We demonstrate NeedleDB, an open-source, deployment-ready database system for answering complex natural language queries over image data. Unlike existing approaches that rely on contrastive-learning embeddings (e.g., CLIP), which degrade on compositional or nuanced queries, NeedleDB leverages generative AI to synthesize guide images that represent the query in the visual domain, transforming the text-to-image retrieval problem into a more tractable image-to-image search. The system aggregates nearest-neighbor results across multiple vision embedders using a weighted rank-fusion strategy grounded in a Monte Carlo estimator with provable error bounds. NeedleDB ships with a full-featured command-line interface (needlectl), a browser-based Web UI, and a modular microservice architecture backed by PostgreSQL and Milvus. On challenging benchmarks, it improves Mean Average Precision by up to 93% over the strongest baseline while maintaining sub-second query latency. In our demonstration, attendees interact with NeedleDB through three hands-on scenarios that showcase its retrieval capabilities, data ingestion workflow, and pipeline configurability.

翻译：我们展示了NeedleDB——一个开源、可部署的数据库系统，用于回答基于图像数据的复杂自然语言查询。不同于依赖对比学习嵌入（如CLIP）的方法——这些方法在组合性或细粒度查询中性能下降——NeedleDB利用生成式AI合成表征查询的引导图像，将文本到图像检索问题转化为更易处理的图像到图像搜索。该系统通过加权排名融合策略聚合多个视觉嵌入器的近邻结果，该策略基于具有可证明误差界的蒙特卡洛估计器。NeedleDB配备功能完备的命令行界面（needlectl）、基于浏览器的Web用户界面，以及由PostgreSQL和Milvus支撑的模块化微服务架构。在具有挑战性的基准测试中，该系统相较于最强基线方法将平均精度均值提升高达93%，同时保持亚秒级查询延迟。在本次演示中，参会者将通过三个实践场景与NeedleDB交互，展示其检索能力、数据摄入流程及流水线可配置性。