41. What Is Search? Module 1 What Is Search? Search is the act of finding the right information out of everything you have, given a question. You type “car repair” into a box, and somethin 01/01 08:00 ♡
42. Where Design Decisions Live Module 4 Where Design Decisions Live A vector search system has five layers. The first four go from easiest to hardest to change. Distribution is separate, because its cost depends 01/01 08:00 ♡
43. Where We Left Off Module 3 Where We Left Off In Module 2, you built a complete ingestion and retrieval pipeline: raw text, vector, store, top-K query. Dense retrieval handles meaning well. It gets s 01/01 08:00 ♡
44. F Filtered search benchmark Filtered search benchmark Applying filters to search results brings a whole new level of complexity. It is no longer enough to apply one algorithm to plain data. With filtering, it 02/13 08:00 ♡
45. Accuracy Recovery with Rescoring Day 4 Accuracy Recovery with Rescoring When we use quantization methods like Scalar, Binary, or Product Quantization, we're compressing our vectors to save memory and improve perfo 01/01 08:00 ♡
46. Collaborative Filtering Build a Recommendation System with Collaborative Filtering using Qdrant Time: 45 min Level: Intermediate Every time Spotify recommends the next song from a band you’ve never 01/01 08:00 ♡
47. Combining Vector Search and Filtering Day 2 Combining Vector Search and Filtering We’ve talked about how Qdrant uses the HNSW graph to efficiently search dense vectors. But in real-world applications, you’l 01/01 08:00 ♡
48. Core Data Model Module 2 Core Data Model Qdrant organizes data in three levels. Understanding this structure is the foundation for everything else in the course. Collection Like a table in a relat 01/01 08:00 ♡
49. Course Completion and Next Steps Day 6 Course Completion and Next Steps Congratulations! You’ve Mastered Vector Search. You’ve built and shipped a complete vector search application and gained the expe 01/01 08:00 ♡
50. Decide Before You Ingest Module 4 Decide Before You Ingest Four decisions belong to the data layer. Changing any one means ingesting the data again. Here’s a news search example: Analysts at a resear 01/01 08:00 ♡
