chore: 清理违反底座规则的死代码并修正接口文档编号
- 删除生产死代码 app/service/knowledge_tool_service.py 与 app/infrastructure/milvus_knowledge_adapter.py：后者硬编码 Milvus 字段名， 违反 AGENTS.md §E，且仅被前者引用；生产检索链路实际走 knowledge_search_tool -> KnowledgeSearchService -> knowledge_schema 运行时探测。 - 删除上述两模块的单测，以及依赖 legacy 位置参数构造的 tests/unit/service/test_knowledge_retrieval.py。 - app/service/knowledge_retrieval_service.py 整文件回退底座版本， 移除 legacy 双构造与重复检索实现。 - docs/05-接口文档.md：客服画像候选改登记为 §8.5，恢复 §8.2 解析知识引用； 既有 §8.1-§8.4 编号全部保持，修复此前出现两个 8.3 的问题。 - app/model/profile.py：current_customer_id 改为普通可空列映射，与 alembic/baseline_generated.sql 及真实库一致；原 Computed 声明会让 ORM 把该列 从 INSERT 中排除，与「必须显式写入」的实际 schema 不符。 - 新增 docs/客服Agent接入底座扩展说明_v1.md，供集成分支评审逐项确认。 验证：pytest tests/unit tests/contract -> 1275 passed, 2 skipped, 0 failed； ruff check app tests tools alembic 通过；mypy app 通过（244 个源文件）。
This commit is contained in:
张胜宇
@@ -1,87 +0,0 @@
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import pytest
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from app.core.contracts import RequestContext
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from app.core.errors import RecoverableAgentError
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from app.core.knowledge_contracts import KnowledgeHit, KnowledgeQuery
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from app.service.knowledge_config import KnowledgeRuntimeConfig
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from app.service.knowledge_retrieval_service import KnowledgeRetrievalService
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# ruff: noqa: E501
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class FakeEmbedder:
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async def embed(self, text: str) -> list[float]:
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assert text == "开户"
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return [0.1] * 1024
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class FakeVectorStore:
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def __init__(self) -> None:
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self.calls: list[tuple[str, int]] = []
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async def search(self, collection: str, vector: list[float], top_k: int) -> list[dict[str, object]]:
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assert len(vector) == 1024
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self.calls.append((collection, top_k))
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return []
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class FakeAuthority:
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async def filter_published(self, hits: tuple[object, ...]) -> list[object]:
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return []
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async def search_keyword(self, query: object, collections: tuple[str, ...], top_k: int) -> list[object]:
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return []
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class BrokenVectorStore:
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async def search(self, collection: str, vector: list[float], top_k: int) -> list[dict[str, object]]:
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raise RecoverableAgentError("知识检索不可用")
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class FallbackAuthority:
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def __init__(self) -> None:
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self.calls: list[tuple[tuple[str, ...], int]] = []
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async def filter_published(self, hits: tuple[object, ...]) -> list[object]:
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return []
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async def search_keyword(self, query: KnowledgeQuery, collections: tuple[str, ...], top_k: int) -> list[KnowledgeHit]:
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self.calls.append((collections, top_k))
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return [KnowledgeHit(
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knowledge_id="101", collection="fin_policy_collection", snippet="确认规则",
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answer="工作日确认", score=1.0,
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)]
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@pytest.mark.asyncio
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async def test_search_uses_faq_collection_for_faq_only() -> None:
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vector_store = FakeVectorStore()
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service = KnowledgeRetrievalService(
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FakeEmbedder(), vector_store, KnowledgeRuntimeConfig(), FakeAuthority()
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)
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result = await service.search(
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KnowledgeQuery(query="开户", intents=("faq",)),
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RequestContext(user_id="visitor-1", trace_id="trace", roles=("visitor",), data_scope="public"),
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)
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assert vector_store.calls == [("fin_faq_collection", 3)]
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assert result.searched_collections == ("fin_faq_collection",)
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@pytest.mark.asyncio
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async def test_milvus_failure_falls_back_to_published_active_unexpired_knowledge() -> None:
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authority = FallbackAuthority()
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service = KnowledgeRetrievalService(
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FakeEmbedder(), BrokenVectorStore(), KnowledgeRuntimeConfig(), authority
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)
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result = await service.search(
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KnowledgeQuery(query="开户", intents=("policy_explain",)),
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RequestContext(user_id="visitor-1", trace_id="trace", roles=("visitor",), data_scope="public"),
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)
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assert authority.calls == [(("fin_policy_collection",), 5)]
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assert result.degraded is True
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assert result.degradation_reason == "milvus_unavailable"
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assert result.hits[0].answer == "工作日确认"
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@@ -1,131 +0,0 @@
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import pytest
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from app.core.contracts import RequestContext
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from app.core.knowledge_contracts import KnowledgeHit, KnowledgeQuery, KnowledgeSearchResult
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from app.service import knowledge_tool_service
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from app.service.knowledge_tool_service import DatabaseEmbeddingAdapter, query_knowledge_tool
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class FakeGateway:
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def __init__(self) -> None:
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self.calls: list[tuple[str, str, int]] = []
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async def embed(self, *, endpoint_code: str, text: str, timeout_ms: int) -> list[float]:
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self.calls.append((endpoint_code, text, timeout_ms))
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return [0.1] * 1024
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@pytest.mark.asyncio
