Vector Database Stories
From company news to technical tutorials – explore the most popular content on the Zilliz blog.
Engineering
Emerging Trends in Vector Database Research and Development
This post discusses the development and anticipated future of vector databases from both technical and practical perspectives, focusing on cost-efficiency and business requirements.
Product
The Evolution and Future of AI and Its Influence on Vector Databases: Insights from Charles, CEO of Zilliz
We continue to explore the dynamic interplay between Artificial Intelligence (AI), particularly large language models and vector databases, guided by the insights of Charles Xie, CEO of Zilliz.
Vector Database 101
Vector Library versus Vector Database
Dive into the differences between these two technologies, their strengths, and their practical applications, providing developers with a comprehensive guide to choosing the right tool for their AI projects.
Engineering
How to Enhance the Performance of Your RAG Pipeline
This article summarizes various popular approaches to enhancing the performance of your RAG applications. We also provided clear illustrations to help you quickly understand these concepts and techniques and expedite their implementation and optimization.
Engineering
Embedding Inference at Scale for RAG Applications with Ray Data and Milvus
This blog showed how to use Ray Data and Milvus Bulk Import features to significantly speed up the vector generation and efficiently batch load them into a vector database.
Vector Database 101
Deploying Vector Databases in Multi-Cloud Environments
Multi-cloud deployment has become increasingly popular for services looking for as much uptime as possible, with organizations leveraging multiple cloud providers to optimize performance, reliability, and cost-efficiency.
Engineering
Monitoring Milvus with Grafana and Loki
This post guides you through setting up Grafana and Loki to monitor your Milvus deployments effectively.
Engineering
What are Private LLMs? Running Large Language Models Privately - privateGPT and Beyond
Private LLMs enhance data control through customization to meet organizational policies and privacy needs, ensuring legal compliance and minimizing risks like data breaches. Operating in a secure environment, they reduce third-party access, protecting sensitive data from unauthorized exposure. Private LLMs can be designed to integrate seamlessly with an organization's existing systems, networks, and databases. Organizations can implement tailored security measures in private LLMs to protect sensitive information.
Engineering
Exploring OpenAI CLIP: The Future of Multi-Modal AI Learning
This article will explore CLIP's inner workings and pioneering potential in multimodal learning.