OpenViSTAI · Agents · Blockchain · Crypto
Menu
RAG & Knowledge Systems

Awesome Rag — coree

Awesome RAG is an open-source directory of papers, learning resources and tools about retrieval-augmented generation in large language models. Its sections cover survey and general papers, lectures and tutorials, workshops, tools and other collections.

Independent OpenViST project profile, based on linked sources.

Awesome Rag — coree — OpenViST project profile in RAG & Knowledge Systems.
OpenViST project illustration. See the linked sources for product details.
Understand the project

Practical overview

When it fits

Use it when you need a starting point for RAG reading or want to discover references by resource type. Follow linked projects or papers for implementation details.

Who it is for

  • Readers learning about RAG concepts and research.
  • Researchers looking for papers, surveys and related code.
  • Developers exploring RAG tools and libraries.

Documented uses

  • Find survey papers that introduce or summarize RAG.
  • Browse research on topics such as benchmarking, query rewriting and active retrieval.
  • Discover tutorials, workshops and tools for further study.
Before you choose

Requirements & limits

The public GitHub repository is available under the CC0-1.0 license.

  • Entries are organized into resource categories, including survey papers, general papers, tutorials, workshops and tools.
  • Some paper entries link to code as well as the paper.
  • The directory points to external work; it does not itself provide a RAG model, API or implementation.

Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.

Source-based practical guide

Browse RAG research papers and surveys

Use the directory to find listed papers about retrieval-augmented generation.

Before you start

  • Access to the Awesome RAG README in a web browser.

Steps

  1. Open the repository README.

  2. Go to the Papers section.

  3. Choose Survey or General to browse entries by category and year.

  4. Open a listed Paper link to read the referenced work.

Expected outcome

You can navigate from the directory to its listed RAG research papers and surveys.

Guide limitations

  • The repository is a curated reference list, not a RAG implementation.
Source-based practical guide

Explore RAG learning resources and tools

Use the directory to locate its learning resources, workshops, tools, and related collections.

Before you start

  • Access to the Awesome RAG README in a web browser.

Steps

  1. Open the repository README.

  2. Use its table of contents to navigate to Resources, Tools, or Other Collections.

  3. Browse the relevant section for listed references.

  4. Follow a resource link to continue learning or explore a listed tool.

Expected outcome

You can find and follow references grouped as RAG resources and tools.

Guide limitations

  • The README presents a directory; it does not document installing or operating the listed tools.
  • Specific links and section contents may vary as the repository changes.
Evidence and transparency

Sources & review

Source record details
Source availability
Active
Sources checked
9 October 2026
Original website title
coree/awesome-rag · Official homepage title · retained in its source language