README

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

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"
      }
    }
  }
}

obsidian-rag

README

Obsidian RAG — Semantic Note Search

Features

Quick Start

Architecture

Tech Stack

MCP Configuration

Recommended Servers