Name: Build an E-commerce Chatbot With Redis, LangChain, OpenAI
Start: 2023-07-18T18:00:00Z
End: 2023-07-18T18:00:21.000Z
Location: BrightTALK
Rating: 5

Redis makes apps faster, by creating a data foundation for a real-time world. It is the driving force behind Open-Source Redis, the world’s most loved in-memory database, and commercial provider of Redis Enterprise, a real time data platform. Redis Enterprise powers real-time services for over 8,000 organizations globally. It builds upon the unmatched simplicity and speed of Open-Source Redis along with an enterprise grade data platform that offers robustness of modern data models, management, automation, performance and resiliency to deploy and run modern applications at any scale from anywhere on the planet.

With so many people – like your CIO – hyped about artificial intelligence, machine learning, and generative AI products like ChatGPT, you’re likely under a lot of pressure to develop applications with those technologies for your company.

Like, yesterday.

But how do you turn terabytes of unstructured data – ranging from text to images, audio, and video – into useful information for the AI application?

The answer is a vector database. Vectorizing converts unstructured data into numerical representations known as vector embeddings, and Redis stores, indexes, and retrieves them. Vectors make searching for similar images or text or documents – vector similarity search (VSS) – faster and easier. Moreover, an external vector database with domain-specific, proprietary, or more recent data – used with public general purpose large language models – improves AI application responses.

Not to mention, keeping it from making things up.  
In this tech talk, find out how to use Redis’s vector database capabilities for real-time AI apps. We show the elements of building generative AI applications – using Redis technology you already know.

Watch to find out more about it.
* What’s a vector database
* Examples
* Why LLMs need real-time response
* Redis Enterprise VSS features and differentiators

Driving AI Innovation: Making It Easy to Build Generative AI Apps with Redis

In this tech talk, find out how to use Redis’s vector database capabilities for real-time AI apps. 

We show the elements of building generative AI applications – using Redis technology you already know.

Driving AI Innovation: Making it easy to build Generative AI Apps

Everyone wants to reduce costs and operate efficiently. But costs for database services can quickly get out of hand as you scale your applications.

Join Redis and Google for a virtual discussion on how Redis Enterprise can help reduce costs and deliver an efficient way to scale application performance on Google Cloud.

Cost Efficiency at Scale With Redis and Google Cloud

IT organizations are increasingly deploying fleets of Kubernetes clusters that need to be centrally managed. There is no shortage of options when it comes to deploying a management platform but managing Kubernetes clusters at scale, running everywhere from the network edge to the cloud, presents IT teams with a unique set of challenges.

Container Kubernetes Management

Everyone is looking to reduce costs and be more efficient. But as you scale your applications, costs for database services can quickly get out of hand.

Join this webinar to learn how Redis Enterprise can help reduce costs as the most cost-efficient way to scale application performance on AWS.

Cost Efficiency at Scale With Redis and AWS

While the application development world continues to thrive from the adoption of DevOps, data management at many companies today lacks the speed, interoperability, and flexibility desired by businesses to effectively compete on intelligence.

Data is still managed through rigid and, oftentimes, manual processes in siloed domains where collaboration is less constant.

There are numerous reasons organizations are hesitant to fully embrace Ops practices in the realm of databases. Concerns over data consistency, testing and deployment, legacy systems integration, and overall complexity can make introducing modern tools and practices challenging.

At the same time, plenty of organizations are finding ways to successfully bridge the gap between data management and DevOps. In fact, as the adoption of cloud-native apps, microservices, and containers continues to grow this will become ever more important.

Achieving Greater Agility: Emerging Trends in Data Management and DevOps

Join Redis Principal Solution Architect, Anton Umnikov, for a discussion on generative artificial intelligence, large language models, and databases. Anton will discuss how large language models have taken the world by storm. There will be specific emphasis on real use cases. While there’s a ton of hype, there’s also an explosion of real potential. Thanks to emerging first-party capabilities such as vector storage and search, Redis fundamentally changes how organizations leverage natural language processing, LLMs, GenAI models, and real-time machine learning workloads.

