RAG & Knowledge
PageIndex
PageIndex: vectorless, reasoning-based RAG that indexes documents like a human expert would
★ 39.0k starsMIT
View guide →RAGApp: deploy a RAG app over your documents with Docker — configure it like a custom GPT
ragapp is an open-source rag & knowledge project by ragapp (ragapp/ragapp on GitHub). It has 4,446 GitHub stars and 471 forks, is released under the Apache-2.0 license, and was last updated on GitHub on 22 January 2025.
Collect the documents or sources you want the app to answer questions over.
Start RAGApp with a single docker run command.
Open the admin page to connect your data and choose a model — OpenAI, Gemini, or local Ollama.
Use the chat UI at the root URL, and put the app behind your API gateway if exposing it publicly.
A single `docker run` starts the whole app on port 8000.
Set up data sources and models from the browser, like configuring a custom GPT.
A user-facing chat interface at / and interactive API documentation at /docs.
Use OpenAI or Gemini with your key, or run fully local models through Ollama.
Provided Docker Compose setups add Ollama + Qdrant, or a management UI to run multiple RAG apps.
Retrieval and indexing ride on the LlamaIndex framework's data connectors and pipelines.
Clone the project and enter its directory (for development or Compose deployments).
git clone https://github.com/ragapp/ragapp.git
cd ragappStart RAGApp with Docker, publishing port 8000.
docker run -p 8000:8000 ragapp/ragappOpen the admin UI and connect your data and model.
# open http://localhost:8000/admin — chat UI at http://localhost:8000/The ragapp project team; it is built on top of LlamaIndex.
Yes, Apache-2.0 licensed. Hosted model usage (OpenAI/Gemini) bills to your own keys; local Ollama models avoid that.
Only if you choose hosted models. With Ollama configured, the app can run entirely on local models.
RAGApp packages the LlamaIndex plumbing into a deployable app with admin and chat UIs, trading deep customization for minutes-to-working setup.
Run `docker run -p 8000:8000 ragapp/ragapp`, then configure data and models at http://localhost:8000/admin.
PageIndex: vectorless, reasoning-based RAG that indexes documents like a human expert would
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