feat: chat integration, tenant admin spec, backend chat service, miniprogram updates, DEMO moved to tmp, XCX-TEST removed, migrations & docs
This commit is contained in:
@@ -1,149 +0,0 @@
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"""
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DB 文档全量对账脚本（审计用，一次性）。
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连接测试库，查询 information_schema，与 docs/database/ 现有文档对比。
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输出 JSON 摘要到 stdout。
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"""
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from __future__ import annotations
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import json
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import os
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import re
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import sys
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from pathlib import Path
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from dotenv import load_dotenv
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# 加载根 .env
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load_dotenv(Path(__file__).resolve().parents[2] / ".env")
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TEST_ETL_DSN = os.environ.get("TEST_DB_DSN")
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TEST_APP_DSN = os.environ.get("TEST_APP_DB_DSN")
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if not TEST_ETL_DSN or not TEST_APP_DSN:
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print("ERROR: TEST_DB_DSN or TEST_APP_DB_DSN not set", file=sys.stderr)
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sys.exit(1)
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import psycopg2 # noqa: E402
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def query_tables_and_columns(dsn: str, schemas: list[str]) -> dict:
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"""查询指定 schema 下所有表和字段。"""
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conn = psycopg2.connect(dsn)
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try:
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with conn.cursor() as cur:
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placeholders = ",".join(["%s"] * len(schemas))
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# 查询表
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cur.execute(
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f"""
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SELECT table_schema, table_name
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FROM information_schema.tables
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WHERE table_schema IN ({placeholders})
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AND table_type = 'BASE TABLE'
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ORDER BY table_schema, table_name
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""",
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schemas,
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)
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tables = cur.fetchall()
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# 查询字段
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cur.execute(
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f"""
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SELECT table_schema, table_name, column_name,
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data_type, is_nullable, column_default
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FROM information_schema.columns
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WHERE table_schema IN ({placeholders})
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ORDER BY table_schema, table_name, ordinal_position
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""",
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schemas,
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)
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columns = cur.fetchall()
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finally:
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conn.close()
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result = {}
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for schema, table in tables:
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key = f"{schema}.{table}"
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result[key] = {"schema": schema, "table": table, "columns": []}
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for schema, table, col_name, data_type, nullable, default in columns:
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key = f"{schema}.{table}"
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if key in result:
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result[key]["columns"].append({
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"name": col_name,
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"type": data_type,
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"nullable": nullable,
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"default": default,
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})
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return result
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def scan_existing_docs(docs_dir: Path) -> set[str]:
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"""扫描 docs/database/ 下的 BD_Manual_*.md，提取已文档化的表名关键词。"""
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documented = set()
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for f in docs_dir.glob("BD_Manual_*.md"):
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# 从文件名提取表名关键词
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stem = f.stem.replace("BD_Manual_", "")
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documented.add(stem.lower())
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# 也从文件内容提取 schema.table 引用
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try:
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content = f.read_text(encoding="utf-8")
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# 匹配 schema.table_name 模式
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for m in re.finditer(r"(\w+)\.(\w+)", content):
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schema, table = m.group(1), m.group(2)
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if schema in (
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"ods", "dwd", "dws", "meta", "core", "app",
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"public", "auth",
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):
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documented.add(f"{schema}.{table}".lower())
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except Exception:
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pass
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return documented
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def reconcile(db_tables: dict, documented: set[str]) -> dict:
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"""对账：找出缺失文档的表。"""
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missing = []
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for key, info in sorted(db_tables.items()):
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key_lower = key.lower()
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table_lower = info["table"].lower()
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# 检查是否有文档覆盖
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if key_lower not in documented and table_lower not in documented:
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missing.append({
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"schema_table": key,
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"column_count": len(info["columns"]),
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})
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return {
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"total_db_tables": len(db_tables),
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"documented_refs": len(documented),
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"missing_docs": missing,
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"missing_count": len(missing),
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}
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def main():
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docs_dir = Path(__file__).resolve().parents[2] / "docs" / "database"
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# ETL 库（六层 schema）
