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。
仓库/Skill 库
294 个 · RAG 检索增强
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.
AI 驱动的学术研究助手,使用 RAG 与 LLM 进行论文分析、生成文献综述、识别研究空白、产出深度解读,并生成带来源引用的演示文稿。AI-powered academic research assistant that uses RAG and LLMs to analyze research papers, generate literature reviews, identify research gaps, create deep explanations, and generate presentations with source citations.
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.
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.
基于关键词反馈的每日 arXiv 论文推荐Daily arXiv paper recommendations with keyword feedback
面向 Zotero 的可解释论文推荐插件,支持可编辑的兴趣画像、AI 摘要与一键导入。An explainable paper recommendation plugin for Zotero, with editable interest profiles, AI summaries, and one-click imports.
🛠️ 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.
基于 LLM 的自优化 markdown 维基构建工具,面向科研场景A self-refining LLM-powered markdown wiki builder for scientific research
Semantic Search for Pi 2026:本地知识库与 AI 工具。Semantic Search for Pi 2026: Local Knowledge Base & AI Tool
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.
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.
PaperClaw 是面向公共管理学者的 AI 研究助手,可帮助用户检索相关文献、定位文本关键概念、获取全文证据、回答研究问题并提出潜在想法,覆盖从检索到洞察的全流程。PaperClaw is an AI research assistant for public administration scholars. It helps users find relevant literature, locate key concepts in texts, retrieve full-text evidence, answer research questions, and suggest potential ideas, improving efficiency from search to insight.
用于文献综述的本地优先 Chrome 扩展:保存文章、生成 AI 摘要、与 Gemini 进行有依据的对话、记录丰富笔记,并导出带样式的 Excel 文件。A local-first Chrome extension for literature reviews: save articles, AI summaries, grounded chat with Gemini, rich notes, and styled Excel exports.
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.