agnes-the-ai-analyst/docs/auto-install.md
Petr 302494b632 Add --format parquet using project's ParquetManager
Generator now supports --format {csv,parquet,both}. Parquet mode
uses src.parquet_manager.ParquetManager for snappy compression,
proper column types (DATE, TIMESTAMP, DOUBLE), and metadata.
No more ad-hoc pandas conversion needed on the server.
2026-03-10 21:46:20 +01:00

450 lines
13 KiB
Markdown

# Automated Installation Guide
Step-by-step deployment of AI Data Analyst on a clean Ubuntu 24.04 VM.
Two repos are involved:
- **OSS repo** (public/private): application code (`padak/tmp_oss`)
- **Instance repo** (private): your config, secrets template, data schema (`padak/tmp_oss_cfg`)
## Architecture on Server
```
/opt/data-analyst/
├── repo/ # OSS repo clone
│ ├── config/
│ │ └── instance.yaml -> ../../instance/config/instance.yaml (symlink)
│ ├── webapp/
│ ├── server/
│ └── ...
├── instance/ # Private instance repo clone
│ ├── config/
│ │ ├── instance.yaml # Branding, auth domains, data source
│ │ └── data_description.md # Data schema (when configured)
│ ├── docs/setup/ # Custom CLAUDE.md template, etc.
│ ├── .env.example # Secrets template
│ └── README.md
├── .env # Secrets (not in git, from .env.example)
├── .venv/ # Python virtual environment
└── logs/ # Application logs
```
Key principle: OSS repo has no secrets/config. Instance repo has no code. Symlinks bridge them.
## Prerequisites
1. **DigitalOcean API token** with `ssh_key` scope (or any Ubuntu 24.04 VM)
2. **Two GitHub repos**: one for OSS code, one for private instance config
3. **SSH key** on your local machine for server access
### Known Issues
- `python3-venv` must be installed before `server/setup.sh` (Ubuntu 24.04 omits it)
- `webapp-setup.sh` generates SSL nginx config - use HTTP-only for IP-only deployments
- DigitalOcean cloud-init cannot override password expiry; must use `ssh_keys` API field
## Step 0: Create Repos
```bash
# Push OSS code to GitHub
git remote add origin git@github.com:YOUR_ORG/YOUR_OSS_REPO.git
git push -u origin main
# Create private instance config repo on GitHub (empty, private)
# We'll populate it from the server after setup
```
## Step 1: Provision VM
### 1a: Create Droplet (DigitalOcean)
```bash
# Register SSH key (requires ssh_key scope on API token)
curl -s -X POST -H 'Content-Type: application/json' \
-H "Authorization: Bearer $DO_TOKEN" \
-d '{"name":"my-key","public_key":"ssh-ed25519 AAAA..."}' \
"https://api.digitalocean.com/v2/account/keys"
# Create droplet with SSH key
curl -s -X POST -H 'Content-Type: application/json' \
-H "Authorization: Bearer $DO_TOKEN" \
-d '{
"name":"data-analyst-1",
"size":"s-1vcpu-2gb",
"region":"ams3",
"image":"ubuntu-24-04-x64",
"ssh_keys":["KEY_ID_OR_FINGERPRINT"]
}' \
"https://api.digitalocean.com/v2/droplets"
```
### 1b: Install Prerequisites
```bash
ssh root@DROPLET_IP
# Wait for apt lock (auto-updates run on first boot)
apt update && apt install -y python3.12-venv python3-pip
```
### 1c: Generate Deploy Keys
Two separate keys - one per repo, for security isolation:
```bash
# Key for OSS repo
ssh-keygen -t ed25519 -f /root/.ssh/deploy_key -N "" -C "oss-app@$(hostname)"
# Key for private instance config repo
ssh-keygen -t ed25519 -f /root/.ssh/instance_key -N "" -C "instance-config@$(hostname)"
```
Add each public key as a **deploy key** on its respective GitHub repo:
- `deploy_key.pub` -> OSS repo Settings > Deploy Keys
- `instance_key.pub` -> Instance repo Settings > Deploy Keys
Configure SSH to use the right key per repo:
```bash
cat > /root/.ssh/config << 'EOF'
# OSS application repo
Host github-oss
HostName github.com
IdentityFile /root/.ssh/deploy_key
StrictHostKeyChecking no
# Instance config repo (private)
Host github-cfg
HostName github.com
IdentityFile /root/.ssh/instance_key
StrictHostKeyChecking no
EOF
chmod 600 /root/.ssh/config
```
### 1d: Clone OSS Repo & Run Setup
```bash
git clone git@github-oss:YOUR_ORG/YOUR_OSS_REPO.git /opt/data-analyst/repo
cd /opt/data-analyst/repo
REPO_URL="git@github-oss:YOUR_ORG/YOUR_OSS_REPO.git" bash server/setup.sh
```
### Step 1 Checklist
| # | Check | Expected |