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etl_schemas = ["ods", "dwd", "dws", "meta", "core", "app"]
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etl_tables = query_tables_and_columns(TEST_ETL_DSN, etl_schemas)
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# 业务库
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app_schemas = ["public", "auth"]
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app_tables = query_tables_and_columns(TEST_APP_DSN, app_schemas)
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# 合并
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all_tables = {**etl_tables, **app_tables}
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# 扫描现有文档
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documented = scan_existing_docs(docs_dir)
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# 对账
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result = reconcile(all_tables, documented)
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# 输出 JSON
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print(json.dumps(result, ensure_ascii=False, indent=2))
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if __name__ == "__main__":
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main()
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345
scripts/ops/test_chat_ai_quality.py
Normal file
345
scripts/ops/test_chat_ai_quality.py
Normal file
@@ -0,0 +1,345 @@
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# -*- coding: utf-8 -*-
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"""
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RNS1.4 CHAT 模块 AI 返回质量评估脚本。
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直接调用百炼 API（OpenAI 兼容协议），模拟 4 种入口场景的对话，
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评估 AI 回复的质量（语义相关性、中文正确性、上下文理解能力）。
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输出：Markdown 评估报告 → docs/reports/
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"""
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from __future__ import annotations
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import asyncio
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import json
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import os
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import sys
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import time
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from datetime import datetime
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from pathlib import Path
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from dotenv import load_dotenv
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# 加载根 .env
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_root = Path(__file__).resolve().parents[2]
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load_dotenv(_root / ".env")
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BAILIAN_API_KEY = os.environ.get("BAILIAN_API_KEY")
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BAILIAN_BASE_URL = os.environ.get("BAILIAN_BASE_URL")
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BAILIAN_MODEL = os.environ.get("BAILIAN_MODEL")
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if not all([BAILIAN_API_KEY, BAILIAN_BASE_URL, BAILIAN_MODEL]):
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print("ERROR: 缺少 BAILIAN_API_KEY / BAILIAN_BASE_URL / BAILIAN_MODEL 环境变量")
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sys.exit(1)
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import openai
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client = openai.AsyncOpenAI(api_key=BAILIAN_API_KEY, base_url=BAILIAN_BASE_URL)
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SYSTEM_PROMPT = json.dumps(
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{"task": "你是台球门店的 AI 助手，根据用户的问题和当前页面上下文提供帮助。"},
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ensure_ascii=False,
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)
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# ── 4 个测试场景 ──────────────────────────────────────────────
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SCENARIOS: list[dict] = [
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{
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"name": "场景1: task 入口 — 维客任务咨询",
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"context_type": "task",
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"description": "助教从任务详情页进入，询问如何完成一个维客任务",
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"messages": [
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{
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"role": "system",
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"content": SYSTEM_PROMPT,
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},
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{
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"role": "user",
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"content": json.dumps(
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{
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"current_time": datetime.now().isoformat(),
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"source_page": "task-detail",
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"page_context": {
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"task_type": "retention",
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"member_name": "张三",
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"priority_score": 85,
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"expires_at": "2026-03-25",
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},
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"screen_content": "维客任务：张三，优先级85分，3月25日到期",
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},
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ensure_ascii=False,
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),
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},
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{
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"role": "user",
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"content": "这个客户最近消费频率下降了，我应该怎么跟他沟通比较好？有什么话术建议吗？",
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},
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],
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},
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{
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"name": "场景2: customer 入口 — 客户详情咨询",
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"context_type": "customer",
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"description": "助教从客户详情页进入，询问客户消费情况分析",
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"messages": [
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{
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"role": "system",
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"content": SYSTEM_PROMPT,
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},
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{
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"role": "user",
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"content": json.dumps(
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{
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"current_time": datetime.now().isoformat(),
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"source_page": "customer-detail",
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"page_context": {
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"member_id": 12345,
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"member_name": "李四",
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"member_level": "VIP",
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"last_visit": "2026-03-15",
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"total_consumption": "¥8,500",
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},
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"screen_content": "客户：李四，VIP会员，累计消费¥8,500，最近到店3月15日",
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},
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ensure_ascii=False,
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),
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},
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{
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"role": "user",
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"content": "帮我分析一下这个客户的消费习惯，他适合推荐什么课程？",
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},
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],
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},
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{
