想给电商搜索加上混合检索？看看这个Lumen demo怎么把语义、关键词和价格过滤组合起来，还能可视化诊断。
推文以“pink headphones for kids with ears under $20”为例，说明电商搜索需同时处理语义意图和数值约束（价格<20美元）。Lumen演示项目通过查询理解分离产品意图与过滤器，使用密集向量搜索、BM25关键词匹配和Milvus过滤（价格、评分、品牌等）。诊断面板展示了Milvus查询、嵌入维度、融合策略和延迟等关键指标。该方案整合了混合检索、元数据过滤和多模态相似度，适用于现代产品搜索。
How do you combine structured filters and keyword signals with semantic similarity in ecommerce sear...
How do you combine structured filters and keyword signals with semantic similarity in ecommerce search? Take this query: “pink headphones for kids with ears under $20” “Pink headphones for kids with ears” expresses semantic intent. “Under $20” is a hard price constraint. Ratings, reviews, brand, category, and visual similarity may also influence the results. 𝗦𝗼 𝗲𝗰𝗼𝗺𝗺𝗲𝗿𝗰𝗲 𝗿𝗲𝘁𝗿𝗶𝗲𝘃𝗮𝗹 𝗻𝗲𝗲𝗱𝘀 𝗺𝗼𝗿𝗲 𝘁𝗵𝗮𝗻 𝘃𝗲𝗰𝘁𝗼𝗿 𝘀𝗲𝗮𝗿𝗰𝗵 𝗮𝗹𝗼𝗻𝗲. Lumen, a demo built by @Simon Hearne, shows how these signals can work together in a small Amazon-style product search experience. The app combines: • Query understanding to separate product intent from numeric filters • Dense vector search for semantic relevance • BM25 for exact keyword matching • Milvus filters for price, rating, reviews, brand, and category • Text and image vectors for “More like this” recommendations A diagnostics panel also makes the retrieval process visible, including the effective Milvus query, embedding dimension, fusion strategy, and latency. That transparency matters. Search quality may feel subjective to users, but builders need to identify exactly which signal failed when the results feel wrong. Lumen keeps the scope intentionally small: one catalog, one search flow, and one clear retrieval stack. It’s a useful example of the components modern product discovery often needs—hybrid retrieval, metadata filtering, multimodal similarity, and an API layer that vdb-ecom.pages.dev ials se github.com/simonhearne/ve… ps://t.co/nMg9Lf9bBo Repo: https://t.co/XYw6BzRVV1 💬 0 🔄 0 ❤️ 0 👀 42 ⚡