Definition
Index Optimization is the process of fine-tuning vector database structures and search algorithms—such as HNSW or IVF—to balance the trade-offs between retrieval latency, memory footprint, and recall accuracy. In RAG pipelines, this involves configuring parameters like quantization and graph connectivity to ensure the LLM receives highly relevant context in milliseconds.
Optimizing vector similarity search structures, not relational database B-Trees or SQL indexes.
"Reorganizing a massive library from a single list into a multi-layered web where similar topics are physically linked by high-speed shortcuts."
- HNSW (Hierarchical Navigable Small Worlds)(Component)
- Product Quantization (PQ)(Component)
- ANN (Approximate Nearest Neighbor)(Underlying technology)
- Latency-Recall Trade-off(Constraint)
Conceptual Overview
Index Optimization is the process of fine-tuning vector database structures and search algorithms—such as HNSW or IVF—to balance the trade-offs between retrieval latency, memory footprint, and recall accuracy. In RAG pipelines, this involves configuring parameters like quantization and graph connectivity to ensure the LLM receives highly relevant context in milliseconds.
Disambiguation
Optimizing vector similarity search structures, not relational database B-Trees or SQL indexes.
Visual Analog
Reorganizing a massive library from a single list into a multi-layered web where similar topics are physically linked by high-speed shortcuts.