Mac Mini M4でプライベートAIサーバーを構築する クラウドAPI不要

# Port mapping summary for the full stack:
#
# Port 443   - nginx (HTTPS, public-facing)
# Port 80    - nginx (HTTP, redirects to HTTPS)
# Port 11434 - Ollama (LLM inference, internal only)
# Port 3000  - Open WebUI (chat interface, proxied via nginx)
# Port 8000  - RAG API (FastAPI, proxied via nginx)
# Port 8200  - ChromaDB (vector database, internal only)
# Port 51820 - WireGuard VPN (optional, for remote access)

# Connect to your Mac Mini M4 via SSH
ssh admin@your-mac-mini.myremotemac.com

# Generate an SSH key pair (if you don't have one)
ssh-keygen -t ed25519 -C "ai-server-admin" -f ~/.ssh/id_ai_server

# Copy your public key to the server
ssh-copy-id -i ~/.ssh/id_ai_server.pub admin@your-mac-mini.myremotemac.com

# Configure SSH client for easier access
cat <<EOF >> ~/.ssh/config
Host ai-server
    HostName your-mac-mini.myremotemac.com
    User admin
    IdentityFile ~/.ssh/id_ai_server
    ForwardAgent no
    ServerAliveInterval 60
    ServerAliveCountMax 3
EOF

# Now connect with just:
ssh ai-server

# Update macOS to latest version
sudo softwareupdate --install --all --agree-to-license

# Install Homebrew (package manager)
/bin/bash -c "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/HEAD/install.sh)"

# Install essential tools
brew install wget curl jq htop python@3.12 git

# Create a dedicated directory for AI services
mkdir -p ~/ai-server/{models,data,logs,config}
mkdir -p ~/ai-server/rag/{documents,vectorstore}

# Set up Python virtual environment for AI tools
python3.12 -m venv ~/ai-server/venv
source ~/ai-server/venv/bin/activate
pip install --upgrade pip setuptools wheel

# Create a dedicated user for AI services (principle of least privilege)
sudo dscl . -create /Users/aiservice
sudo dscl . -create /Users/aiservice UserShell /bin/zsh
sudo dscl . -create /Users/aiservice RealName "AI Service Account"
sudo dscl . -create /Users/aiservice UniqueID 550
sudo dscl . -create /Users/aiservice PrimaryGroupID 20
sudo dscl . -create /Users/aiservice NFSHomeDirectory /Users/aiservice
sudo mkdir -p /Users/aiservice
sudo chown aiservice:staff /Users/aiservice

# Grant access to the AI server directory
sudo chown -R aiservice:staff ~/ai-server

# Download and install Ollama
curl -fsSL https://ollama.com/install.sh | sh

# Verify installation
ollama --version
# ollama version 0.5.4

# Start the Ollama server (runs on localhost:11434 by default)
ollama serve &

# General-purpose assistant (recommended starting point)
ollama pull llama3:8b              # 4.7 GB - fits 16GB RAM

# Instruction-following and reasoning
ollama pull mistral:7b             # 4.1 GB - excellent for RAG

# Code generation and analysis
ollama pull codellama:7b           # 3.8 GB - code-specific model
ollama pull codellama:13b          # 7.4 GB - better code quality (needs 24GB)

# Embedding model for RAG pipeline
ollama pull nomic-embed-text       # 274 MB - text embeddings

# Verify all models are downloaded
ollama list
# NAME                    SIZE      MODIFIED
# llama3:8b               4.7 GB    2 minutes ago
# mistral:7b              4.1 GB    5 minutes ago
# codellama:7b            3.8 GB    8 minutes ago
# nomic-embed-text        274 MB    10 minutes ago

