RAG Data Ingestion Using Apache Beam

Retrieval Augmented Generation (RAG) has emerged as a groundbreaking technique in the field of generative AI. By providing a large language model relevant data to solve a given task, it can generate answers with much higher accuracy. Next to enhanced performance RAG allows us to work with sensitive without resource-intensive in-house model training. However, efficiently preprocessing and ingesting document data into vector databases for RAG applications can be challenging, especially when dealing with real-time updates.

In most RAG applications relevant data is fetched from a vector database through semantic search. From experience working on RAG applications we have noticed that building a robust data processing pipeline to keep the data in the vector database up to date can be a challenge. Beam is a particularly powerful tool to solve this task since it supports both batch and streaming data, allowing us reuse the same data processing pipeline for both processing scale datasets of relevant information as well as keeping the vector database up to date with live data through streaming data.

It is crucial to recognize that proper preprocessing and analysis of data are often underestimated yet fundamental components in building effective RAG applications. As the age-old adage goes, ‘garbage in, garbage out,’ emphasizing the significance of ensuring high-quality input data for optimal output results. Apache Beam can play an important role in this process, offering both batch and streaming data processing capabilities that are essential for developing production-ready RAG applications.