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
Result Fusion and Ranking Strategies
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- Reciprocal Rank Fusion Outperforms Other Fusion Methods for Cold Start, Diversification, and Other Tough Search Problems, Gordon V. Cormack, Maura Grossman, Charles L. A. Clarke, 2009 Proceedings of the 32nd International ACM SIGIR Conference on Research and Development in Information Retrieval (Association for Computing Machinery) DOI: 10.1145/1571941.1572007 - Introduces Reciprocal Rank Fusion (RRF), a rank-based method for combining ranked lists from different retrieval systems.
- Introduction to Information Retrieval, Christopher D. Manning, Prabhakar Raghavan, Hinrich Schütze, 2008 (Cambridge University Press) - A standard textbook covering fundamental information retrieval concepts, including BM25, scoring, and basic result combination methods.
- Neural Information Retrieval, Sean MacAvaney, Andrew Yates, Arman Cohan, Nazir Correia, Ophir Frieder, 2022 The Handbook of Information Retrieval (Springer, Cham) DOI: 10.1007/978-3-030-90981-0_7 - Provides an overview of modern neural information retrieval techniques, including deep learning models for re-ranking in advanced retrieval systems.