108 lines
4.0 KiB
Python
108 lines
4.0 KiB
Python
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# -*- coding: utf-8 -*-
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"""端到端联调用例（Issue #86）—— 跨链路集成场景。
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验证数据流（采集 → 落库）与问答流（RAG → LLM 网关）的协同：
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基于真实采集到的工艺数据，构建 RAG 知识并完成一次安全问答闭环，
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模拟「采集 → 沉淀知识 → 智能问答」的完整业务闭环。
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"""
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from __future__ import annotations
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# 引导加载四个内核模块（必须在测试导入前执行）
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import tests.e2e._bootstrap # noqa: F401
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import tempfile
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import unittest
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from collector.engine import CollectorEngine
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from collector.metrics import HealthMetrics
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from collector.spool import SpoolStore
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from drivers import SimulatorDriver
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from point_dict.loader import Point, PointDict
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from data_bus import BatchWriter, MemorySink
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from rag_kb import RagKnowledgeBase, build_document
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from llm_gateway import (
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GatewayResult,
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LLMGateway,
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LocalBackend,
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PromptRegistry,
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RouteTarget,
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)
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def _collect_samples(tmpdir: str):
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"""跑一轮采集，返回（落库样本, 引擎健康度）。"""
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points = [
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Point(device_id="CLF-01", point_id="CLF-01.TEMP", name="1#炉温",
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unit="℃", data_type="float", sample_rate=1000,
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quality_code=True, row_number=2),
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Point(device_id="CLF-01", point_id="CLF-01.PRES", name="1#炉压",
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unit="kPa", data_type="float", sample_rate=1000,
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quality_code=True, row_number=3),
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]
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pd = PointDict(points)
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spool = SpoolStore(spool_dir=tmpdir, cache_limit_bytes=8 * 1024 * 1024)
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metrics = HealthMetrics()
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engine = CollectorEngine(
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point_dict=pd,
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driver_slots=[("simulator", SimulatorDriver(), [])],
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spool=spool, metrics=metrics, interval_ms=1000, max_pending=100_000,
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)
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engine.collect_once()
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sink = MemorySink()
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writer = BatchWriter(sink=sink, batch_size=10, flush_interval=0.0)
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for s in spool.pending_records():
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writer.push(s)
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spool.ack(s)
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writer.flush()
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return sink.rows, metrics
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class CrossPipelineE2ETest(unittest.TestCase):
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"""采集 → 落库 → 知识沉淀 → 安全问答 全业务闭环。"""
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def test_collect_then_query_closed_loop(self) -> None:
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"""采集数据落库后，结合工艺知识库完成一次安全问答。"""
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tmpdir = tempfile.mkdtemp(prefix="iaop_cross_")
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# 1) 数据流：采集并落库
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rows, metrics = _collect_samples(tmpdir)
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self.assertEqual(len(rows), 2) # 2 点位全部落库
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self.assertTrue(metrics.meets_sla()) # 采集健康度达标
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point_ids = {r["point_id"] for r in rows}
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self.assertEqual(point_ids, {"CLF-01.TEMP", "CLF-01.PRES"})
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# 2) 知识沉淀：工艺知识库（模拟由采集数据衍生的工艺规范）
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kb = RagKnowledgeBase()
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kb.add_documents([
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build_document(
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title="1#氯化炉工艺卡",
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text="1#氯化炉（CLF-01）正常炉温 850~920℃，炉压 0.2~0.4MPa。"
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"CLF-01.TEMP 与 CLF-01.PRES 为关键监控点位。",
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category="process",
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),
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])
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# 3) 问答流：基于知识库回答工艺问题
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prompts = PromptRegistry()
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prompts.update(name="qa", version="1.0.0",
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text="工业 AI 助手回答：{query}")
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gateway = LLMGateway(prompts=prompts, prompt_name="qa",
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local=LocalBackend(echo_context=True))
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query = "CLF-01 的关键监控点位和正常炉温范围？"
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hits = kb.search(query, top_k=3)
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sources = [h.chunk.title for h in hits]
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self.assertGreater(len(sources), 0)
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result = gateway.ask(query, rag_context=sources, confidence=0.95)
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self.assertIsInstance(result, GatewayResult)
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self.assertEqual(result.route.target, RouteTarget.LOCAL)
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self.assertFalse(result.needs_human)
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# 回答引用了知识库来源
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self.assertIn(sources[0], result.answer)
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if __name__ == "__main__":
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unittest.main()
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