Introduction to Multilingual Retrieval Augmented Generation (RAG)

•

0 likes•91 views

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.

Technology

1 | © Copyright 2024 Zilliz
1
Yujian Tang | Zilliz
Multilingual RAG

2 | © Copyright 2024 Zilliz
2
Yujian Tang
Senior Developer Advocate, Zilliz
yujian@zilliz.com
https://www.linkedin.com/in/yujiantang
https://www.twitter.com/yujian_tang
Speaker

3 | © Copyright 2024 Zilliz
3
01 RAG Review
CONTENTS
03
04 Demo
02 LLMs and Embedding Models
Vector Databases

4 | © Copyright 2024 Zilliz
4
01 RAG Review

5 | © Copyright 2024 Zilliz
5
RAG
RAG
Inject your data via a vector
database like Milvus/Zilliz
Primary Use Case
- Factual Recall
- Forced Data Injection
- Cost Optimization

6 | © Copyright 2024 Zilliz
6
Query LLM
Milvus
Your Data
Embedding
Model

7 | © Copyright 2024 Zilliz
7
02 LLMs and Embedding Models

8 | © Copyright 2024 Zilliz
8
How did LLMs come about?

9 | © Copyright 2024 Zilliz
9
A Basic Neural Net

10 | © Copyright 2024 Zilliz
10
A Recurrent Neural Network

11 | © Copyright 2024 Zilliz
11
A Transformer Architecture

13 | © Copyright 2024 Zilliz
13
What about Embedding Models?

14 | © Copyright 2024 Zilliz
14
Vector
Databases
Deep Learning Models w/o Last Layer

15 | © Copyright 2024 Zilliz
15
LLMs
- Large models
- Generate text
- Reasoning capability
- Based on
transformers
Embedding Models
- Smaller
- Non predictive
- Non generative

16 | © Copyright 2024 Zilliz
16
03 Vector Databases

17 | © Copyright 2024 Zilliz
17
Find Semantically Similar Data
Apple made profits of $97 Billion in 2023
I like to eat apple pie for profit in 2023
Apple’s bottom line increased by record numbers in 2023

18 | © Copyright 2024 Zilliz
18
But wait! There’s more!

19 | © Copyright 2024 Zilliz
19
Semantic Similarity
Image from Sutor et al
Woman = [0.3, 0.4]
Queen = [0.3, 0.9]
King = [0.5, 0.7]
Woman = [0.3, 0.4]
Queen = [0.3, 0.9]
King = [0.5, 0.7]
Man = [0.5, 0.2]
Queen - Woman + Man = King
Queen = [0.3, 0.9]
- Woman = [0.3, 0.4]
[0.0, 0.5]
+ Man = [0.5, 0.2]
King = [0.5, 0.7]
Man = [0.5, 0.2]

20 | © Copyright 2024 Zilliz
20
Similarity metrics are ways to measure distance in
vector space

21 | © Copyright 2024 Zilliz
21
Vector Similarity Metric: L2 (Euclidean)
Queen = [0.3, 0.9]
King = [0.5, 0.7]
d(Queen, King) = √(0.3-0.5)2
+ (0.9-0.7)2
= √(0.2)2
+ (0.2)2
= √0.04 + 0.04
= √0.08 ≅ 0.28

22 | © Copyright 2024 Zilliz
22
Vector Similarity Metric: Inner Product (IP)
Queen = [0.3, 0.9]
King = [0.5, 0.7]
Queen · King = (0.3*0.5) + (0.9*0.7)
= 0.15 + 0.63 = 0.78

23 | © Copyright 2024 Zilliz
23
Queen = [0.3, 0.9]
King = [0.5, 0.7]
Vector Similarity Metric: Cosine
𝚹
cos(Queen, King) = (0.3*0.5)+(0.9*0.7)
√0.32
+0.92
* √0.52
+0.72
= 0.15+0.63 _
√0.9 * √0.74
= 0.78 _
√0.666
≅ 0.03

24 | © Copyright 2024 Zilliz
24
Vector Similarity Metrics
Euclidean - Spatial distance
Cosine - Orientational distance
Inner Product - Both
With normalized vectors, IP = Cosine

25 | © Copyright 2024 Zilliz
25
Indexes organize the way we access our data

26 | © Copyright 2024 Zilliz
26
Inverted File Index
Source:
https://towardsdatascience.com/similarity-search-with-ivfpq-9c6348fd4db3

