Chapter 1. AutoRAG overview

When you create an AutoRAG optimization run, AutoRAG tests combinations of chunking, embedding, retrieval, and generation settings against your test data. Each combination produces a RAG pattern with evaluation scores. AutoRAG ranks patterns on a leaderboard and generates Jupyter notebooks that you can use to run the best pattern.

AutoRAG automatically samples up to 1 GiB of relevant documents based on your test data, so you do not need to filter your document set in advance. Documents referenced in your test data are prioritized during sampling.

The following limitations apply during Technology Preview:

If you import an AutoRAG pipeline to your pipeline server and create runs from it, the runs appear on the AutoRAG page in the dashboard. When you import the pipeline, set the pipeline name to documents-rag-optimization-pipeline and the pipeline version name to documents-rag-optimization-pipeline-<version>, such as documents-rag-optimization-pipeline-3.4.0.

For more information about importing pipelines, see Importing a pipeline.

To find the best RAG configuration for your documents, prepare test data, create an AutoRAG optimization run, evaluate the results, and run the best-performing pattern.

Before you begin, ensure that the following prerequisites are met: