594 lines
19 KiB
Python
594 lines
19 KiB
Python
from agent_core.orchestrator import build_messages, run_agent
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from agent_core.rag.ingest import _split_text, ingest_document
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from agent_core.rag.retriever import retrieve
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from agent_core.schemas.outputs import SUPPORTED_OUTPUT_TYPES
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from django.test import override_settings
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def test_run_agent_returns_structured_result_from_llm_output():
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scenario = {
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"id": "knowledge_qa",
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"name": "知识库问答助手",
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"agent": {
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"role": "知识库助手",
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"goal": "基于资料回答问题",
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"instructions": ["仅根据证据回答"],
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},
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"rag": {"enabled": True, "collection": "knowledge_qa", "top_k": 3},
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"tools": ["generate_action_items"],
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"output": {"type": "general_answer"},
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}
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provider_response = """
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{
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"answer": "请先隔离异常现场，再通知负责人。",
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"confidence": "high",
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"references": [
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{"source": "sop.md", "excerpt": "异常处理 SOP：先隔离现场"}
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]
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}
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"""
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class FakeProvider:
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def generate(self, messages, response_format=None):
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from agent_core.llm_provider import LLMResponse
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return LLMResponse(
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content=provider_response,
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model_name="demo-model",
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success=True,
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)
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result = run_agent(
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scenario,
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"如何处理异常？",
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options={"llm_provider": FakeProvider()},
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)
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assert result.status == "success"
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assert result.answer == "请先隔离异常现场，再通知负责人。"
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assert result.structured_output["output_type"] == "general_answer"
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assert result.structured_output["confidence"] == "high"
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assert isinstance(result.references, list)
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assert result.tool_calls[0]["tool_name"] == "generate_action_items"
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assert result.model_name == "demo-model"
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def test_run_agent_falls_back_when_llm_returns_non_json():
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scenario = {
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"id": "document_review",
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"name": "文档审核助手",
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"agent": {
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"role": "审核助手",
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"goal": "总结审核意见",
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"instructions": ["输出重点问题"],
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},
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"rag": {"enabled": False},
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"tools": [],
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"output": {"type": "document_review_report"},
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}
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class FakeProvider:
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def generate(self, messages, response_format=None):
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from agent_core.llm_provider import LLMResponse
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return LLMResponse(
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content="这是非 JSON 的普通回答",
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model_name="demo-model",
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success=True,
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)
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result = run_agent(
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scenario,
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"请检查合同风险",
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options={"llm_provider": FakeProvider()},
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)
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assert result.status == "success"
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assert result.answer == "这是非 JSON 的普通回答"
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assert result.structured_output["output_type"] == "document_review_report"
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assert result.structured_output["summary"] == "这是非 JSON 的普通回答"
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assert result.structured_output["parse_mode"] == "fallback"
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def test_build_messages_contains_role_goal_references_and_tool_results():
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scenario = {
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"name": "质量异常分析助手",
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"agent": {
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"role": "质量管理专家",
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"goal": "生成结构化质量分析报告",
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"instructions": ["必须引用知识库", "缺失信息要说明"],
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},
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"output": {"type": "quality_report"},
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}
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messages = build_messages(
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scenario_config=scenario,
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user_input="分析 A 线异常",
