向量搜索成本是很多团队的痛点，RaBitQ 用 1-bit 压缩加随机旋转做到了低成本低损耗，做向量数据库选型或优化成本的开发者值得看看这个方案。
Zilliz 开发者关系负责人 Jiang Chen 在伦敦非结构化数据 Meetup 上，分享了如何在不牺牲搜索质量的前提下降低向量数据库的 serving 成本。他指出，向量搜索昂贵的主因是索引占用大量 RAM 和 NVMe SSD。RaBitQ 算法通过将 float32 向量压缩到每维度 1 bit，并在量化前加入随机旋转来保留更多信息，从而大幅降低内存和存储开销，同时保持低质量损失。该方法适合需要控制基础设施成本的向量搜索场景。
❓ 𝗛𝗼𝘄 𝗱𝗼 𝘆𝗼𝘂 𝗿𝗲𝗱𝘂𝗰𝗲 𝘀𝗲𝗿𝘃𝗶𝗻𝗴 𝗰𝗼𝘀𝘁𝘀 𝘄𝗶𝘁𝗵𝗼𝘂𝘁 𝗺𝗮𝗸𝗶𝗻𝗴 ...
❓ 𝗛𝗼𝘄 𝗱𝗼 𝘆𝗼𝘂 𝗿𝗲𝗱𝘂𝗰𝗲 𝘀𝗲𝗿𝘃𝗶𝗻𝗴 𝗰𝗼𝘀𝘁𝘀 𝘄𝗶𝘁𝗵𝗼𝘂𝘁 𝗺𝗮𝗸𝗶𝗻𝗴 𝘀𝗲𝗮𝗿𝗰𝗵 𝗾𝘂𝗮𝗹𝗶𝘁𝘆 𝗰𝗼𝗹𝗹𝗮𝗽𝘀𝗲? Jiang Chen, our Head of Developer Relations at Zilliz, unpacked this practical vector database question at last month’s Unstructured Data Meetup London. He starts with the cost issue: vector search is expensive because serving the vector index consumes a lot of 𝗥𝗔𝗠 and NVME 𝗦𝗦𝗗. RaBitQ tackles this by compressing float32 vectors down to 1 bit per dimension, but the key reason it can achieve low quality loss is 𝗿𝗮𝗻𝗱𝗼𝗺 𝗿𝗼𝘁𝗮𝘁𝗶𝗼𝗻 𝗯𝗲𝗳𝗼𝗿𝗲 𝗾𝘂𝗮𝗻𝘁𝗶𝘇𝗮𝘁𝗶𝗼𝗻, making it encode more information than youtu.be/3mDFw933wdE?ut… ion algorithm. 👇 Watch this two-minute video to learn more. 🎬 Full video: https://t.co/rEabJnjjhJ --- #VectorSearch i #RaBitQ i #VectorDatabase r database and vector lakebase updates built for production AI. #VectorSearch #RaBitQ #VectorDatabase Your browser does not support the video tag. 🔗 View on Twitter 💬 0 🔄 0 ❤️ 0 👀 4 ⚡