase.case_id.in_(case_ids)))
|
||||
count = 0
|
||||
for row in rows:
|
||||
db.add(TraditionalCase(**row))
|
||||
count += 1
|
||||
return count
|
||||
|
||||
|
||||
def _sync_teaching_cases(db, case_rows: list[dict[str, Any]], rows: list[dict[str, Any]]) -> int:
|
||||
"""教学互动病例导入：源 SQL 明确提供 teaching_case 时，以源数据为准同步扩展表。"""
|
||||
case_ids = _imported_case_ids(case_rows)
|
||||
if case_ids:
|
||||
db.execute(delete(TeachingCase).where(TeachingCase.case_id.in_(case_ids)))
|
||||
count = 0
|
||||
for row in rows:
|
||||
db.add(TeachingCase(**row))
|
||||
count += 1
|
||||
return count
|
||||
|
||||
|
||||
def _replace_scoring_rules(db, case_rows: list[dict[str, Any]], rows: list[dict[str, Any]]) -> int:
|
||||
"""评分规则导入：按本次导入病例替换 scoring_rule，保持评分规则与源数据一致。"""
|
||||
case_ids = _imported_case_ids(case_rows)
|
||||
for case_id in case_ids:
|
||||
db.execute(delete(ScoringRule).where(ScoringRule.case_id == case_id))
|
||||
for row in rows:
|
||||
db.add(ScoringRule(**row))
|
||||
return len(rows)
|
||||
|
||||
|
||||
def _upsert_generated_exam_items(db, case_rows: list[dict[str, Any]]) -> int:
|
||||
"""检查项目补齐：按 item_code 更新或补齐固定检查结果，避免删除历史训练引用的检查项。"""
|
||||
changed = 0
|
||||
for row in case_rows:
|
||||
case_id = row["id"]
|
||||
items = build_exam_items_from_case(row)
|
||||
for item in items:
|
||||
entity = db.scalar(
|
||||
select(CaseExamItem).where(CaseExamItem.case_id == case_id, CaseExamItem.item_code == item["item_code"])
|
||||
)
|
||||
if not entity:
|
||||
db.add(CaseExamItem(case_id=case_id, **item))
|
||||
else:
|
||||
for key, value in item.items():
|
||||
setattr(entity, key, value)
|
||||
changed += 1
|
||||
return changed
|
||||
|
||||
|
||||
def _imported_case_ids(case_rows: list[dict[str, Any]]) -> list[int]:
|
||||
"""导入范围：提取本次 SQL 涉及的病例 ID，用于同步扩展表。"""
|
||||
return sorted({int(row["id"]) for row in case_rows if row.get("id") is not None})
|
||||
|
||||
|
||||
def build_exam_items_from_case(case_row: dict[str, Any]) -> list[dict[str, Any]]:
|
||||
"""检查项目生成：从病例文本识别常见检查结果，保障 Demo 可继续申请检查。"""
|
||||
text = " ".join(str(case_row.get(key) or "") for key in ("chief_complaint", "description", "knowledge_points"))
|
||||
candidates = [
|
||||
(
|
||||
"blood_routine",
|
||||
"血常规",
|
||||
"lab",
|
||||
"实验室检查",
|
||||
_find_result(text, r"(WBC[^，。；;]*[，,]\s*[^，。；;]*(?:中性|neutrophil)[^，。；;]*)") or "血常规结果见病例资料。",
|
||||
{"source": "case_description"},
|
||||
"WBC" in text or "血常规" in text,
|
||||
),
|
||||
(
|
||||
"crp",
|
||||
"CRP",
|
||||
"lab",
|
||||
"实验室检查",
|
||||
_find_result(text, r"(CRP\s*[^，。；;]*)") or "CRP 结果见病例资料。",
|
||||
{"source": "case_description"},
|
||||
"CRP" in text.upper(),
|
||||
),
|
||||
(
|
||||
"chest_xray",
|
||||
"胸片",
|
||||
"imaging",
|
||||
"影像检查",
|
||||
_find_result(text, r"(胸片[^。；;]*)") or "胸片结果见病例资料。",
|
||||
{"source": "case_description"},
|
||||
"胸片" in text or "X线" in text,
|
||||
),
|
||||
(
|
||||
"spo2",
|
||||
"血氧饱和度",
|
||||
"vital_sign",
|
||||
"生命体征",
|
||||
