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Revornix

mcp
SUMMARY

Revornix is an open-source, local-first AI information/markdown workspace. It helps you collect fragmented inputs, turn them into structured knowledge, generate reports with images and podcast audio, and deliver the output through automated notifications.

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Reject FOMO! When facing the information stream, be lazy, leave the rest to AI!

拒绝 FOMO！面对信息流，做个懒人，剩下的，交给 AI！

Revornix is an open-source, local-first AI information workspace. It helps you collect fragmented inputs, turn them into structured knowledge, generate reports with images and podcast audio, and deliver the output through automated notifications.

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Why Revornix

  • One pipeline for noisy information: from ingestion to summary, graph, podcast, and notification.
  • Built for AI retrieval quality: chunking + vector storage + personalized GraphRAG.
  • Open and controllable: self-host locally and keep your data under your own infra.
  • Model-flexible: any provider compatible with the OpenAI API can be wired in.
  • Collaboration-ready: share private/public knowledge sections and co-create with others.

How It Works

  1. Collect: web pages, PDF, Word, Excel, PPT, text, APIs, library docs, and more.
  2. Understand: parse and normalize with pluggable converters (MinerU, Jina, custom engines).
  3. Organize: store vectors, build graph context, and keep content query-ready.
  4. Deliver: generate rich documents, add illustrations/podcasts, and push notifications.

Project Structure

Revornix/
├── web/                  # Next.js frontend (user interaction + dashboard)
├── api/                  # FastAPI core backend (auth, documents, sections, AI APIs)
├── celery-worker/        # Async workflows (embedding, summary, graph, podcast, notifications)
├── hot-news/             # Trending aggregation service (based on DailyHotApi)
└── docker-compose-local.yaml # Local dependency bootstrap

Core Capabilities

  • Flexible ingestion: multi-format parsing with customizable engines.
  • Advanced transformation: strong markdown/content conversion pipelines.
  • Vector retrieval: chunk-to-vector storage for semantic search and AI context.
  • Graph reasoning: personalized GraphRAG for better context precision.
  • Built-in MCP: both MCP client and MCP server are supported.
  • Auto podcast: generate and update podcast audio for documents/sections.
  • Illustration generation: generate and embed AI images into content.
  • Trending in one place: major platform hot lists via integrated DailyHotApi.
  • Responsive and multilingual: available on mobile/desktop with multi-language support.

Some UI

Dashboard

Revornix-AI

Document

Knowledge Graph

Section

Note: The trending headlines feature is based on DailyHotApi.

Hot-News

Quick Start

[!NOTE]
We recommend creating isolated Python environments per service (for example with conda), because dependencies across services can conflict.

1) Clone repository

git clone [email protected]:Qingyon-AI/Revornix.git
cd Revornix

2) Start base dependencies

[!NOTE]
If you already have postgres, redis, neo4j, minio, and milvus installed, you can reuse them. Otherwise use docker-compose-local.yaml with .env.local.example.

[!WARNING]
If some dependencies are already running on your machine, disable the corresponding services in docker-compose-local.yaml to avoid conflicts.

cp .env.local.example .env.local
docker compose -f ./docker-compose-local.yaml --env-file .env.local up -d

3) Configure env files for microservices

cp ./web/.env.example ./web/.env
cp ./api/.env.example ./api/.env
cp ./celery-worker/.env.example ./celery-worker/.env

Configure env values based on environment docs.

[!WARNING]
For manual deployment, keep OAUTH_SECRET_KEY consistent across services, or cross-service authentication will fail.

4) Initialize required data

cd api
python -m data.milvus.create
python -m data.sql.create

5) Run API service

cd api
conda create -n api python=3.11 -y
pip install -r ./requirements.txt
fastapi run --port 8001

6) Run trending aggregation service

cd hot-news
pnpm build
pnpm start

7) Run Celery worker

cd celery-worker
conda create -n celery-worker python=3.11 -y
pip install -r ./requirements.txt
playwright install
celery -A common.celery.app worker --pool=threads --concurrency=20 --loglevel=info -E

8) Run frontend

cd web
pnpm build
pnpm start

After all services are running, open http://localhost:3000.

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