576 个
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.
🧠 构建模拟人类记忆的 AI 认知架构，分离情景记忆与语义记忆，并保证多语言支持与数据完整性🧠 Build a cognitive architecture for AI that mimics human memory, separating episodic and semantic memory while ensuring multilingual support and data integrity.
基于 Microsoft Agent Framework (.NET) 的 LLM Agent 三层评估：运行时护栏、PR 闸口、校准过的评审者。该框架可自我评估。Three-tier evaluation for LLM agents on Microsoft Agent Framework (.NET): runtime guardrails, a PR gate, and calibrated judges. The framework evaluates itself.
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
面向 AI agent 与 agentic workflow 的开源 MLflow 插件：涵盖 prompt、tool、skill、MCP server、RAG 知识库、评估、部署、可观测性以及 Aria copilot。Open-source MLflow plugin for AI agents and agentic workflows: prompts, tools, skills, MCP servers, RAG knowledge bases, evaluation, deployment, observability, and Aria copilot.
软件工程作品集 | Web 开发 | 桌面应用开发Software Engineering Portfolio | Web Development | Desktop App Development
面向产品和 AI Agent 的自托管时序记忆平台。Self-hosted temporal memory platform for products and AI agents.
🛠️ 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.
通过一次 wrap() 调用为 LLM 流水线构建运行时可靠性守卫，可配置地防御常见生产故障Build runtime reliability guards for LLM pipelines with one wrap() call and configurable protection against common production failures
将 Spark/Hive SQL 转化为结构化、可追溯的事实，供 Agent、RAG 和 AI 知识库使用。Turn Spark/Hive SQL into structured, traceable facts for Agents, RAG, and AI knowledge bases.
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.
基于 LangGraph 的 Agent，可从 arXiv 自动撰写文献综述——每条引用均与源文本片段核对验证，检索策略由实测消融选定，而非凭猜测决定A LangGraph agent that writes literature reviews from arXiv — every citation validated against its source chunk, and the retrieval strategy chosen by measured ablation rather than guessed.
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.
Orchestra 的 AI 服务器——蓝图生成、任务分配、Clover 助手与知识图谱AI server for Orchestra- blueprint generation, task assignment, Clover assistant, and knowledge graph
AI 教材数字化与互动辅导平台：PDF/扫描教材经 MinerU OCR 结构化出题，文本 + 视觉双模型审校与确定性质量门禁；学生端七种题型互动、分层提示与错题多轮陪练闭环；FastAPI + PostgreSQL JSONB + React 19
一本关于生产级 RAG 与 agentic 系统的实用第一性原理手册，支持 RU/EN/SK，基于 Docusaurus 构建。A practical, first-principles handbook on production RAG & agentic systems — RU/EN/SK, built with Docusaurus.
GenAI 职业路线图 2026 🚀 | AI 岗位路径与技能指南GenAI Career Roadmap 2026 🚀 | AI Job Paths & Skills Guide
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.
织光 WeaveMind · 跑在你自己电脑上的多智能体研究团队：一句目标 → 规划/执行/评审/反思全流程可视化，交付全链可溯源报告；看得见、会记忆、会自我进化（Python + React）
🤖 DeepTutor 是一款 AI 驱动的学习助手，可根据个人需求定制教育内容，提供个性化资源与支持🤖 Enhance learning with DeepTutor, an AI-powered assistant that personalizes education to fit your needs. Discover tailored resources and support.
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.
AI 驱动的企业级 Agile OS，使用 Gemini 1.5 将原始客户音频与笔记即时转化为客户 pitch deck、PM epic、UI 规格与后端工程架构。An AI-powered Enterprise Agile OS that instantly translates raw client audio and notes into client pitch decks, PM epics, UI specs, and backend engineering architectures using Gemini 1.5.
面向本地优先应用与 AI Agent 的嵌入式双时态记忆引擎，采用混合检索——进程内运行，无需服务器。Embedded bitemporal memory engine with hybrid retrieval for local-first applications and AI agents — in-process, no server.
🚀 零配置将 LangChain Agent 暴露为服务；以简单包装把脚本变为 Web 服务，无需任何基础设施🚀 Expose your LangChain agents as services with zero configuration; turn scripts into web services using simple wrappers and no infrastructure setup.
🔍 在 Weaviate 混合检索中评测 embedding 模型，基于自有数据或 MTEB 数据集评估 MRR@K、Hit@K、延迟与内存占用🔍 Benchmark embedding models in hybrid search with Weaviate. Evaluate MRR@K, Hit@K, latency, and memory using your data or MTEB datasets.
🛠️ 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.
基于架构决策记录（ADR）理念的 Memory Framework，用于 AI agent（Claude Code、Codex、Antigravity……）之间的知识共享。支持 agent 安装，vector（Postgres + pgvector）+ graph（Neo4j）双骨干，通过本地 gateway 管理，支持多种本地 LLM 后端，具备完整 provenance、合并、遥测等能力。Memory Framework based on the idea of Architectural Decision Records, shared knowledge between AI agents (Claude Code, Codex, Antigravity ...). Agent installable, vector (Postgres + pgvector) + graph (Neo4j) backbone, managed through a local gateway, supporting multiple local LLM backends, with full provenance, consolidation, telemetry and more
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
构建 LongCat-Flash-Prover，利用 LongCat 模型进行快速定理证明与形式化推理。Build LongCat-Flash-Prover for fast theorem proving and formal reasoning with LongCat models
codegraph 是一个 local-first 的代码上下文引擎和面向代码仓库的 MCP server。它将源代码增量索引到轻量级 SQLite 图中，然后提供高信噪比的 tools，支持符号查找、调用图遍历、影响分析、相关测试发现和语义搜索。使用 Go 构建，单二进制工作流，专为……而设计。codegraph is a local-first code context engine and MCP server for repositories. It incrementally indexes source code into a lightweight SQLite graph, then exposes high-signal tools for symbol lookup, call graph traversal, impact analysis, related test discovery, and semantic search. Built in Go with a single-binary workflow, it is designed to work
使用 Next.js、React 和 TypeScript 构建并管理 AI 驱动的 workspace，支持自定义 UI，MIT 许可。Build and manage AI-driven workspaces using Next.js, React, and TypeScript with customizable UI and MIT licensing.
UMD 行星长期记忆算法、公式、基准、SDK 与可复现研究。UMD planetary long-term memory algorithms, formulas, benchmarks, SDKs, and reproducible research