rag-all-techniques and RAG

rag-all-techniques
51
Established
RAG
39
Emerging
Maintenance 2/25
Adoption 10/25
Maturity 15/25
Community 24/25
Maintenance 2/25
Adoption 5/25
Maturity 16/25
Community 16/25
Stars: 453
Forks: 114
Downloads: —
Commits (30d): 0
Language: Jupyter Notebook
License: MIT
Stars: 14
Forks: 6
Downloads: —
Commits (30d): 0
Language: Jupyter Notebook
License: MIT
Stale 6m No Package No Dependents
Stale 6m No Package No Dependents

About rag-all-techniques

liu673/rag-all-techniques

Implementation of all RAG techniques in a simpler way（以简单的方式实现所有 RAG 技术）

This project provides practical, framework-agnostic implementations of various advanced Retrieval Augmented Generation (RAG) techniques. It takes unstructured text data, applies different methods for breaking it down and enriching it, and then uses a large language model to generate improved, contextually relevant answers to user queries. This is for AI practitioners, researchers, or anyone building custom question-answering systems who wants to understand and experiment with core RAG components.

AI-powered question-answering information retrieval natural language processing text analytics knowledge management

About RAG

AashiDutt/RAG

This repo contains self made projects and learnables from various resources on using local LLMs and RAG

Build chatbots that answer questions based on your own specific content, whether it's a website or a PDF document. You provide the content, and the chatbot delivers accurate answers from it. This is ideal for knowledge managers, content creators, or anyone needing to create a dedicated Q&A resource from their existing information.

knowledge-management content-interaction information-retrieval document-query website-Q&A

Related comparisons

rag-all-techniques and advanced-rag rag-all-techniques and RAG-Arena rag-all-techniques and RAG-for-LLMs-demo rag-all-techniques and advanced-rag rag-all-techniques and RAG-for-LLMs-demo

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