Search Results for "databases"

...Its performance benchmarks show it achieving high queries-per-second and fast index build times compared to similar tools. Because it runs in-process, developers can embed it in native apps, microservices, or edge computing scenarios where traditional server-based vector databases might be overkill.

seekdb is an AI-native search database from OceanBase that unifies vector, full-text, relational, JSON, and GIS data into a single query engine. The system is designed to support hybrid search workloads and in-database AI workflows without requiring multiple specialized databases. It enables developers to perform semantic search, keyword search, and structured SQL queries within the same platform, simplifying modern AI application stacks. seekdb also embeds AI capabilities directly in the database layer, including embedding generation, reranking, and LLM inference for end-to-end RAG pipelines. Built on the OceanBase engine, it maintains ACID compliance and MySQL compatibility while delivering real-time analytical performance. ...

...For businesses, the shift to DocWire SDK signifies a leap forward. It promises comprehensive document format support and the ability to extract valuable insights from email boxes, databases, and websites using cutting-edge AI. DocWire SDK aims to expand its capabilities, focusing on versatile data extraction, platform support, and seamless integration with various systems. DocWire SDK is dedicated to streamlining data processing, reducing development time and costs, and harnessing the potential of AI. Its advancements promise a superior experience compared to its predecessor, DocToText.

A developing environment for abstract board games. The user declares game rules in the IDE using a high-level visual language; Abstromatic plays it locally or via Internet, computes databases, openings, heuristics, variants, strategies and much more!

openEAR is the Munich Open-Source Emotion and Affect Recognition Toolkit developed at the Technische Universität München (TUM). It provides efficient (audio) feature extraction algorithms implemented in C++, classfiers, and pre-trained models on well-known emotion databases. It is now maintained and supported by audEERING. Updates will follow soon.