ASIMOV: Enterprise RAG at Dialog Axiata PLC

•

0 likes•134 views

The presentation will delve into the ASIMOV project, a novel initiative that leverages Retrieval-Augmented Generation (RAG) to provide precise, domain-specific assistance to telecommunications engineers and technicians. The session will focus on the unique capabilities of Milvus, the chosen vector database for the project, and its advantages over other vector databases. Attending this session will give you a deeper understanding of the potential of RAG and Milvus DB in telecommunications engineering. You will learn how to address common challenges in the field and enhance the efficiency of their operations. The session will equip you with the knowledge to make informed decisions about the choice of vector databases, and how best to use them for your use-cases

More from Zilliz

Fueling AI with Great Data with Airbyte Webinar

Zilliz

Programming Foundation Models with DSPy - Meetup Slides

Zilliz

Generating privacy-protected synthetic data using Secludy and Milvus

Zilliz

During this demo, the founders of Secludy will demonstrate how their system utilizes Milvus to store and manipulate embeddings for generating privacy-protected synthetic data. Their approach not only maintains the confidentiality of the original data but also enhances the utility and scalability of LLMs under privacy constraints. Attendees, including machine learning engineers, data scientists, and data managers, will witness first-hand how Secludy's integration with Milvus empowers organizations to harness the power of LLMs securely and efficiently.

Building Production Ready Search Pipelines with Spark and Milvus

Zilliz

MemGPT: Introduction to Memory Augmented Chat

Zilliz

Copilot Workspace: What it is, how it works, why it matters

Zilliz

Infrastructure Challenges in Scaling RAG with Custom AI models

Zilliz

Building Retrieval-Augmented Generation (RAG) systems with open-source and custom AI models is a complex task. This talk explores the challenges in productionizing RAG systems, including retrieval performance, response synthesis, and evaluation. We’ll discuss how to leverage open-source models like text embeddings, language models, and custom fine-tuned models to enhance RAG performance. Additionally, we’ll cover how BentoML can help orchestrate and scale these AI components efficiently, ensuring seamless deployment and management of RAG systems in the cloud.

Full-RAG: A modern architecture for hyper-personalization

Zilliz

Mike Del Balso, CEO & Co-Founder at Tecton, presents "Full RAG," a novel approach to AI recommendation systems, aiming to push beyond the limitations of traditional models through a deep integration of contextual insights and real-time data, leveraging the Retrieval-Augmented Generation architecture. This talk will outline Full RAG's potential to significantly enhance personalization, address engineering challenges such as data management and model training, and introduce data enrichment with reranking as a key solution. Attendees will gain crucial insights into the importance of hyperpersonalization in AI, the capabilities of Full RAG for advanced personalization, and strategies for managing complex data integrations for deploying cutting-edge AI solutions.

Building RAG with self-deployed Milvus vector database and Snowpark Container...

Zilliz

Introducing Milvus Lite: Easy-to-Install, Easy-to-Use vector database for you...

Zilliz

Knowledge Graphs in Retrieval Augmented Generation with WhyHow.AI

Zilliz

Answer 'What's for Dinner?' with Vector Search and Natural Language using Hay...

Zilliz

What will you learn? Have you ever wanted a personal chef? You've probably heard the joke "being in a relationship is just asking each other 'what do you want to eat for dinner' until you die." Sure, you can just browse recipes online but who knows if they are any good? LLMs to the rescue! In this session, I'll demonstrate taking a dataset on Kaggle of my favorite cookbook recipes, pulling data into a Milvus vector database instance, and building an agentic Haystack RAG pipeline so I can search for tasty recipes with natural language. I'll even take it one step further with a function call to make an Amazon shopping list with the ingredients. Join us for this session to see how you can solve real-world problems with RAG and answer the age old question "what's for dinner?" Topics Covered - How to build a real-world RAG app - Getting started with Haystack - Ingesting data into Milvus

Advanced Retrieval Augmented Generation Techniques

Zilliz

While achieving a basic Retrieval Augmented Generation (RAG) is relatively straightforward, attaining superior results requires tuning and optimizing various factors, such as a careful selection of embedding models. Additionally, applying advanced techniques, such as multi-stage retrieval with rerankers, is essential. A methodology for quality evaluation is also critical to success in crafting the best strategy for your specific use case. This talk will introduce the landscape of available optimization techniques and provide advice on best practices.

