Qdrant news and milestones

Benchmark filtered vector search in Qdrant: how ACORN, filterable HNSW, and query planning trade recall for latency on one million points.

Discover OpenCrawling

A reproduced vector search benchmark shows Elasticsearch and Qdrant land in the same range once setup and segment count are matched.

Discover how TrustGraph combined Qdrant vector search with graph-native semantics and streaming pipelines to deliver resilient, deterministic, and scalable agentic AI built for p…

Relerance feedback: from ancient history to LLMs. Why relevance feedback techniques are good on paper but not popular in neural search, and what we can do about it.

Qdrant is an Open-Source Vector Search Engine written in Rust. It provides fast and scalable vector similarity search service with convenient API.

Learn how immutable data structures improve vector search performance in Qdrant.

We recently discovered that embedding models can become late interaction models & can perform surprisingly well in some scenarios. See what we learned here.

Vector database offers on-prem, cloud-native, or SaaS deployment, leading performance, a rich set of integrations and language drivers, and a dizzying array of optimization optio…

Qdrant's BM42 is a new search model that extracts data from chunks of a document which it says makes vector search more efficient.

Introducing BM42 - a new sparse embedding approach, which combines the benefits of exact keyword search with the intelligence of transformers.

Intel's 5th gen Xeon processor is made for enterprise-scale operations in vector space.

Redefining freedom in the age of Generative AI. We believe that vendor-dependency comes from hardware, not software.

Enterprises want to make the most of vector databases without sacrificing control of their data.Now, they can do so in the cloud.

Lightweight and open-source. Custom made for RAG and completely integrated with Qdrant.

Explore how RAG enables LLMs to retrieve and utilize relevant external data when generating responses, rather than being limited to their original training data alone.

Uncover the necessity of vector search for RAG and learn how Qdrant empowers enterprise AI with unmatched accuracy and cost-effectiveness.

Introducing Qdrant Summer of Code 2024 program. GSoC alternative.

Discover the power of vector embeddings. Learn how to harness the potential of numerical machine learning representations to create a personalized Neural Search Service with Fast…

Newsfile Press Release.

The first comparative benchmark and benchmarking framework for vector search engines.

Generate Google Gemini embeddings for text and multimodal content, then index them in Qdrant to build multilingual, cross-modal semantic search.