Abstract: Retrieval-augmented generation pipelines store large volumes of embedding vectors in vector databases for semantic search. In Compute Express Link (CXL)-based tiered memory systems, ...

Create collections with configurable vector dimensions Store documents with their vector embeddings in Qdrant Search for similar documents using vector embeddings ...

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.

A study on vector database and AI integration identifies unstable indexing, weak cross-modal fusion, and rigid resource scheduling as key barriers. By introducing HNSW optimization, unified feature ...

IBM worked with Nvidia and Samsung to demonstrate a content-aware storage (CAS) system that can hold a 100-billion-vector database on a single server, work targeted at making retrieval-augmented ...

Qdrant, the open-source vector search engine built in Rust for production workloads, announced it has secured $50 million in Series B funding will enable composable vector search as core ...

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 from Bosch Ventures, Unusual ...

Open-source vector search 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. AVP ...

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 ...

In this tutorial, we build an EverMem-style persistent agent OS. We combine short-term conversational context (STM) with long-term vector memory using FAISS so the agent can recall relevant past ...