Milvus 五步搭建题库混合检索：语义、BM25 与标量过滤结合
Milvus 官方分享的题库检索教程，从嵌入、索引到重排讲得很细，做教育类搜索可以直接照抄这套五步流程。
Milvus 演示了一套面向题库检索的架构，用五步流水线结合语义检索、BM25 关键词搜索和标量过滤。流程包括数据清洗与字段标准化、教育场景向量嵌入、构建 HNSW/COSINE 稠密索引与 BM25 稀疏索引，再通过 WeightedRanker 或 RRFRanker 合并混合检索结果。最后用 Cross-Encoder 重排候选题，支持组卷、错题复习推荐和以题搜题。
Millions of questions. How does Milvus help find the right one? Traditional full-text retrieval finds questions through the words they contain. In a growing question bank, students and teachers also need questions that test similar concepts with different wording and fit the intended curriculum and difficulty level. To support these needs, this Milvus-based architecture combines semantic retrieval, BM25 keyword search, and scalar filtering in a five-step pipeline: 1. Data collection and cleaning. Collect question text, explanations, and metadata; standardize fields such as course, education level, concepts, and difficulty. 2. Vectorization. Use education-tuned embeddings for semantic matching and Milvus’s built-in BM25 function for keyword retrieval. 3. Hybrid index construction. Create HNSW/COSINE indexes for dense vectors, sparse indexes for BM25, and scalar indexes for metadata filtering. 4. Hybrid retrieval. Combine semantic and keyword search under metadata constraints, then merge results with WeightedRanker or RRFRanker. 5. Reranking and output. Use a Cross-Encoder to rerank candidates for test assembly, mistake-based review recommendations, and search by question. The design pairs semantic matching with lexical relevance and explicit teaching constraints, helping students and teachers find questions suited to their learning tasks. #Milvus #EdTech 💬 0 🔄 0 ❤️ 0 👀 31 ⚡