_find_result(text, r"(SpO2\s*\d+%?)") or "血氧饱和度结果见病例资料。",
|
||||
{"source": "case_description"},
|
||||
"SpO2" in text or "血氧" in text,
|
||||
),
|
||||
(
|
||||
"chest_auscultation",
|
||||
"肺部体格检查",
|
||||
"physical_exam",
|
||||
"体格检查",
|
||||
_find_result(text, r"(肺[^。；;]*(?:湿啰音|哮鸣音|呼吸音)[^。；;]*)") or "肺部体格检查结果见病例资料。",
|
||||
{"source": "case_description"},
|
||||
"湿啰音" in text or "哮鸣音" in text or "肺" in text,
|
||||
),
|
||||
(
|
||||
"mp_igm",
|
||||
"肺炎支原体抗体IgM",
|
||||
"lab",
|
||||
"实验室检查",
|
||||
_find_result(text, r"(肺炎支原体抗体IgM[^，。；;]*)") or "肺炎支原体抗体IgM结果见病例资料。",
|
||||
{"source": "case_description"},
|
||||
"肺炎支原体抗体IgM" in text or "支原体" in text,
|
||||
),
|
||||
]
|
||||
items: list[dict[str, Any]] = []
|
||||
for index, (code, name, item_type, category, result_text, structured, detected) in enumerate(candidates, start=1):
|
||||
if detected:
|
||||
items.append(
|
||||
{
|
||||
"item_code": code,
|
||||
"item_name": name,
|
||||
"item_type": item_type,
|
||||
"category": category,
|
||||
"result_text": result_text,
|
||||
"result_structured": structured,
|
||||
"is_key": True,
|
||||
"is_abnormal": True,
|
||||
"score_weight": Decimal("5.00"),
|
||||
"display_order": index,
|
||||
}
|
||||
)
|
||||
if items:
|
||||
return items
|
||||
return [
|
||||
{
|
||||
"item_code": "basic_exam",
|
||||
"item_name": "基础检查",
|
||||
"item_type": "physical_exam",
|
||||
"category": "体格检查",
|
||||
"result_text": "基础检查结果见病例资料。",
|
||||
"result_structured": {"source": "case_description"},
|
||||
"is_key": True,
|
||||
"is_abnormal": False,
|
||||
"score_weight": Decimal("1.00"),
|
||||
"display_order": 1,
|
||||
}
|
||||
]
|
||||
|
||||
|
||||
def _find_result(text: str, pattern: str) -> str | None:
|
||||
"""文本抽取：从病例描述中抽取简短检查结果。"""
|
||||
match = re.search(pattern, text, flags=re.I)
|
||||
return match.group(1).strip() if match else None
|
||||
|
||||
|
||||
def main() -> None:
|
||||
"""命令入口：执行接口 SQL 的安全检查或导入。"""
|
||||
parser = argparse.ArgumentParser(description="Safely import parsed case SQL into current medical agent schema.")
|
||||
parser.add_argument("sql_path", type=Path, help="接口提供的 SQL dump 文件路径")
|
||||
parser.add_argument("--apply", action="store_true", help="确认写入当前数据库；默认只检查不写入")
|
||||
parser.add_argument("--no-generate-exam-items", action="store_true", help="不自动补齐 case_exam_item")
|
||||
args = parser.parse_args()
|
||||
|
||||
try:
|
||||
report = import_source_sql(
|
||||
args.sql_path,
|
||||
apply=args.apply,
|
||||
generate_exam_items=not args.no_generate_exam_items,
|
||||
)
|
||||
except ImportValidationError as exc:
|
||||
print(json.dumps({"ok": False, "error": str(exc)}, ensure_ascii=False, indent=2))
|
||||
raise SystemExit(2) from exc
|
||||
|
||||
print(json.dumps({"ok": True, "report": report.as_dict()}, ensure_ascii=False, indent=2, default=str))
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
@@ -0,0 +1,303 @@
|
||||
from __future__ import annotations
|
||||
|
||||
import sys
|
||||
from pathlib import Path
|
||||
