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 and Next.js. Powered by OpenSearch, Langflow, and Docling.

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.

   git clone https://github.com/langflow-ai/openrag.git
   cd openrag

2. To start the TUI, from the repository root, run:

   # Install dependencies first
   uv sync
   
   # Launch the TUI
   uv run openrag

   The TUI opens and guides you through OpenRAG setup.

For the full TUI installation guide, see TUI.

Docker installation

If you prefer to use Docker to run OpenRAG, the repository includes two Docker Compose .yml files. They deploy the same applications and containers locally, but to different environments.

• 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 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 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 install OpenRAG with Docker:

1. Clone the OpenRAG repository.

   git clone https://github.com/langflow-ai/openrag.git
   cd openrag

2. Install dependencies.

   uv sync

3. Start docling serve on the host machine.

   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:

   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:

   docker compose build
   docker compose up -d

   For environments without GPU support, run:

   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.

   To stop docling serve, run:

   uv run python scripts/docling_ctl.py stop

For more information, see Install with Docker.

Troubleshooting

For common issues and fixes, see Troubleshoot.

Development

For developers wanting to contribute to OpenRAG or set up a development environment, see CONTRIBUTING.md.