Vector Retrieval with Compressed Candidate Filtering
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Solution Overview
Problem
Existing vector retrieval solutions, particularly in shared storage scenarios, suffer from high delays due to synchronous disk I/O operations and network I/O costs, leading to increased latency and reduced user experience.
Innovation Solution
Store compressed vectors in a high-performance first-level storage resource and original vectors in a lower-performance second-level resource, using a pre-trained classification model to identify candidate vectors from the first-level resource, followed by similarity calculations on the second-level resource to reduce data access and volume.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Volume of stationary object
If original vectors are stored in a second-level storage resource with lower data access performance, then storage capacity is improved, but data access delay increases
Solution Approach 1:
The patent divides the storage system into two levels: a first-level storage resource for compressed vectors with high data access performance, and a second-level storage resource for original vectors with larger storage capacity but lower data access performance. This segmentation allows the system to leverage the advantages of both storage levels by performing preliminary similarity calculations on compressed vectors in the first level and only retrieving full original vectors for promising candidates to the second level.
2Measurement precision
If all original vectors are retrieved from the second-level storage resource for similarity calculation, then retrieval accuracy is improved, but data access volume and delay increase
Solution Approach 1:
The patent performs preliminary similarity calculations using compressed vectors stored in the first-level storage resource before retrieving original vectors from the second-level storage resource. This preliminary action filters out most candidate vectors that are unlikely to be the most similar, so that only a small subset of promising candidates requires retrieval of the full original vectors for final accuracy verification.
3Quantity of substance
If compressed vectors are used for preliminary filtering, then data access volume is reduced, but retrieval accuracy may be compromised
Solution Approach 1:
The patent uses compressed vectors as an intermediary representation for preliminary similarity calculations. These compressed vectors serve as a mediator that enables fast filtering while the final retrieval accuracy is ensured by calculating similarity between the query vector and the original vectors for the filtered candidates. The compressed vectors do not directly determine the final result but facilitate the process by reducing the search space.
Data Source
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AI summary
One or more embodiments of this application provide a vector retrieval method and apparatus, a device, and a storage medium. A compressed vector corresponding to an original vector is stored in a first-level storage resource; the original vector is stored in a second-level storage resource; and the method includes: in response to a query vector, obtaining, from the first-level storage resource, at least one compressed vector having a highest similarity with the query vector; performing classification decision-making on each compressed vector based on a classification model, so as to obtain at least one target compressed vector whose corresponding original vector is a candidate vector most similar to the query vector; obtaining at least one target original vector corresponding to the at least one target compressed vector from the second-level storage resource; and respectively calculating a similarity between the query vector and the at least one target original vector, sorting the at least one target original vector based on the calculated similarity, and determining, based on the sorted at least one target original vector, a vector retrieval result corresponding to the query vector.