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Obsidian RAG — Semantic Note Search

Local semantic search for your Obsidian vault, powered by BAAI/bge-m3 embeddings and ChromaDB.

Features

  • Semantic search — find notes by meaning, not just keywords

  • Markdown-aware chunking — splits by heading hierarchy for better context

  • Apple Silicon acceleration — MPS support for M-series Macs

  • Multiple interfaces — CLI, REST API, and MCP server (for Claude/Cursor)

  • Multilingual — full Chinese-English support via bge-m3

Related MCP server: Obsidian RAG MCP Server

Quick Start

# Install
pip install -e .

# Index your vault
export OBSIDIAN_VAULT_PATH="/path/to/your/vault"
python -m obsidian_rag index "$OBSIDIAN_VAULT_PATH" --full

# Search
python -m obsidian_rag search "your question"

# Start API server (for web UI or integrations)
python -m obsidian_rag api

Architecture

Obsidian vault (.md files)
  → chunker.py: split by Markdown headings
  → embeddings.py: BAAI/bge-m3 (MPS accelerated)
  → ChromaDB: cosine similarity vector store
  → Interfaces:
      ├── CLI: python -m obsidian_rag search "query"
      ├── REST API: FastAPI on :8787
      └── MCP: Claude Code / Cursor integration

Tech Stack

Component

Choice

Embedding model

BAAI/bge-m3 (1024d, multilingual)

Vector DB

ChromaDB (local, cosine space)

API

FastAPI + Uvicorn

AI integration

MCP (Model Context Protocol)

MCP Configuration

For Claude Code (.mcp.json):

{
  "mcpServers": {
    "obsidian-rag": {
      "command": "python",
      "args": ["-m", "obsidian_rag.server"],
      "env": {
        "OBSIDIAN_VAULT_PATH": "/path/to/your/vault"
      }
    }
  }
}
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