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Benchmark results and tools by MyScale aimed at measuring the performance of vector databases in various search and retrieval tasks.
A set of benchmarks provided by Qdrant for evaluating vector databases, focusing on speed, scalability, and accuracy of vector search operations.
VectorDBBench is a benchmarking tool developed by ZillizTech for evaluating the performance of various vector databases, aiding users in selecting suitable vector database solutions for their needs.
ANN-Benchmarks is a benchmarking platform specifically for evaluating the performance of approximate nearest neighbor (ANN) search algorithms, which are foundational to vector database evaluation and comparison.
A benchmarking resource for evaluating approximate nearest neighbor search (ANNS) methods on billion-scale datasets, highly relevant for assessing the scalability of vector databases.
This ACL 2023 tutorial reviews retrieval-based language models, which often rely on vector databases and vector search systems to retrieve relevant context. The tutorial covers methods and applications central to the use of vector databases in modern NLP systems.
Category: Benchmarks & Evaluation
Source: GitHub Repository
MyScale's Vector Database Benchmark is an open-source framework designed to assess the performance of fully-managed vector databases across various search and retrieval tasks. It provides tools and datasets for benchmarking and delivers comparative results for different vector database solutions.
Not applicable. This is an open-source tool available for free under the Apache-2.0 license.
benchmark vector-databases performance retrieval