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Weaviate newsletter - Week 17

SeMI Technologies
SeMI Technologies
@weaviate_io
Dear Weaviate follower,
This week I want to share a new podcast and some small updates… Enjoy :)
Weaviate Updates
🤫 Soon to come: Support Filtering by Timestamp and Batch-Delete by Query.
Weaviate Podcast
🎙 Weaviate Podcast #14: Yi-Lin Sung, Jaemin Cho and Mohit Bansal, a research team from UNC, present their incredible work on VL-Adapter
Other Weaviate News
📹 Interesting session about Open Source Databases with Laura Ham and more on dev.to.
💃 Curious about this weeks hot topic benchmarking? Read this thread from Nils Reimers and unravel.
😊 Meet us on the Knowledge Graph Conference next week in New York.
Have a great week,
Donna
Slack–Twitter–Github

Rajko Radovanović
A helpful explainer from the creator of SBERT himself!

It appears research and understanding of Vector DBs and embeddings is still nascent even among leading NLP organizations... 😅

Hard problems are the best ones to solve!! Let's go @weaviate_io 🚀🚀🚀 https://t.co/VsCxNEj20c
Dmitry Kan
An ASR system I helped build just transcribed "vector search" as "better search". I did not do anything, it is all AI.
Etienne Dilocker
Since benchmarking random d=128 vectors is the big topic today: efConstr=512, ef=256, m=64 leads to 93% Recall@10 in @Weaviate. Mean latency of ~12ms with p99 of ~14ms. W/ 30 CPUs that's ~2550 QPS including object retrieval from disk, HTTP request, client-side parsing, etc. https://t.co/EBxFpWu5wk
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SeMI Technologies
SeMI Technologies @weaviate_io

SeMI Technologies is the company behind the Weaviate vector search engine

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