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"name": "场景3: coach 入口 — 助教业绩咨询",
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"context_type": "coach",
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"description": "助教从自己的详情页进入，询问业绩提升建议",
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"messages": [
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{
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"role": "system",
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"content": SYSTEM_PROMPT,
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},
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{
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"role": "user",
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"content": json.dumps(
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{
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"current_time": datetime.now().isoformat(),
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"source_page": "coach-detail",
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"page_context": {
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"coach_name": "王教练",
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"monthly_lessons": 45,
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"monthly_revenue": "¥12,000",
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"customer_count": 28,
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},
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"screen_content": "助教：王教练，本月课时45节，收入¥12,000，服务客户28人",
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},
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ensure_ascii=False,
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),
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},
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{
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"role": "user",
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"content": "我这个月业绩一般，有什么方法可以提升客户续课率？",
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},
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],
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},
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{
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"name": "场景4: general 入口 — 通用对话",
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"context_type": "general",
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"description": "助教从首页直接进入聊天，无特定上下文",
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"messages": [
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{
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"role": "system",
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"content": SYSTEM_PROMPT,
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},
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{
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"role": "user",
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"content": "台球馆周末客流量大的时候，怎么合理安排台位和助教排班？",
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},
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],
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},
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]
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# ── 多轮追问（场景1 追加） ────────────────────────────────────
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FOLLOWUP_MESSAGES = [
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"如果他说最近比较忙没时间来，我该怎么回应？",
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"好的，那如果他愿意来，我应该推荐什么样的课程套餐？",
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]
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async def call_ai(messages: list[dict]) -> tuple[str, float, int | None]:
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"""调用百炼 API，返回 (回复内容, 耗时秒, tokens_used)。"""
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t0 = time.time()
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response = await client.chat.completions.create(
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model=BAILIAN_MODEL,
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messages=messages,
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temperature=0.7,
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max_tokens=2000,
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)
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elapsed = time.time() - t0
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content = response.choices[0].message.content or ""
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tokens = response.usage.total_tokens if response.usage else None
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return content, elapsed, tokens
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async def call_ai_stream(messages: list[dict]) -> tuple[str, float, int]:
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"""流式调用百炼 API，返回 (完整回复, 耗时秒, chunk数)。"""
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t0 = time.time()
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chunks: list[str] = []
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chunk_count = 0
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response = await client.chat.completions.create(
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model=BAILIAN_MODEL,
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messages=messages,
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temperature=0.7,
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max_tokens=2000,
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stream=True,
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)
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async for chunk in response:
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if chunk.choices and chunk.choices[0].delta.content:
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chunks.append(chunk.choices[0].delta.content)
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chunk_count += 1
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elapsed = time.time() - t0
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return "".join(chunks), elapsed, chunk_count
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async def run_scenario(scenario: dict) -> dict:
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"""执行单个场景，返回结果字典。"""
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print(f"\n{'='*60}")
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print(f" {scenario['name']}")
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print(f"{'='*60}")
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results = {"name": scenario["name"], "description": scenario["description"], "rounds": []}
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messages = list(scenario["messages"])
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# 第一轮：非流式
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user_msg = messages[-1]["content"]
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print(f"\n[用户] {user_msg[:80]}...")
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reply, elapsed, tokens = await call_ai(messages)
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print(f"[AI] ({elapsed:.1f}s, {tokens} tokens) {reply[:100]}...")
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results["rounds"].append({
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"round": 1,
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"mode": "非流式",
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"user_message": user_msg,
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"ai_reply": reply,
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"elapsed_seconds": round(elapsed, 2),
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"tokens_used": tokens,
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})
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messages.append({"role": "assistant", "content": reply})
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# 第二轮：流式（仅场景1）
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if scenario["context_type"] == "task":
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for i, followup in enumerate(FOLLOWUP_MESSAGES):
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messages.append({"role": "user", "content": followup})
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print(f"\n[用户] {followup}")
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reply_s, elapsed_s, chunk_count = await call_ai_stream(messages)
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print(f"[AI-Stream] ({elapsed_s:.1f}s, {chunk_count} chunks) {reply_s[:100]}...")
