Master sophisticated vector search techniques to enhance the performance and relevance of Large Language Model applications. This course covers advanced indexing algorithms, optimization strategies, hybrid search methods, and scaling vector search systems in production environments. Suitable for engineers looking to build high-performance semantic search and retrieval-augmented generation (RAG) systems.
Prerequisites ML & Vector Search Basics
Level:
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.