|---|-------|----------|
| 1.1 | Groups | data-ops, dataread, data-private exist |
| 1.2 | Deploy user | uid deploy, groups: deploy, data-ops |
| 1.3 | Directories | /opt/data-analyst/{repo,.venv,logs} |
| 1.4 | Python venv | Flask loads in .venv |
| 1.5 | Scripts | add-analyst, list-analysts in /usr/local/bin |
## Step 2: Webapp Setup
### 2a: Run webapp-setup.sh
```bash
export SERVER_HOSTNAME="your-domain-or-ip"
bash server/webapp-setup.sh
```
For IP-only (no SSL), replace nginx config:
```bash
cat > /etc/nginx/sites-available/webapp << 'NGINX'
server {
listen 80;
server_name _;
location / {
proxy_pass http://unix:/run/webapp/webapp.sock;
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;
proxy_http_version 1.1;
proxy_set_header Upgrade $http_upgrade;
proxy_set_header Connection "upgrade";
}
location /static/ {
alias /opt/data-analyst/repo/webapp/static/;
expires 1d;
}
location /health {
proxy_pass http://unix:/run/webapp/webapp.sock;
proxy_set_header Host $host;
access_log off;
}
}
NGINX
rm -f /etc/nginx/sites-enabled/default
nginx -t && systemctl restart nginx
```
### 2b: Create .env
```bash
SECRET_KEY=$(python3 -c 'import secrets; print(secrets.token_hex(32))')
cat > /opt/data-analyst/.env << EOF
WEBAPP_SECRET_KEY="${SECRET_KEY}"
SERVER_HOST="YOUR_IP"
SERVER_HOSTNAME="YOUR_IP_OR_DOMAIN"
GOOGLE_CLIENT_ID="placeholder"
GOOGLE_CLIENT_SECRET="placeholder"
DATA_SOURCE="local"
DATA_DIR="/data/src_data"
EOF
chown root:data-ops /opt/data-analyst/.env
chmod 640 /opt/data-analyst/.env
```
### 2c: Create Data Directories & Start
```bash
mkdir -p /data/src_data/{parquet,metadata} /data/docs /data/scripts
chown -R root:data-ops /data
chmod -R 2775 /data
mkdir -p /run/webapp
chown www-data:www-data /run/webapp
systemctl daemon-reload
systemctl start webapp
systemctl enable webapp
```
### Step 2 Checklist
| # | Check | Expected |
|---|-------|----------|
| 2.1 | Nginx | active, port 80 |
| 2.2 | Webapp | active (gunicorn) |
| 2.3 | Health | `curl http://IP/health` returns JSON |
| 2.4 | Login page | HTTP 200 at /login |
## Step 3: Instance Configuration (Private Repo)
### 3a: Clone Instance Repo
```bash
git clone git@github-cfg:YOUR_ORG/YOUR_INSTANCE_REPO.git /opt/data-analyst/instance
chown -R root:data-ops /opt/data-analyst/instance
chmod -R 770 /opt/data-analyst/instance
```
### 3b: Initialize Instance Config (if empty repo)
If this is a fresh instance repo, create the initial config:
```bash
cd /opt/data-analyst/instance
mkdir -p config docs/setup
cat > config/instance.yaml << 'YAML'
instance:
name: "My Data Analyst"
subtitle: "My Organization"
copyright: "My Org"
server:
hostname: "YOUR_IP_OR_DOMAIN"
host: "YOUR_IP"
app_dir: "/opt/data-analyst"
auth:
allowed_domain: "mycompany.com"
webapp_secret_key: "${WEBAPP_SECRET_KEY}"
data_source:
type: "local"
catalog:
categories: {}
YAML
# Create .env.example as a template for future deployments
cat > .env.example << 'ENV'
WEBAPP_SECRET_KEY="generate-with: python3 -c 'import secrets; print(secrets.token_hex(32))'"
SERVER_HOST="server-ip"
SERVER_HOSTNAME="server-ip-or-domain"
GOOGLE_CLIENT_ID="placeholder"
GOOGLE_CLIENT_SECRET="placeholder"
DATA_SOURCE="local"
DATA_DIR="/data/src_data"
ENV
cat > .gitignore << 'GI'
.env
.env.local
*.swp
*~
.DS_Store
GI
git add -A && git commit -m "Initial instance config" && git push origin main
```
### 3c: Symlink Config into OSS Repo
```bash
# Remove any existing instance.yaml (from manual setup) and symlink
rm -f /opt/data-analyst/repo/config/instance.yaml
ln -s /opt/data-analyst/instance/config/instance.yaml /opt/data-analyst/repo/config/instance.yaml
# Optional: symlink data_description.md when ready
# ln -s /opt/data-analyst/instance/config/data_description.md /opt/data-analyst/repo/docs/data_description.md
systemctl restart webapp
```
### Step 3 Checklist
| # | Check | Expected |
|---|-------|----------|
| 3.1 | Instance repo | /opt/data-analyst/instance/ exists |
| 3.2 | Symlink | config/instance.yaml -> ../../instance/config/instance.yaml |
| 3.3 | Webapp loads | Instance name shown on login page |
## Step 4: Authentication
Email magic link works without any external service.
