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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

Advanced Vector Search for LLM Applications

Prerequisites: ML & Vector Search Basics

Level:

Advanced

What You'll Learn

Advanced Indexing Algorithms
Implement and analyze complex Approximate Nearest Neighbor (ANN) index structures like HNSW, IVF variations, and graph-based methods.
Vector Search Optimization
Apply techniques such as quantization (scalar, product), advanced filtering, and hardware considerations to optimize search latency and throughput.
Hybrid Search Implementation
Combine vector search with traditional methods like keyword search (BM25) and graph techniques for improved relevance.
Metadata Filtering Strategies
Design and implement efficient pre-filtering and post-filtering mechanisms using associated metadata.
Scalability and Deployment
Architect and manage distributed vector search systems for large-scale, production LLM applications.
Performance Tuning
Tune index parameters and query execution plans for optimal recall, precision, and speed trade-offs.

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