c56905d34f
Open-source AI data analyst platform extracted from internal repo. Includes data sync engine, Keboola adapter, Flask web portal, server deployment scripts, and configuration templates.
64 lines
1.5 KiB
Markdown
64 lines
1.5 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. Create your data description:
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```bash
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cp config/data_description.md.example docs/data_description.md
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# Edit docs/data_description.md to define your tables
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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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