1. Login page shows "Sign in with Email"
2. User enters email with allowed domain
3. Without SMTP: magic link shown in browser (dev mode)
4. With SMTP: link sent via email
5. Click link -> logged in -> dashboard
Optional: add Google OAuth by setting real `GOOGLE_CLIENT_ID`/`GOOGLE_CLIENT_SECRET`.
### Step 4 Checklist
| # | Check | Expected |
|---|-------|----------|
| 4.1 | Email auth | "Sign in with Email" on login page |
| 4.2 | Magic link | Generated for valid domain email |
| 4.3 | Domain check | Rejects wrong domains |
| 4.4 | Login flow | Magic link -> dashboard with session |
## Step 5: Onboarding Flow (End-User)
After server is set up, analysts self-onboard via the webapp:
1. Visit `http://YOUR_SERVER/login` and sign in with email
2. Dashboard shows "Get Started" with 4 steps:
- Create project folder (`mkdir -p data-analyst && cd data-analyst`)
- Generate SSH key (`ssh-keygen -t ed25519 -f ~/.ssh/data_analyst_server -N ''`)
- Copy public key (`cat ~/.ssh/data_analyst_server.pub`)
- Paste key into form, click "Create Account"
3. After account creation, dashboard shows "Set up your local environment"
4. User runs `claude` in their project folder, pastes setup instructions
5. Claude Code configures SSH, rsyncs data, sets up Python + DuckDB
## Step 6: Sample Data (Try Without a Data Adapter)
Before connecting a real data source, you can load sample data to verify the full pipeline
(Parquet files, DuckDB, analyst rsync, Claude Code analysis).
```bash
cd /opt/data-analyst/repo
# Install generator dependency
/opt/data-analyst/.venv/bin/pip install faker
# Generate Parquet files directly (uses project's ParquetManager
# for snappy compression, proper types, and metadata embedding)
/opt/data-analyst/.venv/bin/python scripts/generate_sample_data.py \
--size m --format parquet --output /data/src_data/parquet --seed 42
# Set correct permissions
chown -R root:data-ops /data/src_data/parquet
chmod -R 2775 /data/src_data/parquet
```
Available sizes: `xs` (50 customers, ~1 MB), `s` (500, ~15 MB), `m` (5K, ~150 MB), `l` (50K, ~1.5 GB).
The sample data covers 9 tables: customers, products, campaigns, web_sessions, web_leads,
orders, order_items, payments, support_tickets. See `docs/sample-data.md` for the full
data model, table reference, and built-in analytical patterns.
### Step 6 Checklist
| # | Check | Expected |
|---|-------|----------|
| 6.1 | Parquet files | `ls /data/src_data/parquet/*.parquet` shows 9 files |
| 6.2 | Permissions | Files owned by root:data-ops, group-readable |
| 6.3 | Analyst sync | Analyst can rsync parquet files to local machine |
| 6.4 | DuckDB loads | `SELECT count(*) FROM read_parquet('orders.parquet')` returns rows |
## Step 7: Real Data Source (Production)
When ready, replace sample data with a real data source adapter in `instance/config/instance.yaml`:
```yaml
data_source:
type: "keboola"
keboola:
storage_token: "${KEBOOLA_STORAGE_TOKEN}"
stack_url: "https://connection.keboola.com"
project_id: "12345"
```
Add the token to `.env` and create `config/data_description.md` with table schemas.
Other planned adapters: BigQuery, CSV import.
## Deployment Workflow (Ongoing)
### Update OSS code
```bash
cd /opt/data-analyst/repo && git pull
bash server/deploy.sh # restarts services, syncs scripts/docs
```
### Update instance config
```bash
cd /opt/data-analyst/instance && git pull
systemctl restart webapp # picks up new instance.yaml via symlink
```
### Both at once
```bash
cd /opt/data-analyst/repo && git pull
cd /opt/data-analyst/instance && git pull
bash server/deploy.sh
```
## Server Layout Summary
```
/opt/data-analyst/
├── repo/ -> git@github-oss:ORG/OSS_REPO.git
├── instance/ -> git@github-cfg:ORG/INSTANCE_REPO.git
├── .env # Secrets (not in git)
├── .venv/ # Python
└── logs/ # App logs
/root/.ssh/
├── deploy_key # For OSS repo (github-oss alias)
├── instance_key # For instance repo (github-cfg alias)
└── config # Maps aliases to keys
Symlinks:
repo/config/instance.yaml -> instance/config/instance.yaml
repo/docs/data_description.md -> instance/config/data_description.md (optional)
```
## Quick Verification
```bash
# Health check
curl http://YOUR_IP/health | python3 -m json.tool
# Login page
curl -s -o /dev/null -w "%{http_code}" http://YOUR_IP/login
# Expected: 200
# Instance config loaded
curl -s http://YOUR_IP/login | grep 'YOUR_INSTANCE_NAME'
```