fix: delete agent 500 error + dynamic personality + deployment guide
- Fix delete agent 500: clean up FK records (agent_llm_logs, permissions, schedules, executions, team_members) and unbind goals/tasks before delete - Remove hardcoded personality templates in Android, replace with dynamic system prompt generation from name + description - Set promptSectionsEnabled=false to bypass PromptComposer for personality - Add Tencent Cloud Linux deployment guide (Docker Compose) - Accumulated backend service updates, frontend UI fixes, Android app changes Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
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test/test_stress_long_conversation.py
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test/test_stress_long_conversation.py
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# -*- coding: utf-8 -*-
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"""端到端长对话压力测试 — 50+ 轮验证压缩不丢上下文
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测试逻辑：
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1. Round 1: 注入关键事实（我的名字是小明，最爱《三体》）
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2. Round 2-9: 填充轮 — 正常对话积累 token
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3. Round 10: 验证 — 确认压缩是否触发
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4. Round 11-48: 填充轮 — 继续积累
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5. Round 49: 回忆 — 检查早期上下文是否丢失
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6. Round 50: 最终验证
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观测指标：
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- compaction_triggered: 压缩是否被触发
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- token_usage.input_tokens: 输入 token 走势（应被压缩压低）
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- fact_retained: 50 轮后是否还记得早期事实
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- context_lost_errors: 是否有上下文超限错误
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"""
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import sys, os, io, json, time, requests
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sys.stdout = io.TextIOWrapper(sys.stdout.buffer, encoding="utf-8", errors="replace")
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BASE = "http://localhost:8038/api/v1"
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TOTAL_ROUNDS = 50
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FACT_CHECK_ROUND = 10 # 第 10 轮做第一次找回检查
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FINAL_CHECK_ROUND = 49 # 第 49 轮做最终找回检查
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FILL_MESSAGES = [
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"请用Python写一个简单的冒泡排序函数，只输出代码不要解释",
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"请把下面这段话翻译成英语：今天天气真好，适合出去散步。",
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"写一个SQL查询，找出表中最近7天注册的用户，按注册时间降序排列",
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"解释一下什么是Docker的layer缓存机制，控制在100字以内",
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"用JavaScript写一个防抖函数debounce",
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"Python的GIL是什么？有什么影响？请简短回答",
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"写一个简单的Makefile来编译和运行一个C项目",
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"列出5个提高代码可读性的最佳实践",
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"解释HTTP状态码200、301、404、500分别代表什么",
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"写一个正则表达式来匹配中国手机号",
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"什么是RESTful API？请用三句话内解释",
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"写一个简单的git工作流命令序列（clone→branch→commit→push→PR）",
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"Python装饰器的原理是什么？请简短说明",
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"解释什么是数据库索引以及什么时候该用",
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"写一个shell脚本统计当前目录下每种文件扩展名的数量",
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"什么是CI/CD？请用100字以内解释",
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"写一个Python context manager 示例",
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"解释乐观锁和悲观锁的区别",
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"用TypeScript写一个简单的泛型函数",
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"什么是缓存穿透、缓存击穿、缓存雪崩？简短区分",
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"写一个docker-compose.yml 启动 nginx+mysql",
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"Python中__init__和__new__的区别是什么？",
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"解释TCP三次握手和四次挥手",
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"写一个简单的Redis Lua脚本示例",
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"什么是JWT？在分布式系统中如何使用？",
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"解释CSS盒模型，简短回答",
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"写一个awk命令来统计日志中每个IP的访问次数",
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"Python中asyncio的基本用法是什么？",
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"解释微服务架构的优缺点",
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"写一个简单的React Hook示例",
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"什么是Kubernetes的Pod？简短回答",
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"解释HTTPS的握手过程",
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"写一个Python生成器函数的例子",
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"什么是消息队列？列举几个常用的MQ",
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"解释数据库ACID四个特性",
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"写一个简单的Nginx反向代理配置",
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"Python中*args和**kwargs的含义",
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"什么是WebSocket？与HTTP轮询对比有什么优势？",
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"解释面向对象六大原则中的开闭原则",
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"写一个PostgreSQL的窗口函数查询示例",
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]
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MEMORY_FACT = "我的名字是小明，我最喜欢读的书是刘慈欣写的《三体》三部曲，我的职业是Python后端工程师。"
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def login():
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r = requests.post(f"{BASE}/auth/login", data={"username": "admin", "password": "123456"})
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r.raise_for_status()
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return r.json()["access_token"]
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def test_long_conversation():
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token = login()
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headers = {"Authorization": f"Bearer {token}", "Content-Type": "application/json"}
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print(f"=== 长对话压力测试 — {TOTAL_ROUNDS} 轮 ===\n")
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print(f"注入事实: {MEMORY_FACT}")
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print(f"首次检查: 第 {FACT_CHECK_ROUND} 轮")
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print(f"最终检查: 第 {FINAL_CHECK_ROUND} 轮\n")
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metrics = {
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"token_history": [],
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"compaction_triggered": False,
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"context_lost_errors": 0,
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"fact_retained_at_10": False,
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"fact_retained_at_49": False,
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"total_time_ms": 0,
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}
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start_time = time.time()
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# ── Round 1: 注入记忆事实 ──
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print("─" * 50)
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print(f"Round 1/50: 注入记忆事实…")
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r = requests.post(f"{BASE}/agent-chat/bare", headers=headers,
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json={"message": f"请记住以下关于我的信息：{MEMORY_FACT} 请回复'已记住'即可。",
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"streamlined": True}, timeout=180)
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r.raise_for_status()
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d = r.json()
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sid = d["session_id"]
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tu = d.get("token_usage")
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record_metrics(metrics, 1, tu, d)
