explaingit

marker-inc-korea/autorag

★ 4,756PythonAudience · developerComplexity · 3/5LicenseSetup · moderate

TLDR

A Python tool that automatically tests many combinations of RAG pipeline components against your own documents and questions, then tells you which setup gives the most accurate answers, no manual benchmarking needed.

Mindmap

mindmap
  root((AutoRAG))
    What it does
      Optimizes RAG pipelines
      Benchmarks components
    Inputs
      Document corpus
      QA evaluation set
    Pipeline steps
      Document parsing
      Text chunking
      Embedding models
      Retrieval methods
    Outputs
      Best config export
      Comparison dashboard
    License
      Apache 2.0
Click or tap to explore — scroll the page freely

Code map

Detail Auto

An interactive map of this repo's files and how they connect — its source is parsed live in your browser. Click Visualize to build it.

filefunction / class

Things people build with this

USE CASE 1

Find the best RAG pipeline configuration for your company's PDF knowledge base by running automated trials

USE CASE 2

Auto-generate evaluation question-answer pairs from a document corpus without writing them by hand

USE CASE 3

Compare retrieval methods and embedding models across your documents without manual benchmarking

USE CASE 4

Export the winning pipeline configuration and deploy it to production once optimization is complete

Tech stack

PythonHugging Face

Getting it running

Difficulty · moderate Time to first run · 1h+

Requires an LLM API key (such as OpenAI) for automatic QA generation and evaluation, optimization trial time grows with corpus size.

Use freely for any purpose including commercial, as long as you include the Apache 2.0 license notice and state any changes you made.

In plain English

RAG (Retrieval-Augmented Generation) is a technique for making an AI language model answer questions about your own documents. Instead of the AI relying solely on what it learned during training, a retrieval system first searches your documents for relevant passages, and those passages are fed to the model as context when it generates an answer. The challenge is that building a good RAG system involves many decisions: how to split documents into chunks, which text embedding model to use, which retrieval method to apply, and more. Different combinations work better for different types of data. AutoRAG is a Python tool that automates the process of finding which combination of components works best for a specific dataset. You provide two things: a corpus (your documents) and a QA dataset (sample questions with correct answers drawn from those documents). AutoRAG then runs your documents through many pipeline combinations, measures how accurately each one answers the sample questions using standard evaluation metrics, and reports which pipeline performed best. The tool includes utilities for building those inputs from raw documents. A parser step converts PDFs and other file formats into text. A chunking step splits that text into segments. A QA creation step uses a language model to generate sample questions and answers from your corpus automatically, so you do not need to write evaluation data by hand. Once optimization is complete, AutoRAG provides a dashboard to compare results across pipeline variants and can export the best-performing configuration for deployment. Interactive demos are available on Hugging Face Spaces for trying the tool without any local installation. The library is released under the Apache 2.0 license.

Copy-paste prompts

Prompt 1
I have a folder of PDFs and want to build a RAG chatbot. Show me how to use AutoRAG to parse them, auto-generate QA evaluation pairs, run pipeline optimization, and identify the best configuration.
Prompt 2
I already have a QA dataset for my documents. Show me the AutoRAG config YAML to run optimization comparing at least three chunking strategies and two embedding models.
Prompt 3
How do I use the AutoRAG dashboard to compare trial results and understand why one retrieval method outperformed another on my dataset?
Prompt 4
I want to deploy the best pipeline AutoRAG found. How do I export its configuration and integrate it into a Python application that answers user questions?
Prompt 5
AutoRAG finished optimizing but I'm not sure which metric to trust for my use case, explain what RAGAS, recall, and MRR each measure and when to prioritize each.
Open on GitHub → Explain another repo

← marker-inc-korea on gitmyhub — every repo by this author, as a profile.

Verify against the repo before relying on details.