Revornix

       [](https://deepwiki.com/Qingyon-AI/Revornix) [](https://mseep.ai/app/qingyon-ai-revornix) [English](./README.md) | [中文文档](./README_zh.md) | [日本語ドキュメント](./README_jp.md) > 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. ## Links - Official site: [https://revornix.com](https://revornix.com) - Environment docs: [https://revornix.com/docs/environment](https://revornix.com/docs/environment) - Roadmap: [RoadMap](https://huaqinda.notion.site/RoadMap-224bbdbfa03380fabd7beda0b0337ea3) - Community: [Discord](https://discord.com/invite/3XZfz84aPN) | [WeChat](https://github.com/Qingyon-AI/Revornix/discussions/1#discussioncomment-13638435) | [QQ](https://github.com/Qingyon-AI/Revornix/discussions/1#discussioncomment-13638435) ## 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 ```text Revornix/ ├── web/ # Next.js frontend (user interaction + dashboard) ├── gateway/ # Go public-entry gateway (routing, anti-scraping, upstream failover) ├── 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. - Layered request protection: gateway-level anti-scraping and API-side rate limiting for high-risk public endpoints. ## Some UI        Note: The trending headlines feature is based on [DailyHotApi](https://github.com/imsyy/DailyHotApi).  ## Quick Start > [!NOTE] > We recommend creating isolated Python environments per service (for example with conda), because dependencies across services can conflict. ### 1) Clone repository ```shell git clone git@github.com: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. ```shell 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 ```shell cp ./web/.env.example ./web/.env cp ./gateway/.env.example ./gateway/.env cp ./api/.env.example ./api/.env cp ./celery-worker/.env.example ./celery-worker/.env ``` Configure env values based on [environment docs](https://revornix.com/docs/environment). > [!WARNING] > For manual deployment, keep `OAUTH_SECRET_KEY` consistent across services, or cross-service authentication will fail. ### 4) Initialize required data ```shell cd api python -m data.milvus.create python -m data.sql.create ``` ### 5) Run API service ```shell cd api conda create -n api python=3.11 -y pip install -r ./requirements.txt fastapi run --port 8001 ``` ### 6) Run gateway service ```shell cd gateway go run ./cmd/gateway ``` The gateway is optional for local development, but recommended for production. It handles public routing, failover, and the first layer of anti-scraping protection before traffic reaches `api/`. ### 7) Run trending aggregation service ```shell cd hot-news pnpm build pnpm start ``` ### 8) Run Celery worker ```shell cd celery-worker conda create -n celery-worker python=3.11 -y pip install -r ./requirements.txt playwright install ./start-worker.sh ``` ### 9) Run frontend ```shell cd web pnpm build pnpm start ``` After all services are running, open http://localhost:3000. ## Contributors <a href="https://github.com/Qingyon-AI/Revornix/graphs/contributors"> <img src="https://contrib.rocks/image?repo=Qingyon-AI/Revornix" /> </a>