{"data":{"slug":"jxzhangjhu-awesome-llm-rag","name":"Awesome-LLM-RAG","tagline":"a curated list of advanced retrieval augmented generation (RAG) in Large Language Models","github_url":"https://github.com/jxzhangjhu/Awesome-LLM-RAG","owner":"jxzhangjhu","repo":"Awesome-LLM-RAG","owner_avatar_url":"https://avatars.githubusercontent.com/u/19910910?v=4","primary_language":null,"stars":1343,"forks":94,"topics":["embeddings","large-language-models","llm","rag","rag-embeddings","retrieval-augmented-generation","retrieval-information"],"archived":false,"github_pushed_at":"2026-07-22T03:17:18+00:00","maintenance_label":"Steady","stars_delta_30d":4,"url":"https://www.graphcanon.com/tools/jxzhangjhu-awesome-llm-rag","markdown_url":"https://www.graphcanon.com/tools/jxzhangjhu-awesome-llm-rag.md","api_url":"https://www.graphcanon.com/api/graphcanon/tools/jxzhangjhu-awesome-llm-rag","graph_url":"https://www.graphcanon.com/api/graphcanon/graph?tool=jxzhangjhu-awesome-llm-rag","description":"Awesome-LLM-RAG: a curated list of advanced retrieval augmented generation (RAG) in Large Language Models","homepage_url":null,"license":null,"open_issues":13,"watchers":11,"ai_summary":"Awesome-LLM-RAG provides a comprehensive overview of resources related to Retrieval-Augmented Generation techniques used with Large Language Models.","readme_excerpt":"<div align=\"center\">\n    <h1>Awesome LLM RAG</h1>\n    <a href=\"https://awesome.re\"><img src=\"https://awesome.re/badge.svg\"/></a>\n</div>\n\n\\\n \n\n\n\n\n\nThis repo aims to record advanced papers on Retrieval Augmented Generation (RAG) in LLMs.\n\nWe strongly encourage the researchers that want to promote their fantastic work to the LLM RAG to make pull request to update their paper's information!\n\n\n--- \n\n## Contents\n\n- [Bernstein](https://github.com/chernistry/bernstein) - Multi-agent orchestrator with RAG-enhanced task planning. Decomposes goals using context from codebase analysis.\n- [Resources](#resources)\n  - [Workshops and Tutorials](#workshops-and-tutorials)\n  - [Books](#books)\n- [Papers](#papers)\n  - [Survey and Benchmark](#survey-and-benchmark)\n  - [Retrieval-enhanced LLMs](#retrieval-enhanced-llms)\n  - [RAG Instruction Tuning](#rag-instruction-tuning)\n  - [RAG In-Context Learning](#rag-in-context-learning)\n  - [RAG Embeddings](#rag-embeddings)\n  - [RAG Simulators](#rag-simulators)\n  - [RAG Search](#rag-search)\n  - [RAG Long-text and Memory](#rag-long-text-and-memory)\n  - [RAG Evaluation](#rag-evaluation)\n  - [RAG Optimization](#rag-optimization)\n  - [RAG Application](#rag-application)\n  - [RAG for Missing Modalities](#rag-for-missing-modalities)\n\n\n\n--- \n\n# Resources \n\n- [guardian-agent-prompts](https://github.com/milkomida77/guardian-agent-prompts) - 49 production-tested AI agent system prompts for Claude Code multi-agent orchestration with retrieval-augmented generation patterns. MIT licensed.\n- [CCHub](https://github.com/Moresl/cchub) - A desktop control panel for the Claude Code / Codex / Gemini CLI ecosystem. Manage MCP servers, config profiles, agent skills, CLAUDE.md, hooks, and workflow templates from a single Tauri app (Windows / macOS / Linux).\n- [ChunkTuner](https://github.com/shantanu-deshmukh/chunktuner) - Open-source Python/CLI/MCP tooling to benchmark chunking strategies for RAG and recommend configurations using retrieval metrics (optional RAGAS).\n\n## Workshops and Tutorials\n- [Agent Shadow Brain](https://github.com/theihtisham/agent-shadow-brain) - Self-evolving AI coding intelligence with infinite memory (TurboQuant), genetic algorithm self-evolution, predictive bug detection, PageRank knowledge graphs, swarm intelligence, and adversarial defense.\n- [Omni Skills Forge](https://github.com/theihtisham/omni-skills-forge) - 50,000+ curated AI agent skills for Claude Code, Cursor, Copilot, Windsurf, Cline. Visual dashboard, one-click install, skill doctor, auto-update.\n- [RAG Techniques](https://github.com/NirDiamant/RAG_Techniques) - 35+ runnable Jupyter-notebook tutorials covering advanced RAG techniques: chunking, query transformation/HyDE, reranking, self-RAG, graph RAG, and evaluation.