bd84e500e1
新增完整的长期记忆支持及测试：引入中文记忆检索提示词、query_long_term_memory 检索工具、记忆服务与记忆流程服务，以及 WebUI 的记忆路由。新增大规模测试套件（包括单元测试与基准/在线测试），覆盖聊天历史摘要、知识获取器、事件（episode）生成、写回机制以及用户画像检索等功能。 更新多个模块以集成记忆检索能力（包括 knowledge fetcher、chat summarizer、memory_retrieval、person_info、config/legacy 迁移以及 WebUI 路由），并移除遗留的 lpmm 知识模块。这些变更完成了记忆运行时的接入，同时为基准测试提供嵌入适配器的 mock，并支持新测试与工具所需的导入与 episode 处理流程。
128 lines
4.3 KiB
Python
128 lines
4.3 KiB
Python
from types import SimpleNamespace
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import pytest
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from src.chat.brain_chat.PFC import pfc_KnowledgeFetcher as knowledge_module
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from src.services.memory_service import MemoryHit, MemorySearchResult
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def test_knowledge_fetcher_resolves_private_memory_context(monkeypatch):
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monkeypatch.setattr(knowledge_module, "LLMRequest", lambda *args, **kwargs: object())
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monkeypatch.setattr(
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knowledge_module,
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"_chat_manager",
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SimpleNamespace(get_session_by_session_id=lambda session_id: SimpleNamespace(platform="qq", user_id="42", group_id="")),
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)
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monkeypatch.setattr(
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knowledge_module,
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"resolve_person_id_for_memory",
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lambda *, person_name, platform, user_id: f"{person_name}:{platform}:{user_id}",
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)
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fetcher = knowledge_module.KnowledgeFetcher(private_name="Alice", stream_id="stream-1")
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assert fetcher._resolve_private_memory_context() == {
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"chat_id": "stream-1",
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"person_id": "Alice:qq:42",
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"user_id": "42",
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"group_id": "",
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}
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@pytest.mark.asyncio
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async def test_knowledge_fetcher_memory_get_knowledge_uses_memory_service(monkeypatch):
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monkeypatch.setattr(knowledge_module, "LLMRequest", lambda *args, **kwargs: object())
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monkeypatch.setattr(
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knowledge_module,
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"_chat_manager",
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SimpleNamespace(get_session_by_session_id=lambda session_id: SimpleNamespace(platform="qq", user_id="42", group_id="")),
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)
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monkeypatch.setattr(
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knowledge_module,
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"resolve_person_id_for_memory",
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lambda *, person_name, platform, user_id: f"{person_name}:{platform}:{user_id}",
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)
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calls = []
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async def fake_search(query: str, **kwargs):
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calls.append((query, kwargs))
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return MemorySearchResult(summary="", hits=[MemoryHit(content="她喜欢猫", source="person_fact:qq:42")], filtered=False)
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monkeypatch.setattr(knowledge_module.memory_service, "search", fake_search)
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fetcher = knowledge_module.KnowledgeFetcher(private_name="Alice", stream_id="stream-1")
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result = await fetcher._memory_get_knowledge("她喜欢什么")
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assert "1. 她喜欢猫" in result
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assert calls == [
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(
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"她喜欢什么",
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{
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"limit": 5,
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"mode": "search",
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"chat_id": "stream-1",
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"person_id": "Alice:qq:42",
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"user_id": "42",
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"group_id": "",
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"respect_filter": True,
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},
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)
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]
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@pytest.mark.asyncio
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async def test_knowledge_fetcher_falls_back_to_chat_scope_when_person_scope_misses(monkeypatch):
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monkeypatch.setattr(knowledge_module, "LLMRequest", lambda *args, **kwargs: object())
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monkeypatch.setattr(
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knowledge_module,
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"_chat_manager",
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SimpleNamespace(get_session_by_session_id=lambda session_id: SimpleNamespace(platform="qq", user_id="42", group_id="")),
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)
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monkeypatch.setattr(
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knowledge_module,
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"resolve_person_id_for_memory",
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lambda *, person_name, platform, user_id: "person-1",
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)
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calls = []
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async def fake_search(query: str, **kwargs):
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calls.append((query, kwargs))
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if kwargs.get("person_id"):
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return MemorySearchResult(summary="", hits=[], filtered=False)
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return MemorySearchResult(summary="", hits=[MemoryHit(content="她计划去杭州音乐节", source="chat_summary:stream-1")], filtered=False)
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monkeypatch.setattr(knowledge_module.memory_service, "search", fake_search)
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fetcher = knowledge_module.KnowledgeFetcher(private_name="Alice", stream_id="stream-1")
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result = await fetcher._memory_get_knowledge("Alice 最近在忙什么")
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assert "杭州音乐节" in result
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assert calls == [
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(
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"Alice 最近在忙什么",
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{
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"limit": 5,
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"mode": "search",
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"chat_id": "stream-1",
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"person_id": "person-1",
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"user_id": "42",
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"group_id": "",
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"respect_filter": True,
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},
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),
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(
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"Alice 最近在忙什么",
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{
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"limit": 5,
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"mode": "search",
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"chat_id": "stream-1",
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"person_id": "",
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"user_id": "42",
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"group_id": "",
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"respect_filter": True,
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},
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),
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]
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