Composite Code Sparse Autoencoder for Fast Vector Search
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Solution Overview
Problem
Current information retrieval systems face high latency issues due to the computational challenges of indexing and similarity search on dense vectors, especially in large document collections, which affects user experience and efficiency.
Innovation Solution
The implementation of a Composite Code Sparse Autoencoder (CCSA) method that transforms dense document representations into sparse binary representations, enabling efficient indexing and retrieval by generating composite codes with C clusters of dimension L, which are used to create an index for improved information retrieval.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If dense vector indexing and similarity search are used for information retrieval, then retrieval accuracy is improved, but latency increases significantly
Solution Approach 1:
The patent transforms the representation parameters of document vectors from dense floating-point format to sparse discrete codes. This parameter change enables efficient indexing and search operations while maintaining retrieval accuracy, directly resolving the contradiction between accuracy and latency.
Solution Approach 2:
The patent applies different processing strategies to different parts of the retrieval system: dense vectors are used for accurate similarity computation, while sparse codes are used for efficient indexing and candidate selection. This local differentiation resolves the contradiction by optimizing each component for its specific function.
2Measurement precision
If Transformer-based language models like BERT are used for reranking, then retrieval quality is improved, but computational cost increases
Solution Approach 1:
The patent segments the retrieval pipeline into multiple stages: initial retrieval using efficient sparse code indexing, candidate selection, and final reranking using Transformer models. This segmentation allows computational expensive operations to be applied only to a small subset of candidates, resolving the contradiction between quality and computational cost.
Solution Approach 2:
The patent applies full Transformer-based reranking only to a small subset of candidate documents selected from the sparse code index, rather than applying it to all documents. This partial action maintains high retrieval quality while significantly reducing computational cost.
3Productivity
If approximate nearest neighbor search is used, then throughput is improved, but recall performance degrades
Solution Approach 1:
The patent introduces sparse codes as an intermediary representation that bridges exact search accuracy and approximate search efficiency. The sparse codes enable efficient candidate selection that approximates nearest neighbor search, while the subsequent reranking stage ensures high recall performance by applying precise scoring to selected candidates.
Data Source
AI summary
Information retrieval methods employ a neural network encoder configured to receive a dense representation and generate a composite code comprising C clusters of dimension L from the dense representation. An activation function is configured to generate a sparse composite code from the composite code. The sparse composite code comprises a binary representation. An index can be generated using the sparse composite code.