Presentation: Bringing a naive RAG to production

Presentation synopsis:

In this presentation, I'll take you through our journey from using a simple, local RAG setup to adopting a professional RAG framework. I'll dive into the crucial components and applications of RAG in today's world of machine learning and data management.

RAG has significantly enhanced language models by providing them with the ability to retrieve information. This means they can incorporate the most recent data without constant fine-tuning. By ensuring data stays up-to-date and valid, RAG also enhances the system's transparency and the ease with which we can trace and fix issues.

We've seen RAG used in many parts of organizations. It's used for fetching knowledge, automating processes, and customizing user experiences. This flexibility is useful for handling unstructured data. Unstructured data makes up most corporate data and is often found in PDFs.

We then talk about LlamaIndex. It's a framework for creating applications with language models. We highlight its benefits: easy setup, the ability to work with different systems, and clear documentation. Yet, it's not without its hurdles, including limited depth in integration and a focus that primarily serves projects at the beginning and intermediate stages.

The path to developing with RAG can be hard. This is due to the many unstructured data sources, like PDFs, Excel files, and web pages. Choosing the right vector storage databases, embedding methods, and data segmentation is critical. It helps to manage costs well. LlamaParse stands out here. It can process many formats, especially PDFs. It turns them into markdown to improve organization and readability.

I also outline strategies for making an efficient RAG pipeline. I stress the need for structured data, metadata, summary recursion, and keyword extraction.

We discuss the advantages of using vector storage databases. They are great for fast searches and retrieval. We will focus on authorization, hybrid search, and personalized scoring.

Introducing LlamaIndex Evaluate. We present it as a tool for testing RAG's accuracy and efficiency. It will help plan tests on public and custom datasets.

The last parts of our presentation offer practical tips on deployment, configuration, and budget management. They include methods such as re-ranking and refining prompts. Also, the benefits of using open-source models and hosting services to stay cost-effective.

Moving to an advanced RAG framework means navigating a maze of tough choices. It requires careful optimization at every stage. This journey shows how RAG can transform how we process data and extract knowledge. It's invaluable in many fields, from academic research to business.