27 | © Copyright 2024 Zilliz
27
Hierarchical Navigable Small Worlds (HNSW)
Source:
https://arxiv.org/ftp/arxiv/papers/1603/1603.09320.pdf

28 | © Copyright 2024 Zilliz
28
Scalar Quantization (SQ)

29 | © Copyright 2024 Zilliz
29
Product Quantization
Source:
https://towardsdatascience.com/product-quantization-for-similarity-search-2f1f67c5fddd

30 | © Copyright 2024 Zilliz
30
Indexes Overview
- IVF = Intuitive, medium memory, performant
- HNSW = Graph based, high memory, highly performant
- Flat = brute force
- SQ = bucketize across one dimension, accuracy x
memory tradeoff
- PQ = bucketize across two dimensions, more accuracy x
memory tradeoff

32 | © Copyright 2024 Zilliz
32
Query LLM
Language Data
Embedding
Model(s)

34 | © Copyright 2024 Zilliz
34
Start building
with Zilliz Cloud today!
zilliz.com/cloud

Similar to Introduction to Multilingual Retrieval Augmented Generation (RAG)

Vector Databases 101 - An introduction to the world of Vector Databases

Zilliz

For many of us, building cloud-based systems or using cloud services is our day-to-day reality for quite some time now. What's next then? Commonly driven by digital transformations, you might have already heard several people talk about multi-cloud.. ..But what is multi-cloud exactly? Why and when should you consider it? What challenges and complexities can one face? How can you approach such an endeavor? How can you a build an effective multi-cloud strategy? These are just few examples of what we're going to talk about. This talk is going to focus on building systems in respect to multi-cloud, focusing on architecture and technological concepts in addition to business aspects. One of the key principles to achieve it, is to build the right abstractions. How do you do that though? There are so many options to choose from, what are they, and how do you go about implementing it? Join us in this session as we tackle these subjects head-on while sharing real interesting demos in the process.

Software Architecture in The Multi-Cloud Era AZ

Amir Zuker

Neo4j & AWS Bedrock workshop at GraphSummit London 14 Nov 2023.pptx

Neo4j

Roadmap y Novedades de producto

Neo4j

Neo4j : L’art des Possibles avec la Technologie des Graphes

Neo4j

The Art of the Possible with Graph Technology

Neo4j

Are you drowning in data but lacking in insight? 80% of business leaders say data is critical in decision-making, yet 41% cite a lack of understanding of data because it is too complex or not accessible enough. You’ll learn how companies are using graph technology to leverage the relationships in their connected data to reveal new ways of solving their most pressing business problems and creating new business value for their enterprises. You’ll see real-world use cases that include fraud detection, AI/ML, supply chain management, real-time recommendations, Customer 360, network/IT operations and more.

Keynote: Art of the Possible - Moore

Neo4j

The art of the possible with graph technology_Neo4j GraphSummit Dublin 2023.pptx

Neo4j

Exploring IoT Edge

Codit

Neo4j Keynote: The Art of the Possible with Graph Technology

Neo4j

VMworld 2013: How to Build a Hybrid Cloud in Less than a Day

VMworld

Data Mesh is a new socio-technical approach to data architecture, first described by Zhamak Dehghani and popularised through a guest blog post on Martin Fowler's site. Since then, community interest has grown, due to Data Mesh's ability to explain and address the frustrations that many organisations are experiencing as they try to get value from their data. The 2022 publication of Zhamak's book on Data Mesh further provoked conversation, as have the growing number of experience reports from companies that have put Data Mesh into practice. So what's all the fuss about? On one hand, Data Mesh is a new approach in the field of big data. On the other hand, Data Mesh is application of the lessons we have learned from domain-driven design and microservices to a data context. In this talk, Chris and Pablo will explain how Data Mesh relates to current thinking in software architecture and the historical development of data architecture philosophies. They will outline what benefits Data Mesh brings, what trade-offs it comes with and when organisations should and should not consider adopting it.

