RAG・知識ベース
MinerU
MinerU — turns PDFs and Office documents into LLM-ready Markdown and JSON for agents and RAG pipelines, Apache-2.0 plus terms
★ 82.5k スターApache-2.0 + terms
ガイドを見る →llm-appは、pathwaycomが公開しているオープンソースのRAG・知識ベースプロジェクトです（GitHub: pathwaycom/llm-app）。GitHubスターは58,820、フォークは1,504で、MITライセンスの下で公開されています。GitHubでの最終更新は2026年7月5日です。
Ready-to-run templates for RAG, AI pipelines and enterprise search that stay in sync with live data
Decide what you want from llm-app and check the README and requirements to confirm it fits your setup and Jupyter Notebook environment.
Clone the repository from GitHub (github.com/pathwaycom/llm-app) or use the documented install channel if you do not need the source.
Follow the README quickstart to install dependencies and set any required configuration or credentials.
Work through the README's first example end to end, then adapt it to your own project or data.
Ready-to-run templates for RAG, AI pipelines and enterprise search.
Always in sync with SharePoint, Google Drive, S3, Kafka, PostgreSQL and real-time APIs.
Templates are Docker-friendly for local or cloud deployment.
Built on Pathway's streaming data engine for real-time updates.
Maintains a vector index over your connected sources for retrieval.
Templates address LLM prompting and security concerns for enterprise data.
Start by getting the source and entering the project directory.
git clone https://github.com/pathwaycom/llm-app.git
cd llm-appClone the repo and choose a template folder for your data source and use case.
# see README for the exact command — Getting started sectionStart the template with Docker and connect your data source credentials.
docker compose up # see README for the exact command per templateTwo smaller projects share this shelf's knowledge brief: AppFlowy-Web is the open-source AI workspace for projects, wikis and team docs, and Compiiile renders and deploys a whole folder of markdown as a site in one move. Also on this shelf: paper-qa — PaperQA2 — high-accuracy RAG for answering questions from scientific papers, with citations you can check.
It is maintained by pathwaycom and contributors on GitHub, where the repository github.com/pathwaycom/llm-app is the canonical home for code, issues and releases.
Yes — it is open source under the MIT license. Read the license in the repository for what is permitted, including any conditions on redistribution or commercial use.
Yes — templates call an LLM provider, so you need your own API key (for example OpenAI), plus source-system access.
Compared with LangChain with a static index: llm-app templates keep the index continuously in sync with live sources via Pathway, instead of one-off ingestion.
Clone github.com/pathwaycom/llm-app, follow the README quickstart to install it, and run the documented first example before adapting it to your own work.
MinerU — turns PDFs and Office documents into LLM-ready Markdown and JSON for agents and RAG pipelines, Apache-2.0 plus terms
Unified framework for enterprise RAG pipelines with small, specialized models - local, private and secure.
Free rag & knowledge tool that enterprise-ready vector database toolkit for building s.
Lightweight OCR toolkit turning PDFs and images into structured, LLM-ready data in 100+ languages
Federated AI search and RAG across 100+ apps — answers with sources, without moving your data
AI-native data platform: 60+ connectors, ETL, scheduling, and RAG search.
LibreAIカタログに掲載されたRAG・知識ベースプロジェクトの中で最もスターの多いもの — llm-appが合わなかった場合に検討したい、現実的な候補リストです。
Build Agentic workflows, RAG pipelines, with rich AI model and tool support on one collaborative workspace. Deploy on cloud, VPC, or…
RAGFlow is a leading open-source Retrieval-Augmented Generation (RAG) engine that fuses cutting-edge RAG with Agent capabilities to create a superior context…
Lightweight OCR toolkit turning PDFs and images into structured, LLM-ready data in 100+ languages
MinerU — turns PDFs and Office documents into LLM-ready Markdown and JSON for agents and RAG pipelines, Apache-2.0 plus terms
Stop renting your intelligence. Own it with AnythingLLM. Everything you need for a powerful local-first agent experience
カタログの中でllm-appとスター数が近いRAG・知識ベースプロジェクト — 前後の棚もそのまま見て回れます。
Stop renting your intelligence. Own it with AnythingLLM. Everything you need for a powerful local-first agent experience
Complete API layer for private AI apps on local models: RAG, skills, tools, MCP and text-to-SQL
MinerU — turns PDFs and Office documents into LLM-ready Markdown and JSON for agents and RAG pipelines, Apache-2.0 plus terms
Meta's library for efficient similarity search and clustering of dense vectors, in C++ and Python
Lightweight OCR toolkit turning PDFs and images into structured, LLM-ready data in 100+ languages
Marker — converts PDFs to Markdown and JSON quickly and accurately, with layout, tables and math intact, Apache-2.0