AutoRAG

Visit Tool

AutoRAG is an open-source framework for Retrieval-Augmented Generation (RAG) evaluation and optimization. It uses AutoML-style automation to find an optimal RAG pipeline for specific data.

Claim this tool

2Views

At a glance

Pricing

Open Source

Free tier

Yes

API

Yes

Skill level

Technical

About

What is AutoRAG?

AutoRAG is an open-source framework designed for the evaluation and optimization of Retrieval-Augmented Generation (RAG) pipelines. It leverages AutoML-style automation to help users identify the most effective RAG pipeline for their specific data and use cases. The tool simplifies the otherwise time-consuming and complex process of making and evaluating various RAG modules. Users can automatically evaluate different RAG module combinations with their own evaluation data, ensuring they find the best fit for their needs. AutoRAG supports a wide range of RAG modules, provides detailed metrics for evaluation, and offers quick installation, data creation, and deployment options for optimal pipelines.

Best used for

Ideal for developers and data scientists who need to optimize Retrieval-Augmented Generation (RAG) pipelines, evaluate various RAG modules, and deploy efficient RAG solutions. Especially valuable for those working with custom datasets and seeking automated methods to find the best RAG configuration.

Common actions

optimize RAG pipelines

evaluate RAG performance

automate RAG module selection

deploy RAG solutions

github copilot"AI Agents"face swappingopen-sourceautomated workflowdeepfakeworkflowscollaborationlow-code/no-code

Capabilities

Key features

AutoML-style RAG optimization
RAG pipeline evaluation
Custom data integration
Module combination testing
Metric-based optimization
API server deployment
Web interface deployment

Target Audience

developer

Integrations

Not yet documented

Pricing & Plans

Open Source

Free

FAQs

What kind of data is required to use AutoRAG for optimization?

AutoRAG requires two main types of data for RAG optimization: a QA dataset (qa.parquet) and a Corpus dataset (corpus.parquet). The QA dataset is crucial for accurate evaluation, while the Corpus dataset is used by RAG to retrieve documents and generate answers.

Can I use custom LLM and embedding models with AutoRAG?

Yes, AutoRAG supports the use of custom Large Language Models (LLMs) and embedding models. The documentation and tutorials provide guidance on how to integrate and utilize your preferred custom models within the AutoRAG framework for RAG optimization.

How can I deploy an optimized RAG pipeline found by AutoRAG?

AutoRAG offers multiple deployment options for your optimal RAG pipeline. You can run it directly as code, deploy it as an API server, or even set it up as a web interface. This flexibility allows for easy integration into various applications.

Trending

Subcategories trending in Coding & Development

Open Source & Models DevOps & Infrastructure No-Code / Low-Code Testing & QA Backend & APIs Prompt Engineering

Trending

Explore

Browse AI tools by category

Content & Design Productivity & Business Coding & Development AI Agents & Automation Research & Education Wellness & Lifestyle Career Development Marketing & Growth Data & Analytics Customer Support & CX Finance E-commerce