Data Mesh 101

ChrisFord803185

chapter4.ppt

Rakesh Pogula

FlowForge enables organizations to reliably deliver Node-RED applications in a continuous, collaborative, and secure manner. Node-RED is the popular, low-code programming solution that makes it easy to connect different services using a visual programming environment. InfluxData is the creator of InfluxDB, the purpose-built time series database run by developers at scale and in any environment in the cloud, on-premises, or at the edge. Jump-start monitoring your industrial IoT devices and discover how to build an edge-to-cloud solution with the MING stack. The MING stack includes Mosquitto/MQTT, InfluxDB, Node-RED, and Grafana. This solution can be used to improve fleet management, enable predictive maintenance of industrial machines and power generation equipment (i.e. turbines and generators) and increase safety practices (i.e. buildings, construction sites). Join this webinar to learn best practices from industrial IoT SME's. In this webinar, Robert Marcer and Jay Clifford dive into: Best practices for monitoring sensor data collected by everyone — from the edge to the factory Tips and tricks for using Node-RED and InfluxDB together Demo — see Node-RED and InfluxDB live

Build an Edge-to-Cloud Solution with the MING Stack

InfluxData

"During this session we will cover NetScaler Insight, which gives you visibility across your network AND allows you to see the end user experience. In this session, Ronan will install and configure Insight, and demonstrate how to use it to drill down and pinpoint network or performance issues. During this session, you will learn: - Basics of the HTTP protocol - How to Install & configure Insight - Use Insight to pinpoint performance issues - NetScaler performance metrics when load balancing traffic"

Using NetScaler Insight to Troubleshoot Network and Server Performance Issues

David McGeough

Presented at Spacewalk 2023 Presented by Christian Posta, solo.io Title: The Future of Service Mesh Abstract: Service mesh is a powerful way to solve cross-cutting application-networking concerns, such as load balancing, service resilience, observability, and security. Adopting a mesh for your services can save hundreds of hours of developer time and reduce the burden placed on operations. In this talk we'll explore some common use cases for service mesh, look at some case studies, and then dig into innovation happening in this space such as "sidecar-less" service mesh.

The Future of Service Mesh

All Things Open

In this workshop at Data Innovation Summit 2023, we demonstrated how you could learn from the network structure of a Knowledge Graph and use OpenAI’s GPT engine to populate and enhance your Knowledge Graph. Key takeaways: 1. How Knowledge Graphs grow organically 2. How to deploy Graph Algorithms to learn from the topology of a graph 3. Integrate a Knowledge Graph with OpenAI’s GPT 4. Use Graph Node embeddings to feed Machine Learning workflow

GPT and Graph Data Science to power your Knowledge Graph

Neo4j

Realize True Business Value With ThousandEyes

ThousandEyes

Validation and visualization of Revit BIM Models with FME

GIM_nv

Paul Huppertz Cloud Computing From System Design To Service Composing

Cloudcamp

Similar to Introduction to Multilingual Retrieval Augmented Generation (RAG) (20)

Vector Databases 101 - An introduction to the world of Vector Databases

Software Architecture in The Multi-Cloud Era AZ

Neo4j & AWS Bedrock workshop at GraphSummit London 14 Nov 2023.pptx

Roadmap y Novedades de producto

Neo4j : L’art des Possibles avec la Technologie des Graphes

The Art of the Possible with Graph Technology

Keynote: Art of the Possible - Moore

The art of the possible with graph technology_Neo4j GraphSummit Dublin 2023.pptx

Exploring IoT Edge

Neo4j Keynote: The Art of the Possible with Graph Technology

VMworld 2013: How to Build a Hybrid Cloud in Less than a Day

Data Mesh 101

chapter4.ppt

Build an Edge-to-Cloud Solution with the MING Stack

Using NetScaler Insight to Troubleshoot Network and Server Performance Issues

The Future of Service Mesh

GPT and Graph Data Science to power your Knowledge Graph

Realize True Business Value With ThousandEyes

Validation and visualization of Revit BIM Models with FME

Paul Huppertz Cloud Computing From System Design To Service Composing

More from 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.

Advanced Retrieval Augmented Generation Techniques

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.

Introduction to Open Source RAG and RAG Evaluation

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

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

Zilliz

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

Zilliz

Zilliz - Overview of Generative models in ML

Zilliz

Integrating Multimodal AI in Your Apps with Floom

Zilliz

Build streaming LLM with Timeplus and Zilliz

Zilliz

Voyage AI: cutting-edge embeddings and rerankers for search and RAG

Zilliz

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.

