89 lines
No EOL
3.8 KiB
Markdown
89 lines
No EOL
3.8 KiB
Markdown
<div align="center">
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# OpenRAG
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<div align="center">
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<a href="https://github.com/langflow-ai/langflow"><img src="https://img.shields.io/badge/Langflow-1C1C1E?style=flat&logo=langflow" alt="Langflow"></a>
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<a href="https://github.com/opensearch-project/OpenSearch"><img src="https://img.shields.io/badge/OpenSearch-005EB8?style=flat&logo=opensearch&logoColor=white" alt="OpenSearch"></a>
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<a href="https://github.com/encode/starlette"><img src="https://img.shields.io/badge/Starlette-009639?style=flat&logo=fastapi&logoColor=white" alt="Starlette"></a>
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<a href="https://github.com/vercel/next.js"><img src="https://img.shields.io/badge/Next.js-000000?style=flat&logo=next.js&logoColor=white" alt="Next.js"></a>
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<a href="https://deepwiki.com/phact/openrag"><img src="https://deepwiki.com/badge.svg" alt="Ask DeepWiki"></a>
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</div>
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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.
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</div>
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<div align="center">
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<a href="#quickstart" style="color: #0366d6;">Quickstart</a> |
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<a href="#tui-interface" style="color: #0366d6;">TUI Interface</a> |
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<a href="#docker-deployment" style="color: #0366d6;">Docker Deployment</a> |
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<a href="#development" style="color: #0366d6;">Development</a> |
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<a href="#troubleshooting" style="color: #0366d6;">Troubleshooting</a>
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</div>
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## Quickstart
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To get started quickly, use the OpenRAG Terminal User Interface (TUI) to manage your OpenRAG installation without complex command-line operations.
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To launch OpenRAG with the TUI, do the following:
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1. Clone the OpenRAG repository.
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```bash
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git clone https://github.com/langflow-ai/openrag.git
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cd openrag
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```
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2. To start the TUI, from the repository root, run:
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```bash
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# Install dependencies first
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uv sync
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# Launch the TUI
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uv run openrag
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```
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The TUI opens and guides you through OpenRAG setup.
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For the full TUI guide, see [TUI](docs/docs/get-started/tui.mdx).
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## Docker Deployment
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If you prefer to use Docker to run OpenRAG, the repository includes two Docker Compose `.yml` files.
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They deploy the same applications and containers, but to different environments.
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- [`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.
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- [`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.
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1. Clone the OpenRAG repository.
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```bash
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git clone https://github.com/langflow-ai/openrag.git
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cd openrag
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```
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2. Build and start all services.
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For the GPU-accelerated deployment, run:
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```bash
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docker compose build
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docker compose up -d
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```
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For environments without GPU support, run:
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```bash
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docker compose -f docker-compose-cpu.yml up -d
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```
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For more information, see [Deploy with Docker](docs/docs/get-started/docker.mdx).
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## Troubleshooting
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For common issues and fixes, see [Troubleshoot](docs/docs/support/troubleshoot.mdx).
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## Development
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For developers wanting to contribute to OpenRAG or set up a development environment, see [CONTRIBUTING.md](CONTRIBUTING.md). |