仓库/Skill 库

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

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)

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.

🔬 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.

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)

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.

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.

为 MI Tech Arsenal 定制的基于 RAG 的 AI 助手，具备自动化 sitemap 索引、通过 ChromaDB 进行神经搜索，以及 Streamlit 到 Blogger 的无缝集成。A custom RAG-based AI assistant for MI Tech Arsenal. Features automated sitemap indexing, neural search via ChromaDB, and a seamless Streamlit-to-Blogger integration.

🛠️ 通过 ACG 增强 RAG 系统，依托可靠的外部知识提升准确性与事实一致性，减少 LLM 响应中的幻觉。🛠️ Enhance RAG systems with ACG to reduce hallucinations in LLM responses by improving accuracy and grounding in reliable external knowledge.

使用本地 RAG 系统增强知识库，借助混合搜索实现精准信息检索与最优结果🔍 Enhance your knowledge base with a local RAG system that leverages hybrid search for precise information retrieval and optimal results.

数据在被构建成有意义的东西之前只是噪声——将原始数据转化为真正可用的系统，涵盖欺诈检测、RAG pipeline、计算机视觉追踪器以及数据仓库等领域。FAST NUCES 数据科学本科生，在"搞坏东西"和"交付产品"之间反复横跳。持续构建，持续学习，欢迎合作和实习交流Data is noise until someone builds something meaningful out of it, turn raw data into systems that actually work, from fraud detection and rag pipelines to computer vision trackers and data warehouses. DS undergrad at FAST NUCES, somewhere between breaking things and shipping them. always building, always learning, open to collabs and interns

这是一个个人日志/日记/知识库，集成本地部署的聊天机器人，统一追踪我想要追踪的任何内容。This is a personal journal / diary / knowledge base integrating a locally served chatbot to cohesively track whatever I want it to track.

基于 FastAPI、LangChain、ChromaDB 和 Google Gemini API 构建的快速、轻量级 RAG 聊天机器人。A fast, lightweight Retrieval-Augmented Generation (RAG) chatbot built with FastAPI, LangChain, ChromaDB, and the Google Gemini API.

AI 会议智能工作区，支持视频转录、结构化洞察以及基于转录的 RAG 对话。AI meeting intelligence workspace for video transcription, structured insights, and transcript-grounded RAG conversations.

学术知识图谱系统——符号驱动的研究发现，配合轻量级向量检索。Academic knowledge graph system — symbol-driven research discovery with lightweight vector retrieval

可移植契约与 local-first 运行时，覆盖规范数据、检索、持久执行及面向 agent 的 AI。Portable contracts and local-first runtimes for canonical data, retrieval, durable execution, and agent-facing AI.