Leverage Zilliz Serverless - Up to 50X Saving for Your Vector Storage Cost

Zilliz

Embeddings have become a crucial component in contemporary vector search and Retrieval Augmented Generation (RAG) systems. In this talk, I aim to provide a comprehensive overview of training a versatile embedding model, strategies for encoding longer information within such models, along their benefits and limitations. Additionally, I'll delve into various forms of deep learning-powered retrievers.

Training state-of-the-art general text embedding

Zilliz

The rise of Large Language Models has revolutionized the landscape of AI, unlocking huge potential across society. However, it has also introduced the challenge of hallucinations - instances where the model generates rather trippy content in a scarily convincing way. Rest assured, Morena will guide you through an exploration of how we can automatically detect these instances of hallucination to fully unleash the potential of LLMs.

Fact vs. Fiction: Autodetecting Hallucinations in LLMs

Zilliz

VectorDB Schema Design 101 - Considerations for Building a Scalable and Perfo...

Zilliz

Voyage AI Embedding Models for Retrieval Augmented Generation

Zilliz

Chat with your data, privately and locally

Zilliz

Introducing Milvus and new features in 2.4 release

Zilliz

More from Zilliz (15)

Advanced Retrieval Augmented Generation Techniques

Introduction to Open Source RAG and RAG Evaluation

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

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

Zilliz - Overview of Generative models in ML

Integrating Multimodal AI in Your Apps with Floom

Build streaming LLM with Timeplus and Zilliz

Voyage AI: cutting-edge embeddings and rerankers for search and RAG

Leverage Zilliz Serverless - Up to 50X Saving for Your Vector Storage Cost

Training state-of-the-art general text embedding

Fact vs. Fiction: Autodetecting Hallucinations in LLMs

VectorDB Schema Design 101 - Considerations for Building a Scalable and Perfo...

Voyage AI Embedding Models for Retrieval Augmented Generation

Chat with your data, privately and locally

Introducing Milvus and new features in 2.4 release

Recently uploaded

FDO for Camera, Sensor and Networking Device – Commercial Solutions from VinC...

FIDO Alliance

Webinar Recording: https://www.panagenda.com/webinars/easier-faster-and-more-powerful-notes-document-properties-reimagined/ Have you ever felt frustrated by the small properties dialog in Notes? Had to create an agent or button to quickly change a field? Searched endlessly for the field you wanted to compare each time you selected a new document? Wished you could just make the damned thing bigger? Luckily, there is a solution – and you probably already have it installed! With the free panagenda Document Properties (Pro) you get the properties dialog you always needed. Big, resizable, full-text searchable. View multiple documents at once or compare them with a diff viewer. Modify any field, and finally have an easy way to handle profile documents for all users. Join HCL Lifetime Ambassador Julian Robichaux to discover how Document Properties can simplify your work and assist you daily when using Domino applications – in the client or the designer. You will never look back! Key takeaways from this session - What Document Properties is, which editions there are, and how you can find it in Notes and Domino Designer - How you can search for and edit any field, compare documents, or CSV export all data - How to find, edit, and even delete profile documents - Which configuration settings are available to customize feature

Easier, Faster, and More Powerful – Notes Document Properties Reimagined

panagenda

Intro in Product Management - Коротко про професію продакт менеджера

Mark Opanasiuk

Intrigued by why some of the world's largest companies (Netflix, Google, Cisco, Twitter, Uber etc) are using gRPC? In this demo based talk we delve into the world of gRPC in .Net, what it does and why we should use it. We compare the interface with both Rest and graphQL. We will show you how to implement grpc server-side in .net and in the web. Finally, I will show you how the tooling helps you deliver powerful interfaces and interact with them quickly and simply.

Demystifying gRPC in .Net by John Staveley

John Staveley

Explore the core of Salesforce success in 'Salesforce Adoption – Metrics, Methods, and Motivation.' We will discuss essential metrics, effective methods to drive adoption, and the driving force behind user engagement and explore strategies for onboarding, training, and continuous support that empower users to navigate the platform seamlessly. By leveraging these tools, you can effectively measure adoption against your company’s goals and create an environment where users not only adopt Salesforce but actively contribute to its ongoing success.

