Elastic 9.3.0 is now available, featuring enhanced vector search indexing for RAG applications and significant upgrades to ...

Design intelligent AI agents with retrieval-augmented generation, memory components, and graph-based context integration.

Choosing RAG or long context depends on dataset size, with RAG suited to dynamic knowledge bases and long context best for bounded files.

Qdrant develops a vector search engine designed for production AI systems, enabling teams to configure retrieval, ranking, ...

Open-source vector database startup Qdrant Solutions GmbH today announced it has raised $50 million in early-stage funding to pave the way for smarter and more reactive artificial intelligence apps.

Databricks has released KARL, an RL-trained RAG agent that it says handles all six enterprise search categories at 33% lower ...

Databricks' KARL agent uses reinforcement learning to generalize across six enterprise search behaviors — the problem that breaks most RAG pipelines.

Building a Retrieval-Augmented Generation (RAG) pipeline is easy; building one that doesn’t hallucinate during a 10-K audit is nearly impossible. For devs in the financial sector, the ‘standard’ ...

Alibaba Tongyi Lab research team released ‘Zvec’, an open source, in-process vector database that targets edge and on-device retrieval workloads. It is positioned as ‘the SQLite of vector databases’ ...

Company plans to use funds to accelerate AI database, Genie assistant JPMorgan Chase leads $2 billion debt financing Databricks' AI products cross $1.4 billion in annualized revenue Feb 9 (Reuters) - ...

Abstract: Graph-based vector search that finds best matches to user queries based on their semantic similarities using a graph data structure, becomes instrumental in data science and AI application.

Pictured is a U.S. Air Force photo of U.S. Navy Adm. Rich Correll, the head of U.S. Strategic Command, getting a launch control center briefing from members of the 12th Missile Squadron at Malmstrom ...