Fresh Data, Smart Retrieval: Milvus & Jina CLIP Explained

# Data

# RAG

# Milvus

# Zilliz

Managing and extracting value from unstructured data has become a critical challenge as the volume of data continues to grow. This virtual event brings together industry experts to explore the latest techniques in Retrieval Augmented Generation (RAG) and vector databases.

Discover how RAG systems are revolutionizing natural language processing by seamlessly integrating information retrieval techniques, enabling more accurate and contextual language generation. Gain practical insights into building and optimizing these applications.

This session will also cover how vector databases like Milvus , play a key role in RAG and working with unstructured data. Learn proven strategies for maintaining data freshness, accuracy, and efficiency, ensuring your organization stays ahead of the curve.

Don't miss this opportunity to level up your unstructured data management skills.

Speakers

Ben Epstein

Co-Founder & CTO @ GrottoAI

Stephen Batifol

Developer Advocate @ Zilliz

Andreas Koukounas

ML Engineer Intern @ Jina AI

Saba Sturua

Machine Learning Engineer @ Jina AI

Agenda

4:00 PM

4:05 PM

GMT

Opening / Closing

Introduction

4:05 PM

4:35 PM

GMT

Presentation

Keeping Data Fresh: Mastering Updates in Vector Databases

Have you built a RAG (Retrieval Augmented Generation) system, but now face challenges with updating data? In this talk, we will explore how updates are managed in vector databases, focusing particularly on Milvus. We’ll explore the different challenges and practical solutions for maintaining data freshness in scenarios requiring high throughput and low latency. By the end of this session, you'll understand the mechanics behind data updates in Milvus and how to ensure your database remains both accurate and efficient.

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4:35 PM

4:40 PM

GMT

Question and Answer

Q&A

5 mins Q&A about Stephen's Presentation

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4:40 PM

5:00 PM

GMT

Presentation

Jina CLIP: Your CLIP Model Is Also Your Text Retriever

Your CLIP model under-performs in text-only tasks? In this talk, we will introduce our novel multi-task contrastive training method designed to bridge this performance gap. We developed the Jina CLIP model, having in mind to connect long documents, queries and images in one space. During this session, you will gain insights into the methodology behind our training process and the implications of our findings for future multimodal and text-based retrieval systems.

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