Key consideration for customizing an embedding model through fine-tuning it on company- or domain-specific data to improve the downstream retrieval performance in RAG applications.
Design for speed, build for experience.
Master the art of scaling a vector database like Weaviate.
Weaviate `1.31` implements the MUVERA encoding algorithm for multi-vector embeddings. In this blog, we dive the algorithm in detail, including what MUVERA is, how it works, and whether it might make sense for you.
Learn when and how to use GraphRAG and how it can improve on some search tasks
Learn about how you can use our new agentic personalization service to provide user-catered recommendations from Weaviate collections.
Late interaction allow for semantically rich interactions that enable a precise retrieval process across different modalities of unstructured data, including text and images.
Learn how the new Transformation Agent will change the way we manage data. Say goodbye to the tedious tasks of database management!
Agentic workflows combine AI agents, tools, and agent memory to create adaptive systems. Learn the core patterns, use cases, and real-world examples.
Learn about the Query Agent, our new agentic search service that redefines how you interact with Weaviate’s database!
How Weaviate achieved 10x Faster Keyword Search and 90% index compression
What is an AI Agent? Learn how AI agents work, the benefits of using AI agents and more
