AI Projects
Efficiently handling and retrieving high-dimensional data
Challenges:-
Scalability: Current vector databases struggle to maintain performance as the dataset size increases, especially beyond millions of vectors.
Performance: HNSW-based systems experience a notable drop in search accuracy and speed with increasing dataset sizes.
Cost-Efficiency: Maintaining high performance with HNSW requires substantial computational resources, leading to increased operational costs.
New Trends and Techniques
1. Emergence of ANN Index Algorithm
The Approximate Nearest Neighbor (ANN) index algorithm has emerged from academic research, presenting a promising alternative to HNSW. This algorithm focuses on balancing search accuracy with computational efficiency, making it suitable for both mid-sized and large-scale vector searches.
2. Intra-Query Parallel Graph Traversal
A key innovation in the ANN index algorithm is the use of intra-query parallel graph traversal technology. This technique allows for multiple graph traversal operations to be executed simultaneously within a single query, significantly improving search performance.
Graph 1: Query Latency Comparison :
This graph compares the query latency of HNSW and the new ANN index algorithm across different vector space sizes. Explanation: The ANN index algorithm consistently demonstrates lower latency compared to HNSW, with a more pronounced difference in larger vector spaces.
Scalability Analysis:
Graph 2: Scalability Performance:This graph illustrates the performance of both algorithms as the number of vectors increases from 1M to 10M. Explanation: The ANN index algorithm maintains stable performance across increasing dataset sizes, whereas HNSW shows a decline in efficiency.
Cost-Efficiency :
Graph 3: Cost-Efficiency Analysis:This graph compares the computational cost required to achieve high search accuracy using HNSW and the ANN index algorithm.Explanation: The ANN index algorithm achieves high search accuracy with significantly lower computational costs compared to HNSW.
Solution:- To address the challenges associated with HNSW in high-performance vector databases, a new vector search approach was developed from scratch, emphasizing scalability, performance, and cost-efficiency. This approach leverages the ANN index algorithm, which utilizes intra-query parallel graph traversal, resulting in substantial performance improvements.
