Combined Quivr + MemPalace RAG App

This app combines the local Quivr Core fork with the MemPalace integration and a Gradio browser UI.

The setup script uses uv and installs both local fork-style checkouts: forks\quivr and forks\mempalace.

Install as a pip package

After publishing the package, users can install and run it with:

python -m pip install quivr-mempalace-rag-ui
quivr-mempalace-rag-ui

The package automatically installs its quivr-mempalace dependency. Both packages must be published before the one-command install works for new users.

Install from CMD

cd /d C:\Users\GauravSarma\Downloads\quivr-main\quivr-main\forks\quivr\examples\mempalace_rag_ui
setup.cmd
notepad .env

Start the app

cd /d C:\Users\GauravSarma\Downloads\quivr-main\quivr-main\forks\quivr\examples\mempalace_rag_ui
run_app.cmd

Build and publish from CMD

cd /d C:\Users\GauravSarma\Downloads\quivr-main\quivr-main\forks\quivr\examples\mempalace_rag_ui
build_all.cmd
publish_all.cmd

Set your PyPI token before publishing:

set UV_PUBLISH_TOKEN=pypi-your-token
publish_package.cmd

Open http://127.0.0.1:7862.

1. Choose Groq or NVIDIA NIM.
2. Upload one or more documents.
3. Click Index documents.
4. Click Recall memories to test MemPalace alone.
5. Click Ask to run Quivr document RAG plus MemPalace memory context.

The app uses NVIDIA embeddings for document indexing. Hosted embeddings require NVIDIA_API_KEY; a self-hosted NVIDIA embedding NIM can be selected with NVIDIA_EMBEDDING_BASE_URL. Hugging Face support remains available in the standalone integration package through its optional huggingface extra.