feat: T21-4 RAG 检索编排——rag_service.search_knowledge(query→bge-m3 向量→Milvus 检索→chunks+source_refs 溯源去重), 异常原样上抛不吞(防 LLM 编造回答), 空白 query 短路, ensure_collection 幂等防御; 单测 8 例注入 mock, 412 绿

tests/test_rag_service.py

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+"""T21-4 rag_service 单测：检索编排 + source_refs 溯源（monkeypatch 注入，不连外部）。
+
+embedding 与 Milvus client 均经模块级 _embed/_client 注入点 mock；
+Milvus 检索行为用假 client（FakeClient）模拟，search_kb 参数透传断言。
+"""
+
+from __future__ import annotations
+
+import pytest
+
+from app.service import rag_service as rs
+from app.service.embedding import EmbeddingError
+
+
+class FakeClient:
+    """假 Milvus client：记录 ensure/close 调用，search 透传给 stub。"""
+
+    def __init__(self, search_stub):
+        self.search_stub = search_stub
+        self.closed = False
+        self.last_kwargs: dict = {}
+
+    def close(self):
+        self.closed = True
+
+
+def _client_with(monkeypatch, stub) -> FakeClient:
+    """构造假 client 并注入 rs._client（embed 走假 8 维向量）。"""
+    client = FakeClient(stub)
+    monkeypatch.setattr(rs, "_client", lambda: client)
+    monkeypatch.setattr(rs, "_embed", lambda q: [0.1] * 8)
+    monkeypatch.setattr(
+        rs.milvus_service, "ensure_collection", lambda c, dim=None: None
+    )
+    monkeypatch.setattr(
+        rs.milvus_service,
+        "search_kb",
+        lambda c, vector, **kwargs: (client.last_kwargs.update(kwargs), stub(vector, **kwargs))[1],
+    )
+    return client
+
+
+def _hit(idx: str, doc: str = "KB-P1", ver: str = "v1", pid: str = "PROD-1") -> dict:
+    return {
+        "id": idx,
+        "score": 0.9,
+        "product_id": pid,
+        "product_name": "产品一",
+        "doc_type": "fee",
+        "risk_level": "R2",
+        "source_doc_id": doc,
+        "source_version": ver,
+        "effective_date": "2026-09-01",
+        "chunk_text": "申购费率 1.5%",
+        "chunk_no": 0,
+    }
+
+
+class TestSearch:
+    def test_basic_flow_and_source_refs(self, monkeypatch):
+        _client_with(monkeypatch, lambda v, **k: [_hit("P1_0"), _hit("P1_1", ver="v2")])
+        out = rs.search_knowledge("基金申购费率")
+        assert len(out["results"]) == 2
+        # 同文档不同版本 → 溯源按 (doc, ver, pid) 去重，应为 2 条
+        assert len(out["source_refs"]) == 2
+        assert out["source_refs"][0]["source_doc_id"] == "KB-P1"
+
+    def test_blank_query_short_circuits(self, monkeypatch):
+        # 空白 query：不发 embedding、不连 Milvus
+        def bad_client():
+            raise AssertionError("空白 query 不应触发 Milvus 连接")
+
+        monkeypatch.setattr(rs, "_client", bad_client)
+        monkeypatch.setattr(rs, "_embed", lambda q: [] if False else (_ for _ in ()).throw(AssertionError("不应向量化")))
+        out = rs.search_knowledge("   ")
+        assert out == {"query": "", "results": [], "source_refs": []}
+
+    def test_filter_params_passthrough(self, monkeypatch):
+        client = _client_with(monkeypatch, lambda v, **k: [])
+        rs.search_knowledge("定投规则", product_id="PROD-9", doc_type="rule", top_k=5)
+        assert client.last_kwargs["product_id"] == "PROD-9"
+        assert client.last_kwargs["doc_type"] == "rule"
+        assert client.last_kwargs["top_k"] == 5
+
+    def test_query_whitespace_stripped(self, monkeypatch):
+        _client_with(monkeypatch, lambda v, **k: [])
+        out = rs.search_knowledge("  赎回到账时间  ")
+        assert out["query"] == "赎回到账时间"
+
+
+class TestSourceRefs:
+    def test_dedup_same_doc_version(self, monkeypatch):
+        _client_with(monkeypatch, lambda v, **k: [_hit("P1_0"), _hit("P1_1")])
+        out = rs.search_knowledge("费率")
+        assert len(out["source_refs"]) == 1
+
+    def test_multi_product_refs(self, monkeypatch):
+        _client_with(
+            monkeypatch,
+            lambda v, **k: [_hit("P1_0"), _hit("P2_0", doc="KB-P2", pid="PROD-2")],
+        )
+        out = rs.search_knowledge("费率")
+        assert len(out["source_refs"]) == 2
+        pids = {r["product_id"] for r in out["source_refs"]}
+        assert pids == {"PROD-1", "PROD-2"}
+
+
+class TestFailurePropagation:
+    """失败口径：异常原样上抛，不吞不降级（防止 LLM 编造回答）。"""
+
+    def test_embed_error_propagates(self, monkeypatch):
+        def boom(q):
+            raise EmbeddingError("Ollama 连接失败")
+
+        monkeypatch.setattr(rs, "_embed", boom)
+        with pytest.raises(EmbeddingError):
+            rs.search_knowledge("任意")
+
+    def test_milvus_error_propagates(self, monkeypatch):
+        def stub(vector, **kwargs):
+            raise RuntimeError("milvus down")
+
+        _client_with(monkeypatch, stub)
+        with pytest.raises(RuntimeError):
+            rs.search_knowledge("任意")