Use case: RAG for enhancing information retrieval
In this section, we will mainly discuss the challenges you may face when creating or running your RAG system. We'll go through these challenges at a high level, providing an abstract understanding of some common operational issues you might encounter.
One of the first challenges is Encoding Documents, and how to encode documents effectively. Encoding transforms raw text into vectors, enabling efficient and effective retrieval. Document encoders, often powered by neural networks like transformers, convert each document into a fixed-size representation in a high-dimensional vector space. This representation captures the semantic essence of the document, facilitating accurate similarity searches. Poor encoding can lead to ineffective retrieval and irrelevant responses.
As we encode documents, we establish the groundwork for the subsequent step; document chunking. The encoded vectors are essential for understanding how documents are segmented...
