Remove Docker, update README with setup and auto-start instructions

- Remove Dockerfile and docker-compose.yaml (not suitable for this project)
- Update README.md with comprehensive setup documentation
- Add systemd, tmux, and rc.local auto-start options
- Add troubleshooting section

Dockerfile

@@ -1,33 +0,0 @@
-FROM python:3.11-slim
-
-# Install system dependencies for sentence-transformers
-RUN apt-get update && apt-get install -y --no-install-recommends \
-    gcc \
-    g++ \
-    && rm -rf /var/lib/apt/lists/*
-
-# Set working directory
-WORKDIR /app
-
-# Install uv
-RUN pip install uv
-
-# Copy pyproject.toml
-COPY pyproject.toml .
-
-# Install dependencies
-RUN uv sync --frozen --no-dev
-
-# Copy source code
-COPY src/ ./src/
-
-# Create data directories
-RUN mkdir -p /data/vault /data/chroma_db /data/embeddings_cache
-
-# Set environment variables
-ENV PYTHONUNBUFFERED=1 \
-    VAULT_PATH=/data/vault \
-    EMBEDDINGS_CACHE_DIR=/data/embeddings_cache
-
-# Default command runs the MCP server
-CMD ["python", "-m", "knowledge_rag.server"]

README.md

@@ -1,21 +1,171 @@
-# Knowledge Base
+# Knowledge Base RAG System
 
-Personal knowledge base repository for storing useful information, notes, and documentation.
+A self-hosted RAG (Retrieval Augmented Generation) system for your Obsidian vault with MCP server integration.
 
-## Contents
+## Features
 
-- [Getting Started](#getting-started)
+- **Semantic Search**: Find relevant content using embeddings, not just keywords
-- [Contributing](#contributing)
+- **MCP Server**: Exposes search, indexing, and stats tools via MCP protocol
-- [License](#license)
+- **Local-first**: No external APIs - everything runs locally
+- **Obsidian Compatible**: Works with your existing markdown vault
 
-## Getting Started
+## Requirements
 
-This repository contains various knowledge articles, how-to guides, and reference documentation.
+- Python 3.11+
+- ~2GB disk space for embeddings model
 
-## Contributing
+## Quick Start
 
-Feel free to contribute by creating issues or submitting pull requests.
+### 1. Install uv (if not already)
+
+```bash
+curl -LsSf https://astral.sh/uv/install.sh | sh
+source ~/.local/bin/env
+```
+
+### 2. Clone and setup
+
+```bash
+cd ~/knowledge-base
+cp .env.example .env
+```
+
+### 3. Configure
+
+Edit `.env` to set your vault path:
+
+```bash
+VAULT_PATH=/path/to/your/obsidian-vault
+EMBEDDING_MODEL=all-MiniLM-L6-v2  # optional
+```
+
+### 4. Install dependencies
+
+```bash
+uv sync
+```
+
+### 5. Run the server
+
+```bash
+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
+
+```python
+# 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`:
+
+```ini
+[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:
+
+```bash
+sudo systemctl daemon-reload
+sudo systemctl enable knowledge-rag.service
+sudo systemctl start knowledge-rag.service
+```
+
+### Option 2: tmux/screen
+
+```bash
+# 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:
+
+```bash
+# 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 License
+MIT

docker-compose.yaml

@@ -1,32 +0,0 @@
-version: "3.8"
-
-services:
-  knowledge-rag:
-    build:
-      context: .
-      dockerfile: Dockerfile
-    container_name: knowledge-rag
-    volumes:
-      # Mount your obsidian vault here
-      - ${VAULT_PATH:-./knowledge}:/data/vault
-      # Persist ChromaDB vector store
-      - ./data/chroma_db:/data/chroma_db
-      # Persist embeddings cache
-      - ./data/embeddings_cache:/data/embeddings_cache
-    environment:
-      - VAULT_PATH=/data/vault
-      - EMBEDDING_MODEL=${EMBEDDING_MODEL:-all-MiniLM-L6-v2}
-      - EMBEDDINGS_CACHE_DIR=/data/embeddings_cache
-    restart: unless-stopped
-
-  # Optional: Watchtower for auto-updates
-  # watchtower:
-  #   image: containrr/watchtower
-  #   container_name: watchtower
-  #   volumes:
-  #     - /var/run/docker.sock:/var/run/docker.sock
-  #   environment:
-  #     - WATCHTOWER_CLEANUP=true
-  #     - WATCHTOWER_INCLUDE_STOPPED=true
-  #   command: --interval 3600 knowledge-rag
-  #   restart: unless-stopped

pyproject.toml

@@ -14,6 +14,9 @@ dependencies = [
     "pydantic>=2.0.0",
     "watchdog>=3.0.0",
     "httpx>=0.25.0",
+    # CPU-only PyTorch
+    "torch>=2.0.0",
+    "numpy>=1.24.0",
 ]
 
 [project.optional-dependencies]
@@ -27,6 +30,9 @@ dev = [
 requires = ["hatchling"]
 build-backend = "hatchling.build"
 
+[tool.hatch.build.targets.wheel]
+packages = ["src/knowledge_rag"]
+
 [tool.ruff]
 line-length = 100
 target-version = "py311"

uv.lock

@@ -1265,9 +1265,11 @@ dependencies = [
     { name = "llama-index" },
     { name = "llama-index-vector-stores-chroma" },
     { name = "mcp" },
+    { name = "numpy" },
     { name = "pydantic" },
     { name = "python-dotenv" },
     { name = "sentence-transformers" },
+    { name = "torch" },
     { name = "watchdog" },
 ]
 
@@ -1285,12 +1287,14 @@ requires-dist = [
     { name = "llama-index", specifier = ">=0.10.0" },
     { name = "llama-index-vector-stores-chroma", specifier = ">=0.1.0" },
     { name = "mcp", specifier = ">=1.0.0" },
+    { name = "numpy", specifier = ">=1.24.0" },
     { name = "pydantic", specifier = ">=2.0.0" },
     { name = "pytest", marker = "extra == 'dev'", specifier = ">=7.0.0" },
     { name = "pytest-asyncio", marker = "extra == 'dev'", specifier = ">=0.21.0" },
     { name = "python-dotenv", specifier = ">=1.0.0" },
     { name = "ruff", marker = "extra == 'dev'", specifier = ">=0.1.0" },
     { name = "sentence-transformers", specifier = ">=2.2.0" },
+    { name = "torch", specifier = ">=2.0.0" },
     { name = "watchdog", specifier = ">=3.0.0" },
 ]
 provides-extras = ["dev"]