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print(f" session={sid[:16]}…, tokens_in={tu.get('input_tokens', 'N/A') if tu else 'N/A'}")
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print(f" reply: {d['content'][:100]}")
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# ── Rounds 2-N: 填充 + 定期检查 ──
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for round_num in range(2, TOTAL_ROUNDS + 1):
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if round_num == FACT_CHECK_ROUND:
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msg = "我之前告诉过你我叫什么名字？最喜欢什么书？我的职业是什么？请简要回答。"
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label = f"Round {round_num}/50: 首次记忆检查"
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elif round_num == FINAL_CHECK_ROUND:
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msg = "请回忆一下：我们第一次对话时我告诉你我叫什么名字？我最喜欢的书是什么？我的职业是什么？"
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label = f"Round {round_num}/50: 最终记忆检查"
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else:
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msg = FILL_MESSAGES[(round_num - 2) % len(FILL_MESSAGES)]
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label = f"Round {round_num}/50: 填充"
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print(f"{label}…", end=" ", flush=True)
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try:
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r = requests.post(f"{BASE}/agent-chat/bare", headers=headers,
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json={"message": msg, "session_id": sid, "streamlined": True},
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timeout=180)
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r.raise_for_status()
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d = r.json()
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tu = d.get("token_usage")
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record_metrics(metrics, round_num, tu, d)
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if round_num == FACT_CHECK_ROUND:
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content_lower = d["content"].lower()
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metrics["fact_retained_at_10"] = (
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"小明" in d["content"] and ("三体" in d["content"] or "科幻" in d["content"])
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)
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status = "✓ 记住" if metrics["fact_retained_at_10"] else "✗ 遗忘"
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print(f"{status} | tokens_in={tu.get('input_tokens', '?') if tu else '?'}")
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print(f" reply: {d['content'][:200]}")
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elif round_num == FINAL_CHECK_ROUND:
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content_lower = d["content"].lower()
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metrics["fact_retained_at_49"] = (
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"小明" in d["content"] and ("三体" in d["content"] or "科幻" in d["content"])
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)
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status = "✓ 记住" if metrics["fact_retained_at_49"] else "✗ 遗忘"
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print(f"{status} | tokens_in={tu.get('input_tokens', '?') if tu else '?'}")
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print(f" reply: {d['content'][:200]}")
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elif round_num % 10 == 0:
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print(f"✓ tokens_in={tu.get('input_tokens', '?') if tu else '?'}")
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else:
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print("✓")
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except requests.exceptions.HTTPError as e:
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err_body = e.response.text[:200] if hasattr(e, 'response') else str(e)
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print(f"✗ HTTP {e.response.status_code if hasattr(e, 'response') else '?'}: {err_body}")
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if "context" in str(err_body).lower() or "413" in str(e):
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metrics["context_lost_errors"] += 1
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if metrics["context_lost_errors"] >= 3:
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print(" ⚠ 连续上下文超限，中止测试")
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break
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except requests.exceptions.Timeout:
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print("✗ 超时")
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except Exception as e:
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print(f"✗ {e}")
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metrics["total_time_ms"] = int((time.time() - start_time) * 1000)
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# ── 报告 ──
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print("\n" + "=" * 50)
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print("测试报告")
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print("=" * 50)
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print(f"总轮数: {TOTAL_ROUNDS}")
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print(f"总耗时: {metrics['total_time_ms']/1000:.1f}s")
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print(f"第10轮记忆保留: {'✓ 通过' if metrics['fact_retained_at_10'] else '✗ 失败'}")
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print(f"第49轮记忆保留: {'✓ 通过' if metrics['fact_retained_at_49'] else '✗ 失败'}")
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print(f"上下文超限错误: {metrics['context_lost_errors']} 次")
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if metrics["token_history"]:
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token_entries = metrics["token_history"]
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print(f"\nToken 用量走势:")
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print(f" Round 1: {token_entries[0]['input_tokens']:,} tokens")
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# 找到压缩触发点
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prev = token_entries[0]["input_tokens"]
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for entry in token_entries[1:]:
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if entry["input_tokens"] < prev * 0.7: # 压缩后减少 >30%
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print(f" Round {entry['round']:>2}: {entry['input_tokens']:,} tokens ← 压缩触发")
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prev = entry["input_tokens"]
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print(f" Round {token_entries[-1]['round']:>2}: {token_entries[-1]['input_tokens']:,} tokens")
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passed = metrics["fact_retained_at_10"] and not metrics["context_lost_errors"]
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print(f"\n=== {'测试通过 ✓' if passed else '测试失败 ✗'} ===")
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return passed
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def record_metrics(metrics, round_num, token_usage, response):
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if token_usage:
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metrics["token_history"].append({
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"round": round_num,
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"input_tokens": token_usage.get("input_tokens", 0),
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"cumulative_total": token_usage.get("cumulative_total", 0),
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"is_warning": token_usage.get("is_warning", False),
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"is_critical": token_usage.get("is_critical", False),
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"compaction_attempts": token_usage.get("compaction_attempts", 0),
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})
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if token_usage.get("is_critical"):
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metrics["compaction_triggered"] = True
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if __name__ == "__main__":
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test_long_conversation()
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