Embeddings & Semantic Search

Run a local embedding model so search ranks by meaning, not just keywords.

What This Powers

When embeddings are loaded, memrynote can rank notes by semantic similarity to your query — not just keyword overlap. This affects:

• The search palette (semantic boost on top of keyword match)
• AI Connections in the Journal sidebar
• "Related notes" suggestions in some surfaces

A query like "setting up authentication" can surface a note titled "OAuth flow" even when the words don't overlap.

Enabling

1. Open Settings → AI
2. Toggle Enable
3. Under Embedding Model, click Download to pull the model
4. Wait for the status to say Loaded
5. Click Rebuild Index to embed every existing note (one-time per model)

The first index build can take a few minutes for large vaults — progress is shown.

Model Management

The status line shows:

• Loaded — ready
• Loading — initialization in progress
• Not downloaded — needs download
• Error — see logs; usually disk space or hash mismatch

You can Unload the model from settings to free memory; reload as needed.

memrynote does not load the embedding model just because a vault opens. Semantic surfaces such as search, inbox linked-note suggestions, related notes, and reindexing start the local model on first use. The model runs in a separate utility process and shuts down after an idle period so regular note reading does not keep the embedding runtime resident forever.

Model Size

Models trade off accuracy vs disk and memory. The default is tuned for desktop hardware. The settings page shows dimensions and the current count of embedded notes.

Reindexing

Rebuild the index after:

• Switching models
• Restoring a vault from backup
• A migration that touched note storage

Reindexing is incremental — memrynote skips notes whose content hash hasn't changed.

Privacy

Embeddings are computed on-device. The vectors are stored in the local index database (<vault>/index.db). They are never sent to a server.

Even if you sync across devices, embeddings are recomputed locally — the embedding payload itself is not part of the sync stream.

Performance

Once the index is built, semantic search adds <50ms to a typical query. Embedding is the expensive step (one-time per note); ranking is cheap.

If you have an enormous vault and notice slowdowns, the index can be rebuilt fresh in settings.

Disabling Embeddings

Toggle Enable off. The model unloads. The vector index stays on disk (you can delete the file manually if you want it gone).

Search falls back to keyword-only — fast, but less forgiving of varied phrasing.

See Also

• Search & Command Palette
• Provider Setup — provider config for the inline AI menu (separate from embeddings)