Knowledge Base RAG System

A self-hosted RAG (Retrieval Augmented Generation) system for your Obsidian vault with MCP server integration.

Features

• Semantic Search: Find relevant content using embeddings, not just keywords
• MCP Server: Exposes search, indexing, and stats tools via MCP protocol
• Local-first: No external APIs - everything runs locally
• Obsidian Compatible: Works with your existing markdown vault

Requirements

• Python 3.11+
• ~2GB disk space for embeddings model

Quick Start

1. Install uv (if not already)

curl -LsSf https://astral.sh/uv/install.sh | sh
source ~/.local/bin/env

2. Clone and setup

cd ~/knowledge-base
cp .env.example .env

3. Configure

Edit .env to set your vault path:

VAULT_PATH=/path/to/your/obsidian-vault
EMBEDDING_MODEL=all-MiniLM-L6-v2  # optional

4. Install dependencies

uv sync

5. Run the server

source .venv/bin/activate
VAULT_PATH=./knowledge python -m knowledge_rag.server

The server will:

• Auto-index your vault on startup
• Listen for MCP requests via stdio

MCP Tools

Once running, these tools are available:

Tool	Description
search_knowledge	Semantic search across your vault
index_knowledge	Re-index the vault (use after adding files)
get_knowledge_stats	View indexing statistics

Usage Example

# Example: Searching the knowledge base
# (via MCP client or Claude Desktop integration)

await search_knowledge({
    "query": "how does the RAG system work",
    "top_k": 5
})

Auto-Start on Boot

Option 1: Systemd Service

Create /etc/systemd/system/knowledge-rag.service:

[Unit]
Description=Knowledge Base RAG MCP Server
After=network.target

[Service]
Type=simple
User=ernie
WorkingDirectory=/home/ernie/knowledge-base
Environment="VAULT_PATH=/home/ernie/knowledge"
Environment="PATH=/home/ernie/.local/bin:/usr/bin:/bin"
ExecStart=/home/ernie/knowledge-base/.venv/bin/python -m knowledge_rag.server
Restart=always

[Install]
WantedBy=multi-user.target

Then enable:

sudo systemctl daemon-reload
sudo systemctl enable knowledge-rag.service
sudo systemctl start knowledge-rag.service

Option 2: tmux/screen

# Start in tmux
tmux new -s knowledge-rag
source .venv/bin/activate
VAULT_PATH=./knowledge python -m knowledge_rag.server
# Detach: Ctrl+b, then d

Option 3: rc.local or startup script

Add to your ~/.bashrc or startup script:

# Only start if not already running
if ! pgrep -f "knowledge_rag.server" > /dev/null; then
    cd ~/knowledge-base
    source .venv/bin/activate
    VAULT_PATH=./knowledge nohup python -m knowledge_rag.server > /tmp/knowledge-rag.log 2>&1 &
fi

Project Structure

knowledge-base/
├── src/knowledge_rag/    # Source code
│   ├── server.py         # MCP server
│   ├── chunker.py        # Markdown chunking
│   ├── embeddings.py     # Sentence-transformers wrapper
│   └── vector_store.py  # ChromaDB wrapper
├── knowledge/            # Your Obsidian vault (gitignored)
├── pyproject.toml        # Project config
└── .env.example          # Environment template

Configuration

Variable	Default	Description
VAULT_PATH	/data/vault	Path to your Obsidian vault
EMBEDDING_MODEL	all-MiniLM-L6-v2	Sentence-transformers model
EMBEDDINGS_CACHE_DIR	/data/embeddings_cache	Model cache location

Troubleshooting

First run is slow

The embedding model (~90MB) downloads on first run. Subsequent runs are faster.

No search results

Run index_knowledge tool to index your vault, or restart the server.

Out of memory

The default model is lightweight. For even smaller models, try paraphrase-MiniLM-L3-v2.

License

MIT