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
Inverted File Index (IVF) Variations
Build LLM apps faster with Kerb
Cleaner syntax. Built-in debugging. Production-ready from day one.
Built for the AI systems behind ApX Machine Learning
Was this section helpful?
- Product Quantization for Nearest Neighbor Search, Herve Jégou, Matthijs Douze, Cordelia Schmid, 2011 IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 33 (IEEE) DOI: 10.1109/TPAMI.2011.57 - The foundational paper introducing Product Quantization (PQ) and its integration with Inverted File Index (IVF) to create IVFPQ, which significantly reduces memory and improves search speed for large-scale similarity search.
- Billion-scale similarity search with GPUs, Jeff Johnson, Matthijs Douze, Hervé Jégou, 2019 IEEE Transactions on Big Data, Vol. 7 (IEEE Computer Society) DOI: 10.1109/TBDATA.2019.2921572 - Describes the design and implementation of FAISS, a highly optimized library that includes scalable and efficient implementations of IVF, Product Quantization, and their combinations for billion-scale vector search.
© 2025 ApX Machine LearningEngineered with