Vector Dataset Index Tuning via Variance-Based Parameter Selection
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Solution Overview
Problem
Users face difficulty in selecting suitable ANNS indexes and parameters for vector databases, requiring extensive trial-and-error to achieve high precision and low response time, which is cumbersome due to the need to understand various parameters and user-specific data attributes.
Innovation Solution
An automatic tuning technique that selects suitable indexes and adapts parameters based on user needs, data attributes, and physical resources, using an apparatus to determine optimal build and search parameters for vector dataset indexing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual trial-and-error tuning of ANNS index parameters is performed, then search precision can be improved, but the complexity of operation and time consumption increase significantly
Solution Approach 1:
The system performs automatic parameter tuning without requiring user intervention. The tuning module autonomously selects optimal ANNS index parameters by evaluating multiple candidate configurations and choosing the best one based on performance metrics, eliminating the need for manual trial-and-error tuning by users.
Solution Approach 2:
The system pre-evaluates multiple parameter configurations offline before actual search operations. By performing parameter tuning in advance and storing the optimal configuration, the system avoids the need for users to perform time-consuming manual tuning when conducting searches.
2Measurement precision
If manual trial-and-error tuning of ANNS index parameters is performed, then search precision can be improved, but the time consumption increases significantly
Solution Approach 1:
The system performs parameter tuning offline in advance before actual search operations. By pre-evaluating multiple parameter configurations and storing the optimal one, the system eliminates the need for time-consuming manual tuning at the time of search, significantly reducing time consumption.
Solution Approach 2:
The automatic tuning module independently performs parameter optimization without requiring repeated user intervention. The system autonomously evaluates candidate parameters and selects the optimal configuration, eliminating the iterative time users would spend on manual tuning.
3Ease of operation
If automatic parameter tuning is implemented, then ease of operation is improved, but the computational resources and system complexity increase
Solution Approach 1:
The system introduces a dedicated tuning module as an intermediary between the user and the ANNS index configuration. This module handles the complex parameter selection logic internally, presenting a simplified interface to users while managing the computational complexity within the system architecture.
Solution Approach 2:
The system separates the parameter tuning function into a distinct, modular component. By isolating the tuning logic in a separate module that can be independently configured and executed, the system manages complexity through functional segmentation rather than distributing it throughout the entire system.
4Measurement precision
If multiple parameter configurations are evaluated, then search precision is improved, but the computational energy consumption increases
Solution Approach 1:
The system performs comprehensive parameter evaluation offline before actual search operations. By pre-computing and comparing multiple parameter configurations, the system invests computational energy in advance when energy availability is less critical, reducing real-time energy consumption during actual searches.
Solution Approach 2:
The system evaluates a limited set of pre-selected candidate parameter configurations rather than exhaustively testing all possible parameters. This partial evaluation approach achieves sufficient precision improvement while constraining energy consumption by focusing on the most promising configurations.
Data Source
AI summary
Vector dataset index parameter determination is performed by obtaining a statistical variance of each vector dataset among a plurality of vector datasets, grouping the plurality of vector datasets into a plurality of vector dataset groups based on the statistical variance, determining, for each vector dataset group, build parameter values, obtaining the statistical variance of a target vector dataset, and applying, to the target vector dataset, the determined build parameter values corresponding to a vector dataset group selected based on the statistical variance.