31. MaxSim Distance Metric Module 1 MaxSim Distance Metric MaxSim (Maximum Similarity) is the core distance metric for late interaction models. Unlike traditional vector similarity metrics that operate on pa 01/01 08:00 ♡
32. M Multivectors and Late Interaction Multivector Representations for Reranking in Qdrant Time: 30 min Level: Intermediate Multivector Representations are one of the most powerful features of Qdrant. However, most peop 01/01 08:00 ♡
33. Multivectors for Late Interaction Models Day 5 Multivectors for Late Interaction Models Many embedding models represent data as a single vector. Transformer-based encoders achieve this by pooling the per-token vector matr 01/01 08:00 ♡
34. Points, Vectors and Payloads Day 1 Points, Vectors and Payloads Understanding Qdrant’s core data model is essential for building effective vector search applications. This lesson establishes the precise 01/01 08:00 ♡
35. Project Overview Module 5 Project Overview A factory fire at a supplier’s plant reaches you four ways. A local news report, a satellite image, an earnings call where an executive gets asked a 01/01 08:00 ♡
36. Qdrant Setup Day 0 Qdrant Setup Spin up production-grade vector search in minutes. Qdrant Cloud gives you a managed endpoint with TLS, automatic backups, high-availability options, and a clean 01/01 08:00 ♡
37. Relevance Feedback Retrieval in Qdrant Relevance Feedback in Qdrant Time: 30 min Level: Intermediate Output: GitHub In Qdrant 1.17 we introduced a new Relevance Feedback Query , our scalable, vector index-native approac 01/01 08:00 ♡
38. Sparse Vectors and Inverted Indexes Day 3 Sparse Vectors and Inverted Indexes Create and index sparse vector representations for keywords-based search and recommendations. What You’ll Learn Understanding sparse 01/01 08:00 ♡
39. Vector Quantization Methods Day 4 Vector Quantization Methods Production vector search engines face an inevitable scaling challenge: memory requirements grow with dataset size, while search latency demands ve 01/01 08:00 ♡
40. Vector Quantization Techniques Module 3 Vector Quantization Techniques Vector quantization compresses vectors by reducing the precision of each component. Qdrant supports several quantization methods that can re 01/01 08:00 ♡