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references=[{"source": "sop.md", "content": "先隔离现场"}],
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tool_calls=[
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{
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"tool_name": "query_demo_records",
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"success": True,
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"result": {"records": [{"title": "A线缺陷"}]},
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"error": "",
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}
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],
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)
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assert messages[0]["role"] == "system"
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assert "质量管理专家" in messages[0]["content"]
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assert "生成结构化质量分析报告" in messages[0]["content"]
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assert "quality_report" in messages[0]["content"]
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assert "先隔离现场" in messages[1]["content"]
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assert "A线缺陷" in messages[1]["content"]
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assert "分析 A 线异常" in messages[2]["content"]
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def test_rag_ingest_and_retrieve_filters_by_scenario_and_query(tmp_path):
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store_path = tmp_path / "rag_store.json"
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text = "设备点检需要先断电挂牌。质量异常需要记录批次、工位和缺陷现象。"
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result = ingest_document(
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scenario_id="quality_analysis",
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source_file="quality.md",
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text=text,
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collection="quality_analysis",
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store_path=store_path,
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)
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ingest_document(
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scenario_id="risk_audit",
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source_file="risk.md",
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text="报销审核需要检查发票、金额和审批链。",
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collection="risk_audit",
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store_path=store_path,
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)
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chunks = retrieve(
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scenario_id="quality_analysis",
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query="质量异常批次",
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collection="quality_analysis",
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top_k=3,
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store_path=store_path,
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)
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assert result.success is True
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assert result.chunks_count >= 1
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assert chunks
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assert chunks[0]["source"] == "quality.md"
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assert "质量异常" in chunks[0]["content"]
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assert all(chunk["scenario_id"] == "quality_analysis" for chunk in chunks)
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def test_rag_reingest_replaces_same_document_and_retrieve_filters_document_ids(tmp_path):
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store_path = tmp_path / "rag_store.json"
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ingest_document(
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document_id=1,
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scenario_id="knowledge_qa",
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source_file="old.md",
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text="旧制度要求人工登记。",
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collection="knowledge_qa",
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store_path=store_path,
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)
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ingest_document(
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document_id=1,
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scenario_id="knowledge_qa",
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source_file="new.md",
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text="新制度要求系统自动登记。",
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collection="knowledge_qa",
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store_path=store_path,
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)
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ingest_document(
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document_id=2,
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scenario_id="knowledge_qa",
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source_file="other.md",
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text="系统自动登记后需要生成审计记录。",
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collection="knowledge_qa",
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store_path=store_path,
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)
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chunks = retrieve(
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scenario_id="knowledge_qa",
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query="系统自动登记",
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collection="knowledge_qa",
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top_k=5,
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document_ids=[1],
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store_path=store_path,
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)
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assert chunks
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assert {chunk["document_id"] for chunk in chunks} == {1}
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assert all(chunk["source"] == "new.md" for chunk in chunks)
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assert all("旧制度" not in chunk["content"] for chunk in chunks)
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def test_run_agent_uses_retrieved_document_chunks(tmp_path):
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store_path = tmp_path / "rag_store.json"
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ingest_document(
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scenario_id="knowledge_qa",
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source_file="sop.md",
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text="异常处理 SOP：先隔离现场，再通知负责人。",
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collection="knowledge_qa",
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store_path=store_path,
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)