Salesforce Adoption – Metrics, Methods, and Motivation, Antone Kom

CzechDreamin

Where to Learn More About FDO _ Richard at FIDO Alliance.pdf

FIDO Alliance

How Red Hat Uses FDO in Device Lifecycle _ Costin and Vitaliy at Red Hat.pdf

FIDO Alliance

The presentation underscores the strategic advantage of treating design systems not just as technical assets but as vital business components that require thoughtful management, robust planning, and strategic alignment with organizational goals. Key Points Covered: - Understanding Design Systems as Business Entities: Conceptualizing design systems as internal business entities can streamline their integration and evolution within a company. - Adoption and Expansion: Elaborating on the importance of tactical adoption across organizational structures, enhancing product suites to cater to user needs and broadening scope to mobile and content authoring solutions. - Data-Driven Development: Utilizing data insights for component development ensures that resources are allocated to create valuable, widely used features. - Financial Modeling for Design Systems: Developing sustainable funding models is crucial for long-term support and success of design systems. - Promoting Internal Buy-In: Stressing on strategies for promoting design systems within the organization to increase engagement and investment from internal stakeholders.

A Business-Centric Approach to Design System Strategy

UXDXConf

Speed Wins: From Kafka to APIs in Minutes

confluent

Syngulon’s technology expands the capacity for selection of microorganisms. The ability to select individual microbes with a behavior of interest is essential, whether for simple cloning at the bench, or for industry-scale production. Synthetic biology uses the concept of “bioengineering” to improve or modify existing genetic systems to create microbes with desired behaviors, and Syngulon uses this approach to develop its selection technologies. This selection technology is based on bacteriocins, ribosomally-produced peptides naturally made by most bacteria to kill competitive microbial species. These bacteriocins can have a limited or wide target range against other microbial species. This technology offers advantageous over antibiotic selection for several reasons: it avoids the use of antibiotics in the first place, helping to reduce the spread of antibiotic resistant microbes. The technology also increases product yield; as bacteriocins are generally smaller peptides, they do not impose a heavy metabolic burden on the producing cell. They can have a wide target specificity, helping to avoid genetic drift. Finally, our system is 100% plasmid-based (e.g. without chromosomal mutations), making it applicable for use in any E. coli strains.

Syngulon - Selection technology May 2024.pdf

Syngulon

This talk offers actionable insights at an executive level for enhancing productivity and refining your portfolio management approach to propel your organization to greater heights. Key Points Covered: 1. Experience Transformation: - The core challenge remains consistent across organizations: converting budget into user-centric designs. - Strategies for deploying design resources effectively in both startups and large enterprises. 2. Strategic Frameworks: - Introduction to the "Ziggurat of Impact" model, detailing layers from basic system interactions to comprehensive customer experiences. - Practical insights on creating frameworks that scale with organizational complexity. 3. Organizational Impact: - Real-world examples of navigating design in large settings, focusing on the synthesis of consumer products and customer experiences. - Emphasis on the importance of designing systems that directly influence customer interactions. 4. Design Execution: - Detailed walkthrough of organizational layers affecting design execution, from touchpoints and customer activities to shared capabilities. - How to ensure design influences both the micro and macro aspects of customer interactions. 5. Measurement and Adaptation: - Techniques for measuring the impact of design decisions and adapting strategies based on data-driven insights. - The critical role of continuous improvement and feedback in refining customer experiences.

Structuring Teams and Portfolios for Success

UXDXConf

Screen flow is a powerful automation tool that is commonly designed for internal and external users. However, what about the guest users? We will dive into various methods of launching screen flows and understand how to make them publicly accessible, extending their usability to a broader audience. The presentation will also cover the implementation of security layers and highlight best practices for a smooth and protected user experience. Discover the potential of screen flows beyond conventional use and learn how to leverage them effectively.

Free and Effective: Making Flows Publicly Accessible, Yumi Ibrahimzade

CzechDreamin

Join me in this session where I'll share our journey of building a fully serverless application that flawlessly managed check-ins for an event with a staggering 80 thousand registrations. We'll dive into three key strategies that made this possible. Firstly, by harnessing DynamoDB global tables, we ensured global service availability and data replication across regions, boosting performance and disaster recovery. Next, we'll explore how we seamlessly integrated real-time updates into the app using Appsync subscriptions, making the experience dynamic and engaging for users. Finally, I'll discuss how provisioned concurrency not only improved performance but also kept costs in check, highlighting the cost-effectiveness of serverless architectures. Through these strategies and the inherent scalability of serverless technology, our application effortlessly handled massive user loads without manual intervention. This session is a real world example to the power and efficiency of modern cloud-based solutions in enabling seamless scalability and robust performance with Serverless

