Weaviate Newsletter - Cross-encoders, GOMEMLIMIT and BetterFeedback integrates Weaviate

Hiiiiii Weaviate Friends!!!

There has been a lot going on in the last few weeks. We have a few exciting blogs, podcasts and stories to share with you.

Cross-encoders

Our very own Laura Ham has been researching: how we could combine the power of cross-encoders and bi-encoders to get more accurate results without sacrificing performance.

✍️ Read Using Cross-Encoders as reranker in multistage vector search - to learn about:

• What cross-encoders and bi-encoders are

• How to combine them

• Pre-trained cross-encoders

GOMEMLIMIT – a game changer for high-memory applications

The Go team have released GOMEMLIMT – an exciting feature that we particularly feel very excited about. It allows us to better control the garbage collection process, and avoid the rare out-of-memory application crashes.

✍️ Read GOMEMLIMIT is a game changer for high-memory applications - by Etienne Dilocker – to learn about:

• The memory allocation process in Go

• The Garbage Collector

• Scenarios that could result in OOM

• How GOMEMLIMIT helps

• The results of our experiments

If you find this interesting, join us in the conversation on Reddit.

Integration story - BetterFeedback

We've been collaborating with our friends at BetterFeedback.ai, who in their quest to give their customers a better analytics experience, decided to join the Semantic Search movement. But don't take my word for it:

✍️ Read The Full Power of Semantic Search and Vector Database - by Rafał Muszyński - to learn about their motivations and why Weaviate was their pick.

Weaviate helps 🚀NASA's GES-DISC team

We are very excited to see Weaviate in a presentation by NASA's GES-DISC team.

👀 See GES-DISC Graph-Enabled Vector Search - where they show how they are using language embeddings and graph-enabled vector search (Weaviate) to improve their search capabilities.

Podcasts

Last week, Connor met with Yaoshiang Ho – a Co-Founder of Masterful AI.

🎙Watch Weaviate Podcast #22 – to learn more about:

• cutting edge Computer Vision algorithms such as Noisy Student, SimCLR, and Barlow Twins

• broader topic of Semi-Supervised Learning

Updates

We had an update to the Multi2Vec CLIP inference module, now on version 1.1.4 (notes)

• Fix unserializable values in /meta endpoint