Scaling RAG Bank Customer Data with Pinecone

Scaling up RAG documents, whether text-based or multimodal, isn’t just about piling on and accumulating more data—it fundamentally changes how an application works. Firstly, scaling is about finding the right amount of data, not just more of it. Secondly, as you add more data, the demands on an application can change—it might need new features to handle the bigger load. Finally, cost monitoring and speed performance will constrain our projects when scaling. Hence, this chapter is designed to equip you with cutting-edge techniques for leveraging AI in solving the real-world scaling challenges you may face in your projects. For this, we will be building a recommendation system based on pattern-matching using Pinecone to minimize bank customer churn (customers choosing to leave a bank).

We will start with a step-by-step approach to developing the first program of our pipeline. Here, you will learn how to download a...