Selective Vector Embedding Updates Using Hash-Based Change Detection
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Solution Overview
Problem
Existing AI data processing pipelines generate embeddings for entire data assets, even if only a portion has changed, leading to computational overhead and delays.
Innovation Solution
A data processing pipeline that maps data assets to hash values and compares them to a lookup table, selectively generating new embeddings only for changed portions, reusing existing embeddings for unchanged parts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If embeddings are generated for entire data assets, then completeness of data representation is improved, but computational overhead and processing time increase
Solution Approach 1:
The patent segments data assets into multiple data segments and generates embeddings only for changed segments rather than processing entire data assets. This is achieved by dividing data assets into manageable portions, tracking changes at the segment level, and selectively processing only those segments that have changed, thereby reducing computational overhead while maintaining complete representation of changed data.
Solution Approach 2:
The patent applies partial action by generating embeddings only for the necessary portion of data assets that have changed, rather than performing excessive action of regenerating embeddings for entire data assets. The system determines the extent of changes and performs embedding generation only to the degree required, avoiding unnecessary computational work while ensuring complete representation of changed portions.
2Reliability
If embeddings are generated for entire data assets, then data completeness is improved, but computational overhead increases
Solution Approach 1:
The system segments data assets into multiple data segments and processes only changed segments for embedding generation. This segmentation allows the system to maintain complete representation of changed data while avoiding the computational overhead of processing entire data assets, as embeddings are generated only for the necessary segments that contain changes.
Solution Approach 2:
The patent implements partial action by performing embedding generation only for the extent necessary to represent changed data. The system determines which data segments have changed and performs computational work only on those segments, avoiding excessive computational overhead associated with processing entire data assets while ensuring data completeness for changed portions.
3Manufacturing precision
If embeddings are generated for entire data assets, then representation accuracy is improved, but processing efficiency decreases
Solution Approach 1:
The patent segments data assets into multiple data segments and generates embeddings only for changed segments. This segmentation approach maintains representation accuracy for changed portions by ensuring they are fully processed, while improving processing efficiency by avoiding redundant processing of unchanged segments. The system tracks changes at the segment level and performs embedding generation only where necessary.
Solution Approach 2:
The system applies partial action by performing embedding generation only to the extent necessary to maintain representation accuracy for changed data. Rather than performing excessive action of processing entire data assets, the system determines the precise scope of changes and performs computational work only on affected segments, thereby maintaining accuracy while improving processing efficiency.
Data Source
AI summary
This disclosure provides methods, devices, and systems for generating vector embeddings. The present implementations more specifically relate to detecting changes in a data asset for targeted embeddings generation. For example, a data processing pipeline may receive a data asset to be converted to a set of vector embeddings. In some aspects, the data processing pipeline may map the data asset to one or more hash values and compare the hash values to a lookup table. The lookup table stores known hash values associated with previously generated vector embeddings stored in a vector repository. The data processing pipeline selectively maps the data asset to one or more vector embeddings based on whether the hash values match any of the known hash values in the lookup table. More specifically, the data processing pipeline may refrain from generating any new vector embeddings if each of the hash values matches a known hash value.