Qdrant Database is an open source engine for similarity search, semantic retrieval, and AI applications using high-performance vector storage. Download qdrant documentation to set up reliable semantic ...

Vector databases have graduated from experimental tooling to mission-critical infrastructure. In 2026, vector databases serve as the core retrieval layer for RAG pipelines, semantic search systems, ...

As more AI systems become mission-critical for the agentic era and enterprise companies begin to adopt retrieval-augmented generation, also known as RAG, vector search has become the go-to for data ...

Skills encode deep Qdrant knowledge so coding agents can make the engineering decisions that determine whether vector search works well: quantization, sharding, tenant isolation, hybrid search, model ...

This voice experience is generated by AI. Learn more. This voice experience is generated by AI. Learn more. Oracle announced a suite of agentic AI capabilities integrated directly into Oracle AI ...

To continue reading this content, please enable JavaScript in your browser settings and refresh this page. Preview this article 1 min Vector Systems is betting big on ...

What's the role of vector databases in the agentic AI world? That's a question that organizations have been coming to terms with in recent months. The narrative had real momentum. As large language ...

Qdrant, an open-source vector search engine, has closed a $50 million Series B funding round led by AVP, with participation from Bosch Ventures, Unusual Ventures, Spark Capital, and 42CAP. Vector ...

BERLIN & NEW YORK--(BUSINESS WIRE)--Qdrant, the open-source vector search engine built in Rust for production workloads, today announced $50 million in Series B funding led by AVP, with participation ...

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 ...