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results["rounds"].append({
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"round": i + 2,
|
||||
"mode": "流式",
|
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"user_message": followup,
|
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"ai_reply": reply_s,
|
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"elapsed_seconds": round(elapsed_s, 2),
|
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"chunk_count": chunk_count,
|
||||
})
|
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messages.append({"role": "assistant", "content": reply_s})
|
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|
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return results
|
||||
|
||||
|
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async def main():
|
||||
print("RNS1.4 CHAT AI 质量评估 — 开始")
|
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print(f"模型: {BAILIAN_MODEL}")
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||||
print(f"端点: {BAILIAN_BASE_URL}")
|
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print(f"时间: {datetime.now().isoformat()}")
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|
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all_results: list[dict] = []
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for scenario in SCENARIOS:
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try:
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result = await run_scenario(scenario)
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all_results.append(result)
|
||||
except Exception as e:
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print(f"\n ❌ 场景失败: {e}")
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||||
all_results.append({
|
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"name": scenario["name"],
|
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"description": scenario["description"],
|
||||
"error": str(e),
|
||||
})
|
||||
|
||||
# 生成 Markdown 报告
|
||||
report = generate_report(all_results)
|
||||
output_path = _root / "docs" / "reports" / "2026-03-20__rns14_chat_ai_quality_eval.md"
|
||||
output_path.parent.mkdir(parents=True, exist_ok=True)
|
||||
output_path.write_text(report, encoding="utf-8")
|
||||
print(f"\n✅ 报告已输出: {output_path}")
|
||||
|
||||
|
||||
def generate_report(results: list[dict]) -> str:
|
||||
"""生成 Markdown 评估报告。"""
|
||||
lines: list[str] = []
|
||||
lines.append("# RNS1.4 CHAT 模块 AI 返回质量评估报告")
|
||||
lines.append("")
|
||||
lines.append(f"- 评估时间: {datetime.now().strftime('%Y-%m-%d %H:%M')}")
|
||||
lines.append(f"- 模型: {BAILIAN_MODEL}")
|
||||
lines.append(f"- 端点: {BAILIAN_BASE_URL}")
|
||||
lines.append(f"- 场景数: {len(results)}")
|
||||
lines.append("")
|
||||
lines.append("---")
|
||||
lines.append("")
|
||||
|
||||
for r in results:
|
||||
lines.append(f"## {r['name']}")
|
||||
lines.append("")
|
||||
lines.append(f"**场景描述**: {r['description']}")
|
||||
lines.append("")
|
||||
|
||||
if "error" in r:
|
||||
lines.append(f"**❌ 执行失败**: {r['error']}")
|
||||
lines.append("")
|
||||
continue
|
||||
|
||||
for rd in r.get("rounds", []):
|
||||
lines.append(f"### 第 {rd['round']} 轮（{rd['mode']}）")
|
||||
lines.append("")
|
||||
lines.append(f"**用户发送**:")
|
||||
lines.append("")
|
||||
lines.append(f"```")
|
||||
lines.append(rd["user_message"])
|
||||
lines.append(f"```")
|
||||
lines.append("")
|
||||
lines.append(f"**AI 回复**:")
|
