The Weaviate team was in London last month, where the presentations were packed with insights from the field, real-world use cases, and inspiration for what’s next in AI. From streamlining development to tackling challenges in nuclear energy, here’s a recap of the standout moments.
Moving your Weaviate OSS instance into production? Check out our handy guide to securing your Weaviate database in the cloud, including all the helpful modules we’ve added to make it as easy as possible to keep your vectors safe.
Read about a preview of role-based access control, improved indexing, Weaviate Embeddings, new Voyage Multimodal model and a Japanese BM25 tokenizer!
Unbody has enhanced Weaviate's generative capabilities by developing a custom Generative API that addresses limitations such as static project configurations, limited RAG syntax, and text-only input restrictions.
Say goodbye to the headaches of creating and managing vector embeddings.
Learn about the partnership between AWS and Weaviate.
Learn about vector search, a technique that uses mathematical representations of data to find similar items in large data sets.
Learn about the challenges of filtered vector search and how Weaviate tackles them with ACORN.
Learn about Agentic Retrieval Augmented Generation (RAG), including architecture, implementation, and and difference to vanilla RAG.
Learn about Retrieval Augmented Generation (RAG), including architecture, use cases, implementation, and evaluation.
1.27 adds filtered search and multi-target vector improvements, Jina V3 embedding support and more!
