# 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](https://github.com/Kludex/starlette) and [Next.js](https://github.com/vercel/next.js). Powered by [OpenSearch](https://github.com/opensearch-project/OpenSearch), [Langflow](https://github.com/langflow-ai/langflow), and [Docling](https://github.com/docling-project/docling).

Quickstart | Python package | Docker or Podman | Development | Troubleshooting

## Quickstart The recommended way to get started with OpenRAG is through the official documentation quickstart guide, which contains up-to-date installation steps, prerequisites, and usage examples: 👉 https://docs.openr.ag/quickstart The GitHub repository focuses on the source code. Please refer to the documentation for setup and usage instructions. ## Install Python package OpenRAG can be installed as a Python package. For detailed installation steps, environment requirements, and environment setup guidance (e.g., managing virtual environments), see the official documentation: 👉 https://docs.openr.ag/install 👉 https://docs.astral.sh/uv/pip/environments This ensures you are following the most current and supported setup. ## 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](https://docs.openr.ag/docker). ## Development For developers wanting to contribute to OpenRAG or set up a development environment, see [CONTRIBUTING.md](CONTRIBUTING.md). ## Troubleshooting For common issues and fixes, see [Troubleshoot OpenRAG](https://docs.openr.ag/support/troubleshoot).