仓库/Skill 库

[EMNLP2025] "LightRAG:简单且快速的检索增强生成"[EMNLP2025] LightRAG: Simple and Fast Retrieval-Augmented Generation

模块化的、基于图结构的 Retrieval-Augmented Generation (RAG) 系统。A modular graph-based Retrieval-Augmented Generation (RAG) system

📑 PageIndex：面向无向量、基于推理的 RAG 的文档索引📑 PageIndex: Document Index for Vectorless, Reasoning-based RAG

带有生成式 UI 的 AI 驱动搜索引擎。An AI-powered search engine with a generative UI

LLM 实战指南：从基础到使用 LLMOps 最佳实践将 LLM 和 RAG 应用部署到 AWSThe LLM's practical guide: From the fundamentals to deploying advanced LLM and RAG apps to AWS using LLMOps best practices

面向 monorepo 的终极 RAG。借助 AI 与知识图谱的能力，对多语言代码库进行查询、理解与编辑。The ultimate RAG for your monorepo. Query, understand, and edit multi-language codebases with the power of AI and knowledge graphs

免费学习如何使用 LLMOps 最佳实践构建端到端生产级 LLM & RAG 系统：源码 + 12 个实操课程。🤖 𝗟𝗲𝗮𝗿𝗻 for 𝗳𝗿𝗲𝗲 how to 𝗯𝘂𝗶𝗹𝗱 an end-to-end 𝗽𝗿𝗼𝗱𝘂𝗰𝘁𝗶𝗼𝗻-𝗿𝗲𝗮𝗱𝘆 𝗟𝗟𝗠 & 𝗥𝗔𝗚 𝘀𝘆𝘀𝘁𝗲𝗺 using 𝗟𝗟𝗠𝗢𝗽𝘀 best practices: ~ 𝘴𝘰𝘶𝘳𝘤𝘦 𝘤𝘰𝘥𝘦 + 12 𝘩𝘢𝘯𝘥𝘴-𝘰𝘯 𝘭𝘦𝘴𝘴𝘰𝘯𝘴

⚡FlashRAG：面向高效 RAG 研究的 Python 工具包（WWW2025 Resource）⚡FlashRAG: A Python Toolkit for Efficient RAG Research (WWW2025 Resource)

RAG 领域新 SOTA —— 一种全新的原创检索架构，以及面向人类与 Agent 的开源知识库。A new SOTA for RAG — an original retrieval architecture and an open-source knowledge base for humans and agents.

基于 OpenSearch 构建的开源、自托管企业及站内搜索服务器，爬取网页、文件、数据库与云端数据源，支持 20+ 语言、REST API，以及 AI/RAG 与语义搜索。Apache-2.0。Open-source, self-hosted enterprise & site search server built on OpenSearch. Crawls web / file / DB / cloud sources, 20+ languages, REST API, and AI/RAG & semantic search. Apache-2.0.

使用开源 LLM 摄取文件用于检索增强生成（RAG），无需第三方，数据不出你的网络。Ingest files for retrieval augmented generation (RAG) with open-source Large Language Models (LLMs), all without 3rd parties or sensitive data leaving your network.

极简网络搜索平台，配有可在浏览器直接运行的 AI 助手。Demo：https://felladrin-minisearch.hf.spaceMinimalist web-searching platform with an AI assistant that runs directly from your browser. Demo: https://felladrin-minisearch.hf.space

带标签、评分与 LLM 的新闻阅读器News reader with tags, scoring, and LLM

デジタル庁のガバメントAI「源内(GENAI)」を完全ローカル(ローカルLLM/OpenAI互換)で動かす非公式プロジェクト。SAML認証(Keycloak)・RAG(Qdrant)・文字起こし(Whisper)・画像生成(SD)・チーム単位ナレッジをローカル完結。

本地代码搜索，结合 BM25、向量相似度与 cross-encoder 重排序。使用 tree-sitter 解析 60+ 种语言，完全离线运行，返回包含文件路径、行号范围与符号元数据的结构化结果。使用 Rust 构建。Local code search combining BM25, vector similarity, and cross-encoder reranking. Parses 60+ languages with tree-sitter, runs entirely offline, and returns structured results with file paths, line ranges, and symbol metadata. Built in Rust.

面向实验的 GenAI/RAG 优化器与工具包，基于 Oracle Database AI Vector Search 与 NL2SQLGenAI/RAG Optimizer and Toolkit for experimentation using Oracle Database AI Vector Search and NL2SQL

为大型语言模型（LLM）和 RAG 系统转换并优化你的 markdown 文档，自动生成 llms.txt。Transform and optimize your markdown documentation for Large Language Models (LLMs) and RAG systems. Generate llms.txt automatically.

