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| """bench_vector_baseline — 轻量级向量库基线测试（V47）
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| 用户需求：先做基线测试。确定性语料+实测数字（零虚构：本脚本输出的
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| 数字即投喂包 §基线报告数字的来源）。跑法：
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| python scripts/bench_vector_baseline.py
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| """
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| import os
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| import sys
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| import time
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|
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| sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
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|
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| from holoaeon.deploy_gate import VectorStore
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| TEMPLATES = [
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| "{s}患者应定期复查相关指标并做好记录",
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| "{s}的日常管理包括生活方式干预与依从性教育",
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| "{s}稳定期以常规随访为主，控制不满意时加密随访频次",
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| "{s}合并多种慢病时须综合评估并整合随访安排",
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| "{s}的健康指导应个体化，结合患者实际条件",
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| "{s}急性加重的识别与及时转诊是管理关键环节",
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| "{s}康复训练应循序渐进并定期评估效果",
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| "{s}患者的自我监测与自我管理能力决定长期效果",
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| "{s}的中医药调理可融入全流程健康管理",
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| "{s}高危人群的早期筛查有助于延缓疾病进展",
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| ]
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| SYSTEMS = ("高血压", "2型糖尿病", "慢阻肺病", "冠心病", "脑卒中后遗症",
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| "骨质疏松")
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| PARAPHRASE_QUERIES = {
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| "高血压": "血压高的病人平时要注意什么，定期量血压记下来",
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| "2型糖尿病": "血糖高的人日常怎么管，生活上怎么干预",
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| "脑卒中后遗症": "中风后半边身子不利索，怎么做恢复锻炼",
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| }
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| LITERAL_QUERIES = {
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| "高血压": "高血压患者应定期复查相关指标并做好记录",
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| "慢阻肺病": "慢阻肺病急性加重的识别与及时转诊是管理关键环节",
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| "骨质疏松": "骨质疏松的康复训练应循序渐进并定期评估效果",
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| }
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| def build_corpus():
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| entries = []
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| for si, sys_name in enumerate(SYSTEMS):
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| for ti, tpl in enumerate(TEMPLATES):
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| eid = "k%02d_%02d" % (si, ti)
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| entries.append((eid, tpl.format(s=sys_name),
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| {"system": sys_name}))
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| return entries
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| def _hit(store, queries, top_k=5):
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| lat, top1, top5 = [], 0, 0
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| for sys_name, q in queries.items():
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| res = store.search(q, top_k=top_k)
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| lat.append(res["latency_ms"])
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| ids = [h["meta"].get("system") for h in res["hits"]]
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| top1 += 1 if ids and ids[0] == sys_name else 0
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| top5 += 1 if sys_name in ids else 0
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| return lat, top1, top5, len(queries)
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|
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| def main():
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| store = VectorStore(dim=256)
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| entries = build_corpus()
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|
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| t0 = time.perf_counter()
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| r = store.batch_add(entries)
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| add_ms = round((time.perf_counter() - t0) * 1000, 1)
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|
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| lat_l, t1_l, t5_l, n_l = _hit(store, LITERAL_QUERIES)
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| lat_p, t1_p, t5_p, n_p = _hit(store, PARAPHRASE_QUERIES)
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|
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| report = {
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| "corpus_entries": r["added"],
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| "add_batch_ms": add_ms,
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| "dim": 256,
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| "literal_group": {
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| "queries": n_l,
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| "top1_hit_rate": round(t1_l / n_l, 4),
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| "top5_hit_rate": round(t5_l / n_l, 4),
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| "avg_latency_ms": round(sum(lat_l) / n_l, 3),
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| "note": "字面查询对照组（positive control）",
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| },
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| "paraphrase_group": {
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| "queries": n_p,
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| "top1_hit_rate": round(t1_p / n_p, 4),
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| "top5_hit_rate": round(t5_p / n_p, 4),
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| "avg_latency_ms": round(sum(lat_p) / n_p, 3),
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| "note": "同义改写组——字面混合通道的诚实天花板：跨词汇语义"
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| "召回需共现统计语料(semvec 路线)或 bridges 层神经旁挂",
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| },
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| "channel": "hybrid=0.6*trigram_cos + 0.4*bigram_dice",
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| "deterministic": True,
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| "note": "确定性语料（模板×系统，零随机）；混合通道为基线驱动优化"
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| "（纯 trigram 同义组 Top5=0.0）",
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| }
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| import json
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| print(json.dumps(report, ensure_ascii=False, indent=2))
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| return report
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|
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| if __name__ == "__main__":
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| main() |
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|