Redis Real-Time Data in Action from ChatGPT to Hello World!

In this webinar, speakers Sam Partee, Principal Applied AI Engineer, Redis and Microsoft’s Kyle Teegarden, Senior Product Manager, Azure Cache for Redis will walk you through it step by step. This session will provide a deep dive into how vector databases can be used in conjunction with large language models (LLMs) and demonstrate how to make a custom AI-based semantic answer engine entirely using Azure managed services.

Combining Redis with Azure OpenAI Service

Amid the tragedy, the COVID-19 pandemic presented unique opportunities for e-commerce firms, especially for online delivery services prepared to handle an escalating volume of orders. 

At iFood, one of the largest food delivery companies in Latin America, the technology team shifted into high gear as millions of new users began relying on its service, and thousands of new restaurants joined its growing platform.

Join experts from Redis and Amazon Web Services to learn how iFood uses Redis Enterprise Cloud on AWS as the foundation of its rapidly evolving ML feature store to create an optimal experience for users.

You will learn how iFood’s Redis-based infrastructure:
- Delivers sub-millisecond performance for ML operations
- Powers a highly efficient microservices architecture that optimizes the user experience
- Allows for massive scalability, enabling rapid corporate growth
- Saves money on infrastructure costs

Optimize the User Experience with AI Leveraging Redis Enterprise on AWS

Join us for this free instructor-led professional services workshop that shows you how to simplify your microservices architecture and build performant, resilient, and highly available microservices using Redis Enterprise.

After completing this session, you will:

Learn how to implement 3 caching design patterns to address read latency challenges while maintaining independent, highly available services.
Understand how to use Redis Streams as an asynchronous message broker for interservice communications. 
See how Redis Enterprise supports multiple Redis data types and models including JSON and Search for Domain Driven Design.
Understand how to simplify Change Data Capture with Redis Data Integration tool including demo.

Redis Enterprise Microservices Virtual Workshop

In today’s market everyone is looking to reduce costs and be more efficient. ElastiCache is a great solution for starting out, but can become difficult to manage at scale.

Join this webinar to learn about the differences between Redis Enterprise and ElastiCache and hear from Redis customer iFood on how they successfully moved their online feature store from Amazon ElastiCache to Redis Enterprise.

Cost Efficiency at Scale: Redis Enterprise vs. ElastiCache

Organizations must make data-driven decisions and respond quickly to changing customer needs in today’s rapidly evolving digital landscape. Customers are demanding intelligent applications that can keep up with their needs.

However, building these intelligent applications creates its own set of challenges. That’s where we come in.

These applications harness the power of artificial intelligence and machine learning, employing ultra-low latency databases to provide real-time insights, enabling organizations to better serve their customers.

Watch our on-demand session to learn how the joint technologies from Redis and Amazon Web Services (AWS) can help make it easier for your company to build these intelligent applications leveraging Redis Enterprise Cloud on AWS.

During this on-demand webinar, you will learn:
- What benefits customers who choose Redis Enterprise Cloud on AWS are seeing
- How to create applications with Amazon SageMaker and Redis Enterprise Cloud that can more accurately predict what your customers need
- How a unified database can help you stay ahead of the competition and better serve customers with real-time insights

Developing Real-time Intelligent Apps with Redis Enterprise Cloud on AWS

Data modeling can be challenging.  We will go through 8 different scenarios and show how to model them in Redis along with code snippets.

8 Data Modeling Patterns in Redis

Microservice-based architecture is an architecture style that helps development teams launch new products faster, scale more easily, and respond better to customer demands while minimizing costs and complexity. The result is that enterprises reduce time-to-market for new service development, often from projects measured in months to days. 

In a microservices environment, services running in real time must compensate for networking overhead. Redis Enterprise delivers sub-millisecond latency for all Redis data types and models. In addition, it scales quickly and linearly to almost any throughput needed.

