Building end-to-end RAG system using Solar LLM and MongoDB Atlas

As an enterprise AI startup building large-scale AI solutions for leading enterprises globally, Upstage AI offers a suite of full-stack LLM components, including Document AI (converting unstructured documents into machine-readable data through key information extractor, layout analysis, and OCR), embedding models, Solar LLM, and Groundedness Checker.

With these components and a powerful vector DB like MongoDB Atlas , developers can build a Retrieval Augmented Generation (RAG) application.

RAG applications are particularly useful for enterprise users like Upstage.

They generate answers from LLMs based on the data retrieved from the private database, which lowers the risk of hallucination and increases productivity.

In this blog, we will explain how enterprises can build an internal RAG application.

We will use Solar LLM and MongoDB Atlas to improve access to information for employees and increase work productivity.

The RAG system will find relevant information from separate sources like Google Workspace, Notion, GitHub, and Linear.

It will then use this data to generate answers.

The end interface will be a chatbot.

Employees can ask questions and search for information.

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