Agents need vector search more than RAG ever did

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

Qdrant closes $50M Series B to expand vector search infrastructure

Qdrant develops a vector search engine designed for production AI systems, enabling teams to configure retrieval, ranking, and filtering to support scalable applications such as semantic search and AI ...
dbta

Qdrant 1.17 Supercharges Vector Search with a Variety of Updates

Qdrant is releasing platform version 1.17.0—updating search latency, introducing relevance feedback query, and deploying greater operational observability. This release introduces a new Relevance ...
Salina Journal

Endee.io Open Sources its High-Performance Vector Database for Scalable AI

Endee.io launches Endee, an open source vector database delivering fast, accurate, and cost-efficient AI and semantic search at scale. Endee rethinks vector DBs for high recall, low latency, and low ...
IEEE

Comparison of Vector Database Management Systems for Retrieval Augmented Generation

Abstract: Retrieval Augmented Generation (RAG) is the de-facto technology used by pre-trained large language models to access data in databases, in addition to the data stored in their parameters.
SecurityWeek

DockerDash Flaw in Docker AI Assistant Leads to RCE, Data Theft

The critical vulnerability exists in the contextual trust in MCP Gateway architecture, as instructions are passed without validation. A critical-severity bug in Docker’s Ask Gordon AI assistant can be ...