|
||||
from sqlalchemy import select
|
||||
|
||||
sys.path.insert(0, str(Path(__file__).resolve().parents[1]))
|
||||
|
||||
from app.db.base import Base
|
||||
from app.db.session import SessionLocal, engine
|
||||
from app.models import (
|
||||
CaseBase,
|
||||
CaseExamItem,
|
||||
Department,
|
||||
KnowledgeChunk,
|
||||
KnowledgeDocument,
|
||||
KnowledgeSource,
|
||||
PromptTemplate,
|
||||
ScoringRule,
|
||||
TeachingCase,
|
||||
TraditionalCase,
|
||||
User,
|
||||
)
|
||||
|
||||
|
||||
def init_database() -> None:
|
||||
"""数据库初始化：创建当前新表体系并写入第一版 Demo 种子数据。"""
|
||||
Base.metadata.create_all(bind=engine)
|
||||
with SessionLocal() as db:
|
||||
seed_demo_data(db)
|
||||
db.commit()
|
||||
|
||||
|
||||
def seed_demo_data(db) -> None:
|
||||
"""病例导入：写入儿科支气管肺炎病例、检查项目、评分规则和提示词元数据。"""
|
||||
department = _get_or_create_department(db)
|
||||
user = _get_or_create_seed_user(db)
|
||||
case = _get_or_create_case_base(db, department.id, user.id)
|
||||
_seed_traditional_case(db, case.id)
|
||||
_seed_teaching_case(db, case.id)
|
||||
_seed_exam_items(db, case.id)
|
||||
_seed_scoring_rules(db, case.id)
|
||||
_seed_knowledge(db, department.id)
|
||||
_seed_prompts(db)
|
||||
|
||||
|
||||
def _get_or_create_department(db) -> Department:
|
||||
"""科室种子：写入儿科科室。"""
|
||||
department = db.scalar(select(Department).where(Department.code == "PEDIATRICS"))
|
||||
if department:
|
||||
return department
|
||||
department = Department(name="儿科", code="PEDIATRICS", sort_order=1, is_active=True)
|
||||
db.add(department)
|
||||
db.flush()
|
||||
return department
|
||||
|
||||
|
||||
def _get_or_create_seed_user(db) -> User:
|
||||
"""用户占位：写入系统种子用户，不承担登录职责。"""
|
||||
user = db.scalar(select(User).where(User.external_user_id == "system_seed"))
|
||||
if user:
|
||||
return user
|
||||
user = User(external_user_id="system_seed", display_name="系统种子数据")
|
||||
db.add(user)
|
||||
db.flush()
|
||||
return user
|
||||
|
||||
|
||||
def _get_or_create_case_base(db, department_id: int, user_id: int) -> CaseBase:
|
||||
"""病例主表种子：以 case_base 作为病例唯一主表。"""
|
||||
case = db.scalar(select(CaseBase).where(CaseBase.title == "支气管肺炎 - 6岁男性患儿"))
|
||||
if case:
|
||||
return case
|
||||
case = CaseBase(
|
||||
title="支气管肺炎 - 6岁男性患儿",
|
||||
case_type="diagnosis_treatment",
|
||||
difficulty="medium",
|
||||
difficulty_score=2,
|
||||
chief_complaint="发热、咳嗽4天，喘息1天",
|
||||
description=(
|
||||
"患儿4天前无明显诱因出现发热，最高体温39.2℃，伴阵发性咳嗽，后有少量白色黏痰。"
|
||||
"1天前出现喘息，夜间明显，活动后加重。精神较差，食欲下降，小便略少。"
|
||||
),
|
||||
patient_age=6,
|
||||
patient_gender="male",
|
||||
tags="pediatrics,pneumonia,demo",
|
||||
symptom_tags=["发热", "咳嗽", "喘息", "精神食纳差"],
|
||||
disease_tags=["支气管肺炎"],
|
||||
competency_tags=["问诊完整性", "儿科查体规范", "关键症状识别", "诊断准确性", "治疗计划合理性"],
|
||||
guideline_tags=["CAP_2019", "HUMANISTIC_CARE"],
|
||||
knowledge_points=["血常规", "CRP", "胸片", "血氧饱和度", "肺部湿啰音"],
|
||||
icd_codes="",
|
||||
estimated_minutes=20,
|
||||
osce_enabled=False,
|
||||
rag_enabled=True,
|
||||
