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
Binary Hashing and Locality Sensitive Hashing (LSH) Refresher
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- Similarity Search in High Dimensions via Hashing, Aristides Gionis, Piotr Indyk, Rajeev Motwani, 1999 Proceedings of the 25th International Conference on Very Large Data Bases (VLDB '99) (Morgan Kaufmann Publishers Inc.) DOI: 10.5555/635606.635655 - Presents early LSH theory and algorithms, including MinHash, for high-dimensional similarity search.
- Mining of Massive Datasets, Jure Leskovec, Anand Rajaraman, Jeffrey David Ullman, 2020 (Cambridge University Press) - A standard textbook providing accessible explanations of LSH, MinHash, and other related methods for similarity search. (3rd edition)
- Similarity Estimation Techniques from Rounding Algorithms, Moses S. Charikar, 2002 Proceedings of the 34th Annual ACM Symposium on Theory of Computing (STOC) (ACM) DOI: 10.1145/509907.509965 - Introduces signed random projections (SRP) as an LSH technique effective for estimating cosine similarity in high dimensions.
- Locality-Sensitive Hashing Scheme Based on p-Stable Distributions, Mayur Datar, Nicole Immorlica, Piotr Indyk, Vahab S. Mirrokni, 2004 Proceedings of the Twentieth Annual Symposium on Computational Geometry (SoCG'04) (ACM) DOI: 10.1145/997817.997857 - Proposes an LSH family based on p-stable distributions, suitable for Lp distance metrics like Euclidean distance.