做向量搜索或 RAG 系统的团队，Booking.com 的 1 亿+嵌入生产实战比任何论文都实在，看完能避开不少坑。
Booking.com 的 Başak Eskili 在 Weaviate Podcast 上分享了他们从关键词匹配到语义检索的 AI 进化之路，最终在 AWS 上使用 OpenSearch 并迁移到 Weaviate 以应对 1 亿+嵌入向量的生产级规模。他们构建的合作伙伴到客人的消息代理是真实的智能体 AI 案例：Weaviate 检索回复模板，API 获取上下文，智能体推荐或生成回复，必要时转人工。评估体系包括离线数据集、LLM 作为裁判、A/B 测试和实时反馈。他们还测试了过滤向量搜索、多线程并发、读写并发和成本优化，并展望了带记忆系统的个性化旅行代理。
Most companies talk about vector search. Few share what it actually takes to scale to 100M+ embeddi...
Most companies talk about vector search. Few share what it actually takes to scale to 100M+ embeddings in production. Başak Eskili from @bookingcom joined the Weaviate Podcast to break down their AI journey, and it's packed with insights about what building production systems at massive scale actually looks like. 𝗧𝗵𝗲 𝗘𝘃𝗼𝗹𝘂𝘁𝗶𝗼𝗻: • Started with keyword matching → semantic retrieval with 𝗢𝗽𝗲𝗻𝗦𝗲𝗮𝗿𝗰𝗵 on AWS • Scaled to hundreds of millions of embeddings with strict latency requirements • Migrated to 𝗪𝗲𝗮𝘃𝗶𝗮𝘁𝗲 to handle complex filtering, rising concurrency, and production-scale demands 𝗣𝗿𝗼𝗱𝘂𝗰𝘁𝗶𝗼𝗻 𝗚𝗲𝗻𝗔𝗜 𝗶𝗻 𝗔𝗰𝘁𝗶𝗼𝗻: Their partner-to-guest messaging agent is a real-world example of 𝗮𝗴𝗲𝗻𝘁𝗶𝗰 𝗔𝗜: • 𝗪𝗲𝗮𝘃𝗶𝗮𝘁𝗲 retrieves relevant response templates • 𝗔𝗣𝗜𝘀 fetch property and booking context • The agent suggests templates, crafts grounded replies, or defers to humans (human-in-the-loop design!) • Evaluation spans offline datasets, LLM-as-a-judge, A/B testing, and live partner feedback @CShorten30 and Başak talk about how 𝗕𝗼𝗼𝗸𝗶𝗻𝗴.𝗰𝗼𝗺 tested with 100 million embeddings, filtered vector search, multi-threaded concurrency, reads during writes, and cost-efficient infrastructure provisioning to evaluate Weaviate, as well as a look ahead at personalized travel agents with memory systems that capture user prefere youtube.com/watch?v=O9edM9… and long-term personalization! Watch the full podcast here: https://t.co/HB9ceB7JWD 💬 0 🔄 5 ❤️ 12 👀 1242 📊 4 ⚡