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.

Qdrant's $50M Series B and version 1.17 release make the case that agentic AI didn't simplify vector search — it scaled the ...

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.

Building an open-source data lakehouse costs $520K/year in engineering time, before licenses and infra. The real all-in cost ...

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.

Build intelligent applications that combine large language models with your enterprise data using battle-tested RAG patterns and native vector search capabilities.

Step-by-step tutorial perfect for understanding core concepts. Start here if you're new to Agentic RAG or want to experiment quickly. 2️⃣ Building Path: Modular Project Flexible architecture where ...