feat: add governed public knowledge retrieval

tests/unit/service/test_knowledge_tool_service.py

@@ -0,0 +1,131 @@
+import pytest
+
+from app.core.contracts import RequestContext
+from app.core.knowledge_contracts import KnowledgeHit, KnowledgeQuery, KnowledgeSearchResult
+from app.service import knowledge_tool_service
+from app.service.knowledge_tool_service import DatabaseEmbeddingAdapter, query_knowledge_tool
+
+
+class FakeGateway:
+    def __init__(self) -> None:
+        self.calls: list[tuple[str, str, int]] = []
+
+    async def embed(self, *, endpoint_code: str, text: str, timeout_ms: int) -> list[float]:
+        self.calls.append((endpoint_code, text, timeout_ms))
+        return [0.1] * 1024
+
+
+@pytest.mark.asyncio
+async def test_embedding_adapter_uses_single_text_gateway_contract() -> None:
+    gateway = FakeGateway()
+    adapter = DatabaseEmbeddingAdapter("knowledge-embedding", 15000, gateway=gateway)
+
+    vector = await adapter.embed("基金开户")
+
+    assert len(vector) == 1024
+    assert gateway.calls == [("knowledge-embedding", "基金开户", 15000)]
+
+
+@pytest.mark.asyncio
+async def test_query_tool_degrades_when_embedding_endpoint_is_unconfigured(monkeypatch) -> None:
+    class Settings:
+        knowledge_embedding_endpoint_code = ""
+
+    monkeypatch.setattr("app.service.knowledge_tool_service.get_settings", lambda: Settings())
+
+    result = await query_knowledge_tool(
+        KnowledgeQuery(query="基金开户", intents=("faq",)),
+        RequestContext(
+            user_id="visitor-1", trace_id="trace", roles=("visitor",), data_scope="public"
+        ),
+    )
+
+    assert result.degraded is True
+    assert result.degradation_reason == "embedding_endpoint_unconfigured"
+
+
+@pytest.mark.asyncio
+async def test_query_tool_uses_configured_embedding_endpoint_and_read_only_dependencies(
+    monkeypatch,
+) -> None:
+    class Settings:
+        knowledge_embedding_endpoint_code = "knowledge-embedding"
+        knowledge_embedding_timeout_ms = 15000
+        milvus_uri = "http://milvus:19530"
+        milvus_token = ""
+
+    class FakeGateway:
+        calls: list[tuple[str, str, int]] = []
+
+        async def embed(
+            self, *, endpoint_code: str, text: str, timeout_ms: int
+        ) -> list[float]:
+            self.calls.append((endpoint_code, text, timeout_ms))
+            return [0.1] * 1024
+
+    class FakeMilvus:
+        def __init__(self, uri: str, token: str | None) -> None:
+            self.uri = uri
+            self.token = token
+
+    class FakeSession:
+        async def __aenter__(self) -> object:
+            return object()
+
+        async def __aexit__(self, exc_type, exc, traceback) -> None:
+            return None
+
+    class FakeAuthority:
+        def __init__(self, session: object) -> None:
+            self.session = session
+
+    class FakeRetrievalService:
+        def __init__(self, embedder, vector_store, config, authority) -> None:
+            self.embedder = embedder
+            self.vector_store = vector_store
+            self.config = config
+            self.authority = authority
+
+        async def search(
+            self, query: KnowledgeQuery, context: RequestContext
+        ) -> KnowledgeSearchResult:
+            vector = await self.embedder.embed(query.query)
+            assert len(vector) == 1024
+            assert isinstance(self.vector_store, FakeMilvus)
+            assert isinstance(self.authority, FakeAuthority)
+            assert self.config.routes["faq"] == ("fin_faq_collection", 3)
+            assert context.data_scope == "public"
+            return KnowledgeSearchResult(
+                hits=(
+                    KnowledgeHit(
+                        knowledge_id="1",
+                        collection="fin_faq_collection",
+                        snippet="snippet",
+                        answer="answer",
+                    ),
+                ),
+                searched_collections=("fin_faq_collection",),
+            )
+
+    gateway = FakeGateway()
+    monkeypatch.setattr(knowledge_tool_service, "get_settings", lambda: Settings())
+    monkeypatch.setattr(knowledge_tool_service, "DatabaseModelGateway", lambda: gateway)
+    monkeypatch.setattr(knowledge_tool_service, "MilvusKnowledgeClient", FakeMilvus)
+    monkeypatch.setattr(knowledge_tool_service, "KnowledgeMysqlAuthority", FakeAuthority)
+    monkeypatch.setattr(
+        knowledge_tool_service, "KnowledgeRetrievalService", FakeRetrievalService
+    )
+    monkeypatch.setattr(knowledge_tool_service, "SessionFactory", FakeSession)
+
+    result = await query_knowledge_tool(
+        KnowledgeQuery(query="基金开户", intents=("faq",)),
+        RequestContext(
+            user_id="visitor-1",
+            trace_id="trace",
+            roles=("visitor",),
+            data_scope="public",
+        ),
+    )
+
+    assert result.hits[0].answer == "answer"
+    assert gateway.calls == [("knowledge-embedding", "基金开户", 15000)]