# Test a model interactively
ollama run llama3:8b "What are the benefits of self-hosted AI?"

# Create a launchd plist for Ollama to start on boot
cat <<'EOF' > ~/Library/LaunchAgents/com.ollama.server.plist
<?xml version="1.0" encoding="UTF-8"?>
<!DOCTYPE plist PUBLIC "-//Apple//DTD PLIST 1.0//EN"
  "http://www.apple.com/DTDs/PropertyList-1.0.dtd">
<plist version="1.0">
<dict>
    <key>Label</key>
    <string>com.ollama.server</string>
    <key>ProgramArguments</key>
    <array>
        <string>/usr/local/bin/ollama</string>
        <string>serve</string>
    </array>
    <key>EnvironmentVariables</key>
    <dict>
        <key>OLLAMA_HOST</key>
        <string>127.0.0.1:11434</string>
        <key>OLLAMA_KEEP_ALIVE</key>
        <string>-1</string>
        <key>OLLAMA_NUM_PARALLEL</key>
        <string>4</string>
    </dict>
    <key>RunAtLoad</key>
    <true/>
    <key>KeepAlive</key>
    <true/>
    <key>StandardOutPath</key>
    <string>/Users/admin/ai-server/logs/ollama.log</string>
    <key>StandardErrorPath</key>
    <string>/Users/admin/ai-server/logs/ollama-error.log</string>
</dict>
</plist>
EOF

# Load the service
launchctl load ~/Library/LaunchAgents/com.ollama.server.plist

# Verify Ollama is running
curl -s http://localhost:11434/api/tags | jq '.models[].name'
# "llama3:8b"
# "mistral:7b"
# "codellama:7b"
# "nomic-embed-text"

# Activate the virtual environment
source ~/ai-server/venv/bin/activate

# Install RAG pipeline dependencies
pip install \
    langchain==0.1.20 \
    langchain-community==0.0.38 \
    langchain-chroma==0.1.0 \
    chromadb==0.4.24 \
    sentence-transformers==2.7.0 \
    pypdf==4.2.0 \
    docx2txt==0.8 \
    fastapi==0.111.0 \
    uvicorn==0.29.0 \
    python-multipart==0.0.9 \
    pydantic==2.7.1

# ~/ai-server/rag/ingest.py
"""
Document ingestion pipeline for the private RAG system.
Loads PDFs, DOCX, and text files, splits them into chunks,
generates embeddings via Ollama, and stores them in ChromaDB.
"""
import os
import sys
from pathlib import Path
from langchain_community.document_loaders import (
    PyPDFLoader,
    Docx2txtLoader,
    TextLoader,
    DirectoryLoader,
)
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain_community.embeddings import OllamaEmbeddings
from langchain_chroma import Chroma

# Configuration
DOCUMENTS_DIR = os.path.expanduser("~/ai-server/rag/documents")
VECTORSTORE_DIR = os.path.expanduser("~/ai-server/rag/vectorstore")
OLLAMA_BASE_URL = "http://localhost:11434"
EMBEDDING_MODEL = "nomic-embed-text"
CHUNK_SIZE = 1000
CHUNK_OVERLAP = 200

def load_documents(directory: str):
    """Load all supported document types from a directory."""
    documents = []
    path = Path(directory)

    # Load PDFs
    for pdf_file in path.glob("**/*.pdf"):
        loader = PyPDFLoader(str(pdf_file))
        documents.extend(loader.load())
        print(f"  Loaded: {pdf_file.name} ({len(loader.load())} pages)")

    # Load DOCX files
    for docx_file in path.glob("**/*.docx"):
        loader = Docx2txtLoader(str(docx_file))
        documents.extend(loader.load())
        print(f"  Loaded: {docx_file.name}")

    # Load text files
    for txt_file in path.glob("**/*.txt"):
        loader = TextLoader(str(txt_file))
        documents.extend(loader.load())
        print(f"  Loaded: {txt_file.name}")

    # Load markdown files
    for md_file in path.glob("**/*.md"):
        loader = TextLoader(str(md_file))
        documents.extend(loader.load())
        print(f"  Loaded: {md_file.name}")

    return documents

def create_vectorstore(documents):
    """Split documents into chunks and store embeddings in ChromaDB."""
    # Split documents into chunks
    text_splitter = RecursiveCharacterTextSplitter(
        chunk_size=CHUNK_SIZE,
        chunk_overlap=CHUNK_OVERLAP,
        length_function=len,
        separators=["\n\n", "\n", ". ", " ", ""],
    )
    chunks = text_splitter.split_documents(documents)
    print(f"\nSplit {len(documents)} documents into {len(chunks)} chunks")

    # Create embeddings using Ollama (runs locally!)
    embeddings = OllamaEmbeddings(
        model=EMBEDDING_MODEL,
        base_url=OLLAMA_BASE_URL,
    )