ai_prompt_template="app/prompts/patient/practice.md",
|
||||
multimodal_assets=[],
|
||||
vector_status=0,
|
||||
publish_status=1,
|
||||
status=1,
|
||||
created_by_id=user_id,
|
||||
department_id=department_id,
|
||||
)
|
||||
db.add(case)
|
||||
db.flush()
|
||||
return case
|
||||
|
||||
|
||||
def _seed_traditional_case(db, case_id: int) -> None:
|
||||
"""传统病例种子：练习模式读取 case_base + traditional_case。"""
|
||||
if db.scalar(select(TraditionalCase).where(TraditionalCase.case_id == case_id)):
|
||||
return
|
||||
db.add(
|
||||
TraditionalCase(
|
||||
case_id=case_id,
|
||||
standard_diagnosis="支气管肺炎",
|
||||
standard_treatment=(
|
||||
"抗感染、止咳平喘、改善氧合、严密观察病情变化；必要时雾化吸入缓解喘息，"
|
||||
"监测体温、呼吸、血氧、精神反应和饮水尿量，出现低氧或呼吸困难加重时及时升级处理。"
|
||||
),
|
||||
guideline_reference=(
|
||||
"诊断依据：发热、咳嗽、喘息，肺部湿啰音，炎症指标升高，胸片提示右下肺片状模糊影，"
|
||||
"符合儿童社区获得性肺炎/支气管肺炎诊断思路。严重程度需结合呼吸频率、SpO2、意识、循环和进食饮水情况。"
|
||||
),
|
||||
)
|
||||
)
|
||||
|
||||
|
||||
def _seed_teaching_case(db, case_id: int) -> None:
|
||||
"""教学互动病例种子：教学互动模式读取 case_base + teaching_case。"""
|
||||
if db.scalar(select(TeachingCase).where(TeachingCase.case_id == case_id)):
|
||||
return
|
||||
db.add(
|
||||
TeachingCase(
|
||||
case_id=case_id,
|
||||
teaching_goal="围绕儿科肺炎问诊、检查选择、诊断依据、治疗决策和医患沟通完成互动训练。",
|
||||
discussion_questions="如何判断病情严重程度？哪些检查是关键检查？治疗方案如何兼顾抗感染、平喘和氧合监测？",
|
||||
teacher_guide="观察学生是否完整追问发热、咳嗽、喘息、既往史、接触史，并能解释胸片、炎症指标和血氧。",
|
||||
scoring_focus="问诊完整性、检查合理性、诊断准确性、治疗计划、风险预案、人文沟通。",
|
||||
)
|
||||
)
|
||||
|
||||
|
||||
def _seed_exam_items(db, case_id: int) -> None:
|
||||
"""检查项目种子：写入病例可申请检查和固定返回结果。"""
|
||||
if db.scalar(select(CaseExamItem).where(CaseExamItem.case_id == case_id)):
|
||||
return
|
||||
items = [
|
||||
("blood_routine", "血常规", "lab", "WBC 12.5×10^9/L，中性粒细胞比例72%，提示感染及炎症反应。", {"wbc": "12.5×10^9/L", "neutrophil": "72%"}, True, True, 1),
|
||||
("crp", "CRP", "lab", "CRP 28 mg/L，提示炎症反应升高。", {"crp": "28 mg/L"}, True, True, 2),
|
||||
("chest_xray", "胸片", "imaging", "双下肺纹理增多，右下肺片状模糊影，支持肺部感染。", {"finding": "右下肺片状模糊影"}, True, True, 3),
|
||||
("spo2", "血氧饱和度", "vital_sign", "室内空气 SpO2 94%，处于临界偏低范围。", {"spo2": "94%"}, True, True, 4),
|
||||
("mp_igm", "肺炎支原体IgM", "lab", "肺炎支原体IgM阴性。", {"mp_igm": "negative"}, False, False, 5),
|
||||
]
|
||||
for code, name, item_type, result_text, structured, is_key, abnormal, order in items:
|
||||
db.add(
|
||||
CaseExamItem(
|
||||
case_id=case_id,
|
||||
item_code=code,
|
||||
item_name=name,
|
||||
item_type=item_type,
|
||||
category=item_type,
|
||||
result_text=result_text,
|
||||
result_structured=structured,
|
||||
is_key=is_key,
|
||||
is_abnormal=abnormal,
|
||||
score_weight=5.0 if is_key else 1.0,
|
||||
display_order=order,
|
||||
)
|
||||
)
|
||||
|
||||
|
||||
def _seed_scoring_rules(db, case_id: int) -> None:
|
||||
"""评分规则种子：写入 scoring_rule，评价时作为基础评分细则。"""
|
||||
if db.scalar(select(ScoringRule).where(ScoringRule.case_id == case_id)):
|
||||
return
|
||||
rules = [
|
||||
("信息获取", "问诊完整性", 25, "覆盖现病史、既往史、个人史、家族史、儿科特异性症状与家属担忧。"),
