用视觉图像直接检索企业文档,速度更快且更准。
PULSAR: Pooled Unified Late-Interaction Search and Retrieval for Enterprise Visual Document RAG

- 先用压缩摘要粗筛,再用精细表示精排,提升检索效率。
- 延迟降低15.1倍,每页处理成本降为原方案的1/20。
- 适合高频更新的金融文档检索,已服务超3000个交易项目。
机构投资者需在接近交易截止时,快速查找包含大量图表的融资演示文稿、董事会材料和尽职调查文件。传统基于OCR和图文描述的方法在如此规模下刷新成本高,且易丢失图表细节。我们提出PULSAR,一个部署于Mubadala Investment Company的生产级视觉优先检索系统。PULSAR使用冻结的ColPali风格主干网络索引页面图像,并采用池化两阶段后期交互索引:紧凑的页面摘要支持初步检索,随后通过更精细的池化表示进行精确的MaxSim重评分。在ViDoRe V3数据集上,该设计使中位向量搜索延迟相比非池化配置降低15.1倍,且NDCG@10和Recall@10损失均低于0.01;生产环境下的中位向量搜索延迟为156毫秒。在并发负载下,池化索引的每秒查询率(QPS)约为非池化索引的88倍。事件驱动的摄入路径估计每页成本仅为原OCR+描述基线的约1/20。自2026年3月起,PULSAR已服务7.8万份文档,约240万页,覆盖超过3000个交易项目。在生产环境的顶部K检索结果中,其答案事实召回率是原基线的两倍以上。
原文摘要 · Abstract (English)
Institutional investors search visually dense pitch decks, board packs, and diligence materials that change hourly near deal closing. OCR followed by figure verbalisation is costly to refresh at this scale and can lose chart detail. We present PULSAR, a production vision-first retrieval system deployed at Mubadala Investment Company. PULSAR indexes page images with a frozen ColPali-style backbone and uses a pooled two-stage late-interaction index: compact page summaries support initial retrieval, followed by exact MaxSim rescoring over a finer pooled representation. On ViDoRe V3, this design reduces median vector-search latency by 15.1 times against an unpooled configuration with less than 0.01 absolute NDCG@10 and Recall@10 loss; production median vector-search latency is 156 ms. Under concurrent load, the pooled index sustains approximately 88 times higher QPS than an unpooled index. The event-driven ingestion path is estimated to be approximately 20 times cheaper per page than the OCR+verbalisation baseline it replaced. Since March 2026, PULSAR has served 78 thousand documents and approximately 2.4 million pages across more than 3,000 deals. At the production top K, it more than doubles answer-fact recall over the OCR+verbalisation baseline.
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