    # Store in ChromaDB
    vectorstore = Chroma.from_documents(
        documents=chunks,
        embedding=embeddings,
        persist_directory=VECTORSTORE_DIR,
        collection_name="private_docs",
    )
    print(f"Stored {len(chunks)} chunks in ChromaDB at {VECTORSTORE_DIR}")
    return vectorstore

if __name__ == "__main__":
    print("=== Private RAG Document Ingestion ===\n")
    print(f"Loading documents from: {DOCUMENTS_DIR}")
    docs = load_documents(DOCUMENTS_DIR)
    print(f"\nTotal documents loaded: {len(docs)}")

    if not docs:
        print("No documents found. Add files to ~/ai-server/rag/documents/")
        sys.exit(1)

    print("\nCreating vector store...")
    create_vectorstore(docs)
    print("\nIngestion complete!")

# ~/ai-server/rag/api.py
"""
RAG Query API - FastAPI service for document Q&A.
Retrieves relevant chunks from ChromaDB and generates
answers using Ollama. Everything runs locally.
"""
from fastapi import FastAPI, UploadFile, File, HTTPException
from fastapi.middleware.cors import CORSMiddleware
from pydantic import BaseModel
from typing import Optional, List
import os
import shutil

from langchain_community.embeddings import OllamaEmbeddings
from langchain_chroma import Chroma
from langchain_community.llms import Ollama
from langchain.chains import RetrievalQA
from langchain.prompts import PromptTemplate

# Configuration
VECTORSTORE_DIR = os.path.expanduser("~/ai-server/rag/vectorstore")
DOCUMENTS_DIR = os.path.expanduser("~/ai-server/rag/documents")
OLLAMA_BASE_URL = "http://localhost:11434"
EMBEDDING_MODEL = "nomic-embed-text"
LLM_MODEL = "mistral:7b"

app = FastAPI(
    title="Private RAG API",
    description="Self-hosted document Q&A with zero cloud dependencies",
    version="1.0.0",
)

app.add_middleware(
    CORSMiddleware,
    allow_origins=["*"],
    allow_methods=["*"],
    allow_headers=["*"],
)

# Initialize components
embeddings = OllamaEmbeddings(
    model=EMBEDDING_MODEL,
    base_url=OLLAMA_BASE_URL,
)

vectorstore = Chroma(
    persist_directory=VECTORSTORE_DIR,
    embedding_function=embeddings,
    collection_name="private_docs",
)

llm = Ollama(
    model=LLM_MODEL,
    base_url=OLLAMA_BASE_URL,
    temperature=0.3,
    num_ctx=4096,
)

# Custom prompt template
PROMPT_TEMPLATE = """Use the following context to answer the question.
If you cannot find the answer in the context, say "I don't have enough
information in the provided documents to answer this question."

Context:
{context}

Question: {question}

Answer:"""

prompt = PromptTemplate(
    template=PROMPT_TEMPLATE,
    input_variables=["context", "question"],
)

qa_chain = RetrievalQA.from_chain_type(
    llm=llm,
    chain_type="stuff",
    retriever=vectorstore.as_retriever(
        search_type="similarity",
        search_kwargs={"k": 4},
    ),
    chain_type_kwargs={"prompt": prompt},
    return_source_documents=True,
)


class QueryRequest(BaseModel):
    question: str
    model: Optional[str] = "mistral:7b"
    num_results: Optional[int] = 4


class QueryResponse(BaseModel):
    answer: str
    sources: List[dict]
    model: str


@app.post("/query", response_model=QueryResponse)
async def query_documents(request: QueryRequest):
    """Query your private documents using RAG."""
    try:
        result = qa_chain.invoke({"query": request.question})
        sources = [
            {
                "content": doc.page_content[:200] + "...",
                "source": doc.metadata.get("source", "unknown"),
                "page": doc.metadata.get("page", None),
            }
            for doc in result.get("source_documents", [])
        ]
        return QueryResponse(
            answer=result["result"],
            sources=sources,
            model=request.model,
        )
    except Exception as e:
        raise HTTPException(status_code=500, detail=str(e))


@app.post("/upload")
async def upload_document(file: UploadFile = File(...)):
    """Upload a document for ingestion into the RAG pipeline."""
    allowed_types = [".pdf", ".docx", ".txt", ".md"]
    ext = os.path.splitext(file.filename)[1].lower()

    if ext not in allowed_types:
        raise HTTPException(
            status_code=400,
            detail=f"Unsupported file type. Allowed: {allowed_types}",
        )

    file_path = os.path.join(DOCUMENTS_DIR, file.filename)
    with open(file_path, "wb") as buffer:
        shutil.copyfileobj(file.file, buffer)

    return {"message": f"Uploaded {file.filename}", "path": file_path}


@app.get("/health")
async def health_check():
    """Health check endpoint."""
    return {"status": "healthy", "model": LLM_MODEL, "vectorstore": "chromadb"}


