# OpenRAG

OpenRAG is a comprehensive Retrieval-Augmented Generation platform that enables intelligent document search and AI-powered conversations. Users can upload, process, and query documents through a chat interface backed by large language models and semantic search capabilities. The system utilizes Langflow for document ingestion, retrieval workflows, and intelligent nudges, providing a seamless RAG experience. Built with Starlette, Next.js, OpenSearch, and Langflow integration.

Quickstart | TUI Interface | Docker Deployment | Development | Troubleshooting

## Quickstart Use the OpenRAG Terminal User Interface (TUI) to manage your OpenRAG installation without complex command-line operations. To launch OpenRAG with the TUI, do the following: 1. Clone the OpenRAG repository. ```bash git clone https://github.com/langflow-ai/openrag.git cd openrag ``` 2. To start the TUI, from the repository root, run: ```bash # Install dependencies first uv sync # Launch the TUI uv run openrag ``` The TUI opens and guides you through OpenRAG setup. For the full TUI guide, see [TUI](docs/docs/get-started/tui.mdx). ## Docker Deployment If you prefer to use Docker to run OpenRAG, the repository includes two Docker Compose `.yml` files. They deploy the same applications and containers, but to different environments. - [`docker-compose.yml`](https://github.com/langflow-ai/openrag/blob/main/docker-compose.yml) is an OpenRAG deployment for environments with GPU support. GPU support requires an NVIDIA GPU with CUDA support and compatible NVIDIA drivers installed on the OpenRAG host machine. - [`docker-compose-cpu.yml`](https://github.com/langflow-ai/openrag/blob/main/docker-compose-cpu.yml) is a CPU-only version of OpenRAG for systems without GPU support. Use this Docker compose file for environments where GPU drivers aren't available. Both Docker deployments depend on `docling serve` to be running on port `5001` on the host machine. This enables [Mac MLX](https://opensource.apple.com/projects/mlx/) support for document processing. Installing OpenRAG with the TUI starts `docling serve` automatically, but for a Docker deployment you must manually start the `docling serve` process. To deploy OpenRAG with Docker: 1. Clone the OpenRAG repository. ```bash git clone https://github.com/langflow-ai/openrag.git cd openrag ``` 2. Install dependencies. ```bash uv sync ``` 3. Start `docling serve` on the host machine. ```bash uv run python scripts/docling_ctl.py start --port 5001 ``` 4. Confirm `docling serve` is running. ``` uv run python scripts/docling_ctl.py status ``` Successful result: ```bash Status: running Endpoint: http://127.0.0.1:5001 Docs: http://127.0.0.1:5001/docs PID: 27746 ``` 5. Build and start all services. For the GPU-accelerated deployment, run: ```bash docker compose build docker compose up -d ``` For environments without GPU support, run: ```bash docker compose -f docker-compose-cpu.yml up -d ``` The OpenRAG Docker Compose file starts five containers: | Container Name | Default Address | Purpose | |---|---|---| | OpenRAG Backend | http://localhost:8000 | FastAPI server and core functionality. | | OpenRAG Frontend | http://localhost:3000 | React web interface for users. | | Langflow | http://localhost:7860 | AI workflow engine and flow management. | | OpenSearch | http://localhost:9200 | Vector database for document storage. | | OpenSearch Dashboards | http://localhost:5601 | Database administration interface. | 6. Access the OpenRAG application at `http://localhost:3000` and continue with the [Quickstart](docs/docs/get-started/quickstart.mdx). To stop `docling serve`, run: ```bash uv run python scripts/docling_ctl.py stop ``` For more information, see [Deploy with Docker](docs/docs/get-started/docker.mdx). ## Troubleshooting For common issues and fixes, see [Troubleshoot](docs/docs/support/troubleshoot.mdx). ## Development For developers wanting to contribute to OpenRAG or set up a development environment, see [CONTRIBUTING.md](CONTRIBUTING.md).