zhanghongyu_0626
7dadec279d
- Implemented `_merged_items` and `_merged_memory_text` functions to consolidate consult and chitchat memories, improving context awareness in intent classification and response generation. - Updated intent prompts to include recent dialogue history, aiding in the resolution of ambiguous user queries. - Enhanced `search_knowledge` tool to utilize context window for better query understanding, addressing issues with omitted references in user inputs. - Fixed existing test cases to reflect changes in intent constants and ensure accurate context handling during tests. This update significantly improves the handling of multi-turn dialogues, ensuring a more coherent and contextually aware interaction for users.
230 lines
9.5 KiB
Python
230 lines
9.5 KiB
Python
"""T21-5 知识库 Tool 单测：注册表合并 / skip_access_check / 意图扩展 / 带参注入。
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覆盖：
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1. kb_tools.search_knowledge Tool 函数（rag_service mock，返回结构）；
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2. get_registered_tool 三层合并（core → risk → kb）；
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3. run_tool 对 skip_access_check 的处理（advisor 无绑定客户 → success 而非
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AUTH_403 blocked；未知角色也放行——公开知识无归属语义，意图层已限范围）；
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4. match_intent：customer/advisor 命中 kb 词、Core 词优先、risk 不命中；
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5. tool_node 按 spec 白名单注入 query（agent_service 集成，FakeLLM 模式同既有测试）。
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"""
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from __future__ import annotations
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import pytest
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from app.service import rag_service, tool_service
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from app.service.agent_service import tool_node
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from app.tool import kb_tools
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@pytest.fixture()
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def fake_rag(monkeypatch):
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"""mock rag_service.search_knowledge（不连 Ollama/Milvus）。"""
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calls: dict = {}
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def fake_search(query, *, product_id=None, doc_type=None, top_k=3):
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calls.update({"query": query, "top_k": top_k})
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return {
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"query": query,
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"results": [
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{
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"id": "PROD-110022_1",
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"score": 0.82,
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"product_id": "PROD-110022",
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"product_name": "稳健债基 A",
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"doc_type": "rule",
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"risk_level": "R1",
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"source_doc_id": "KB-PROD-110022",
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"source_version": "2026.09",
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"effective_date": "2026-09-01",
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"chunk_text": "申购 T 日 15:00 前提交按当日净值确认，T+1 确认份额。",
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"chunk_no": 1,
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}
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],
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"source_refs": [
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{
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"source_doc_id": "KB-PROD-110022",
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"source_version": "2026.09",
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"product_id": "PROD-110022",
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"product_name": "稳健债基 A",
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}
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],
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}
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monkeypatch.setattr(rag_service, "search_knowledge", fake_search)
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return calls
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CUSTOMER_ACTOR = {"actor_id": "CUST-1", "roles": ["customer"], "token_type": "dev"}
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ADVISOR_ACTOR = {"actor_id": "ADV-1", "roles": ["advisor"], "token_type": "dev"}
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class TestToolFunction:
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def test_returns_hits_and_refs(self, fake_rag):
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out = kb_tools.search_knowledge(query="申购规则")
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assert out["hit_count"] == 1
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assert out["results"][0]["source_doc_id"] == "KB-PROD-110022"
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assert fake_rag["query"] == "申购规则"
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def test_registry_shape(self):
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spec = kb_tools.KB_TOOL_REGISTRY["search_knowledge"]
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assert spec["requires_customer"] is False
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assert spec["skip_access_check"] is True
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assert "query" in spec["param_whitelist"]
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class TestRegistryMerge:
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def test_three_layer_merge(self):
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# core / risk / kb 各取一个代表
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assert tool_service.get_registered_tool("query_holdings") is not None
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assert tool_service.get_registered_tool("alert_query") is not None
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assert tool_service.get_registered_tool("search_knowledge") is not None
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assert tool_service.get_registered_tool("no_such_tool") is None
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class TestRunToolSkipAccess:
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def test_advisor_without_customer_succeeds(self, fake_rag, monkeypatch):
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# 核心：advisor 无绑定客户（customer_id=""）查知识库必须 success
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# （无 skip_access_check 时会被 assert_tool_access 拒 AUTH_403_NOT_ASSIGNED）
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monkeypatch.setattr(tool_service, "_core_ro", lambda: object())
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monkeypatch.setattr(tool_service, "_risk_repo", lambda: object())
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monkeypatch.setattr(
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tool_service, "_session_repo", lambda: type("R", (), {"insert_tool_call": lambda *a, **k: None})()
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)
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record = tool_service.run_tool(
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tool_name="search_knowledge",
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agent_type="advisor",
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actor=ADVISOR_ACTOR,
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customer_id="",
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tool_input={"query": "基金申购费率"},
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session_id="S1",
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)
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assert record["status"] == "success"
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assert record["data"]["hit_count"] == 1
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def test_unknown_role_also_passes(self, fake_rag, monkeypatch):
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# 公开知识：非四角色（如 platform 误调）也放行——意图层已限定开放范围
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monkeypatch.setattr(tool_service, "_core_ro", lambda: object())
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monkeypatch.setattr(tool_service, "_risk_repo", lambda: object())
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monkeypatch.setattr(
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tool_service, "_session_repo", lambda: type("R", (), {"insert_tool_call": lambda *a, **k: None})()