# Run: uvicorn api:app --host 127.0.0.1 --port 8000 --workers 2

# Step 1: Add your documents
cp /path/to/your/documents/*.pdf ~/ai-server/rag/documents/
cp /path/to/your/documents/*.docx ~/ai-server/rag/documents/

# Step 2: Run ingestion
cd ~/ai-server/rag
python ingest.py
# === Private RAG Document Ingestion ===
# Loading documents from: /Users/admin/ai-server/rag/documents
#   Loaded: company-handbook.pdf (45 pages)
#   Loaded: api-documentation.md
#   Loaded: compliance-policy.docx
# Total documents loaded: 47
# Split 47 documents into 312 chunks
# Stored 312 chunks in ChromaDB

# Step 3: Start the RAG API server
uvicorn api:app --host 127.0.0.1 --port 8000 --workers 2 &

# Step 4: Test with a query
curl -s http://localhost:8000/query \
  -H "Content-Type: application/json" \
  -d '{"question": "What is our company vacation policy?"}' | jq .
# {
#   "answer": "According to the company handbook, employees receive...",
#   "sources": [...],
#   "model": "mistral:7b"
# }

# Install Docker Desktop for Mac (Apple Silicon native)
brew install --cask docker

# Start Docker Desktop
open -a Docker

# Verify Docker is running
docker --version
# Docker version 26.1.0, build 9714adc
docker compose version
# Docker Compose version v2.27.0

# ~/ai-server/docker-compose.yml
version: '3.8'

services:
  open-webui:
    image: ghcr.io/open-webui/open-webui:main
    container_name: open-webui
    restart: unless-stopped
    ports:
      - "127.0.0.1:3000:8080"
    environment:
      # Connect to Ollama running on the host
      - OLLAMA_BASE_URL=http://host.docker.internal:11434
      # Disable telemetry and external connections
      - ENABLE_SIGNUP=false
      - ENABLE_COMMUNITY_SHARING=false
      - WEBUI_AUTH=true
      - WEBUI_SECRET_KEY=your-strong-secret-key-change-this
      # Data privacy settings
      - ENABLE_OPENAI_API=false
      - ENABLE_OLLAMA_API=true
      - SAFE_MODE=true
    volumes:
      - open-webui-data:/app/backend/data
    extra_hosts:
      - "host.docker.internal:host-gateway"

  # Optional: ChromaDB as a persistent service
  chromadb:
    image: chromadb/chroma:latest
    container_name: chromadb
    restart: unless-stopped
    ports:
      - "127.0.0.1:8200:8000"
    volumes:
      - chromadb-data:/chroma/chroma
    environment:
      - IS_PERSISTENT=TRUE
      - ANONYMIZED_TELEMETRY=FALSE

volumes:
  open-webui-data:
  chromadb-data:

# Start the services
cd ~/ai-server
docker compose up -d

# Check that containers are running
docker compose ps
# NAME          STATUS          PORTS
# open-webui    Up 2 minutes    127.0.0.1:3000->8080/tcp
# chromadb      Up 2 minutes    127.0.0.1:8200->8000/tcp

# View logs
docker compose logs -f open-webui

# Open WebUI is now accessible at http://localhost:3000
# On first visit, create an admin account (this account is local only)

# Verify Ollama connectivity from Open WebUI
curl -s http://localhost:3000/api/config | jq '.ollama'

# To update Open WebUI later:
docker compose pull && docker compose up -d

# Install nginx
brew install nginx

# Generate a self-signed SSL certificate (or use Let's Encrypt)
mkdir -p ~/ai-server/config/ssl
openssl req -x509 -nodes -days 365 -newkey rsa:2048 \
    -keyout ~/ai-server/config/ssl/server.key \
    -out ~/ai-server/config/ssl/server.crt \
    -subj "/CN=ai-server.local/O=Private AI/C=US"

