WikiRag and RAG-Simplified

WikiRag
34
Emerging
RAG-Simplified
34
Emerging
Maintenance 0/25
Adoption 5/25
Maturity 16/25
Community 13/25
Maintenance 0/25
Adoption 4/25
Maturity 16/25
Community 14/25
Stars: 10
Forks: 2
Downloads: —
Commits (30d): 0
Language: Jupyter Notebook
License: Apache-2.0
Stars: 5
Forks: 3
Downloads: —
Commits (30d): 0
Language: Python
License: MIT
Stale 6m No Package No Dependents
Stale 6m No Package No Dependents

About WikiRag

MauroAndretta/WikiRag

WikiRag is a Retrieval-Augmented Generation (RAG) system designed for question answering, it reduces hallucination thanks to the RAG architecture. It leverages Wikipedia content as a knowledge base.

This tool helps researchers, students, and curious individuals quickly get answers to factual questions by searching Wikipedia and, if needed, the broader web. You input a question in natural language, and it provides a concise, accurate answer, leveraging a vast knowledge base to avoid common AI inaccuracies. Anyone who frequently needs to extract specific, reliable information from Wikipedia will find this useful.

information-retrieval research-support knowledge-discovery fact-checking educational-tools

About RAG-Simplified

ShahMitul-GenAI/RAG-Simplified

Enhance GPT-3.5-Turbo output using Retrieval-Augmented Generation (RAG) with a user-friendly interface. Select between Wikipedia or integrate external documents to experience precise, context-aware responses.

Related comparisons

WikiRag and wiki-rag WikiRag and RAG-system WikiRag and wiki-rag WikiRag and RAG-system

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