Neum AI is a data platform for large-scale embeddings management, optimization, and synchronization. In this blog, they discuss the challenges of scaling a Retrieval Augmented Generation (RAG) application and provide technical and architectural details on how they solved these problems for a pipeline syncing 1 billion vectors. The blog covers topics such as ingestion at large scale, embedding data efficiently, and storing data in a vector database. They also mention their use of the Replicate embedding model and the Weaviate vector database for this particular case study. It’s important to note that their system has robust monitoring, logging, and retry mechanisms in place to ensure efficiency and accuracy.
https://medium.com/@neum_ai/retrieval-augmented-generation-at-scale-building-a-distributed-system-for-synchronizing-and-eaa29162521