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scenario = {
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"id": "knowledge_qa",
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"name": "知识库问答助手",
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"rag": {"enabled": True, "collection": "knowledge_qa", "top_k": 3},
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"tools": [],
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"output": {"type": "general_answer"},
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}
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result = run_agent(scenario, "异常处理怎么做？", options={"rag_store_path": store_path})
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assert result.references[0]["source"] == "sop.md"
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assert "隔离现场" in result.references[0]["content"]
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def test_rag_split_text_keeps_overlap_and_non_empty_chunks():
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chunks = _split_text("A" * 20, chunk_size=8, overlap=3)
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assert chunks == ["AAAAAAAA", "AAAAAAAA", "AAAAAAAA", "AAAAA"]
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def test_retrieve_returns_empty_when_query_has_no_overlap(tmp_path):
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store_path = tmp_path / "rag_store.json"
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ingest_document(
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scenario_id="knowledge_qa",
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source_file="rules.md",
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text="这里描述的是报销流程和审批链。",
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collection="knowledge_qa",
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store_path=store_path,
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)
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chunks = retrieve(
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scenario_id="knowledge_qa",
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query="设备点检",
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collection="knowledge_qa",
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top_k=3,
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store_path=store_path,
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)
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assert chunks == []
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def test_registration_risk_result_includes_owner_fields_and_notification_payload():
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scenario = {
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"id": "document_review",
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"name": "注册审核智能体",
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"agent": {
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"role": "注册审核助手",
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"goal": "输出风险结果",
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"instructions": ["输出结构化风险结果"],
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},
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"rag": {"enabled": False},
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"tools": [],
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"output": {"type": "registration_risk_report"},
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}
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provider_response = """
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{
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"summary": "存在高风险项，需人工复核。",
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"highest_risk_level": "high",
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"pass_status": "blocked",
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"owner_roles": [
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{
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"owner_role": "注册资料负责人",
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"owner_name": "张三",
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"department": "注册事务部",
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"chapter_scope": "CH1",
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"risk_scope": "字段冲突",
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"feishu_user_id": "ou_demo_1",
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"feishu_open_id": "on_demo_1",
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"feishu_name": "张三",
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"notify_enabled": true
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}
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],
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"notify_reason": "task_completed"
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}
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"""
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class FakeProvider:
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def generate(self, messages, response_format=None):
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from agent_core.llm_provider import LLMResponse
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return LLMResponse(content=provider_response, model_name="demo-model", success=True)
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result = run_agent(scenario, "请输出风险结果", options={"llm_provider": FakeProvider()})
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owner = result.notification_payload["owners"][0]
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assert result.structured_output["output_type"] == "registration_risk_report"
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assert owner["owner_role"] == "注册资料负责人"
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assert owner["feishu_user_id"] == "ou_demo_1"
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assert owner["feishu_open_id"] == "on_demo_1"
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assert result.notification_payload["notify_reason"] == "task_completed"
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def test_supported_output_types_include_word_export_and_feishu_notification():
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assert "registration_word_export_report" in SUPPORTED_OUTPUT_TYPES
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assert "feishu_notification_report" in SUPPORTED_OUTPUT_TYPES
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def test_registration_risk_report_builds_eight_business_nodes():
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scenario = {
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"id": "document_review",
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"name": "注册审核智能体",
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"agent": {
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"role": "注册审核助手",
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"goal": "输出风险结果",
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"instructions": ["输出结构化风险结果"],
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},
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"rag": {"enabled": False},
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"tools": [],