# /opt/homebrew/etc/nginx/nginx.conf

worker_processes auto;
error_log /Users/admin/ai-server/logs/nginx-error.log;

events {
    worker_connections 1024;
}

http {
    include       mime.types;
    default_type  application/octet-stream;

    # Logging
    access_log /Users/admin/ai-server/logs/nginx-access.log;

    # Rate limiting zones
    limit_req_zone $binary_remote_addr zone=api:10m rate=30r/m;
    limit_req_zone $binary_remote_addr zone=chat:10m rate=60r/m;
    limit_req_zone $binary_remote_addr zone=upload:10m rate=5r/m;

    # Connection limits
    limit_conn_zone $binary_remote_addr zone=addr:10m;

    # SSL settings
    ssl_protocols TLSv1.2 TLSv1.3;
    ssl_ciphers HIGH:!aNULL:!MD5;
    ssl_prefer_server_ciphers on;
    ssl_session_cache shared:SSL:10m;
    ssl_session_timeout 10m;

    # Security headers
    add_header X-Frame-Options DENY always;
    add_header X-Content-Type-Options nosniff always;
    add_header X-XSS-Protection "1; mode=block" always;
    add_header Strict-Transport-Security "max-age=31536000; includeSubDomains" always;
    add_header Content-Security-Policy "default-src 'self'" always;

    # Redirect HTTP to HTTPS
    server {
        listen 80;
        server_name ai-server.local;
        return 301 https://$host$request_uri;
    }

    # Main HTTPS server
    server {
        listen 443 ssl;
        server_name ai-server.local;

        ssl_certificate     /Users/admin/ai-server/config/ssl/server.crt;
        ssl_certificate_key /Users/admin/ai-server/config/ssl/server.key;

        # Client body size limit (for document uploads)
        client_max_body_size 50M;

        # Open WebUI (chat interface)
        location / {
            limit_req zone=chat burst=20 nodelay;
            limit_conn addr 10;

            proxy_pass http://127.0.0.1:3000;
            proxy_set_header Host $host;
            proxy_set_header X-Real-IP $remote_addr;
            proxy_set_header X-Forwarded-For $proxy_add_x_forwarded_for;
            proxy_set_header X-Forwarded-Proto $scheme;

            # WebSocket support for streaming
            proxy_http_version 1.1;
            proxy_set_header Upgrade $http_upgrade;
            proxy_set_header Connection "upgrade";
            proxy_read_timeout 300s;
        }

        # Ollama API (for programmatic access)
        location /ollama/ {
            limit_req zone=api burst=10 nodelay;
            limit_conn addr 5;

            # Basic auth for API access
            auth_basic "Private AI API";
            auth_basic_user_file /Users/admin/ai-server/config/.htpasswd;

            rewrite ^/ollama/(.*) /$1 break;
            proxy_pass http://127.0.0.1:11434;
            proxy_set_header Host $host;
            proxy_set_header X-Real-IP $remote_addr;
            proxy_read_timeout 300s;
        }

        # RAG API
        location /rag/ {
            limit_req zone=api burst=10 nodelay;
            limit_conn addr 5;

            auth_basic "Private AI API";
            auth_basic_user_file /Users/admin/ai-server/config/.htpasswd;

            rewrite ^/rag/(.*) /$1 break;
            proxy_pass http://127.0.0.1:8000;
            proxy_set_header Host $host;
            proxy_set_header X-Real-IP $remote_addr;
            proxy_read_timeout 120s;
        }

        # Document upload endpoint
        location /rag/upload {
            limit_req zone=upload burst=3 nodelay;

            auth_basic "Private AI API";
            auth_basic_user_file /Users/admin/ai-server/config/.htpasswd;

            rewrite ^/rag/(.*) /$1 break;
            proxy_pass http://127.0.0.1:8000;
            proxy_set_header Host $host;
            proxy_set_header X-Real-IP $remote_addr;
        }

        # Health check (no auth required)
        location /health {
            proxy_pass http://127.0.0.1:8000/health;
        }

        # Deny access to hidden files
        location ~ /\. {
            deny all;
        }
    }
}

# Install htpasswd utility
brew install httpd

# Create API credentials
htpasswd -c ~/ai-server/config/.htpasswd api-user
# Enter a strong password when prompted

# Test nginx configuration
nginx -t
# nginx: configuration file /opt/homebrew/etc/nginx/nginx.conf test is successful

# Start nginx
brew services start nginx

# Test HTTPS access
curl -k https://localhost/health
# {"status": "healthy", "model": "mistral:7b", "vectorstore": "chromadb"}