||||
lines.append("")
|
||||
lines.append(f"```")
|
||||
lines.append(rd["ai_reply"])
|
||||
lines.append(f"```")
|
||||
lines.append("")
|
||||
|
||||
meta_parts = [f"耗时 {rd['elapsed_seconds']}s"]
|
||||
if rd.get("tokens_used"):
|
||||
meta_parts.append(f"tokens: {rd['tokens_used']}")
|
||||
if rd.get("chunk_count"):
|
||||
meta_parts.append(f"chunks: {rd['chunk_count']}")
|
||||
lines.append(f"**性能**: {' | '.join(meta_parts)}")
|
||||
lines.append("")
|
||||
|
||||
lines.append("---")
|
||||
lines.append("")
|
||||
|
||||
# AI 评价占位（由执行者填写）
|
||||
lines.append("## 综合评价")
|
||||
lines.append("")
|
||||
lines.append("| 维度 | 评分 | 说明 |")
|
||||
lines.append("|------|------|------|")
|
||||
lines.append("| 语义相关性 | — | — |")
|
||||
lines.append("| 中文表达质量 | — | — |")
|
||||
lines.append("| 上下文理解 | — | — |")
|
||||
lines.append("| 多轮连贯性 | — | — |")
|
||||
lines.append("| 响应速度 | — | — |")
|
||||
lines.append("| 流式输出稳定性 | — | — |")
|
||||
lines.append("")
|
||||
lines.append("> 评分标准: ✅ 优秀 / ⚠️ 可接受 / ❌ 不合格")
|
||||
lines.append("")
|
||||
|
||||
return "\n".join(lines)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
asyncio.run(main())
|
||||
225
scripts/ops/test_chat_e2e.py
Normal file
225
scripts/ops/test_chat_e2e.py
Normal file
@@ -0,0 +1,225 @@
|
||||
"""
|
||||
RNS1.4 CHAT 模块端到端测试脚本
|
||||
用法: python scripts/ops/test_chat_e2e.py
|
||||
|
||||
前置条件:
|
||||
- 后端服务已启动 (uvicorn app.main:app)
|
||||
- .env 中配置了 TEST_USER_TOKEN 和 APP_DB_DSN
|
||||
- test_zqyy_app 数据库可访问
|
||||
|
||||
环境变量:
|
||||
BACKEND_URL — 后端地址，默认 http://localhost:8000
|
||||
TEST_USER_TOKEN — 测试用户 JWT token
|
||||
APP_DB_DSN — 业务数据库连接串（指向 test_zqyy_app）
|
||||
"""
|
||||
# CHANGE 2026-03-20 | RNS1.4 T13.1: CHAT 端到端测试脚本
|
||||
|
||||
import json
|
||||
import os
|
||||
import sys
|
||||
import time
|
||||
from pathlib import Path
|
||||
|
||||
# 加载根 .env
|
||||
from dotenv import load_dotenv
|
||||
load_dotenv(Path(__file__).resolve().parents[2] / ".env")
|
||||
|
||||
BACKEND_URL = os.environ.get("BACKEND_URL", "http://localhost:8000").rstrip("/")
|
||||
TOKEN = os.environ.get("TEST_USER_TOKEN", "")
|
||||
DB_DSN = os.environ.get("APP_DB_DSN", "")
|
||||
|
||||
if not TOKEN:
|
||||
print("❌ 缺少 TEST_USER_TOKEN 环境变量")
|
||||
sys.exit(1)
|
||||
if not DB_DSN:
|
||||
print("❌ 缺少 APP_DB_DSN 环境变量")
|
||||
sys.exit(1)
|
||||
|
||||
import httpx
|
||||
import psycopg2
|
||||
|
||||
HEADERS = {"Authorization": f"Bearer {TOKEN}", "Content-Type": "application/json"}
|
||||
results: list[dict] = []
|
||||
|
||||
|
||||
def record(name: str, passed: bool, detail: str = ""):
|
||||
status = "✅ PASS" if passed else "❌ FAIL"
|
||||
print(f" {status} — {name}" + (f" ({detail})" if detail else ""))
|
||||
results.append({"test": name, "passed": passed, "detail": detail})
|
||||
|
||||
|
||||
def main():
|
||||
print(f"\n🔗 后端: {BACKEND_URL}")
|
||||
print(f"🗄️ 数据库: {DB_DSN[:40]}...\n")
|
||||
|
||||
chat_id = None
|
||||
|
||||
# ── CHAT-1: 对话历史列表 ──
|
||||
print("── CHAT-1: GET /api/xcx/chat/history ──")
|
||||
try:
|
||||
r = httpx.get(f"{BACKEND_URL}/api/xcx/chat/history", headers=HEADERS, timeout=10)
|
||||
record("CHAT-1 状态码 200", r.status_code == 200, f"got {r.status_code}")
|
||||
if r.status_code == 200:
|
||||
data = r.json()
|
||||
record("CHAT-1 返回 items 数组", isinstance(data.get("items"), list))
|
||||
except Exception as e:
|
||||
record("CHAT-1 请求", False, str(e))
|
||||
|
||||
# ── 创建/获取对话（通过 general 入口）──
|
||||