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"output": {"type": "registration_risk_report"},
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}
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provider_response = """
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{
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"summary": "存在高风险项，需人工复核。",
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"highest_risk_level": "high",
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"pass_status": "blocked",
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"owner_roles": [],
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"notify_reason": "task_completed"
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}
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"""
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class FakeProvider:
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def generate(self, messages, response_format=None):
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from agent_core.llm_provider import LLMResponse
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return LLMResponse(content=provider_response, model_name="demo-model", success=True)
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result = run_agent(scenario, "请输出风险结果", options={"llm_provider": FakeProvider()})
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assert len(result.node_results) == 8
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assert [node["label"] for node in result.node_results] == [
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"资料包导入",
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"目录汇总",
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"法规完整性检查",
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"字段抽取",
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"一致性核查",
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"风险预警",
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"Word 回填导出",
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"飞书通知",
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]
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assert result.node_results[5]["status"] == "已阻断"
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assert result.node_results[6]["status"] == "待处理"
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assert result.node_results[7]["status"] == "待处理"
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@override_settings(GOVERNANCE_CONFIG_PATH="")
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def test_registration_risk_payload_falls_back_to_governance_owner_mappings(tmp_path):
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config_path = tmp_path / "governance.yaml"
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config_path.write_text(
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"""
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owner_mappings:
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- owner_role: 法规负责人
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owner_name: 赵六
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department: 法规事务部
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chapter_scope: CH1
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risk_scope: 高风险缺失
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feishu_user_id: ou_governance_1
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feishu_open_id: on_governance_1
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feishu_name: 赵六
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notify_enabled: 是
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""".strip(),
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encoding="utf-8",
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)
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scenario = {
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"id": "document_review",
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"name": "注册审核智能体",
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"agent": {
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"role": "注册审核助手",
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"goal": "输出风险结果",
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"instructions": ["输出结构化风险结果"],
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},
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"rag": {"enabled": False},
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"tools": [],
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"output": {"type": "registration_risk_report"},
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}
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provider_response = """
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{
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"summary": "存在高风险项，需人工复核。",
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"highest_risk_level": "high",
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"pass_status": "blocked",
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"owner_roles": [],
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"notify_reason": "task_completed"
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}
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"""
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class FakeProvider:
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def generate(self, messages, response_format=None):
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from agent_core.llm_provider import LLMResponse
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return LLMResponse(content=provider_response, model_name="demo-model", success=True)
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with override_settings(GOVERNANCE_CONFIG_PATH=config_path):
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result = run_agent(scenario, "请输出风险结果", options={"llm_provider": FakeProvider()})
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owner = result.notification_payload["owners"][0]
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assert owner["owner_role"] == "法规负责人"
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assert owner["feishu_user_id"] == "ou_governance_1"
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assert result.notification_payload["notify_reason"] == "task_completed"
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@override_settings(GOVERNANCE_CONFIG_PATH="")
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def test_failed_agent_result_uses_governance_owner_mappings_for_failed_notification(tmp_path):
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config_path = tmp_path / "governance.yaml"
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config_path.write_text(
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"""
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owner_mappings:
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- owner_role: 注册资料负责人
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owner_name: 孙七
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department: 注册事务部
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chapter_scope: CH2
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risk_scope: 执行异常
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feishu_user_id: ou_failed_1
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feishu_open_id: on_failed_1