# Test API access with authentication
curl -k -u api-user:your-password \
    https://localhost/ollama/api/tags | jq '.models[].name'

# Test RAG query through nginx
curl -k -u api-user:your-password \
    https://localhost/rag/query \
    -H "Content-Type: application/json" \
    -d '{"question": "What is our data retention policy?"}'

# ~/ai-server/config/pf.rules
#
# Packet Filter rules for private AI server
# Only allow SSH, HTTPS, and WireGuard VPN from outside

# Define macros
ext_if = "en0"
vpn_if = "utun1"

# Default: block everything
block all

# Allow loopback traffic
pass quick on lo0 all

# Allow established connections
pass in quick on $ext_if proto tcp from any to any flags A/A

# Allow SSH (port 22) - restrict to known IPs if possible
pass in on $ext_if proto tcp from any to any port 22

# Allow HTTPS (port 443) through nginx
pass in on $ext_if proto tcp from any to any port 443

# Allow HTTP (port 80) for redirect to HTTPS
pass in on $ext_if proto tcp from any to any port 80

# Allow WireGuard VPN (port 51820)
pass in on $ext_if proto udp from any to any port 51820

# Allow all traffic on VPN interface
pass on $vpn_if all

# Allow all outbound traffic
pass out on $ext_if all

# Block everything else inbound (implicit from "block all")
# Internal services (11434, 3000, 8000, 8200) are NOT exposed

# --- Load these rules ---
# sudo pfctl -f ~/ai-server/config/pf.rules
# sudo pfctl -e   # Enable pf
# sudo pfctl -sr  # Show active rules

# Harden SSH configuration
# Edit /etc/ssh/sshd_config (requires sudo)

# Disable password authentication (key-only)
PasswordAuthentication no
ChallengeResponseAuthentication no
UsePAM no

# Disable root login
PermitRootLogin no

# Only allow specific users
AllowUsers admin

# Use strong key exchange algorithms
KexAlgorithms curve25519-sha256,curve25519-sha256@libssh.org
HostKeyAlgorithms ssh-ed25519,rsa-sha2-512,rsa-sha2-256
Ciphers chacha20-poly1305@openssh.com,aes256-gcm@openssh.com

# Reduce login grace time and max attempts
LoginGraceTime 30
MaxAuthTries 3
MaxSessions 5

# Disable unused features
X11Forwarding no
AllowTcpForwarding no
AllowAgentForwarding no

# Restart SSH
# sudo launchctl stop com.openssh.sshd
# sudo launchctl start com.openssh.sshd

# Install WireGuard
brew install wireguard-tools

# Generate server keys
wg genkey | tee ~/ai-server/config/wg-server-private.key | \
    wg pubkey > ~/ai-server/config/wg-server-public.key

# Generate client keys
wg genkey | tee ~/ai-server/config/wg-client-private.key | \
    wg pubkey > ~/ai-server/config/wg-client-public.key

# Server configuration
cat <<EOF > ~/ai-server/config/wg0.conf
[Interface]
PrivateKey = $(cat ~/ai-server/config/wg-server-private.key)
Address = 10.66.66.1/24
ListenPort = 51820
PostUp = echo "WireGuard started"
PostDown = echo "WireGuard stopped"

[Peer]
# Client 1 - Your workstation
PublicKey = $(cat ~/ai-server/config/wg-client-public.key)
AllowedIPs = 10.66.66.2/32
PersistentKeepalive = 25
EOF

# Client configuration (copy to your workstation)
cat <<EOF > ~/ai-server/config/wg-client.conf
[Interface]
PrivateKey = $(cat ~/ai-server/config/wg-client-private.key)
Address = 10.66.66.2/24
DNS = 1.1.1.1

[Peer]
PublicKey = $(cat ~/ai-server/config/wg-server-public.key)
Endpoint = your-mac-mini.myremotemac.com:51820
AllowedIPs = 10.66.66.0/24
PersistentKeepalive = 25
EOF

# Start WireGuard on the server
sudo wg-quick up ~/ai-server/config/wg0.conf

# Verify connection
sudo wg show
# interface: utun1
#   public key: 
#   listening port: 51820
#
# peer: 
#   allowed ips: 10.66.66.2/32

