Booking.com 和 Weaviate
向量搜索看起来很简单，直到你面临生产规模的挑战。
我非常激动地与大家分享Weaviate播客的新一期，邀请到了来自@bookingcom的Başak，讨论生产规模的向量搜索、RAG和自主智能体（agentic AI）！
播客开始时讨论了Booking在采用向量搜索方面的转折点以及新兴的应用案例。
仅仅是合作伙伴与客户之间的消息交流量就令人震惊！每天有近250,000次这样的交流，而Booking的智能助手已经在帮助处理其中的数万条信息！
Başak描述了团队如何应对不断增加的规模和工作负载的复杂性。他们对Weaviate进行了全面评估，使用了1亿个嵌入，并进行了许多常见近似最近邻（ANN）基准测试中常常遗漏的测试。这包括过滤向量搜索、支持多线程并发，以及同时进行读写的测试。
播客最后，Başak分享了她的职业旅程以及对旅行代理人的看法！
YouTube: https://www.youtube.com/watch?v=O9edM9ZS_FQ
Spotify: https://spotifycreators-web.app.link/e/8tc6Dyb7e3b
查看原文
Vector search looks easy, until you hit production scale.<p>I'm super excited to share a new episode of the Weaviate Podcast with Başak from @bookingcom on production-scale vector search, RAG, and agentic AI with @weaviate_io!<p>The podcast begins by discussing Booking's tipping point into adopting vector search and emerging use cases.<p>The scale of Partner-to-Guest messaging alone is insane! There are nearly 250,000 such exchanges <i>daily</i>, and Booking's Agent is already helping with 10s of thousands of these!<p>Başak describes how the team navigated increasing scale and workload complexity. They ran an exhaustive evaluation of Weaviate with 100M embeddings and tests often left out of common ANN benchmarks. This includes Filtered Vector Search, Multi-Threaded Concurrency, and testing with simultaneous Reads and Writes.<p>The podcast concludes with Başak's career journey to Booking and her thoughts on Travel Agents!<p>YouTube: https://www.youtube.com/watch?v=O9edM9ZS_FQ<p>Spotify: https://spotifycreators-web.app.link/e/8tc6Dyb7e3b