Highly Accurate Retrieval for your RAG Application with ColBERT and Astra DB

In this article, we explore the use of ColBERT, an alternative method for improving retrieval in Retrieval-Augmented Generation (RAG) applications. Unlike traditional methods that turn a passage into a single vector, ColBERT uses Google's open source BERT model to create vectors for each token in a piece of text. This approach captures better context for terms not part of the training data and overcomes issues with chunking strategies. However, it requires more storage capacity and may result in increased latency compared to regular vector search. ColBERT is available in Astra DB through both LangChain and LlamaIndex, making it a viable option for improving accuracy and relevance in RAG systems.