Introduction to Open Source RAG and RAG Evaluation

Zilliz

You’ve heard good data matters in Machine Learning, but does it matter for Generative AI applications? Corporate data often differs significantly from the general Internet data used to train most foundation models. Join me for a demo on building an open source RAG (Retrieval Augmented Generation) stack using Milvus vector database for Retrieval, LangChain, Llama 3 with Ollama, Ragas RAG Eval, and optional Zilliz cloud, OpenAI.

Emergent Methods: Multilingual narrative tracking in the news - real-time exp...

Zilliz

We present an architecture of embedding models, vector databases, LLMs, and narrow ML for tracking global news narratives across a variety of countries/languages/news sources in https://asknews.app/. As an example, we explore the real-time application of this architecture for tracking the news narrative surrounding the death of Russian opposition leader Alexei Navalny coming from Russian, French, and English sources

Introduction to Multilingual Retrieval Augmented Generation (RAG)

Zilliz

Retrieval augmented generation (RAG) is the most popular style of large language model application to emerge from 2023. The most basic style of RAG works by vectorizing your data and injecting it into a vector database like Milvus for retrieval to augment the text output generated by an LLM. This is just the beginning. One of the ways that we can extend RAG, and extend AI, is through multilingual use cases. Typical RAG is done in English using embedding models that are trained in English. In this talk, we’ll explore how RAG could work in languages other than English. We’ll explore French, Chinese, and Polish.

"I see eyes in my soup": How Delivery Hero implemented the safety system for ...

Zilliz

Exploring Multimodal Embeddings with Milvus

Zilliz

A Beginners Guide to Building a RAG App Using Open Source Milvus

Zilliz

Zilliz - Overview of Generative models in ML

Zilliz

More from Zilliz (20)

Fueling AI with Great Data with Airbyte Webinar

Programming Foundation Models with DSPy - Meetup Slides

Generating privacy-protected synthetic data using Secludy and Milvus

Building Production Ready Search Pipelines with Spark and Milvus

MemGPT: Introduction to Memory Augmented Chat

Copilot Workspace: What it is, how it works, why it matters

Infrastructure Challenges in Scaling RAG with Custom AI models

Full-RAG: A modern architecture for hyper-personalization

Building RAG with self-deployed Milvus vector database and Snowpark Container...

Introducing Milvus Lite: Easy-to-Install, Easy-to-Use vector database for you...

Knowledge Graphs in Retrieval Augmented Generation with WhyHow.AI

Answer 'What's for Dinner?' with Vector Search and Natural Language using Hay...

Advanced Retrieval Augmented Generation Techniques

Introduction to Open Source RAG and RAG Evaluation

Emergent Methods: Multilingual narrative tracking in the news - real-time exp...

Introduction to Multilingual Retrieval Augmented Generation (RAG)

"I see eyes in my soup": How Delivery Hero implemented the safety system for ...

Exploring Multimodal Embeddings with Milvus

A Beginners Guide to Building a RAG App Using Open Source Milvus

Zilliz - Overview of Generative models in ML

Recently uploaded

Vulnerability Management: A Comprehensive Overview

Steven Carlson

Introduction-to-the-IAM-Platform-Implementation-Plan.pptx

313mohammedarshad

Types of Weaving loom machine & it's technology

ldtexsolbl

Welcome to the presentation on the types of weaving loom machines, brought to you by LD Texsol, a leading manufacturer of electronic Jacquard machines. Weaving looms are pivotal in textile production, enabling the interlacing of warp and weft threads to create diverse fabrics. Our exploration begins with traditional handlooms, which have been in use since ancient times, preserving artisanal craftsmanship. We then move to frame and pit looms, simple yet effective tools for small-scale and traditional weaving. Advancing to modern industrial applications, we discuss power looms, the backbone of high-speed textile manufacturing. These looms, integral to LD Texsol's product range, offer unmatched productivity and consistent quality, essential for large-scale apparel, home textiles, and technical fabrics. Rapier looms, another modern marvel, use rapier rods for versatile and rapid weaving of complex patterns. Next, we explore air and water jet looms, known for their efficiency in lightweight fabric production. LD Texsol's state-of-the-art electronic Jacquard machines exemplify technological advancements, enabling intricate designs and patterns with precision control. Lastly, we examine dobby looms, ideal for medium-complexity patterns and versatile fabric production. This presentation will deepen your understanding of weaving looms, their applications, and the innovations LD Texsol brings to the textile industry. Join us as we weave through the history, technology, and future of textile production.

