Advanced Vector Search for LLM Applications
Chapter 1: Approximate Nearest Neighbor Algorithms
Revisiting Vector Embeddings and Search Fundamentals
Hierarchical Navigable Small Worlds (HNSW) Internals
Inverted File Index (IVF) Variations
Product Quantization (PQ) Mechanics
Other Graph-Based ANN Methods (e.g., NSG, Vamana)
Selecting the Right ANN Algorithm: Trade-offs
Hands-on Practical: Implementing and Tuning HNSW
Chapter 2: Optimizing Vector Search Performance and Efficiency
Quantization Techniques: Scalar vs. Product
Implementing Optimized Product Quantization (OPQ)
Binary Hashing and Locality Sensitive Hashing (LSH) Refresher
Advanced Filtering Strategies: Pre vs. Post Filtering
Indexing Metadata Efficiently alongside Vectors
Hardware Acceleration Considerations (CPU SIMD, GPU)
Memory Management and Caching Strategies
Practice: Applying Quantization and Filtering
Chapter 3: Hybrid Search Approaches
Limitations of Pure Vector Search
Integrating Keyword Search (BM25, TF-IDF)
Result Fusion and Ranking Strategies
Reciprocal Rank Fusion (RRF) and Other Fusion Algorithms
Graph-Based Augmentation for Vector Search
Multi-Modal Search Considerations
Hands-on Practical: Building a Hybrid Search Pipeline
Chapter 4: Scaling Vector Search for Production Systems
Distributed Vector Database Architectures
Sharding Strategies for Vector Indexes
Replication and High Availability
Load Balancing Search Queries
Monitoring Vector Search Performance Metrics
Index Updates and Maintenance in Production
Cost Optimization for Large-Scale Deployments
Practice: Configuring a Distributed Setup
Chapter 5: Advanced Tuning and Evaluation
Evaluation Metrics Revisited: Recall, Precision, Latency
Building Ground Truth Datasets for Evaluation
Parameter Sensitivity Analysis (HNSW, IVF)
A/B Testing Frameworks for Search Algorithms
Debugging Search Relevance Issues
Online vs. Offline Evaluation Techniques
Tuning for Specific Application Needs (RAG vs. Semantic Search)
Hands-on Practical: Comprehensive Performance Evaluation
A/B Testing Frameworks for Search Algorithms
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- Trustworthy Online Controlled Experiments: A Practical Guide to A/B Testing, Ron Kohavi, Diane Tang, Ya Xu, 2020 (Cambridge University Press) DOI: 10.1017/9781108653985 - This guide from pioneers in the field offers a thorough and practical treatment of designing, executing, and analyzing online controlled experiments, covering all aspects discussed in the section.
- Controlled experiments on the web: survey and practical guide, Ron Kohavi, Roger Longbotham, Dan Sommerfield, Randal M. Henne, 2009 Data Mining and Knowledge Discovery, Vol. 18 (Springer) DOI: 10.1007/s10618-008-0114-1 - This foundational survey paper offers a practical guide to designing, running, and analyzing controlled experiments on the web, providing a comprehensive background for A/B testing applications in search.
- Online Experimentation in Large-Scale Production Systems, Dmitriy Fabijan, Pavel Dmitriev, Peter Shirobokov, 2019 IEEE Software, Vol. 36 (IEEE) DOI: 10.1109/MS.2019.2929910 - This paper provides an up-to-date overview of the engineering challenges and best practices for implementing and managing online experimentation platforms in large-scale production environments, relevant to building A/B testing infrastructure for search.