Configuration
Vault Intelligence is designed to be powerful out of the box, but you can customise it to suit different hardware, budgets, and workflows.
Connection
| Setting | Default | Description |
|---|---|---|
| Google API key | None | Your secret key from Google AI Studio. Stored in plain text in your plugin settings (data.json), but masked for display and logging using SHA-256 fingerprints. Required for all Gemini models and Gemini embeddings. |
| Refresh models | None | A manual trigger to force a fresh fetch of available models from the Gemini API. |
Researcher
Consolidate settings for the Research Assistant agent.
| Setting | Default | Description |
|---|---|---|
| Chat model | gemini-3-flash-preview | The main intelligence engine. • Flash: Best for speed and agentic loops. • Pro: Best for deep reasoning or creative writing. |
| Agent language | English (US) | The primary language for all agent interactions. Affects the default system prompt and response style. |
| System instruction | Default (Managed) | The core personality, role, and rules for the Researcher. Leave as default to receive automatic improvements in future updates. |
| Context window budget | 200,000 | Max tokens the AI can consider at once. Automatically scales proportionally when you switch models. |
| Max agent steps | 5 | Limits reasoning loops to prevent infinite "thinking" or high costs. |
| Web search model | gemini-2.5-flash-lite | Model used specifically for web searches and fact-checking. |
| Enable computational solver | On | Allows the agent to write and execute Python code for math and data analysis. |
| Code execution model | gemini-3-flash-preview | The specialised model used for generating Python code. |
| Vault reading limit | 25 | Max notes the Researcher can retrieve to answer a single question. |
Explorer
Configure how connections and similar notes are discovered.
| Setting | Default | Description |
|---|---|---|
| Embedding provider | gemini | Google Gemini: Cloud-based. Requires API key. Local: Offline. Runs on your CPU. |
| Embedding model | gemini-embedding-001 | The vector engine. Choose from Gemini presets or various local ONNX models. |
| Minimum similarity score | 0.5 | Relevance threshold (0.0 to 1.0). Matches below this are ignored. |
| Similar notes limit | 20 | Max number of related notes displayed in the sidebar. |
| GARS Similarity weight | 1.0 | Importance of vector similarity in hybrid scoring. |
| GARS Centrality weight | 0.2 | Importance of graph centrality (popularity) in hybrid scoring. |
| GARS Activation weight | 0.4 | Importance of semantic activation (connections) in hybrid scoring. |
| Re-index vault | None | Wipe and rebuild all embeddings. Required after changing models. |
Gardener
Configure the Gardener agent for ontology maintenance and vault hygiene.
| Setting | Default | Description |
|---|---|---|
| Gardener model | gemini-3-flash-preview | The model used for analyzing vault structure and recommending improvements. |
| Gardener rules | Default (Managed) | The persona and hygiene instructions for the Gardener. Leave as default to receive automatic improvements. |
| Ontology path | Ontology | Folder where concepts, entities, and MOCs are stored. |
| Gardener plans path | Gardener/Plans | Folder where proposed plans are saved. |
| Plans retention | 7 days | How long to keep gardener plans before purging. |
| Excluded folders | Default | Folders the gardener should ignore. |
| Recent note limit | 10 | Max notes to scan in a single session. |
| Context budget | 100,000 | Max token usage for a single gardener analysis. |
Performance and System
Technical tuning for power users.
| Setting | Default | Description |
|---|---|---|
| Indexing delay | 5000ms | Wait time after typing stops before re-indexing the current note. |
| Bulk scan delay | 300ms | Delay between files during full vault scans. |
| Local worker threads | 1-2 | CPU threads for local embeddings. Higher is faster but heavier. |
| Local SIMD acceleration | Auto | Enables SIMD instructions for local models. Faster but may be unstable on older hardware. |
| Gemini API retries | 10 | Number of retries for spotty connections. |
| Model cache duration | 7 days | Duration to cache Gemini model list locally. |
| Log level | Warn | Developer console verbosity (Debug for full CoT). |
Gemini vs Local Models
Google Gemini (Cloud)
- Pros: Highest quality (
gemini-embedding-001), zero local CPU/RAM overhead, handles large documents. - Cons: Requires API key, internet dependent, remote processing.
Transformers.js (Local)
- Pros: 100% private, offline, no API costs.
- Cons: Uses local resources, slightly lower quality on smaller presets.
Privacy and Git Sync
Vault Intelligence stores its search index and relationship graph in a specialized binary format inside the plugin's data/ directory.
- Automated .gitignore: The plugin automatically creates and maintains a
.gitignorefile inside its internal data folder. - Why?: These index files can be very large (up to 100MB+ for massive vaults) and change frequently. Excluding them from Git prevents repository bloat and sync conflicts while using plugins like Obsidian Git.
- Data safety: Since the index is a derived cache, it can be regenerated automatically from your notes at any time.