Agentic RAG

One of the most powerful aspects of Nuclia is its ability to find the perfect combination of embeddings, LLMs, prompts, and retrieval strategies tailored to any specific use case. Whether you are dealing with highly specialized industry data or general-purpose information, Nuclia’s flexible framework allows you to configure and optimize every aspect of your RAG process. This means that no matter your specific requirements, you can rely on Nuclia to deliver the most effective and efficient results.

Nuclia’s RAG evaluation tools, REMi and nuclia-eval, can be used to identify and resolve issues in a failing RAG pipeline. The tools are based on the RAG Triad framework, which evaluates the query, contexts, and answer in relation to each other using these metrics:

Using RAG is about getting the most from LLMs ability to phrase proper answers and at the same time to make sure it uses the most relevant and up-to-date data according to the user’s question. The objective is to deliver high-quality answers to users.

Nuclia has been building something for the last two years. Our vision is to deliver an engine that allows engineers and builders to search on any domain specific set of data, focusing on unstructured data like video, text, PDFs, links, conversations, layouts and many other sources.