rag-web-ui and rag-arena

These are complements: the web UI provides an interface for RAG dialogue systems while the evaluation arena assesses RAG quality through user feedback, addressing different stages of the RAG development lifecycle.

rag-web-ui
52
Established
rag-arena
45
Emerging
Maintenance 6/25
Adoption 10/25
Maturity 16/25
Community 20/25
Maintenance 0/25
Adoption 10/25
Maturity 16/25
Community 19/25
Stars: 2,818
Forks: 293
Downloads:
Commits (30d): 0
Language: TypeScript
License: Apache-2.0
Stars: 216
Forks: 33
Downloads:
Commits (30d): 0
Language: TypeScript
License: MIT
No Package No Dependents
Stale 6m No Package No Dependents

About rag-web-ui

rag-web-ui/rag-web-ui

RAG Web UI is an intelligent dialogue system based on RAG (Retrieval-Augmented Generation) technology.

This system helps organizations create an intelligent Q&A system using their own documents. You upload various document types like PDFs or Word files, and it processes them to answer questions accurately. This is ideal for knowledge managers, support teams, or anyone needing to make internal knowledge easily searchable and conversational for their users.

knowledge-management customer-support internal-communications information-retrieval corporate-training

About rag-arena

firecrawl/rag-arena

Open-source RAG evaluation through users' feedback

This project helps evaluate and compare how different retrieval-augmented generation (RAG) methods perform for a chatbot. You input a question, and the system provides multiple answers, each generated by a different RAG approach. You then vote on the best response, which helps benchmark the effectiveness of various data retrieval strategies. This tool is designed for AI practitioners, data scientists, or product managers who are building or optimizing RAG-powered chatbots and need to understand which retrieval techniques work best for their specific data.

AI-evaluation chatbot-development natural-language-processing machine-learning-operations data-science

Scores updated daily from GitHub, PyPI, and npm data. How scores work