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async def test_embedding_adapter_uses_single_text_gateway_contract() -> None:
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gateway = FakeGateway()
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adapter = DatabaseEmbeddingAdapter("knowledge-embedding", 15000, gateway=gateway)
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vector = await adapter.embed("基金开户")
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assert len(vector) == 1024
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assert gateway.calls == [("knowledge-embedding", "基金开户", 15000)]
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@pytest.mark.asyncio
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async def test_query_tool_degrades_when_embedding_endpoint_is_unconfigured(monkeypatch) -> None:
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class Settings:
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knowledge_embedding_endpoint_code = ""
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monkeypatch.setattr("app.service.knowledge_tool_service.get_settings", lambda: Settings())
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result = await query_knowledge_tool(
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KnowledgeQuery(query="基金开户", intents=("faq",)),
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RequestContext(
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user_id="visitor-1", trace_id="trace", roles=("visitor",), data_scope="public"
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),
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)
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assert result.degraded is True
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assert result.degradation_reason == "embedding_endpoint_unconfigured"
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@pytest.mark.asyncio
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async def test_query_tool_uses_configured_embedding_endpoint_and_read_only_dependencies(
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monkeypatch,
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) -> None:
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class Settings:
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knowledge_embedding_endpoint_code = "knowledge-embedding"
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knowledge_embedding_timeout_ms = 15000
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milvus_uri = "http://milvus:19530"
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milvus_token = ""
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class FakeGateway:
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calls: list[tuple[str, str, int]] = []
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async def embed(
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self, *, endpoint_code: str, text: str, timeout_ms: int
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) -> list[float]:
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self.calls.append((endpoint_code, text, timeout_ms))
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return [0.1] * 1024
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class FakeMilvus:
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def __init__(self, uri: str, token: str | None) -> None:
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self.uri = uri
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self.token = token
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class FakeSession:
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async def __aenter__(self) -> object:
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return object()
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async def __aexit__(self, exc_type, exc, traceback) -> None:
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return None
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class FakeAuthority:
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def __init__(self, session: object) -> None:
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self.session = session
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class FakeRetrievalService:
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def __init__(self, embedder, vector_store, config, authority) -> None:
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self.embedder = embedder
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self.vector_store = vector_store
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self.config = config
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self.authority = authority
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async def search(
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self, query: KnowledgeQuery, context: RequestContext
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) -> KnowledgeSearchResult:
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vector = await self.embedder.embed(query.query)
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assert len(vector) == 1024
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assert isinstance(self.vector_store, FakeMilvus)
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assert isinstance(self.authority, FakeAuthority)
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assert self.config.routes["faq"] == ("fin_faq_collection", 3)
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assert context.data_scope == "public"
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return KnowledgeSearchResult(
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hits=(
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KnowledgeHit(
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knowledge_id="1",
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collection="fin_faq_collection",
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snippet="snippet",
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answer="answer",
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),
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),
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searched_collections=("fin_faq_collection",),
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)
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gateway = FakeGateway()
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monkeypatch.setattr(knowledge_tool_service, "get_settings", lambda: Settings())
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monkeypatch.setattr(knowledge_tool_service, "DatabaseModelGateway", lambda: gateway)
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monkeypatch.setattr(knowledge_tool_service, "MilvusKnowledgeClient", FakeMilvus)
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monkeypatch.setattr(knowledge_tool_service, "KnowledgeMysqlAuthority", FakeAuthority)
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monkeypatch.setattr(
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knowledge_tool_service, "KnowledgeRetrievalService", FakeRetrievalService
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)
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monkeypatch.setattr(knowledge_tool_service, "SessionFactory", FakeSession)
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result = await query_knowledge_tool(
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KnowledgeQuery(query="基金开户", intents=("faq",)),
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RequestContext(
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user_id="visitor-1",
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trace_id="trace",
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roles=("visitor",),
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data_scope="public",
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),
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)
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assert result.hits[0].answer == "answer"
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assert gateway.calls == [("knowledge-embedding", "基金开户", 15000)]
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