retrieval-augmented-generation and RAG-Playground

RAG-Playground
34
Emerging
Maintenance 10/25
Adoption 7/25
Maturity 16/25
Community 20/25
Maintenance 2/25
Adoption 3/25
Maturity 15/25
Community 14/25
Stars: 33
Forks: 24
Downloads:
Commits (30d): 0
Language: Jupyter Notebook
License: Apache-2.0
Stars: 4
Forks: 3
Downloads:
Commits (30d): 0
Language: Jupyter Notebook
License: MIT
No Package No Dependents
Stale 6m No Package No Dependents

About retrieval-augmented-generation

VectorInstitute/retrieval-augmented-generation

Reference Implementations for the RAG bootcamp

This collection provides examples for building applications that can answer questions using up-to-date or private information, going beyond what a large language model was originally trained on. You input a question and relevant external data (like documents, web pages, or database records), and it outputs an accurate, specific answer. It's designed for developers, data scientists, and AI engineers looking to create smart assistants or search tools.

AI development natural language processing information retrieval question answering data integration

About RAG-Playground

prasanna00019/RAG-Playground

A comprehensive collection of RAG (Retrieval Augmented Generation) implementations 📚✨, from foundational concepts to advanced agentic 🤖 and knowledge graph 🌐 RAGs

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