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.

Use this post, company, and trend context to find content marketing opportunities, perform competitive analysis, or address product feature gaps via the Plushcap MCP server or the Plushcap API.