Context engineering is how AI agents manage LLM memory—selecting, retrieving, and organizing context from short-term and long-term memory to improve reliability in production.
Weaviate recognized by AWS Partners in Benelux as leaders in helping customers drive innovation
The Weaviate Java client v6 is now generally available! This release brings a completely redesigned API that embraces modern Java patterns, simplifies common operations, and makes working with vector databases more intuitive than ever.
Learn how to use the Dify and Weaviate integration to build RAG applications.
1.34 introduces flat index support with RQ quantization, server-side batching improvements, new client libraries, Contextual AI integration and much more.
Weaviate announces two CVEs that are fixed in updated versions of our product.
What building AI apps with Weaviate and cooking have in common? Let’s find out!
Hands-on patterns: Design pattern for gen-AI enterprise applications, with Arize AI.
Learn how Confluent’s Streaming Agents and Weaviate combine real-time context with semantic understanding for agentic AI.
Weaviate Cloud gets an updated pricing model.
A guide to seamlessly transform data sitting in lakes and warehouses for GenAI capable applications
A strategic guide to the costs, risks and rewards of upgrading embedding models in production AI
