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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"
      }
    }
  }
}

Tool Schema Changelog

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    • A
      license
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      Enables semantic search and content retrieval from local Obsidian vaults through the Model Context Protocol. It allows AI assistants to query notes by meaning, filter by tags, and access full note content for enhanced knowledge integration.
      MIT
    • A
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      D
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      Enables AI assistants to interact with Obsidian vaults through vector search, vault indexing, and file monitoring. It provides a standardized interface for searching and managing markdown-based personal knowledge management data within the Model Context Protocol.
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    • F
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      Enables semantic search and note management for Obsidian vaults via the Model Context Protocol, allowing LLMs to search, read, and index notes, PDFs, and web pages locally.
      -