How to run scalable, fault-tolerant RAG with a vector database


Prototyping retrieval augmented generation (RAG) apps can be easy with modern generative AI tools and frameworks. But the journey from prototyping to production is anything but easy with many traps and pitfalls.

In this workshop, you'll get to see first-hand some of the potential risks, and how the right infrastructure tool can help you to mitigate them.

You will get hands-on with an AI-native, vector databases to provide you with an experiential perspective on how to not only build a proof of concept, but go beyond it. You'll learn about some of the key tools that help RAG-powered applications to achieve scalability, fault tolerance and data isolation.

During the workshop, you will implement features like quantization, multi-tenancy, replication, and horizontal scaling. In turn, you will experience how big a difference each of these can make to the RAG system's performance.

You will be guided through each step, to not only run each piece of code, but also to understand what is happening under-the-hood.

By the end, you'll understand some of the key considerations in selecting your infrastructure tools, and what to consider when going to production.

Background Knowledge:

The participants may choose to follow along, which will require some Python knowledge. The level of Kubernetes & Docker knowledge required is minimal.
However, they can also simply listen & watch, too.


JP Hwang is a developer and educator at Weaviate. He loves to enhance others' learning through everything from hands-on coding projects to engaging and informative talks. He brings a combination of technical expertise, empathy, and commitment, to help ensure that learning is fun and empowering for both the educator and the audience.

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