Approximate Nearest Neighbor Search Index with Tombstone Filtering
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Solution Overview
Problem
Existing approximate nearest neighbor search algorithms in content repositories face challenges in providing immediate searchability of new content, as building a search index requires significant computational resources and time, leading to stale search results.
Innovation Solution
A method and system that utilize a prebuilt search index with a neighborhood graph, allowing new content to be added to the index as if it were present initially, and using a tombstone mechanism for deleted content, ensuring immediate searchability and minimal impact on search latency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a search index is rebuilt to include new content, then search completeness is improved, but computational resources and time are consumed
Solution Approach 1:
The system performs preliminary actions by pre-computing and storing nearest neighbor relationships in a search index before they are needed. When new content is added, the system has already prepared the indexing structure, allowing for rapid insertion without full re-computation. This preliminary preparation resolves the contradiction by having the computational work done in advance rather than at the moment of content addition.
Solution Approach 2:
The search index is segmented into manageable components (neighborhood graphs with nodes representing content items). Instead of rebuilding the entire index when adding new content, only the relevant segments need to be updated. This segmentation allows the system to maintain search completeness while minimizing the computational overhead to only the affected portions of the index.
2Measurement precision
If a search index is rebuilt to include new content, then search accuracy is improved, but computational resources are consumed
Solution Approach 1:
The system performs partial action by computing and storing only the necessary nearest neighbor relationships required for accurate search, rather than computing all possible relationships. The search index stores approximate nearest neighbors that are sufficient for accurate search results without requiring exhaustive computation. This partial computation approach maintains search accuracy while significantly reducing computational resource consumption.
3Loss of information
If the search index is updated frequently to include new content, then result freshness is improved, but system complexity increases
Solution Approach 1:
The system prepares the indexing structure in advance with pre-computed nearest neighbor relationships, allowing new content to be integrated seamlessly without complex update procedures. This preliminary preparation simplifies the actual update process while maintaining result freshness, as the infrastructure is already in place to handle new content efficiently.
4Adaptability or versatility
If new content is added to the content repository, then content availability is improved, but search index completeness deteriorates
Solution Approach 1:
The system performs preliminary indexing of new content as it is added to the repository, rather than waiting for batch processing. By pre-computing the vector representations and nearest neighbor relationships immediately upon content addition, the system ensures that newly available content is simultaneously searchable, maintaining both content availability and search index completeness without contradiction.
Data Source
AI summary
System and methods are directed to operations associated with an approximate nearest neighbor search engine. More specifically, a vector semantically representing content to be added to a search index may be received. The search index may include a neighborhood graph having a plurality of nodes, where each node of the plurality of nodes is associated with content in a content repository. A plurality of nodes within the search index determined to be most semantically similar to the received vector semantically representing content to be added to the search index may be identified. The node corresponding to the received vector semantically representing content to be added to the search index to the search index may be added to the search index and a listing of nearest neighbors associated with each of the of the plurality of nodes may be updated to include an identifier associated with the added node.