feat: chat integration, tenant admin spec, backend chat service, miniprogram updates, DEMO moved to tmp, XCX-TEST removed, migrations & docs
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scripts/ops/test_chat_ai_quality.py
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345
scripts/ops/test_chat_ai_quality.py
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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,
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"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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})
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messages.append({"role": "assistant", "content": reply_s})
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return results
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async def main():
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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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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)
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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"],
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"error": str(e),
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})
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# 生成 Markdown 报告
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report = generate_report(all_results)
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output_path = _root / "docs" / "reports" / "2026-03-20__rns14_chat_ai_quality_eval.md"
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output_path.parent.mkdir(parents=True, exist_ok=True)
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output_path.write_text(report, encoding="utf-8")
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print(f"\n✅ 报告已输出: {output_path}")
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def generate_report(results: list[dict]) -> str:
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"""生成 Markdown 评估报告。"""
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lines: list[str] = []
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lines.append("# RNS1.4 CHAT 模块 AI 返回质量评估报告")
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lines.append("")
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lines.append(f"- 评估时间: {datetime.now().strftime('%Y-%m-%d %H:%M')}")
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lines.append(f"- 模型: {BAILIAN_MODEL}")
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lines.append(f"- 端点: {BAILIAN_BASE_URL}")
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lines.append(f"- 场景数: {len(results)}")
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lines.append("")
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lines.append("---")
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lines.append("")
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for r in results:
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lines.append(f"## {r['name']}")
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lines.append("")
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lines.append(f"**场景描述**: {r['description']}")
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lines.append("")
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if "error" in r:
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lines.append(f"**❌ 执行失败**: {r['error']}")
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lines.append("")
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continue
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for rd in r.get("rounds", []):
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lines.append(f"### 第 {rd['round']} 轮（{rd['mode']}）")
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lines.append("")
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lines.append(f"**用户发送**:")
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lines.append("")
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lines.append(f"```")
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lines.append(rd["user_message"])
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lines.append(f"```")
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lines.append("")
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lines.append(f"**AI 回复**:")
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lines.append("")
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lines.append(f"```")
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lines.append(rd["ai_reply"])
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lines.append(f"```")
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lines.append("")
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meta_parts = [f"耗时 {rd['elapsed_seconds']}s"]
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if rd.get("tokens_used"):
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meta_parts.append(f"tokens: {rd['tokens_used']}")
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if rd.get("chunk_count"):
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meta_parts.append(f"chunks: {rd['chunk_count']}")
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lines.append(f"**性能**: {' | '.join(meta_parts)}")
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lines.append("")
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lines.append("---")
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lines.append("")
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# AI 评价占位（由执行者填写）
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lines.append("## 综合评价")
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lines.append("")
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lines.append("| 维度 | 评分 | 说明 |")
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lines.append("|------|------|------|")
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lines.append("| 语义相关性 | — | — |")
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lines.append("| 中文表达质量 | — | — |")
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lines.append("| 上下文理解 | — | — |")
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lines.append("| 多轮连贯性 | — | — |")
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lines.append("| 响应速度 | — | — |")
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lines.append("| 流式输出稳定性 | — | — |")
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lines.append("")
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lines.append("> 评分标准: ✅ 优秀 / ⚠️ 可接受 / ❌ 不合格")
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lines.append("")
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return "\n".join(lines)
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if __name__ == "__main__":
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asyncio.run(main())
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