# ~/ai-server/scripts/monitor-ssh.sh
#!/bin/bash
# Simple SSH brute-force detection and blocking for macOS
# Run via cron every 5 minutes

LOG_FILE="/var/log/system.log"
BLOCK_THRESHOLD=5
BLOCK_FILE="$HOME/ai-server/config/blocked_ips.txt"

# Find IPs with failed SSH attempts in the last 10 minutes
failed_ips=$(log show --predicate 'process == "sshd" AND eventMessage CONTAINS "Failed"' \
    --last 10m 2>/dev/null | \
    grep -oE '([0-9]{1,3}\.){3}[0-9]{1,3}' | \
    sort | uniq -c | sort -rn)

echo "$failed_ips" | while read count ip; do
    if [ "$count" -ge "$BLOCK_THRESHOLD" ] && [ -n "$ip" ]; then
        # Check if already blocked
        if ! grep -q "$ip" "$BLOCK_FILE" 2>/dev/null; then
            echo "$(date): Blocking $ip ($count failed attempts)" >> ~/ai-server/logs/security.log
            echo "$ip" >> "$BLOCK_FILE"

            # Add pf block rule
            echo "block in quick from $ip to any" | sudo pfctl -f - -a "blocked/$ip" 2>/dev/null
        fi
    fi
done

# Make executable and add to crontab:
# chmod +x ~/ai-server/scripts/monitor-ssh.sh
# crontab -e
# */5 * * * * ~/ai-server/scripts/monitor-ssh.sh

# nginx upstream configuration for multi-node load balancing

# Define upstream groups by model type
upstream ollama_general {
    # Round-robin across general chat nodes
    server 10.66.66.10:11434;  # Node 1
    server 10.66.66.11:11434;  # Node 1 replica (if needed)
    keepalive 8;
}

upstream ollama_code {
    server 10.66.66.20:11434;  # Node 2 - Code models
    keepalive 4;
}

upstream ollama_reasoning {
    server 10.66.66.30:11434;  # Node 3 - Large models
    keepalive 4;
}

# Model routing based on request path
server {
    listen 443 ssl;
    server_name ai-cluster.local;

    # Route general chat requests
    location /v1/chat/ {
        proxy_pass http://ollama_general;
        proxy_read_timeout 300s;
    }

    # Route code generation requests
    location /v1/code/ {
        proxy_pass http://ollama_code;
        proxy_read_timeout 300s;
    }

    # Route reasoning/analysis requests
    location /v1/reasoning/ {
        proxy_pass http://ollama_reasoning;
        proxy_read_timeout 600s;
    }
}

# ~/ai-server/scripts/model_router.py
"""
Intelligent model router that directs requests to the appropriate
Mac Mini node based on the requested model and current load.
"""
from fastapi import FastAPI, Request
import httpx
import asyncio

app = FastAPI()

NODES = {
    "general": {
        "url": "http://10.66.66.10:11434",
        "models": ["llama3:8b", "mistral:7b"],
    },
    "code": {
        "url": "http://10.66.66.20:11434",
        "models": ["codellama:7b", "codellama:13b"],
    },
    "reasoning": {
        "url": "http://10.66.66.30:11434",
        "models": ["llama3:70b", "mixtral:8x7b"],
    },
}

def get_node_for_model(model: str) -> str:
    """Find which node hosts the requested model."""
    for node_name, config in NODES.items():
        if model in config["models"]:
            return config["url"]
    # Default to general node
    return NODES["general"]["url"]

@app.post("/v1/chat/completions")
async def route_chat(request: Request):
    body = await request.json()
    model = body.get("model", "llama3:8b")
    target_url = get_node_for_model(model)

    async with httpx.AsyncClient(timeout=300) as client:
        response = await client.post(
            f"{target_url}/v1/chat/completions",
            json=body,
        )
        return response.json()

@app.get("/v1/models")
async def list_all_models():
    """Aggregate model lists from all nodes."""
    all_models = []
    async with httpx.AsyncClient(timeout=10) as client:
        for node_name, config in NODES.items():
            try:
                resp = await client.get(f"{config['url']}/api/tags")
                models = resp.json().get("models", [])
                for m in models:
                    m["node"] = node_name
                all_models.extend(models)
            except Exception:
                pass
    return {"models": all_models}

# Run: uvicorn model_router:app --host 0.0.0.0 --port 8080