AI-ML-Roadmap-from-scratch
Become skilled in Artificial Intelligence, Machine Learning, Generative AI, Deep Learning, Data Science, Natural Language Processing, Reinforcement Learning and more with this complete 0 to 100 repository.
Retrieval-augmented generation (RAG) makes a language model answer from your own documents. The text is split into chunks, turned into vectors and stored in an index, and the most relevant chunks are added to the prompt when a question comes in. The projects here cover the whole pipeline or one part of it: document parsing, vector search, or a complete question-answering app.
Answer quality in RAG depends mostly on retrieval, not on the model. Compare how each project handles difficult documents such as PDFs, tables and scans, whether it supports hybrid search that combines keywords with vectors and re-ranking, and whether it shows which sources an answer came from.
Become skilled in Artificial Intelligence, Machine Learning, Generative AI, Deep Learning, Data Science, Natural Language Processing, Reinforcement Learning and more with this complete 0 to 100 repository.
Generative AI reference workflows optimized for accelerated infrastructure and microservice architecture.
A modular Agentic RAG built with LangGraph — learn Retrieval-Augmented Generation Agents in minutes.
Everything you need to know to build your own RAG application
Vision infrastructure to turn complex documents into RAG/LLM-ready data
Harness LLMs with Multi-Agent Programming
企业级 Agentic RAG 智能体 - 全链路覆盖文档解析、多路检索、意图识别、问题重写、会话记忆、MCP 工具调用与深度思考。面向真实业务场景,从 0 到 1 完整工程实现。
[NeurIPS'24] HippoRAG is a novel RAG framework inspired by human long-term memory that enables LLMs to continuously integrate knowledge across external documents. RAG + Knowledge Graphs + Personalized PageRank.
A minimal Python framework for building custom AI inference servers with full control over logic, batching, and scaling.
Hypergraph is more powerful. Transform unstructured text into structured knowledge with LLMs. Graphs, hypergraphs, and spatio-temporal extractions — with one command.
Easiest and laziest way for building multi-agent LLMs applications.
Biomni: a general-purpose biomedical AI agent
Portable AI agent runtime for the JVM. One @Agent class runs on Spring AI, LangChain4j, Anthropic, or 9 more behind one SPI. Token streaming, tool calls, human approvals, and governance over WebSocket, SSE, gRPC, or WebTransport/HTTP3. Speaks MCP, A2A, and AG-UI.
A C#/.NET library to run LLM (🦙LLaMA/LLaVA) on your local device efficiently.
SimpleMem: Efficient Lifelong Memory for LLM Agents — Text & Multimodal
PipesHub is an open-source platform for securely connecting enterprise knowledge to AI. Give AI agents trusted context and your team permission-aware search with verified citations across your business systems.
2026 年最新的免费编程资源大全,持续更新!🔥 覆盖各种语言和方向(Java / Python / C++ / JavaScript / TypeScript / Golang / 前端 / 后端 / AI大模型应用开发 / AI Agent开发等)的学习路线、零基础入门教程、项目实战教程、经典编程书籍、面试题合集、求职经验分享、简历模板、开源项目推荐、开发工具推荐、实用技术资源等,对程序员和计算机专业学生非常有帮助!⭐️ 学编程,先收藏这个仓库!
Open-source multimodal retrieval engine (Morphik Core). By Morphik — AI back office for skilled nursing & senior living (morphik.ai).
A collection of scientific methods, processes, algorithms, and systems to build stories & models.
A curated collection of practical AI projects implementing OCR systems, RAG, AI agents, and other AI use cases.
A personal knowledge base that builds and maintains itself. Drop in sources — Claude (or Codex/Gemini) reads them, extracts knowledge, and maintains a persistent interlinked wiki. Works with Claude Code, Codex, OpenCode, Gemini CLI. No API key needed.
ReMe: Memory Management Kit for Agents - Remember Me, Refine Me.
AnyCrawl 🚀: A Node.js/TypeScript crawler that turns websites into LLM-ready data and extracts structured SERP results from Google/Bing/Baidu/etc. Native multi-threading for bulk processing.
总结Prompt&LLM论文,开源数据&模型,AIGC应用