RAG・知識ベース
PageIndex
PageIndex: vectorless, reasoning-based RAG that indexes documents like a human expert would
★ 39.0k スターMIT
ガイドを見る →ragappは、ragapp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: ragapp/ragapp）。GitHubスターは4,446、フォークは471で、Apache-2.0ライセンスの下で公開されています。GitHubでの最終更新は2025年1月22日です。
RAGApp: deploy a RAG app over your documents with Docker — configure it like a custom GPT
Collect the documents or sources you want the app to answer questions over.
Start RAGApp with a single docker run command.
Open the admin page to connect your data and choose a model — OpenAI, Gemini, or local Ollama.
Use the chat UI at the root URL, and put the app behind your API gateway if exposing it publicly.
A single `docker run` starts the whole app on port 8000.
Set up data sources and models from the browser, like configuring a custom GPT.
A user-facing chat interface at / and interactive API documentation at /docs.
Use OpenAI or Gemini with your key, or run fully local models through Ollama.
Provided Docker Compose setups add Ollama + Qdrant, or a management UI to run multiple RAG apps.
Retrieval and indexing ride on the LlamaIndex framework's data connectors and pipelines.
Clone the project and enter its directory (for development or Compose deployments).
git clone https://github.com/ragapp/ragapp.git
cd ragappStart RAGApp with Docker, publishing port 8000.
docker run -p 8000:8000 ragapp/ragappOpen the admin UI and connect your data and model.
# open http://localhost:8000/admin — chat UI at http://localhost:8000/The ragapp project team; it is built on top of LlamaIndex.
Yes, Apache-2.0 licensed. Hosted model usage (OpenAI/Gemini) bills to your own keys; local Ollama models avoid that.
Only if you choose hosted models. With Ollama configured, the app can run entirely on local models.
RAGApp packages the LlamaIndex plumbing into a deployable app with admin and chat UIs, trading deep customization for minutes-to-working setup.
Run `docker run -p 8000:8000 ragapp/ragapp`, then configure data and models at http://localhost:8000/admin.
PageIndex: vectorless, reasoning-based RAG that indexes documents like a human expert would
Knowhere turns documents into structured, hierarchy-native chunks ready for agents and RAG.
The smallest vector index in the world: private RAG over everything on your laptop with 97% less storage.
A private AI platform for agents, assistants and enterprise search over your documents
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
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 — ragapp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ragapp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。
AI-native database for hybrid search across dense, sparse, and tensor vectors plus full text
Ragent (容纳智能体): a production-grade Java Agentic RAG platform with hybrid retrieval and an admin console
OpenKB compiles your documents into a persistent, interlinked wiki knowledge base — no vector DB
nano-graphrag: a small, hackable GraphRAG implementation in about 1,100 lines — no Microsoft GraphRAG bulk
An agent that finds anything on your computer — federated search over your files, in place.
The open-source, permission-aware context layer that lets agents search all your company knowledge.