IPLOOK Remote-Sensing Satellite Solution

IPLOOK Networks

Feature sql server terbaru performance.pptx

ssuser1915fe1

CiscoIconsLibrary cours de réseau VLAN.ppt

moinahousna

RPA In Healthcare Benefits, Use Case, Trend And Challenges 2024.pptx

SynapseIndia

(CISOPlatform Summit & SACON 2024) Digital Personal Data Protection Act.pdf

Priyanka Aash

High Profile Girls Call ServiCe Hyderabad 0000000000 Tanisha Best High Class ...

aslasdfmkhan4750

How Social Media Hackers Help You to See Your Wife's Message.pdf

HackersList

How to Build a Profitable IoT Product.pptx

Adam Dunkels

The Rise of AI in Cybersecurity How Machine Learning Will Shape Threat Detect...

digitalxplive

leewayhertz.com-AI agents for healthcare Applications benefits and implementa...

alexjohnson7307

In recent years, the integration of artificial intelligence (AI) in various sectors has revolutionized traditional practices, and healthcare is no exception. AI agents for healthcare have emerged as powerful tools, enhancing the efficiency, accuracy, and accessibility of medical services. This article explores the multifaceted role of AI agents in healthcare, shedding light on their applications, benefits, and the future they herald.

“Deploying Large Language Models on a Raspberry Pi,” a Presentation from Usef...

Edge AI and Vision Alliance

For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2024/07/deploying-large-language-models-on-a-raspberry-pi-a-presentation-from-useful-sensors/ Pete Warden, CEO of Useful Sensors, presents the “Deploying Large Language Models on a Raspberry Pi,” tutorial at the May 2024 Embedded Vision Summit. In this presentation, Warden outlines the key steps required to implement a large language model (LLM) on a Raspberry Pi. He begins by outlining the motivations for running LLMs on the edge and exploring practical use cases for LLMs at the edge. Next, he provides some rules of thumb for selecting hardware to run an LLM. Warden then walks through the steps needed to adapt an LLM for an application using prompt engineering and LoRA retraining. He demonstrates how to build and run an LLM from scratch on a Raspberry Pi. Finally, he shows how to integrate an LLM with other edge system building blocks, such as a speech recognition engine to enable spoken input and application logic to trigger actions.

WhatsApp Spy Online Trackers and Monitoring Apps

HackersList

The Role of IoT in Australian Mobile App Development - PDF Guide

Shiv Technolabs

Three New Criminal Laws in India 1 July 2024

aakash malhotra

In Deloitte's latest article, discover the impact of India's three new criminal laws, effective July 1, 2024. These laws, replacing the IPC, CrPC, and Indian Evidence Act, promise a more contemporary, concise, and accessible legal framework, enhancing forensic investigations and aligning with current societal needs. Learn how these Three New Criminal Laws will shape the future of criminal justice in India Read More Deloitte India's Latest Article on Three New Criminal Laws https://www2.deloitte.com/in/en/pages/finance/articles/three-new-criminal-laws-in-India.html

Girls Call Churchgate 9910780858 Provide Best And Top Girl Service And No1 in...

maigasapphire

Uncharted Together- Navigating AI's New Frontiers in Libraries

Brian Pichman

Journey into the heart of innovation where the collaborative spirit between information professionals, technologists, and researchers illuminates the path forward through AI's uncharted territories. This opening keynote celebrates the unique potential of special libraries to spearhead AI-driven transformations. Join Brian Pichman as we saddle up to ride into the history of Artificial Intelligence, how its evolved over the years, and how its transforming today's frontiers. We will explore a variety of tools and strategies that leverage AI including some new ideas that may enhance cataloging, unlock personalized user experiences, or pioneer new ways to access specialized research. As with any frontier exploration, we will confront shared ethical challenges and explore how joint efforts can not only navigate but also shape AI's impact on equitable access and information integrity in special libraries. For the remainder of the conference, we will equip you with a "digital compass" where you can submit ideas and thoughts of what you've learned in sessions for a final reveal in the closing keynote.

BLOCKCHAIN TECHNOLOGY - Advantages and Disadvantages

SAI KAILASH R

Explore the advantages and disadvantages of blockchain technology in this comprehensive SlideShare presentation. Blockchain, the backbone of cryptocurrencies like Bitcoin, is revolutionizing various industries by offering enhanced security, transparency, and efficiency. However, it also comes with challenges such as scalability issues and energy consumption. This presentation provides an in-depth analysis of the key benefits and drawbacks of blockchain, helping you understand its potential impact on the future of technology and business.