EzRAG – AI-Powered Search for Obsidian Notes

EzRAG turns your Obsidian vault into a Gemini File Search index so you can run semantic search, chat over your notes, and expose your vault through MCP tools. Everything stays in your Google account; the plugin simply keeps the index up to date.

<img width="716" height="507" alt="Chat Interface Screenshot" src="https://github.com/user-attachments/assets/4026c1aa-0a9e-43f0-bbb8-31b95e645244" />

Highlights

• Semantic search + AI chat with inline citations
• Smart runner pattern: one desktop keeps the index in sync, other devices can query
• Built-in MCP server so external agents can query or fetch notes
• Automatic deduplication, queue persistence, and rebuild workflows

Getting Started

Requirements

• Google Gemini API key (get one free)
• Obsidian desktop app for indexing (mobile can query/read-only)

Install

Option 1 – BRAT (recommended)

1. Install BRAT from Community Plugins.
2. BRAT settings → Add Beta Plugin → https://github.com/benbjurstrom/ezrag.
3. Enable EzRAG in Community Plugins.

Option 2 – Manual

1. Clone into your vault:

   cd /path/to/vault/.obsidian/plugins
   git clone https://github.com/benbjurstrom/ezrag
   

2. Build once:

   cd ezrag
   npm install
   npm run build
   

3. Restart Obsidian and enable EzRAG.

Configure

1. Settings → EzRAG → enter your Gemini API key.
2. On desktop, toggle This machine is the runner to let it index.

<img width="826" height="591" alt="Settings Screenshot" src="https://github.com/user-attachments/assets/8d3d2470-b305-4114-91ed-b8778af66e1e" />

Using EzRAG

Chat

Open via the ribbon icon or EzRAG: Open Chat. Try prompts like:

• “What are my notes about the Johnson project?”
• “Summarize yesterday’s meeting notes.”
• “Find all mentions of machine learning.”

MCP Server

Enable Settings → EzRAG → MCP Server to let tools connect.

Connect from Claude Code:

claude mcp add --transport http ezrag-obsidian-notes http://localhost:42427/mcp

Tools provided:

• keywordSearch – keyword/regex search
• semanticSearch – Gemini-backed semantic search with citations
• note:///<path> – direct note retrieval

How It Works

Indexing basics

• Only .md files are indexed; changes trigger hashing + re-upload if content changed.
• Runner enforcement prevents multiple machines from uploading the same file.
• Upload queue persists across restarts and surfaces status in the UI.

<img width="881" height="500" alt="Upload Queue Screenshot" src="https://github.com/user-attachments/assets/a1a51b87-2e8a-461a-8f6b-59ef0dea1098" />

Limits & costs

Gemini File Search pricing (details):

• Indexing: ~$0.15 per 1M tokens (storage free; standard model rates for queries)
• Max file size: 100 MB; free tier ≈1 GB total storage (higher tiers up to 1 TB)
• For best performance keep stores under ~20 GB

Data control

• Documents live in your Google account. Manage/delete stores via Settings → Manage Stores.
• No telemetry or note data leaves your machine beyond the Gemini File Search uploads.

Links

• Issues
• Discussions
• License (ISC)