feat(iit): harden QC pipeline consistency and release artifacts

Implement IIT quality workflow hardening across eQuery deduplication, guard metadata validation, timeline/readability improvements, and chat evidence fallbacks, then synchronize release and development documentation for deployment handoff.

Includes migration/scripts for open eQuery dedupe guards, orchestration/status semantics, report/tool readability fixes, and updated module status plus deployment checklist.

Made-with: Cursor

backend/scripts/iit_chat_regression.py

@@ -0,0 +1,126 @@
+import json
+import sys
+from typing import List, Dict
+
+import requests
+
+
+BASE = "http://localhost:3001"
+PROJECT_ID = "1d80f270-6a02-4b58-9db3-6af176e91f3c"
+USER_ID = "diag-user-p1-regression"
+
+
+def login() -> str:
+    resp = requests.post(
+        f"{BASE}/api/v1/auth/login/password",
+        json={"phone": "13800000001", "password": "123456"},
+        timeout=10,
+    )
+    resp.raise_for_status()
+    return resp.json()["data"]["tokens"]["accessToken"]
+
+
+def ask(question: str) -> Dict:
+    resp = requests.post(
+        f"{BASE}/api/v1/iit/chat",
+        json={"message": question, "projectId": PROJECT_ID, "userId": USER_ID},
+        timeout=80,
+    )
+    resp.raise_for_status()
+    return resp.json()
+
+
+def must_contain(text: str, keywords: List[str]) -> bool:
+    return all(k in text for k in keywords)
+
+def must_not_contain(text: str, keywords: List[str]) -> bool:
+    return all(k not in text for k in keywords)
+
+
+def main() -> int:
+    # Ensure backend is alive and creds are valid before running chat checks.
+    _ = login()
+
+    cases = [
+        {
+            "name": "知情统计",
+            "q": "目前已经有几个患者签署知情了？",
+            "must": ["结论", "证据", "签署", "12"],
+        },
+        {
+            "name": "纳排合规",
+            "q": "3号患者的纳入排除标准都符合要求吗？",
+            "must": ["结论", "证据", "3号", "规则"],
+        },
+        {
+            "name": "项目总览",
+            "q": "最新质控报告怎么样？",
+            "must": ["结论", "通过率", "严重问题"],
+        },
+        {
+            "name": "患者明细",
+            "q": "查询一下患者ID为2的患者数据",
+            "must": ["结论", "证据", "2"],
+        },
+        {
+            "name": "访视进度",
+            "q": "4号患者到第几次访视了",
+            "must": ["结论", "证据", "4号", "访视"],
+        },
+        {
+            "name": "eQuery状态",
+            "q": "目前eQuery总体状态如何？",
+            "must": ["结论", "待处理", "证据"],
+        },
+        {
+            "name": "通过率口径",
+            "q": "现在通过率是多少，怎么算出来的？",
+            "must": ["结论", "证据", "通过率", "计算方法"],
+        },
+        {
+            "name": "D6风险",
+            "q": "现在方案偏离风险大吗？",
+            "must": ["结论", "D6"],
+            "must_not": ["156条严重问题", "284条警告问题"],
+        },
+        {
+            "name": "D1维度风险",
+            "q": "D1数据一致性风险现在怎么样？",
+            "must": ["结论", "D1", "证据"],
+            "must_not": ["D6问题总数"],
+        },
+        {
+            "name": "D2维度风险",
+            "q": "D2数据完整性现在风险大吗？",
+            "must": ["结论", "D2", "证据"],
+            "must_not": ["D1问题总数", "D6问题总数"],
+        },
+    ]
+
+    failed = 0
+    print("IIT Chat Regression Start\n")
+    for idx, c in enumerate(cases, 1):
+        try:
+            out = ask(c["q"])
+            reply = out.get("reply", "")
+            ok = must_contain(reply, c["must"])
+            if ok and c.get("must_not"):
+                ok = must_not_contain(reply, c["must_not"])
+            status = "PASS" if ok else "FAIL"
+            print(f"[{idx}] {status} {c['name']}")
+            print(f"Q: {c['q']}")
+            print(f"A: {reply[:220].replace(chr(10), ' | ')}")
+            print("")
+            if not ok:
+                failed += 1
+        except Exception as exc:
+            failed += 1
+            print(f"[{idx}] FAIL {c['name']}: {exc}\n")
+
+    print(f"Done. total={len(cases)} failed={failed}")
+    return 1 if failed else 0
+
+
+if __name__ == "__main__":
+    sys.exit(main())
+