22cfbfe5fb
- QUICKSTART.md: replace data_description.md.example copy step with note that tables are registered via the admin API or web UI - NOTIFICATIONS.md: replace examples/ section with planned-feature note - telegram_bot.md: remove examples/notifications/ rows from deployment table and example scripts section; note feature is planned - dev_docs/README.md: remove plan-corporate-memory.md entry - duckdb_manager.py: update comment from remote_query.py to query API endpoint
63 lines
1.4 KiB
Markdown
63 lines
1.4 KiB
Markdown
# Quick Start Guide
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## Prerequisites
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- Python 3.10+
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- SSH access to a Linux server (for production deployment)
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- Data source credentials (Keboola token, BigQuery service account, etc.)
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## Local Development Setup
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1. Clone the repository:
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```bash
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git clone <repo-url>
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cd ai-data-analyst
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```
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2. Run the initialization script:
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```bash
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bash scripts/init.sh
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```
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3. Configure your instance:
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```bash
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cp config/instance.yaml.example config/instance.yaml
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# Edit config/instance.yaml with your settings
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```
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4. Set up environment variables:
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```bash
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# Edit .env with your data source credentials
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```
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5. Register your tables:
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```bash
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# Tables are registered via the admin API or web UI — no config file needed
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```
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6. Sync data:
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```bash
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source .venv/bin/activate
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python -m src.data_sync
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```
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## Server Deployment
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See [DEPLOYMENT.md](DEPLOYMENT.md) for full server setup instructions.
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## Using with Claude Code
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Open the project in Claude Code. The CLAUDE.md file will guide the AI assistant through setup and analysis workflows.
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### Analyst Setup
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1. Visit your instance URL (e.g., https://data.yourcompany.com)
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2. Sign in with your company email
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3. Register your SSH key
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4. Follow the setup instructions to sync data locally
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### Analysis Workflow
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1. Sync latest data: `bash server/scripts/sync_data.sh`
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2. Open Claude Code in your project directory
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3. Ask Claude to analyze your data using DuckDB
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