|
||||
("分析推理", "诊断与鉴别诊断", 25, "结合症状、体征、胸片、炎症指标和血氧支持支气管肺炎诊断，并列出合理鉴别诊断。"),
|
||||
("处置决策", "检查与治疗方案", 20, "检查申请合理，治疗原则覆盖抗感染、止咳平喘、改善氧合、风险预案和随访。"),
|
||||
("沟通人文", "家属沟通", 15, "向家属说明病情、用药注意事项、危险信号、复诊或住院指征，并回应焦虑。"),
|
||||
("临床整合", "流程与整体思维", 15, "流程连贯，把问诊、检查、诊断、治疗和沟通整合成完整临床决策。"),
|
||||
]
|
||||
for dimension, competency, weight, standard in rules:
|
||||
db.add(
|
||||
ScoringRule(
|
||||
case_id=case_id,
|
||||
dimension=dimension,
|
||||
competency_dimension=competency,
|
||||
score_weight=weight,
|
||||
ai_auto_score=True,
|
||||
osce_dimension=False,
|
||||
scoring_standard=standard,
|
||||
rubric_json={"max_score": weight, "criteria": standard},
|
||||
)
|
||||
)
|
||||
|
||||
|
||||
def _seed_knowledge(db, department_id: int) -> None:
|
||||
"""知识库种子：写入评分参考指南和人文沟通片段。"""
|
||||
if db.scalar(select(KnowledgeSource).where(KnowledgeSource.source_code == "CAP_2019")):
|
||||
return
|
||||
source = KnowledgeSource(
|
||||
source_code="CAP_2019",
|
||||
source_name="儿童社区获得性肺炎诊疗规范（2019年版）",
|
||||
source_type="clinical_guideline",
|
||||
authority_level=5,
|
||||
is_active=True,
|
||||
)
|
||||
human = KnowledgeSource(
|
||||
source_code="HUMANISTIC_CARE",
|
||||
source_name="问诊沟通与人文关怀要求",
|
||||
source_type="humanistic_care",
|
||||
authority_level=4,
|
||||
is_active=True,
|
||||
)
|
||||
db.add_all([source, human])
|
||||
db.flush()
|
||||
doc = KnowledgeDocument(
|
||||
source_id=source.id,
|
||||
department_id=department_id,
|
||||
title="儿童社区获得性肺炎诊疗规范摘要",
|
||||
task_type="diagnosis_treatment",
|
||||
summary="用于肺炎病例诊断、严重程度评估和治疗评分参考。",
|
||||
file_path="docs/knowledge/cap_2019.md",
|
||||
is_active=True,
|
||||
)
|
||||
human_doc = KnowledgeDocument(
|
||||
source_id=human.id,
|
||||
department_id=department_id,
|
||||
title="儿科问诊人文关怀要点",
|
||||
task_type="diagnosis_treatment",
|
||||
summary="用于评价家属沟通、知情告知和健康教育。",
|
||||
file_path="docs/knowledge/humanistic_care.md",
|
||||
is_active=True,
|
||||
)
|
||||
db.add_all([doc, human_doc])
|
||||
db.flush()
|
||||
db.add_all(
|
||||
[
|
||||
KnowledgeChunk(
|
||||
document_id=doc.id,
|
||||
department_id=department_id,
|
||||
task_type="diagnosis_treatment",
|
||||
chunk_text="儿童肺炎诊断需综合发热、咳嗽、喘息、肺部湿啰音、炎症指标和胸部影像学新发浸润影。",
|
||||
keywords=["发热", "咳嗽", "喘息", "胸片异常"],
|
||||
weight=5.0,
|
||||
is_active=True,
|
||||
),
|
||||
KnowledgeChunk(
|
||||
document_id=doc.id,
|
||||
department_id=department_id,
|
||||
task_type="diagnosis_treatment",
|
||||
chunk_text="严重程度评估应关注呼吸频率、血氧饱和度、意识状态、循环状态和进食饮水情况。",
|
||||
keywords=["血氧饱和度下降", "呼吸", "严重程度"],
|
||||
weight=4.5,
|
||||
is_active=True,
|
||||
),
|
||||
KnowledgeChunk(
|
||||
document_id=human_doc.id,
|
||||
department_id=department_id,
|
||||
task_type="diagnosis_treatment",
|
||||
chunk_text="儿科问诊需要向家属说明病情观察指标、用药注意事项、复诊指征，并给予情绪安抚。",
|
||||
keywords=["沟通", "健康教育", "家属"],
|
||||
weight=4.0,
|
||||
is_active=True,
|
||||
),
|
||||
]
|
||||
)
|
||||
|
||||
|
||||
def _seed_prompts(db) -> None:
|
||||
"""提示词种子：写入 Markdown 模板元数据，正文保存在 prompts 目录。"""
