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e31db5fe19
* docs: rename vault_dir to workspace_dir in documentation and examples * refactor(extract): format long method call across multiple lines * refactor(extract): format system prompt parameters for better readability
261 lines
9.3 KiB
Python
261 lines
9.3 KiB
Python
"""Integration test for the auto_memory job (single-step).
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Drives the ``auto_memory`` step against a real LLM. Two scenarios:
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1. **CREATE**: calls ``auto_memory`` with a fresh ``session_id`` and
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conversation messages. Expects a new note with the key facts.
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2. **UPDATE**: seeds an existing daily note, calls ``auto_memory`` with
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the same ``session_id`` and new conversation messages. Expects the
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old facts to survive and new facts to land.
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Requires LLM_API_KEY (and optionally LLM_BASE_URL / LLM_MODEL_NAME) in the
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environment or a .env file at the repo root. Hits the real LLM API.
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"""
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import asyncio
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import sys
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from pathlib import Path
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INTEGRATION_DIR = Path(__file__).resolve().parent
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sys.path.insert(0, str(INTEGRATION_DIR))
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# pylint: disable=wrong-import-position
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from _workspace_fixture import workspace_env # noqa: E402
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SEED_STEM = "auth-middleware-rewrite"
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SEED_BODY = """---
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name: auth-middleware-rewrite
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description: JWT auth middleware rewrite driven by legal/compliance requirements around session token storage
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---
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# 背景
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- 项目：JWT auth middleware 重写，替换旧的 session middleware
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- 动机：legal/compliance 要求，旧的 session token 存储方式不符合新合规要求
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- 决策：采用 RS256 签名，密钥放在 KMS，refresh token 写 redis 集群
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- 团队：Alice 主导，Bob 协助
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# 时间线
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- 2026-05-20 立项 kickoff
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- 2026-05-23 设计评审通过
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# 当下状态
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- 进度：实现中
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- 卡点：暂无
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- 下一步：完成 refresh token 写入流程
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"""
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def _auth_messages() -> list[dict]:
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"""Messages continuing the auth middleware thread."""
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return [
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{
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"name": "user",
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"role": "user",
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"content": "状态更新：PR #432（auth middleware rewrite）今天已经合并到 dev 分支，等待 staging 验收。",
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},
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{
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"name": "assistant",
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"role": "assistant",
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"content": "好的，已记录。staging 验收前要先跑回归测试吗？",
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},
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{
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"name": "user",
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"role": "user",
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"content": (
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"对。测试时发现 refresh token TTL 设 7d 在 redis 集群挂了——"
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"redis maxmemory-policy 默认 allkeys-lru，会随机驱逐 token，导致用户被强制登出。"
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),
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},
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{
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"name": "assistant",
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"role": "assistant",
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"content": "理解，要切到 volatile-ttl 才能只驱逐带 TTL 的 key，对吧？",
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},
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{
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"name": "user",
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"role": "user",
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"content": (
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"对，下一步：周五 2026-05-29 前把 redis 配置改成 volatile-ttl 并重测，" "blocked 在 SRE @lihua 的排期。"
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),
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},
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]
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def _pytorch_messages() -> list[dict]:
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"""Messages about a brand-new pytorch topic."""
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return [
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{
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"name": "user",
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"role": "user",
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"content": ("最近在调 pytorch 分布式训练。结论：DDP 启动推荐用 torchrun，" "比 mp.spawn 稳很多。"),
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},
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{
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"name": "assistant",
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"role": "assistant",
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"content": "是因为信号处理的原因吗？",
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},
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{
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"name": "user",
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"role": "user",
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"content": (
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"主要是 NCCL backend 初始化更干净。mp.spawn 在 4 卡以上偶尔会卡死握手；"
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"复现版本 pytorch 2.5.1 + nccl 2.21.5。"
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),
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},
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{
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"name": "user",
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"role": "user",
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"content": (
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"另外 batch size 用 64*world_size，per-rank lr 用 linear scaling rule "
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"（lr = base_lr * world_size）。"
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),
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},
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{
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"name": "user",
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"role": "user",
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"content": "先记一下。",
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},
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]
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def _read_text(p: Path) -> str:
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return p.read_text(encoding="utf-8")
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def test_auto_memory_create():
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"""CREATE a new note from scratch with a fresh session_id."""
