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
Building Ground Truth Datasets for Evaluation
New · Open Source
Kerb - LLM Development Toolkit
Python toolkit for building production-ready LLM applications. Modular utilities for prompts, RAG, agents, structured outputs, and multi-provider support.
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- Introduction to Information Retrieval, C.D. Manning, P. Raghavan, H. Schütze, 2008 (Cambridge UP) - Covers fundamental concepts of information retrieval, including evaluation metrics, relevance, and the design of search systems.
- Relevance Assessments for Information Retrieval Evaluation, Ellen M. Voorhees, Donna Harman, 2011 (John Wiley & Sons, Inc.) DOI: 10.1002/9781119125307.ch8 - Explores methodologies and challenges in creating high-quality relevance assessments and test collections for information retrieval evaluation.
- BEIR: A Heterogeneous Benchmark for Zero-shot Evaluation of Information Retrieval Models, Nandan Thakur, Nils Reimers, Andreas Rücklé, Abhishek Srivastava, Iryna Gurevych, 2021 NeurIPS 2021 Dataset and Benchmark Track DOI: 10.48550/arXiv.2104.08663 - Introduces a comprehensive benchmark for evaluating information retrieval models across diverse tasks, highlighting the importance of standardized evaluation.
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