239 个 · RAG 检索增强
🤖 自主研究论文分析器——下载 arXiv 论文，通过 HuggingFace Transformers 运行本地 LLM 分析，并将 AI 生成的文献综述发布至 GitHub Pages。🤖 Autonomous Research Paper Analyzer — Downloads arXiv papers, runs local LLM analysis via HuggingFace Transformers, and publishes AI-generated literature reviews to GitHub Pages
[进行中 / 实验性] 关于免疫检查点抑制剂耐药性的活态文献综述。[WIP / experiment] Living literature review of resistance to immune checkpoint inhibitors
AI Research Wiki 2026：通过深度引用综合自动构建知识库AI Research Wiki 2026: Auto-Building Knowledge Base with Deep Citation Syntheses
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 FastAPI + LangChain + RAG 的 AI 智能对话助手，支持多轮对话记忆、图片分析、流式回复、知识库 RAG 检索、上传定义知识库。
搜索 Google Scholar，使用 OpenAlex 增强论文信息，并在本地构建结构化文献综述Search Google Scholar, enrich papers with OpenAlex, and build structured literature reviews locally.
基于 LLM 的科研 Wiki，支持 Zotero 文献导入、MinerU PDF 转换以及知识图谱辅助的科学写作A llm research wiki for Zotero-backed literature ingestion, MinerU PDF conversion, and knowledge-graph-assisted scientific writing.
自托管、100% 本地的 AI 平台——在一个 Docker 栈中集成 LLM 推理、RAG 与知识图谱。无需 API key。Self-hosted, 100% local AI platform — LLM inference, RAG, and knowledge graphs in one Docker stack. No API keys.
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.
本仓库包含一个纯 Python 工作流，用于消费级可穿戴设备系统综述论文中的两项可复现任务。This repository contains a plain-Python workflow for two reproducible tasks used in a consumer wearable systematic review paper.
证据把关的研究生论文写作工作台：涵盖章节规划、文献综述、引文核验、DOCX 编辑、配图、作者署名/AIGC 感知审阅与最终 QAEvidence-gated graduate thesis writing workbench: chapter planning, literature review, citation verification, DOCX editing, figures, authorship/AIGC-aware review, and final QA.
Rust 可嵌入的混合搜索原语：BM25、HNSW、reciprocal-rank fusion、UTF-8 安全的 chunking，零依赖。Embeddable hybrid search primitives for Rust: BM25, HNSW, reciprocal-rank fusion, UTF-8-safe chunking. Zero dependencies.
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
语义优先的 Codex skill，用于可审计的 BERTopic 调参、主题解释、模型比较与可复现研究交付。Semantic-first Codex skill for auditable BERTopic tuning, topic interpretation, model comparison, and reproducible research delivery.
🛠️ 使用 Haystack 轻松构建强大搜索系统，该框架用于开发端到端问答与搜索应用。🛠️ Build powerful search systems effortlessly with Haystack, a framework for developing end-to-end question answering and search applications.
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.
Review Hub - 用于系统文献综述的 FastAPI 后端Review Hub - FastAPI Backend for Systematic Literature Review
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.
AI 辅助的文献综述与综合工作流。AI-assisted literature review and synthesis workflows
本地优先的桌面端研究与引用管理器，具备 AI 辅助引用建议、语义文献库搜索、PDF 批注以及 Microsoft Word 插件。Local-first desktop research & citation manager with AI-assisted citation suggestions, semantic library search, PDF annotation, and a Microsoft Word add-in.
📚 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.
Daily Paper Update 是一个精选仓库，提供人工智能、机器学习与计算机科学多领域近期研究论文的结构化技术摘要。目标是加速文献综述，为研究人员、工程师与学生提供便利。Daily Paper Update is a curated repository that provides structured and technical summaries of recent research papers across multiple domains in Artificial Intelligence, Machine Learning, and Computer Science. The goal is to accelerate literature review, facilitate researchers, engineers, and students.
从首次检索到完成的证据表，一站式文献综述。面向需要可复现系统综述的团队，以及在同一机制上完成论文检索的学生。A literature review, from the first search to the finished evidence table. For teams running a systematic review that has to be reproducible, and for students running a thesis search on the same machinery.
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.
跨平台桌面端（EXE）学术论文聚合客户端，支持自定义 JSON 数据源规则、多标签页隔离搜索、内置 PDF 阅读与翻译、批量下载，灵感来自 Legado。A cross-platform desktop client (EXE) for aggregating academic papers. Supports custom JSON data-source rules, multi-tab isolated search, built-in PDF reader with translation, and batch downloads. Inspired by Legado.
为 Ramone 提供的容器化本地 RAG 服务，使用 atlas-corpus 检索、ChromaDB 会话记忆和 Ollama 生成。Containerised local RAG service for Ramone using atlas-corpus retrieval, ChromaDB session memory and Ollama generation.