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) ## Introduction 🖥️ Official site: [https://revornix.com](https://revornix.com) 🚀 Development plan: [RoadMap](https://huaqinda.notion.site/RoadMap-224bbdbfa03380fabd7beda0b0337ea3) ❤️ Join the 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) One-sentence description: Revornix is a highly customizable information and document management tool for the AI era. It helps you easily consolidate any information source, generate rich reports with images and podcast audio, and then notify you. **Some UI**    Note: **The trending headlines feature comes from the [DailyHotApi](https://github.com/imsyy/DailyHotApi) project**  ## Features - Input sources stay flexible: Depending on different parsing engines, we currently support web pages, PDF, Word, Excel, PPT, manually entered text, APIs, PIP libraries, Node.js libraries, and more. Additional third-party platforms will be integrated in the future. - Advanced text conversion: Powered by cutting-edge text transformation technologies such as MinerU and Jina, delivering industry-leading conversion quality with support for custom conversion engines. - Vectorized storage: Revornix breaks all input content into chunks, converts them into vectors, and stores them in a vector database to make retrieval easy and to provide richer context to AI models. - Knowledge graphs: Based on personalized GraphRag, Revornix analyzes information sources and generates knowledge graphs, greatly improving context accuracy while giving you a satisfying big-picture view. - Integrated sharing: Built-in sharing lets users share specific knowledge bases for co-creation, push their knowledge bases to the public for search engine indexing, or use other public knowledge bases. Knowledge sharing is a core focus for Revornix. - Local-first & open source: The code is open source, and with local deployment all data is stored locally, so you don’t need to worry about data leakage. - Intelligent assistant & built-in MCP: Revornix includes both an MCP client and server, letting you provide MCP services to third parties or let the AI assistant call local or external MCP services. - Seamless model integration: Models are not fixed. Any model compatible with the OpenAI API can be used, and different features can use independent models. - Multilingual & responsive: Whether you use Chinese or English, on mobile or desktop, you’ll get a great experience. - Automatic podcasts: Enable automatic podcast generation/updates for documents/columns. Revornix will automatically generate podcast audio files, offering a more convenient way to consume information. - Trending integration: Revornix embeds the [DailyHotApi](https://github.com/imsyy/DailyHotApi) service so you can view trending lists from major platforms in one place. - Illustration generation: Leveraging powerful image-generation models such as Banana Pro, Revornix can generate high-quality images and embed them into documents/columns. ## Quick Start > [!NOTE] > We strongly recommend using conda to create different Python virtual environments for each service because Python dependencies may conflict across services. If you prefer another virtual environment manager, feel free to use it. ### Clone the repository locally ```shell git clone git@github.com:Qingyon-AI/Revornix.git cd Revornix ``` ### Install and start the base services > [!NOTE] > If you haven’t installed postgres, redis, neo4j, minio, milvus, rsshub, or browserless, you need to install them manually and configure environment variables based on your needs. Refer to each service’s requirements and the [Revornix environment variables](https://revornix.com/docs/environment). > > To avoid that busywork, we provide `docker-compose-local.yaml` and `.env.local.example`. You can use `docker-compose-local.yaml` to pull these services and `.env.local.example` as the environment configuration. > [!WARNING] > If some of these services are already installed locally, disable the corresponding services in `docker-compose-local.yaml` according to your setup to prevent unexpected issues. Copy the example file provided, then adjust fields as needed with the [Revornix environment variables](https://revornix.com/docs/environment). If you have no special requirements, you typically only need to change the `OAUTH_SECRET_KEY` field. ```shell cp .env.local.example .env.local ``` Start postgres, redis, neo4j, minio, milvus, rsshub, and browserless. ```shell docker compose -f ./docker-compose-local.yaml --env-file .env.local up -d ``` ### Environment variables for each microservice ```shell cp ./web/.env.example ./web/.env cp ./api/.env.example ./api/.env cp ./celery-worker/.env.example ./celery-worker/.env ``` Configure the corresponding environment variable files. See the [Revornix environment variables](https://revornix.com/docs/environment) for details. > [!WARNING] > If you use manual deployment, the `SECRET_KEY` must stay consistent across services; otherwise, user authentication won’t work across services. ### Initialize the necessary data ```shell cd api python -m data.milvus.create python -m data.sql.create ``` ### Start the core backend service ```shell cd api conda create -n api python=3.11 -y pip install -r ./requirements.txt fastapi run --port 8001 ``` ### Start the trending aggregation service ```shell cd hot-news pnpm build pnpm start ``` ### Start the Celery task queue ```shell 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=10 --loglevel=info -E ``` ### Start the frontend service ```shell cd web pnpm build pnpm start ``` After all services are running, visit http://localhost:3000 to view the frontend. ## Contributors <a href="https://github.com/Qingyon-AI/Revornix/graphs/contributors"> <img src="https://contrib.rocks/image?repo=Qingyon-AI/Revornix" /> </a>