Quantized Vector Indices for Fast, Memory-Efficient Multi-Graph Search
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Solution Overview
Problem
Conventional quantization techniques for vector indices require high memory storage and have lower accuracy, while single-graph search algorithms are computationally inefficient, and multi-graph merging algorithms are complex and resource-intensive.
Innovation Solution
A search system that employs quantization constants to optimize vector quantization, computes similarity distances with error approximation, and implements adaptive indexing strategies for merging multiple graphs efficiently, using parallel processing to reduce computational resources and improve accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If conventional quantization techniques are used for vector indices, then memory storage requirements are reduced, but accuracy of representing the underlying data deteriorates
Solution Approach 1:
The patent applies parameter changes by dynamically adjusting quantization parameters (such as quantization bits and precision levels) based on the specific requirements of different vector indices and search operations. This allows the system to optimize the balance between memory storage and representation accuracy by changing quantization parameters rather than using fixed conventional quantization techniques.
2Device complexity
If single graph search algorithms are used, then system complexity is reduced, but computational efficiency deteriorates
Solution Approach 1:
The patent applies segmentation by dividing the search process into multiple independent graph searches that can be executed in parallel. Instead of using a single complex graph search algorithm, the system segments the data into multiple graphs and performs concurrent searches, thereby improving computational efficiency while maintaining manageable system complexity through modular design.
Solution Approach 2:
The patent applies merging by combining results from multiple parallel graph searches into a unified result set. The system merges the outputs of several simpler graph searches to achieve the effectiveness of a more complex search algorithm, thereby improving computational efficiency without requiring a single overly complex search structure.
3Measurement precision
If multiple graphs are used for indexing, then search accuracy is improved, but indexing complexity and computing resources increase
Solution Approach 1:
The patent applies segmentation by organizing data into multiple separate graph structures that can be independently built and maintained. Each graph can be optimized for specific data characteristics or query types, improving search accuracy while keeping individual graph structures simple and manageable, thereby controlling indexing complexity through modular organization.
Data Source
AI summary
According to an aspect, a search system is provided that executes quantization techniques and/or multi-graph searching and/or merging techniques that may increase the speed of searching and/or indexing while reducing the amount of computing resources that are used to perform these computer tasks as compared with conventional approaches.