bayesian_limit

I also noticed, from same contributors, this repo with goal of enabling import/export of vector datasets, that can be migrated from one vectorDB vendor to another. The in-common format vectordb data can be stored/shared on HuggingFace datasets, using the in-built flag for it. Then anyone using another vectordb could download it.

https://github.com/AI-Northstar-Tech/vector-io/tree/main?tab...

I could not help but notice the Contriever curve is so much higher on y-axis Recall than the other methods (figure 11 in https://arxiv.org/pdf/2307.03172.pdf).

Has anyone come across more recent experiments, results, or papers related to this? I'm acquainted with the: - Contriever 2021 paper https://aclanthology.org/2021.eacl-main.74.pdf - Hyde 2022 https://arxiv.org/pdf/2212.10496.pdf

My suspicion is some pre-logic such as is the user's question dense enough then use Hyde with chat history. If anyone has more recent experience with Contrievers, would love to learn more about it!

Feel free to contact me directly on LinkedIn. https://www.linkedin.com/in/christybergman/

We conducted benchmark tests on Elastic's queries per second (QPS) performance using datasets of 500,000 and 1 million vectors. Result was Zilliz is 13x and 22x faster, per number of vectors respectively. https://zilliz.com/blog/elasticsearch-cloud-vs-zilliz

We also conducted a benchmark comparing Pgvector to both Milvus (open source) and Zilliz (managed, with a free tier option). When running the OSS Milvus on 2 CPUs and 8 GiB memory, Pgvector was found to be 5 times slower. You can check out the detailed performance charts at the bottom of this blog post: https://zilliz.com/blog/getting-started-pgvector-guide-devel...

Feel free to explore our open-source benchmarking tool, which allows you to examine our methodology and even compare it with your vector database. https://github.com/zilliztech/VectorDBBench

We conducted benchmark tests on Elastic's queries per second (QPS) performance using datasets of 500,000 and 1 million vectors. Result was Zilliz is 13x and 22x faster, per number of vectors respectively. https://zilliz.com/blog/elasticsearch-cloud-vs-zilliz

Feel free to explore our open-source benchmarking tool, which allows you to examine our methodology and even compare it with your vector database. https://github.com/zilliztech/VectorDBBench

Zilliz just published an article comparing QPS (queries per second) with pg vector vs. Milvus. The results are clear - Milvus, a database designed ground-up for handling vector indexes, outperformed in terms of speed and latency. Dive into the details here. https://zilliz.com/blog/getting-started-pgvector-guide-devel...

Full disclosure, I just joined Zilliz this week as a Dev Advocate.