🎥 Once a month, we'll meet, socialize, and hear speakers present topics on unstructured data and generative AI. This event was sponsored by Zilliz.
Timeline:
00:21 - An introduction to Milvus/Zilliz by Christy
02:36 - Speaker James Luan, Leverage Zilliz Serverless - Up to 50X Saving for Your Vector Storage Cost
21:00 - Speaker Tengyu Ma, Voyage AI: cutting-edge embeddings and rerankers for search and RAG
47:06 - Speaker Laurie Voss, Advanced Retrieval-Augmented Generation apps with LlamaIndex
01:16:52 - Community Demo, Kasey, About Upstage and Demo
~~~~~~~~~~~~~~~ CONNECT ~~~~~~~~~~~~~~~
🎥 Playlist • Unstructured Data Meetup
🖥️ Website: www.meetup.com...
X Twitter - / milvusio
🔗 Linkedin: / zilliz
😺 GitHub: github.com/mil...
🦾 Invitation to join discord: / discord
~~~~~~~~~~~~~~ MEETUP VIDEO CONTENTS ~~~~~~~~~~~~~~
1. Host: Christy Bergman
Linkedin: / christybergman
2. Speaker: James Luan, VP of Engineering at Zilliz
Title: Leverage Zilliz Serverless - Up to 50X Saving for Your Vector Storage Cost
Abstract: If you are building a RAG application that serves millions of users, you should consider how to scale your system seamlessly and cost-efficiently. The Zilliz Serverless tier represents a significant innovation in the field of vector search, enabling you to rapidly scale to millions of tenants and billions of vectors, while fully leveraging the hot/cold characteristics across tenants to reduce data storage costs. It enables vector storage at costs comparable to S3 and facilitates vector search times in the hundreds of milliseconds for tens of millions of data points!
In this talk, we will delve into the implementation details, usage patterns, and performance metrics of Zilliz Serverless. We will discuss how it empowers AI-native applications to achieve rapid business growth by providing a cost-effective and scalable vector storage and search solution.
3. Speaker: Tengyu Ma, CEO of Voyage AI
Title: Voyage AI: cutting-edge embeddings and rerankers for search and RAG
4. Speaker: Laurie Voss, VP of Developer Relations at LlamaIndex
Title: Advanced Retrieval-Augmented Generation apps with LlamaIndex
Abstract: Retrieval-Augmented Generation (RAG) is critical to LLM-powered applications and LlamaIndex is critical to getting up and running with RAG. This talk will start with the basics of how RAG works under the hood and show you how to build a basic RAG app, ending with advanced RAG techniques to get you into production.
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