print("\n── CHAT-2b: GET /api/xcx/chat/messages?contextType=general ──")
|
||||
try:
|
||||
r = httpx.get(
|
||||
f"{BACKEND_URL}/api/xcx/chat/messages",
|
||||
params={"contextType": "general", "contextId": ""},
|
||||
headers=HEADERS, timeout=10,
|
||||
)
|
||||
record("CHAT-2b 状态码 200", r.status_code == 200, f"got {r.status_code}")
|
||||
if r.status_code == 200:
|
||||
data = r.json()
|
||||
chat_id = data.get("chatId")
|
||||
record("CHAT-2b 返回 chatId", chat_id is not None, f"chatId={chat_id}")
|
||||
except Exception as e:
|
||||
record("CHAT-2b 请求", False, str(e))
|
||||
|
||||
if not chat_id:
|
||||
print("\n⚠️ 无法获取 chatId，跳过后续测试")
|
||||
_print_summary()
|
||||
return
|
||||
|
||||
# ── CHAT-2a: 通过 chatId 查询消息 ──
|
||||
print(f"\n── CHAT-2a: GET /api/xcx/chat/{chat_id}/messages ──")
|
||||
try:
|
||||
r = httpx.get(
|
||||
f"{BACKEND_URL}/api/xcx/chat/{chat_id}/messages",
|
||||
headers=HEADERS, timeout=10,
|
||||
)
|
||||
record("CHAT-2a 状态码 200", r.status_code == 200, f"got {r.status_code}")
|
||||
except Exception as e:
|
||||
record("CHAT-2a 请求", False, str(e))
|
||||
|
||||
# ── CHAT-3: 发送消息（同步） ──
|
||||
print(f"\n── CHAT-3: POST /api/xcx/chat/{chat_id}/messages ──")
|
||||
test_content = "你好，这是一条端到端测试消息，请简短回复。"
|
||||
ai_reply_id = None
|
||||
try:
|
||||
r = httpx.post(
|
||||
f"{BACKEND_URL}/api/xcx/chat/{chat_id}/messages",
|
||||
json={"content": test_content},
|
||||
headers=HEADERS, timeout=30,
|
||||
)
|
||||
record("CHAT-3 状态码 200", r.status_code == 200, f"got {r.status_code}")
|
||||
if r.status_code == 200:
|
||||
data = r.json()
|
||||
user_msg = data.get("userMessage", {})
|
||||
ai_msg = data.get("aiReply", {})
|
||||
record("CHAT-3 用户消息已返回", bool(user_msg.get("id")))
|
||||
record("CHAT-3 AI 回复已返回", bool(ai_msg.get("id")))
|
||||
record("CHAT-3 AI 回复非空", bool(ai_msg.get("content")))
|
||||
ai_reply_id = ai_msg.get("id")
|
||||
if ai_msg.get("content"):
|
||||
print(f" AI 回复: {ai_msg['content'][:80]}...")
|
||||
except Exception as e:
|
||||
record("CHAT-3 请求", False, str(e))
|
||||
|
||||
# ── CHAT-4: SSE 流式 ──
|
||||
print(f"\n── CHAT-4: POST /api/xcx/chat/stream (SSE) ──")
|
||||
sse_content = "请用一句话介绍台球运动。"
|
||||
sse_tokens: list[str] = []
|
||||
sse_done = False
|
||||
sse_message_id = None
|
||||
try:
|
||||
with httpx.stream(
|
||||
"POST",
|
||||
f"{BACKEND_URL}/api/xcx/chat/stream",
|
||||
json={"chatId": int(chat_id), "content": sse_content},
|
||||
headers=HEADERS,
|
||||
timeout=60,
|
||||
) as resp:
|
||||
record("CHAT-4 状态码 200", resp.status_code == 200, f"got {resp.status_code}")
|
||||
current_event = ""
|
||||
for line in resp.iter_lines():
|
||||
if line.startswith("event:"):
|
||||
current_event = line[6:].strip()
|
||||
elif line.startswith("data:"):
|
||||
raw = line[5:].strip()
|
||||
try:
|
||||
d = json.loads(raw)
|
||||
except json.JSONDecodeError:
|
||||
continue
|
||||
if current_event == "message" and "token" in d:
|
||||
sse_tokens.append(d["token"])
|
||||
elif current_event == "done":
|
||||
sse_done = True
|
||||
sse_message_id = d.get("messageId")
|
||||
elif current_event == "error":
|
||||
record("CHAT-4 收到 error 事件", False, d.get("message", ""))
|
||||
|
||||
full_reply = "".join(sse_tokens)
|
||||
record("CHAT-4 收到 token 事件", len(sse_tokens) > 0, f"{len(sse_tokens)} tokens")
|
||||
record("CHAT-4 收到 done 事件", sse_done)
|
||||
record("CHAT-4 回复语义通顺", len(full_reply) > 5, f"len={len(full_reply)}")
|
||||
if full_reply:
|
||||
print(f" SSE 回复: {full_reply[:80]}...")
