December 26, 2023 · 7 min read
Weaviate 2023 Recap
A reflection on 2023 from team Weaviate!
Weaviate Blog
Keep up-to-date on the latest product updates, how-tos, industry insights, and community spotlights.
December 19, 2023 · 0 min read
Weaviate 1.23 Release
Weaviate 1.23 released with AutoPQ, flat indexing + Binary Quantization, OSS LLM support through Anyscale, and more!
December 5, 2023 · 12 min read
Multimodal Retrieval-Augmented Generation (RAG)
Learn how to build Multimodal Retrieval Augmented Generation (MM-RAG) systems that combine text, images, audio, and video. Discover contrastive learning, any-to-any search with vector databases, and practical code examples using Weaviate and OpenAI GPT-4V.
December 5, 2023 · 6 min read
Weaviate’s re:Invent 2023 recap
Recap the first Weaviate visit to Amazon re:Invent in Las Vegas
November 30, 2023 · 11 min read
Achieve Zero-Downtime Upgrades with Weaviate’s Multi-Node Setup
Learn about high-availability setups with Weaviate, which can allow upgrades and other maintenance with zero downtime.
November 21, 2023 · 30 min read
An Overview on RAG Evaluation
Learn about new trends in RAG evaluation and the current state of the art.
November 15, 2023 · 4 min read
Building an AI-Powered Shopping Copilot with Weaviate
UK-based startup Moonsift is harnessing the power of AI with Weaviate.
October 23, 2023 · 0 min read
Weaviate 1.22 Release
Weaviate 1.22 released with nest object storage, async indexing, further gRPC support, and more!
October 2, 2023 · 5 min read
Hacktoberfest 2023 - Celebrating Open Source with Weaviate
Join us in celebrating Hacktoberfest, a month-long celebration of open source!
September 26, 2023 · 12 min read
Shape the Future - Try Our New Python Client API
A preview release of our new Python client is now available! Help us make it better by trying it out and providing your feedback.
September 26, 2023 · 10 min read
Make Real-Time AI a Reality with Weaviate + Confluent
Learn how to build an application using Weaviate and Confluent
September 19, 2023 · 17 min read
How to Reduce Memory Requirements by up to 90%+ using Product Quantization
The details behind how you can compress vectors using PQ with little loss of recall!