On November 12, 2024, the second session in the MatrixOne 2.0 interpretation livestream series was successfully held. In this livestream, we explored in depth the applications and technical innovation of cloud-native databases in AI search. The event attracted many technology enthusiasts and industry experts, who together witnessed how MatrixOne uses its unique technical architecture to drive breakthroughs and applications in GenAI capabilities.
1. Product and Architecture Highlights
The event first reviewed MatrixOne's development history. From the project launch in October 2021 to today's mature 2.0 version, MatrixOne has consistently adhered to independent R&D, continuously optimized its technical architecture, and integrated multiple database capabilities. These include HTAP (Hybrid Transactional/Analytical Processing), vector databases, time-series data processing, search engines, and more. MatrixOne uses shared storage (S3) as the file-service foundation, combined with a transaction layer, compute nodes (CN), and a shared log service based on the Raft protocol, ensuring elastic scaling of compute resources and seamless data migration and management.
2. GenAI Capability Analysis
One focus of the livestream was MatrixOne's innovative solution for GenAI applications. The team analyzed in depth the limitations of traditional LLMs (large language models) in enterprise-grade applications, such as inability to access private data and insufficient understanding of industry knowledge. To address these challenges, MatrixOne introduced an "LLM + enterprise private hybrid multimodal data RAG (search-based augmented generation) enhancement solution." Through data governance, vectorized storage, and efficient search mechanisms, MatrixOne enhances hybrid multimodal data and helps enterprises improve the accuracy and practicality of AI generation.
3. AI Search Application Case
MatrixOne's practice in AI search was another highlight of the event. The team demonstrated core features of its vector database, including data storage and indexing, vector types and indexes, and full-text indexes. It particularly emphasized MatrixOne's advantages in high performance, easy management, simplified development, and data consistency. Through successful implementation in an image retrieval application, MatrixOne demonstrated its powerful search capabilities. With a dedicated image-search app, MatrixOne helps users quickly find matching sample brands and product numbers, significantly improving work efficiency and customer satisfaction.
4. Event Replay Videos
MatrixOne Capabilities for GenAI
5. Get the PPT
Follow our WeChat official account and reply with the keyword "1112" in the background to get the PDF PPTs for "Born for AI: Technical Analysis of MatrixOne Capabilities for GenAI" and "MO Empowers AI Search."