Tax season rolls around and you're hunting through folders for last year's W-2. Your kid's school sends a permission slip that you need to find again three weeks later. The mechanic gives you a receipt that you swear you saved somewhere.
The Vault solves this. It's Meggy's local document store — drop in your files, and your assistant organizes, indexes, and retrieves them for you. Ask "find my car insurance policy" or "what was the total on that restaurant receipt from December?" and Meggy searches through everything instantly.
It supports 25+ file formats, processes them into semantically chunked segments, and makes them searchable through a hybrid retrieval pipeline — all stored locally in SQLite. Your documents never leave your machine.
The document parser handles a wide range of formats:
| Category | Formats |
|---|---|
| Documents | PDF, DOCX, TXT, RTF, MD, HTML, JSON, EML |
| Spreadsheets | XLSX, CSV |
| Presentations | PPTX |
| Code | JS, TS, PY, Java, C, C++, Go, Rust, and more |
| Markup | HTML, XML, JSON, YAML |
| Images | PNG, JPG, WEBP, SVG, GIF, BMP (with vision-based captioning) |
| Audio | MP3, WAV, M4A, OGG (transcribed via STT model role) |
When you add a file to the Vault, it goes through a multi-stage pipeline:
embedding role. For media files (images, audio), the system can embed the binary content natively when the embedding model supports multimodal input — otherwise it falls back to embedding the text caption or transcriptNote: Multimodal embedding (native binary input for images and audio) currently requires a Gemini embedding model. Other providers do not yet expose multimodal embedding APIs, so media files will be embedded using their text representation (caption or transcript) when a non-Gemini model is selected.
You can drag and drop files, use the file picker, paste a URL, or let the AI ingest documents programmatically during conversations.
Paste a URL and the Vault downloads, parses, and indexes the page content — including YouTube videos (transcripts extracted automatically). All fetches go through an SSRF protection layer that blocks requests to internal networks, so it's safe even on shared machines.
The Vault can also watch URLs you've previously ingested and re-crawl them on a schedule, creating a new version when the content changes.
Finding documents is more than just keyword matching. The Vault uses a two-stage hybrid retrieval pipeline that understands both meaning and exact terms:
Stage 1 — Parallel retrieval:
Stage 2 — Reciprocal Rank Fusion (RRF): Both result sets are merged using RRF, which weights each result based on its rank position across both retrieval methods. This produces a single, reranked list that captures both semantic relevance and term precision.
Smart Folders can be configured with auto-population rules. When new documents are ingested, they're automatically assigned to matching folders based on content type, keywords, or custom filters. Set up folders like "Medical," "Financial," or "Recipes," and Meggy sorts incoming documents for you.
Watched Directories let you link a local folder on your computer directly to the Vault. Not only are files automatically ingested as you add them, but the entire internal directory structure is mirrored. If you have nested subfolders in your OS, they appear as nested folders in the Vault. Delete a folder on your computer, and the Vault stays perfectly in sync.
Every document you add is automatically scanned for entities — people, organizations, dates, locations, concepts, and events. The Vault builds a local knowledge graph that connects these entities across all your documents, so you can ask questions like "which documents mention Dr. Smith?" or "what's related to our home insurance?"
When the AI searches the Vault, it doesn't just return raw text chunks. The Intelligence Layer includes:
Generate a podcast-style audio summary of any document or group of documents. The Vault creates a conversational script and synthesizes it with TTS — perfect for catching up on long reports while commuting or cooking.
Workspaces are virtual collections that let you group documents by project or theme without moving files. A document can belong to multiple workspaces.
Versioning tracks changes to ingested documents non-destructively. When a file is re-ingested or a URL is re-crawled, the Vault creates a new version snapshot. You can view the full history, compare any two versions, and restore a previous version at any time.
The Vault exposes 13 tools that the AI assistant can invoke during conversations:
vault_search — Search the vault for relevant documents given a natural language queryvault_ingest — Add a new document to the vault from a file pathvault_ingest_url — Add a web page or YouTube URL to the vaultvault_list — List documents and folders in the vaultvault_delete — Remove a document from the vaultPlus folder management, smart folders, and media-specific ingestion tools.
This means the AI can autonomously pull context from your documents during a conversation — ask "what does my lease say about pets?" and Meggy searches the vault, finds the relevant section, cites the exact page, and gives you the answer.
Five pre-built slash commands give you quick access to common vault workflows:
| Command | What it does |
|---|---|
/summarize-vault |
Summarize all vault documents or a specific folder |
/compare-docs |
Compare two vault documents side-by-side |
/ask-vault |
Ask a question answered from vault knowledge |
/find-related |
Find documents related to a given topic |
/vault-gaps |
Identify gaps or missing topics in your collection |