ylhan
20cfaa6ef7
这是进入 Pipecat Flows 改造前的回滚点。 提交时已核对本地与线上代码 md5 一致（engine.py 7e97bad8、 bot_server.py 1cf543ae），所以这个提交就是当前线上状态。 内容（此前一直在线上运行但未提交的改动）： - 姓名档案：用户自报"我叫林"同步写 confirmed 姓名，重连后恢复 - 疑问句防误写："谁吗"之类的疑问句不再被覆盖成姓名 - 新增 test_profile_memory.py（姓名档案专项回归） - 新增 test_profile_live.py（需要真实 LLM 的现场测试） - 搜索、身份、记忆测试的相应补充 背景：下一步要按 P1-Pipecat Flows Agent化与记忆改造交接.md 渐进引入 Pipecat Flows，交接文档明确要求先保护基线、不得 reset/checkout/覆盖现有改动。此提交即该基线。
115 lines
4.0 KiB
Python
115 lines
4.0 KiB
Python
#!/usr/bin/env python3
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"""用户称呼跨会话持久化与内部 ID 隔离回归测试。"""
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from __future__ import annotations
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import asyncio
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import shutil
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import sys
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import tempfile
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from pathlib import Path
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import numpy as np
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ROOT = Path(__file__).resolve().parent.parent.parent
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sys.path.insert(0, str(ROOT))
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from app.config import AppConfig # noqa: E402
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from app.engine import ( # noqa: E402
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ConversationEngine, extract_display_name, extract_memories,
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)
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from app.identity import ManualIdentityProvider # noqa: E402
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from app.llm.base import Chunk, ChunkKind # noqa: E402
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from app.memory import ConfirmStatus, SQLiteMemoryStore # noqa: E402
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class FakeLLM:
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name = "fake"
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last_backend = "fake"
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def __init__(self):
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self.system_prompts = []
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async def stream(self, messages, *, tools=None, max_tokens=None):
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self.system_prompts.append(messages[0]["content"])
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yield Chunk(kind=ChunkKind.CONTENT, text="好的。")
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yield Chunk(kind=ChunkKind.DONE, finish_reason="stop")
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class FakeTTS:
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sample_rate = 16000
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def synthesize(self, _text):
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return np.ones(160, dtype=np.float32) * 0.1
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async def consume(engine: ConversationEngine, text: str) -> None:
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async for _event in engine.respond_to_text(text):
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pass
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async def test_cross_session_profile() -> None:
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tmp = Path(tempfile.mkdtemp(prefix="profile_memory_"))
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store = SQLiteMemoryStore(tmp / "memory.db")
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await store.open()
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try:
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cfg = AppConfig()
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cfg.search.enabled = False
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cfg.memory.auto_extract_enabled = False
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first_llm = FakeLLM()
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first = ConversationEngine(
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cfg, first_llm, store, ManualIdentityProvider(), FakeTTS())
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await first.start_session("profile_s1", user_id="u_internal")
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await consume(first, "你好，我叫林。")
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await first.end_session()
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assert await store.get_user_display_name("u_internal") == "林"
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memories = await store.list_memories("u_internal")
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names = [m for m in memories if m.key == "姓名"]
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assert len(names) == 1
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assert names[0].value == "林"
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assert names[0].confirm_status == ConfirmStatus.CONFIRMED
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# 模拟 disconnect 后重新创建整个 ConversationEngine/IdentityProvider。
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second_llm = FakeLLM()
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second = ConversationEngine(
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cfg, second_llm, store, ManualIdentityProvider(), FakeTTS())
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await second.start_session("profile_s2", user_id="u_internal")
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assert second.identity.current().display_name == "林"
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await consume(second, "我是谁？")
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prompt = second_llm.system_prompts[-1]
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assert "林" in prompt
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assert "u_internal" not in prompt
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assert "内部 user_id" in prompt
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await consume(second, "知道我是谁吗？")
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assert await store.get_user_display_name("u_internal") == "林"
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await consume(second, "我叫什么？")
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assert await store.get_user_display_name("u_internal") == "林"
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await second.end_session()
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finally:
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await store.close()
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shutil.rmtree(tmp, ignore_errors=True)
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async def main() -> int:
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assert extract_display_name("我叫林") == "林"
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assert extract_display_name("你好我叫lin。") == "lin"
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assert extract_display_name("我的名字是小林。") == "小林"
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assert extract_display_name("你可以叫我老韩。") == "老韩"
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assert extract_display_name("我是林。") == "林"
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assert extract_display_name("我是程序员。") is None
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assert extract_display_name("我叫什么？") is None
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assert extract_display_name("我是谁？") is None
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assert extract_display_name("知道我是谁吗？") is None
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assert extract_display_name("我是林吗？") is None
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assert extract_display_name("我叫什么名字？") is None
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assert extract_memories("记住，我叫林。", "") == []
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await test_cross_session_profile()
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print("用户称呼跨会话回归测试：22/22 PASS")
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return 0
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if __name__ == "__main__":
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raise SystemExit(asyncio.run(main()))
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