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   |   Python package   |   Docker or Podman   |   Development   |   Troubleshooting

Quickstart

To run OpenRAG without creating or modifying any project files, use uvx:

uvx openrag

This command runs OpenRAG without installing it to your project or globally.

To run a specific version of OpenRAG, run uvx --from openrag==VERSION openrag.

Install Python package

To add the OpenRAG Python package to a Python project, use uv:

1. Create a new project with a virtual environment using uv init:

   uv init YOUR_PROJECT_NAME
   cd YOUR_PROJECT_NAME
   

   The (venv) prompt doesn't change, but uv commands will automatically use the project's virtual environment. For more information on virtual environments, see the uv documentation.

2. Add OpenRAG to your project:

   uv add openrag
   

   To add a specific version of OpenRAG, run uv add openrag==VERSION.

3. Start the OpenRAG terminal user interface (TUI):

   uv run openrag
   

4. Continue with the Quickstart.

For all installation options, see the OpenRAG installation guide.

Docker or Podman installation

By default, OpenRAG automatically starts the required containers and helps you manage them. To install OpenRAG with self-managed containers, see the OpenRAG installation guide.

Development

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

Troubleshooting

For common issues and fixes, see Troubleshoot OpenRAG.