Rags

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RAGs is an open-source AI Agents & Automation tool that allows users to build Retrieval Augmented Generation (RAG) pipelines using natural language. It enables the creation and configuration of RAG agents over custom data sources.

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At a glance

Pricing

Open Source

Free tier

Yes

API

Skill level

Technical

About

What is rags?

RAGs is a Streamlit application designed to simplify the creation of Retrieval Augmented Generation (RAG) pipelines. Users can describe their task and desired RAG system parameters using natural language, such as specifying data sources like local files or web pages, and defining parameters like top-k retrieval or summarization. The tool provides a configuration view to inspect and modify generated parameters, offering full control over the RAG setup. Once configured, a standard chatbot interface allows users to query the RAG agent over their data. It supports various LLMs and embedding models, including OpenAI, Anthropic, Replicate, and HuggingFace, making it a flexible solution for developers and data scientists looking to implement RAG systems.

Best used for

Ideal for developers and data scientists who need to quickly build and customize RAG pipelines over their data, configure agent parameters, and interact with the generated RAG agent. Especially valuable for experimenting with different LLMs and embedding models for specific use cases.

Common actions

build RAG pipelines

configure AI agents

query custom data

experiment with LLMs

face swapping"AI Agents"workflowsdeepfakecollaborationautomated workflowlow-code/no-codeopen-sourcegithub copilot

Capabilities

Key features

Natural language RAG creation
Configurable RAG parameters
Chatbot query interface
Multiple LLM support
Multiple embedding support
Streamlit app

Target Audience

developerdata scientiststartup founder

Integrations

Not yet documented

Pricing & Plans

Open Source

Free

FAQs

What data sources can RAGs use to build a RAG pipeline?

RAGs currently supports building RAG pipelines from either a single local file or a web page. The developers are open to suggestions for additional data sources to integrate into the system.

Can I customize the LLM and embedding models used by RAGs?

Yes, RAGs allows customization of both the LLM and embedding models. It supports OpenAI, Anthropic, Replicate, and HuggingFace LLMs, and text-embedding-ada-002 or Hugging Face embedding models.

What are the typical RAG parameters I can configure in RAGs?

You can configure parameters such as the system prompt, whether to include summarization, top-k retrieval, chunk size, and specify the embed model and LLM to be used by your RAG agent.

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AI Frameworks & Infra Chatbots & Conversational AI Workflow Agents Personal Assistants RAG & Document AI Voice Agents

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