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docs: Change README links to docs

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Also removes Starlette and Next.js badges, but still calls out to them in the prose.
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Phil Nash 2025-10-10 15:29:05 +01:00
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README.md
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@ -7,14 +7,13 @@
  
<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>
&nbsp;&nbsp;
<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>
<a href="https://github.com/docling-project/docling"><img src="https://img.shields.io/badge/Docling-000000?style=flat" alt="Langflow"></a>
&nbsp;&nbsp;
<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>
&nbsp;&nbsp;
<a href="https://deepwiki.com/phact/openrag"><img src="https://deepwiki.com/badge.svg" alt="Ask DeepWiki"></a>
</div>
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.
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).
<a href="https://deepwiki.com/phact/openrag"><img src="https://deepwiki.com/badge.svg" alt="Ask DeepWiki"></a>
</div>
<div align="center">
@ -48,7 +47,7 @@ To launch OpenRAG with the TUI, do the following:
The TUI opens and guides you through OpenRAG setup.
For the full TUI guide, see [TUI](docs/docs/get-started/tui.mdx).
For the full TUI guide, see [TUI](https://docs.openr.ag/get-started/tui).
## Docker Deployment
@ -114,7 +113,7 @@ To deploy OpenRAG with Docker:
| 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](docs/docs/get-started/quickstart.mdx).
6. Access the OpenRAG application at `http://localhost:3000` and continue with the [Quickstart](https://docs.openr.ag/quickstart).
To stop `docling serve`, run:
@ -122,11 +121,11 @@ To deploy OpenRAG with Docker:
uv run python scripts/docling_ctl.py stop
```
For more information, see [Deploy with Docker](docs/docs/get-started/docker.mdx).
For more information, see [Deploy with Docker](https://docs.openr.ag/get-started/docker).
## Troubleshooting
For common issues and fixes, see [Troubleshoot](docs/docs/support/troubleshoot.mdx).
For common issues and fixes, see [Troubleshoot](https://docs.openr.ag/support/troubleshoot).
## Development