Knowledge catalogue
TL;DR Elastic recently published a benchmark claiming that their proprietary, disk-based index (dubbed “DiskBBQ”) delivers up to 7x higher throughput than Qdrant when deployed on nodes with network-at
Now Available on Qdrant Cloud: GPU Indexing, Multi-AZ, and Audit Logging We’re excited to announce some Qdrand Cloud upgrades to address AI workloads that write continuously, must meet higher uptime S
Open-source vector database startup Qdrant Solutions GmbH today announced three new enterprise-grade capabilities on its cloud service to address performance, availability and compliance requirements
A discussion about AI agents that can remember information from one interaction to the next, even across separate sessions , exploring how to implement persistent memory systems for personal AI assist
Qdrant 1.18.0 is out! Let’s look at the main features for this version: TurboQuant: A new quantization method that, at twice the compression ratio of scalar quantization, delivers similar recall and s
Reducing Retrieval and Calls When a retrieval-augmented generation (RAG) agent runs, it often has to plan a search, check the evidence it gets back, and try again when that evidence falls short. Those
Qdrant has released **Qdrant‑Fineweb‑10B**, a public benchmark dataset comprising dense and sparse embeddings for 10 billion documents from the FineWeb corpus and exact results for 120,000 queries to
Every crawl, retried job, and embedding pipeline change writes points into a vector collection. The stored data keeps moving even when the query code never changes, and the top results move with it. A
On June 11th, 2026, over 350 developers, researchers, and engineers came together at The Midway in San Francisco for Vector Space Day, our first event of its kind in the United States and our first ma
I decided to run my knowledge base inside a @Cloudflare Worker👀 How? Surprisingly easily: • Spin up a @qdrant_engine cluster and grab the credentials • Process incoming files at the edge: parse with @
Most people don’t read their insurance pamphlet. The benefits are there: deductibles, copays, in-network providers, what dental covers, what dermatology covers, when an optometry visit is included in
GoPerfect mission is to use an AI recruiting workforce that replaces the manual, low-leverage parts of recruiting. Instead, an agent decomposes recruiter intent and runs the work end to end to find to
Sapu is an early-stage biopharmaceutical company developing treatments for hard-to-treat cancers. From its San Diego facility, the team is pioneering a nanomedicine pipeline that takes existing FDA-ap
arXiv:2609.04999v1 Announce Type: new Abstract: This paper describes the participation of the BIT.UA team from the University of Aveiro in the 14th edition of the BioASQ Task B challenge on biomedical
arXiv:2609.22770v2 Announce Type: cross Abstract: We study a parameterized hybrid ranker that fuses a dense embedding list and a sparse lexical list. The method has a small, explicit parameter vector:
Google’s new EmbeddingGemma 2 can run on a phone. The model travels light. The vectors need their own suitcase. At 10 million documents, full-size float32 vectors alone take 30.7 GB of RAM. We got ear
Vector Space Day 2026: Powered by Qdrant About We’re hosting our second-ever full-day in-person Vector Space Day (https://luma.com/vsd-sf) on June 11th at The Midway in San Francisco, and you’re invit
As query traffic grows, so does the compute bill for embedding it. On a low-power device, a large model may not fit in memory. A smaller query model could reduce that cost, but switching usually means
Classification is one of the oldest problems in machine learning. Unlike modern decoding transformers, most classification models were non-autoregressive by nature. Before Jev, the common approaches w
When Ali Aoun Mehdi watched two major global news outlets report opposite facts during the Iran-US conflict, he saw a critical problem: misinformation spreading in real-time. From Islamabad, participa
OpenCodeRAG is a local embedding service using FastAPI and SentenceTransformer, paired with a Node.js plugin that integrates RAG tools with Qdrant vector database via YAML configuration. It provides a
I'm going all in on Hermes (@NousResearch, @Teknium1) as my entire agent and coding stack. Six profiles. One shared self-hosted memory store. Zero hosted-coder dependencies. The fleet: - pmax-mousa —
New from the LangSmith Signal: The vector space rapidly diversified since December. ✅ Managed cloud surged. MongoDB Atlas, Pinecone, + Qdrant tripled their combined share to 21%. ✅ Only 3 vector store
We’ve been building PipesHub for a while now, and I’d love to get more teams to try it and tell us where it breaks. The problem we kept running into was pretty simple: Building an AI app over company
So I've been messinga round with embedding models for a bit, and I think they are interesting enough to experiment with. They are useful for rag, especially in a localllm sense because you can ground
arXiv:2512.04888v4 Announce Type: replace Abstract: Object detection constitutes the primary task within the domain of computer vision. It is utilized in numerous domains. Nonetheless, object detectio
Written by: Robin Grunewald, Supriya Mazumdar, Kelli Vanderlee Introduction Google Threat Intelligence Group (GTIG) examines vulnerability disclosure and exploitation statistics to evaluate the impact
As enterprise organizations scale mission-critical applications, storage I/O and memory access can become severe operational bottlenecks. Whether its Oracle databases, in-memory databases like SAP HAN
AI Engineer Paris is one week away! A huge thank you to all our incredible sponsors for making this special event possible: @nvidia @FactoryAI @sentry @CockroachDB @SonarSource @NotionHQ @SierraPlatfo
So I was playingw ith embedding models I saw that when you upgrade from model A to B, you face a very big backfilling cost Ie, suppose you have a 1b vectors from model A, and then you want to use mode
arXiv:2609.01617v1 Announce Type: cross Abstract: Getting accurate, grounded answers out of large enterprise document repositories is a difficult problem. Dense vector retrieval alone frequently perfo
Build a 100% offline fast Retrieval Augmented Generation (RAG) system that runs without an internet connection, without cloud APIs, without OpenAI/Ollama Published a video where you can build a fully
I am building a domain-specific AI agent powered by Ollama (using the qwen:2.5 model). For data retrieval, the agent utilizes multiple search APIs: DuckDuckGo Search (DDGS), Tavily, Serper, and Google
How should your security team manage shadow AI? Workloads deployed by developers without formal registration can often evade traditional security scanners, because organizations are reluctant to slow
arXiv:2605.14700v1 Announce Type: new Abstract: Generating robot simulation environments remains a major bottleneck in simulation-based robot learning. Constructing a training-ready MuJoCo scene typic
arXiv:2604.13236v1 Announce Type: new Abstract: Semiconductor failure analysis (FA) requires engineers to examine inspection images, correlate equipment telemetry, consult historical defect records, a
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