benchmark-qed 0.2.0

BenchmarkQED: Automated benchmarking of retrieval-augmented generation (RAG) systems

BenchmarkQED

👉 Microsoft Research Blog Post
👉 BenchmarkQED Docs

Overview

flowchart LR
    AutoQ["<span style='font-size:1.5em; color:black'><b>AutoQ</b></span><br>LLM synthesis of<br>local-to-global<br>queries for target<br>datasets"] -- creates queries <br>for evaluation --> AutoE["<span style='font-size:1.5em; color:black'><b>AutoE</b></span><br>LLM evaluation of<br>relative answer <br>quality on target <br>metrics"]
    AutoE ~~~ AutoD["<span style='font-size:1.5em; color:black'><b>AutoD</b></span><br>LLM summarization<br>of datasets samples<br>to a curated target<br>structures"]
    AutoD -- curates datasets <br>for evaluation --> AutoE
    AutoD -- creates dataset summaries <br>for query synthesis --> AutoQ
    style AutoQ fill:#a8d0ed,color:black,font-weight:normal
    style AutoE fill:#a8d0ed,color:black,font-weight:normal
    style AutoD fill:#a8d0ed,color:black,font-weight:normal
    linkStyle 0 stroke:#0077b6,stroke-width:2px
    linkStyle 2 stroke:#0077b6,stroke-width:2px
    linkStyle 3 stroke:#0077b6,stroke-width:2px

BenchmarkQED is a suite of tools designed for automated benchmarking of retrieval-augmented generation (RAG) systems. It provides components for query generation, evaluation, and dataset preparation to facilitate reproducible testing at scale.

• AutoQ: Generates four classes of synthetic queries with variable data scope, ranging from local queries (answered using a small number of text regions) to global queries (requiring reasoning over large portions or the entirety of a dataset).
• AutoE: Evaluates RAG answers by comparing them side-by-side on key metrics—relevance, comprehensiveness, diversity, and empowerment—using the LLM-as-a-Judge approach. When ground truth is available, AutoE can also assess correctness, completeness, and other custom metrics.
• AutoD: Provides data utilities for sampling and summarizing datasets, ensuring consistent inputs for query synthesis.

In addition to the tools, we also release two datasets to support the development and evaluation of RAG systems:

• Podcast Transcripts: Transcripts of 70 episodes of the Behind the Tech podcast series. This is an updated version of the podcast transcript dataset used in the GraphRAG paper.
• AP News: A collection of 1,397 health-related news articles from the Associated Press.

Getting Started

Instructions for getting started can be found here.

Contributing

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