68 lines
1.7 KiB
Markdown
68 lines
1.7 KiB
Markdown
# Quick Start Guide
|
|
|
|
## Prerequisites
|
|
|
|
- Python 3.10+
|
|
- SSH access to a Linux server (for production deployment)
|
|
- Data source credentials (Keboola token, BigQuery service account, etc.)
|
|
|
|
## Local Development Setup
|
|
|
|
1. Clone the repository:
|
|
```bash
|
|
git clone <repo-url>
|
|
cd ai-data-analyst
|
|
```
|
|
|
|
2. Run the initialization script:
|
|
```bash
|
|
bash scripts/init.sh
|
|
```
|
|
|
|
3. Configure your instance:
|
|
```bash
|
|
cp config/instance.yaml.example config/instance.yaml
|
|
# Edit config/instance.yaml with your settings
|
|
```
|
|
|
|
4. Set up environment variables:
|
|
```bash
|
|
# Edit .env with your data source credentials
|
|
```
|
|
|
|
5. Register your tables:
|
|
```bash
|
|
# Tables are registered via the admin API or web UI — no config file needed
|
|
```
|
|
|
|
6. Sync data:
|
|
```bash
|
|
source .venv/bin/activate
|
|
python -m src.data_sync
|
|
```
|
|
|
|
## Server Deployment
|
|
|
|
See [DEPLOYMENT.md](DEPLOYMENT.md) for full server setup instructions.
|
|
|
|
## Using with Claude Code
|
|
|
|
Open the project in Claude Code. The CLAUDE.md file will guide the AI assistant through setup and analysis workflows.
|
|
|
|
### Analyst Setup
|
|
|
|
1. Visit your instance URL (e.g., https://data.yourcompany.com)
|
|
2. Sign in with your company email
|
|
3. Register your SSH key
|
|
4. Follow the setup instructions to sync data locally
|
|
|
|
### Analysis Workflow
|
|
|
|
1. Sync latest data: `bash server/scripts/sync_data.sh`
|
|
2. Open Claude Code in your project directory
|
|
3. Ask Claude to analyze your data using DuckDB
|
|
|
|
## Hackathon
|
|
|
|
Point the shared `agnes-dev` VM at your branch image with `scripts/switch-dev-vm.sh <branch-slug>`. See [`HACKATHON.md`](HACKATHON.md) for the full deploy-and-develop playbook.
|
|
<!-- dryrun 2026-04-21T19:12:08Z -->
|