Weaviate 搞了个demo，用向量聚类把新闻标题自动归类成故事，每2小时更新，还能自己动手搭建。
Weaviate Playground 发布了一个报纸主题demo，每2小时从6个新闻主题（general、world、business、technology、sports、health）采集标题。每个标题被转化为1536维向量，利用Leiden算法进行聚类。LLM为每个聚类生成自然语言标签，用户可通过混合搜索（语义+关键词）探索不同角度。该demo展示了向量数据库自动发现潜在故事的能力，并附带构建教程。
Most vector database demos show you search. This one shows you how information organizes itself. T...
Most vector database demos show you search. This one shows you how information organizes itself. This newspaper-themed demo in Weaviate Playground showcases vector clustering in action - and it updates every 2 hours with real headlines. Here's how it works: 𝟭. 𝗛𝗲𝗮𝗱𝗹𝗶𝗻𝗲𝘀 𝗚𝗮𝘁𝗵𝗲𝗿𝗲𝗱 Six news topics polled every 2 hours: general, world, business, technology, sports, and health. 𝟮. 𝗘𝗮𝗰𝗵 𝗦𝘁𝗼𝗿𝘆 𝗨𝗻𝗱𝗲𝗿𝘀𝘁𝗼𝗼𝗱 Every headline becomes a 1,536-dimensional vector capturing its meaning, not just its words. 𝟯. 𝗦𝘁𝗼𝗿𝗲𝗱 𝗶𝗻 𝗪𝗲𝗮𝘃𝗶𝗮𝘁𝗲 Vectors land in a Weaviate collection, searchable by meaning, keyword, or both (hybrid search). 𝟰. 𝗖𝗹𝘂𝘀𝘁𝗲𝗿𝘀 𝗙𝗼𝗿𝗺 & 𝗚𝗲𝘁 𝗡𝗮𝗺𝗲𝗱 The 𝗟𝗲𝗶𝗱𝗲𝗻 𝗮𝗹𝗴𝗼𝗿𝗶𝘁𝗵𝗺 groups similar headlines into communities - these become the actual "stories" underneath. Then an LLM writes a natural label for each cluster. 𝟱. 𝗗𝗲𝗹𝗶𝘃𝗲𝗿𝗲𝗱 𝘁𝗼 𝘁𝗵𝗲 𝗥𝗲𝗮𝗱𝗲𝗿 One card per story. Readers can tune between semantic and keyword search to explore different angles. This demo combines vector embeddings, hybrid similarity, and graph-based community detection to automatically discover which headlines are really about the same underlying story. Plus, the demo includes a copy prompt so you can build it yourself 🔥 This is what vector databases were built for: taking unstructured data (news headlines), understanding meaning (embeddings), chronicles.playground.weaviate.io/?utm_source=ch… tering), and making it searchable (hybrid search). Check it out in Weaviate Playground 💙 https://t.co/gYhQRlvzRq 💬 1 🔄 2 ❤️ 3 👀 389 📊 2 ⚡