This tutorial walks you through creating a document vault, importing a file, and asking questions about it. By the end, you'll have a personal knowledge base you can query with AI.

## 1. Open the Vault

Click the **folder icon** on the Daneel launcher bubble to open the vault overlay.

## 2. Create a vault

If this is your first time, you'll see an empty vault list. Click **Create Vault** and give it a name — something like "Research Papers" or "Project Docs".

## 3. Import a document

Click the **Import** button inside your vault. You can:

- **Click** to open a file picker
- **Drag and drop** files directly onto the vault area

Supported formats: PDF, DOCX, TXT, HTML, PPTX, Excel (XLS, XLSX).

Pick a document — a PDF works well for this tutorial. Daneel converts it to text, splits it into chunks, generates embeddings, and stores everything locally.

You'll see the document appear in the vault with its name, format icon, and chunk count.

## 4. Ask a question

With your document imported, type a question in the chat input at the bottom of the vault overlay:

> *What are the main conclusions of this paper?*

Daneel searches the vault's vector index, finds the most relevant chunks, and generates an answer using your active AI model.

## 5. Import more documents

Add more files to the same vault. Daneel deduplicates by content hash (SHA-256), so importing the same file twice won't create duplicates.

## 6. Try the knowledge graph (optional)

If you want to visualize entity relationships across your documents:

1. Open the vault's settings (gear icon)
2. Enable **Knowledge Graph**
3. Daneel extracts named entities (people, organizations, places, concepts) using a local NER model and builds an interactive 3D graph

See [How to Build a Knowledge Graph](/how-to/knowledge-graph/) for the full guide.

## What just happened

Daneel converted your document to structured Markdown (using EdgeParse for PDFs, Mammoth for DOCX), chunked it into overlapping segments, and embedded each chunk with the BGE Small model on WebGPU. The vectors are stored in IndexedDB, partitioned by vault ID. Queries run semantic search over those vectors and feed the top matches into a RAG prompt.

Everything stays in your browser. Your documents are never uploaded anywhere.

## Free vs. paid limits

| | Free | Paid |
|---|---|---|
| Vaults | 1 | Unlimited |
| Documents per vault | 5 | 50 |
| Max file size | 1 MB | 10 MB |
| Max characters per doc | 50,000 | 500,000 |

[Upgrade your license](/how-to/offline/#license-activation) to unlock the full limits.

## Next steps

- [Connect a Cloud Provider](/guides/connect-provider/) for more powerful AI responses
- Learn [how to create a custom agent](/how-to/agents/) to specialize your vault's AI
- [Browse linked pages from a vault document](/how-to/vault-mini-browser/) — turn web-origin docs into a navigable surface
- Read about [the privacy model](/concepts/privacy/) to understand what stays local