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
Hierarchical Navigable Small Worlds (HNSW) Internals
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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- Efficient and Robust Approximate Nearest Neighbor Search Using Hierarchical Navigable Small World Graphs, Yury Malkov, Dmitry Yashunin, 2018 IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 40 (IEEE) DOI: 10.1109/TPAMI.2018.2889473 - The definitive academic paper introducing the HNSW algorithm, detailing its multi-layer graph structure, construction, and search procedures.
- A Survey of Approximate Nearest Neighbor Search Algorithms, Jingjing Wang, Bin Cui, Chunmei Wang, Jun Xu, Shuaichao Zhang, 2020 Neurocomputing, Vol. 399 (Elsevier) DOI: 10.1016/j.neucom.2019.09.094 - Provides a broad overview and classification of various ANN algorithms, including graph-based methods like HNSW, offering context for its position within the field.
- Quantizing HNSW for the small-memory regime, Thomas Joliot, Remi Tissier, Benoit Bideau, François Le Lay, Gatien Couairon, Grégoire Lemaire, 2020 Proceedings of the 2020 on International Conference on Multimedia Retrieval (ICMR) (ACM) DOI: 10.1145/3379553.3400224 - Discusses methods for integrating quantization techniques with HNSW to reduce memory footprint, addressing a common challenge in large-scale vector search systems.
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