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async def run():
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with workspace_env() as env:
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app = await env.make_app()
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try:
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today = env.today
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print("\n" + "=" * 70)
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print("[setup] workspace_root =", env.workspace_dir)
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print("[setup] today =", today)
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print("=" * 70)
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pytorch_session_id = "pytorch-distributed-training"
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expected_stem = pytorch_session_id
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with env.record_agents(prefix="agent_create") as recorder:
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response = await app.run_job(
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"auto_memory",
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messages=_pytorch_messages(),
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session_id=pytorch_session_id,
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)
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dumped = await recorder.dump()
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for p in dumped:
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print(f"[CREATE] agent memory dumped: {p}")
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assert response.success is True, f"CREATE job failed: {response.answer!r}"
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meta = response.metadata or {}
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assert meta.get("created") is True, f"Expected created=True, got {meta!r}"
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assert meta.get("path") == f"daily/{today}/{expected_stem}.md"
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pytorch_path = env.workspace_dir / meta["path"]
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assert pytorch_path.is_file(), f"created note not found at {pytorch_path}"
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pytorch_text = _read_text(pytorch_path)
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print("\n" + "=" * 70)
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print(f"[CREATE] {pytorch_path} ({len(pytorch_text)} bytes)")
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print(f"[CREATE] body:\n{pytorch_text}")
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print("=" * 70)
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topic_hits = [
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needle
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for needle in ("torchrun", "mp.spawn", "NCCL", "2.5.1", "linear scaling", "world_size")
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if needle in pytorch_text
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]
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print(f"[CREATE] landed topic facts: {topic_hits}")
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assert (
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len(topic_hits) >= 3
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), f"CREATE only captured {topic_hits!r} of expected facts\n--- CREATE ---\n{pytorch_text}"
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stem = pytorch_path.stem
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assert (
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f"name: {stem}" in pytorch_text
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), f"frontmatter name does not match stem {stem!r}\n{pytorch_text[:400]}"
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print("\n" + "=" * 70)
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print("test_auto_memory_create passed")
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print("=" * 70)
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finally:
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await env.close_all()
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asyncio.run(run())
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def test_auto_memory_update():
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"""UPDATE an existing note — old facts must survive, new facts must land."""
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async def run():
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with workspace_env() as env:
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app = await env.make_app()
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try:
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today = env.today
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expected_stem = SEED_STEM
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seed_path = env.seed_daily_note(expected_stem, SEED_BODY)
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seed_before = _read_text(seed_path)
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assert "legal/compliance" in seed_before
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print("\n" + "=" * 70)
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print("[setup] workspace_root =", env.workspace_dir)
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print("[setup] today =", today)
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print("[setup] seed_path =", seed_path)
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print("=" * 70)
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with env.record_agents(prefix="agent_update") as recorder:
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response = await app.run_job(
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"auto_memory",
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messages=_auth_messages(),
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session_id=SEED_STEM,
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)
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dumped = await recorder.dump()
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for p in dumped:
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print(f"[UPDATE] agent memory dumped: {p}")
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assert response.success is True, f"UPDATE job failed: {response.answer!r}"
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meta = response.metadata or {}
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assert meta.get("created") is False, f"Expected created=False, got {meta!r}"
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assert meta.get("path") == f"daily/{today}/{expected_stem}.md"
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seed_after = _read_text(seed_path)
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print("\n" + "=" * 70)
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print(f"[UPDATE] {seed_path} ({len(seed_before)} -> {len(seed_after)} bytes)")
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print(f"[UPDATE] body after:\n{seed_after}")
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print("=" * 70)
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for old_fact in ("legal/compliance", "RS256", "Alice"):
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assert (
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old_fact in seed_after
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), f"UPDATE dropped pre-existing fact {old_fact!r}\n--- AFTER ---\n{seed_after}"
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new_hits = [
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needle
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for needle in ("PR #432", "432", "volatile-ttl", "maxmemory-policy", "2026-05-29")
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if needle in seed_after
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]
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print(f"[UPDATE] preserved old facts, landed new facts: {new_hits}")
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assert (
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len(new_hits) >= 2
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), f"UPDATE only landed {new_hits!r} of expected new facts\n--- AFTER ---\n{seed_after}"
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print("\n" + "=" * 70)
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print("test_auto_memory_update passed")
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print("=" * 70)
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finally:
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await env.close_all()
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asyncio.run(run())
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if __name__ == "__main__":
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print("=== auto_memory integration test ===")
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test_auto_memory_create()
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test_auto_memory_update()
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print("\nAll integration tests passed!")
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