|
||||
templates = [
|
||||
("patient_practice", "patient", "practice", "v1", "fast", "text", "app/prompts/patient/practice.md"),
|
||||
("patient_teaching", "patient", "teaching", "v1", "fast", "text", "app/prompts/patient/teaching.md"),
|
||||
("novice_case_hint", "hint", "novice", "v1", "fast", "json", "app/prompts/hint/novice_case_hint.md"),
|
||||
("scoring_pediatrics_pneumonia", "scoring", "pediatrics_pneumonia", "v1", "fast", "json", "app/prompts/scoring/pediatrics_pneumonia.md"),
|
||||
("report_evaluation", "report", "evaluation", "v1", "fast", "json", "app/prompts/report/evaluation_report.md"),
|
||||
]
|
||||
for code, agent_type, scene, version, model_type, output_format, file_path in templates:
|
||||
template = db.scalar(select(PromptTemplate).where(PromptTemplate.template_code == code, PromptTemplate.version_no == version))
|
||||
if not template:
|
||||
template = PromptTemplate(template_code=code, version_no=version)
|
||||
template.agent_type = agent_type
|
||||
template.scene = scene
|
||||
template.model_type = model_type
|
||||
template.output_format = output_format
|
||||
template.file_path = file_path
|
||||
template.is_active = True
|
||||
db.add(template)
|
||||
|
||||
|
||||
def ensure_storage_dirs() -> None:
|
||||
"""目录初始化：创建报告导出目录。"""
|
||||
Path("storage/reports").mkdir(parents=True, exist_ok=True)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
ensure_storage_dirs()
|
||||
init_database()
|
||||
print("Demo database initialized.")
|
||||
@@ -0,0 +1,45 @@
|
||||
from __future__ import annotations
|
||||
|
||||
import sys
|
||||
from pathlib import Path
|
||||
|
||||
from sqlalchemy import text
|
||||
|
||||
sys.path.insert(0, str(Path(__file__).resolve().parents[1]))
|
||||
|
||||
from app.db.session import SessionLocal
|
||||
from scripts.init_demo_db import init_database
|
||||
|
||||
|
||||
def main() -> None:
|
||||
"""新表迁移：创建并补齐 case_base、traditional_case、teaching_case、case_exam_item、training_* 和 training_record。"""
|
||||
init_database()
|
||||
with SessionLocal() as db:
|
||||
_apply_table_comments(db)
|
||||
db.commit()
|
||||
print("new schema migration completed")
|
||||
|
||||
|
||||
def _apply_table_comments(db) -> None:
|
||||
"""表注释补齐：为当前业务表写入中文说明，便于数据库工具查看。"""
|
||||
comments = {
|
||||
"case_base": "病例主表",
|
||||
"traditional_case": "传统病例扩展表",
|
||||
"teaching_case": "教学互动病例扩展表",
|
||||
"scoring_rule": "评分规则表",
|
||||
"case_exam_item": "病例检查检验项目表",
|
||||
"training_session": "训练会话表",
|
||||
"training_order": "训练检查申请表",
|
||||
"training_submission": "训练诊断治疗提交表",
|
||||
"training_record": "训练记录表",
|
||||
}
|
||||
dialect = db.bind.dialect.name if db.bind else ""
|
||||
if dialect != "mysql":
|
||||
return
|
||||
for table_name, comment in comments.items():
|
||||
safe_comment = comment.replace("'", "''")
|
||||
db.execute(text(f"ALTER TABLE `{table_name}` COMMENT='{safe_comment}'"))
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
Reference in New Issue
Block a user