MS Series: Getting Started With Microservice-Based Architectures

A microservice architecture can be a game-changer that helps organizations reduce barriers to application modernization and cloud migration journeys – and let them beat their competition to market.

However, breaking up a monolithic application into microservices usually creates a more complex system. Redis Enterprise reduces that complexity by caching data and optimizing data transfer between microservices – which offers the additional benefit of reducing latency and, consequently, improving performance.

Microservices also improve resiliency and provide read-optimized queries for global data, which means an organization can simplify its processes and automate them to reduce risk and make its architecture more mobile – without adding overhead.

The result is a single data platform for caching that supports optimal data models  — enough to support each microservice in isolation — and acts as a lightweight message broker for inter-service communication. This enhances developer productivity, provides faster time to market, and streamlines an application’s architecture.

This webinar shows how Redis Enterprise has helped customers simplify microservice-based architecture challenges in four ways:
- Query caching
- CQRS pattern
- API gateway caching
- Interservice communication

Getting Started Series: Simplify Microservice-Based Architectures with Redis

As one of the world’s most loved and used databases, Redis is likely widely used across your company. But the truth is; ineffectively deploying 
and managing Redis across large complex organizations can be challenging, time-consuming, expensive, and wasteful. 

Join John Noonan, Sr. Product Marketing Manager and Manish Arora, Solution Architectat at Redis to explore winning strategies to implement Redis at scale. Learn when, why, and how you can set up Redis-as-a-Service or establish a Redis Center of Excellence to:

●        Standardize deployments 
●        Simplify and centralize operations
●        Cut costs and reduce duplication of efforts
●        Improving service speed and quality

Winning Techniques to Deploy and Operate Redis at Scale

Hybrid Cloud gives you the best of both worlds: the advantages of the cloud and the control of local infrastructure. And thanks to the portability of Kubernetes, Redis Enterprise makes hybrid deployment easier than ever, on the cloud and on-prem.

Watch our Tech Talk to learn how to maximize flexibility and scalability, while reducing complexity and costs. In this session, we also show you:
- How a hybrid deployment increases flexibility while ensuring data security
- How Kubernetes with Redis Enterprise lets you easily scale up and down to handle spikes in throughput
- How Hybrid Cloud simplifies disaster recovery without dedicated backup IT Infrastructure

Join our experts to explore the easy way to deploy Hybrid Cloud.

Getting Started Series: The Easy Way to Deploy Hybrid Cloud

Vital data can end up locked in legacy databases because it seems too complex, time-consuming, and costly to unlock its full potential.

Not with Redis Enterprise. At this half-hour Tech Talk, you’ll see how to use the Redis Connect tool to quickly and easily ingest and replicate data from sources into Redis Enterprise.

Whether you’re replatforming to a hybrid architecture, migrating databases, optimizing Redis Enterprise, or enriching microservices, you’ll learn how to:
1. Understand architectural patterns and main use cases
2. Optimize performance with change data capture (CDC)
3. Deliver real-time data to employees, customers, and partner applications
4. Run ETL and searches in real time for your source applications

Tech Talk Series 5: Rapid Data Ingestion with Redis Enterprise

Unlock the Power of AI

Learn from Principal Applied AI Engineer, Sam Partee, how to Build an Ecommerce Chatbot With Redis, LangChain, and OpenAI. OpenAI language models were not trained on your company’s data. If you want the chatbot to rely on it, you must retrieve and provide OpenAI with relevant data at runtime. This retrieval process is critically important in ensuring the accuracy and speed of your Chatbot, and is the focus of this session.

Build an E-commerce Chatbot With Redis, LangChain, OpenAI

Database Management

Machine Learning

Analytics

ChatOps

Artificial Intelligence

Chatbots

Data Architecture

Ecommerce

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Build an E-commerce Chatbot With Redis, LangChain, OpenAI

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