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 Filtering Strategies: Pre vs. Post Filtering
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- Efficient and Robust Approximate Nearest Neighbor Search Using Hierarchical Navigable Small World Graphs, Yu. A. Malkov, D. A. Yashunin, 2018 CoRR DOI: 10.48550/arXiv.1603.09320 - This paper presents the HNSW algorithm, which is foundational for many vector search systems. Understanding its graph structure helps clarify why pre-filtering on arbitrary subsets can be inefficient and provides context for the 'Impact on ANN Index Structure' section.
- Efficient Constrained Nearest Neighbor Search for Large-Scale Recommendation Systems, Zheng Shen, Shihua Zhang, Chuanqi Liu, Dehui Xu, Ruimao Li, 2020 Proceedings of the VLDB Endowment, Vol. 14 (VLDB Endowment) DOI: 10.14778/3429329.3429337 - This academic paper presents techniques for integrating constraints, akin to metadata filters, directly into nearest neighbor search for large-scale systems. It offers insights into performance implications and strategies similar to pre-filtering methods.
- Filtering vector search results, Pinecone, 2024 (Pinecone) - This official documentation from a leading vector database provides practical guidance and an industry perspective on implementing metadata filtering, including a discussion of the trade-offs between pre- and post-filtering.
- How to filter vector search results in Milvus, Milvus Team, 2024 (Milvus) - This documentation from an open-source vector database offers another practical example of how filtering is handled, which can illustrate different implementation details or hybrid strategies in a real-world system.