How we scaled to 80K users by doing nothing!.pdf

Srushith Repakula

Are you wondering why everyone is talking about the Flutterwave scandal? You’re not alone! It’s been in the news a lot. We’re going to tell you all about the Flutterwave Scandal in a way that’s easy to get. Keep reading, because we’re going to share the whole story with you. The company Flutterwave is headquartered in San Francisco, California, United States. In 2016, Iyinoluwa Aboyeji, Olugbenga Agboola, and Adeleke Adekoya established Flutterwave. It offers a payment infrastructure to international merchants and payment service providers throughout Africa. The company operates in various African countries including Nigeria, Kenya, Uganda, Ghana, South Africa, and others. They focus on digital payments, helping businesses accept and process payments on various channels like the web, mobile, ATM, and POS. Recently, they’ve been in the news due to allegations of misconduct within the company, which has affected their reputation and value. Flutterwave is an essential player in the African fintech landscape, aiming to drive growth for banks and businesses through digital payment solutions. The Flutterwave scandal involves allegations of misconduct and inappropriate behaviour towards female employees by the company’s co-founder and CEO, Olugbenga Agboola. Reports have surfaced from both current and former employees about bullying, intimidation, and sexual harassment at work. The allegations of inappropriate behaviour towards female employees at Flutterwave, specifically involving the CEO Olugbenga Agboola, were brought to public attention in April 2022.This was when Clara Wanjiku Odero, an ex-employee and current CEO of Credrails, published a Medium post and a series of tweets on April 4, 2022, accusing Flutterwave and Agboola of bullying. These allegations were part of a broader range of misconduct claims that surfaced around the same time The reasons behind the scandal are rooted in accusations of unethical behavior within the company. The allegations suggest a workplace culture that allowed for misconduct and failed to protect employees from harassment and intimidation. Specifically, the Flutterwave scandal refers to the series of events where the CEO was accused of engaging in improper conduct with female colleagues. This led to a broader investigation into the company’s practices and raised serious concerns about the fintech’s corporate governance. The scandal had significant repercussions, including a drop in the company’s stock price and a loss of trust among customers and partners. The trust that customers and investors had in Flutterwave was greatly damaged. People started to doubt the company’s integrity and whether their money and personal information were safe. Market Impact The scandal shook the entire fintech market. Flutterwave’s competitors saw this as an opportunity and started to attract customers who were leaving Flutterwave.

Breaking Down the Flutterwave Scandal What You Need to Know.pdf

UK Journal

This talk focuses on the practical aspects of integrating various telephony systems with Salesforce, drawing on examples from implementations in the Czech scene. It aims to inform attendees about the spectrum of telephony solutions available, from small to large scale, and their compatibility with Salesforce. The presentation will highlight key considerations for selecting a telephony provider that integrates smoothly with Salesforce, including important questions to support the decision-making process. It will also discuss methods for integrating existing telephony systems with Salesforce, aimed at companies contemplating or in the process of adopting this CRM platform. The discussion is designed to provide a straightforward overview of the steps and considerations involved in telephony and Salesforce integration, with an emphasis on functionality, compatibility, and the practical experiences of Czech companies.

Integrating Telephony Systems with Salesforce: Insights and Considerations, B...

CzechDreamin

Ever caught yourself nodding along when someone mentions "delivering value" in Agile, but secretly wondering what the heck they actually mean? You're not alone! Join us for an eye-opening session where we'll strip away the buzzwords and dive into the heart of Agile—value delivery. But what is "value"? Is it a mythical unicorn in the world of software development, or is there more to this overused term? This isn't going to be a sit-and-get lecture. We're talking about a face-to-face, interactive meetup where YOU play a crucial role. Come along to: Define It: What does "value" really mean? We’ll build a definition that’s not just words, but a compass for your Agile journey. Contextualise It: Discover what value means specifically to you, your team, your company, and your industry. Because one size does not fit all. Deliver It: Share strategies and gather new ones for uncovering and delivering true value—no more shooting in the dark!

