deploy/huggingface/README_SPACE_TEMPLATE.md

metadata

title: Auralynq
emoji: 🎙️
colorFrom: blue
colorTo: purple
sdk: docker
app_port: 7860
pinned: false
license: apache-2.0
short_description: Local-first, voice-native, agentic RAG with visual citation grounding

Auralynq (demo Space)

This Space runs Auralynq, a local-first, voice-native, agentic RAG platform, in its lightweight offline demo configuration:

• Hash embeddings + in-memory vector store + extractive answering — no GPU, no model downloads, no paid API keys.
• Pre-loaded with the project's small, original, CC0-licensed demo corpus (three documents — no private or third-party data).
• Document uploads are disabled by default on this Space (AURALYNQ_ALLOW_UPLOADS=false) — you can browse and ask questions, but can't add your own files, so nothing you type gets stored beyond the current session.

What data is stored, and for how long

• This Space has no persistent storage attached. Everything under AURALYNQ_DATA_DIR — the demo corpus's vector index, any chat history — is held in memory/container-local disk only and is wiped on every restart or redeploy of the Space. Nothing survives a rebuild.
• No analytics, no logging of your questions to a third party, no data sent anywhere except (if you've set an LLM key) to that provider's API.

Try it

• Ask a question from examples/demo_corpus/expected_questions.md.
• Click a citation to open the Source Workspace and see the exact bounding-box highlight in the sample PDF.
• Visit /modelfit to see the hardware-aware model-selection tool (numbers reflect this Space's container, not your own machine).

Limitations of this demo

• Offline extractive answering verifies pipeline correctness, not answer quality — see the Limitations section in the main repo's README.md.
• CPU-only; ModelFit's speed/recommendation numbers reflect this container, not any GPU you may have.
• No uploads, no persistence, single small demo corpus.

Duplicate this Space to make it your own

Click "Duplicate this Space" (top right). Duplicating copies the Variables above but not Secrets — you'll start with the same safe defaults. From there you can:

• Enable uploads: set AURALYNQ_ALLOW_UPLOADS=true as a Variable — understand first that a public, unauthenticated Space would let anyone upload documents that persist for as long as the container runs.
• Add real model quality: set AURALYNQ_LLM__PROVIDER and the matching key (OPENAI_API_KEY, ANTHROPIC_API_KEY, COHERE_API_KEY) as Secrets (never Variables).
• Add persistent storage: attach Hugging Face Persistent Storage and point AURALYNQ_DATA_DIR at the mounted path — read the storage-ownership caveat in deploy/huggingface/README.md first; the container runs as a non-root user and the mount may need its ownership fixed on first boot.
• Upgrade hardware: this demo needs no GPU; only upgrade if you've also configured a real embedding/LLM provider that would benefit from it.

Source

Full source, docs, and the no-Podman / Podman / server run modes: https://github.com/MHHamdan/Auralynq. License: Apache-2.0 (code); CC0-1.0 (this Space's demo corpus).