\n**Personalized Generative AI**  \n*Zheng Chen, Ziyan Jiang, Fan Yang, Zhankui He, Yupeng Hou, Eunah Cho, Julian McAuley, Aram Galstyan, Xiaohua Hu, Jie Yang*  \nCIKM 23 – Oct 2023 [[link](https://sites.google.com/view/pgai2023/home)]\n\n**First Workshop on Recommendation with Generative Models**  \n*Wenjie Wang, Yong Liu, Yang Zhang, Weiwen Liu, Fuli Feng, Xiangnan He, Aixin Sun*  \nCIKM 23 – Oct 2023 [[link](https://rgm-cikm23.github.io/)]\n\n**First Workshop on Generative Information Retrieval**  \n*Gabriel Bénédict, Ruqing Zhang, Donald Metzler*  \nSIGIR 23 – Jul 2023 [[link](https://coda.io/@sigir/gen-ir)]\n\n**Retrieval-based Language Models and Applications**  \n*Akari Asai,\tSewon Min,\tZexuan Zhong,\tDanqi Chen*  \nACL 23 – Jul 2023 [[link](https://acl2023-retrieval-lm.github.io/)]\n\n**Become a Generative AI Developer**\n*Richie Cotton, Olivier Mertens, Korey Stegared-Pace, James Briggs, Vincent Vankrunkelsven, Alara Dirik, Jacob Marquez, Priyanka Asnani*\nDataCamp [[link](https://www.datacamp.com/ai-code-alongs)]\n\n## Books\n\n**Build a Large Language Model (From Scratch)**  \n*Sebastian Raschka*  \nManning Publications - Sep 2024 [[link](https://www.manning.com/books/build-a-large-language-model-from-scratch)]\n\n**Build a Reasoning Model (From S","github_created_at":"2023-10-26T17:47:05+00:00","created_at":"2026-07-11T11:30:55.706761+00:00","updated_at":"2026-08-22T12:01:23.361618+00:00","categories":[{"slug":"data-retrieval","name":"Data & Retrieval","url":"https://www.graphcanon.com/categories/data-retrieval","markdown_url":"https://www.graphcanon.com/categories/data-retrieval.md","api_url":"https://www.graphcanon.com/api/graphcanon/categories/data-retrieval"},{"slug":"llm-frameworks","name":"LLM Frameworks","url":"https://www.graphcanon.com/categories/llm-frameworks","markdown_url":"https://www.graphcanon.com/categories/llm-frameworks.md","api_url":"https://www.graphcanon.com/api/graphcanon/categories/llm-frameworks"}],"tags":[{"slug":"embeddings","name":"embeddings"},{"slug":"large-language-models","name":"large language models"},{"slug":"llm","name":"llm"},{"slug":"rag","name":"rag"},{"slug":"rag-embeddings","name":"rag-embeddings"},{"slug":"retrieval-augmented-generation","name":"retrieval-augmented-generation"},{"slug":"retrieval-information","name":"retrieval-information"}],"trust":{"provenance":{"is_fork":false,"github_id":710443541,"owner_type":"User","methodology":"github_public_v1","parent_repo":null,"near_duplicate_slugs":[]},"computed_at":"2026-08-22T12:01:22.663Z","maintenance":{"label":"Steady","score":60,"methodology":"github_public_v1","releases_90d":0,"days_since_push":31,"last_release_at":null,"stars_delta_30d":4,"open_issues_delta_30d":4},"security_summary":{"status":"no_lockfile","scanner":null,"low_count":0,"high_count":0,"last_scan_at":"2026-07-11T11:30:57.475Z","medium_count":0,"scan_profile":"none","critical_count":0}},"capability_facts":{"scan":{"source":"repo_scan","observed_at":"2026-08-22T12:01:23.082Z"}},"decision_facts":{"hosting":null,"pricing":null,"requirements":null,"constraints":null,"when_to_use":["When you are focusing on the detailed implementation and utilization of RAG in large language models, as Awesome-LLM-RAG provides a deep dive into advanced RAG approaches.","For researchers or developers interested in curating their resources around specific RAG advancements and embedding techniques within LLMs."],"when_not_to_use":["If you are looking for introductory material on LLM frameworks broadly; Awesome-LLM-RAG does not cover basics of large language models but rather focuses on advanced topics.","Not recommended if your interest is in broad categories like general vector databases or data retrieval without a focus on RAG within LLMs, as the content is highly specialized."],"source":"enrich:decision_facts","observed_at":"2026-07-12T03:50:24.499Z"},"constraint_facets":null,"decision_summary":[{"label":"Adopt for","value":"Awesome-LLM-RAG is a curated list specific to advanced retrieval augmented generation (RAG) techniques for Large Language Models."}]}}