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)
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record = tool_service.run_tool(
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tool_name="search_knowledge",
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agent_type="customer",
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actor={"actor_id": "X", "roles": ["unknown_role"], "token_type": "dev"},
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customer_id="",
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tool_input={"query": "费率"},
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session_id="S1",
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)
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assert record["status"] == "success"
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def test_bad_param_still_blocked(self, fake_rag, monkeypatch):
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# 白名单外参数仍拒（skip_access_check 只豁免归属，不豁免入参校验）
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monkeypatch.setattr(tool_service, "_core_ro", lambda: object())
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monkeypatch.setattr(tool_service, "_risk_repo", lambda: object())
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monkeypatch.setattr(
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tool_service, "_session_repo", lambda: type("R", (), {"insert_tool_call": lambda *a, **k: None})()
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)
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record = tool_service.run_tool(
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tool_name="search_knowledge",
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agent_type="customer",
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actor=CUSTOMER_ACTOR,
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customer_id="CUST-1",
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tool_input={"customer_id": "HACK"},
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session_id="S1",
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)
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assert record["status"] == "blocked"
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assert record["error_code"] == "TOOL_BAD_PARAM"
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class TestIntent:
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def test_customer_kb_keywords(self):
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assert tool_service.match_intent("customer", "稳健债基的申购费率是多少") == "search_knowledge"
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assert tool_service.match_intent("customer", "赎回几天到账") == "search_knowledge"
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assert tool_service.match_intent("advisor", "这只基金的定投起点") == "search_knowledge"
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def test_core_keywords_take_priority(self):
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# Core 词更特异（组序在前）：持仓类问句不被 kb 词抢走
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assert tool_service.match_intent("customer", "看一下我的持仓") == "query_holdings"
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assert tool_service.match_intent("customer", "我的风险测评结果") == "query_customer_profile"
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def test_risk_branch_no_kb(self):
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# 拍板：风控不开放知识检索
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assert tool_service.match_intent("risk", "基金申购费率") is None
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assert tool_service.match_intent("analyst", "基金申购费率") is None
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class TestToolNodeInjection:
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"""tool_node 按 spec 白名单注入 query（state→tool_input）。"""
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def _state(self, agent_type="customer", msg="基金申购费率"):
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return {
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"agent_type": agent_type,
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"history": [],
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"user_message": msg,
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"messages": [],
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"reply": "",
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"has_disclaimer": False,
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"session_id": "S1",
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"trace_id": "T1",
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"actor": CUSTOMER_ACTOR,
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"customer_id": "CUST-1",
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"tool_results": [],
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}
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def test_query_injected_from_user_message(self, fake_rag, monkeypatch):
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captured: dict = {}
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def fake_run_tool(**kwargs):
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captured.update(kwargs)
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return {"tool_name": kwargs["tool_name"], "status": "success", "error_code": None,
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"data": {"hit_count": 0, "results": [], "source_refs": []}, "latency_ms": 1}
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monkeypatch.setattr(tool_service, "run_tool", fake_run_tool)
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tool_node(self._state())
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assert captured["tool_input"] == {"query": "基金申购费率", "_context_window": ""}
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def test_no_input_for_core_tools(self, fake_rag, monkeypatch):
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# Core Tool（白名单无 query）不注入 tool_input，维持 T-04 口径
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captured: dict = {}
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def fake_run_tool(**kwargs):
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captured.update(kwargs)
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return {"tool_name": kwargs["tool_name"], "status": "success", "error_code": None,
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"data": {}, "latency_ms": 1}
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monkeypatch.setattr(tool_service, "run_tool", fake_run_tool)
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tool_node(self._state(msg="看一下我的持仓"))
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assert captured["tool_input"] is None
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class TestSummarize:
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def test_kb_summary_with_refs(self):
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record = {
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"tool_name": "search_knowledge",
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"status": "success",
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"error_code": None,
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"data": {
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"hit_count": 1,
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"results": [{
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"product_name": "稳健债基 A", "doc_type": "rule", "score": 0.82,
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"chunk_text": "申购 T 日 15:00 前提交按当日净值确认，T+1 确认份额。本金部分。",
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}],
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"source_refs": [{"source_doc_id": "KB-PROD-110022", "source_version": "2026.09"}],
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},
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}
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text = tool_service.summarize(record)
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assert "命中 1 条" in text
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assert "稳健债基 A" in text
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assert "KB-PROD-110022@2026.09" in text
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def test_kb_summary_empty(self):
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record = {
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"tool_name": "search_knowledge", "status": "success", "error_code": None,
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"data": {"hit_count": 0, "results": [], "source_refs": []},
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}
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text = tool_service.summarize(record)
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assert "未命中" in text and "不要编造" in text
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