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feishu_name: 孙七
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notify_enabled: 是
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""".strip(),
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encoding="utf-8",
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)
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scenario = {
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"id": "document_review",
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"name": "注册审核智能体",
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"agent": {
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"role": "注册审核助手",
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"goal": "输出风险结果",
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"instructions": ["输出结构化风险结果"],
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},
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"rag": {"enabled": False},
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"tools": [],
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"output": {"type": "registration_risk_report"},
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}
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class FailedProvider:
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def generate(self, messages, response_format=None):
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from agent_core.llm_provider import LLMResponse
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return LLMResponse(
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content="",
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model_name="demo-model",
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success=False,
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error="provider down",
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)
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with override_settings(GOVERNANCE_CONFIG_PATH=config_path):
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result = run_agent(scenario, "请输出风险结果", options={"llm_provider": FailedProvider()})
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owner = result.notification_payload["owners"][0]
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assert result.status == "failed"
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assert result.notification_payload["notify_reason"] == "task_failed"
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assert owner["owner_name"] == "孙七"
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assert owner["feishu_open_id"] == "on_failed_1"
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def test_feishu_notification_report_builds_notification_payload_with_receipt_and_node_status():
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scenario = {
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"id": "document_review",
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"name": "注册审核智能体",
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"agent": {
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"role": "注册审核助手",
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"goal": "输出通知结果",
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"instructions": ["输出结构化通知结果"],
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},
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"rag": {"enabled": False},
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"tools": [],
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"output": {"type": "feishu_notification_report"},
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}
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provider_response = """
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{
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"batch_id": "SUB-20260604-003",
|
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"conversation_id": "conv-003",
|
||
"notify_reason": "task_completed",
|
||
"mentioned_users": ["ou_demo_1"],
|
||
"message_status": "sent",
|
||
"web_detail_url": "https://example.com/audit/3",
|
||
"receipt": {
|
||
"message_id": "msg-3",
|
||
"status": "sent"
|
||
}
|
||
}
|
||
"""
|
||
|
||
class FakeProvider:
|
||
def generate(self, messages, response_format=None):
|
||
from agent_core.llm_provider import LLMResponse
|
||
|
||
return LLMResponse(content=provider_response, model_name="demo-model", success=True)
|
||
|
||
result = run_agent(
|
||
scenario,
|
||
"请生成通知结果",
|
||
options={
|
||
"llm_provider": FakeProvider(),
|
||
"batch_id": "SUB-20260604-003",
|
||
"conversation_id": "conv-003",
|
||
"product_name": "产品C",
|
||
},
|
||
)
|
||
|
||
assert result.node_results[7]["status"] == "已发送"
|
||
assert result.notification_payload["message_status"] == "sent"
|
||
assert result.notification_payload["web_detail_url"] == "https://example.com/audit/3"
|
||
assert result.notification_payload["receipt"]["message_id"] == "msg-3"
|
||
|
||
|
||
def test_notification_payload_normalizes_unsupported_notify_reason():
|
||
scenario = {
|
||
"id": "document_review",
|
||
"name": "注册审核智能体",
|
||
"agent": {
|
||
"role": "注册审核助手",
|
||
"goal": "输出通知结果",
|
||
"instructions": ["输出结构化通知结果"],
|
||
},
|
||
"rag": {"enabled": False},
|
||
"tools": [],
|
||
"output": {"type": "feishu_notification_report"},
|
||
}
|
||
provider_response = """
|
||
{
|
||
"batch_id": "SUB-20260604-004",
|
||
"conversation_id": "conv-004",
|
||
"notify_reason": "custom_reason",
|
||
"mentioned_users": ["ou_demo_1"],
|
||
"message_status": "sent"
|
||
}
|
||
"""
|
||
|
||
class FakeProvider:
|
||
def generate(self, messages, response_format=None):
|
||
from agent_core.llm_provider import LLMResponse
|
||
|
||
return LLMResponse(content=provider_response, model_name="demo-model", success=True)
|
||
|
||
result = run_agent(
|
||
scenario,
|
||
"请生成通知结果",
|
||
options={
|
||
"llm_provider": FakeProvider(),
|
||
"batch_id": "SUB-20260604-004",
|
||
"conversation_id": "conv-004",
|
||
"product_name": "产品D",
|
||
},
|
||
)
|
||
|
||
assert result.notification_payload["notify_reason"] == "task_completed"
|
||
|
||
|
||
def test_registration_word_export_report_preserves_formal_export_flag_and_blocked_items():
|
||
scenario = {
|
||
"id": "document_review",
|
||
"name": "注册审核智能体",
|
||
"agent": {
|
||
"role": "注册审核助手",
|
||
"goal": "输出导出结果",
|
||
"instructions": ["输出结构化导出结果"],
|
||
},
|
||
"rag": {"enabled": False},
|
||
"tools": [],
|
||
"output": {"type": "registration_word_export_report"},
|
||
}
|
||
provider_response = """
|
||
{
|
||
"summary": "已生成草稿导出包。",
|
||
"export_status": "draft_only",
|
||
"can_export_formally": false,
|
||
"blocked_items": ["风险项未清零", "需人工复核后再导出"],
|
||
"download_url": "/downloads/registration-draft.docx"
|
||
}
|
||
"""
|
||
|
||
class FakeProvider:
|
||
def generate(self, messages, response_format=None):
|
||
from agent_core.llm_provider import LLMResponse
|
||
|
||
return LLMResponse(content=provider_response, model_name="demo-model", success=True)
|
||
|
||
result = run_agent(scenario, "请输出导出结果", options={"llm_provider": FakeProvider()})
|
||
|
||
assert result.structured_output["can_export_formally"] is False
|
||
assert result.structured_output["download_url"] == "/downloads/registration-draft.docx"
|
||
assert result.structured_output["blocked_items"] == ["风险项未清零", "需人工复核后再导出"]
|
||
assert result.node_results[6]["status"] == "待复核"
|