Unpacking Value Delivery - Agile Oxford Meetup - May 2024.pptx

David Michel

Using IESVE for Room Loads Analysis - UK & Ireland

IES VE

Linux Foundation Edge _ Overview of FDO Software Components _ Randy at Intel.pdf

FIDO Alliance

Oauth 2.0 Introduction and Flows with MuleSoft

shyamraj55

New customer? New industry? New cloud? New team? A lot to handle! How to ensure the success of the project? Start it well! I've created the 3 areas of focus at the beginning of the project that helped me in multiple roles (BA, PO, and Consultant). Learn from real-world experiences and discover how these insights can empower you to deliver unparalleled value to your customers right from the project's start.

Powerful Start- the Key to Project Success, Barbara Laskowska

CzechDreamin

Recently uploaded (20)

FDO for Camera, Sensor and Networking Device – Commercial Solutions from VinC...

Easier, Faster, and More Powerful – Notes Document Properties Reimagined

Intro in Product Management - Коротко про професію продакт менеджера

Demystifying gRPC in .Net by John Staveley

Salesforce Adoption – Metrics, Methods, and Motivation, Antone Kom

Where to Learn More About FDO _ Richard at FIDO Alliance.pdf

How Red Hat Uses FDO in Device Lifecycle _ Costin and Vitaliy at Red Hat.pdf

A Business-Centric Approach to Design System Strategy

Speed Wins: From Kafka to APIs in Minutes

Syngulon - Selection technology May 2024.pdf

Structuring Teams and Portfolios for Success

Free and Effective: Making Flows Publicly Accessible, Yumi Ibrahimzade

How we scaled to 80K users by doing nothing!.pdf

Breaking Down the Flutterwave Scandal What You Need to Know.pdf

Integrating Telephony Systems with Salesforce: Insights and Considerations, B...

Unpacking Value Delivery - Agile Oxford Meetup - May 2024.pptx

Using IESVE for Room Loads Analysis - UK & Ireland

Linux Foundation Edge _ Overview of FDO Software Components _ Randy at Intel.pdf

Oauth 2.0 Introduction and Flows with MuleSoft

Powerful Start- the Key to Project Success, Barbara Laskowska

Introduction to Multilingual Retrieval Augmented Generation (RAG)

17. 17 | © Copyright 2024 Zilliz 17 Find Semantically Similar Data Apple made profits of $97 Billion in 2023 I like to eat apple pie for profit in 2023 Apple’s bottom line increased by record numbers in 2023

19. 19 | © Copyright 2024 Zilliz 19 Semantic Similarity Image from Sutor et al Woman = [0.3, 0.4] Queen = [0.3, 0.9] King = [0.5, 0.7] Woman = [0.3, 0.4] Queen = [0.3, 0.9] King = [0.5, 0.7] Man = [0.5, 0.2] Queen - Woman + Man = King Queen = [0.3, 0.9] - Woman = [0.3, 0.4] [0.0, 0.5] + Man = [0.5, 0.2] King = [0.5, 0.7] Man = [0.5, 0.2]

21. 21 | © Copyright 2024 Zilliz 21 Vector Similarity Metric: L2 (Euclidean) Queen = [0.3, 0.9] King = [0.5, 0.7] d(Queen, King) = √(0.3-0.5)2 + (0.9-0.7)2 = √(0.2)2 + (0.2)2 = √0.04 + 0.04 = √0.08 ≅ 0.28

23. 23 | © Copyright 2024 Zilliz 23 Queen = [0.3, 0.9] King = [0.5, 0.7] Vector Similarity Metric: Cosine 𝚹 cos(Queen, King) = (0.3*0.5)+(0.9*0.7) √0.32 +0.92 * √0.52 +0.72 = 0.15+0.63 _ √0.9 * √0.74 = 0.78 _ √0.666 ≅ 0.03

30. 30 | © Copyright 2024 Zilliz 30 Indexes Overview - IVF = Intuitive, medium memory, performant - HNSW = Graph based, high memory, highly performant - Flat = brute force - SQ = bucketize across one dimension, accuracy x memory tradeoff - PQ = bucketize across two dimensions, more accuracy x memory tradeoff

Introduction to Multilingual Retrieval Augmented Generation (RAG)

Recommended

Recommended

More Related Content

Similar to Introduction to Multilingual Retrieval Augmented Generation (RAG)

Similar to Introduction to Multilingual Retrieval Augmented Generation (RAG) (20)

More from Zilliz

More from Zilliz (15)

Recently uploaded

Recently uploaded (20)

Introduction to Multilingual Retrieval Augmented Generation (RAG)