Vector Dataset Index Selection for Precision and Query Speed
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Solution Overview
Problem
Users face challenges in optimizing vector database query parameters for high precision and low response time, as manual selection relies on engineer expertise and is time-consuming, with inconsistent performance across different data sets, lacking ground truth, and requiring extensive trial-and-error.
Innovation Solution
A method for automatically selecting suitable indexes and parameters in vector databases using unsupervised scenarios, adapting to user needs and data attributes, and dynamically adjusting parameters based on desired accuracy and delay, without requiring manual intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual parameter selection is used, then expertise-based optimization is achieved, but time consumption and inconsistency increase
Solution Approach 1:
The system performs self-optimization by automatically selecting index parameters and configuring ANNS algorithms without requiring manual engineer intervention. The optimization process is autonomous, using the vector dataset characteristics to determine optimal parameters automatically.
Solution Approach 2:
The system dynamically adjusts multiple parameters including index type selection, number of lists, search probes, and algorithm configurations based on vector dataset attributes such as dimensionality, size, and distribution characteristics to achieve optimal search performance.
2Measurement precision
If extensive trial-and-error is performed, then parameter optimization is attempted, but productivity decreases
Solution Approach 1:
The system performs preliminary optimization by pre-determining the optimal index parameters and algorithm configurations before actual search operations. This upfront configuration eliminates the need for repeated trial-and-error during query processing, improving overall productivity.
Solution Approach 2:
The system uses feedback from vector dataset characteristics (dimensionality, size, distribution) to automatically adjust parameters. This feedback mechanism replaces manual trial-and-error with an automated adaptive process that efficiently converges on optimal settings.
3Measurement precision
If manual expertise-based selection is used, then accurate parameter choice is possible, but adaptability to different data sets decreases
Solution Approach 1:
The system provides universal adaptability by automatically adjusting to different vector dataset types, sizes, and dimensions. The same optimization framework handles diverse datasets including images, audio, and text embeddings without requiring dataset-specific manual configuration.
Solution Approach 2:
The system dynamically adapts parameters based on the specific characteristics of each vector dataset. Index type, number of lists, search probes, and algorithm configurations are all dynamically adjusted according to dataset attributes, enabling versatile adaptation across different applications.
Data Source
AI summary
Vector dataset index parameter determination is performed by building an index of a vector dataset according to each build parameter group among a plurality of build parameter groups, generating search parameter groups, submitting each vector query in a vector query set to each index according to the candidate search parameter values from each search parameter group to obtain, for each submission, a result content and a result delay value, obtaining, for each submission, a vector distance between at least one result vector of the result content and the submitted vector query, determining a relative accuracy value for each submission by comparing the vector distance of the result content to the vector distance of at least one other submission, correlating build parameter values and search parameter values with the relative accuracy values and the result delay values, and fixing build parameter values based on the correlating.