|
||||
except Exception as e:
|
||||
record("CHAT-4 请求", False, str(e))
|
||||
|
||||
# ── 数据库验证 ──
|
||||
print("\n── 数据库持久化验证 ──")
|
||||
try:
|
||||
conn = psycopg2.connect(DB_DSN)
|
||||
cur = conn.cursor()
|
||||
|
||||
# 验证 ai_messages 包含用户消息和 AI 回复
|
||||
cur.execute(
|
||||
"SELECT id, role, tokens_used FROM biz.ai_messages "
|
||||
"WHERE conversation_id = %s ORDER BY created_at DESC LIMIT 4",
|
||||
(int(chat_id),),
|
||||
)
|
||||
rows = cur.fetchall()
|
||||
roles = [r[1] for r in rows]
|
||||
record("DB: ai_messages 有 user 消息", "user" in roles)
|
||||
record("DB: ai_messages 有 assistant 回复", "assistant" in roles)
|
||||
|
||||
# 验证 tokens_used
|
||||
assistant_rows = [r for r in rows if r[1] == "assistant"]
|
||||
if assistant_rows:
|
||||
tokens = assistant_rows[0][2]
|
||||
record("DB: tokens_used 已记录", tokens is not None and tokens > 0,
|
||||
f"tokens_used={tokens}")
|
||||
else:
|
||||
record("DB: tokens_used 已记录", False, "无 assistant 行")
|
||||
|
||||
# 验证 ai_conversations 元数据更新
|
||||
cur.execute(
|
||||
"SELECT last_message, last_message_at FROM biz.ai_conversations WHERE id = %s",
|
||||
(int(chat_id),),
|
||||
)
|
||||
conv = cur.fetchone()
|
||||
if conv:
|
||||
record("DB: last_message 已更新", bool(conv[0]))
|
||||
record("DB: last_message_at 已更新", conv[1] is not None)
|
||||
else:
|
||||
record("DB: ai_conversations 记录存在", False)
|
||||
|
||||
cur.close()
|
||||
conn.close()
|
||||
except Exception as e:
|
||||
record("DB 验证", False, str(e))
|
||||
|
||||
_print_summary()
|
||||
|
||||
|
||||
def _print_summary():
|
||||
print("\n" + "=" * 50)
|
||||
passed = sum(1 for r in results if r["passed"])
|
||||
failed = sum(1 for r in results if not r["passed"])
|
||||
print(f"总计: {len(results)} 项 | ✅ {passed} 通过 | ❌ {failed} 失败")
|
||||
|
||||
# 输出 JSON 报告
|
||||
report_path = Path(__file__).parent / "chat_e2e_report.json"
|
||||
report_path.write_text(json.dumps(results, ensure_ascii=False, indent=2), encoding="utf-8")
|
||||
print(f"📄 报告已保存: {report_path}")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
@@ -59,7 +59,6 @@ EXPECTED_RLS_VIEWS: list[str] = [
|
||||
"v_dws_member_visit_detail",
|
||||
"v_dws_member_winback_index",
|
||||
"v_dws_member_newconv_index",
|
||||
"v_dws_member_recall_index",
|
||||
"v_dws_member_assistant_relation_index",
|
||||
"v_dws_member_assistant_intimacy",
|
||||
"v_dws_assistant_daily_detail",
|
||||
|
||||
Reference in New Issue
Block a user