ICLR 2026 Oral 论文 "Q-RAG: Long Context Multi-Step Retrieval via Value-Based Embedder Training" 的官方仓库。Official repository for the ICLR 2026 Oral Paper🔥 “Q-RAG: Long Context Multi-Step Retrieval via Value-Based Embedder Training”

SPY put-credit 验证与真金白银的 control plane，具备券商账本支持、确定性 risk gates、hybrid RAG、对账及每月 $1k 税后收益证据追踪。SPY put-credit validation and real-money control plane with broker-backed ledgers, deterministic risk gates, hybrid RAG, reconciliation, and $1k/mo after-tax evidence tracking.

RAG 管道的回归测试与配置扫描，附带统计学指标以判断变更是否真正带来改进。Regression testing and configuration sweeps for RAG pipelines, with the statistics to know whether a change actually helped.

面向 Node.js 与 TypeScript 的 AI firewall。阻止 prompt injection、音频幻觉与 RAG 数据爬取。零依赖。MIT 许可。AI firewall for Node.js & TypeScript. Stop prompt injection, audio hallucinations, and RAG data scraping. Zero dependencies. MIT

动态 README，包含 AI 生成的 SCP 基金会与 Wikipedia 随机文章摘要。A dynamic README with AI-generated summaries of random articles from the SCP Foundation and Wikipedia.

金融与生活交易学习 RAG 与 QuantConnect LEAN 回测工作流（FastAPI、Qdrant、pgvector、Docker）。금융·생활거래 학습 RAG와 QuantConnect LEAN 백테스트 워크플로우 (FastAPI, Qdrant, pgvector, Docker)

🔬 基于 AI 与研究论文对话，通过高级语义搜索与 RAG（检索增强生成）技术提取洞见与摘要。🔬 Chat with research papers using AI, extracting insights and summaries through advanced semantic search and Retrieval-Augmented Generation techniques.

支持从多源智能检索、筛选与总结科学论文，提升研究与报告生成效率Enable intelligent retrieval, filtering, and summarization of scientific papers from multiple sources for efficient research and report generation.

LOCAH.ai —— 基于 Wilfrid Laurier University 公开信息的助手。提供引用标注的回答、显式呈现的矛盾，以及 Laurier 信息在何处未能服务学生的证据。LOCAH.ai — an assistant over Wilfrid Laurier University's public information. Cited answers, surfaced contradictions, and evidence on where Laurier's information fails students.

使用本地 LLM 与私密法律文档对话，在自有硬件上获得带引用、可验证的答案。Chat with private legal documents using local LLMs. Get cited, verifiable answers on your own hardware.

用于拆解复杂技术文档的 RAG 应用A RAG app to breakdown complex technical documents

基于 LlamaIndex、Redis 与 PII 脱敏的 RAG 搜索引擎。RAG search engine using LlamaIndex, Redis, and PII masking.

使用先进的 RAG 系统发现你的下一部心仪动画，提供精准推荐与增强语义搜索。🎬 Discover your next favorite anime with this advanced Retrieval-Augmented Generation system, offering precise recommendations and enriched semantic search.

一个面向 PDF 文档问答的全栈 RAG 应用：上传 PDF，将其索引到本地向量库，然后基于页面级答案进行对话，并在内置阅读器中通过可点击引用跳转到对应页面。A full-stack retrieval-augmented generation (RAG) application for question answering over PDF documents. Upload a PDF, index it into a local vector store, then chat with page-grounded answers and clickable citations that jump to the right page in the built-in viewer.

基于 RAG 的文档问答机器人，可对任意 PDF/文本文件提问。使用本地 embeddings + Groq API。学习要点：embeddings、向量搜索、分块、RAG 流水线。Document Q&A bot using RAG (Retrieval-Augmented Generation). Ask questions about any PDF/text file. Uses local embeddings + Groq API. Learns: embeddings, vector search, chunking, RAG pipeline.

面向业务的 RAG | 要么有引用，要么不要 | 客服团队需要一份可核验的答案RAG for business | Citations or nothing | Support teams need an answer they can verify

RAG 真的物有所值吗？ragornot 在真实 AWS Lambda + Bedrock 后端上，通过四种检索模式（Flat/BM25、Hierarchical、LLM-only、RAG）运行相同查询，并测量延迟、质量、成本和碳排放——用数据帮你决定是否使用 RAG。静态 Next.js 部署于 GitHub Pages。Does RAG actually earn its cost? ragornot runs the same query through four retrieval modes (Flat/BM25, Hierarchical, LLM-only, RAG) against a live AWS Lambda + Bedrock backend and measures latency, quality, cost, and carbon — so you can decide RAG-or-not with data. Static Next.js on GitHub Pages.

在同一 chunk 集合上对比 Lexical / Vector / Graph RAG，提供确定性评估、自我修正的 LangGraph agent 循环、PII 治理与 RAG 就绪度分析器。Lexical vs Vector vs Graph RAG over one identical chunk set, with deterministic evaluation, a self-correcting LangGraph agent loop, PII governance, and a RAG-readiness analyzer.

📚 构建并评估 RAG 流水线，实现数据导入、嵌入、检索与问答，并提供准确性与相关性指标。📚 Build and evaluate RAG pipelines to ingest, embed, retrieve, and answer questions with metrics for accuracy and relevance.