Building your own RAG application using Together AI and MongoDB Atlas

Together AI provides a fast cloud platform for building and running generative AI applications, including the launch of its Together Embeddings endpoint, which allows users to build powerful RAG-based applications using MongoDB's Atlas Vector Search. RAG combines generative models with retrieval models to improve performance and accuracy in knowledge-intensive tasks. To use RAG, users populate a vector database using an embedding model, retrieve relevant data examples, augment the retrieved information, and obtain the final output from a generative model. The Together AI platform provides a step-by-step guide on how to implement RAG with MongoDB Atlas, including setting up an account, creating an embedding function, storing embeddings, creating a vector search index, retrieving data, augmenting and generating outputs, and fine-tuning models. This tutorial demonstrates how to build a RAG application using the Airbnb listing review dataset and showcases the potential of combining generative AI with semantic search for high-quality applications.