USearch and ScyllaDB for Vector Search Guide

Welcome to the comprehensive guide on USearch and ScyllaDB for vector search. This section outlines all the chapters, leading you from foundational concepts to advanced deployment and optimization techniques. Prepare to master efficient vector search implementations.

Chapters

1. 01 What are Vector Embeddings? The Language of AI 10m
2. 02 USearch: Core Concepts, Installation, and Vector Search 11m
3. 03 Your First Vector Search with USearch 11m
4. 04 ScyllaDB: A Real-time Database for AI (Overview) 9m
5. 05 Storing Vectors in ScyllaDB: The Vector Data Type 10m
6. 06 Perform Vector Similarity Search Directly in ScyllaDB 12m
7. 07 Understanding USearch Indexing Strategies 17m
8. 08 Vector Distance Metrics and Their Impact 14m
9. 09 Optimizing USearch Performance: Memory & Latency 16m
10. 10 Scaling ScyllaDB Vector Search for Billions of Vectors 15m
11. 11 Advanced USearch Features: Quantization & Compression 12m
12. 12 Designing Real-world Vector Search Systems with ScyllaDB and USearch 16m
13. 13 Building a Movie Recommendation System 19m
14. 14 Implementing Semantic Search for Documents 18m
15. 15 Fraud Detection with Vector Similarity 15m
16. 16 Monitoring and Debugging Vector Search Systems 18m
17. 17 Deployment Strategies for High-Availability 15m
18. 18 Data Lifecycle Management for Embeddings 15m
19. 19 The Future of Vector Search with USearch and ScyllaDB 10m