# 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). ## Install OpenRAG To get started with OpenRAG, see the installation guides in the OpenRAG documentation: * [Quickstart](https://docs.openr.ag/quickstart) * [Install the OpenRAG Python package](https://docs.openr.ag/install-options) * [Deploy self-managed services with Docker or Podman](https://docs.openr.ag/docker) ## Development For developers who want to [contribute to OpenRAG](https://docs.openr.ag/support/contribute) or set up a development environment, see [CONTRIBUTING.md](CONTRIBUTING.md). ## Troubleshooting For assistance with OpenRAG, see [Troubleshoot OpenRAG](https://docs.openr.ag/support/troubleshoot) and visit the [Discussions page](https://github.com/langflow-ai/openrag/discussions). To report a bug or submit a feature request, visit the [Issues page](https://github.com/langflow-ai/openrag/issues).