Multimodal Vector Database Indexing for Accurate Similarity Search
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Solution Overview
Problem
Existing vector databases face inefficiencies in searching multimodal data due to differences in vector distributions across various data modalities, leading to distorted similarity calculations and suboptimal search performance.
Innovation Solution
A method and apparatus that generate separate vector index structures for different modalities and connect them to facilitate accurate similarity searches by using a vector embedding model to align semantically-aligned representations in a common embedding space, followed by modality transformation and alignment to form a hierarchical vector database structure.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a single vector index structure is used for all modalities, then the device complexity is reduced, but the measurement precision of similarity calculations deteriorates due to distorted similarity across heterogeneous data distributions
Solution Approach 1:
The patent divides the single vector index structure into multiple modality-specific index structures (first vector index structure for first modality data, second vector index structure for second modality data). Each index structure is optimized for its specific modality's data distribution, thereby improving similarity calculation accuracy while maintaining manageable complexity through modular organization.
2Measurement precision
If separate vector index structures are created for each modality, then the measurement precision of similarity calculations is improved, but the device complexity increases
Solution Approach 1:
The patent merges multiple modality-specific vector index structures into a unified hierarchical vector database structure with a common embedding space. This allows the system to maintain separate optimized index structures for each modality while presenting a unified interface for queries, thereby managing overall system complexity.
Solution Approach 2:
The hierarchical vector database structure serves multiple functions: it stores and indexes data from different modalities, provides modality-specific optimization for similarity calculations, and offers a unified query interface. This multi-functionality reduces the need for separate systems for each modality.
3Ease of operation
If vector distributions for different modalities are not aligned, then the ease of operation is maintained, but the measurement precision of cross-modality search deteriorates
Solution Approach 1:
The patent introduces a modality alignment layer that acts as an intermediary between different modalities and the embedding space. This alignment layer transforms and aligns vector distributions from different modalities into a common representation space, enabling accurate cross-modality similarity calculations while maintaining simple query operations through a unified interface.
Data Source
AI summary
Provided are a method and apparatus for improving vector search efficiency for multimodal data in vector databases. The method includes: generating a first vector index structure for first modality data; generating a second vector index structure for second modality data different from the first modality data; connecting the first vector index structure and the second vector index structure; and searching for a node similar to a query vector using the connected first vector index structure and second vector index structure. According to the method, it is possible to improve the accuracy of a vector search for multimodal data.